diff --git a/CMakeLists.txt b/CMakeLists.txt index 5ea7732..2f34f87 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -19,6 +19,8 @@ cmake_minimum_required(VERSION 2.8.4 FATAL_ERROR) include(ExternalProject) include(CheckCXXCompilerFlag) +ADD_DEFINITIONS(-DPGXC) +ADD_DEFINITIONS(-DMADLIB) # -- Local definitions (filenames, paths, etc.) -------------------------------- @@ -70,7 +72,7 @@ if(CMAKE_COMPILER_IS_GNUCC) # http://pubs.opengroup.org/onlinepubs/9699919799/functions/V2_chap02.html#tag_15_02_01_01 # We specify that we are POSIX.1-2001 compliant and XSI-conforming. We only # need to specify _XOPEN_SOURCE as _POSIX_C_SOURCE will be set implicitly. - set(CMAKE_C_FLAGS "-std=c99 -D_GLIBCXX_USE_CXX11_ABI=0 -pedantic -Wall -Wextra -Wno-clobbered -D_XOPEN_SOURCE=600" + set(CMAKE_C_FLAGS "-std=c99 -D_GLIBCXX_USE_CXX11_ABI=0 -fsigned-char -fpermissive -pedantic -Wall -Wextra -Wno-clobbered -D_XOPEN_SOURCE=600" CACHE STRING "Flags used by the compiler during all build types." FORCE) if((CMAKE_C_COMPILER_VERSION VERSION_EQUAL 5.0) OR (CMAKE_C_COMPILER_VERSION VERSION_GREATER 5.0)) @@ -106,7 +108,7 @@ if(CMAKE_COMPILER_IS_GNUCXX) # - long long is not part of the C++ 1998/2003 standard, but it is such a # common (and useful) extension that we do not want to hear warnings about # it. - set(CMAKE_CXX_FLAGS "-std=gnu++98 -D_GLIBCXX_USE_CXX11_ABI=0 -fdiagnostics-show-option -Wall -Wextra -pedantic -Wconversion -Wno-long-long -Wno-clobbered ${SSE_DISABLE_OPTIONS} -fstrict-aliasing" + set(CMAKE_CXX_FLAGS "-std=gnu++98 -D_GLIBCXX_USE_CXX11_ABI=0 -fsigned-char -fpermissive -fdiagnostics-show-option -Wall -Wextra -pedantic -Wconversion -Wno-long-long -Wno-clobbered ${SSE_DISABLE_OPTIONS} -fstrict-aliasing" CACHE STRING "Flags used by the compiler during all build types." FORCE) diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake index 4a6e496..707ec3b 100644 --- a/cmake/Utils.cmake +++ b/cmake/Utils.cmake @@ -22,7 +22,7 @@ function(define_m4_macros OUT_M4_CMD_LINE OUT_M4_CODE) "__DBMS_VERSION_MAJOR__=${${DBMS_UC}_VERSION_MAJOR}" "__DBMS_VERSION_MINOR__=${${DBMS_UC}_VERSION_MINOR}" "__DBMS_VERSION_PATCH__=${${DBMS_UC}_VERSION_PATCH}" - "__DBMS_ARCHITECTURE__=${${DBMS_UC}_ARCHITECTURE}" + "__DBMS_ARCHITECTURE__=x86_64" "__MADLIB_VERSION__=${MADLIB_VERSION_STRING}" "__MADLIB_VERSION_MAJOR__=${MADLIB_VERSION_MAJOR}" "__MADLIB_VERSION_MINOR__=${MADLIB_VERSION_MINOR}" diff --git a/methods/array_ops/src/pg_gp/array_ops.c b/methods/array_ops/src/pg_gp/array_ops.cpp similarity index 97% rename from methods/array_ops/src/pg_gp/array_ops.c rename to methods/array_ops/src/pg_gp/array_ops.cpp index a842a60..936fd39 100644 --- a/methods/array_ops/src/pg_gp/array_ops.c +++ b/methods/array_ops/src/pg_gp/array_ops.cpp @@ -20,6 +20,46 @@ PG_MODULE_MAGIC; #endif + +#ifdef __cplusplus +extern "C" { +#endif + +Datum array_of_float(PG_FUNCTION_ARGS); +Datum array_of_bigint(PG_FUNCTION_ARGS); +Datum array_stddev(PG_FUNCTION_ARGS); +Datum array_mean(PG_FUNCTION_ARGS); +Datum array_sum_big(PG_FUNCTION_ARGS); +Datum array_sum(PG_FUNCTION_ARGS); +Datum array_abs_sum(PG_FUNCTION_ARGS); +Datum array_min(PG_FUNCTION_ARGS); +Datum array_max(PG_FUNCTION_ARGS); +Datum array_max_index(PG_FUNCTION_ARGS); +Datum array_min_index(PG_FUNCTION_ARGS); +Datum array_dot(PG_FUNCTION_ARGS); +Datum array_contains(PG_FUNCTION_ARGS); +Datum array_add(PG_FUNCTION_ARGS); +Datum array_sub(PG_FUNCTION_ARGS); +Datum array_mult(PG_FUNCTION_ARGS); +Datum array_div(PG_FUNCTION_ARGS); +Datum array_abs(PG_FUNCTION_ARGS); +Datum array_sqrt(PG_FUNCTION_ARGS); +Datum array_pow(PG_FUNCTION_ARGS); +Datum array_square(PG_FUNCTION_ARGS); +Datum array_fill_(PG_FUNCTION_ARGS); +Datum array_cos(PG_FUNCTION_ARGS); +Datum array_scalar_mult(PG_FUNCTION_ARGS); +Datum array_scalar_add(PG_FUNCTION_ARGS); +Datum array_cum_sum(PG_FUNCTION_ARGS); +Datum array_cum_prod(PG_FUNCTION_ARGS); +Datum array_filter(PG_FUNCTION_ARGS); +Datum array_normalize(PG_FUNCTION_ARGS); +Datum array_contains_null(PG_FUNCTION_ARGS); +Datum array_to_bytea(PG_FUNCTION_ARGS); + +#ifdef __cplusplus +} +#endif /* This module provides c implementations for several postgres array operations. @@ -378,7 +418,7 @@ array_of_float(PG_FUNCTION_ARGS){ errmsg("invalid array length"), errdetail("array_of_float: Size should be in [1, 1e7], %d given", size))); } - Datum* array = palloc (sizeof(Datum)*size); + Datum* array = (Datum *) palloc (sizeof(Datum)*size); for(int i = 0; i < size; ++i) { array[i] = Float8GetDatum(0); } @@ -405,7 +445,7 @@ array_of_bigint(PG_FUNCTION_ARGS){ errmsg("invalid array length"), errdetail("array_of_bigint: Size should be in [1, 1e7], %d given", size))); } - Datum* array = palloc (sizeof(Datum)*size); + Datum* array = (Datum *) palloc (sizeof(Datum)*size); for(int i = 0; i < size; ++i) { array[i] = Int64GetDatum(0); } @@ -823,9 +863,9 @@ array_square(PG_FUNCTION_ARGS){ /* * This function sets all elements to be the specified value. */ -PG_FUNCTION_INFO_V1(array_fill); +PG_FUNCTION_INFO_V1(array_fill_); Datum -array_fill(PG_FUNCTION_ARGS){ +array_fill_(PG_FUNCTION_ARGS){ if (PG_ARGISNULL(0)) { PG_RETURN_NULL(); } if (PG_ARGISNULL(1)) { PG_RETURN_NULL(); } @@ -2129,7 +2169,7 @@ array_to_bytea(PG_FUNCTION_ARGS) data_length = (nitems * TI->typlen); } - bytea *ba = palloc(VARHDRSZ + data_length); + bytea *ba = (bytea *)palloc(VARHDRSZ + data_length); SET_VARSIZE(ba, VARHDRSZ + data_length); diff --git a/methods/array_ops/src/pg_gp/array_ops.sql_in b/methods/array_ops/src/pg_gp/array_ops.sql_in index c1ec853..fc7036b 100644 --- a/methods/array_ops/src/pg_gp/array_ops.sql_in +++ b/methods/array_ops/src/pg_gp/array_ops.sql_in @@ -268,7 +268,7 @@ File array_ops.sql_in for list of functions and usage. */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_add(x anyarray, y anyarray) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_add' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -295,7 +295,7 @@ CREATE AGGREGATE MADLIB_SCHEMA.sum(/* x */ anyarray) ( */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_sub(x anyarray, y anyarray) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_sub' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -308,7 +308,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_mult(x anyarray, y anyarray) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_mult' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -321,7 +321,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_div(x anyarray, y anyarray) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_div' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -334,7 +334,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_dot(x anyarray, y anyarray) RETURNS float8 AS 'MODULE_PATHNAME', 'array_dot' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -347,7 +347,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_contains(x anyarray, y anyarray) RETURNS BOOL AS 'MODULE_PATHNAME', 'array_contains' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -359,7 +359,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_max(x anyarray) RETURNS anyelement AS 'MODULE_PATHNAME', 'array_max' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -371,7 +371,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_max_index(x anyarray) RETURNS float8[] AS 'MODULE_PATHNAME', 'array_max_index' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -383,7 +383,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_min(x anyarray) RETURNS anyelement AS 'MODULE_PATHNAME', 'array_min' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -395,7 +395,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_min_index(x anyarray) RETURNS float8[] AS 'MODULE_PATHNAME', 'array_min_index' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -407,7 +407,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_sum(x anyarray) RETURNS anyelement AS 'MODULE_PATHNAME', 'array_sum' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -420,7 +420,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_sum_big(x anyarray) RETURNS float8 AS 'MODULE_PATHNAME', 'array_sum_big' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -431,7 +431,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_abs_sum(x anyarray) RETURNS anyelement AS 'MODULE_PATHNAME', 'array_abs_sum' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -443,7 +443,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_abs(x anyarray) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_abs' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -455,7 +455,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_mean(x anyarray) RETURNS float8 AS 'MODULE_PATHNAME', 'array_mean' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -467,7 +467,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_stddev(x anyarray) RETURNS float8 AS 'MODULE_PATHNAME', 'array_stddev' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -479,7 +479,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_of_float(k integer) RETURNS float8[] AS 'MODULE_PATHNAME', 'array_of_float' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -491,7 +491,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_of_bigint(k integer) RETURNS bigint[] AS 'MODULE_PATHNAME', 'array_of_bigint' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -503,8 +503,8 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); * */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_fill(x anyarray, k anyelement) RETURNS anyarray -AS 'MODULE_PATHNAME', 'array_fill' -LANGUAGE C IMMUTABLE +AS 'MODULE_PATHNAME', 'array_fill_' +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -515,7 +515,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_cos(x anyarray) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_cos' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -527,7 +527,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_scalar_mult(x anyarray, k anyelement) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_scalar_mult' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -539,7 +539,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_scalar_add(x anyarray, k anyelement) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_scalar_add' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); @@ -551,7 +551,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); * @returns Filtered array. */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_filter(x anyarray, k anyelement, op text) RETURNS anyarray -AS 'MODULE_PATHNAME' LANGUAGE C IMMUTABLE +AS 'MODULE_PATHNAME' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -561,7 +561,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); * @returns Filtered array. */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_filter(x anyarray, k anyelement) RETURNS anyarray -AS 'MODULE_PATHNAME' LANGUAGE C IMMUTABLE +AS 'MODULE_PATHNAME' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -570,7 +570,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); * @returns Filtered array. */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_filter(x anyarray) RETURNS anyarray -AS 'MODULE_PATHNAME' LANGUAGE C IMMUTABLE +AS 'MODULE_PATHNAME' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -583,7 +583,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.array_sqrt(x anyarray) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.array_sqrt(x anyarray) RETURNS float8[] AS 'MODULE_PATHNAME', 'array_sqrt' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -597,7 +597,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.array_pow(x anyarray, y anyelement) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.array_pow(x anyarray, y anyelement) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_pow' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -610,7 +610,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.array_square(x anyarray) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.array_square(x anyarray) RETURNS float8[] AS 'MODULE_PATHNAME', 'array_square' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -622,7 +622,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ DROP FUNCTION IF EXISTS MADLIB_SCHEMA.normalize(float8[]) CASCADE; CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normalize(x anyarray) RETURNS float8[] -AS 'MODULE_PATHNAME', 'array_normalize' LANGUAGE C IMMUTABLE STRICT +AS 'MODULE_PATHNAME', 'array_normalize' LANGUAGE C IMMUTABLE STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -634,7 +634,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ DROP FUNCTION IF EXISTS MADLIB_SCHEMA.array_contains_null(float8[]) CASCADE; CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_contains_null(x anyarray) RETURNS BOOLEAN -AS 'MODULE_PATHNAME', 'array_contains_null' LANGUAGE C IMMUTABLE STRICT +AS 'MODULE_PATHNAME', 'array_contains_null' LANGUAGE C IMMUTABLE STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -648,7 +648,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_cum_sum(x anyarray) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_cum_sum' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -662,7 +662,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_cum_prod(x anyarray) RETURNS anyarray AS 'MODULE_PATHNAME', 'array_cum_prod' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -741,7 +741,115 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_to_bytea(ANYARRAY) RETURNS BYTEA AS 'MODULE_PATHNAME', 'array_to_bytea' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED; !>) m4_changequote(`,') m4_ifdef(__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); +------------------------------------------------------------------ +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_sort(x ANYARRAY) RETURNS ANYARRAY AS +$BODY$ +SELECT ARRAY( + SELECT $1[s.i] AS "foo" + FROM + generate_series(array_lower($1,1), array_upper($1,1)) AS s(i) + ORDER BY foo); +$BODY$ LANGUAGE SQL IMMUTABLE; +------------------------------------------------------------------ + +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.percentile_cont(myarray anyarray, percentile double precision)RETURNS double precision AS $$ +DECLARE + ary_cnt bigint; + row_num bigint; + crn double precision; + frn double precision; + calc_result double precision; + new_array double precision[]; +BEGIN + new_array := array_sort(myarray); + ary_cnt := array_length(new_array,1); + row_num := 1 + ( percentile * ( ary_cnt - 1 )); + crn := ceiling(row_num); + frn := floor(row_num); + if crn = frn and frn = row_num then + calc_result := new_array[row_num]; + else + calc_result := (crn - row_num) * new_array[frn] + (row_num - frn) * new_array[crn]; + end if; + RETURN calc_result; +END; +$$ LANGUAGE plpgsql IMMUTABLE; + +------------------------------------------------------------------ +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.percentile_disc(myarray anyarray, percentile double precision) RETURNS double precision AS $$ +DECLARE + ary_cnt bigint; + row_num double precision; + frn bigint; + calc_result double precision; + new_array double precision[]; +BEGIN + new_array := array_sort(myarray); + ary_cnt := array_length(new_array,1); + row_num := percentile * ary_cnt; + frn := ceil(row_num); + calc_result := new_array[frn]; + RETURN calc_result; +END; +$$ LANGUAGE plpgsql IMMUTABLE; + +----------------------------------------------------------------- +-- aggregation for independent var +DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.agg_array_concat(REAL[]); +CREATE AGGREGATE MADLIB_SCHEMA.agg_array_concat(REAL[]) ( + SFUNC = array_cat, + PREFUNC = array_cat, + STYPE = REAL[] + + ); + +-- aggregation for dependent var +DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.agg_array_concat(SMALLINT[]); +CREATE AGGREGATE MADLIB_SCHEMA.agg_array_concat(SMALLINT[]) ( + SFUNC = array_cat, + PREFUNC = array_cat, + STYPE = SMALLINT[] + + ); + +CREATE FUNCTION MADLIB_SCHEMA.convert_array_to_bytea(var REAL[]) +RETURNS BYTEA +AS +$$ +import numpy as np + +return np.array(var, dtype=np.float32).tobytes() +$$ LANGUAGE plpythonu; + +CREATE FUNCTION MADLIB_SCHEMA.convert_array_to_bytea(var SMALLINT[]) +RETURNS BYTEA +AS +$$ +import numpy as np + +return np.array(var, dtype=np.int16).tobytes() +$$ LANGUAGE plpythonu; + + +CREATE FUNCTION MADLIB_SCHEMA.convert_bytea_to_real_array(var BYTEA) +RETURNS REAL[] +AS +$$ +import numpy as np + +return np.frombuffer(var, dtype=np.float32) +$$ LANGUAGE plpythonu; + + +CREATE FUNCTION MADLIB_SCHEMA.convert_bytea_to_smallint_array(var BYTEA) +RETURNS SMALLINT[] +AS +$$ +import numpy as np + +return np.frombuffer(var, dtype=np.int16) +$$ LANGUAGE plpythonu; diff --git a/methods/kmeans/src/pg_gp/kmeans.c b/methods/kmeans/src/pg_gp/kmeans.cpp similarity index 95% rename from methods/kmeans/src/pg_gp/kmeans.c rename to methods/kmeans/src/pg_gp/kmeans.cpp index d74f65b..a4785b0 100644 --- a/methods/kmeans/src/pg_gp/kmeans.c +++ b/methods/kmeans/src/pg_gp/kmeans.cpp @@ -17,6 +17,19 @@ typedef enum { TANIMOTO } KMeansMetric; +#ifdef __cplusplus +extern "C" { +#endif +Datum internal_get_array_of_close_canopies(PG_FUNCTION_ARGS); +Datum internal_kmeans_closest_centroid(PG_FUNCTION_ARGS); +Datum internal_kmeans_agg_centroid_trans(PG_FUNCTION_ARGS); +Datum internal_kmeans_agg_centroid_merge(PG_FUNCTION_ARGS); +Datum internal_kmeans_canopy_transition(PG_FUNCTION_ARGS); +Datum internal_remove_close_canopies(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + static inline int @@ -128,7 +141,7 @@ internal_get_array_of_close_canopies(PG_FUNCTION_ARGS) get_svec_array_elms(PG_GETARG_ARRAYTYPE_P(verify_arg_nonnull(fcinfo, 1)), &all_canopies, &num_all_canopies); threshold = PG_GETARG_FLOAT8(verify_arg_nonnull(fcinfo, 2)); - metric_fn = get_metric_fn(PG_GETARG_INT32(verify_arg_nonnull(fcinfo, 3))); + metric_fn = get_metric_fn((KMeansMetric)PG_GETARG_INT32(verify_arg_nonnull(fcinfo, 3))); mem_context_for_function_calls = setup_mem_context_for_functional_calls(); close_canopies = (int32 *) palloc(sizeof(int32) * num_all_canopies); @@ -382,7 +395,7 @@ internal_kmeans_closest_centroid(PG_FUNCTION_ARGS) { cid = indirect ? canopy_ids[i] - ARR_LBOUND(canopy_ids_arr)[0] : i; double * centroid = c_centroids_array+cid*dimension; - MetricFunc func = get_metric_fn_for_array(dist_metric); + MetricFunc func = get_metric_fn_for_array((KMeansMetric)dist_metric); distance = (*func)(centroid, c_point_array, dimension); if (distance < min_distance) { @@ -459,7 +472,7 @@ internal_kmeans_agg_centroid_trans(PG_FUNCTION_ARGS) { if (PG_ARGISNULL(0)) { - c_array = palloc0(expected_array_len*sizeof(float8)); + c_array = (float8 *) palloc0(expected_array_len*sizeof(float8)); rebuild_array = true; } else @@ -565,7 +578,7 @@ internal_kmeans_canopy_transition(PG_FUNCTION_ARGS) { canopies_arr = PG_GETARG_ARRAYTYPE_P(verify_arg_nonnull(fcinfo, 0)); get_svec_array_elms(canopies_arr, &canopies, &num_canopies); point = PG_GETARG_SVECTYPE_P(verify_arg_nonnull(fcinfo, 1)); - metric_fn = get_metric_fn(PG_GETARG_INT32(verify_arg_nonnull(fcinfo, 2))); + metric_fn = get_metric_fn((KMeansMetric)PG_GETARG_INT32(verify_arg_nonnull(fcinfo, 2))); threshold = PG_GETARG_FLOAT8(verify_arg_nonnull(fcinfo, 3)); mem_context_for_function_calls = setup_mem_context_for_functional_calls(); @@ -609,7 +622,7 @@ internal_remove_close_canopies(PG_FUNCTION_ARGS) { all_canopies_arr = PG_GETARG_ARRAYTYPE_P(verify_arg_nonnull(fcinfo, 0)); get_svec_array_elms(all_canopies_arr, &all_canopies, &num_all_canopies); - metric_fn = get_metric_fn(PG_GETARG_INT32(verify_arg_nonnull(fcinfo, 1))); + metric_fn = get_metric_fn((KMeansMetric)PG_GETARG_INT32(verify_arg_nonnull(fcinfo, 1))); threshold = PG_GETARG_FLOAT8(verify_arg_nonnull(fcinfo, 2)); mem_context_for_function_calls = setup_mem_context_for_functional_calls(); diff --git a/methods/sketch/src/pg_gp/countmin.c b/methods/sketch/src/pg_gp/countmin.cpp similarity index 97% rename from methods/sketch/src/pg_gp/countmin.c rename to methods/sketch/src/pg_gp/countmin.cpp index 8b70ec6..152d51a 100644 --- a/methods/sketch/src/pg_gp/countmin.c +++ b/methods/sketch/src/pg_gp/countmin.cpp @@ -39,6 +39,17 @@ #include "sketch_support.h" #include "countmin.h" +#ifdef __cplusplus +extern "C" { +#endif +Datum __cmsketch_int8_trans(PG_FUNCTION_ARGS); +Datum __cmsketch_base64_final(PG_FUNCTION_ARGS); +Datum __cmsketch_merge(PG_FUNCTION_ARGS); +Datum cmsketch_dump(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + PG_FUNCTION_INFO_V1(__cmsketch_int8_trans); /* @@ -248,7 +259,7 @@ Datum __cmsketch_base64_final(PG_FUNCTION_ARGS) elog(ERROR, "invalid transition state for cmsketch"); } - out = palloc0(len); + out = (bytea *) palloc0(len); if (VARSIZE(blob) > VARHDRSZ) { sketch = (cmtransval *)VARDATA(blob); memcpy((uint8 *)VARDATA(out), sketch->sketches, len - VARHDRSZ); @@ -263,7 +274,7 @@ Datum __cmsketch_base64_final(PG_FUNCTION_ARGS) res; datalen = VARSIZE(data) - VARHDRSZ; resultlen = b64_enc_len(datalen); - result = palloc(VARHDRSZ + resultlen); + result = (text *) palloc(VARHDRSZ + resultlen); res = b64_encode(VARDATA(data), datalen, VARDATA(result)); /* Make this FATAL 'cause we've trodden on memory ... */ if (res > resultlen) diff --git a/methods/sketch/src/pg_gp/countmin.h b/methods/sketch/src/pg_gp/countmin.h index 28f1347..11476d8 100644 --- a/methods/sketch/src/pg_gp/countmin.h +++ b/methods/sketch/src/pg_gp/countmin.h @@ -172,6 +172,10 @@ int cnt_cmp_desc(const void *i, const void *j); /* UDF protos */ +#ifdef __cplusplus +extern "C" { +#endif + Datum __cmsketch_int8_trans(PG_FUNCTION_ARGS); Datum cmsketch_width_histogram(PG_FUNCTION_ARGS); Datum cmsketch_dhistogram(PG_FUNCTION_ARGS); @@ -187,5 +191,8 @@ Datum __mfvsketch_trans(PG_FUNCTION_ARGS); Datum __mfvsketch_final(PG_FUNCTION_ARGS); Datum __mfvsketch_merge(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif #endif /* _COUNTMIN_H_ */ diff --git a/methods/sketch/src/pg_gp/fm.c b/methods/sketch/src/pg_gp/fm.cpp similarity index 98% rename from methods/sketch/src/pg_gp/fm.c rename to methods/sketch/src/pg_gp/fm.cpp index 39f3413..c8b8011 100644 --- a/methods/sketch/src/pg_gp/fm.c +++ b/methods/sketch/src/pg_gp/fm.cpp @@ -104,6 +104,17 @@ typedef struct { char storage[]; } fmtransval; +#ifdef __cplusplus +extern "C" { +#endif +Datum __fmsketch_trans(PG_FUNCTION_ARGS); +Datum __fmsketch_count_distinct(PG_FUNCTION_ARGS); +Datum __fmsketch_merge(PG_FUNCTION_ARGS); +Datum big_or(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + /* check whter the contents in fmtransval::storage is safe for sortasort */ void check_sortasort(sortasort *st, size_t st_size) { size_t left_len = st_size; @@ -317,7 +328,7 @@ Datum __fmsketch_trans(PG_FUNCTION_ARGS) * zero bits for an empty FM sketch. * \param template an optional pre-existing transval whose fields we can copy in */ -bytea *fm_new(fmtransval *template) +bytea *fm_new(fmtransval *template1) { int fmsize = VARHDRSZ + sizeof(fmtransval) + FMSKETCH_SZ; /* use palloc0 to make sure it's initialized to 0 */ @@ -327,8 +338,8 @@ bytea *fm_new(fmtransval *template) SET_VARSIZE(newblob, fmsize); transval = (fmtransval *)VARDATA(newblob); /* copy over the struct values */ - if (template != NULL) - memcpy(transval, template, sizeof(fmtransval)); + if (template1 != NULL) + memcpy(transval, template1, sizeof(fmtransval)); /* set status to BIG, possibly overwriting what was in template */ transval->status = BIG; diff --git a/methods/sketch/src/pg_gp/mfvsketch.c b/methods/sketch/src/pg_gp/mfvsketch.cpp similarity index 98% rename from methods/sketch/src/pg_gp/mfvsketch.c rename to methods/sketch/src/pg_gp/mfvsketch.cpp index 57e8c5d..6192ad6 100644 --- a/methods/sketch/src/pg_gp/mfvsketch.c +++ b/methods/sketch/src/pg_gp/mfvsketch.cpp @@ -45,6 +45,16 @@ #include +#ifdef __cplusplus +extern "C" { +#endif +Datum __mfvsketch_trans(PG_FUNCTION_ARGS); +Datum __mfvsketch_final(PG_FUNCTION_ARGS); +Datum __mfvsketch_merge(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + /* check whether the content in the given bytea is safe for mfvtransval */ void check_mfvtransval(bytea *storage) { size_t left_len = VARSIZE(storage); @@ -515,7 +525,7 @@ bytea *mfvsketch_merge_c(bytea *transblob1, bytea *transblob2) { mfvtransval *transval1 = (mfvtransval *)VARDATA(transblob1); mfvtransval *transval2 = (mfvtransval *)VARDATA(transblob2); - void *newblob; + bytea *newblob; mfvtransval *newval; uint32 i, j, cnt; @@ -572,7 +582,7 @@ bytea *mfvsketch_merge_c(bytea *transblob1, bytea *transblob2) qsort(transval1->mfvs, transval1->next_mfv, sizeof(offsetcnt), cnt_cmp_desc); qsort(transval2->mfvs, transval2->next_mfv, sizeof(offsetcnt), cnt_cmp_desc); - Datum *values_seen = palloc0((2 * newval->max_mfvs) * sizeof(Datum)); + Datum *values_seen = (Datum *)palloc0((2 * newval->max_mfvs) * sizeof(Datum)); unsigned int k; /* choose top k from transval1 and transval2 */ diff --git a/methods/sketch/src/pg_gp/sketch.sql_in b/methods/sketch/src/pg_gp/sketch.sql_in index 2ae36a2..60e4ae8 100644 --- a/methods/sketch/src/pg_gp/sketch.sql_in +++ b/methods/sketch/src/pg_gp/sketch.sql_in @@ -381,28 +381,28 @@ DROP FUNCTION IF EXISTS MADLIB_SCHEMA.big_or(bitmap1 bytea, bitmap2 bytea) CASCA CREATE FUNCTION MADLIB_SCHEMA.big_or(bitmap1 bytea, bitmap2 bytea) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__fmsketch_trans(bitmaps bytea, input anyelement) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__fmsketch_trans(bitmaps bytea, input anyelement) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__fmsketch_count_distinct(bitmaps bytea) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__fmsketch_count_distinct(bitmaps bytea) RETURNS int8 AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__fmsketch_merge(bitmaps1 bytea, bitmaps2 bytea) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__fmsketch_merge(bitmaps1 bytea, bitmaps2 bytea) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.fmsketch_dcount(anyelement); @@ -431,42 +431,42 @@ DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__cmsketch_int8_trans(bytea, int8) CASCADE CREATE FUNCTION MADLIB_SCHEMA.__cmsketch_int8_trans(bitmaps bytea, input int8) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__cmsketch_int8_trans(bytea, int8, int8) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__cmsketch_int8_trans(bitmaps bytea, input int8, arg1 int8) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__cmsketch_int8_trans(bytea, int8, int8, int8) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__cmsketch_int8_trans(bitmaps bytea, input int8, arg1 int8, arg2 int8) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__cmsketch_int8_trans(bytea, int8, int8, int8, int8) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__cmsketch_int8_trans(bitmaps bytea, input int8, arg1 int8, arg2 int8, arg3 int8) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__cmsketch_base64_final(bytea) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__cmsketch_base64_final(sketch bytea) RETURNS text AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__cmsketch_merge(bytea, bytea) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__cmsketch_merge(bytea, bytea) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.cmsketch(int8); @@ -580,33 +580,33 @@ DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__mfvsketch_trans(bytea, anyelement, int4) CREATE FUNCTION MADLIB_SCHEMA.__mfvsketch_trans(bytea, anyelement, int4) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__mfvsketch_final(bytea) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__mfvsketch_final(bytea) RETURNS text[][] AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__mfvsketch_merge(bytea, bytea) CASCADE; CREATE FUNCTION MADLIB_SCHEMA.__mfvsketch_merge(bytea, bytea) RETURNS bytea AS 'MODULE_PATHNAME' -LANGUAGE C STRICT +LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__sketch_rightmost_one(bytea, integer, integer) -RETURNS integer AS 'MODULE_PATHNAME', 'sketch_rightmost_one' LANGUAGE C STRICT +RETURNS integer AS 'MODULE_PATHNAME', 'sketch_rightmost_one' LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__sketch_leftmost_zero(bytea, integer, integer) -RETURNS integer AS 'MODULE_PATHNAME', 'sketch_leftmost_zero' LANGUAGE C STRICT +RETURNS integer AS 'MODULE_PATHNAME', 'sketch_leftmost_zero' LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__sketch_array_set_bit_in_place(bytea, integer, integer, integer, integer) -RETURNS bytea AS 'MODULE_PATHNAME', 'sketch_array_set_bit_in_place' LANGUAGE C STRICT +RETURNS bytea AS 'MODULE_PATHNAME', 'sketch_array_set_bit_in_place' LANGUAGE C STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.mfvsketch_top_histogram( anyelement, int4); diff --git a/methods/sketch/src/pg_gp/sketch_support.c b/methods/sketch/src/pg_gp/sketch_support.cpp similarity index 98% rename from methods/sketch/src/pg_gp/sketch_support.c rename to methods/sketch/src/pg_gp/sketch_support.cpp index 1cdeb90..ca57b07 100644 --- a/methods/sketch/src/pg_gp/sketch_support.c +++ b/methods/sketch/src/pg_gp/sketch_support.cpp @@ -50,7 +50,15 @@ #include #include "sketch_support.h" - +#ifdef __cplusplus +extern "C" { +#endif +Datum sketch_array_set_bit_in_place(PG_FUNCTION_ARGS); +Datum sketch_rightmost_one(PG_FUNCTION_ARGS); +Datum sketch_leftmost_zero(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif /*! * Simple linear function to find the rightmost bit that's set to one @@ -302,7 +310,7 @@ bytea *sketch_md5_bytea(Datum dat, Oid typOid) // according to postgres' libpq/md5.c, need 33 bytes to hold // null-terminated md5 string char outbuf[MD5_HASHLEN*2+1]; - bytea *out = palloc0(MD5_HASHLEN+VARHDRSZ); + bytea *out = (bytea *) palloc0(MD5_HASHLEN+VARHDRSZ); bool byval = get_typbyval(typOid); int len = ExtractDatumLen(dat, get_typlen(typOid), byval, -1); void *datp = DatumExtractPointer(dat, byval); diff --git a/methods/sketch/src/pg_gp/sortasort.c b/methods/sketch/src/pg_gp/sortasort.cpp similarity index 99% rename from methods/sketch/src/pg_gp/sortasort.c rename to methods/sketch/src/pg_gp/sortasort.cpp index c052ca7..1bc9a72 100644 --- a/methods/sketch/src/pg_gp/sortasort.c +++ b/methods/sketch/src/pg_gp/sortasort.cpp @@ -30,6 +30,13 @@ #define SORTASORT_DATA(s) (((char *)(s->dir)) + \ (s->capacity * (sizeof (s->dir[0])))) +#ifdef __cplusplus +extern "C" { +#endif +#ifdef __cplusplus +} +#endif + /* * get the ith item stored in sortasort */ diff --git a/methods/stemmer/src/pg_gp/porter_stemmer.c b/methods/stemmer/src/pg_gp/porter_stemmer.cpp similarity index 97% rename from methods/stemmer/src/pg_gp/porter_stemmer.c rename to methods/stemmer/src/pg_gp/porter_stemmer.cpp index 598e232..2a65862 100644 --- a/methods/stemmer/src/pg_gp/porter_stemmer.c +++ b/methods/stemmer/src/pg_gp/porter_stemmer.cpp @@ -61,6 +61,15 @@ */ #include "libstemmer/include/libstemmer.h" +#ifdef __cplusplus +extern "C" { +#endif +Datum stem_token(PG_FUNCTION_ARGS); +Datum stem_token_arr(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + static const char * stem_token_text(struct sb_stemmer *stemmer, text *token) { char * stemmed_token = TextDatumGetCString(PointerGetDatum(token)); if (strcmp(stemmed_token, "") == 0) { diff --git a/methods/stemmer/src/pg_gp/porter_stemmer.sql_in b/methods/stemmer/src/pg_gp/porter_stemmer.sql_in index 43e2805..1e39b5f 100644 --- a/methods/stemmer/src/pg_gp/porter_stemmer.sql_in +++ b/methods/stemmer/src/pg_gp/porter_stemmer.sql_in @@ -172,7 +172,7 @@ File porter_stemmer.sql_in for list of functions and usage. */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.stem_token(token text) RETURNS text AS 'MODULE_PATHNAME', 'stem_token' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /** @@ -184,7 +184,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); */ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.stem_token_arr(token_arr text[]) RETURNS text[] AS 'MODULE_PATHNAME', 'stem_token_arr' -LANGUAGE C IMMUTABLE +LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); diff --git a/methods/svec/src/pg_gp/SparseData.c b/methods/svec/src/pg_gp/SparseData.cpp similarity index 100% rename from methods/svec/src/pg_gp/SparseData.c rename to methods/svec/src/pg_gp/SparseData.cpp diff --git a/methods/svec/src/pg_gp/operators.c b/methods/svec/src/pg_gp/operators.cpp similarity index 93% rename from methods/svec/src/pg_gp/operators.c rename to methods/svec/src/pg_gp/operators.cpp index fb83998..4c41fd5 100644 --- a/methods/svec/src/pg_gp/operators.c +++ b/methods/svec/src/pg_gp/operators.cpp @@ -24,6 +24,65 @@ PG_MODULE_MAGIC; #endif +#ifdef __cplusplus +extern "C" { +#endif +Datum svec_concat_replicate(PG_FUNCTION_ARGS); +Datum svec_concat(PG_FUNCTION_ARGS); +Datum svec_cmp(PG_FUNCTION_ARGS); +Datum svec_ge(PG_FUNCTION_ARGS); +Datum svec_lt(PG_FUNCTION_ARGS); +Datum svec_le(PG_FUNCTION_ARGS); +Datum svec_gt(PG_FUNCTION_ARGS); +Datum svec_ne(PG_FUNCTION_ARGS); +Datum svec_eq(PG_FUNCTION_ARGS); +Datum svec_l2_cmp(PG_FUNCTION_ARGS); +Datum svec_l2_lt(PG_FUNCTION_ARGS); +Datum svec_l2_le(PG_FUNCTION_ARGS); +Datum svec_l2_eq(PG_FUNCTION_ARGS); +Datum svec_l2_ne(PG_FUNCTION_ARGS); +Datum svec_l2_gt(PG_FUNCTION_ARGS); +Datum svec_l2_ge(PG_FUNCTION_ARGS); +Datum svec_pow(PG_FUNCTION_ARGS); +Datum svec_minus(PG_FUNCTION_ARGS); +Datum svec_plus(PG_FUNCTION_ARGS); +Datum svec_mult(PG_FUNCTION_ARGS); +Datum svec_div(PG_FUNCTION_ARGS); +Datum svec_dot(PG_FUNCTION_ARGS); +Datum svec_cast_int2(PG_FUNCTION_ARGS); +Datum svec_cast_int4(PG_FUNCTION_ARGS); +Datum svec_cast_int8(PG_FUNCTION_ARGS); +Datum svec_cast_float4(PG_FUNCTION_ARGS); +Datum svec_cast_float8(PG_FUNCTION_ARGS); +Datum svec_cast_numeric(PG_FUNCTION_ARGS); +Datum float8arr_cast_int2(PG_FUNCTION_ARGS); +Datum float8arr_cast_int4(PG_FUNCTION_ARGS); +Datum float8arr_cast_int8(PG_FUNCTION_ARGS); +Datum float8arr_cast_float4(PG_FUNCTION_ARGS); +Datum float8arr_cast_float8(PG_FUNCTION_ARGS); +Datum float8arr_cast_numeric(PG_FUNCTION_ARGS); +Datum svec_cast_float8arr(PG_FUNCTION_ARGS); +Datum svec_cast_positions_float8arr(PG_FUNCTION_ARGS); +Datum float8arr_equals(PG_FUNCTION_ARGS); +Datum float8arr_dot(PG_FUNCTION_ARGS); +Datum float8arr_minus_float8arr(PG_FUNCTION_ARGS); +Datum svec_minus_float8arr(PG_FUNCTION_ARGS); +Datum float8arr_minus_svec(PG_FUNCTION_ARGS); +Datum float8arr_plus_float8arr(PG_FUNCTION_ARGS); +Datum svec_plus_float8arr(PG_FUNCTION_ARGS); +Datum float8arr_plus_svec(PG_FUNCTION_ARGS); +Datum float8arr_mult_float8arr(PG_FUNCTION_ARGS); +Datum svec_mult_float8arr(PG_FUNCTION_ARGS); +Datum float8arr_mult_svec(PG_FUNCTION_ARGS); +Datum float8arr_div_float8arr(PG_FUNCTION_ARGS); +Datum svec_div_float8arr(PG_FUNCTION_ARGS); +Datum float8arr_div_svec(PG_FUNCTION_ARGS); +Datum svec_dot_float8arr(PG_FUNCTION_ARGS); +Datum float8arr_dot_svec(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + /** * For many functions defined in this module, the operation has no meaning * if the array dimensions aren't the same, unless one of the inputs is a diff --git a/methods/svec/src/pg_gp/sparse_vector.c b/methods/svec/src/pg_gp/sparse_vector.cpp similarity index 97% rename from methods/svec/src/pg_gp/sparse_vector.c rename to methods/svec/src/pg_gp/sparse_vector.cpp index 7944419..f0177d6 100644 --- a/methods/svec/src/pg_gp/sparse_vector.c +++ b/methods/svec/src/pg_gp/sparse_vector.cpp @@ -27,6 +27,17 @@ #include "utils/memutils.h" #include "sparse_vector.h" +#ifdef __cplusplus +extern "C" { +#endif +Datum svec_send(PG_FUNCTION_ARGS); +Datum svec_recv(PG_FUNCTION_ARGS); +Datum svec_return_array(PG_FUNCTION_ARGS); +Datum svec_out(PG_FUNCTION_ARGS); +Datum svec_in(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif /** * @return An array of float8s obtained by converting a given sparse vector */ diff --git a/methods/svec/src/pg_gp/sparse_vector.h b/methods/svec/src/pg_gp/sparse_vector.h index e29185b..18a5171 100644 --- a/methods/svec/src/pg_gp/sparse_vector.h +++ b/methods/svec/src/pg_gp/sparse_vector.h @@ -104,6 +104,10 @@ void check_dimension(SvecType *svec1, SvecType *svec2, char *msg); double svec_svec_dot_product(SvecType *svec1, SvecType *svec2); SparseData sdata_uncompressed_from_float8arr_internal(ArrayType *array); +#ifdef __cplusplus +extern "C" { +#endif + Datum svec_in(PG_FUNCTION_ARGS); Datum svec_out(PG_FUNCTION_ARGS); Datum svec_return_vector(PG_FUNCTION_ARGS); @@ -154,4 +158,8 @@ Datum float8arr_cast_numeric(PG_FUNCTION_ARGS); Datum svec_cast_float8arr(PG_FUNCTION_ARGS); Datum svec_cast_positions_float8arr(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + #endif /* SPARSEVECTOR_H */ diff --git a/methods/svec/src/pg_gp/svec.sql_in b/methods/svec/src/pg_gp/svec.sql_in index 2d0e21e..4f484c0 100644 --- a/methods/svec/src/pg_gp/svec.sql_in +++ b/methods/svec/src/pg_gp/svec.sql_in @@ -634,7 +634,7 @@ CREATE TYPE MADLIB_SCHEMA.svec; CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_in(cstring) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME' - LANGUAGE C IMMUTABLE STRICT + LANGUAGE C IMMUTABLE STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Converts SVEC to CSTRING. @@ -642,7 +642,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_in(cstring) CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_out(MADLIB_SCHEMA.svec) RETURNS cstring AS 'MODULE_PATHNAME' - LANGUAGE C IMMUTABLE STRICT + LANGUAGE C IMMUTABLE STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Converts SVEC internal representation to SVEC. @@ -650,7 +650,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_out(MADLIB_SCHEMA.svec) CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_recv(internal) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME' - LANGUAGE C IMMUTABLE STRICT + LANGUAGE C IMMUTABLE STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Converts SVEC to BYTEA. @@ -658,7 +658,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_recv(internal) CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_send(MADLIB_SCHEMA.svec) RETURNS bytea AS 'MODULE_PATHNAME' - LANGUAGE C IMMUTABLE STRICT + LANGUAGE C IMMUTABLE STRICT NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); CREATE TYPE MADLIB_SCHEMA.svec ( @@ -673,232 +673,232 @@ CREATE TYPE MADLIB_SCHEMA.svec ( --! Adds two SVECs together, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_plus(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_plus' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_plus(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_plus' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Minus second SVEC from the first, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_minus(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_minus' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_minus(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_minus' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Divides the first SVEC by the second, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_div(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_div' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_div(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_div' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Multiplies two SVEVs together, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_mult(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_mult' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_mult(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_mult' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Raises each element of the first SVEC to the power given by second SVEC, which must have dimension 1 (a scalar). --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_pow(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_pow' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_pow(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_pow' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Compare two SVECs, -1 for left side smaller, 1 for greater, 0 for equal --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cmp(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS integer AS 'MODULE_PATHNAME', 'svec_cmp' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cmp(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS integer AS 'MODULE_PATHNAME', 'svec_cmp' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if the left SVEC is greater or equal to the right. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_ge(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_ge' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_ge(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_ge' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if the left SVEC is less than the right. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_lt(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_lt' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_lt(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_lt' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if the left SVEC is less or equal to the right. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_le(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_le' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_le(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_le' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if the left SVEC is greater. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_gt(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_gt' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_gt(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_gt' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if two SVECs are not equal. If the two SVECs are of different size, then will return true. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_ne(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_ne' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_ne(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_ne' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if two SVECs are equal. If the two SVECs are of different size, then will return false. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_eq(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_eq' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_eq(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS boolean AS 'MODULE_PATHNAME', 'svec_eq' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if two float8 arrays are equal --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_eq(float8[],float8[]) RETURNS boolean AS 'MODULE_PATHNAME', 'float8arr_equals' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_eq(float8[],float8[]) RETURNS boolean AS 'MODULE_PATHNAME', 'float8arr_equals' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Minus second array from the first array, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_minus_float8arr(float8[],float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_minus_float8arr' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_minus_float8arr(float8[],float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_minus_float8arr' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Minus second SVEC from the first array, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_minus_svec(float8[],MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_minus_svec' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_minus_svec(float8[],MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_minus_svec' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Minus second array from the first SVEC, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_minus_float8arr(MADLIB_SCHEMA.svec,float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_minus_float8arr' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_minus_float8arr(MADLIB_SCHEMA.svec,float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_minus_float8arr' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Adds two arrays together, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_plus_float8arr(float8[],float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_plus_float8arr' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_plus_float8arr(float8[],float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_plus_float8arr' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Adds an array and an SVEC, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_plus_svec(float8[],MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_plus_svec' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_plus_svec(float8[],MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_plus_svec' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Adds an SVEC and an array, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_plus_float8arr(MADLIB_SCHEMA.svec,float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_plus_float8arr' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_plus_float8arr(MADLIB_SCHEMA.svec,float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_plus_float8arr' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Multiplies two float8 arrays, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_mult_float8arr(float8[],float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_mult_float8arr' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_mult_float8arr(float8[],float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_mult_float8arr' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Multiplies an array and an SVEC, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_mult_svec(float8[],MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_mult_svec' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_mult_svec(float8[],MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_mult_svec' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Multiplies an SVEC and an array, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_mult_float8arr(MADLIB_SCHEMA.svec,float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_mult_float8arr' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_mult_float8arr(MADLIB_SCHEMA.svec,float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_mult_float8arr' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Divides a float8 array by another, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_div_float8arr(float8[],float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_div_float8arr' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_div_float8arr(float8[],float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_div_float8arr' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Divides a float8 array by an SVEC, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_div_svec(float8[],MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_div_svec' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_div_svec(float8[],MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'float8arr_div_svec' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Divides an SVEC by a float8 array, element by element. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_div_float8arr(MADLIB_SCHEMA.svec,float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_div_float8arr' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_div_float8arr(MADLIB_SCHEMA.svec,float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_div_float8arr' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Computes the dot product of two SVECs. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_dot(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS float8 AS 'MODULE_PATHNAME', 'svec_dot' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_dot(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS float8 AS 'MODULE_PATHNAME', 'svec_dot' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Computes the dot product of two float8 arrays. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_dot(float8[],float8[]) RETURNS float8 AS 'MODULE_PATHNAME', 'float8arr_dot' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_dot(float8[],float8[]) RETURNS float8 AS 'MODULE_PATHNAME', 'float8arr_dot' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Computes the dot product of an SVEC and a float8 array. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_dot(MADLIB_SCHEMA.svec,float8[]) RETURNS float8 AS 'MODULE_PATHNAME', 'svec_dot_float8arr' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_dot(MADLIB_SCHEMA.svec,float8[]) RETURNS float8 AS 'MODULE_PATHNAME', 'svec_dot_float8arr' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Computes the dot product of a float8 array and an SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_dot(float8[],MADLIB_SCHEMA.svec) RETURNS float8 AS 'MODULE_PATHNAME', 'float8arr_dot_svec' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_dot(float8[],MADLIB_SCHEMA.svec) RETURNS float8 AS 'MODULE_PATHNAME', 'float8arr_dot_svec' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts an int2 into an SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_int2(int2) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_int2' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_int2(int2) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_int2' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts an int4 into an SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_int4(int4) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_int4' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_int4(int4) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_int4' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts an int8 into an SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_int8(bigint) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_int8' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_int8(bigint) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_int8' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts a float4 into an SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_float4(float4) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_float4' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_float4(float4) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_float4' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts a float8 into an SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_float8(float8) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_float8' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_float8(float8) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_float8' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts a numeric into an SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_numeric(numeric) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_numeric' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_numeric(numeric) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_numeric' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts an int2 into a float8 array. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_int2(int2) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_int2' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_int2(int2) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_int2' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts an int4 into a float8 array. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_int4(int4) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_int4' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_int4(int4) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_int4' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts an int8 into a float8 array. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_int8(bigint) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_int8' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_int8(bigint) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_int8' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts a float4 into a float8 array. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_float4(float4) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_float4' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_float4(float4) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_float4' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts a float8 into a float8 array. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_float8(float8) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_float8' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_float8(float8) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_float8' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts a numeric into a float8 array. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_numeric(numeric) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_numeric' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.float8arr_cast_numeric(numeric) RETURNS float8[] AS 'MODULE_PATHNAME', 'float8arr_cast_numeric' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts a float8 into an SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_float8arr(float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_float8arr' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_float8arr(float8[]) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_float8arr' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts an array of int8 positions, float8 values into an SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_positions_float8arr(int8[],float8[],int8,float8) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_positions_float8arr' STRICT LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_cast_positions_float8arr(int8[],float8[],int8,float8) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_cast_positions_float8arr' STRICT LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Casts an SVEC into a float8 array. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_return_array(MADLIB_SCHEMA.svec) RETURNS float8[] AS 'MODULE_PATHNAME', 'svec_return_array' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_return_array(MADLIB_SCHEMA.svec) RETURNS float8[] AS 'MODULE_PATHNAME', 'svec_return_array' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Concatenates two SVECs. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_concat(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_concat' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_concat(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_concat' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Replicates n copies of an SVEC and concatenates them together. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_concat_replicate(int4,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_concat_replicate' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_concat_replicate(int4,MADLIB_SCHEMA.svec) RETURNS MADLIB_SCHEMA.svec AS 'MODULE_PATHNAME', 'svec_concat_replicate' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /* @@ -1108,37 +1108,37 @@ CREATE OPERATOR MADLIB_SCHEMA.= ( -- Comparisons based on L2 Norm --! Returns true if the l2 norm of the first SVEC is less than that of the second SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_lt(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_lt' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_lt(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_lt' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if the l2 norm of the first SVEC is less than or equal to that of the second SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_le(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_le' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_le(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_le' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if the l2 norm of the first SVEC is equal to that of the second SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_eq(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_eq' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_eq(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_eq' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if the l2 norm of the first SVEC is not equal to that of the second SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_ne(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_ne' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_ne(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_ne' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if the l2 norm of the first SVEC is greater than that of the second SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_gt(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_gt' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_gt(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_gt' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns true if the l2 norm of the first SVEC is greater than or equal to that of the second SVEC. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_ge(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_ge' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_ge(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS bool AS 'MODULE_PATHNAME', 'svec_l2_ge' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); --! Returns a value indicating the relative values of the l2 norms of two SVECs. --! -CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_cmp(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS integer AS 'MODULE_PATHNAME', 'svec_l2_cmp' LANGUAGE C IMMUTABLE +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.svec_l2_cmp(MADLIB_SCHEMA.svec,MADLIB_SCHEMA.svec) RETURNS integer AS 'MODULE_PATHNAME', 'svec_l2_cmp' LANGUAGE C IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); /* diff --git a/methods/svec_util/src/pg_gp/generate_svec.c b/methods/svec_util/src/pg_gp/generate_svec.cpp similarity index 95% rename from methods/svec_util/src/pg_gp/generate_svec.c rename to methods/svec_util/src/pg_gp/generate_svec.cpp index 1d7012c..b081b82 100644 --- a/methods/svec_util/src/pg_gp/generate_svec.c +++ b/methods/svec_util/src/pg_gp/generate_svec.cpp @@ -9,6 +9,14 @@ #include "../../../svec/src/pg_gp/sparse_vector.h" +#ifdef __cplusplus +extern "C" { +#endif +Datum generate_sparse_vector(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + PG_FUNCTION_INFO_V1(generate_sparse_vector); Datum generate_sparse_vector(PG_FUNCTION_ARGS) { diff --git a/methods/svec_util/src/pg_gp/gp_sfv.c b/methods/svec_util/src/pg_gp/gp_sfv.cpp similarity index 99% rename from methods/svec_util/src/pg_gp/gp_sfv.c rename to methods/svec_util/src/pg_gp/gp_sfv.cpp index 7b2aeb6..1cbdb1b 100644 --- a/methods/svec_util/src/pg_gp/gp_sfv.c +++ b/methods/svec_util/src/pg_gp/gp_sfv.cpp @@ -17,7 +17,13 @@ #include "../../../svec/src/pg_gp/sparse_vector.h" +#ifdef __cplusplus +extern "C" { +#endif Datum gp_extract_feature_histogram(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif static void gp_extract_feature_histogram_errout(char *msg); diff --git a/methods/svec_util/src/pg_gp/svec_util.c b/methods/svec_util/src/pg_gp/svec_util.cpp similarity index 95% rename from methods/svec_util/src/pg_gp/svec_util.c rename to methods/svec_util/src/pg_gp/svec_util.cpp index 12c839c..23e43d8 100644 --- a/methods/svec_util/src/pg_gp/svec_util.c +++ b/methods/svec_util/src/pg_gp/svec_util.cpp @@ -24,11 +24,54 @@ PG_MODULE_MAGIC; #endif +#ifdef __cplusplus +extern "C" { +#endif +Datum float8_min(PG_FUNCTION_ARGS); +Datum float8_max(PG_FUNCTION_ARGS); +Datum svec_to_string(PG_FUNCTION_ARGS); +Datum svec_from_string(PG_FUNCTION_ARGS); +Datum svec_unnest(PG_FUNCTION_ARGS); +Datum svec_dimension(PG_FUNCTION_ARGS); +Datum svec_lapply(PG_FUNCTION_ARGS); +Datum svec_append(PG_FUNCTION_ARGS); +Datum svec_proj(PG_FUNCTION_ARGS); +Datum svec_subvec(PG_FUNCTION_ARGS); +Datum svec_reverse(PG_FUNCTION_ARGS); +Datum svec_change(PG_FUNCTION_ARGS); +Datum svec_eq_non_zero(PG_FUNCTION_ARGS); +Datum svec_contains(PG_FUNCTION_ARGS); +Datum svec_count(PG_FUNCTION_ARGS); +Datum svec_l2norm(PG_FUNCTION_ARGS); +Datum svec_svec_l2norm(PG_FUNCTION_ARGS); +Datum svec_svec_l1norm(PG_FUNCTION_ARGS); +Datum svec_svec_angle(PG_FUNCTION_ARGS); +Datum svec_svec_tanimoto_distance(PG_FUNCTION_ARGS); +Datum svec_normalize(PG_FUNCTION_ARGS); +Datum svec_l1norm(PG_FUNCTION_ARGS); +Datum svec_summate(PG_FUNCTION_ARGS); +Datum svec_log(PG_FUNCTION_ARGS); +Datum float8arr_l1norm(PG_FUNCTION_ARGS); +Datum float8arr_summate(PG_FUNCTION_ARGS); +Datum float8arr_l2norm(PG_FUNCTION_ARGS); +Datum float8arr_hash(PG_FUNCTION_ARGS); +Datum svec_pivot(PG_FUNCTION_ARGS); +Datum float8arr_median(PG_FUNCTION_ARGS); +Datum svec_median(PG_FUNCTION_ARGS); +Datum svec_nonbase_positions(PG_FUNCTION_ARGS); +Datum svec_nonbase_values(PG_FUNCTION_ARGS); +Datum svec_hash(PG_FUNCTION_ARGS); +Datum svec_mean_transition(PG_FUNCTION_ARGS); +Datum svec_mean_prefunc(PG_FUNCTION_ARGS); +Datum svec_mean_final(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + /* * Basic floating point operators like MIN,MAX */ PG_FUNCTION_INFO_V1( float8_min ); -Datum float8_min(PG_FUNCTION_ARGS); Datum float8_min(PG_FUNCTION_ARGS) { if (PG_ARGISNULL(0)) { @@ -137,7 +180,7 @@ Datum svec_unnest(PG_FUNCTION_ARGS) /* stuff done on every call of the function */ funcctx = SRF_PERCALL_SETUP(); - fctx = funcctx->user_fctx; + fctx = (svec_unnest_fctx *) funcctx->user_fctx; run_length = compword_to_int8(fctx->index_position); @@ -573,7 +616,7 @@ Datum svec_normalize(PG_FUNCTION_ARGS) norm = l2norm_sdata_values_double(sdata); - op_sdata_by_scalar_inplace( 3, (char *)&norm, sdata, 2); + op_sdata_by_scalar_inplace( (operation_t)3, (char *)&norm, sdata, 2); PG_RETURN_SVECTYPE_P( svec_from_sparsedata( sdata, true)); } @@ -915,7 +958,7 @@ partition_select (char **lists, size_t nlists, size_t *widths, tmp = (char **)palloc(nlists*sizeof(char *)); for (uint64 i=0;i +#ifdef __cplusplus +extern "C" { +#endif +Datum exec_sql_using(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + // SearchSysCache1 first appeared with commit e26c539 by Robert Haas // on Sun, 14 Feb 2010 18:42:19 +0000. If this macro is // not defined, we assume to be on a version of PostgreSQL that expects five @@ -105,7 +113,7 @@ exec_sql_using(PG_FUNCTION_ARGS) { for (int i = 1; i < nargs; i++){ if (PG_ARGISNULL(i)) { if (nulls == NULL) { - nulls = palloc0(sizeof(char) * (nargs - 1)); + nulls = (char *) palloc0(sizeof(char) * (nargs - 1)); memset(nulls, ' ', nargs - 1); } nulls[i - 1] = 'n'; diff --git a/methods/utils/src/pg_gp/noop.c b/methods/utils/src/pg_gp/noop.cpp similarity index 59% rename from methods/utils/src/pg_gp/noop.c rename to methods/utils/src/pg_gp/noop.cpp index 5521cad..7ac77cd 100644 --- a/methods/utils/src/pg_gp/noop.c +++ b/methods/utils/src/pg_gp/noop.cpp @@ -1,6 +1,14 @@ #include #include +#ifdef __cplusplus +extern "C" { +#endif +Datum noop(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + PG_FUNCTION_INFO_V1(noop); Datum noop(PG_FUNCTION_ARGS) { diff --git a/methods/utils/src/pg_gp/to_char.c b/methods/utils/src/pg_gp/to_char.cpp similarity index 89% rename from methods/utils/src/pg_gp/to_char.c rename to methods/utils/src/pg_gp/to_char.cpp index 2d33676..2dc2f43 100644 --- a/methods/utils/src/pg_gp/to_char.c +++ b/methods/utils/src/pg_gp/to_char.cpp @@ -7,6 +7,14 @@ PG_MODULE_MAGIC; #endif +#ifdef __cplusplus +extern "C" { +#endif +Datum __to_text(PG_FUNCTION_ARGS); +#ifdef __cplusplus +} +#endif + PG_FUNCTION_INFO_V1(__to_text); /* * @brief Cast a value to text. On some databases, there diff --git a/pom.xml b/pom.xml index 4d0eec1..d487d18 100644 --- a/pom.xml +++ b/pom.xml @@ -122,20 +122,20 @@ examples/gpce/sketches.sql examples/gpce/svd-mf.sql examples/gpce/svm.sql - methods/array_ops/src/pg_gp/array_ops.c + methods/array_ops/src/pg_gp/array_ops.cpp methods/array_ops/src/pg_gp/array_ops.sql_in methods/array_ops/src/pg_gp/test/array_ops.sql_in - methods/kmeans/src/pg_gp/kmeans.c + methods/kmeans/src/pg_gp/kmeans.cpp methods/sketch/src/pg_gp/__init__.py_in - methods/sketch/src/pg_gp/countmin.c + methods/sketch/src/pg_gp/countmin.cpp methods/sketch/src/pg_gp/countmin.h methods/sketch/src/pg_gp/countmin.py_in methods/sketch/src/pg_gp/fm.c - methods/sketch/src/pg_gp/mfvsketch.c + methods/sketch/src/pg_gp/mfvsketch.cpp methods/sketch/src/pg_gp/sketch.sql_in - methods/sketch/src/pg_gp/sketch_support.c + methods/sketch/src/pg_gp/sketch_support.cpp methods/sketch/src/pg_gp/sketch_support.h - methods/sketch/src/pg_gp/sortasort.c + methods/sketch/src/pg_gp/sortasort.cpp methods/sketch/src/pg_gp/sortasort.h methods/sketch/src/pg_gp/sql/cm.sql_in methods/sketch/src/pg_gp/sql/fm.sql_in @@ -143,25 +143,25 @@ methods/sketch/src/pg_gp/sql/support.sql_in methods/sketch/src/pg_gp/uncrustify.cfg methods/svec/src/pg_gp/float_specials.h - methods/svec/src/pg_gp/operators.c - methods/svec/src/pg_gp/sparse_vector.c + methods/svec/src/pg_gp/operators.cpp + methods/svec/src/pg_gp/sparse_vector.cpp methods/svec/src/pg_gp/sparse_vector.h - methods/svec/src/pg_gp/SparseData.c + methods/svec/src/pg_gp/SparseData.cpp methods/svec/src/pg_gp/SparseData.h methods/svec/src/pg_gp/svec.sql_in methods/svec_util/src/pg_gp/__init__.py_in - methods/svec_util/src/pg_gp/generate_svec.c + methods/svec_util/src/pg_gp/generate_svec.cpp methods/svec_util/src/pg_gp/generate_svec.py_in - methods/svec_util/src/pg_gp/gp_sfv.c + methods/svec_util/src/pg_gp/gp_sfv.cpp methods/svec_util/src/pg_gp/sql/gp_sfv.sql_in.todo.sql methods/svec_util/src/pg_gp/sql/gp_sfv_sort_order.sql_in methods/svec_util/src/pg_gp/sql/svec_test.sql_in - methods/svec_util/src/pg_gp/svec_util.c + methods/svec_util/src/pg_gp/svec_util.cpp methods/svec_util/src/pg_gp/svec_util.h methods/svec_util/src/pg_gp/svec_util.sql_in - methods/utils/src/pg_gp/exec_sql_using.c - methods/utils/src/pg_gp/noop.c - methods/utils/src/pg_gp/to_char.c + methods/utils/src/pg_gp/exec_sql_using.cpp + methods/utils/src/pg_gp/noop.cpp + methods/utils/src/pg_gp/to_char.cpp src/bin/CMakeLists.txt src/bin/madpack src/CMakeLists.txt diff --git a/src/config/Modules.yml b/src/config/Modules.yml index f2da10f..c1e5004 100644 --- a/src/config/Modules.yml +++ b/src/config/Modules.yml @@ -11,8 +11,8 @@ modules: - name: convex depends: ['utilities'] - name: crf - - name: deep_learning - depends: ['utilities'] +# - name: deep_learning +# depends: ['utilities'] - name: elastic_net - name: glm depends: ['utilities'] @@ -29,7 +29,7 @@ modules: depends: ['utilities'] - name: pca depends: ['linalg', 'convex'] - - name: pmml +# - name: pmml - name: prob - name: recursive_partitioning depends: ['utilities'] @@ -52,3 +52,9 @@ modules: - name: validation depends: ['array_ops', 'regress'] - name: stemmer + - name: xgboost_gs + depends: ['utilities'] + - name: facebook_prophet + depends: ['utilities'] + - name: gbdt + depends: ['utilities', 'recursive_partitioning'] diff --git a/src/config/Ports.yml b/src/config/Ports.yml index 78327d7..6502c96 100644 --- a/src/config/Ports.yml +++ b/src/config/Ports.yml @@ -9,3 +9,6 @@ postgres: greenplum: name: Greenplum DB + +opengauss: + name: openGauss DB diff --git a/src/madpack/madpack.py b/src/madpack/madpack.py index 2af0480..68a6566 100644 --- a/src/madpack/madpack.py +++ b/src/madpack/madpack.py @@ -24,7 +24,7 @@ from utilities import is_rev_gte from utilities import remove_comments_from_sql from utilities import run_query # Required Python version -py_min_ver = [2, 6] +py_min_ver = [2, 7] # Check python version if sys.version_info[:2] < py_min_ver: @@ -59,7 +59,7 @@ portid_list = [] for port in ports: portid_list.append(port) -SUPPORTED_PORTS = ('postgres', 'greenplum') +SUPPORTED_PORTS = ('postgres', 'greenplum', 'opengauss') # Global variables portid = None # Target port ID (eg: pg90, gp40) @@ -110,7 +110,7 @@ def _get_relative_maddir(maddir, port): madlib function definition. Replacing the GPHOME path with the equivalent relative path makes it simpler to perform DB upgrades without breaking MADlib. """ - if port == 'postgres': + if port == 'postgres' or port == 'opengauss': # do nothing for postgres return maddir @@ -167,18 +167,22 @@ def _run_m4_and_append(schema, maddir_mod_py, module, sqlfile, if pre_sql: output_filehandle.writelines([pre_sql, '\n\n']) # Find the madpack dir (platform specific or generic) + global portid + portid_old = portid + portid = 'postgres' if portid == 'opengauss' else portid if os.path.isdir(maddir + "/ports/" + portid + "/" + dbver + "/madpack"): maddir_madpack = maddir + "/ports/" + portid + "/" + dbver + "/madpack" else: maddir_madpack = maddir + "/madpack" maddir_ext_py = maddir + "/lib/python" + portid = portid_old m4args = ['m4', '-P', '-DMADLIB_SCHEMA=' + schema, '-DPLPYTHON_LIBDIR=' + maddir_mod_py, '-DEXT_PYTHON_LIBDIR=' + maddir_ext_py, - '-DMODULE_PATHNAME=' + maddir_lib, + '-DMODULE_PATHNAME=' + '$libdir/libmadlib.so', '-DMODULE_NAME=' + module, '-I' + maddir_madpack, sqlfile] @@ -213,7 +217,7 @@ def _run_install_check_sql(schema, maddir_mod_py, module, sqlfile, sub_module = '' # Run the SQL using DB command-line utility if portid in SUPPORTED_PORTS: - sqlcmd = 'psql' + sqlcmd = 'gsql' # Test the DB cmd line utility std, err = subprocess.Popen(['which', sqlcmd], stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate() @@ -226,12 +230,8 @@ def _run_install_check_sql(schema, maddir_mod_py, module, sqlfile, '-p', con_args['host'].split(':')[1], '-d', con_args['database'], '-U', con_args['user'], - '--no-password', + '-W', con_args['password'], '-f', tmpfile] - runenv = os.environ - if 'password' in con_args: - runenv["PGPASSWORD"] = con_args['password'] - runenv["PGOPTIONS"] = '-c client_min_messages=notice' # Open log file try: @@ -243,7 +243,7 @@ def _run_install_check_sql(schema, maddir_mod_py, module, sqlfile, # Run the SQL try: info_(this, "> ... executing " + tmpfile, verbose) - retval = subprocess.call(runcmd, env=runenv, stdout=log, stderr=log) + retval = subprocess.call(runcmd, stdout=log, stderr=log) except: error_(this, "Failed executing %s" % tmpfile, False) raise Exception @@ -261,7 +261,7 @@ def _run_sql_file(schema, sqlfile): """ # Run the SQL using DB command-line utility if portid in SUPPORTED_PORTS: - sqlcmd = 'psql' + sqlcmd = 'gsql' # Test the DB cmd line utility std, err = subprocess.Popen(['which', sqlcmd], stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate() @@ -274,13 +274,9 @@ def _run_sql_file(schema, sqlfile): '-p', con_args['host'].split(':')[1], '-d', con_args['database'], '-U', con_args['user'], - '--no-password', - '--single-transaction', + '-W', con_args['password'], + #'--single-transaction', '-f', sqlfile] - runenv = os.environ - if 'password' in con_args: - runenv["PGPASSWORD"] = con_args['password'] - runenv["PGOPTIONS"] = '-c client_min_messages=notice' # Open log file logfile = sqlfile + '.log' @@ -294,7 +290,7 @@ def _run_sql_file(schema, sqlfile): try: info_(this, "> ... executing " + sqlfile, verbose) info_(this, ' '.join(runcmd), verbose) - retval = subprocess.call(runcmd, env=runenv, stdout=log, stderr=log) + retval = subprocess.call(runcmd, stdout=log, stderr=log) except: error_(this, "Failed executing %s" % sqlfile, False) raise Exception @@ -353,6 +349,12 @@ def _check_db_port(portid): return True elif portid == 'greenplum': return True + if row and row[0]['version'].lower().find('opengauss') >= 0: + if portid == 'opengauss': + return True + if row and row[0]['version'].find('GaussDB') >= 0: + if portid == 'opengauss': + return True return False # ------------------------------------------------------------------------------ @@ -469,6 +471,8 @@ def _db_upgrade(schema, filehandle, db_madlib_ver): info_(this, "\tDetecting dependencies...", True) info_(this, "\tLoading change list...", True) + portid_old = portid + portid = 'postgres' if portid == 'opengauss' else portid ch = uu.ChangeHandler(schema, portid, con_args, maddir, db_madlib_ver, filehandle) info_(this, "\tDetecting table dependencies...", True) @@ -476,7 +480,7 @@ def _db_upgrade(schema, filehandle, db_madlib_ver): info_(this, "\tDetecting view dependencies...", True) vd = uu.ViewDependency(schema, portid, con_args) - + portid = portid_old abort = False if td.has_dependency(): info_(this, "*" * 50, True) @@ -562,7 +566,10 @@ def _db_upgrade(schema, filehandle, db_madlib_ver): info_(this, "No dependency problem found, continuing to upgrade ...", True) info_(this, "\tReading existing UDAs/UDTs...", False) + portid_old = portid + portid = 'postgres' if portid == 'opengauss' else portid sc = uu.ScriptCleaner(schema, portid, con_args, ch) + portid = portid_old info_(this, "Script Cleaner initialized ...", False) ch.drop_changed_uda() @@ -632,6 +639,7 @@ def _process_py_sql_files_in_modules(modset, args_dict): error_(this, "Invalid madpack operation: %s" % madpack_cmd, True) # Perform operations on all modules + portid_new = 'postgres' if portid == 'opengauss' else portid for moduleinfo in portspecs['modules']: # Get the module name module = moduleinfo['name'] @@ -641,14 +649,14 @@ def _process_py_sql_files_in_modules(modset, args_dict): continue # Find the Python module dir (platform specific or generic) - if os.path.isdir(maddir + "/ports/" + portid + "/" + dbver + "/modules/" + module): - maddir_mod_py = maddir + "/ports/" + portid + "/" + dbver + "/modules" + if os.path.isdir(maddir + "/ports/" + portid_new + "/" + dbver + "/modules/" + module): + maddir_mod_py = maddir + "/ports/" + portid_new + "/" + dbver + "/modules" else: maddir_mod_py = maddir + "/modules" # Find the SQL module dir (platform specific or generic) - if os.path.isdir(maddir + "/ports/" + portid + "/modules/" + module): - maddir_mod_sql = maddir + "/ports/" + portid + "/modules" + if os.path.isdir(maddir + "/ports/" + portid_new + "/modules/" + module): + maddir_mod_sql = maddir + "/ports/" + portid_new + "/modules" elif os.path.isdir(maddir + "/modules/" + module): maddir_mod_sql = maddir + "/modules" else: @@ -757,8 +765,7 @@ def _execute_per_module_unit_test_algo(module, pyfile, cur_tmpdir): run_start = datetime.datetime.now() # Run the python unit test file runcmd = ["python", pyfile] - runenv = os.environ - retval = subprocess.call(runcmd, env=runenv, stdout=log, stderr=log) + retval = subprocess.call(runcmd, stdout=log, stderr=log) run_end = datetime.datetime.now() milliseconds = round((run_end - run_start).seconds * 1000 + (run_end - run_start).microseconds / 1000) @@ -783,15 +790,16 @@ def _execute_per_module_install_dev_check_algo(schema, test_user, test_schema = "madlib_installcheck_%s" % (module) _internal_run_query("DROP SCHEMA IF EXISTS %s CASCADE; CREATE SCHEMA %s;" % (test_schema, test_schema), True) - _internal_run_query("GRANT ALL ON SCHEMA %s TO \"%s\";" % + _internal_run_query("GRANT ALL ON SCHEMA %s TO %s;" % (test_schema, test_user), True) # Switch to test user and prepare the search_path pre_sql = '-- Switch to test user:\n' \ - 'SET ROLE \"%s\";\n' \ + "SET ROLE %s password '%s';\n" \ '-- Set SEARCH_PATH for install-check:\n' \ 'SET search_path=%s,%s;\n' \ - % (test_user, test_schema, schema) + 'SET behavior_compat_options = \'bind_procedure_searchpath\';\n' \ + % (test_user, con_args['password'], test_schema, schema) # Set file names @@ -1031,9 +1039,10 @@ def run_install_check(args, testcase, madpack_cmd): _internal_run_query("DROP OWNED BY \"%s\" CASCADE;" % (test_user), True) _internal_run_query("DROP USER IF EXISTS \"%s\";" % (test_user), True) - _internal_run_query("CREATE USER \"%s\" WITH SUPERUSER NOINHERIT;" % (test_user), True) - _internal_run_query("GRANT USAGE ON SCHEMA %s TO \"%s\";" % (schema, test_user), True) - _internal_run_query("GRANT ALL PRIVILEGES ON DATABASE \"%s\" TO \"%s\";" % (db_name, test_user), True) + _internal_run_query("CREATE USER %s WITH SYSADMIN NOINHERIT password '%s';" % (test_user, con_args['password']), True) + + _internal_run_query("GRANT USAGE ON SCHEMA %s TO %s;" % (schema, test_user), True) + _internal_run_query("GRANT ALL PRIVILEGES ON DATABASE %s TO %s;" % (db_name, test_user), True) # 2) Run test SQLs info_(this, "> Running %s scripts for:" % madpack_cmd, verbose) @@ -1262,8 +1271,7 @@ def main(argv): con_args['host'] = c_host + ':' + c_port con_args['database'] = c_db con_args['user'] = c_user - if c_pass is not None: - con_args['password'] = c_pass + con_args['password'] = getpass.getpass("Password for user %s: " % c_user) # Try connecting to the database info_(this, "Testing database connection...", verbose) @@ -1288,7 +1296,7 @@ def main(argv): # Get MADlib version in DB db_madlib_ver = get_db_madlib_version(con_args, schema) - portdir = os.path.join(maddir, "ports", portid) + portdir = os.path.join(maddir, "ports", 'postgres' if portid == 'opengauss' else portid) supportedVersions = [dirItem for dirItem in os.listdir(portdir) if os.path.isdir(os.path.join(portdir, dirItem)) and re.match("^\d+", dirItem)] @@ -1319,7 +1327,7 @@ def main(argv): else: # only need the first two digits for <= 4.3.4 dbver = '.'.join(map(str, dbver_split[:2])) - elif portid == 'postgres': + elif portid == 'postgres' or portid == 'opengauss': if is_rev_gte(dbver_split, get_rev_num('10.0')): # Postgres starting 10.0 uses semantic versioning. Hence, # only need first digit for major version. @@ -1335,6 +1343,8 @@ def main(argv): # Adjust MADlib directories for this port (if they exist) global maddir_conf + portid_old = portid + portid = 'postgres' if portid == 'opengauss' else portid if os.path.isdir(maddir + "/ports/" + portid + "/" + dbver + "/config"): maddir_conf = maddir + "/ports/" + portid + "/" + dbver + "/config" else: @@ -1347,7 +1357,7 @@ def main(argv): "/lib/libmadlib.so" else: maddir_lib = maddir + "/lib/libmadlib.so" - + portid = portid_old # Get the list of modules for this port global portspecs portspecs = configyml.get_modules(maddir_conf) diff --git a/src/madpack/utilities.py b/src/madpack/utilities.py index 9e4e95b..304f7a3 100644 --- a/src/madpack/utilities.py +++ b/src/madpack/utilities.py @@ -83,7 +83,7 @@ def remove_comments_from_sql(sql): def run_query(sql, con_args, show_error=True): # Define sqlcmd - sqlcmd = 'psql' + sqlcmd = 'gsql' delimiter = ' <$madlib_delimiter$> ' # Test the DB cmd line utility @@ -98,10 +98,7 @@ def run_query(sql, con_args, show_error=True): '-p', con_args['host'].split(':')[1], '-d', con_args['database'], '-U', con_args['user'], - '-F', delimiter, - '--no-password', - '--no-psqlrc', - '--no-align', + '-W', con_args['password'], '-c', sql] runenv = os.environ if 'password' in con_args: @@ -123,22 +120,23 @@ def run_query(sql, con_args, show_error=True): i = 0 for line in std.splitlines(): if i == 0: - cols = [name for name in line.split(delimiter)] + cols = [name.strip() for name in line.split(delimiter)] else: row = {} # dict of col_name:col_value pairs c = 0 for val in line.split(delimiter): - row[cols[c]] = val + row[cols[c]] = val.strip() c += 1 results.insert(i, row) i += 1 - # Drop the last line: "(X rows)" + # Drop the last 2 lines: "(X rows), ("\n")" try: results.pop() + results.pop() except Exception: pass - return results + return results[1:] # ------------------------------------------------------------------------------ @@ -176,7 +174,11 @@ def get_db_madlib_version(con_args, schema): def get_dbver(con_args, portid): """ Read version number from database (of form X.Y) """ try: - versionStr = run_query("SELECT pg_catalog.version()", con_args, True)[0]['version'] + versionStr = run_query("SELECT pg_catalog.version()", con_args, True) + versionStr = versionStr[0]['version'] + if portid == 'opengauss' and ('openGauss' in versionStr or 'GaussDB' in versionStr): + return '9.2' + if portid == 'postgres': match = re.search("PostgreSQL[a-zA-Z\s]*(\d+\.\d+)", versionStr) elif portid == 'greenplum': diff --git a/src/ports/CMakeLists.txt b/src/ports/CMakeLists.txt index bb08e83..f7ec404 100644 --- a/src/ports/CMakeLists.txt +++ b/src/ports/CMakeLists.txt @@ -1,2 +1,2 @@ add_subdirectory(postgres) -add_subdirectory(greenplum) +#add_subdirectory(greenplum) diff --git a/src/ports/postgres/CMakeLists.txt b/src/ports/postgres/CMakeLists.txt index 6c7b3a8..6919024 100644 --- a/src/ports/postgres/CMakeLists.txt +++ b/src/ports/postgres/CMakeLists.txt @@ -59,7 +59,7 @@ list(APPEND MAD_DBAL_SOURCES # BEGIN Legacy Code file(GLOB_RECURSE LEGACY_C_FILES - "${CMAKE_SOURCE_DIR}/methods/*.c") + "${CMAKE_SOURCE_DIR}/methods/*.cpp") list(APPEND MAD_DBAL_SOURCES ${LEGACY_C_FILES}) # END Legacy Code @@ -154,7 +154,7 @@ function(add_${PORT_LC}_library IN_PORT_VERSION) # The warning GCC would output without -fno-strict-aliasing is: # dereferencing type-punned pointer will break strict-aliasing rules set_source_files_properties(${LEGACY_C_FILES} PROPERTIES - COMPILE_FLAGS "-fno-strict-aliasing") + COMPILE_FLAGS "-fno-strict-aliasing -fsigned-char") endif(CMAKE_COMPILER_IS_GNUCC) # errOmitLocation is used in legacy C code, but it is Greenplum only @@ -226,6 +226,11 @@ function(add_${PORT_LC}_library IN_PORT_VERSION) DESTINATION ports/${PORT_DIR_NAME}/${IN_PORT_VERSION}/madpack COMPONENT ${DBMS} ) + # copy libmadlib.so to $GAUSSHOME/lib/postgresql + MESSAGE(STATUS "HOME dir: $ENV{GAUSSHOME}") + if(DEFINED ENV{GAUSSHOME}) + install(TARGETS madlib_${DBMS} LIBRARY DESTINATION $ENV{GAUSSHOME}/lib/postgresql) + endif() endfunction(add_${PORT_LC}_library) diff --git a/src/ports/postgres/dbconnector/AnyType_impl.hpp b/src/ports/postgres/dbconnector/AnyType_impl.hpp index 41fcd47..4f0011c 100644 --- a/src/ports/postgres/dbconnector/AnyType_impl.hpp +++ b/src/ports/postgres/dbconnector/AnyType_impl.hpp @@ -258,7 +258,7 @@ AnyType::numFields() const { case Scalar: return 1; case ReturnComposite: return static_cast(mChildren.size()); case FunctionComposite: return PG_NARGS(); - case NativeComposite: return HeapTupleHeaderGetNatts(mTupleHeader); + case NativeComposite: return ((mTupleHeader)->t_infomask2 & HEAP_NATTS_MASK); //HeapTupleHeaderGetNatts1(mTupleHeader); default: // This should never happen throw std::logic_error("Unhandled case in AnyType::numFields()."); diff --git a/src/ports/postgres/dbconnector/dbconnector.hpp b/src/ports/postgres/dbconnector/dbconnector.hpp index 523fd1c..e6222e0 100644 --- a/src/ports/postgres/dbconnector/dbconnector.hpp +++ b/src/ports/postgres/dbconnector/dbconnector.hpp @@ -18,9 +18,7 @@ // (commit https://github.com/postgres/postgres/commit/a919937f112eb2f548d5f9bd1b3a7298375e6380) // Since we don't need anything from ports.h we can cheat and say its already been declared. // Warning: This could cause problems in the future... -#define PG_PORT_H -extern "C" { #include #include // Use the macro defined in the header to detect the platform #include @@ -40,7 +38,6 @@ extern "C" { #include // for direct access to catalog, e.g., SearchSysCache() #include // type conversion, e.g., lookup_rowtype_tupdesc #include "../../../../methods/svec/src/pg_gp/sparse_vector.h" // Legacy sparse vectors -} // extern "C" #include "Compatibility.hpp" @@ -77,6 +74,10 @@ extern "C" { #undef dngettext #endif +#ifdef PM +#undef PM +#endif + // Note: If errors occur in the following include files, it could indicate that // new macros have been added to PostgreSQL header files. #include diff --git a/src/ports/postgres/dbconnector/main.cpp b/src/ports/postgres/dbconnector/main.cpp index 1422780..160b11d 100644 --- a/src/ports/postgres/dbconnector/main.cpp +++ b/src/ports/postgres/dbconnector/main.cpp @@ -11,9 +11,7 @@ #include #include -extern "C" { - PG_MODULE_MAGIC; -} // extern "C" +PG_MODULE_MAGIC; namespace madlib { diff --git a/src/ports/postgres/modules/assoc_rules/assoc_rules.py_in b/src/ports/postgres/modules/assoc_rules/assoc_rules.py_in index 6b7a7c9..e21268b 100644 --- a/src/ports/postgres/modules/assoc_rules/assoc_rules.py_in +++ b/src/ports/postgres/modules/assoc_rules/assoc_rules.py_in @@ -12,6 +12,7 @@ import plpy from utilities.validate_args import columns_exist_in_table from utilities.validate_args import table_exists from utilities.control import MinWarning +from utilities.utilities import unique_string """ @@ -144,7 +145,7 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, plpy.execute(""" CREATE TEMP TABLE assoc_rules_aux_tmp ( - ruleId SERIAL, + ruleId BIGINT, pre TEXT, post TEXT, support FLOAT8, @@ -165,17 +166,17 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, plpy.execute(""" CREATE TEMP TABLE assoc_input_unique AS SELECT - dense_rank() OVER (ORDER BY tid)::BIGINT as tid, + dense_rank() OVER (ORDER BY artid)::BIGINT as artid, item FROM ( - SELECT {0} as tid, + SELECT {0} as artid, {1}::text as item FROM {2} WHERE {3} IS NOT NULL AND {4} IS NOT NULL GROUP BY 1,2 ) t - m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (tid)') + m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (artid)') """.format(tid_col, item_col, input_table, tid_col, item_col) ); @@ -183,7 +184,7 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, plpy.info("finished removing duplicates"); rv = plpy.execute(""" - SELECT count(DISTINCT tid) as c1, count(DISTINCT item) as c2 + SELECT count(DISTINCT artid) as c1, count(DISTINCT item) as c2 FROM assoc_input_unique """); num_tranx = rv[0]["c1"]; @@ -203,7 +204,7 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, item::TEXT, cnt::FLOAT8 FROM ( - SELECT item AS item, count(tid) as cnt + SELECT item AS item, count(artid) as cnt FROM assoc_input_unique GROUP BY item ) t @@ -219,11 +220,11 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, plpy.execute("DROP TABLE IF EXISTS assoc_enc_input"); plpy.execute(""" - CREATE TEMP TABLE assoc_enc_input (tid, item, cnt) AS - SELECT t1.tid, t2.item_id, t2.cnt + CREATE TEMP TABLE assoc_enc_input (artid, item, cnt) AS + SELECT t1.artid, t2.item_id, t2.cnt FROM assoc_input_unique t1, assoc_item_uniq t2 WHERE t1.item = t2.item_text - m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (tid)') + m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (artid)') """); if verbose : @@ -281,7 +282,7 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, (ARRAY[t.item], ARRAY[1], {1}, 0), t.cnt::FLOAT8 / {2}, {3}.svec_cast_positions_float8arr - (array_agg(t.tid), array_agg(1), {4}, 0) + (array_agg(t.artid), array_agg(1), {4}, 0) FROM assoc_enc_input t GROUP BY t.item, t.cnt """.format(madlib_schema, num_supp_prod, num_tranx, madlib_schema, @@ -314,7 +315,7 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, plpy.execute(""" CREATE TEMP TABLE assoc_loop_aux ( - id BIGSERIAL, + id BIGINT, set_list {0}.svec, support FLOAT8, tids {1}.svec @@ -380,13 +381,15 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, # The iterp1 check ensures that the new itemset is of a certain size. # At every iteration, we increase the target itemset size by one. - plpy.execute("ALTER SEQUENCE assoc_loop_aux_id_seq RESTART WITH 1"); + # plpy.execute("ALTER SEQUENCE assoc_loop_aux_id_seq RESTART WITH 1"); + assoc_loop_aux_sequence = unique_string(desp='s1') plpy.execute("TRUNCATE TABLE assoc_loop_aux"); + plpy.execute("""CREATE SEQUENCE {s} START WITH 1 INCREMENT BY 1 NO MINVALUE NO MAXVALUE CACHE 1; + """.format(s=assoc_loop_aux_sequence)) plpy.execute(""" - INSERT INTO assoc_loop_aux(set_list, support, tids) - SELECT DISTINCT ON({madlib_schema}.svec_to_string(set_list)) set_list, - {madlib_schema}.svec_l1norm(tids)::FLOAT8 / {num_tranx}, - tids + INSERT INTO assoc_loop_aux(set_list, support, tids, id) + SELECT set_list, {madlib_schema}.svec_l1norm(tids)::FLOAT8 / {num_tranx}, tids, nextval('{assoc_loop_aux_sequence}') FROM( + SELECT DISTINCT ON({madlib_schema}.svec_to_string(set_list)) set_list, tids FROM ( SELECT {madlib_schema}.svec_minus( @@ -401,9 +404,11 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, ) t WHERE {madlib_schema}.svec_l1norm(set_list)::INT = {iterp1} AND {madlib_schema}.svec_l1norm(tids)::FLOAT8 >= {min_supp_tranx} + ) q """.format(iterp1=iter + 1, **locals()) ); + plpy.execute("""DROP SEQUENCE IF EXISTS {s};""".format(s=assoc_loop_aux_sequence)) plpy.execute("TRUNCATE TABLE assoc_rule_sets_loop"); plpy.execute(""" INSERT INTO assoc_rule_sets_loop(id, set_list, support, tids) @@ -470,10 +475,14 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, begin_step_exec = time.time(); # generate all the final rules + assoc_rules_aux_tmp_sequence = unique_string(desp='s2') + plpy.execute("""CREATE SEQUENCE {s} START WITH 1 INCREMENT BY 1 NO MINVALUE NO MAXVALUE CACHE 1; + """.format(s=assoc_rules_aux_tmp_sequence)) plpy.execute(""" INSERT INTO assoc_rules_aux_tmp - (pre, post, support, confidence, lift, conviction) + (ruleId, pre, post, support, confidence, lift, conviction) SELECT + nextval('{assoc_rules_aux_tmp_sequence}'), t.item[1], t.item[2], t.support_xy, @@ -499,6 +508,7 @@ def assoc_rules(madlib_schema, support, confidence, tid_col, (t.support_xy / x.support) >= {confidence} """.format(**locals()) ); + plpy.execute("""DROP SEQUENCE IF EXISTS {s};""".format(s=assoc_rules_aux_tmp_sequence)) # generate the readable rules plpy.execute("DROP TABLE IF EXISTS pre_tmp_table"); diff --git a/src/ports/postgres/modules/assoc_rules/assoc_rules.sql_in b/src/ports/postgres/modules/assoc_rules/assoc_rules.sql_in index a7f639b..4d8531a 100644 --- a/src/ports/postgres/modules/assoc_rules/assoc_rules.sql_in +++ b/src/ports/postgres/modules/assoc_rules/assoc_rules.sql_in @@ -529,7 +529,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gen_rules_from_cfp INT ) RETURNS SETOF TEXT[] AS 'MODULE_PATHNAME' -LANGUAGE C STRICT IMMUTABLE +LANGUAGE C STRICT IMMUTABLE NOT FENCED m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `'); diff --git a/src/ports/postgres/modules/bayes/bayes.py_in b/src/ports/postgres/modules/bayes/bayes.py_in index b97929f..7507515 100644 --- a/src/ports/postgres/modules/bayes/bayes.py_in +++ b/src/ports/postgres/modules/bayes/bayes.py_in @@ -709,7 +709,6 @@ def create_prepared_data(**kwargs): {attr_counts_sql} m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (attr)'); ALTER TABLE {attrCountsSource} ADD PRIMARY KEY (attr); - ANALYZE {attrCountsSource}; DROP TABLE IF EXISTS {attrValuesSource}; CREATE TEMPORARY TABLE {attrValuesSource} @@ -718,7 +717,6 @@ def create_prepared_data(**kwargs): m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (attr, value)'); ALTER TABLE {attrValuesSource} ADD PRIMARY KEY (attr, value); - ANALYZE {attrValuesSource}; """.format( attrCountsSource = kwargs['attrCountsSource'], attrValuesSource = kwargs['attrValuesSource'], @@ -741,7 +739,6 @@ def create_prepared_data(**kwargs): if kwargs['whatToCreate'] == 'TABLE': plpy.execute(""" ALTER TABLE {classPriorsDestName} ADD PRIMARY KEY (class); - ANALYZE {classPriorsDestName}; """.format(**kwargs)) kwargs.update(dict( @@ -786,7 +783,6 @@ def create_prepared_data(**kwargs): if kwargs['whatToCreate'] == 'TABLE': plpy.execute(""" ALTER TABLE {featureProbsDestName} ADD PRIMARY KEY (class, attr, value); - ANALYZE {featureProbsDestName}; DROP TABLE {attrCountsSource}; DROP TABLE {attrValuesSource}; """.format(**kwargs)) @@ -795,7 +791,6 @@ def create_prepared_data(**kwargs): if kwargs['whatToCreate'] == 'TABLE': plpy.execute(""" ALTER TABLE {numericFeatureStatsDestName} ADD PRIMARY KEY (class, attr); - ANALYZE {numericFeatureStatsDestName}; """.format(**kwargs)) @@ -1173,7 +1168,7 @@ def __verify_attr_num(sourceTable, attrColumn, numAttr): Verify the number of attributes for each record is as expected. """ result = plpy.execute(""" - SELECT count(*) size FROM {0} + SELECT count(*) AS size FROM {0} WHERE array_upper({1}, 1) <> {2} """.format(sourceTable, attrColumn, numAttr) ) diff --git a/src/ports/postgres/modules/convex/lmf.sql_in b/src/ports/postgres/modules/convex/lmf.sql_in index e608d0a..700e34f 100644 --- a/src/ports/postgres/modules/convex/lmf.sql_in +++ b/src/ports/postgres/modules/convex/lmf.sql_in @@ -132,7 +132,7 @@ INSERT INTO lmf_data VALUES (999, 10000, 2.0); -# Call the lmf_igd_run() stored procedure. 
 DROP TABLE IF EXISTS lmf_model;
-SELECT madlib.lmf_igd_run( 'lmf_model',
+SELECT MADLIB_SCHEMA.lmf_igd_run( 'lmf_model',
                            'lmf_data',
                            'row',
                            'col',
@@ -245,7 +245,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lmf_igd_transition(
         scale_factor    DOUBLE PRECISION)
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lmf_igd_merge(
@@ -253,14 +253,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lmf_igd_merge(
         state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lmf_igd_final(
         state DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -293,14 +293,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_lmf_igd_distance(
     /*+ state2 */ DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_lmf_igd_result(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.lmf_result AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_execute_using_lmf_igd_args(
@@ -309,7 +309,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_execute_using_lmf_igd_args(
 ) RETURNS VOID
 IMMUTABLE
 CALLED ON NULL INPUT
-LANGUAGE c
+LANGUAGE c NOT FENCED
 AS 'MODULE_PATHNAME', 'exec_sql_using'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
@@ -382,8 +382,9 @@ BEGIN
     -- PostgreSQL 8.4:
     -- http://www.postgresql.org/docs/8.4/static/plpgsql-statements.html#PLPGSQL-STATEMENTS-EXECUTING-DYN
     -- We therefore have to emulate.
+    DROP TABLE IF EXISTS pg_temp._madlib_lmf_igd_args;
+
     PERFORM MADLIB_SCHEMA.internal_execute_using_lmf_igd_args($sql$
-        DROP TABLE IF EXISTS pg_temp._madlib_lmf_igd_args;
         CREATE TABLE pg_temp._madlib_lmf_igd_args AS
         SELECT
             $1 AS row_dim,
diff --git a/src/ports/postgres/modules/convex/mlp.sql_in b/src/ports/postgres/modules/convex/mlp.sql_in
index d6ce7ce..6caaae5 100644
--- a/src/ports/postgres/modules/convex/mlp.sql_in
+++ b/src/ports/postgres/modules/convex/mlp.sql_in
@@ -1442,7 +1442,7 @@ CREATE FUNCTION MADLIB_SCHEMA.mlp_igd_transition(
     )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE;
+LANGUAGE C IMMUTABLE NOT FENCED;
 
 CREATE FUNCTION MADLIB_SCHEMA.mlp_minibatch_transition(
         state              DOUBLE PRECISION[],
@@ -1463,33 +1463,33 @@ CREATE FUNCTION MADLIB_SCHEMA.mlp_minibatch_transition(
     )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE;
+LANGUAGE C IMMUTABLE NOT FENCED;
 
 CREATE FUNCTION MADLIB_SCHEMA.mlp_igd_merge(
         state1 DOUBLE PRECISION[],
         state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT;
+LANGUAGE C IMMUTABLE STRICT NOT FENCED;
 
 CREATE FUNCTION MADLIB_SCHEMA.mlp_igd_final(
         state DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT;
+LANGUAGE C IMMUTABLE STRICT NOT FENCED;
 
 CREATE FUNCTION MADLIB_SCHEMA.mlp_minibatch_merge(
         state1 DOUBLE PRECISION[],
         state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT;
+LANGUAGE C IMMUTABLE STRICT NOT FENCED;
 
 CREATE FUNCTION MADLIB_SCHEMA.mlp_minibatch_final(
         state DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT;
+LANGUAGE C IMMUTABLE STRICT NOT FENCED;
 
 /**
  * @internal
@@ -1550,19 +1550,19 @@ CREATE FUNCTION MADLIB_SCHEMA.internal_mlp_igd_distance(
     /*+ state2 */ DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT;
+LANGUAGE c IMMUTABLE STRICT NOT FENCED;
 
 CREATE FUNCTION MADLIB_SCHEMA.internal_mlp_igd_result(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.mlp_result AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT;
+LANGUAGE c IMMUTABLE STRICT NOT FENCED;
 
 CREATE FUNCTION MADLIB_SCHEMA.internal_mlp_minibatch_result(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.mlp_result AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT;
+LANGUAGE c IMMUTABLE STRICT NOT FENCED;
 -------------------------------------------------------------------------
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mlp_classification(
@@ -1834,7 +1834,7 @@ CREATE FUNCTION MADLIB_SCHEMA.internal_predict_mlp(
     )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT;
+LANGUAGE C IMMUTABLE STRICT NOT FENCED;
 
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mlp_classification(
diff --git a/src/ports/postgres/modules/convex/utils_regularization.sql_in b/src/ports/postgres/modules/convex/utils_regularization.sql_in
index 0bcef63..a4dc35f 100644
--- a/src/ports/postgres/modules/convex/utils_regularization.sql_in
+++ b/src/ports/postgres/modules/convex/utils_regularization.sql_in
@@ -12,7 +12,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__utils_var_scales_result (
     in_state            DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__utils_scales AS
 'MODULE_PATHNAME', '__utils_var_scales_result'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -23,7 +23,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__utils_var_scales_transition (
     dimension           INTEGER
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'utils_var_scales_transition'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -33,7 +33,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__utils_var_scales_merge (
     state2              DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'utils_var_scales_merge'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -42,14 +42,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__utils_var_scales_final (
     state               DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'utils_var_scales_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__utils_var_scales_non_zero_std_final (
     state               DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'utils_var_scales_non_zero_std_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -88,5 +88,5 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.utils_normalize_data (
     std         DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'utils_normalize_data'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
diff --git a/src/ports/postgres/modules/crf/crf.py_in b/src/ports/postgres/modules/crf/crf.py_in
index 2eaa1e8..e812134 100644
--- a/src/ports/postgres/modules/crf/crf.py_in
+++ b/src/ports/postgres/modules/crf/crf.py_in
@@ -209,7 +209,7 @@ def lincrf_train(schema_madlib, train_feature_tbl, train_featureset_tbl,
                             schema_madlib = schema_madlib))
 
     # Enforce ANALYZE to gather proper table statistics required to generate optimized query plans
-    plpy.execute(""" ANALYZE {crf_weights_tbl} """.format(crf_weights_tbl = crf_weights_tbl))
+    # plpy.execute(""" ANALYZE {crf_weights_tbl} """.format(crf_weights_tbl = crf_weights_tbl))
 
     return "CRF Train successful. Results stored in the specified CRF stats and weights table"
 
diff --git a/src/ports/postgres/modules/crf/crf.sql_in b/src/ports/postgres/modules/crf/crf.sql_in
index f9f6358..230df8e 100644
--- a/src/ports/postgres/modules/crf/crf.sql_in
+++ b/src/ports/postgres/modules/crf/crf.sql_in
@@ -708,33 +708,33 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lincrf_lbfgs_step_transition(
     DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE;
+LANGUAGE C IMMUTABLE NOT FENCED;
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lincrf_lbfgs_step_merge_states(
     state1 DOUBLE PRECISION[],
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT;
+LANGUAGE C IMMUTABLE STRICT NOT FENCED;
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lincrf_lbfgs_step_final(
     state DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT;
+LANGUAGE C IMMUTABLE STRICT NOT FENCED;
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_lincrf_lbfgs_converge(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT;
+LANGUAGE c IMMUTABLE STRICT NOT FENCED;
 
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_lincrf_lbfgs_result(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.lincrf_result AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT;
+LANGUAGE c IMMUTABLE STRICT NOT FENCED;
 
 /**
  * @internal
diff --git a/src/ports/postgres/modules/crf/crf_feature_gen.py_in b/src/ports/postgres/modules/crf/crf_feature_gen.py_in
index f55037f..7f9857b 100644
--- a/src/ports/postgres/modules/crf/crf_feature_gen.py_in
+++ b/src/ports/postgres/modules/crf/crf_feature_gen.py_in
@@ -138,7 +138,7 @@ def generate_train_features(schema_madlib, train_segment_tbl, regex_tbl, label_t
                     FROM   """ + tmp1_feature + """;""")
 
     # Enforce ANALYZE to gather proper table statistics required to generate optimized query plans
-    plpy.execute("""ANALYZE {tmp1_feature} """.format(tmp1_feature = tmp1_feature))
+    # plpy.execute("""ANALYZE {tmp1_feature} """.format(tmp1_feature = tmp1_feature))
 
     plpy.execute("""DROP SEQUENCE IF EXISTS seq;
                     CREATE  SEQUENCE seq START 1 INCREMENT 1;""")
@@ -152,7 +152,7 @@ def generate_train_features(schema_madlib, train_segment_tbl, regex_tbl, label_t
                    tmp_featureset = tmp_featureset))
 
     # Enforce ANALYZE to gather proper table statistics required to generate optimized query plans
-    plpy.execute(""" ANALYZE {train_featureset_tbl} """.format(train_featureset_tbl = train_featureset_tbl))
+    # plpy.execute(""" ANALYZE {train_featureset_tbl} """.format(train_featureset_tbl = train_featureset_tbl))
 
     rv = plpy.execute("""SELECT COUNT(*) AS total_feature FROM """ + train_featureset_tbl + """;""")
 
@@ -205,7 +205,7 @@ def generate_train_features(schema_madlib, train_segment_tbl, regex_tbl, label_t
                 dense_mtbl = dense_mtbl))
 
     # Enforce ANALYZE to gather proper table statistics required to generate optimized query plans
-    plpy.execute(""" ANALYZE {train_feature_tbl} """.format(train_feature_tbl = train_feature_tbl))
+    # plpy.execute(""" ANALYZE {train_feature_tbl} """.format(train_feature_tbl = train_feature_tbl))
 
     plpy.execute("""SET client_min_messages TO """ + str(origClientMinMessages[0]['setting']) + """;""")
 
@@ -412,8 +412,8 @@ m4_ifdef(`__HAS_ORDERED_AGGREGATES__', `
                         GROUP BY seg_text,label;""")
 
     # Enforce ANALYZE to gather proper table statistics required to generate optimized query plans
-    plpy.execute(""" ANALYZE {viterbi_mtbl} """.format(viterbi_mtbl = viterbi_mtbl))
-    plpy.execute(""" ANALYZE {viterbi_rtbl} """.format(viterbi_rtbl = viterbi_rtbl))
+    # plpy.execute(""" ANALYZE {viterbi_mtbl} """.format(viterbi_mtbl = viterbi_mtbl))
+    # plpy.execute(""" ANALYZE {viterbi_rtbl} """.format(viterbi_rtbl = viterbi_rtbl))
 
 
 def _validate_label_tbl(label_tbl):
diff --git a/src/ports/postgres/modules/crf/viterbi.sql_in b/src/ports/postgres/modules/crf/viterbi.sql_in
index d3c20cd..8aa0b08 100644
--- a/src/ports/postgres/modules/crf/viterbi.sql_in
+++ b/src/ports/postgres/modules/crf/viterbi.sql_in
@@ -35,7 +35,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
  * @returns the top1 label sequence, the last two elements in the array is used to calculate the top1 probability
  */
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.vcrf_top1_label(mArray DOUBLE PRECISION[], rArray DOUBLE PRECISION[], nlabel int)
-returns int[] as 'MODULE_PATHNAME' language c strict;
+returns int[] as 'MODULE_PATHNAME' language c strict NOT FENCED;
 
 
 /**
diff --git a/src/ports/postgres/modules/deep_learning/gpu_info_from_tf.py_in b/src/ports/postgres/modules/deep_learning/gpu_info_from_tf.py_in
deleted file mode 100644
index b1e59b1..0000000
--- a/src/ports/postgres/modules/deep_learning/gpu_info_from_tf.py_in
+++ /dev/null
@@ -1,39 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-"""
-@file gpu_info_from_tf.py_in
-
-@brief This file prints out the gpu descriptions using tensorflow API. This file
-is intended to be called using subprocess. See madlib_keras_gpu_info.py_in
-for more details.
-"""
-
-from tensorflow.python.client import device_lib
-from keras import backend as K
-
-config = K.tf.ConfigProto()
-config.gpu_options.allow_growth = True
-sess = K.tf.Session(config=config)
-local_device_protos = device_lib.list_local_devices()
-K.clear_session()
-sess.close()
-if local_device_protos:
-    for x in local_device_protos:
-        if x.device_type == 'GPU':
-            print x.physical_device_desc
diff --git a/src/ports/postgres/modules/deep_learning/input_data_preprocessor.py_in b/src/ports/postgres/modules/deep_learning/input_data_preprocessor.py_in
deleted file mode 100644
index 605d439..0000000
--- a/src/ports/postgres/modules/deep_learning/input_data_preprocessor.py_in
+++ /dev/null
@@ -1,1036 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-
-"""
-@file input_data_preprocessor.py_in
-
-"""
-from math import ceil
-import plpy
-
-from internal.db_utils import get_distinct_col_levels
-from internal.db_utils import quote_literal
-from internal.db_utils import get_product_of_dimensions
-from utilities.minibatch_preprocessing import MiniBatchBufferSizeCalculator
-from utilities.control import OptimizerControl
-from utilities.control import HashaggControl
-from utilities.utilities import _assert
-from utilities.utilities import add_postfix
-from utilities.utilities import is_platform_pg
-from utilities.utilities import is_psql_char_type
-from utilities.utilities import is_valid_psql_type
-from utilities.utilities import is_var_valid
-from utilities.utilities import BOOLEAN, NUMERIC, ONLY_ARRAY, TEXT
-from utilities.utilities import py_list_to_sql_string
-from utilities.utilities import split_quoted_delimited_str
-from utilities.utilities import strip_end_quotes
-from utilities.utilities import unique_string
-from utilities.utilities import validate_module_input_params
-from utilities.utilities import get_seg_number
-from utilities.validate_args import input_tbl_valid
-from utilities.validate_args import get_expr_type
-
-from madlib_keras_helper import *
-import time
-
-NUM_CLASSES_COLNAME = "num_classes"
-
-class InputDataPreprocessorDL(object):
-    def __init__(self, schema_madlib, source_table, output_table,
-                 dependent_varname, independent_varname, buffer_size,
-                 normalizing_const, num_classes, distribution_rules, module_name):
-        self.schema_madlib = schema_madlib
-        self.source_table = source_table
-        self.output_table = output_table
-        self.dependent_varname = dependent_varname
-        self.independent_varname = independent_varname
-        self.buffer_size = buffer_size
-        self.normalizing_const = normalizing_const
-        self.num_classes = num_classes
-        self.distribution_rules = distribution_rules if distribution_rules else 'all_segments'
-        self.module_name = module_name
-        self.output_summary_table = None
-        self.dependent_vartype = None
-        self.independent_vartype = None
-        self.gpu_config = '$__madlib__$all_segments$__madlib__$'
-        if self.output_table:
-            self.output_summary_table = add_postfix(self.output_table, "_summary")
-
-        ## Validating input args prior to using them in _set_validate_vartypes()
-        self._validate_args()
-        self._set_validate_vartypes()
-        self.dependent_levels = None
-        # The number of padded zeros to include in 1-hot vector
-        self.padding_size = 0
-
-    def _set_one_hot_encoding_variables(self):
-        """
-            Set variables such as dependent_levels and padding_size.
-            If necessary, NULLs are padded to dependent_levels list.
-        """
-        if self.dependent_levels:
-            # if any class level was NULL in sql, that would show up as
-            # None in self.dependent_levels. Replace all None with NULL
-            # in the list.
-            self.dependent_levels = ['NULL' if level is None else level
-                for level in self.dependent_levels]
-            self._validate_num_classes()
-            # Try computing padding_size after running all necessary validations.
-            if self.num_classes:
-                self.padding_size = self.num_classes - len(self.dependent_levels)
-
-    def _validate_num_classes(self):
-        if self.num_classes is not None and \
-            self.num_classes < len(self.dependent_levels):
-            plpy.error("{0}: Invalid num_classes value specified. It must "\
-                "be equal to or greater than distinct class values found "\
-                "in table ({1}).".format(
-                    self.module_name, len(self.dependent_levels)))
-
-    def _validate_distribution_table(self):
-
-        input_tbl_valid(self.distribution_rules, self.module_name,
-                        error_suffix_str="""
-                        segments_to_use table ({self.distribution_rules}) doesn't exist.
-                        """.format(self=self))
-        _assert(is_var_valid(self.distribution_rules, 'dbid'),
-                "{self.module_name}: distribution rules table must contain dbib column".format(
-                    self=self))
-        dbids = plpy.execute("""
-            SELECT array_agg(dbid) AS dbids FROM gp_segment_configuration
-            WHERE content >= 0 AND role = 'p'
-            """)[0]['dbids']
-        dist_result = plpy.execute("""
-            SELECT array_agg(dbid) AS dbids,
-                   count(dbid) AS c1,
-                   count(DISTINCT dbid) AS c2
-            FROM {0} """.format(self.distribution_rules))
-
-        _assert(dist_result[0]['c1'] == dist_result[0]['c2'],
-            '{self.module_name}: distribution rules table contains duplicate dbids'.format(
-                self=self))
-
-        for i in dist_result[0]['dbids']:
-            _assert(i in dbids,
-                '{self.module_name}: invalid dbid:{i} in the distribution rules table'.format(
-                    self=self, i=i))
-
-    def get_one_hot_encoded_dep_var_expr(self):
-        """
-        :param dependent_varname: Name of the dependent variable
-        :param num_classes: Number of class values to consider in 1-hot
-        :return:
-            This function returns a tuple of
-            1. A string with transformed dependent varname depending on it's type
-            2. All the distinct dependent class levels encoded as a string
-
-            If dep_type == numeric[] , do not encode
-                    1. dependent_varname = rings
-                        transformed_value = ARRAY[rings]
-                    2. dependent_varname = ARRAY[a, b, c]
-                        transformed_value = ARRAY[a, b, c]
-            else if dep_type in ("text", "boolean"), encode:
-                    3. dependent_varname = rings (encoding)
-                        transformed_value = ARRAY[rings=1, rings=2, rings=3]
-        """
-        # Assuming the input NUMERIC[] is already one_hot_encoded,
-        # so casting to INTEGER[]
-        if is_valid_psql_type(self.dependent_vartype, NUMERIC | ONLY_ARRAY):
-            return "{0}::{1}[]".format(self.dependent_varname, SMALLINT_SQL_TYPE)
-
-        # For DL use case, we want to allow NULL as a valid class value,
-        # so the query must have 'IS NOT DISTINCT FROM' instead of '='
-        # like in the generic get_one_hot_encoded_expr() defined in
-        # db_utils.py_in. We also have this optional 'num_classes' param
-        # that affects the logic of 1-hot encoding. Since this is very
-        # specific to input_preprocessor_dl for now, let's keep
-        # it here instead of refactoring it out to a generic helper function.
-        one_hot_encoded_expr = ["({0}) IS NOT DISTINCT FROM {1}".format(
-            self.dependent_varname, c) for c in self.dependent_levels]
-        if self.num_classes:
-            one_hot_encoded_expr.extend(['false'
-                for i in range(self.padding_size)])
-        # In psql, we can't directly convert boolean to smallint, so we firstly
-        # convert it to integer and then cast to smallint
-        return 'ARRAY[{0}]::INTEGER[]::{1}[]'.format(
-            ', '.join(one_hot_encoded_expr), SMALLINT_SQL_TYPE)
-
-    def _get_independent_var_shape(self):
-
-        shape = plpy.execute(
-            "SELECT array_dims({0}) AS shape FROM {1} LIMIT 1".format(
-            self.independent_varname, self.source_table))[0]['shape']
-        return parse_shape(shape)
-
-    def _get_dependent_var_shape(self):
-
-        if self.num_classes:
-            shape = [self.num_classes]
-        elif self.dependent_levels:
-            shape = [len(self.dependent_levels)]
-        else:
-            shape = plpy.execute(
-                "SELECT array_dims({0}) AS shape FROM {1} LIMIT 1".format(
-                self.dependent_varname, self.source_table))[0]['shape']
-            shape = parse_shape(shape)
-        return shape
-
-    def input_preprocessor_dl(self, order_by_random=True):
-        """
-            Creates the output and summary table that does the following
-            pre-processing operations on the input data:
-            1) Normalizes the independent variable.
-            2) Minibatches the normalized independent variable.
-            3) One-hot encodes the dependent variable.
-            4) Minibatches the one-hot encoded dependent variable.
-        """
-        # setup for 1-hot encoding
-        self._set_one_hot_encoding_variables()
-
-        # Generate random strings for TEMP tables
-        series_tbl = unique_string(desp='series')
-        dist_key_tbl = unique_string(desp='dist_key')
-        normalized_tbl = unique_string(desp='normalized_table')
-        batched_table = unique_string(desp='batched_table')
-
-        # Used later in locals() for formatting queries
-        x=MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL
-        y=MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL
-        float32=FLOAT32_SQL_TYPE
-        dep_shape_col = add_postfix(y, "_shape")
-        ind_shape_col = add_postfix(x, "_shape")
-
-        ind_shape = self._get_independent_var_shape()
-        ind_shape = ','.join([str(i) for i in ind_shape])
-        dep_shape = self._get_dependent_var_shape()
-        dep_shape = ','.join([str(i) for i in dep_shape])
-
-        one_hot_dep_var_array_expr = self.get_one_hot_encoded_dep_var_expr()
-
-        # skip normalization step if normalizing_const = 1.0
-        if self.normalizing_const and (self.normalizing_const < 0.999999 or self.normalizing_const > 1.000001):
-            rescale_independent_var = """{self.schema_madlib}.array_scalar_mult(
-                                         {self.independent_varname}::{float32}[],
-                                         (1/{self.normalizing_const})::{float32})
-                                      """.format(**locals())
-        else:
-            self.normalizing_const = DEFAULT_NORMALIZING_CONST
-            rescale_independent_var = "{self.independent_varname}::{float32}[]".format(**locals())
-
-        # It's important that we shuffle all rows before batching for fit(), but
-        #  we can skip that for predict()
-        order_by_clause = " ORDER BY RANDOM()" if order_by_random else ""
-
-        # This query template will be used later in pg & gp specific code paths,
-        #  where {make_buffer_id} and {dist_by_buffer_id} are filled in
-        batching_query = """
-            CREATE TEMP TABLE {batched_table} AS SELECT
-                {{make_buffer_id}} buffer_id,
-                {self.schema_madlib}.agg_array_concat(
-                    ARRAY[x_norm::{float32}[]]) AS {x},
-                {self.schema_madlib}.agg_array_concat(
-                    ARRAY[y]) AS {y},
-                COUNT(*) AS count
-            FROM {normalized_tbl}
-            GROUP BY buffer_id
-            {{dist_by_buffer_id}}
-        """.format(**locals())
-
-        # This query template will be used later in pg & gp specific code paths,
-        #  where {dist_key_col_comma} and {dist_by_dist_key} will be filled in
-        bytea_query = """
-            CREATE TABLE {self.output_table} AS SELECT
-                {{dist_key_col_comma}}
-                {self.schema_madlib}.array_to_bytea({x}) AS {x},
-                {self.schema_madlib}.array_to_bytea({y}) AS {y},
-                ARRAY[count,{ind_shape}]::SMALLINT[] AS {ind_shape_col},
-                ARRAY[count,{dep_shape}]::SMALLINT[] AS {dep_shape_col},
-                buffer_id
-            FROM {batched_table}
-            {{dist_by_dist_key}}
-        """.format(**locals())
-
-        if is_platform_pg():
-            # used later for writing summary table
-            self.distribution_rules = '$__madlib__$all_segments$__madlib__$'
-
-            #
-            # For postgres, we just need 3 simple queries:
-            #   1-hot-encode/normalize + batching + bytea conversion
-            #
-
-            # see note in gpdb code branch (lower down) on
-            # 1-hot-encoding of dependent var
-            one_hot_sql = """
-                CREATE TEMP TABLE {normalized_tbl} AS SELECT
-                    (ROW_NUMBER() OVER({order_by_clause}) - 1)::INTEGER as row_id,
-                    {rescale_independent_var} AS x_norm,
-                    {one_hot_dep_var_array_expr} AS y
-                FROM {self.source_table}
-            """.format(**locals())
-
-            plpy.execute(one_hot_sql)
-
-            self.buffer_size = self._get_buffer_size(1)
-
-            # Used to format query templates with locals()
-            make_buffer_id = 'row_id / {0} AS '.format(self.buffer_size)
-            dist_by_dist_key = ''
-            dist_by_buffer_id = ''
-            dist_key_col_comma = ''
-
-            # Disable hashagg since large number of arrays being concatenated
-            # could result in excessive memory usage.
-            with HashaggControl(False):
-                # Batch rows with GROUP BY
-                plpy.execute(batching_query.format(**locals()))
-
-            plpy.execute("DROP TABLE {0}".format(normalized_tbl))
-
-            # Convert to BYTEA and output final (permanent table)
-            plpy.execute(bytea_query.format(**locals()))
-
-            plpy.execute("DROP TABLE {0}".format(batched_table))
-
-            self._create_output_summary_table()
-
-            return
-
-        # Done with postgres, rest is all for gpdb
-        #
-        # This gpdb code path is far more complex, and depends on
-        #   how the user wishes to distribute the data.  Even if
-        #   it's to be spread evenly across all segments, we still
-        #   need to do some extra work to ensure that happens.
-
-        if self.distribution_rules == 'all_segments':
-            all_segments = True
-            self.distribution_rules = '$__madlib__$all_segments$__madlib__$'
-            num_segments = get_seg_number()
-        else:
-            all_segments = False
-
-        if self.distribution_rules == 'gpu_segments':
-            #TODO can we reuse the function `get_accessible_gpus_for_seg` from
-            # madlib_keras_helper
-            gpu_info_table = unique_string(desp='gpu_info')
-            plpy.execute("""
-                SELECT {self.schema_madlib}.gpu_configuration('{gpu_info_table}')
-            """.format(**locals()))
-            gpu_query = """
-                SELECT array_agg(DISTINCT(hostname)) as gpu_config
-                FROM {gpu_info_table}
-            """.format(**locals())
-            gpu_query_result = plpy.execute(gpu_query)[0]['gpu_config']
-            if not gpu_query_result:
-               plpy.error("{self.module_name}: No GPUs configured on hosts.".format(self=self))
-            plpy.execute("DROP TABLE IF EXISTS {0}".format(gpu_info_table))
-
-            gpu_config_hostnames = "ARRAY{0}".format(gpu_query_result)
-            # find hosts with gpus
-            get_segment_query = """
-                SELECT array_agg(content) as segment_ids,
-                       array_agg(dbid) as dbid,
-                       count(*) as count
-                FROM gp_segment_configuration
-                WHERE content != -1 AND role = 'p'
-                AND hostname=ANY({gpu_config_hostnames})
-            """.format(**locals())
-            segment_ids_result = plpy.execute(get_segment_query)[0]
-
-            self.gpu_config = "ARRAY{0}".format(sorted(segment_ids_result['segment_ids']))
-            self.distribution_rules = "ARRAY{0}".format(sorted(segment_ids_result['dbid']))
-
-            num_segments = segment_ids_result['count']
-
-        elif not all_segments:  # Read from a table with dbids to distribute the data
-            self._validate_distribution_table()
-            gpu_query = """
-                SELECT array_agg(content) as gpu_config,
-                       array_agg(gp_segment_configuration.dbid) as dbid
-                FROM {self.distribution_rules} JOIN gp_segment_configuration
-                ON {self.distribution_rules}.dbid = gp_segment_configuration.dbid
-            """.format(**locals())
-            gpu_query_result = plpy.execute(gpu_query)[0]
-            self.gpu_config = "ARRAY{0}".format(sorted(gpu_query_result['gpu_config']))
-            num_segments = plpy.execute("SELECT count(*) as count FROM {self.distribution_rules}".format(**locals()))[0]['count']
-            self.distribution_rules = "ARRAY{0}".format(sorted(gpu_query_result['dbid']))
-
-        join_key = 't.buffer_id % {num_segments}'.format(**locals())
-
-        if not all_segments:
-            join_key = '({self.gpu_config})[{join_key} + 1]'.format(**locals())
-
-        # Create large temp table such that there is atleast 1 row on each segment
-        # Using 999999 would distribute data(atleast 1 row on each segment) for
-        # a cluster as large as 20000
-        dist_key_col = DISTRIBUTION_KEY_COLNAME
-        query = """
-            CREATE TEMP TABLE {series_tbl} AS
-                SELECT generate_series(0, 999999) {dist_key_col}
-                DISTRIBUTED BY ({dist_key_col})
-            """.format(**locals())
-
-        plpy.execute(query)
-
-        # Used in locals() to format queries, including template queries
-        #  bytea_query & batching_query defined in section common to
-        #  pg & gp (very beginning of this function)
-        dist_by_dist_key = 'DISTRIBUTED BY ({dist_key_col})'.format(**locals())
-        dist_by_buffer_id = 'DISTRIBUTED BY (buffer_id)'
-        dist_key_col_comma = dist_key_col + ' ,'
-        make_buffer_id = ''
-
-        dist_key_query = """
-                CREATE TEMP TABLE {dist_key_tbl} AS
-                SELECT min({dist_key_col}) AS {dist_key_col}
-                FROM {series_tbl}
-                GROUP BY gp_segment_id
-                DISTRIBUTED BY ({dist_key_col})
-        """.format(**locals())
-
-        plpy.execute(dist_key_query)
-
-        plpy.execute("DROP TABLE {0}".format(series_tbl))
-
-        # Always one-hot encode the dependent var. For now, we are assuming
-        # that input_preprocessor_dl will be used only for deep
-        # learning and mostly for classification. So make a strong
-        # assumption that it is only for classification, so one-hot
-        # encode the dep var, unless it's already a numeric array in
-        # which case we assume it's already one-hot encoded.
-
-        # While 1-hot-encoding is done, we also normalize the independent
-        # var and randomly shuffle the rows on each segment.  (The dist key
-        # we're adding avoids any rows moving between segments.  This may
-        # make things slightly less random, but helps with speed--probably
-        # a safe tradeoff to make.)
-
-        norm_tbl = unique_string(desp='norm_table')
-
-        one_hot_sql = """
-            CREATE TEMP TABLE {norm_tbl} AS
-            SELECT {dist_key_col},
-                {rescale_independent_var} AS x_norm,
-                {one_hot_dep_var_array_expr} AS y
-            FROM {self.source_table} s JOIN {dist_key_tbl} AS d
-                ON (s.gp_segment_id = d.gp_segment_id)
-            {order_by_clause}
-            DISTRIBUTED BY ({dist_key_col})
-        """.format(**locals())
-        plpy.execute(one_hot_sql)
-
-        rows_per_seg_tbl = unique_string(desp='rows_per_seg')
-        start_rows_tbl = unique_string(desp='start_rows')
-
-        #  Generate rows_per_segment table; this small table will
-        #  just have one row on each segment containing the number
-        #  of rows on that segment in the norm_tbl
-        sql = """
-            CREATE TEMP TABLE {rows_per_seg_tbl} AS SELECT
-                COUNT(*) as rows_per_seg,
-                {dist_key_col}
-            FROM {norm_tbl}
-            GROUP BY {dist_key_col}
-            DISTRIBUTED BY ({dist_key_col})
-        """.format(**locals())
-
-        plpy.execute(sql)
-
-        #  Generate start_rows_tbl from rows_per_segment table.
-        #  This assigns a start_row number for each segment based on
-        #  the sum of all rows in previous segments.  These will be
-        #  added to the row numbers within each segment to get an
-        #  absolute index into the table.  All of this is to accomplish
-        #  the equivalent of ROW_NUMBER() OVER() on the whole table,
-        #  but this way is much faster because we don't have to do an
-        #  N:1 Gather Motion (moving entire table to a single segment
-        #  and scanning through it).
-        #
-        sql = """
-            CREATE TEMP TABLE {start_rows_tbl} AS SELECT
-                {dist_key_col},
-                SUM(rows_per_seg) OVER (ORDER BY gp_segment_id) - rows_per_seg AS start_row
-            FROM {rows_per_seg_tbl}
-            DISTRIBUTED BY ({dist_key_col})
-        """.format(**locals())
-
-        plpy.execute(sql)
-
-        plpy.execute("DROP TABLE {0}".format(rows_per_seg_tbl))
-
-        self.buffer_size = self._get_buffer_size(num_segments)
-
-        # The query below assigns slot_id's to each row within
-        #  a segment, computes a row_id by adding start_row for
-        #  that segment to it, then divides by buffer_size to make
-        #  this into a buffer_id
-        # ie:
-        #  buffer_id = row_id / buffer_size
-        #     row_id = start_row + slot_id
-        #    slot_id = ROW_NUMBER() OVER(PARTITION BY )::INTEGER
-        #
-        #   Instead of partitioning by gp_segment_id itself, we
-        # use __dist_key__ col instead.  This is the same partition,
-        # since there's a 1-to-1 mapping between the columns; but
-        # using __dist_key__ avoids an extra Redistribute Motion.
-        #
-        # Note: even though the ordering of these two columns is
-        #  different, this doesn't matter as each segment is being
-        #  numbered separately (only the start_row is different,
-        #  and those are fixed to the correct segments by the JOIN
-        #  condition.
-
-        sql = """
-        CREATE TEMP TABLE {normalized_tbl} AS SELECT
-            {dist_key_col},
-            x_norm,
-            y,
-            (ROW_NUMBER() OVER( PARTITION BY {dist_key_col} ))::INTEGER as slot_id,
-            ((start_row +
-               (ROW_NUMBER() OVER( PARTITION BY {dist_key_col} ) - 1)
-             )::INTEGER / {self.buffer_size}
-            ) AS buffer_id
-        FROM {norm_tbl} JOIN {start_rows_tbl}
-            USING ({dist_key_col})
-        ORDER BY buffer_id
-        DISTRIBUTED BY (slot_id)
-        """.format(**locals())
-
-        plpy.execute(sql)   # label buffer_id's
-
-        # A note on DISTRIBUTED BY (slot_id) in above query:
-        #
-        #     In the next query, we'll be doing the actual batching.  Due
-        #  to the GROUP BY, gpdb will Redistribute on buffer_id.  We could
-        #  avoid this by using DISTRIBUTED BY (buffer_id) in the above
-        #  (buffer-labelling) query.  But this also causes the GROUP BY
-        #  to use single-stage GroupAgg instead of multistage GroupAgg,
-        #  which for unknown reasons is *much* slower and often runs out
-        #  of VMEM unless it's set very high!
-
-        plpy.execute("DROP TABLE {norm_tbl}, {start_rows_tbl}".format(**locals()))
-
-        # Disable optimizer (ORCA) for platforms that use it
-        # since we want to use a groupagg instead of hashagg
-        with OptimizerControl(False):
-            with HashaggControl(False):
-                # Run actual batching query
-                plpy.execute(batching_query.format(**locals()))
-
-        plpy.execute("DROP TABLE {0}".format(normalized_tbl))
-
-        if not all_segments: # remove any segments we don't plan to use
-            sql = """
-                DELETE FROM {dist_key_tbl}
-                    WHERE NOT gp_segment_id = ANY({self.gpu_config})
-            """.format(**locals())
-
-        plpy.execute("ANALYZE {dist_key_tbl}".format(**locals()))
-        plpy.execute("ANALYZE {batched_table}".format(**locals()))
-
-        # Redistribute from buffer_id to dist_key
-        #
-        #  This has to be separate from the batching query, because
-        #   we found that adding DISTRIBUTED BY (dist_key) to that
-        #   query causes it to run out of VMEM on large datasets such
-        #   as places100.  Possibly this is because the memory available
-        #   for GroupAgg has to be shared with an extra slice if they
-        #   are part of the same query.
-        #
-        #  We also tried adding this to the BYTEA conversion query, but
-        #   that resulted in slower performance than just keeping it
-        #   separate.
-        #
-        sql = """CREATE TEMP TABLE {batched_table}_dist_key AS
-                    SELECT {dist_key_col}, t.*
-                        FROM {batched_table} t
-                            JOIN {dist_key_tbl} d
-                                ON {join_key} = d.gp_segment_id
-                            DISTRIBUTED BY ({dist_key_col})
-              """.format(**locals())
-
-        # match buffer_id's with dist_keys
-        plpy.execute(sql)
-
-        sql = """DROP TABLE {batched_table}, {dist_key_tbl};
-                 ALTER TABLE {batched_table}_dist_key RENAME TO {batched_table}
-              """.format(**locals())
-        plpy.execute(sql)
-
-        # Convert batched table to BYTEA and output as final (permanent) table
-        plpy.execute(bytea_query.format(**locals()))
-
-        plpy.execute("DROP TABLE {0}".format(batched_table))
-
-        # Create summary table
-        self._create_output_summary_table()
-
-    def _create_output_summary_table(self):
-        class_level_str='NULL::{0}[]'.format(self.dependent_vartype)
-        if self.dependent_levels:
-            # Update dependent_levels to include NULL when
-            # num_classes > len(self.dependent_levels)
-            if self.num_classes:
-                self.dependent_levels.extend(['NULL'
-                    for i in range(self.padding_size)])
-            else:
-                self.num_classes = len(self.dependent_levels)
-            class_level_str=py_list_to_sql_string(
-                self.dependent_levels, array_type=self.dependent_vartype,
-                long_format=True)
-        if self.num_classes is None:
-            self.num_classes = 'NULL::INTEGER'
-        query = """
-            CREATE TABLE {self.output_summary_table} AS
-            SELECT
-                $__madlib__${self.source_table}$__madlib__$::TEXT AS source_table,
-                $__madlib__${self.output_table}$__madlib__$::TEXT AS output_table,
-                $__madlib__${self.dependent_varname}$__madlib__$::TEXT AS {dependent_varname_colname},
-                $__madlib__${self.independent_varname}$__madlib__$::TEXT AS {independent_varname_colname},
-                $__madlib__${self.dependent_vartype}$__madlib__$::TEXT AS {dependent_vartype_colname},
-                {class_level_str} AS {class_values_colname},
-                {self.buffer_size} AS buffer_size,
-                {self.normalizing_const}::{FLOAT32_SQL_TYPE} AS {normalizing_const_colname},
-                {self.num_classes} AS {num_classes_colname},
-                {self.distribution_rules} AS distribution_rules,
-                {self.gpu_config} AS {internal_gpu_config}
-            """.format(self=self, class_level_str=class_level_str,
-                       dependent_varname_colname=DEPENDENT_VARNAME_COLNAME,
-                       independent_varname_colname=INDEPENDENT_VARNAME_COLNAME,
-                       dependent_vartype_colname=DEPENDENT_VARTYPE_COLNAME,
-                       class_values_colname=CLASS_VALUES_COLNAME,
-                       normalizing_const_colname=NORMALIZING_CONST_COLNAME,
-                       num_classes_colname=NUM_CLASSES_COLNAME,
-                       internal_gpu_config=INTERNAL_GPU_CONFIG,
-                       FLOAT32_SQL_TYPE=FLOAT32_SQL_TYPE)
-        plpy.execute(query)
-
-    def _validate_args(self):
-        validate_module_input_params(
-            self.source_table, self.output_table, self.independent_varname,
-            self.dependent_varname, self.module_name, None,
-            [self.output_summary_table])
-        if self.buffer_size is not None:
-            _assert(self.buffer_size > 0,
-                    "{0}: The buffer size has to be a "
-                    "positive integer or NULL.".format(self.module_name))
-        if self.normalizing_const is not None:
-            _assert(self.normalizing_const > 0,
-                "{0}: The normalizing constant has to be a "
-                "positive integer or NULL.".format(self.module_name))
-
-    def _set_validate_vartypes(self):
-        self.independent_vartype = get_expr_type(self.independent_varname,
-                                                     self.source_table)
-        self.dependent_vartype = get_expr_type(self.dependent_varname,
-                                                   self.source_table)
-        num_of_independent_cols = split_quoted_delimited_str(self.independent_varname)
-        _assert(len(num_of_independent_cols) == 1,
-                "Invalid independent_varname: only one column name is allowed "
-                "as input.")
-        _assert(is_valid_psql_type(self.independent_vartype,
-                                   NUMERIC | ONLY_ARRAY),
-                "Invalid independent variable type, should be an array of "
-                "one of {0}".format(','.join(NUMERIC)))
-        # The dependent variable needs to be either:
-        # 1. NUMERIC, TEXT OR BOOLEAN, which we always one-hot encode
-        # 2. NUMERIC ARRAY, which we assume it is already one-hot encoded, and we
-        #    just cast it the INTEGER ARRAY
-        num_of_dependent_cols = split_quoted_delimited_str(self.dependent_varname)
-        _assert(len(num_of_dependent_cols) == 1,
-                "Invalid dependent_varname: only one column name is allowed "
-                "as input.")
-        _assert((is_valid_psql_type(self.dependent_vartype, NUMERIC | TEXT | BOOLEAN) or
-                 is_valid_psql_type(self.dependent_vartype, NUMERIC | ONLY_ARRAY)),
-                """Invalid dependent variable type, should be one of the types in this list:
-                numeric, text, boolean, or numeric array""")
-
-    def get_distinct_dependent_levels(self, table, dependent_varname,
-                                      dependent_vartype):
-        # Refactoring this out into the parent class to ensure include_nulls
-        # is passed in as true for both training and validation tables
-        return get_distinct_col_levels(table, dependent_varname,
-            dependent_vartype, include_nulls=True)
-
-    def _get_buffer_size(self, num_segments):
-        num_rows_in_tbl = plpy.execute("""
-                SELECT count(*) AS cnt FROM {0}
-            """.format(self.source_table))[0]['cnt']
-        buffer_size_calculator = MiniBatchBufferSizeCalculator()
-        indepdent_var_dim = get_product_of_dimensions(self.source_table,
-            self.independent_varname)
-        buffer_size = buffer_size_calculator.calculate_default_buffer_size(
-            self.buffer_size, num_rows_in_tbl, indepdent_var_dim, num_segments)
-        num_buffers = num_segments * ceil((1.0 * num_rows_in_tbl) / buffer_size / num_segments)
-        return int(ceil(num_rows_in_tbl / num_buffers))
-
-class ValidationDataPreprocessorDL(InputDataPreprocessorDL):
-    def __init__(self, schema_madlib, source_table, output_table,
-                 dependent_varname, independent_varname,
-                 training_preprocessor_table, buffer_size, distribution_rules,
-                 **kwargs):
-        """
-            This prepares the variables that are required by
-            InputDataPreprocessorDL.
-        """
-        self.module_name = "validation_preprocessor_dl"
-        self.training_preprocessor_table = training_preprocessor_table
-        summary_table = self._validate_and_process_training_preprocessor_table()
-        num_classes = summary_table[NUM_CLASSES_COLNAME]
-        InputDataPreprocessorDL.__init__(
-            self, schema_madlib, source_table, output_table,
-            dependent_varname, independent_varname, buffer_size,
-            summary_table[NORMALIZING_CONST_COLNAME], num_classes,
-            distribution_rules, self.module_name)
-        # Update value of dependent_levels from training batch summary table.
-        self.dependent_levels = self._get_dependent_levels(
-            summary_table[CLASS_VALUES_COLNAME],
-            summary_table[DEPENDENT_VARTYPE_COLNAME])
-
-    def _get_dependent_levels(self, training_dependent_levels,
-                              training_dependent_vartype):
-        """
-            Return the distinct dependent levels to be considered for
-            one-hot encoding the dependent var. This is inferred from
-            the class_values column in the training_preprocessor_table
-            summary table. Note that class_values in that summary table
-            already has padding in it, so we have to strip it out here
-            in that case.
-            This function also quotes class levels if they are text.
-        """
-        # Validate that dep var type is exactly the same as what was in
-        # trainig_preprocessor_table's input.
-        _assert(self.dependent_vartype == training_dependent_vartype,
-            "{0}: the dependent variable's type in {1} must be {2}.".format(
-                self.module_name, self.source_table,
-                training_dependent_vartype))
-        # training_dependent_levels is the class_values column from the
-        # training batch summary table. This already has the padding with
-        # NULLs in it based on num_classes that was provided to
-        # training_preprocessor_dl(). We have to work our way backwards
-        # to strip out those trailing NULLs from class_values, since
-        # they will anyway get added later in
-        # InputDataPreprocessorDL._set_one_hot_encoding_variables.
-        dependent_levels = strip_trailing_nulls_from_class_values(
-            training_dependent_levels)
-        if training_dependent_levels:
-            dependent_levels_val_data = self.get_distinct_dependent_levels(
-                self.source_table, self.dependent_varname,
-                self.dependent_vartype)
-            unquoted_dependent_levels_val_data = [strip_end_quotes(level, "'")
-                                                  for level in dependent_levels_val_data]
-            # Assert to check if the class values in validation data is a subset
-            # of the class values in training data.
-            _assert(set(unquoted_dependent_levels_val_data).issubset(set(dependent_levels)),
-                    "{0}: the class values in {1} ({2}) should be a "
-                    "subset of class values in {3} ({4})".format(
-                        self.module_name, self.source_table,
-                        unquoted_dependent_levels_val_data,
-                        self.training_preprocessor_table, dependent_levels))
-        if is_psql_char_type(self.dependent_vartype):
-            dependent_levels = [quote_literal(level) if level is not None else level
-                                for level in dependent_levels]
-        return dependent_levels
-
-    def _validate_and_process_training_preprocessor_table(self):
-        """
-            Validate training_preprocessor_table param passed. That and
-            the corresponding summary tables must exist. The summary
-            table must also have columns such as normalizing_const,
-            class_values, num_classes and dependent_vartype in it.
-        """
-        input_tbl_valid(self.training_preprocessor_table, self.module_name)
-        training_summary_table = add_postfix(
-            self.training_preprocessor_table, "_summary")
-        input_tbl_valid(training_summary_table, self.module_name,
-                        error_suffix_str="Please ensure that table '{0}' "
-                                         "has been preprocessed using "
-                                         "training_preprocessor_dl()."
-                                        .format(self.training_preprocessor_table))
-        summary_table = plpy.execute("SELECT * FROM {0} LIMIT 1".format(
-            training_summary_table))[0]
-        _assert(NORMALIZING_CONST_COLNAME in summary_table,
-            "{0}: Expected column {1} in {2}.".format(
-                self.module_name, NORMALIZING_CONST_COLNAME,
-                training_summary_table))
-        _assert(CLASS_VALUES_COLNAME in summary_table,
-            "{0}: Expected column {1} in {2}.".format(
-                self.module_name, CLASS_VALUES_COLNAME,
-                training_summary_table))
-        _assert(NUM_CLASSES_COLNAME in summary_table,
-            "{0}: Expected column {1} in {2}.".format(
-                self.module_name, NUM_CLASSES_COLNAME,
-                training_summary_table))
-        _assert(DEPENDENT_VARTYPE_COLNAME in summary_table,
-            "{0}: Expected column {1} in {2}.".format(
-                self.module_name, DEPENDENT_VARTYPE_COLNAME,
-                training_summary_table))
-        return summary_table
-
-    def validation_preprocessor_dl(self):
-        self.input_preprocessor_dl(order_by_random=False)
-
-class TrainingDataPreprocessorDL(InputDataPreprocessorDL):
-    def __init__(self, schema_madlib, source_table, output_table,
-                 dependent_varname, independent_varname, buffer_size,
-                 normalizing_const, num_classes, distribution_rules,
-                **kwargs):
-        """
-            This prepares the variables that are required by
-            InputDataPreprocessorDL.
-        """
-        self.module_name = "training_preprocessor_dl"
-        InputDataPreprocessorDL.__init__(
-            self, schema_madlib, source_table, output_table,
-            dependent_varname, independent_varname, buffer_size,
-            normalizing_const, num_classes, distribution_rules,
-            self.module_name)
-        # Update default value of dependent_levels in superclass
-        self.dependent_levels = self._get_dependent_levels()
-
-    def _get_dependent_levels(self):
-        """
-            Return the distinct dependent levels to be considered for
-            one-hot encoding the dependent var. class level values of
-            type text are quoted.
-        """
-        if is_valid_psql_type(self.dependent_vartype, NUMERIC | ONLY_ARRAY):
-            dependent_levels = None
-        else:
-            dependent_levels = get_distinct_col_levels(
-                self.source_table, self.dependent_varname,
-                self.dependent_vartype, include_nulls=True)
-        return dependent_levels
-
-    def training_preprocessor_dl(self):
-        self.input_preprocessor_dl(order_by_random=True)
-
-class InputDataPreprocessorDocumentation:
-    @staticmethod
-    def validation_preprocessor_dl_help(schema_madlib, message):
-        method = "validation_preprocessor_dl"
-        summary = """
-        ----------------------------------------------------------------
-                            SUMMARY
-        ----------------------------------------------------------------
-        For Deep Learning based techniques such as Convolutional Neural Nets,
-        the input data is mostly images. These images can be represented as an
-        array of numbers where each element represents a pixel/color intensity.
-        It is standard practice to normalize the image data before use.
-        minibatch_preprocessor() is for general use-cases, but for deep learning
-        based use-cases we provide training_preprocessor_dl() that is
-        light-weight and is specific to image datasets.
-
-        If you want to evaluate the model, a validation dataset has to
-        be prepared. This validation data has to be in the same format
-        as the corresponding batched training data used for training, i.e.,
-        the two datasets must be normalized using the same normalizing
-        constant, and the one-hot encoding of the dependent variable must
-        follow the same convention. validation_preprocessor_dl() can be
-        used to pre-process the validation data. To ensure that the format
-        is similar to the corresponding training data, this function takes
-        the output table name of training_preprocessor_dl() as an input
-        param.
-
-        For more details on function usage:
-        SELECT {schema_madlib}.{method}('usage')
-        """.format(**locals())
-
-        usage = """
-        ---------------------------------------------------------------------------
-                                        USAGE
-        ---------------------------------------------------------------------------
-        SELECT {schema_madlib}.{method}(
-            source_table,          -- TEXT. Name of the table containing input
-                                      data.  Can also be a view.
-            output_table,          -- TEXT. Name of the output table for
-                                      mini-batching.
-            dependent_varname,     -- TEXT. Name of the dependent variable column.
-            independent_varname,   -- TEXT. Name of the independent variable
-                                      column.
-            training_preprocessor_table, -- TEXT. packed training data table.
-            buffer_size            -- INTEGER. Default computed automatically.
-                                      Number of source input rows to pack into a buffer.
-            distribution_rules     -- TEXT. Default: 'all_segments'. Specifies how to
-                                      distribute the 'output_table'. This is important
-                                      for how the fit function will use resources on the
-                                      cluster.  The default 'all_segments' means the
-                                      'output_table' will be distributed to all segments
-                                      in the database cluster.
-        );
-
-
-        ---------------------------------------------------------------------------
-                                        OUTPUT
-        ---------------------------------------------------------------------------
-        The output table produced by validation_preprocessor_dl contains the
-        following columns:
-
-        buffer_id               -- INTEGER.  Unique id for packed table.
-        dependent_varname       -- BYTEA. Packed array of dependent variables.
-        independent_varname     -- BYTEA. Packed array of independent
-                                   variables.
-        dependent_varname       -- TEXT. Shape of the dependent variable buffer.
-        independent_varname     -- TEXT. Shape of the independent variable buffer.
-
-        ---------------------------------------------------------------------------
-        The algorithm also creates a summary table named _summary
-        that has the following columns:
-
-        source_table              -- Source table name.
-        output_table              -- Output table name from preprocessor.
-        dependent_varname         -- Dependent variable values from the original table
-                                     (encoded by one_hot_encode, if specified).
-        independent_varname       -- Independent variable values from the original
-                                     table.
-        dependent_vartype         -- Type of the dependent variable from the
-                                     original table.
-        class_values              -- Class values of the dependent variable
-                                     (‘NULL’(as TEXT type) for non
-                                     categorical vars).
-        buffer_size               -- Buffer size used in preprocessing step.
-        normalizing_const         -- Normalizing constant used for standardizing.
-                                     arrays in independent_varname.
-        num_classes               -- num_classes value passed by user while
-                                     generating training_preprocessor_table.
-        gpu_config                -- List of segment id's the data is distributed
-                                     on depending on the 'distribution_rules' parameter
-                                     specified as input. Set to 'all_segments' if
-                                     'distribution_rules' is specified as 'all_segments'.
-
-        ---------------------------------------------------------------------------
-        """.format(**locals())
-
-        if not message:
-            return summary
-        elif message.lower() in ('usage', 'help', '?'):
-            return usage
-        return """
-            No such option. Use "SELECT {schema_madlib}.{method}()"
-            for help.
-        """.format(**locals())
-
-    @staticmethod
-    def training_preprocessor_dl_help(schema_madlib, message):
-        method = "training_preprocessor_dl"
-        summary = """
-        ----------------------------------------------------------------
-                            SUMMARY
-        ----------------------------------------------------------------
-        For Deep Learning based techniques such as Convolutional Neural Nets,
-        the input data is mostly images. These images can be represented as an
-        array of numbers where each element represents a pixel/color intensity.
-        It is standard practice to normalize the image data before use.
-        minibatch_preprocessor() is for general use-cases, but for deep learning
-        based use-cases we provide training_preprocessor_dl() that is
-        light-weight and is specific to image datasets.
-
-        The normalizing constant is parameterized, and can be specified based
-        on the kind of image data used.
-
-        An optional param named num_classes can be used to specify the length
-        of the one-hot encoded array for the dependent variable. This value if
-        specified must be greater than equal to the total number of distinct
-        class values found in the input table.
-
-        For more details on function usage:
-        SELECT {schema_madlib}.{method}('usage')
-        """.format(**locals())
-
-        usage = """
-        ---------------------------------------------------------------------------
-                                        USAGE
-        ---------------------------------------------------------------------------
-        SELECT {schema_madlib}.{method}(
-            source_table,          -- TEXT. Name of the table containing input
-                                      data.  Can also be a view.
-            output_table,          -- TEXT. Name of the output table for
-                                      mini-batching.
-            dependent_varname,     -- TEXT. Name of the dependent variable column.
-            independent_varname,   -- TEXT. Name of the independent variable
-                                      column.
-            buffer_size            -- INTEGER. Default computed automatically.
-                                      Number of source input rows to pack into a buffer.
-            normalizing_const      -- REAL. Default 1.0. The normalizing constant to
-                                      use for standardizing arrays in independent_varname.
-            num_classes            -- INTEGER. Default NULL. Number of class labels
-                                      to be considered for 1-hot encoding. If NULL,
-                                      the 1-hot encoded array length will be equal to
-                                      the number of distinct class values found in the
-                                      input table.
-            distribution_rules     -- TEXT. Default: 'all_segments'. Specifies how to
-                                      distribute the 'output_table'. This is important
-                                      for how the fit function will use resources on the
-                                      cluster.  The default 'all_segments' means the
-                                      'output_table' will be distributed to all segments
-                                      in the database cluster.
-        );
-
-
-        ---------------------------------------------------------------------------
-                                        OUTPUT
-        ---------------------------------------------------------------------------
-        The output table produced by MiniBatch Preprocessor contains the
-        following columns:
-
-        buffer_id               -- INTEGER.  Unique id for packed table.
-        dependent_varname       -- BYTEA. Packed array of dependent variables.
-        independent_varname     -- BYTEA. Packed array of independent
-                                   variables.
-        dependent_varname       -- TEXT. Shape of the dependent variable buffer.
-        independent_varname     -- TEXT. Shape of the independent variable buffer.
-
-        ---------------------------------------------------------------------------
-        The algorithm also creates a summary table named _summary
-        that has the following columns:
-
-        source_table              -- Source table name.
-        output_table              -- Output table name from preprocessor.
-        dependent_varname         -- Dependent variable values from the original table
-                                     (encoded by one_hot_encode, if specified).
-        independent_varname       -- Independent variable values from the original
-                                     table.
-        dependent_vartype         -- Type of the dependent variable from the
-                                     original table.
-        class_values              -- Class values of the dependent variable
-                                     (‘NULL’(as TEXT type) for non
-                                     categorical vars).
-        buffer_size               -- Buffer size used in preprocessing step.
-        normalizing_const         -- Normalizing constant used for standardizing
-                                     arrays in independent_varname.
-        num_classes               -- num_classes input param passed to function.
-        gpu_config                -- List of segment id's the data is distributed
-                                     on depending on the 'distribution_rules' param
-                                     specified as input. Set to 'all_segments' if
-                                     'distribution_rules' is specified as 'all_segments'.
-
-
-        ---------------------------------------------------------------------------
-        """.format(**locals())
-
-        if not message:
-            return summary
-        elif message.lower() in ('usage', 'help', '?'):
-            return usage
-        return """
-            No such option. Use "SELECT {schema_madlib}.{method}()"
-            for help.
-        """.format(**locals())
diff --git a/src/ports/postgres/modules/deep_learning/input_data_preprocessor.sql_in b/src/ports/postgres/modules/deep_learning/input_data_preprocessor.sql_in
deleted file mode 100644
index f243417..0000000
--- a/src/ports/postgres/modules/deep_learning/input_data_preprocessor.sql_in
+++ /dev/null
@@ -1,1056 +0,0 @@
-/* ----------------------------------------------------------------------- */
-/**
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- * @file input_preprocessor_dl.sql_in
- * @brief Utilities to prepare input image data for use by deep learning modules.
- * @date December 2018
- *
- */
-/* ----------------------------------------------------------------------- */
-
-m4_include(`SQLCommon.m4')
-
-/**
-@addtogroup grp_input_preprocessor_dl
-
-@brief Utilities that prepare input image data for use by deep learning
-modules.
-
-\warning  This MADlib method is still in early stage development.
-Interface and implementation are subject to change. 
-
-
Contents

-
-This preprocessor is a utility that prepares image data for use
-by frameworks like Keras and TensorFlow that support mini-batching
-as an optimization option.  The advantage of using mini-batching is that
-it can perform better than stochastic gradient descent
-because it uses more than one training example at a time, typically
-resulting in faster and smoother convergence [1].
-
-Images can be
-represented as an array of numbers
-where each element represents grayscale,
-RGB or other channel values for each
-pixel in the image. It is standard practice to
-normalize the image data before training.
-The normalizing constant in this module is parameterized,
-so it can be set depending on
-the format of image data used.
-
-There are two versions of the preprocessor:
-training_preprocessor_dl() preprocesses input image data to be
-used for training a deep learning model, while
-validation_preprocessor_dl() preprocesses validation
-image data used for model evaluation.
-
-@anchor training_preprocessor_dl
-@par Preprocessor for Training Image Data
-
-
-training_preprocessor_dl(source_table,
-                         output_table,
-                         dependent_varname,
-                         independent_varname,
-                         buffer_size,
-                         normalizing_const,
-                         num_classes,
-                         distribution_rules
-                        )
-

-
-\b Arguments
-

-  
source_table

-  
TEXT. Name of the table containing training dataset.
-  Can also be a view.
-  

-
-  
output_table

-  
TEXT.  Name of the output table from the training preprocessor which
-  will be used as input to algorithms that support mini-batching.
-  Note that the arrays packed into the output table are shuffled
-  and normalized, by dividing each element in the independent variable array
-  by the optional 'normalizing_const' parameter. For performance reasons,
-  packed arrays are converted to PostgreSQL bytea format, which is a
-  variable-length binary string.
-
-  In the case a validation data set is used (see
-  later on this page), this output table is also used
-  as an input to the validation preprocessor
-  so that the validation and training image data are
-  both preprocessed in an identical manner.
-  

-
-  
dependent_varname

-  
TEXT. Name of the dependent variable column.
-  @note The mini-batch preprocessor automatically 1-hot encodes
-  dependent variables of all types.  The exception is numeric array types
-  (integer and float), where we assume these are already 1-hot encoded,
-  so these will just be passed through as is.
-  

-
-  
independent_varname

-  
TEXT. Name of the independent variable column. The column must be
-  a numeric array type.
-  

-
-  
buffer_size (optional)

-  
INTEGER, default: computed. Buffer size is the number of rows from
-  the source table that are packed into one row of the preprocessor output
-  table. In the case of images, the source table will have one image per row,
-  and the output table will have multiple images per row. The default value is
-  computed considering the sizes of the source table and images and the number
-  of segments in the database cluster.
-  @note Using the default for 'buffer_size' will produce buffers that are relatively
-  large, which generally results in the fastest fit() runtime with Keras. Setting a
-  smaller buffer size may cause the preprocessor to run faster (although this is not
-  guaranteed, since it depends on database cluster size, data set, and other factors).
-  But since preprocessing is usually a one-time operation and fit() is called many times,
-  by default buffer sizes are optimized to make fit() as fast as possible.  Note that
-  specifying a 'buffer_size' does not guarantee that exact value will be used. Actual buffer
-  size is adjusted to avoid data skew, which adversely impacts fit() runtime.
-  

-
-  
normalizing_const (optional)

-  
REAL, default: 1.0. The normalizing constant to divide
-  each value in the 'independent_varname' array by.  For example,
-  you would use 255 for this value if the image data is in the form 0-255.
-  

-
-  
num_classes (optional)

-  
INTEGER, default: NULL. Number of class labels for 1-hot
-  encoding. If NULL, the 1-hot encoded array
-  length will be equal to the number
-  of distinct class values found in the input table.
-  

-
-  
distribution_rules (optional)

-  
TEXT, default: 'all_segments'. Specifies how to distribute the
-  'output_table'.  This is important for how the fit function will use
-  resources on the cluster.  The default 'all_segments' means the
-  'output_table' will be distributed to all segments in the database
-  cluster.
-
-  If you specify 'gpu_segments' then the 'output_table' will be distributed
-  to all segments that are on hosts that have GPUs attached. This will make
-  maximum use of GPU resources when training a deep learning model.
-
-  You can also specify the name of a resources table containing the segments
-  to be used for training.  This table must contain a column called 'dbid' that
-  specifies the segment id from the 'gp_segment_configuration' table [2].
-  Refer to the utility function GPU Configuration
-  for more information on how to
-  identify segments attached to hosts that are GPU enabled.
-  

-

-
-@anchor validation_preprocessor_dl
-@par Preprocessor for Validation Image Data
-
-validation_preprocessor_dl(source_table,
-                           output_table,
-                           dependent_varname,
-                           independent_varname,
-                           training_preprocessor_table,
-                           buffer_size,
-                           distribution_rules
-                          )
-

-
-\b Arguments
-

-  
source_table

-  
TEXT. Name of the table containing validation dataset.
-  Can also be a view.
-  

-
-  
output_table

-  
TEXT.  Name of the output table from the validation
-  preprocessor which will be used as input to algorithms that support mini-batching.
-  The arrays packed into the output table are
-  normalized using the same normalizing constant from the
-  training preprocessor as specified in
-  the 'training_preprocessor_table' parameter described below.
-  Validation data is not shuffled.
-  For performance reasons,
-  packed arrays are converted to PostgreSQL bytea format, which is a
-  variable-length binary string.
-  

-
-  
dependent_varname

-  
TEXT. Name of the dependent variable column.
-  @note The mini-batch preprocessor automatically 1-hot encodes
-  dependent variables of all types.  The exception is numeric array types
-  (integer and float), where we assume these are already 1-hot encoded,
-  so these will just be passed through as is.
-  

-
-  
independent_varname

-  
TEXT. Name of the independent variable column. The column must be
-  a numeric array type.
-  

-
-  
training_preprocessor_table

-  
TEXT. The output table obtained by
-  running training_preprocessor_dl().
-  Validation data is preprocessed in the same way as
-  training data, i.e., same normalizing constant and dependent
-  variable class values.
-  

-
- 
buffer_size (optional)

-  
INTEGER, default: computed. Buffer size is the number of rows from
-  the source table that are packed into one row of the preprocessor output
-  table. In the case of images, the source table will have one image per row,
-  and the output table will have multiple images per row. The default value is
-  computed considering the sizes of the source table and images and the number
-  of segments in the database cluster.
-  @note Using the default for 'buffer_size' will produce buffers that are relatively
-  large, which generally results in the fastest fit() runtime with Keras. Setting a
-  smaller buffer size may cause the preprocessor to run faster (although this is not
-  guaranteed, since it depends on database cluster size, data set, and other factors).
-  But since preprocessing is usually a one-time operation and fit() is called many times,
-  by default buffer sizes are optimized to make fit() as fast as possible.  Note that
-  specifying a 'buffer_size' does not guarantee that exact value will be used. Actual buffer
-  size is adjusted to avoid data skew, which adversely impacts fit() runtime.
-  

-
- 
distribution_rules (optional)

-  
TEXT, default: 'all_segments'. Specifies how to distribute the
-  'output_table'.  This is important for how the fit function will use
-  resources on the cluster.  The default 'all_segments' means the
-  'output_table' will be distributed to all segments in the database
-  cluster.
-
-  If you specify 'gpu_segments' then the 'output_table' will be distributed
-  to all segments that are on hosts that have GPUs attached. This will make
-  maximum use of GPU resources when training a deep learning model.
-
-  You can also specify the name of a resources table containing the segments
-  to be used for training.  This table must contain a column called 'dbid' that
-  specifies the segment id from the 'gp_segment_configuration' table [2].
-  Refer to the utility function GPU Configuration
-  for more information on how to
-  identify segments attached to hosts that are GPU enabled.
-  

-
-

-
-@anchor output
-@par Output Tables
-

-    The output tables produced by both training_preprocessor_dl() and
-    validation_preprocessor_dl() contain the following columns:
-    
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-    
independent_varBYTEA. Packed array of independent variables in PostgreSQL bytea format.
-        Arrays of independent variables packed into the output table are
-        normalized by dividing each element in the independent variable array by the
-        optional 'normalizing_const' parameter.  Training data is shuffled, but
-        validation data is not.
-        
dependent_varBYTEA. Packed array of dependent variables in PostgreSQL bytea format.
-        The dependent variable is always one-hot encoded as an
-        integer array. For now, we are assuming that
-        input_preprocessor_dl() will be used
-        only for classification problems using deep learning. So
-        the dependent variable is one-hot encoded, unless it's already a
-        numeric array in which case we assume it's already one-hot
-        encoded and just cast it to an integer array.
-        
independent_var_shapeINTEGER[]. Shape of the independent variable array after preprocessing.
-        The first element is the number of images packed per row, and subsequent
-        elements will depend on how the image is described (e.g., channels first or last).
-        
dependent_var_shapeINTEGER[]. Shape of the dependent variable array after preprocessing.
-        The first element is the number of images packed per row, and the second
-        element is the number of class values.
-        
buffer_idINTEGER. Unique id for each row in the packed table.
-        

-
-A summary table named \_summary is also created, which
-has the following columns (the columns are the same for
-both validation_preprocessor_dl() and training_preprocessor_dl() ):
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-   
source_tableName of the source table.
output_tableName of output table generated by preprocessor.
dependent_varnameDependent variable from the source table.
independent_varnameIndependent variable from the source table.
dependent_vartypeType of the dependent variable from the source table.
class_valuesThe dependent level values that one-hot encoding maps to.
buffer_sizeBuffer size used in preprocessing step.
normalizing_constThe value used to normalize the input image data.
num_classesNumber of dependent levels the one-hot encoding is created
-        for. NULLs are padded at the end if the number of distinct class
-        levels found in the input data is less than the 'num_classes' parameter
-        specified in training_preprocessor_dl().
distribution_rulesThis is the list of segment id's in the form of 'dbid'
-        describing how the 'output_table' is distributed,
-        as per the 'distribution_rules' input parameter.
-        If the 'distribution_rules' parameter is set to 'all_segments', then
-        this will also be set to 'all_segments'.
__internal_gpu_config__For internal use.  (Note: this is the list of segment id's
-        where data is distributed in the form of 'content' id, which
-        is different from 'dbid' [2].)

-
-@anchor example
-@par Examples
--#  Create an artificial 2x2 resolution color image data set with 3 possible classifications.
-The RGB values are per-pixel arrays:
-
-DROP TABLE IF EXISTS image_data;
-CREATE TABLE image_data AS (
-    SELECT ARRAY[
-        ARRAY[
-            ARRAY[(random() * 256)::integer, -- pixel (1,1)
-                (random() * 256)::integer,
-                (random() * 256)::integer],
-            ARRAY[(random() * 256)::integer, -- pixel (2,1)
-                (random() * 256)::integer,
-                (random() * 256)::integer]
-        ],
-        ARRAY[
-            ARRAY[(random() * 256)::integer, -- pixel (1,2)
-                (random() * 256)::integer,
-                (random() * 256)::integer],
-            ARRAY[(random() * 256)::integer, -- pixel (2,1)
-                (random() * 256)::integer,
-                (random() * 256)::integer]
-        ]
-    ] as rgb, ('{cat,dog,bird}'::text[])[ceil(random()*3)] as species
-    FROM generate_series(1, 52)
-);
-SELECT * FROM image_data;
-

-
-                             rgb                              | species
---------------------------------------------------------------+---------
- {{{124,198,44},{91,47,130}},{{24,175,69},{196,189,166}}}     | dog
- {{{111,202,129},{198,249,254}},{{141,37,88},{187,167,113}}}  | dog
- {{{235,53,39},{145,167,209}},{{197,147,222},{55,218,53}}}    | dog
- {{{231,48,125},{248,233,151}},{{63,125,230},{33,24,70}}}     | dog
- {{{92,146,121},{163,241,110}},{{75,88,72},{218,90,12}}}      | bird
- {{{88,114,59},{202,211,152}},{{92,76,58},{77,186,134}}}      | dog
- {{{2,96,255},{14,48,19}},{{240,55,115},{137,255,245}}}       | dog
- {{{165,122,98},{16,115,240}},{{4,106,116},{108,242,210}}}    | dog
- {{{155,207,101},{214,167,24}},{{118,240,228},{199,230,21}}}  | dog
- {{{94,212,15},{48,66,170}},{{255,167,128},{166,191,246}}}    | dog
- {{{169,69,131},{16,98,225}},{{228,113,17},{38,27,17}}}       | bird
- {{{156,183,139},{146,77,46}},{{80,202,230},{146,84,239}}}    | dog
- {{{190,210,147},{227,31,66}},{{229,251,84},{51,118,240}}}    | bird
- {{{253,175,200},{237,151,107}},{{207,56,162},{133,39,35}}}   | cat
- {{{146,185,108},{14,10,105}},{{188,210,86},{83,61,36}}}      | dog
- {{{223,169,177},{3,200,250}},{{112,91,16},{193,32,151}}}     | cat
- {{{249,145,240},{144,153,58}},{{131,156,230},{56,50,75}}}    | dog
- {{{212,186,229},{52,251,197}},{{230,121,201},{35,215,119}}}  | cat
- {{{234,94,23},{114,196,94}},{{242,249,90},{223,24,109}}}     | bird
- {{{111,36,145},{77,135,123}},{{171,158,237},{111,252,222}}}  | dog
- {{{90,74,240},{231,133,95}},{{11,21,173},{146,144,88}}}      | cat
- {{{170,52,237},{13,114,71}},{{87,99,46},{220,194,56}}}       | bird
- {{{8,17,92},{64,2,203}},{{10,131,145},{4,129,30}}}           | cat
- {{{217,218,207},{74,68,186}},{{127,107,76},{38,60,16}}}      | bird
- {{{193,34,83},{203,99,58}},{{251,224,50},{228,118,113}}}     | dog
- {{{146,218,155},{32,159,243}},{{146,218,189},{101,114,25}}}  | bird
- {{{179,160,74},{204,81,246}},{{50,189,39},{60,42,185}}}      | cat
- {{{13,82,174},{198,151,84}},{{65,249,100},{179,234,104}}}    | cat
- {{{162,190,124},{184,66,138}},{{10,240,80},{161,68,145}}}    | dog
- {{{164,144,199},{53,42,111}},{{122,174,128},{220,143,100}}}  | cat
- {{{160,138,104},{177,86,3}},{{104,226,149},{181,16,229}}}    | dog
- {{{246,119,211},{229,249,119}},{{117,192,172},{159,47,38}}}  | cat
- {{{175,1,220},{18,78,124}},{{156,181,45},{242,185,148}}}     | bird
- {{{50,113,246},{101,213,180}},{{56,103,151},{87,169,124}}}   | cat
- {{{73,109,147},{22,81,197}},{{135,71,42},{91,251,98}}}       | bird
- {{{206,61,255},{25,151,211}},{{211,124,7},{206,64,237}}}     | cat
- {{{201,71,34},{182,142,43}},{{198,172,171},{230,1,23}}}      | bird
- {{{142,158,2},{223,45,205}},{{118,177,223},{232,178,141}}}   | cat
- {{{86,190,128},{195,172,14}},{{97,173,237},{142,123,99}}}    | cat
- {{{26,72,148},{79,226,156}},{{96,62,220},{99,9,230}}}        | bird
- {{{154,234,103},{184,18,65}},{{146,225,139},{214,156,10}}}   | cat
- {{{244,169,103},{218,143,2}},{{196,246,186},{214,55,76}}}    | bird
- {{{20,226,7},{96,153,200}},{{130,236,147},{229,38,142}}}     | bird
- {{{172,102,107},{50,11,109}},{{145,9,123},{193,28,107}}}     | bird
- {{{143,243,247},{132,104,137}},{{94,3,169},{253,246,59}}}    | bird
- {{{78,74,228},{51,200,218}},{{170,155,190},{164,18,51}}}     | dog
- {{{163,226,161},{56,182,239}},{{129,154,35},{73,116,205}}}   | bird
- {{{74,243,3},{172,182,149}},{{101,34,163},{111,138,95}}}     | cat
- {{{224,178,126},{4,61,93}},{{174,238,96},{118,232,208}}}     | bird
- {{{55,236,249},{7,189,242}},{{151,173,130},{49,232,5}}}      | bird
- {{{9,16,30},{128,32,85}},{{108,25,91},{41,11,243}}}          | bird
- {{{141,35,191},{146,240,141}},{{207,239,166},{102,194,121}}} | bird
-(52 rows)
-

--#  Run the preprocessor for training image data:
-
-DROP TABLE IF EXISTS image_data_packed, image_data_packed_summary;
-SELECT madlib.training_preprocessor_dl('image_data',         -- Source table
-                                        'image_data_packed',  -- Output table
-                                        'species',            -- Dependent variable
-                                        'rgb',                -- Independent variable
-                                        NULL,                 -- Buffer size
-                                        255                   -- Normalizing constant
-                                        );
-

-For small datasets like in this example, buffer size is mainly determined
-by the number of segments in the database. For a Greenplum database with 2 segments,
-there will be 2 rows with a buffer size of 26. For PostgresSQL, there would
-be only one row with a buffer size of 52 since it is a single node database.
-For larger data sets, other factors go into computing buffers size besides
-number of segments.
-Here is the packed output table of training data for our simple example:
-
-SELECT independent_var_shape, dependent_var_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;
-

-
- independent_var_shape | dependent_var_shape | buffer_id
------------------------+---------------------+-----------
- {26,2,2,3}            | {26,3}              |         0
- {26,2,2,3}            | {26,3}              |         1
-(2 rows)
-

-Review the output summary table:
-
-\\x on
-SELECT * FROM image_data_packed_summary;
-

-
--[ RECORD 1 ]-----------+------------------
-source_table            | image_data
-output_table            | image_data_packed
-dependent_varname       | species
-independent_varname     | rgb
-dependent_vartype       | text
-class_values            | {bird,cat,dog}
-buffer_size             | 26
-normalizing_const       | 255
-num_classes             | 3
-distribution_rules      | all_segments
-__internal_gpu_config__ | all_segments
-

-
--#  Run the preprocessor for the validation dataset.
-In this example, we use the same images for
-validation to demonstrate, but normally validation data
-is different than training data:
-
-DROP TABLE IF EXISTS val_image_data_packed, val_image_data_packed_summary;
-SELECT madlib.validation_preprocessor_dl(
-      'image_data',             -- Source table
-      'val_image_data_packed',  -- Output table
-      'species',                -- Dependent variable
-      'rgb',                    -- Independent variable
-      'image_data_packed',      -- From training preprocessor step
-      NULL                      -- Buffer size
-      );
-

-We could choose to use a different buffer size compared to the
-training_preprocessor_dl run (but generally don't need to).
-Other parameters such as num_classes and
-normalizing_const that were passed to training_preprocessor_dl are
-automatically inferred using the image_data_packed param that is passed.
-Here is the packed output table of validation data for our simple example:
-
-SELECT independent_var_shape, dependent_var_shape, buffer_id FROM val_image_data_packed ORDER BY buffer_id;
-

-
- independent_var_shape | dependent_var_shape | buffer_id
------------------------+---------------------+-----------
- {26,2,2,3}            | {26,3}              |         0
- {26,2,2,3}            | {26,3}              |         1
-(2 rows)
-

-Review the output summary table:
-
-\\x on
-SELECT * FROM val_image_data_packed_summary;
-

-
--[ RECORD 1 ]-----------+----------------------
-source_table            | image_data
-output_table            | val_image_data_packed
-dependent_varname       | species
-independent_varname     | rgb
-dependent_vartype       | text
-class_values            | {bird,cat,dog}
-buffer_size             | 26
-normalizing_const       | 255
-num_classes             | 3
-distribution_rules      | all_segments
-__internal_gpu_config__ | all_segments
-

-
--#  Load data in another format.  Create an artificial 2x2 resolution color image
-data set with 3 possible classifications. The RGB values are unrolled into a flat array:
-
-DROP TABLE IF EXISTS image_data;
-CREATE TABLE image_data AS (
-SELECT ARRAY[
-        (random() * 256)::integer, -- R values
-        (random() * 256)::integer,
-        (random() * 256)::integer,
-        (random() * 256)::integer,
-        (random() * 256)::integer, -- G values
-        (random() * 256)::integer,
-        (random() * 256)::integer,
-        (random() * 256)::integer,
-        (random() * 256)::integer, -- B values
-        (random() * 256)::integer,
-        (random() * 256)::integer,
-        (random() * 256)::integer
-    ] as rgb, ('{cat,dog,bird}'::text[])[ceil(random()*3)] as species
-FROM generate_series(1, 52)
-);
-SELECT * FROM image_data;
-

-
-                       rgb                        | species
---------------------------------------------------+---------
- {26,150,191,113,235,57,145,143,44,145,85,25}     | dog
- {240,43,225,15,220,136,186,209,49,130,55,111}    | bird
- {25,191,37,77,193,62,249,228,97,33,81,7}         | cat
- {141,223,46,195,201,19,207,78,160,130,157,89}    | cat
- {39,249,168,164,223,193,99,4,14,37,66,7}         | cat
- {159,250,127,44,151,254,11,211,247,137,79,233}   | cat
- {19,230,76,253,42,175,230,143,184,133,27,215}    | cat
- {199,224,144,5,64,19,200,186,109,218,108,70}     | bird
- {148,136,4,41,185,104,203,253,113,151,166,76}    | bird
- {230,132,114,213,210,139,91,199,240,142,203,75}  | bird
- {166,188,96,217,135,70,93,249,27,47,132,118}     | bird
- {118,120,222,236,110,83,240,47,19,206,222,51}    | bird
- {230,3,26,47,93,144,167,59,123,21,142,107}       | cat
- {250,224,62,136,112,142,88,187,24,1,168,216}     | bird
- {52,144,231,12,76,1,162,11,114,141,69,3}         | cat
- {166,172,246,169,200,102,62,57,239,75,165,88}    | dog
- {151,50,112,227,199,97,47,4,43,123,116,133}      | bird
- {39,185,96,127,80,248,177,191,218,120,32,9}      | dog
- {25,172,34,34,40,109,166,23,60,216,246,54}       | bird
- {163,39,89,170,95,230,137,141,169,82,159,121}    | dog
- {131,143,183,138,151,90,177,240,4,16,214,141}    | dog
- {99,233,100,9,159,140,30,202,29,169,120,62}      | bird
- {99,162,69,10,204,169,219,20,106,170,111,16}     | bird
- {16,246,27,32,187,226,0,75,231,64,94,175}        | bird
- {25,135,244,101,50,4,91,77,36,22,47,37}          | dog
- {22,101,191,197,96,138,78,198,155,138,193,51}    | bird
- {236,22,110,30,181,20,218,21,236,97,91,73}       | dog
- {160,57,34,212,239,197,233,174,164,97,88,153}    | cat
- {226,170,192,123,242,224,190,51,163,192,91,105}  | bird
- {149,174,12,72,112,1,37,153,118,201,79,121}      | bird
- {34,250,232,222,218,221,234,201,138,66,186,58}   | bird
- {162,55,85,159,247,234,77,3,50,189,4,87}         | dog
- {122,32,164,243,0,198,237,232,164,199,197,142}   | dog
- {80,209,75,138,169,236,193,254,140,184,232,217}  | bird
- {112,148,114,137,13,107,105,75,243,218,218,75}   | dog
- {241,76,61,202,76,112,90,51,125,166,52,30}       | bird
- {75,132,239,207,49,224,250,19,238,214,154,169}   | dog
- {203,43,222,58,231,5,243,71,131,67,63,52}        | cat
- {229,12,133,142,179,80,185,145,138,160,149,125}  | bird
- {64,251,61,153,13,100,145,181,8,112,118,107}     | dog
- {128,223,60,248,126,124,243,188,20,0,31,166}     | bird
- {39,22,43,146,138,174,33,65,56,184,155,234}      | dog
- {177,247,133,154,159,37,148,30,81,43,29,92}      | bird
- {56,127,199,118,105,120,109,239,18,12,20,166}    | cat
- {101,209,72,193,207,91,166,27,88,209,203,62}     | dog
- {131,195,122,90,18,178,217,217,40,66,81,149}     | cat
- {203,137,103,17,60,251,152,64,36,81,168,239}     | cat
- {239,97,10,20,194,32,121,129,228,217,11,50}      | dog
- {117,4,193,192,223,176,33,232,196,226,8,61}      | dog
- {162,21,190,223,120,170,245,230,200,170,250,163} | bird
- {32,67,65,195,2,39,198,28,86,35,172,254}         | dog
- {39,19,236,146,87,140,203,121,96,187,62,73}      | dog
-(52 rows)
-

-
--#  Run the preprocessor for training image data:
-
-DROP TABLE IF EXISTS image_data_packed, image_data_packed_summary;
-SELECT madlib.training_preprocessor_dl('image_data',         -- Source table
-                                        'image_data_packed',  -- Output table
-                                        'species',            -- Dependent variable
-                                        'rgb',                -- Independent variable
-                                        NULL,                 -- Buffer size
-                                        255                   -- Normalizing constant
-                                        );
-

-Here is a sample of the packed output table:
-
-SELECT independent_var_shape, dependent_var_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;
-

-
- independent_var_shape | dependent_var_shape | buffer_id
------------------------+---------------------+-----------
- {26,12}               | {26,3}              |         0
- {26,12}               | {26,3}              |         1
-(2 rows)
-

-
--#  Run the preprocessor for the validation dataset.
-In this example, we use the same images for
-validation to demonstrate, but normally validation data
-is different than training data:
-
-DROP TABLE IF EXISTS val_image_data_packed, val_image_data_packed_summary;
-SELECT madlib.validation_preprocessor_dl(
-    'image_data',             -- Source table
-    'val_image_data_packed',  -- Output table
-    'species',                -- Dependent variable
-    'rgb',                    -- Independent variable
-    'image_data_packed',      -- From training preprocessor step
-    NULL                      -- Buffer size
-    );
-

-Here is a sample of the packed output summary table:
-
-SELECT independent_var_shape, dependent_var_shape, buffer_id FROM val_image_data_packed ORDER BY buffer_id;
-

-
- independent_var_shape | dependent_var_shape | buffer_id
------------------------+---------------------+-----------
- {26,12}               | {26,3}              |         0
- {26,12}               | {26,3}              |         1
-(2 rows)
-

-
--# Generally the default buffer size will work well,
-but if you have occasion to change it:
-
-DROP TABLE IF EXISTS image_data_packed, image_data_packed_summary;
-SELECT madlib.training_preprocessor_dl('image_data',         -- Source table
-                                       'image_data_packed',  -- Output table
-                                       'species',            -- Dependent variable
-                                       'rgb',                -- Independent variable
-                                        10,                   -- Buffer size
-                                        255                   -- Normalizing constant
-                                        );
-SELECT independent_var_shape, dependent_var_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;
-

-
- independent_var_shape | dependent_var_shape | buffer_id
------------------------+---------------------+-----------
- {8,12}                | {8,3}               |         0
- {9,12}                | {9,3}               |         1
- {9,12}                | {9,3}               |         2
- {9,12}                | {9,3}               |         3
- {9,12}                | {9,3}               |         4
- {8,12}                | {8,3}               |         5
-(6 rows)
-

-Review the output summary table:
-
-\\x on
-SELECT * FROM image_data_packed_summary;
-

-
--[ RECORD 1 ]-----------+------------------
-source_table            | image_data
-output_table            | image_data_packed
-dependent_varname       | species
-independent_varname     | rgb
-dependent_vartype       | text
-class_values            | {bird,cat,dog}
-buffer_size             | 10
-normalizing_const       | 255
-num_classes             | 3
-distribution_rules      | all_segments
-__internal_gpu_config__ | all_segments
-

-
--#  Run the preprocessor for image data with num_classes greater than 3 (distinct class values found in table):
-
-DROP TABLE IF EXISTS image_data_packed, image_data_packed_summary;
-SELECT madlib.training_preprocessor_dl('image_data',         -- Source table
-                                        'image_data_packed',  -- Output table
-                                        'species',            -- Dependent variable
-                                        'rgb',                -- Independent variable
-                                        NULL,                 -- Buffer size
-                                        255,                  -- Normalizing constant
-                                        5                     -- Number of desired class values
-                                        );
-

-Here is a sample of the packed output table with the padded 1-hot vector:
-
-SELECT independent_var_shape, dependent_var_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;
-

-
- independent_var_shape | dependent_var_shape | buffer_id
------------------------+---------------------+-----------
- {26,12}               | {26,5}              |         0
- {26,12}               | {26,5}              |         1
-(2 rows)
-

-Review the output summary table:
-
-\\x on
-SELECT * FROM image_data_packed_summary;
-

-
--[ RECORD 1 ]-----------+-------------------------
-source_table            | image_data
-output_table            | image_data_packed
-dependent_varname       | species
-independent_varname     | rgb
-dependent_vartype       | text
-class_values            | {bird,cat,dog,NULL,NULL}
-buffer_size             | 26
-normalizing_const       | 255
-num_classes             | 5
-distribution_rules      | all_segments
-__internal_gpu_config__ | all_segments
-

-
--#  Using distribution rules to specify how to distribute
-the 'output_table'. This is important for how the fit function
-will use resources on the cluster. To distribute to all segments
-on hosts with GPUs attached:
-
-DROP TABLE IF EXISTS image_data_packed, image_data_packed_summary;
-SELECT madlib.training_preprocessor_dl('image_data',          -- Source table
-                                        'image_data_packed',  -- Output table
-                                        'species',            -- Dependent variable
-                                        'rgb',                -- Independent variable
-                                        NULL,                 -- Buffer size
-                                        255,                  -- Normalizing constant
-                                        NULL,                 -- Number of classes
-                                        'gpu_segments'        -- Distribution rules
-                                        );
-\\x on
-SELECT * FROM image_data_packed_summary;
-

-
--[ RECORD 1 ]-----------+------------------
-source_table            | image_data
-output_table            | image_data_packed
-dependent_varname       | species
-independent_varname     | rgb
-dependent_vartype       | text
-class_values            | {bird,cat,dog}
-buffer_size             | 26
-normalizing_const       | 255
-num_classes             | 3
-distribution_rules      | {2,3,4,5}
-__internal_gpu_config__ | {0,1,2,3}
-

-To distribute to only specified segments, create a
-distribution table with a column called 'dbid' that
-lists the segments you want:
-
-DROP TABLE IF EXISTS segments_to_use;
-CREATE TABLE segments_to_use(
-    dbid INTEGER,
-    hostname TEXT
-);
-INSERT INTO segments_to_use VALUES
-(2, 'hostname-01'),
-(3, 'hostname-01');
-DROP TABLE IF EXISTS image_data_packed, image_data_packed_summary;
-SELECT madlib.training_preprocessor_dl('image_data',          -- Source table
-                                        'image_data_packed',  -- Output table
-                                        'species',            -- Dependent variable
-                                        'rgb',                -- Independent variable
-                                        NULL,                 -- Buffer size
-                                        255,                  -- Normalizing constant
-                                        NULL,                 -- Number of classes
-                                        'segments_to_use'     -- Distribution rules
-                                        );
-\\x on
-SELECT * FROM image_data_packed_summary;
-

-
--[ RECORD 1 ]-----------+------------------
-source_table            | image_data
-output_table            | image_data_packed
-dependent_varname       | species
-independent_varname     | rgb
-dependent_vartype       | text
-class_values            | {bird,cat,dog}
-buffer_size             | 26
-normalizing_const       | 255
-num_classes             | 3
-distribution_rules      | {2,3}
-__internal_gpu_config__ | {0,1}
-

-
-@anchor references
-@par References
-
-[1] "Neural Networks for Machine Learning", Lectures 6a and 6b on mini-batch gradient descent,
-Geoffrey Hinton with Nitish Srivastava and Kevin Swersky, http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
-
-[2] Greenplum 'gp_segment_configuration' table https://gpdb.docs.pivotal.io/latest/ref_guide/system_catalogs/gp_segment_configuration.html
-
-@anchor related
-@par Related Topics
-
-training_preprocessor_dl()
-
-validation_preprocessor_dl()
-
-gpu_configuration()
-
-*/
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.validation_preprocessor_dl(
-    source_table                VARCHAR,
-    output_table                VARCHAR,
-    dependent_varname           VARCHAR,
-    independent_varname         VARCHAR,
-    training_preprocessor_table VARCHAR,
-    buffer_size                 INTEGER,
-    distribution_rules          TEXT
-) RETURNS VOID AS $$
-    PythonFunctionBodyOnly(deep_learning, input_data_preprocessor)
-    from utilities.control import MinWarning
-    with AOControl(False):
-        with MinWarning('error'):
-            validation_preprocessor_obj = input_data_preprocessor.ValidationDataPreprocessorDL(**globals())
-            validation_preprocessor_obj.validation_preprocessor_dl()
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.validation_preprocessor_dl(
-    source_table                VARCHAR,
-    output_table                VARCHAR,
-    dependent_varname           VARCHAR,
-    independent_varname         VARCHAR,
-    training_preprocessor_table VARCHAR,
-    buffer_size                 INTEGER
-) RETURNS VOID AS $$
-  SELECT MADLIB_SCHEMA.validation_preprocessor_dl($1, $2, $3, $4, $5, $6, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.validation_preprocessor_dl(
-    source_table                VARCHAR,
-    output_table                VARCHAR,
-    dependent_varname           VARCHAR,
-    independent_varname         VARCHAR,
-    training_preprocessor_table VARCHAR
-) RETURNS VOID AS $$
-  SELECT MADLIB_SCHEMA.validation_preprocessor_dl($1, $2, $3, $4, $5, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.validation_preprocessor_dl(
-    message VARCHAR
-) RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(deep_learning, input_data_preprocessor)
-    return input_data_preprocessor.InputDataPreprocessorDocumentation.validation_preprocessor_dl_help(schema_madlib, message)
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.validation_preprocessor_dl()
-RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(deep_learning, input_data_preprocessor)
-    return input_data_preprocessor.InputDataPreprocessorDocumentation.validation_preprocessor_dl_help(schema_madlib, '')
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
--------------------------------------------------------------------------------
-
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.training_preprocessor_dl(
-    source_table                VARCHAR,
-    output_table                VARCHAR,
-    dependent_varname           VARCHAR,
-    independent_varname         VARCHAR,
-    buffer_size                 INTEGER,
-    normalizing_const           REAL,
-    num_classes                 INTEGER,
-    distribution_rules          TEXT
-) RETURNS VOID AS $$
-    PythonFunctionBodyOnly(deep_learning, input_data_preprocessor)
-    from utilities.control import MinWarning
-    with AOControl(False):
-        with MinWarning('error'):
-            training_preprocessor_obj = input_data_preprocessor.TrainingDataPreprocessorDL(**globals())
-            training_preprocessor_obj.training_preprocessor_dl()
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.training_preprocessor_dl(
-    source_table            VARCHAR,
-    output_table            VARCHAR,
-    dependent_varname       VARCHAR,
-    independent_varname     VARCHAR,
-    buffer_size             INTEGER,
-    normalizing_const       REAL,
-    num_classes             INTEGER
-) RETURNS VOID AS $$
-  SELECT MADLIB_SCHEMA.training_preprocessor_dl($1, $2, $3, $4, $5, $6, $7, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.training_preprocessor_dl(
-    source_table            VARCHAR,
-    output_table            VARCHAR,
-    dependent_varname       VARCHAR,
-    independent_varname     VARCHAR,
-    buffer_size             INTEGER,
-    normalizing_const       REAL
-) RETURNS VOID AS $$
-  SELECT MADLIB_SCHEMA.training_preprocessor_dl($1, $2, $3, $4, $5, $6, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.training_preprocessor_dl(
-    source_table            VARCHAR,
-    output_table            VARCHAR,
-    dependent_varname       VARCHAR,
-    independent_varname     VARCHAR,
-    buffer_size             INTEGER
-) RETURNS VOID AS $$
-  SELECT MADLIB_SCHEMA.training_preprocessor_dl($1, $2, $3, $4, $5, 1.0, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.training_preprocessor_dl(
-    source_table            VARCHAR,
-    output_table            VARCHAR,
-    dependent_varname       VARCHAR,
-    independent_varname     VARCHAR
-) RETURNS VOID AS $$
-  SELECT MADLIB_SCHEMA.training_preprocessor_dl($1, $2, $3, $4, NULL, 1.0, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.training_preprocessor_dl(
-    message VARCHAR
-) RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(deep_learning, input_data_preprocessor)
-    return input_data_preprocessor.InputDataPreprocessorDocumentation.training_preprocessor_dl_help(schema_madlib, message)
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.training_preprocessor_dl()
-RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(deep_learning, input_data_preprocessor)
-    return input_data_preprocessor.InputDataPreprocessorDocumentation.training_preprocessor_dl_help(schema_madlib, '')
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
--- aggregation for independent var
-DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.agg_array_concat(REAL[]);
-CREATE AGGREGATE MADLIB_SCHEMA.agg_array_concat(REAL[]) (
-   SFUNC = array_cat,
-   PREFUNC = array_cat,
-   STYPE = REAL[]
-   );
-
--- aggregation for dependent var
-DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.agg_array_concat(SMALLINT[]);
-CREATE AGGREGATE MADLIB_SCHEMA.agg_array_concat(SMALLINT[]) (
-   SFUNC = array_cat,
-   PREFUNC = array_cat,
-   STYPE = SMALLINT[]
-   );
-
-CREATE FUNCTION MADLIB_SCHEMA.convert_array_to_bytea(var REAL[])
-RETURNS BYTEA
-AS
-$$
-import numpy as np
-
-return np.array(var, dtype=np.float32).tobytes()
-$$ LANGUAGE plpythonu;
-
-CREATE FUNCTION MADLIB_SCHEMA.convert_array_to_bytea(var SMALLINT[])
-RETURNS BYTEA
-AS
-$$
-import numpy as np
-
-return np.array(var, dtype=np.int16).tobytes()
-$$ LANGUAGE plpythonu;
-
-
-CREATE FUNCTION MADLIB_SCHEMA.convert_bytea_to_real_array(var BYTEA)
-RETURNS REAL[]
-AS
-$$
-import numpy as np
-
-return np.frombuffer(var, dtype=np.float32)
-$$ LANGUAGE plpythonu;
-
-
-CREATE FUNCTION MADLIB_SCHEMA.convert_bytea_to_smallint_array(var BYTEA)
-RETURNS SMALLINT[]
-AS
-$$
-import numpy as np
-
-return np.frombuffer(var, dtype=np.int16)
-$$ LANGUAGE plpythonu;
-
diff --git a/src/ports/postgres/modules/deep_learning/keras_model_arch_table.py_in b/src/ports/postgres/modules/deep_learning/keras_model_arch_table.py_in
deleted file mode 100644
index 0d6fc7c..0000000
--- a/src/ports/postgres/modules/deep_learning/keras_model_arch_table.py_in
+++ /dev/null
@@ -1,231 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-"""
-@file keras_model_arch_table.py_in
-
-@brief keras model arch table management helper functions
-
-@namespace keras_model_arch_table
-"""
-
-from internal.db_utils import quote_literal
-import plpy
-from utilities.control import MinWarning
-from utilities.utilities import get_col_name_type_sql_string
-from utilities.utilities import unique_string
-from utilities.validate_args import columns_missing_from_table
-from utilities.validate_args import input_tbl_valid
-from utilities.validate_args import quote_ident
-from utilities.validate_args import table_exists
-
-class ModelArchSchema:
-    """Expected format of keras_model_arch_table.
-       Example uses:
-
-           from utilities.validate_args import columns_missing_from_table
-           from keras_model_arch_table import Format
-
-           # Validate names in cols list against actual table
-           missing_cols = columns_missing_from_table('my_arch_table', Format.col_names)
-
-           # Get model arch from keras model arch table, without hard coding column names
-           sql = "SELECT {arch} FROM {table} WHERE {id} = {my_id}"
-                 .format(arch=Format.model_arch,
-                         table='my_arch_table',
-                         id=Format.model_id,
-                         my_id=1)
-           arch = plpy.execute(sql)[0]
-
-    """
-    col_names = ('model_id', 'model_arch', 'model_weights', 'name', 'description',
-                 '__internal_madlib_id__')
-    col_types = ('SERIAL PRIMARY KEY', 'JSON', 'bytea', 'TEXT', 'TEXT', 'TEXT')
-    (MODEL_ID, MODEL_ARCH, MODEL_WEIGHTS, NAME, DESCRIPTION,
-     __INTERNAL_MADLIB_ID__) = col_names
-
-@MinWarning("error")
-def load_keras_model(keras_model_arch_table, model_arch, model_weights,
-                     name, description, **kwargs):
-    model_arch_table = quote_ident(keras_model_arch_table)
-    if not table_exists(model_arch_table):
-        col_defs = get_col_name_type_sql_string(ModelArchSchema.col_names,
-                                                ModelArchSchema.col_types)
-
-        sql = "CREATE TABLE {model_arch_table} ({col_defs})" \
-              .format(**locals())
-
-        plpy.execute(sql, 0)
-        plpy.info("Keras Model Arch: Created new keras model architecture table {0}." \
-            .format(model_arch_table))
-    else:
-        missing_cols = columns_missing_from_table(model_arch_table,
-                                                  ModelArchSchema.col_names)
-        if len(missing_cols) > 0:
-            plpy.error("Keras Model Arch: Invalid keras model architecture table {0},"
-                       " missing columns: {1}".format(model_arch_table,
-                                                      missing_cols))
-
-    unique_str = unique_string(prefix_has_temp=False)
-    insert_query = plpy.prepare("INSERT INTO {model_arch_table} "
-                                "VALUES(DEFAULT, $1, $2, $3, $4, $5);".format(**locals()),
-                                ModelArchSchema.col_types[1:])
-    insert_res = plpy.execute(insert_query,[model_arch, model_weights, name, description,
-                               unique_str], 0)
-
-    select_query = """SELECT {model_id_col}, {model_arch_col} FROM {model_arch_table}
-                   WHERE {internal_id_col} = '{unique_str}'""".format(
-                    model_id_col=ModelArchSchema.MODEL_ID,
-                    model_arch_col=ModelArchSchema.MODEL_ARCH,
-                    model_arch_table=model_arch_table,
-                    internal_id_col=ModelArchSchema.__INTERNAL_MADLIB_ID__,
-                    unique_str=unique_str)
-    select_res = plpy.execute(select_query,1)
-
-    plpy.info("Keras Model Arch: Added model id {0} to {1} table".
-              format(select_res[0][ModelArchSchema.MODEL_ID], model_arch_table))
-
-@MinWarning("error")
-def delete_keras_model(keras_model_arch_table, model_id, **kwargs):
-    model_arch_table = quote_ident(keras_model_arch_table)
-    input_tbl_valid(model_arch_table, "Keras Model Arch")
-
-    missing_cols = columns_missing_from_table(model_arch_table, ModelArchSchema.col_names)
-    if len(missing_cols) > 0:
-        plpy.error("Keras Model Arch: Invalid keras model architecture table {0},"
-                   " missing columns: {1}".format(model_arch_table, missing_cols))
-
-    sql = """
-           DELETE FROM {model_arch_table} WHERE {model_id_col}={model_id}
-          """.format(model_arch_table=model_arch_table, model_id_col=ModelArchSchema.MODEL_ID,
-                     model_id=model_id)
-    res = plpy.execute(sql, 0)
-
-    if res.nrows() > 0:
-        plpy.info("Keras Model Arch: Model id {0} has been deleted from {1}.".
-            format(model_id, model_arch_table))
-    else:
-        plpy.error("Keras Model Arch: Model id {0} not found".format(model_id))
-
-    sql = "SELECT {0} FROM {1}".format(ModelArchSchema.MODEL_ID, model_arch_table)
-    res = plpy.execute(sql, 0)
-    if not res:
-        plpy.info("Keras Model Arch: Dropping empty keras model architecture "\
-            "table {model_arch_table}".format(model_arch_table=model_arch_table))
-        sql = "DROP TABLE {0}".format(model_arch_table)
-        plpy.execute(sql, 0)
-
-class KerasModelArchDocumentation:
-    @staticmethod
-    def _returnHelpMsg(schema_madlib, message, summary, usage, method):
-        if not message:
-            return summary
-        elif message.lower() in ('usage', 'help', '?'):
-            return usage
-        return """
-            No such option. Use "SELECT {schema_madlib}.{method}()"
-            for help.
-        """.format(**locals())
-
-    @staticmethod
-    def load_keras_model_help(schema_madlib, message):
-        method = "load_keras_model"
-        summary = """
-        ----------------------------------------------------------------
-                            SUMMARY
-        ----------------------------------------------------------------
-        The architecture of the model to be used in madlib_keras_train()
-        function must be stored in a table, the details of which must be
-        provided as parameters to the madlib_keras_train module. This is
-        a helper function to help users insert JSON blobs of Keras model
-        architectures into a table.
-        If the output table already exists, the model_arch specified will
-        be added as a new row into the table. The output table could thus
-        act as a repository of Keras model architectures.
-
-        For more details on function usage:
-        SELECT {schema_madlib}.{method}('usage')
-        """.format(**locals())
-
-        usage = """
-        ---------------------------------------------------------------------------
-                                        USAGE
-        ---------------------------------------------------------------------------
-        SELECT {schema_madlib}.{method}(
-            keras_model_arch_table, --  Output table to load keras model arch.
-            model_arch,             --  JSON of the model architecture to insert.
-            model_weights,          --  Model weights to load as a PostgreSQL
-                                        binary data type.
-            name,                   --  Free text string to identify a name
-            description             --  Free text string to provide a description
-        );
-
-
-        ---------------------------------------------------------------------------
-                                        OUTPUT
-        ---------------------------------------------------------------------------
-        The output table produced by load_keras_model contains the following columns:
-
-        'model_id'                -- SERIAL PRIMARY KEY. Model ID.
-        'model_arch'              -- JSON. JSON blob of the model architecture.
-        'model_weights'           -- bytea. weights of the model for warm start.
-        '__internal_madlib_id__'  -- TEXT. Unique id for model arch.
-
-        """.format(**locals())
-
-        return KerasModelArchDocumentation._returnHelpMsg(
-            schema_madlib, message, summary, usage, method)
-# ---------------------------------------------------------------------
-
-    @staticmethod
-    def delete_keras_model_help(schema_madlib, message):
-        method = "delete_keras_model"
-        summary = """
-        ----------------------------------------------------------------
-                            SUMMARY
-        ----------------------------------------------------------------
-        Delete the model architecture corresponding to the provided model_id
-        from the model architecture repository table (keras_model_arch_table).
-
-        For more details on function usage:
-        SELECT {schema_madlib}.{method}('usage')
-        """.format(**locals())
-
-        usage = """
-        ---------------------------------------------------------------------------
-                                        USAGE
-        ---------------------------------------------------------------------------
-        SELECT {schema_madlib}.{method}(
-            keras_model_arch_table VARCHAR, -- Table containing Keras model architectures.
-            model_id               INTEGER  -- The id of the model arch to be deleted.
-        );
-
-
-        ---------------------------------------------------------------------------
-                                        OUTPUT
-        ---------------------------------------------------------------------------
-        This method deletes the row corresponding to the given model_id in
-        keras_model_arch_table. This also tries to drop the table if the table is
-        empty after dropping the model_id. If there are any views depending on the
-        table, a warning message is displayed and the table is not dropped.
-
-        ---------------------------------------------------------------------------
-        """.format(**locals())
-
-        return KerasModelArchDocumentation._returnHelpMsg(
-            schema_madlib, message, summary, usage, method)
diff --git a/src/ports/postgres/modules/deep_learning/keras_model_arch_table.sql_in b/src/ports/postgres/modules/deep_learning/keras_model_arch_table.sql_in
deleted file mode 100644
index cc915bb..0000000
--- a/src/ports/postgres/modules/deep_learning/keras_model_arch_table.sql_in
+++ /dev/null
@@ -1,429 +0,0 @@
-/* ----------------------------------------------------------------------- *//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *
- * @file model_arch_table.sql_in
- *
- * @brief SQL functions for multilayer perceptron
- * @date June 2012
- *
- *
- *//* ----------------------------------------------------------------------- */
-
-m4_include(`SQLCommon.m4')
-/**
-@addtogroup grp_keras_model_arch
-
-@brief Utility function to load model architectures and weights into a table.
-
-\warning  This MADlib method is still in early stage development.
-Interface and implementation are subject to change. 
-
-
Contents

-
-This utility function loads model architectures and
-weights into a table for use by deep learning algorithms.
-Model architecture is in JSON form
-and model weights are in the form of PostgreSQL binary data types (bytea).
-If the output table already exists, a new row is inserted
-into the table so it can act as a repository for multiple model
-architectures and weights.
-
-There is also a utility function to delete a model
-from the table.
-
-@anchor load_keras_model
-@par Load Model
-
-
-load_keras_model(
-    keras_model_arch_table,
-    model_arch,
-    model_weights,
-    name,
-    description
-    )
-

-\b Arguments
-

-  
keras_model_arch_table

-  
VARCHAR. Output table to load keras model architecture and weights.
-  

-
-  
model_arch

-  
JSON. JSON of the model architecture to load.
-  

-
-  
model_weights (optional)

-  
bytea. Model weights to load as a PostgreSQL
-  binary data type.
-  

-
-  
name (optional)

-  
TEXT, default: NULL. Free text string to provide
-  a name, if desired.
-  

-
-  
description (optional)

-  
TEXT, default: NULL. Free text string to provide
-  a description, if desired.
-  

-
-

-
-Output table
-

-    The output table contains the following columns:
-    
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-    
model_idSERIAL PRIMARY KEY. Model ID.
-        
model_archJSON. JSON blob of the model architecture.
-        
model_weightsBYTEA. Weights of the model which may be used for warm start
-        or transfer learning.
-        Weights are stored as a PostgreSQL binary data type.
-        
nameTEXT. Name of model (free text).
-        
descriptionTEXT. Description of model (free text).
-        
__internal_madlib_id__TEXT. Unique id for model arch.  This is an id used internally be MADlib.
-        

-

-
-@anchor delete_keras_model
-@par Delete Model
-
-
-delete_keras_model(
-    keras_model_arch_table
-    model_id
-)
-

-\b Arguments
-

-  
keras_model_arch_table

-  
VARCHAR. Table containing model architectures and weights.
-  

-
-  
model_id

-  
INTEGER. The id of the model to be deleted.
-  

-

-
-@anchor example
-@par Examples
--# Define model architecture.  Use Keras to define
-the model architecture:
-
-import keras
-from keras.models import Sequential
-from keras.layers import Dense
-model_simple = Sequential()
-model_simple.add(Dense(10, activation='relu', input_shape=(4,)))
-model_simple.add(Dense(10, activation='relu'))
-model_simple.add(Dense(3, activation='softmax'))
-model_simple.summary()
-\verbatim
-
-_________________________________________________________________
-Layer (type)                 Output Shape              Param #
-=================================================================
-dense_1 (Dense)              (None, 10)                50
-_________________________________________________________________
-dense_2 (Dense)              (None, 10)                110
-_________________________________________________________________
-dense_3 (Dense)              (None, 3)                 33
-=================================================================
-Total params: 193
-Trainable params: 193
-Non-trainable params: 0
-\endverbatim
-

-Export the model to JSON:
-
-model_simple.to_json()
-

-
-'{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}'
-

--# Load into model architecture table:
-
-DROP TABLE IF EXISTS model_arch_library;
-SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
-$$
-{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
-$$
-::json,  -- JSON blob
-                               NULL,                  -- Weights
-                               'Sophie',              -- Name
-                               'A simple model'       -- Descr
-);
-SELECT COUNT(*) FROM model_arch_library;
-

-
- count
--------+
-     1
-

-Load another model architecture:
-
-SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
-$$
-{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
-$$
-::json,  -- JSON blob
-                               NULL,                  -- Weights
-                               'Maria',               -- Name
-                               'Also a simple model'  -- Descr
-);
-SELECT COUNT(*) FROM model_arch_library;
-

-
- count
--------+
-     2
-

--# Load model weights.  To load weights from previous
-MADlib run, use UPDATE to load directly into the table.
-For example, if 'model_weights' are the weights in the
-output table 'iris_model' from a previous run
-of 'madlib_keras_fit()' :
-
-UPDATE model_arch_library SET model_weights = model_weights FROM iris_model WHERE model_id = 2;
-

-To load weights from Keras using a PL/Python function,
-we need to flatten then serialize the weights to store
-as a PostgreSQL binary data type. Byte format is more
-efficient on space and memory compared to a numeric array.
-The model weights will be de-serialized when
-passed to Keras functions.
-
-CREATE OR REPLACE FUNCTION load_weights() RETURNS VOID AS
-$$
-from keras.layers import *
-from keras import Sequential
-import numpy as np
-import plpy
-\#
-\# create model
-model = Sequential()
-model.add(Dense(10, activation='relu', input_shape=(4,)))
-model.add(Dense(10, activation='relu'))
-model.add(Dense(3, activation='softmax'))
-\#
-\# get weights, flatten and serialize
-weights = model.get_weights()
-weights_flat = [w.flatten() for w in weights]
-weights1d =  np.concatenate(weights_flat).ravel()
-weights_bytea = weights1d.tostring()
-\#
-\# load query
-load_query = plpy.prepare("""SELECT madlib.load_keras_model(
-                        'model_arch_library',
-                        $1, $2)
-                    """, ['json','bytea'])
-plpy.execute(load_query, [model.to_json(), weights_bytea])
-$$ language plpythonu;
--- Call load function
-SELECT load_weights();
--- Check weights loaded OK
-SELECT COUNT(*) FROM model_arch_library WHERE model_weights IS NOT NULL;
-

-
- count
--------+
-     1
-

-Load weights from Keras using psycopg2.  (Psycopg is a PostgreSQL database adapter for the
-Python programming language.) As above we need to flatten then serialize the weights to store as a
-PostgreSQL binary data type.  Note that the psycopg2.Binary function used below will increase the size of the
-Python object for the weights, so if your model is large it might be better to use a PL/Python function as above.
-
-import psycopg2
-import psycopg2 as p2
-conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')
-cur = conn.cursor()
-from keras.layers import *
-from keras import Sequential
-import numpy as np
-\#
-\# create model
-model = Sequential()
-model.add(Dense(10, activation='relu', input_shape=(4,)))
-model.add(Dense(10, activation='relu'))
-model.add(Dense(3, activation='softmax'))
-\#
-\# get weights, flatten and serialize
-weights = model.get_weights()
-weights_flat = [w.flatten() for w in weights]
-weights1d =  np.concatenate(weights_flat).ravel()
-weights_bytea = psycopg2.Binary(weights1d.tostring())
-\#
-\# load query
-query = "SELECT madlib.load_keras_model('model_arch_library', %s,%s)"
-cur.execute(query,[model.to_json(),weights_bytea])
-conn.commit()
-

-From SQL check if weights loaded OK:
-
-SELECT COUNT(*) FROM model_arch_library WHERE model_weights IS NOT NULL;
-

-
- count
--------+
-     2
-

--# Delete one of the models:
-
-SELECT madlib.delete_keras_model('model_arch_library',   -- Output table
-                                  1                      -- Model id
-                                );
-SELECT COUNT(*) FROM model_arch_library;
-

-
- count
--------+
-     3
-

-
-@anchor related
-@par Related Topics
-
-See keras_model_arch_table.sql_in
-
-*/
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_keras_model(
-    keras_model_arch_table VARCHAR,
-    model_arch             JSON,
-    model_weights          bytea,
-    name                   TEXT,
-    description            TEXT
-)
-    RETURNS VOID AS $$
-    PythonFunctionBodyOnlyNoSchema(`deep_learning', `keras_model_arch_table')
-    from utilities.control import AOControl
-    with AOControl(False):
-        keras_model_arch_table.load_keras_model(**globals())
-$$ LANGUAGE plpythonu VOLATILE;
-
--- Function to add a keras model to arch table
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_keras_model(
-    keras_model_arch_table VARCHAR,
-    model_arch             JSON
-)
-RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.load_keras_model($1, $2, NULL, NULL, NULL)
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_keras_model(
-    keras_model_arch_table VARCHAR,
-    model_arch             JSON,
-    model_weights          bytea
-)
-    RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.load_keras_model($1, $2, $3, NULL, NULL)
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_keras_model(
-    keras_model_arch_table VARCHAR,
-    model_arch             JSON,
-    model_weights          bytea,
-    name                   TEXT
-)
-    RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.load_keras_model($1, $2, $3, $4, NULL)
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-
-
-
-
-
--- Functions for online help
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_keras_model(
-    message VARCHAR
-) RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(deep_learning, keras_model_arch_table)
-    return keras_model_arch_table.KerasModelArchDocumentation.load_keras_model_help(schema_madlib, message)
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_keras_model()
-RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(deep_learning, keras_model_arch_table)
-    return keras_model_arch_table.KerasModelArchDocumentation.load_keras_model_help(schema_madlib, '')
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
--- Function to delete a keras model from arch table
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.delete_keras_model(
-    keras_model_arch_table VARCHAR,
-    model_id INTEGER
-)
-RETURNS VOID AS $$
-    PythonFunctionBodyOnlyNoSchema(`deep_learning',`keras_model_arch_table')
-    from utilities.control import AOControl
-    with AOControl(False):
-        keras_model_arch_table.delete_keras_model(**globals())
-$$ LANGUAGE plpythonu VOLATILE;
-
--- Functions for online help
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.delete_keras_model(
-    message VARCHAR
-) RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(deep_learning, keras_model_arch_table)
-    return keras_model_arch_table.KerasModelArchDocumentation.delete_keras_model_help(schema_madlib, message)
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.delete_keras_model()
-RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(deep_learning, keras_model_arch_table)
-    return keras_model_arch_table.KerasModelArchDocumentation.delete_keras_model_help(schema_madlib, '')
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras.py_in
deleted file mode 100644
index ee27554..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras.py_in
+++ /dev/null
@@ -1,944 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import datetime
-import os
-import plpy
-import sys
-import time
-
-from keras import backend as K
-from keras.layers import *
-from keras.models import *
-from keras.optimizers import *
-from keras.regularizers import *
-from madlib_keras_helper import *
-from madlib_keras_validator import *
-from madlib_keras_wrapper import *
-from model_arch_info import *
-
-from utilities.utilities import _assert
-from utilities.utilities import add_postfix
-from utilities.utilities import is_platform_pg
-from utilities.utilities import get_segments_per_host
-from utilities.utilities import get_seg_number
-from utilities.utilities import madlib_version
-from utilities.utilities import unique_string
-from utilities.validate_args import get_expr_type
-from utilities.control import MinWarning
-import tensorflow as tf
-
-class SD_STORE:
-    SESS = 'sess'
-    SEGMENT_MODEL = 'segment_model'
-
-    @staticmethod
-    def init_SD(SD, sess, segment_model):
-        SD[SD_STORE.SESS] = sess
-        SD[SD_STORE.SEGMENT_MODEL] = segment_model
-
-    @staticmethod
-    def clear_SD(SD):
-        del SD[SD_STORE.SEGMENT_MODEL]
-        del SD[SD_STORE.SESS]
-
-def get_init_model_and_sess(SD, device_name, gpu_count, segments_per_host,
-                               model_architecture, compile_params):
-    # If a live session is present, re-use it. Otherwise, recreate it.
-    if SD_STORE.SESS in SD :
-        if SD_STORE.SEGMENT_MODEL not in SD:
-            plpy.error("Session and model should exist in SD after the first row"
-                       "of the first iteration")
-        sess = SD[SD_STORE.SESS]
-        segment_model = SD[SD_STORE.SEGMENT_MODEL]
-        K.set_session(sess)
-    else:
-        sess = get_keras_session(device_name, gpu_count, segments_per_host)
-        K.set_session(sess)
-        segment_model = init_model(model_architecture, compile_params)
-        SD_STORE.init_SD(SD, sess, segment_model)
-    return segment_model, sess
-
-@MinWarning("warning")
-def fit(schema_madlib, source_table, model, model_arch_table,
-        model_id, compile_params, fit_params, num_iterations,
-        use_gpus, validation_table=None,
-        metrics_compute_frequency=None, warm_start=False, name="",
-        description="", **kwargs):
-
-    module_name = 'madlib_keras_fit'
-    fit_params = "" if not fit_params else fit_params
-    _assert(compile_params, "Compile parameters cannot be empty or NULL.")
-
-    mb_dep_var_col = MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL
-    mb_indep_var_col = MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL
-
-    dep_shape_col = add_postfix(
-        MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL, "_shape")
-    ind_shape_col = add_postfix(
-        MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL, "_shape")
-
-    segments_per_host = get_segments_per_host()
-    use_gpus = use_gpus if use_gpus else False
-    if use_gpus:
-        accessible_gpus_for_seg = get_accessible_gpus_for_seg(schema_madlib, segments_per_host, module_name)
-    else:
-        accessible_gpus_for_seg = get_seg_number()*[0]
-
-    fit_validator = FitInputValidator(
-        source_table, validation_table, model, model_arch_table,
-        model_id, mb_dep_var_col, mb_indep_var_col,
-        num_iterations, metrics_compute_frequency, warm_start,
-        use_gpus, accessible_gpus_for_seg)
-    if metrics_compute_frequency is None:
-        metrics_compute_frequency = num_iterations
-
-
-    warm_start = bool(warm_start)
-
-    # The following two times must be recorded together.
-    metrics_elapsed_start_time = time.time()
-    start_training_time = datetime.datetime.now()
-    #TODO add a unit test for this in a future PR
-    # save the original value of the env variable so that we can reset it later.
-    original_cuda_env = None
-    if CUDA_VISIBLE_DEVICES_KEY in os.environ:
-        original_cuda_env = os.environ[CUDA_VISIBLE_DEVICES_KEY]
-
-    # Get the serialized master model
-    start_deserialization = time.time()
-    model_arch, model_weights = get_model_arch_weights(model_arch_table, model_id)
-    num_classes = get_num_classes(model_arch)
-    input_shape = get_input_shape(model_arch)
-    fit_validator.validate_input_shapes(input_shape)
-    dist_key_col = '0' if is_platform_pg() else DISTRIBUTION_KEY_COLNAME
-    gp_segment_id_col = '0' if is_platform_pg() else GP_SEGMENT_ID_COLNAME
-
-    serialized_weights = get_initial_weights(model, model_arch, model_weights,
-                                             warm_start, use_gpus, accessible_gpus_for_seg)
-    # Compute total images on each segment
-    dist_key_mapping, images_per_seg_train = get_image_count_per_seg_for_minibatched_data_from_db(source_table)
-
-
-    if validation_table:
-        seg_ids_val, images_per_seg_val = get_image_count_per_seg_for_minibatched_data_from_db(validation_table)
-
-    # Construct validation dataset if provided
-    validation_set_provided = bool(validation_table)
-    validation_metrics = []; validation_loss = []
-
-    # Prepare the SQL for running distributed training via UDA
-    compile_params_to_pass = "$madlib$" + compile_params + "$madlib$"
-    fit_params_to_pass = "$madlib$" + fit_params + "$madlib$"
-    run_training_iteration = plpy.prepare("""
-        SELECT {schema_madlib}.fit_step(
-            {mb_dep_var_col},
-            {mb_indep_var_col},
-            {dep_shape_col},
-            {ind_shape_col},
-            $MAD${model_arch}$MAD$::TEXT,
-            {compile_params_to_pass}::TEXT,
-            {fit_params_to_pass}::TEXT,
-            {dist_key_col},
-            ARRAY{dist_key_mapping},
-            {gp_segment_id_col},
-            {segments_per_host},
-            ARRAY{images_per_seg_train},
-            {use_gpus}::BOOLEAN,
-            ARRAY{accessible_gpus_for_seg},
-            $1,
-            $2
-        ) AS iteration_result
-        FROM {source_table}
-        """.format(**locals()), ["bytea", "boolean"])
-
-    # Define the state for the model and loss/metric storage lists
-    training_loss, training_metrics, metrics_elapsed_time = [], [], []
-    metrics_iters = []
-
-    # get the size of serialized model weights string in KB
-    model_size = sys.getsizeof(serialized_weights)/1024.0
-
-    # Run distributed training for specified number of iterations
-    for i in range(1, num_iterations+1):
-        start_iteration = time.time()
-        is_final_iteration = (i == num_iterations)
-        serialized_weights = plpy.execute(run_training_iteration,
-                                        [serialized_weights, is_final_iteration]
-                                        )[0]['iteration_result']
-        end_iteration = time.time()
-        info_str = "\tTime for training in iteration {0}: {1} sec".format(i,
-            end_iteration - start_iteration)
-
-        if should_compute_metrics_this_iter(i, metrics_compute_frequency,
-                                            num_iterations):
-            # Compute loss/accuracy for training data.
-            compute_out = compute_loss_and_metrics(
-                schema_madlib, source_table, compile_params_to_pass, model_arch,
-                serialized_weights, use_gpus, accessible_gpus_for_seg, dist_key_mapping,
-                images_per_seg_train, training_metrics, training_loss, i, is_final_iteration)
-            metrics_iters.append(i)
-            compute_time, compute_metrics, compute_loss = compute_out
-
-            info_str += "\n\tTime for evaluating training dataset in "\
-                        "iteration {0}: {1} sec\n".format(i, compute_time)
-            info_str += "\tTraining set metric after iteration {0}: {1}\n".format(
-                i, compute_metrics)
-            info_str += "\tTraining set loss after iteration {0}: {1}".format(
-                i, compute_loss)
-
-            if validation_set_provided:
-                # Compute loss/accuracy for validation data.
-                val_compute_out = compute_loss_and_metrics(
-                    schema_madlib, validation_table, compile_params_to_pass,
-                    model_arch, serialized_weights, use_gpus, accessible_gpus_for_seg,
-                    seg_ids_val, images_per_seg_val, validation_metrics,
-                    validation_loss, i, is_final_iteration)
-                val_compute_time, val_compute_metrics, val_compute_loss = val_compute_out
-
-                info_str += "\n\tTime for evaluating validation dataset in "\
-                        "iteration {0}: {1} sec\n".format(i, val_compute_time)
-                info_str += "\tValidation set metric after iteration {0}: {1}\n".format(
-                    i, val_compute_metrics)
-                info_str += "\tValidation set loss after iteration {0}: {1}".format(
-                    i, val_compute_loss)
-
-            metrics_elapsed_end_time = time.time()
-            metrics_elapsed_time.append(
-                metrics_elapsed_end_time-metrics_elapsed_start_time)
-        plpy.info("\n"+info_str)
-    end_training_time = datetime.datetime.now()
-
-    version = madlib_version(schema_madlib)
-    src_summary_dict = get_source_summary_table_dict(fit_validator)
-    class_values = src_summary_dict['class_values']
-    class_values_type = src_summary_dict['class_values_type']
-    norm_const = src_summary_dict['norm_const']
-    norm_const_type = src_summary_dict['norm_const_type']
-    dep_vartype = src_summary_dict['dep_vartype']
-    dependent_varname = src_summary_dict['dependent_varname_in_source_table']
-    independent_varname = src_summary_dict['independent_varname_in_source_table']
-    # Define some constants to be inserted into the summary table.
-    model_type = "madlib_keras"
-    compile_params_dict = convert_string_of_args_to_dict(compile_params)
-    metrics_list = get_metrics_from_compile_param(compile_params)
-    is_metrics_specified = True if metrics_list else False
-    metrics_type = 'ARRAY{0}'.format(metrics_list) if is_metrics_specified else 'NULL'
-    metrics_iters = metrics_iters if metrics_iters else 'NULL'
-    # We always compute the training loss and metrics, at least once.
-    training_loss_final = training_loss[-1]
-    training_loss = 'ARRAY{0}'.format(training_loss) if training_loss else 'NULL'
-    training_metrics_final, training_metrics = get_metrics_sql_string(
-        training_metrics, is_metrics_specified)
-    # Validation loss and metrics are computed only if validation_table
-    # is provided.
-    if validation_set_provided:
-        validation_metrics_final, validation_metrics = get_metrics_sql_string(
-            validation_metrics, is_metrics_specified)
-        validation_loss_final = validation_loss[-1]
-        validation_loss = 'ARRAY{0}'.format(validation_loss)
-        # Must quote the string before inserting to table. Explicitly
-        # quoting it here since this can also take a NULL value, done
-        # in the else part.
-        validation_table = "$MAD${0}$MAD$".format(validation_table)
-    else:
-        validation_metrics = validation_loss = 'NULL'
-        validation_metrics_final = validation_loss_final = 'NULL'
-        validation_table = 'NULL'
-
-    if warm_start:
-        plpy.execute("DROP TABLE {0}, {1}".format
-                     (model, fit_validator.output_summary_model_table))
-    create_output_summary_table = plpy.prepare("""
-        CREATE TABLE {output_summary_model_table} AS
-        SELECT
-            $MAD${source_table}$MAD$::TEXT AS source_table,
-            $MAD${model}$MAD$::TEXT AS model,
-            $MAD${dependent_varname}$MAD$::TEXT AS dependent_varname,
-            $MAD${independent_varname}$MAD$::TEXT AS independent_varname,
-            $MAD${model_arch_table}$MAD$::TEXT AS model_arch_table,
-            {model_id}::INTEGER AS {model_id_colname},
-            $1 AS compile_params,
-            $2 AS fit_params,
-            {num_iterations}::INTEGER AS num_iterations,
-            {validation_table}::TEXT AS validation_table,
-            {metrics_compute_frequency}::INTEGER AS metrics_compute_frequency,
-            $3 AS name,
-            $4 AS description,
-            '{model_type}'::TEXT AS model_type,
-            {model_size}::DOUBLE PRECISION AS model_size,
-            '{start_training_time}'::TIMESTAMP AS start_training_time,
-            '{end_training_time}'::TIMESTAMP AS end_training_time,
-            $5 AS metrics_elapsed_time,
-            '{version}'::TEXT AS madlib_version,
-            {num_classes}::INTEGER AS num_classes,
-            $6 AS {class_values_colname},
-            $MAD${dep_vartype}$MAD$::TEXT AS {dependent_vartype_colname},
-            {norm_const}::{FLOAT32_SQL_TYPE} AS {normalizing_const_colname},
-            {metrics_type}::TEXT[] AS metrics_type,
-            {training_metrics_final}::DOUBLE PRECISION AS training_metrics_final,
-            {training_loss_final}::DOUBLE PRECISION AS training_loss_final,
-            {training_metrics}::DOUBLE PRECISION[] AS training_metrics,
-            {training_loss}::DOUBLE PRECISION[] AS training_loss,
-            {validation_metrics_final}::DOUBLE PRECISION AS validation_metrics_final,
-            {validation_loss_final}::DOUBLE PRECISION AS validation_loss_final,
-            {validation_metrics}::DOUBLE PRECISION[] AS validation_metrics,
-            {validation_loss}::DOUBLE PRECISION[] AS validation_loss,
-            ARRAY{metrics_iters}::INTEGER[] AS metrics_iters
-        """.format(output_summary_model_table=fit_validator.output_summary_model_table,
-                   class_values_colname=CLASS_VALUES_COLNAME,
-                   dependent_vartype_colname=DEPENDENT_VARTYPE_COLNAME,
-                   normalizing_const_colname=NORMALIZING_CONST_COLNAME,
-                   FLOAT32_SQL_TYPE = FLOAT32_SQL_TYPE,
-                   model_id_colname = ModelArchSchema.MODEL_ID,
-                   **locals()),
-                   ["TEXT", "TEXT", "TEXT", "TEXT", "DOUBLE PRECISION[]", class_values_type])
-    plpy.execute(create_output_summary_table,
-                 [compile_params, fit_params, name,
-                  description, metrics_elapsed_time, class_values])
-
-    plpy.execute("""
-        CREATE TABLE {0}
-        (model_weights bytea,
-        {1} json)""".format(model, ModelArchSchema.MODEL_ARCH))
-    insert_output_table = plpy.prepare("""
-        INSERT INTO {0} SELECT model_weights, {1}
-        FROM (VALUES($1, $2))t(model_weights, {1})
-        """.format(model, ModelArchSchema.MODEL_ARCH), ["bytea", "json"])
-    plpy.execute(insert_output_table, [serialized_weights, model_arch])
-
-    #TODO add a unit test for this in a future PR
-    reset_cuda_env(original_cuda_env)
-
-def get_initial_weights(model_table, model_arch, serialized_weights, warm_start,
-                        use_gpus, accessible_gpus_for_seg, mst_filter=''):
-    """
-        If warm_start is True, return back initial weights from model table.
-        If warm_start is False, first try to get the weights from model_arch
-        table, if no weights are defined there, randomly initialize it using
-        keras.
-        We also need to set the cuda environment variable based on the platform.
-        1. For postgres, if user specifies use_gpus=False which means they want
-        to use CPU, then we have to set CUDA_VISIBLE_DEVICES to -1 to disable gpu.
-        Otherwise model.get_weights() will use gpu if available.
-
-        2. For gpdb, we want to disable gpu on gpdb's master node because GPUs
-        will only be used for segment nodes.
-        @args:
-            @param model_table: Output model table passed in to fit.
-            @param model_arch_result: Dict containing model architecture info.
-            @param warm_start: Boolean flag indicating warm start or not.
-    """
-    if is_platform_pg():
-        _ = get_device_name_and_set_cuda_env(accessible_gpus_for_seg[0], None)
-    else:
-        _ = get_device_name_and_set_cuda_env(0, None)
-
-    if warm_start:
-        serialized_weights = plpy.execute("""
-            SELECT model_weights FROM {model_table} {mst_filter} LIMIT 1
-        """.format(**locals()))[0]['model_weights']
-    else:
-        if not serialized_weights:
-            model = model_from_json(model_arch)
-            serialized_weights = madlib_keras_serializer.serialize_nd_weights(
-                model.get_weights())
-    return serialized_weights
-
-def get_source_summary_table_dict(fit_validator):
-    source_summary = plpy.execute("""
-            SELECT
-                {class_values} AS class_values,
-                {norm_const} AS norm_const,
-                {dep_vartype} AS dep_vartype,
-                {dep_varname} AS dependent_varname_in_source_table,
-                {indep_varname} AS independent_varname_in_source_table
-            FROM {tbl}
-        """.format(class_values=CLASS_VALUES_COLNAME,
-                   norm_const=NORMALIZING_CONST_COLNAME,
-                   dep_vartype=DEPENDENT_VARTYPE_COLNAME,
-                   dep_varname='dependent_varname',
-                   indep_varname='independent_varname',
-                   tbl=fit_validator.source_summary_table))[0]
-    source_summary['class_values_type'] = get_expr_type(
-        CLASS_VALUES_COLNAME, fit_validator.source_summary_table)
-    source_summary['norm_const_type'] = get_expr_type(
-        NORMALIZING_CONST_COLNAME, fit_validator.source_summary_table)
-    return source_summary
-
-def get_metrics_sql_string(metrics_list, is_metrics_specified):
-    """
-        Return the SQL string to use for creating metrics SQL values.
-    """
-    if is_metrics_specified:
-        metrics_final = metrics_list[-1]
-        metrics_all = 'ARRAY{0}'.format(metrics_list)
-    else:
-        metrics_final = metrics_all = 'NULL'
-    return metrics_final, metrics_all
-
-def compute_loss_and_metrics(schema_madlib, table, compile_params, model_arch,
-                             serialized_weights, use_gpus, accessible_gpus_for_seg,
-                             dist_key_mapping, images_per_seg_val, metrics_list, loss_list,
-                             curr_iter, is_final_iteration):
-    """
-    Compute the loss and metric using a given model (serialized_weights) on the
-    given dataset (table.)
-    """
-    start_val = time.time()
-    evaluate_result = get_loss_metric_from_keras_eval(schema_madlib,
-                                                   table,
-                                                   compile_params,
-                                                   model_arch,
-                                                   serialized_weights,
-                                                   use_gpus,
-                                                   accessible_gpus_for_seg,
-                                                   dist_key_mapping,
-                                                   images_per_seg_val,
-                                                   is_final_iteration)
-    end_val = time.time()
-
-    if len(evaluate_result) not in [1, 2]:
-        plpy.error('Calling evaluate on table {0} returned < 2 '
-                   'metrics. Expected both loss and a metric.'.format(table))
-    loss = evaluate_result[0]
-    metric = evaluate_result[1]
-    metrics_list.append(metric)
-    loss_list.append(loss)
-    return end_val - start_val, metric, loss
-
-def should_compute_metrics_this_iter(curr_iter, metrics_compute_frequency,
-                                     num_iterations):
-    """
-    Check if we want to compute loss/accuracy for the current iteration
-    :param curr_iter:
-    :param metrics_compute_frequency:
-    :param num_iterations:
-    :return: Returns a boolean
-            return TRUE, if it is the last iteration, or if metrics_compute_frequency
-            iterations have elapsed since the last time it was computed.
-            return FALSE otherwise.
-    """
-    # Compute loss/accuracy every metrics_compute_frequency'th iteration,
-    # and also for the last iteration.
-    return (curr_iter)%metrics_compute_frequency == 0 or \
-           curr_iter == num_iterations
-
-def init_model(model_architecture, compile_params):
-    """
-        Should only be called at the first row of first iteration.
-    """
-    segment_model = model_from_json(model_architecture)
-    compile_model(segment_model, compile_params)
-    return segment_model
-
-def update_model(segment_model, prev_serialized_weights):
-    """
-        Happens at first row of each iteration.
-    """
-    model_shapes = get_model_shapes(segment_model)
-    model_weights = madlib_keras_serializer.deserialize_as_nd_weights(
-        prev_serialized_weights, model_shapes)
-    segment_model.set_weights(model_weights)
-
-def fit_transition(state, dependent_var, independent_var, dependent_var_shape,
-                   independent_var_shape, model_architecture,
-                   compile_params, fit_params, dist_key, dist_key_mapping,
-                   current_seg_id, segments_per_host, images_per_seg, use_gpus,
-                   accessible_gpus_for_seg, prev_serialized_weights, is_final_iteration=True,
-                   is_multiple_model=False, **kwargs):
-    """
-    This transition function is common for madlib_keras_fit() and
-    madlib_keras_fit_multiple_model(). The important difference between
-    these two calls is the way this function handles the input param
-    prev_serialized_weights and clearing keras session.
-    For madlib_keras_fit_multiple_model,
-        a. prev_serialized_weights is always passed in as the state
-        (image count, serialized weights), since it is fetched in the
-        table for each hop of the model between segments.
-        b. keras session is cleared at the end of each iteration, i.e,
-        last row of each iteration.
-    For madlib_keras_fit,
-        a. prev_serialized_weights is passed in as serialized weights
-        b. keras session is cleared at the end of the final iteration,
-        i.e, last row of last iteration.
-    """
-    if not independent_var or not dependent_var:
-        return state
-    SD = kwargs['SD']
-    device_name = get_device_name_and_set_cuda_env(accessible_gpus_for_seg[current_seg_id], current_seg_id)
-
-    segment_model, sess = get_init_model_and_sess(SD, device_name,
-                                                  accessible_gpus_for_seg[current_seg_id],
-                                                  segments_per_host,
-                                                  model_architecture, compile_params)
-    if not state:
-        agg_image_count = 0
-        set_model_weights(segment_model, prev_serialized_weights)
-    else:
-        agg_image_count = float(state)
-
-    # Prepare the data
-    x_train = np_array_float32(independent_var, independent_var_shape)
-    y_train = np_array_int16(dependent_var, dependent_var_shape)
-
-    # Fit segment model on data
-    #TODO consider not doing this every time
-    fit_params = parse_and_validate_fit_params(fit_params)
-    segment_model.fit(x_train, y_train, **fit_params)
-
-    # Aggregating number of images, loss and accuracy
-    agg_image_count += len(x_train)
-    total_images = get_image_count_per_seg_from_array(dist_key_mapping.index(dist_key),
-                                                      images_per_seg)
-    is_last_row = agg_image_count == total_images
-    return_state = get_state_to_return(segment_model, is_last_row, is_multiple_model,
-                                  agg_image_count, total_images)
-    if is_last_row:
-        if is_final_iteration or is_multiple_model:
-            SD_STORE.clear_SD(SD)
-            clear_keras_session(sess)
-
-    return return_state
-
-def get_state_to_return(segment_model, is_last_row, is_multiple_model, agg_image_count,
-                        total_images):
-    """
-    1. For both model averaging fit_transition and fit multiple transition, the
-    state only needs to have the image count except for the last row.
-    2. For model averaging fit_transition, the last row state must always contain
-    the image count as well as the model weights
-    3. For fit multiple transition, the last row state only needs the model
-    weights. This state is the output of the UDA for that hop. We don't need
-    the image_count here because unlike model averaging, model hopper does
-    not have a merge/final function and there is no need to average the weights
-    based on the image count.
-    :param segment_model: cached model for that segment
-    :param is_last_row: boolean to indicate if last row for that hop
-    :param is_multiple_model: boolean
-    :param agg_image_count: aggregated image count per hop
-    :param total_images: total images per segment
-    :return:
-    """
-    if is_last_row:
-        updated_model_weights = segment_model.get_weights()
-        if is_multiple_model:
-            new_state = madlib_keras_serializer.serialize_nd_weights(updated_model_weights)
-        else:
-            updated_model_weights = [total_images * w for w in updated_model_weights]
-            new_state = madlib_keras_serializer.serialize_state_with_nd_weights(
-                agg_image_count, updated_model_weights)
-    else:
-        new_state = float(agg_image_count)
-
-    return new_state
-
-def fit_merge(state1, state2, **kwargs):
-
-    # Return if called early
-    if not state1 or not state2:
-        return state1 or state2
-
-    # Deserialize states
-    image_count1, weights1 = madlib_keras_serializer.deserialize_as_image_1d_weights(state1)
-    image_count2, weights2 = madlib_keras_serializer.deserialize_as_image_1d_weights(state2)
-
-    # Compute total image counts
-    image_count = (image_count1 + image_count2) * 1.0
-
-    # Aggregate the weights
-    total_weights = weights1 + weights2
-
-    # Return the merged state
-    return madlib_keras_serializer.serialize_state_with_1d_weights(
-        image_count, total_weights)
-
-def fit_final(state, **kwargs):
-    # Return if called early
-    if not state:
-        return state
-
-    image_count, weights = madlib_keras_serializer.deserialize_as_image_1d_weights(state)
-    if image_count == 0:
-        plpy.error("fit_final: Total images processed is 0")
-
-    # Averaging the weights
-    weights /= image_count
-    return madlib_keras_serializer.serialize_nd_weights(weights)
-
-
-def evaluate(schema_madlib, model_table, test_table, output_table,
-             use_gpus, mst_key, **kwargs):
-
-    module_name = 'madlib_keras_evaluate'
-    is_mult_model = mst_key is not None
-    if test_table:
-        test_summary_table = add_postfix(test_table, "_summary")
-    model_summary_table = None
-    if model_table:
-        model_summary_table = add_postfix(model_table, "_summary")
-
-    mult_where_clause = ""
-    if is_mult_model:
-        mult_where_clause = "WHERE mst_key = {0}".format(mst_key)
-        model_summary_table = create_summary_view(module_name, model_table, mst_key)
-
-    validate_evaluate(module_name, model_table, model_summary_table, test_table, test_summary_table, output_table, is_mult_model)
-
-    segments_per_host = get_segments_per_host()
-    if use_gpus:
-        accessible_gpus_for_seg = get_accessible_gpus_for_seg(schema_madlib, segments_per_host, module_name)
-    else:
-        accessible_gpus_for_seg = get_seg_number()*[0]
-
-    model_weights_query = "SELECT model_weights, model_arch FROM {0} {1}".format(
-        model_table, mult_where_clause)
-
-    res = plpy.execute(model_weights_query)[0]
-    _assert(res, "{0}: The model does not exist.")
-    model_weights = res['model_weights']
-    model_arch = res['model_arch']
-
-    input_shape = get_input_shape(model_arch)
-    InputValidator.validate_input_shape(
-        test_table, MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL, input_shape, 2, True)
-
-    compile_params_query = "SELECT compile_params, metrics_type FROM {0}".format(model_summary_table)
-    res = plpy.execute(compile_params_query)[0]
-    metrics_type = res['metrics_type']
-    compile_params = "$madlib$" + res['compile_params'] + "$madlib$"
-
-    dist_key_mapping, images_per_seg = get_image_count_per_seg_for_minibatched_data_from_db(test_table)
-
-    loss, metric = \
-        get_loss_metric_from_keras_eval(
-            schema_madlib, test_table, compile_params, model_arch,
-            model_weights, use_gpus, accessible_gpus_for_seg, dist_key_mapping,
-            images_per_seg)
-
-    if not metrics_type:
-        metrics_type = None
-        metric = None
-
-    with MinWarning("error"):
-        create_output_table = plpy.prepare("""
-            CREATE TABLE {0} AS
-            SELECT $1 as loss, $2 as metric, $3 as metrics_type""".format(output_table), ["FLOAT", "FLOAT", "TEXT[]"])
-        plpy.execute(create_output_table, [loss, metric, metrics_type])
-
-    if is_mult_model:
-        plpy.execute("DROP VIEW IF EXISTS {0}".format(model_summary_table))
-
-def validate_evaluate(module_name, model_table, model_summary_table, test_table, test_summary_table, output_table, is_mult_model):
-    def _validate_test_summary_tbl():
-        input_tbl_valid(test_summary_table, module_name,
-                error_suffix_str="Please ensure that the test table ({0}) "
-                                 "has been preprocessed by "
-                                 "the image preprocessor.".format(test_table))
-        cols_in_tbl_valid(test_summary_table, [CLASS_VALUES_COLNAME,
-            NORMALIZING_CONST_COLNAME, DEPENDENT_VARTYPE_COLNAME,
-            DEPENDENT_VARNAME_COLNAME, INDEPENDENT_VARNAME_COLNAME], module_name)
-
-    input_tbl_valid(model_table, module_name)
-    if is_mult_model and not columns_exist_in_table(model_table, ['mst_key']):
-        plpy.error("{module_name}: Single model should not pass mst_key".format(**locals()))
-    if not is_mult_model and columns_exist_in_table(model_table, ['mst_key']):
-        plpy.error("{module_name}: Multi-model needs to pass mst_key".format(**locals()))
-    InputValidator.validate_predict_evaluate_tables(
-        module_name, model_table, model_summary_table,
-        test_table, output_table, MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL)
-    _validate_test_summary_tbl()
-    validate_bytea_var_for_minibatch(test_table,
-                                     MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL)
-
-def get_loss_metric_from_keras_eval(schema_madlib, table, compile_params,
-                                    model_arch, serialized_weights, use_gpus,
-                                    accessible_gpus_for_seg, dist_key_mapping, images_per_seg,
-                                    is_final_iteration=True):
-
-    dist_key_col = '0' if is_platform_pg() else DISTRIBUTION_KEY_COLNAME
-    gp_segment_id_col = '0' if is_platform_pg() else GP_SEGMENT_ID_COLNAME
-    segments_per_host = get_segments_per_host()
-
-    mb_dep_var_col = MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL
-    mb_indep_var_col = MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL
-
-    dep_shape_col = add_postfix(
-        MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL, "_shape")
-    ind_shape_col = add_postfix(
-        MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL, "_shape")
-    """
-    This function will call the internal keras evaluate function to get the loss
-    and accuracy of each tuple which then gets averaged to get the final result.
-    """
-    use_gpus = use_gpus if use_gpus else False
-    evaluate_query = plpy.prepare("""
-        select ({schema_madlib}.internal_keras_evaluate(
-                                            {mb_dep_var_col},
-                                            {mb_indep_var_col},
-                                            {dep_shape_col},
-                                            {ind_shape_col},
-                                            $MAD${model_arch}$MAD$,
-                                            $1,
-                                            {compile_params},
-                                            {dist_key_col},
-                                            ARRAY{dist_key_mapping},
-                                            {gp_segment_id_col},
-                                            {segments_per_host},
-                                            ARRAY{images_per_seg},
-                                            {use_gpus}::BOOLEAN,
-                                            ARRAY{accessible_gpus_for_seg},
-                                            {is_final_iteration}
-                                            )) as loss_metric
-        from {table}
-        """.format(**locals()), ["bytea"])
-    res = plpy.execute(evaluate_query, [serialized_weights])
-    loss_metric = res[0]['loss_metric']
-    return loss_metric
-
-
-def internal_keras_eval_transition(state, dependent_var, independent_var,
-                                   dependent_var_shape, independent_var_shape,
-                                   model_architecture, serialized_weights, compile_params,
-                                   dist_key, dist_key_mapping, current_seg_id,
-                                   segments_per_host, images_per_seg,
-                                   use_gpus, accessible_gpus_for_seg,
-                                   is_final_iteration, **kwargs):
-    SD = kwargs['SD']
-    device_name = get_device_name_and_set_cuda_env(accessible_gpus_for_seg[current_seg_id], current_seg_id)
-    agg_loss, agg_metric, agg_image_count = state
-
-    # This transition function is common to evaluate as well as the fit functions
-    # and is used to determine when to clear the session.
-    # For evaluate,
-    #   is_final_iteration is always set to true, so the session is cleared once
-    #   evaluated the last buffer on each segment.
-    # When called from fit functions,
-    #  if is_final_iteration is false, the fit function has already created a
-    #   session and a graph that can be used between iterations and cleared only
-    #   for the last buffer of last iteration
-    #  if is_final_iteration is false, we can clear the
-
-    segment_model, sess = get_init_model_and_sess(SD, device_name,
-                                                  accessible_gpus_for_seg[current_seg_id],
-                                                  segments_per_host,
-                                                  model_architecture,
-                                                  compile_params)
-    if not agg_image_count:
-        # These should already be 0, but just in case make sure
-        agg_metric = 0
-        agg_loss = 0
-        set_model_weights(segment_model, serialized_weights)
-
-    x_val = np_array_float32(independent_var, independent_var_shape)
-    y_val = np_array_int16(dependent_var, dependent_var_shape)
-
-    with K.tf.device(device_name):
-        res = segment_model.evaluate(x_val, y_val)
-
-    # if metric is None, model.evaluate will only return loss as a scalar
-    # Otherwise, it will return a list which has loss and metric
-    if type(res) is list:
-        loss, metric = res
-    else:
-        loss = res
-        metric = 0
-
-    image_count = len(y_val)
-
-    agg_image_count += image_count
-    agg_loss += (image_count * loss)
-    agg_metric += (image_count * metric)
-
-    total_images = get_image_count_per_seg_from_array(dist_key_mapping.index(dist_key),
-                                                      images_per_seg)
-
-    if agg_image_count == total_images and is_final_iteration:
-        K.clear_session()
-        sess.close()
-        SD_STORE.clear_SD(SD)
-        del segment_model
-        del sess
-
-    state[0] = agg_loss
-    state[1] = agg_metric
-    state[2] = agg_image_count
-
-    return state
-
-def internal_keras_eval_merge(state1, state2, **kwargs):
-    # If either state is None, return the other one
-    if not state1 or not state2:
-        return state1 or state2
-
-    loss1, metric1, image_count1 = state1
-    loss2, metric2, image_count2 = state2
-
-    merged_loss = loss1 + loss2
-    merged_metric = metric1 + metric2
-    total_image_count = image_count1 + image_count2
-
-    merged_state = [ merged_loss, merged_metric , total_image_count ]
-
-    return merged_state
-
-def internal_keras_eval_final(state, **kwargs):
-    loss, metric, image_count = state
-
-    if image_count == 0:
-        plpy.error("internal_keras_eval_final: Total images processed is 0")
-
-    loss /= image_count
-    metric /= image_count
-
-    return loss, metric
-
-def fit_help(schema_madlib, message, **kwargs):
-    """
-    Help function for keras fit
-
-    Args:
-        @param schema_madlib
-        @param message: string, Help message string
-        @param kwargs
-
-    Returns:
-        String. Help/usage information
-    """
-    if not message:
-        help_string = """
------------------------------------------------------------------------
-                            SUMMARY
------------------------------------------------------------------------
-This module allows you to use SQL to call deep learning
-models designed in Keras, which is a high-level neural
-network API written in Python.
-Keras was developed for fast experimentation.  It can run
-on top of different backends and the one that is currently
-supported by MADlib is TensorFlow.  The implementation
-in MADlib is distributed and designed to train
-a single large model across multiple segments (workers)
-in a Greenplum database.  PostgreSQL is also supported.
-
-For more details on function usage:
-    SELECT {schema_madlib}.madlib_keras_fit('usage')
-            """
-    elif message in ['usage', 'help', '?']:
-        help_string = """
------------------------------------------------------------------------
-                            USAGE
------------------------------------------------------------------------
- SELECT {schema_madlib}.madlib_keras_fit(
-    source_table,               --  Name of the table containing the
-                                    training data
-    model,                      --  Name of the output table containing
-                                    the model
-    model_arch_table,           --  Name of the table containing the
-                                    model architecture
-    model_id,                   --  This is the id in 'model_arch_table'
-                                    containing the model architecture
-    compile_params,             --  Parameters passed to the compile
-                                    method of the Keras model class
-    fit_params,                 --  Parameters passed to the fit method
-                                    of the Keras model class
-    num_iterations,             --  Number of iterations to train.
-    use_gpus,                   --  Flag to enable GPU support
-    validation_table,           --  Name of the table containing
-                                    the validation dataset
-    metrics_compute_frequency,  --  Frequency to compute per-iteration
-                                    metrics
-    warm_start,                 --  Flag to enable warm start
-    name,                       --  Free text string to identify a name
-    description                 --  Free text string to provide a description
-    )
- );
-
------------------------------------------------------------------------
-                            OUTPUT
------------------------------------------------------------------------
-The output table ('model' above) contains the following columns:
-
-model_weights: Byte array containing the weights of the neural net.
-model_arch: A JSON representation of the model architecture used in
-            training.
-
-A summary table ('_summary') is created to store various training
-statistics as well as the input parameters.
-"""
-    else:
-        help_string = "No such option. Use {schema_madlib}.madlib_keras_fit()"
-
-    return help_string.format(schema_madlib=schema_madlib)
-# ---------------------------------------------------------------------
-
-
-def evaluate_help(schema_madlib, message, **kwargs):
-    """
-    Help function for keras evaluate
-
-    Args:
-        @param schema_madlib
-        @param message: string, Help message string
-        @param kwargs
-
-    Returns:
-        String. Help/usage information
-    """
-    if not message:
-        help_string = """
------------------------------------------------------------------------
-                            SUMMARY
------------------------------------------------------------------------
-This function allows the user to evaluate a madlib_keras_fit trained
-model.
-
-For more details on function usage:
-    SELECT {schema_madlib}.madlib_keras_evaluate('usage')
-            """
-    elif message in ['usage', 'help', '?']:
-        help_string = """
------------------------------------------------------------------------
-                            USAGE
------------------------------------------------------------------------
- SELECT {schema_madlib}.madlib_keras_evaluate(
-    model_table,    --  Name of the table containing the model
-    test_table,     --  Name of the table containing the evaluation dataset
-    output_table,   --  Name of the output table
-    use_gpus,       --  Flag to enable GPU support
-    mst_key         --  Identifier for the desired model out of multimodel
-                        training output
-    )
- );
-
------------------------------------------------------------------------
-                            OUTPUT
------------------------------------------------------------------------
-The output table ('output_table' above) contains the following columns:
-
-loss:           Loss value on evaluation dataset.
-metric:         Metric value on evaluation dataset, where 'metrics_type'
-                below identifies the type of metric.
-metrics_type:   Type of metric used that was used in the training step.
-"""
-    else:
-        help_string = "No such option. Use {schema_madlib}.madlib_keras_evaluate()"
-
-    return help_string.format(schema_madlib=schema_madlib)
-# ---------------------------------------------------------------------
-
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras.sql_in b/src/ports/postgres/modules/deep_learning/madlib_keras.sql_in
deleted file mode 100644
index 90e7a98..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras.sql_in
+++ /dev/null
@@ -1,2162 +0,0 @@
-/* ----------------------------------------------------------------------- *//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *
- * @file madlib_keras.sql_in
- *
- * @brief SQL functions for distributed deep learning with keras
- * @date June 2019
- *
- *
- *//* ----------------------------------------------------------------------- */
-
-m4_include(`SQLCommon.m4')
-
-/**
-@addtogroup grp_keras
-
-@brief Fit, evaluate and predict using the Keras API.
-
-
Contents

-
-\warning  This MADlib method is still in early stage development.
-Interface and implementation are subject to change. 
-
-This module allows you to use SQL to call deep learning
-models designed in Keras [1], which is a high-level neural
-network API written in Python.
-Keras was developed for fast experimentation.  It can run
-on top of different backends and the one that is currently
-supported by MADlib is TensorFlow [2].  The implementation
-in MADlib is distributed and designed to train
-a single model across multiple segments (workers)
-in Greenplum database.  (PostgreSQL is also supported.)
-Alternatively, to train multiple models at the same time for model
-architecture search or hyperparameter tuning, you can
-use Model Selection.
-
-The main use case is image classification
-using sequential models, which are made up of a
-linear stack of layers.  This includes multilayer perceptrons (MLPs)
-and convolutional neural networks (CNNs).  Regression is not
-currently supported.
-
-Before using Keras in MADlib you will need to mini-batch
-your training and evaluation datasets by calling the
-Preprocessor
-for Images which is a utility that prepares image data for
-use by models that support mini-batch as an optimization option.
-This is a one-time operation and you would only
-need to re-run the preprocessor if your input data has changed.
-The advantage of using mini-batching is that it
-can perform better than stochastic gradient descent
-because it uses more than one training example at a time,
-typically resulting faster and smoother convergence [3].
-
-You can also do inference on models that have not been trained in MADlib,
-but rather imported from an external source.  This is in the section
-called "Predict BYOM" below, where "BYOM" stands for "Bring Your Own Model."
-
-Note that the following MADlib functions are targeting a specific Keras
-version (2.2.4) with a specific TensorFlow kernel version (1.14).
-Using a newer or older version may or may not work as intended.
-
-@note CUDA GPU memory cannot be released until the process holding it is terminated.
-When a MADlib deep learning function is called with GPUs, Greenplum internally
-creates a process (called a slice) which calls TensorFlow to do the computation.
-This process holds the GPU memory until one of the following two things happen:
-query finishes and user logs out of the Postgres client/session; or,
-query finishes and user waits for the timeout set by gp_vmem_idle_resource_timeout.
-The default value for this timeout is 18 sec [8].  So the recommendation is:
-log out/reconnect to the session after every GPU query; or
-wait for gp_vmem_idle_resource_timeout before you run another GPU query (you can
-also set it to a lower value).
-
-@anchor keras_fit
-@par Fit
-The fit (training) function has the following format:
-
-
-madlib_keras_fit(
-    source_table,
-    model,
-    model_arch_table,
-    model_id,
-    compile_params,
-    fit_params,
-    num_iterations,
-    use_gpus,
-    validation_table,
-    metrics_compute_frequency,
-    warm_start,
-    name,
-    description
-    )
-

-
-\b Arguments
-

-  
source_table

-  
TEXT. Name of the table containing the training data.
-  This is the name of the output
-  table from the image preprocessor.  Independent
-  and dependent variables are specified in the preprocessor
-  step which is why you do not need to explictly state
-  them here as part of the fit function.

-
-  
model

-  
TEXT. Name of the output table containing the model.
-  Details of the output table are shown below.
-  

-
-  
model_arch_table

-  
TEXT. Name of the table containing the model
-  architecture and (optionally) initial weights to use for
-  training.
-  

-
-  
model_id

-  
INTEGER. This is the id in 'model_arch_table'
-  containing the model architecture and (optionally)
-  initial weights to use for training.
-  

-
-  
compile_params

-  
TEXT.
-    Parameters passed to the compile method of the Keras
-    model class [4].  These parameters will be passed through as is
-    so they must conform to the Keras API definition.
-    As an example, you might use something like: loss='categorical_crossentropy', optimizer='adam', metrics=['acc'].
-    The mandatory parameters that must be specified are 'optimizer'
-    and 'loss'.  Others are optional and will use the default
-    values as per Keras if not specified here.  Also, when
-    specifying 'loss' and 'metrics' do not include the
-    module and submodule prefixes
-    like loss='losses.categorical_crossentropy'
-    or optimizer='keras.optmizers.adam'.
-
-    @note
-    The following loss function is
-    not supported: sparse_categorical_crossentropy.
-    The following metrics are not
-    supported: sparse_categorical_accuracy, top_k_categorical_accuracy, sparse_top_k_categorical_accuracy and custom metrics.
-
-  

-
-  
fit_params 

-  
TEXT.  Parameters passed to the fit method of the Keras
-    model class [4].  These will be passed through as is
-    so they must conform to the Keras API definition.
-    As an example, you might use something like:
-    batch_size=128, epochs=4.
-    There are no mandatory parameters so
-    if you specify NULL, it will use all default
-    values as per Keras.
-  

-
-  
num_iterations

-  
INTEGER.  Number of iterations to train.
-  

-
-  
use_gpus (optional)

-  
BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs
-  are to be used for training the neural network.  Set to TRUE to use GPUs.
-
-    @note
-    This parameter must not conflict with how the distribution rules are set in
-    the preprocessor function.  For example, if you set a distribution rule to use
-    certain segments on hosts that do not have GPUs attached, you will get an error
-    if you set ‘use_gpus’ to TRUE.  Also, we have seen some memory related issues
-    when segments share GPU resources.
-    For example, if you have 1 GPU per segment host and your cluster has 4
-    segments per segment host, it means that all 4
-    segments will share the same
-    GPU on each host. The current recommended
-    configuration is 1 GPU per segment.
-  

-
-  
validation_table (optional)

-  
TEXT, default: none. Name of the table containing
-  the validation dataset.
-  Note that the validation dataset must be preprocessed
-  in the same way as the training dataset, so this
-  is the name of the output
-  table from running the image preprocessor on the validation dataset.
-  Using a validation dataset can mean a
-  longer training time, depending on its size.
-  This can be controlled using the 'metrics_compute_frequency'
-  paremeter described below.

-
-  
metrics_compute_frequency (optional)

-  
INTEGER, default: once at the end of training
-  after 'num_iterations'.  Frequency to compute per-iteration
-  metrics for the training dataset and validation dataset
-  (if specified).  There can be considerable cost to
-  computing metrics every iteration, especially if the
-  training dataset is large.  This parameter is a way of
-  controlling the frequency of those computations.
-  For example, if you specify 5, then metrics will be computed
-  every 5 iterations as well as at the end of training
-  after 'num_iterations'.  If you use the default,
-  metrics will be computed only
-  once after 'num_iterations' have completed.
-  

-
-  
warm_start (optional)

-  
BOOLEAN, default: FALSE.
-    Initalize weights with the coefficients
-    from the last call of the fit
-    function. If set to TRUE, weights will be
-    initialized from the model table
-    generated by the previous training run.
-
-    @note
-    The warm start feature works based on the name of the
-    model output table from a previous training run.
-    When using warm start, do not drop the model output table
-    or the model output summary table
-    before calling the fit function, since these are needed to obtain the
-    weights from the previous run.
-    If you are not using warm start, the model output table
-    and the model output table summary must be dropped in
-    the usual way before calling the training function.
-  

-
-  
name (optional)

-  
TEXT, default: NULL.
-    Free text string to identify a name, if desired.
-  

-
-  
description (optional)

-  
TEXT, default: NULL.
-    Free text string to provide a description, if desired.
-  

-

-
-Output tables
-

-    The model table produced by fit contains the following columns:
-    
-      
-        
-        
-      
-      
-        
-        
-      
-    
model_weightsBYTEA8. Byte array containing the weights of the neural net.
model_archTEXT. A JSON representation of the model architecture
-        used in training.

-
-A summary table named \_summary is also created, which has the following columns:
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-   
source_tableSource table used for training.
modelModel output table produced by training.
independent_varnameIndependent variables column from the original
-        source table in the image preprocessing step.
dependent_varnameDependent variable column from the original
-        source table in the image preprocessing step.
model_arch_tableName of the table containing
-        the model architecture and (optionally) the
-        initial model weights.
model_idThe id of the model in
-        the model architecture table used for training.
compile_paramsCompile parameters passed to Keras.
fit_paramsFit parameters passed to Keras.
num_iterationsNumber of iterations of training completed.
validation_tableName of the table containing
-        the validation dataset (if specified).
metrics_compute_frequencyFrequency that per-iteration metrics are computed
-        for the training dataset and validation
-        dataset.
nameName of the training run (free text).
descriptionDescription of the training run (free text).
model_typeGeneral identifier for type of model trained.
-        Currently says 'madlib_keras'.
model_sizeSize of the model in KB.  Models are stored in
-        'bytea' data format which is used for binary strings
-        in PostgreSQL type databases.
start_training_timeTimestamp for start of training.
end_training_timeTimestamp for end of training.
metrics_elapsed_time Array of elapsed time for metric computations as
-        per the 'metrics_compute_frequency' parameter.
-        Useful for drawing a curve showing loss, accuracy or
-        other metrics as a function of time.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of elapsed time for every 5th
-        iteration, plus the last iteration.
madlib_versionVersion of MADlib used.
num_classesCount of distinct classes values used.
class_valuesArray of actual class values used.
dependent_vartypeData type of the dependent variable.
normalizing_constantNormalizing constant used from the
-        image preprocessing step.
metrics_typeMetric specified in the 'compile_params'.
training_metrics_finalFinal value of the training
-        metric after all iterations have completed.
-        The metric reported is the one
-        specified in the 'metrics_type' parameter.
training_loss_finalFinal value of the training loss after all
-        iterations have completed.
training_metricsArray of training metrics as
-        per the 'metrics_compute_frequency' parameter.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of metrics for every 5th
-        iteration, plus the last iteration.
training_lossArray of training losses as
-        per the 'metrics_compute_frequency' parameter.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of losses for every 5th
-        iteration, plus the last iteration.
validation_metrics_finalFinal value of the validation
-        metric after all iterations have completed.
-        The metric reported is the one
-        specified in the 'metrics_type' parameter.
validation_loss_finalFinal value of the validation loss after all
-        iterations have completed.
validation_metricsArray of validation metrics as
-        per the 'metrics_compute_frequency' parameter.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of metrics for every 5th
-        iteration, plus the last iteration.
validation_lossArray of validation losses as
-        per the 'metrics_compute_frequency' parameter.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of losses for every 5th
-        iteration, plus the last iteration.
metrics_itersArray indicating the iterations for which
-        metrics are calculated, as derived from the
-        parameters 'num_iterations' and 'metrics_compute_frequency'.
-        For example, if 'num_iterations=5'
-        and 'metrics_compute_frequency=2', then 'metrics_iters' value
-        would be {2,4,5} indicating that metrics were computed
-        at iterations 2, 4 and 5 (at the end).
-        If 'num_iterations=5'
-        and 'metrics_compute_frequency=1', then 'metrics_iters' value
-        would be {1,2,3,4,5} indicating that metrics were computed
-        at every iteration.

-
-@anchor keras_evaluate
-@par Evaluate
-The evaluation function has the following format:
-
-
-madlib_keras_evaluate(
-    model_table,
-    test_table,
-    output_table,
-    use_gpus,
-    mst_key
-    )
-

-
-\b Arguments
-

-
-
model_table

-  
TEXT. Name of the table containing the model
-  to use for validation.
-  

-
-  
test_table

-  
TEXT. Name of the table containing the evaluation dataset.
-  Note that test/validation data must be preprocessed in the same
-  way as the training dataset, so
-  this is the name of the output
-  table from the image preprocessor.  Independent
-  and dependent variables are specified in the preprocessor
-  step which is why you do not need to explictly state
-  them here as part of the fit function.

-
-  
output_table

-  
TEXT. Name of table that validation output will be
-  written to. Table contains:

-    
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-
-  
use_gpus (optional)

-  
BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs
-  are to be used for training the neural network.  Set to TRUE to use GPUs.
-
-    @note
-    This parameter must not conflict with how the distribution rules are set in
-    the preprocessor function.  For example, if you set a distribution rule to use
-    certain segments on hosts that do not have GPUs attached, you will get an error
-    if you set ‘use_gpus’ to TRUE.  Also, we have seen some memory related issues
-    when segments share GPU resources.
-    For example, if you have 1 GPU per segment host and your cluster has 4
-    segments per segment host, it means that all 4
-    segments will share the same
-    GPU on each host. The current recommended
-    configuration is 1 GPU per segment.
-  

-
-  
mst_key (optional)

-  
INTEGER, default: NULL. ID that defines a unique tuple for
-  model architecture-compile parameters-fit parameters in a model
-  selection table.  Do not use this if training one model at a time using madlib_keras_fit().
-  See the Model Selection section
-  for more details on model selection by training multiple models at a time.
-  

-
-
-@anchor keras_predict
-@par Predict
-The prediction function has the following format:
-
-madlib_keras_predict(
-    model_table,
-    test_table,
-    id_col,
-    independent_varname,
-    output_table,
-    pred_type,
-    use_gpus,
-    mst_key
-    )
-

-
-\b Arguments
-

-
-
model_table

-  
TEXT. Name of the table containing the model
-  to use for prediction.
-  

-
-  
test_table

-  
TEXT. Name of the table containing the dataset to
-  predict on.  Note that test data is not preprocessed (unlike
-  fit and evaluate) so put one test image per row for prediction.
-  Also see the comment below for the 'independent_varname' parameter
-  regarding normalization.
-
-  

-
-  
id_col

-  
TEXT. Name of the id column in the test data table.
-  

-
-  
independent_varname

-  
TEXT. Column with independent variables in the test table.
-  If a 'normalizing_const' is specified when preprocessing the
-  training dataset, this same normalization will be applied to
-  the independent variables used in predict.
-  

-
-  
output_table

-  
TEXT. Name of the table that prediction output will be
-  written to. Table contains:

-    
lossLoss value on evaluation dataset.
metricMetric value on evaluation dataset, where 'metrics_type'
-        below identifies the type of metric.
metrics_typeType of metric used that was used in the training step.

-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-
-  
pred_type (optional)

-  
TEXT, default: 'response'. The type of output
-  desired, where 'response' gives the actual prediction
-  and 'prob' gives the probability value for each class.
-  

-
-  
use_gpus (optional)

-  
BOOLEAN, default: FALSE (i.e., CPU).
-    Flag to enable GPU support for training neural network.
-    The number of GPUs to use is determined by the parameters
-    passed to the preprocessor.
-
-    @note
-    We have seen some memory related issues when segments
-    share GPU resources.
-    For example, if you provide 1 GPU and your
-    database cluster is set up to have 4
-    segments per segment host, it means that all 4
-    segments on a segment host will share the same
-    GPU. The current recommended
-    configuration is 1 GPU per segment.
-  

-
-  
mst_key (optional)

-  
INTEGER, default: NULL. ID that defines a unique tuple for
-  model architecture-compile parameters-fit parameters in a model
-  selection table.  Do not use this if training one model at a time using madlib_keras_fit().
-  See the Model Selection section
-  for more details on model selection by training multiple models at a time.
-  

-
-
-
-@anchor keras_predict_byom
-@par Predict BYOM (bring your own model)
-The predict BYOM function allows you to do inference on models that
-have not been trained on MADlib, but rather imported from elsewhere.
-It has the following format:
-
-madlib_keras_predict_byom(
-    model_arch_table,
-    model_id,
-    test_table,
-    id_col,
-    independent_varname,
-    output_table,
-    pred_type,
-    use_gpus,
-    class_values,
-    normalizing_const
-    )
-

-
-
-\b Arguments
-

-
-
model_arch_table

-  
TEXT. Name of the architecture table containing the model
-  to use for prediction. The model weights and architecture can be loaded to
-  this table by using the
-  load_keras_model function.
-  

-
-  
model_id

-  
INTEGER. This is the id in 'model_arch_table' containing the model
-  architecture and model weights to use for prediction.
-  

-
-  
test_table

-  
TEXT. Name of the table containing the dataset to
-  predict on.  Note that test data is not preprocessed (unlike
-  fit and evaluate) so put one test image per row for prediction.
-  Set the 'normalizing_const' below for the independent variable if necessary.
-  

-
-  
id_col

-  
TEXT. Name of the id column in the test data table.
-  

-
-  
independent_varname

-  
TEXT. Column with independent variables in the test table.
-  Set the 'normalizing_const' below if necessary.
-  

-
-  
output_table

-  
TEXT. Name of the table that prediction output will be
-  written to. Table contains:

-    
idGives the 'id' for each prediction, corresponding to each row from the test_table.
estimated_COL_NAME
-        (For pred_type='response') The estimated class
-         for classification, where
-         COL_NAME is the name of the column to be
-         predicted from test data.
-        
prob_CLASS
-        (For pred_type='prob' for classification) The
-        probability of a given class.
-        There will be one column for each class
-        in the training data.
-        

-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-
-  
pred_type (optional)

-  
TEXT, default: 'response'. The type of output desired, where 'response'
-   gives the actual prediction and 'prob' gives the probability value for each class.
-  

-
-  
use_gpus (optional)

-  
BOOLEAN, default: FALSE (i.e., CPU).
-    Flag to enable GPU support for training neural network.
-    The number of GPUs to use is determined by the parameters
-    passed to the preprocessor.
-
-    @note
-    We have seen some memory related issues when segments
-    share GPU resources.
-    For example, if you provide 1 GPU and your
-    database cluster is set up to have 4
-    segments per segment host, it means that all 4
-    segments on a segment host will share the same
-    GPU. The current recommended
-    configuration is 1 GPU per segment.
-  

-
-  
class_values (optional)

-  
TEXT[], default: NULL.
-    List of class labels that were used while training the model. See the 'output_table'
-    column above for more details.
-
-    @note
-    If you specify the class values parameter,
-    it must reflect how the dependent variable was 1-hot encoded for training. If you accidently
-    pick another order that does not match the 1-hot encoding, the predictions would be wrong.
-  

-
-  
normalizing_const (optional)

-  
DOUBLE PRECISION, default: 1.0.
-  The normalizing constant to divide each value in the 'independent_varname'
-  array by. For example, you would use 255 for this value if the image data is
-  in the form 0-255.
-  

-
-
-
-@anchor example
-@par Examples
-
-@note
-Deep learning works best on very large datasets,
-but that is not convenient for a quick introduction
-to the syntax. So in this example we use an MLP on the well
-known iris data set from https://archive.ics.uci.edu/ml/datasets/iris.
-For more realistic examples with images please refer
-to the deep learning notebooks
-at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts.
-
-
Classification

-
--#  Create an input data set.
-
-DROP TABLE IF EXISTS iris_data;
-CREATE TABLE iris_data(
-    id serial,
-    attributes numeric[],
-    class_text varchar
-);
-INSERT INTO iris_data(id, attributes, class_text) VALUES
-(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),
-(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),
-(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),
-(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),
-(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),
-(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),
-(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),
-(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),
-(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),
-(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
-(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),
-(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),
-(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),
-(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),
-(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),
-(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),
-(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),
-(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),
-(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),
-(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),
-(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),
-(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),
-(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),
-(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),
-(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),
-(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),
-(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),
-(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),
-(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),
-(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),
-(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),
-(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),
-(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),
-(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),
-(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
-(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),
-(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),
-(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
-(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),
-(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),
-(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),
-(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),
-(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),
-(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),
-(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),
-(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),
-(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),
-(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),
-(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),
-(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),
-(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),
-(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),
-(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),
-(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),
-(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),
-(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),
-(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),
-(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),
-(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),
-(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),
-(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),
-(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),
-(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),
-(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),
-(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),
-(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),
-(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),
-(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),
-(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),
-(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),
-(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),
-(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),
-(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),
-(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),
-(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),
-(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),
-(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),
-(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),
-(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),
-(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),
-(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),
-(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),
-(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),
-(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),
-(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),
-(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),
-(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),
-(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),
-(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),
-(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),
-(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),
-(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),
-(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),
-(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),
-(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),
-(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),
-(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),
-(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),
-(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),
-(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),
-(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),
-(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
-(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),
-(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),
-(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),
-(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),
-(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),
-(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),
-(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),
-(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),
-(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),
-(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),
-(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),
-(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),
-(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),
-(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),
-(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),
-(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),
-(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),
-(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),
-(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),
-(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),
-(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),
-(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),
-(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),
-(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),
-(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),
-(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),
-(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),
-(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),
-(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),
-(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),
-(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),
-(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),
-(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),
-(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),
-(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),
-(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),
-(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),
-(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),
-(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),
-(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),
-(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
-(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),
-(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),
-(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),
-(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),
-(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),
-(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),
-(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');
-

-Create a test/validation dataset from the training data:
-
-DROP TABLE IF EXISTS iris_train, iris_test;
--- Set seed so results are reproducible
-SELECT setseed(0);
-SELECT madlib.train_test_split('iris_data',     -- Source table
-                               'iris',          -- Output table root name
-                                0.8,            -- Train proportion
-                                NULL,           -- Test proportion (0.2)
-                                NULL,           -- Strata definition
-                                NULL,           -- Output all columns
-                                NULL,           -- Sample without replacement
-                                TRUE            -- Separate output tables
-                              );
-SELECT COUNT(*) FROM iris_train;
-

-
- count
-------+
-   120
-

-
--# Call the preprocessor for deep learning.  For the training dataset:
-
-\\x off
-DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;
-SELECT madlib.training_preprocessor_dl('iris_train',         -- Source table
-                                       'iris_train_packed',  -- Output table
-                                       'class_text',         -- Dependent variable
-                                       'attributes'          -- Independent variable
-                                        );
-SELECT * FROM iris_train_packed_summary;
-

-
--[ RECORD 1 ]-------+---------------------------------------------
-source_table        | iris_train
-output_table        | iris_train_packed
-dependent_varname   | class_text
-independent_varname | attributes
-dependent_vartype   | character varying
-class_values        | {Iris-setosa,Iris-versicolor,Iris-virginica}
-buffer_size         | 60
-normalizing_const   | 1.0
-num_classes         | 3
-

-For the validation dataset:
-
-DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;
-SELECT madlib.validation_preprocessor_dl('iris_test',          -- Source table
-                                         'iris_test_packed',   -- Output table
-                                         'class_text',         -- Dependent variable
-                                         'attributes',         -- Independent variable
-                                         'iris_train_packed'   -- From training preprocessor step
-                                          );
-SELECT * FROM iris_test_packed_summary;
-

-
--[ RECORD 1 ]-------+---------------------------------------------
-source_table        | iris_test
-output_table        | iris_test_packed
-dependent_varname   | class_text
-independent_varname | attributes
-dependent_vartype   | character varying
-class_values        | {Iris-setosa,Iris-versicolor,Iris-virginica}
-buffer_size         | 15
-normalizing_const   | 1.0
-num_classes         | 3
-

-
--# Define and load model architecture.  Use Keras to define
-the model architecture:
-
-import keras
-from keras.models import Sequential
-from keras.layers import Dense
-model_simple = Sequential()
-model_simple.add(Dense(10, activation='relu', input_shape=(4,)))
-model_simple.add(Dense(10, activation='relu'))
-model_simple.add(Dense(3, activation='softmax'))
-model_simple.summary()
-\verbatim
-
-_________________________________________________________________
-Layer (type)                 Output Shape              Param #
-=================================================================
-dense_1 (Dense)              (None, 10)                50
-_________________________________________________________________
-dense_2 (Dense)              (None, 10)                110
-_________________________________________________________________
-dense_3 (Dense)              (None, 3)                 33
-=================================================================
-Total params: 193
-Trainable params: 193
-Non-trainable params: 0
-\endverbatim
-

-Export the model to JSON:
-
-model_simple.to_json()
-

-
-'{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}'
-

-Load into model architecture table:
-
-DROP TABLE IF EXISTS model_arch_library;
-SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
-$$
-{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
-$$
-::json,  -- JSON blob
-                               NULL,                  -- Weights
-                               'Sophie',              -- Name
-                               'A simple model'       -- Descr
-);
-

-
--# Train model and view summary table:
-
-DROP TABLE IF EXISTS iris_model, iris_model_summary;
-SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
-                               'iris_model',          -- model output table
-                               'model_arch_library',  -- model arch table
-                                1,                    -- model arch id
-                                $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
-                                $$ batch_size=5, epochs=3 $$,  -- fit_params
-                                10                    -- num_iterations
-                              );
-SELECT * FROM iris_model_summary;
-

-
--[ RECORD 1 ]-------------+--------------------------------------------------------------------------
-source_table              | iris_train_packed
-model                     | iris_model
-dependent_varname         | class_text
-independent_varname       | attributes
-model_arch_table          | model_arch_library
-model_id                  | 1
-compile_params            |  loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']
-fit_params                |  batch_size=5, epochs=3
-num_iterations            | 10
-validation_table          |
-metrics_compute_frequency | 10
-name                      |
-description               |
-model_type                | madlib_keras
-model_size                | 0.7900390625
-start_training_time       | 2019-06-05 20:55:15.785034
-end_training_time         | 2019-06-05 20:55:25.373035
-metrics_elapsed_time      | {9.58799290657043}
-madlib_version            | 1.17.0
-num_classes               | 3
-class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
-dependent_vartype         | character varying
-normalizing_const         | 1
-metrics_type              | {accuracy}
-training_metrics_final    | 0.766666650772
-training_loss_final       | 0.721103310585
-training_metrics          | {0.766666650772095}
-training_loss             | {0.721103310585022}
-validation_metrics_final  |
-validation_loss_final     |
-validation_metrics        |
-validation_loss           |
-metrics_iters             | {10}
-

-
--# Use the test dataset to evaluate the model we built above:
-
-DROP TABLE IF EXISTS iris_validate;
-SELECT madlib.madlib_keras_evaluate('iris_model',       -- model
-                                   'iris_test_packed',  -- test table
-                                   'iris_validate'      -- output table
-                                   );
-SELECT * FROM iris_validate;
-

-
-       loss        |      metric       | metrics_type
--------------------+-------------------+--------------
- 0.719491899013519 | 0.800000011920929 | {accuracy}
-(1 row)
-

-
--# Predict.  We will use the validation dataset for prediction
-as well, which is not usual but serves to show the
-syntax. The prediction is in the 'estimated_class_text'
-column:
-
-DROP TABLE IF EXISTS iris_predict;
-SELECT madlib.madlib_keras_predict('iris_model', -- model
-                                   'iris_test',  -- test_table
-                                   'id',  -- id column
-                                   'attributes', -- independent var
-                                   'iris_predict'  -- output table
-                                   );
-SELECT * FROM iris_predict ORDER BY id;
-

-
- id  | estimated_class_text
------+----------------------
-   4 | Iris-setosa
-   6 | Iris-setosa
-   8 | Iris-setosa
-  12 | Iris-setosa
-  13 | Iris-setosa
-  15 | Iris-setosa
-  24 | Iris-setosa
-  30 | Iris-setosa
-  38 | Iris-setosa
-  49 | Iris-setosa
-  60 | Iris-virginica
-  68 | Iris-versicolor
-  69 | Iris-versicolor
-  76 | Iris-versicolor
-  78 | Iris-versicolor
-  81 | Iris-versicolor
-  85 | Iris-virginica
-  90 | Iris-versicolor
-  91 | Iris-versicolor
-  94 | Iris-virginica
- 104 | Iris-virginica
- 106 | Iris-versicolor
- 107 | Iris-virginica
- 110 | Iris-virginica
- 119 | Iris-versicolor
- 127 | Iris-virginica
- 129 | Iris-virginica
- 134 | Iris-versicolor
- 139 | Iris-virginica
- 144 | Iris-virginica
-(30 rows)
-

-Count missclassifications:
-
-SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id)
-WHERE iris_predict.estimated_class_text != iris_test.class_text;
-

-
- count
--------+
-     6
-(1 row)
-

-Accuracy:
-
-SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from
-    (select iris_test.class_text as actual, iris_predict.estimated_class_text as estimated
-     from iris_predict inner join iris_test
-     on iris_test.id=iris_predict.id) q
-WHERE q.actual=q.estimated;
-

-
- test_accuracy_percent
------------------------+
-                 80.00
-(1 row)
-

-
--# Predict BYOM.
-We will use the validation dataset for prediction
-as well, which is not usual but serves to show the
-syntax. See load_keras_model
-for details on how to load the model architecture and weights.
-In this example we will use weights we already have:
-
-UPDATE model_arch_library
-SET model_weights = iris_model.model_weights
-FROM iris_model
-WHERE model_arch_library.model_id = 1;
-

-Now train using a model from the model architecture table directly
-without referencing the model table from the MADlib training.  Note that if you
-specify the class values parameter as we do below, it must reflect how the dependent
-variable was 1-hot encoded for training. In this example the 'training_preprocessor_dl()'
-in Step 2 above encoded in the order {'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'} so
-this is the order we pass in the parameter. If we accidently pick another order that does
-not match the 1-hot encoding, the predictions would be wrong.
-
-DROP TABLE IF EXISTS iris_predict_byom;
-SELECT madlib.madlib_keras_predict_byom('model_arch_library',  -- model arch table
-                                         1,                    -- model arch id
-                                        'iris_test',           -- test_table
-                                        'id',                  -- id column
-                                        'attributes',          -- independent var
-                                        'iris_predict_byom',   -- output table
-                                        'response',            -- prediction type
-                                         FALSE,                -- use GPUs
-                                         ARRAY['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], -- class values
-                                         1.0                   -- normalizing const
-                                   );
-SELECT * FROM iris_predict_byom ORDER BY id;
-

-The prediction is in the 'estimated_dependent_var' column:
-
- id  | estimated_dependent_var
------+----------------------
-   4 | Iris-setosa
-   6 | Iris-setosa
-   8 | Iris-setosa
-  12 | Iris-setosa
-  13 | Iris-setosa
-  15 | Iris-setosa
-  24 | Iris-setosa
-  30 | Iris-setosa
-  38 | Iris-setosa
-  49 | Iris-setosa
-  60 | Iris-virginica
-  68 | Iris-versicolor
-  69 | Iris-versicolor
-  76 | Iris-versicolor
-  78 | Iris-versicolor
-  81 | Iris-versicolor
-  85 | Iris-virginica
-  90 | Iris-versicolor
-  91 | Iris-versicolor
-  94 | Iris-virginica
- 104 | Iris-virginica
- 106 | Iris-versicolor
- 107 | Iris-virginica
- 110 | Iris-virginica
- 119 | Iris-versicolor
- 127 | Iris-virginica
- 129 | Iris-virginica
- 134 | Iris-versicolor
- 139 | Iris-virginica
- 144 | Iris-virginica
-(30 rows)
- 

-Count missclassifications:
-
-SELECT COUNT(*) FROM iris_predict_byom JOIN iris_test USING (id)
-WHERE iris_predict_byom.estimated_dependent_var != iris_test.class_text;
-

-
- count
--------+
-     6
-(1 row)
-

-Accuracy:
-
-SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from
-    (select iris_test.class_text as actual, iris_predict_byom.estimated_dependent_var as estimated
-     from iris_predict_byom inner join iris_test
-     on iris_test.id=iris_predict_byom.id) q
-WHERE q.actual=q.estimated;
-

-
- test_accuracy_percent
------------------------+
-                 80.00
-(1 row)
-

-
-
-
Classification with Other Parameters

-
--# Validation dataset.  Now use a validation dataset
-and compute metrics every 3rd iteration using
-the 'metrics_compute_frequency' parameter. This can
-help reduce run time if you do not need metrics
-computed at every iteration.
-
-DROP TABLE IF EXISTS iris_model, iris_model_summary;
-SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
-                               'iris_model',          -- model output table
-                               'model_arch_library',  -- model arch table
-                                1,                    -- model arch id
-                                $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
-                                $$ batch_size=5, epochs=3 $$,  -- fit_params
-                                10,                   -- num_iterations
-                                FALSE,                -- use GPUs
-                                'iris_test_packed',   -- validation dataset
-                                3,                    -- metrics compute frequency
-                                FALSE,                -- warm start
-                               'Sophie L.',           -- name
-                               'Simple MLP for iris dataset'  -- description
-                              );
-SELECT * FROM iris_model_summary;
-

-
--[ RECORD 1 ]-------------+--------------------------------------------------------------------------
-source_table              | iris_train_packed
-model                     | iris_model
-dependent_varname         | class_text
-independent_varname       | attributes
-model_arch_table          | model_arch_library
-model_id                  | 1
-compile_params            |  loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']
-fit_params                |  batch_size=5, epochs=3
-num_iterations            | 10
-validation_table          | iris_test_packed
-metrics_compute_frequency | 3
-name                      | Sophie L.
-description               | Simple MLP for iris dataset
-model_type                | madlib_keras
-model_size                | 0.7900390625
-start_training_time       | 2019-06-05 20:58:23.224629
-end_training_time         | 2019-06-05 20:58:35.477499
-metrics_elapsed_time      | {4.69859290122986,8.2062520980835,10.8104848861694,12.2528700828552}
-madlib_version            | 1.17.0
-num_classes               | 3
-class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
-dependent_vartype         | character varying
-normalizing_const         | 1
-metrics_type              | {accuracy}
-training_metrics_final    | 0.941666662693
-training_loss_final       | 0.40586027503
-training_metrics          | {0.699999988079071,0.800000011920929,0.899999976158142,0.941666662693024}
-training_loss             | {0.825238645076752,0.534248650074005,0.427499741315842,0.405860275030136}
-validation_metrics_final  | 0.866666674614
-validation_loss_final     | 0.409001916647
-validation_metrics        | {0.733333349227905,0.733333349227905,0.866666674613953,0.866666674613953}
-validation_loss           | {0.827081918716431,0.536275088787079,0.431326270103455,0.409001916646957}
-metrics_iters             | {3,6,9,10}
-

-
--# Predict probabilities for each class:
-
-DROP TABLE IF EXISTS iris_predict;
-SELECT madlib.madlib_keras_predict('iris_model',      -- model
-                                   'iris_test',       -- test_table
-                                   'id',              -- id column
-                                   'attributes',      -- independent var
-                                   'iris_predict',    -- output table
-                                   'prob'             -- response type
-                                   );
-SELECT * FROM iris_predict ORDER BY id;
-

-
- id  | prob_Iris-setosa | prob_Iris-versicolor | prob_Iris-virginica
------+------------------+----------------------+---------------------
-   4 |        0.9241953 |          0.059390426 |          0.01641435
-   6 |        0.9657151 |           0.02809224 |        0.0061926916
-   8 |        0.9543316 |           0.03670931 |         0.008959154
-  12 |       0.93851465 |          0.048681837 |         0.012803554
-  13 |       0.93832576 |           0.04893658 |         0.012737647
-  15 |       0.98717564 |           0.01091238 |        0.0019119986
-  24 |        0.9240628 |          0.060805064 |         0.015132156
-  30 |       0.92063266 |          0.062279057 |         0.017088294
-  38 |        0.9353765 |          0.051353406 |         0.013270103
-  49 |        0.9709265 |          0.023811856 |         0.005261566
-  60 |      0.034395564 |            0.5260507 |          0.43955377
-  68 |      0.031360663 |           0.53689945 |          0.43173987
-  69 |     0.0098787155 |           0.46121457 |          0.52890676
-  76 |      0.031186827 |            0.5644549 |          0.40435827
-  78 |       0.00982633 |           0.48929632 |           0.5008774
-  81 |       0.03658528 |           0.53248984 |           0.4309249
-  85 |      0.015423619 |           0.48452598 |           0.5000504
-  90 |      0.026857043 |            0.5155698 |          0.45757324
-  91 |      0.013675574 |           0.47155368 |           0.5147708
-  94 |      0.073440716 |            0.5418821 |           0.3846772
- 104 |     0.0021637122 |            0.3680499 |          0.62978643
- 106 |    0.00052832486 |           0.30891812 |           0.6905536
- 107 |      0.007315576 |           0.40949163 |           0.5831927
- 110 |     0.0022259138 |            0.4058138 |          0.59196025
- 119 |    0.00018505375 |           0.24510723 |           0.7547077
- 127 |      0.009542585 |           0.46958733 |          0.52087003
- 129 |     0.0019719477 |           0.36288205 |            0.635146
- 134 |     0.0056418083 |           0.43401477 |          0.56034344
- 139 |       0.01067015 |            0.4755573 |          0.51377255
- 144 |     0.0018909549 |           0.37689638 |           0.6212126
-(30 rows)
-

-
--# Warm start.  Next, use the warm_start parameter
-to continue learning, using the coefficients from
-the run above. Note that we don't drop the
-model table or model summary table:
-
-SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
-                               'iris_model',          -- model output table
-                               'model_arch_library',  -- model arch table
-                                1,                    -- model arch id
-                                $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
-                                $$ batch_size=5, epochs=3 $$,  -- fit_params
-                                5,                   -- num_iterations
-                                FALSE,               -- use GPUs
-                                'iris_test_packed',   -- validation dataset
-                                1,                    -- metrics compute frequency
-                                TRUE,                 -- warm start
-                               'Sophie L.',           -- name
-                               'Simple MLP for iris dataset'  -- description
-                              );
-SELECT * FROM iris_model_summary;
-

-
--[ RECORD 1 ]-------------+--------------------------------------------------------------------------------------------
-source_table              | iris_train_packed
-model                     | iris_model
-dependent_varname         | class_text
-independent_varname       | attributes
-model_arch_table          | model_arch_library
-model_id                  | 1
-compile_params            |  loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']
-fit_params                |  batch_size=5, epochs=3
-num_iterations            | 5
-validation_table          | iris_test_packed
-metrics_compute_frequency | 1
-name                      | Sophie L.
-description               | Simple MLP for iris dataset
-model_type                | madlib_keras
-model_size                | 0.7900390625
-start_training_time       | 2019-06-05 20:59:43.971792
-end_training_time         | 2019-06-05 20:59:51.654586
-metrics_elapsed_time      | {2.89326310157776,4.14273309707642,5.24781513214111,6.34498596191406,7.68279695510864}
-madlib_version            | 1.17.0
-num_classes               | 3
-class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
-dependent_vartype         | character varying
-normalizing_const         | 1
-metrics_type              | {accuracy}
-training_metrics_final    | 0.933333337307
-training_loss_final       | 0.334455043077
-training_metrics          | {0.933333337306976,0.933333337306976,0.975000023841858,0.975000023841858,0.933333337306976}
-training_loss             | {0.386842548847198,0.370587915182114,0.357161343097687,0.344598710536957,0.334455043077469}
-validation_metrics_final  | 0.866666674614
-validation_loss_final     | 0.34414178133
-validation_metrics        | {0.866666674613953,0.866666674613953,0.933333337306976,0.866666674613953,0.866666674613953}
-validation_loss           | {0.391442179679871,0.376414686441422,0.362262904644012,0.351912915706635,0.344141781330109}
-metrics_iters             | {1,2,3,4,5}
-

-Note that the loss and accuracy values pick up from where the previous run left off.
-
-
Transfer Learning

-Here we want
-to start with initial weights from a pre-trained model
-rather than training from scratch. We also want to use
-a model architecture with the earlier feature layer(s)
-frozen to save on training time. The example below is
-somewhat contrived but gives you the idea of the steps.
-
--# Define and load a model architecture with
-the 1st hidden layer frozen:
-
-
-model_transfer = Sequential()
-model_transfer.add(Dense(10, activation='relu', input_shape=(4,), trainable=False))
-model_transfer.add(Dense(10, activation='relu'))
-model_transfer.add(Dense(3, activation='softmax'))
-model_simple.summary()
-\verbatim
-
-_________________________________________________________________
-Layer (type)                 Output Shape              Param #
-=================================================================
-dense_1 (Dense)              (None, 10)                50
-_________________________________________________________________
-dense_2 (Dense)              (None, 10)                110
-_________________________________________________________________
-dense_3 (Dense)              (None, 3)                 33
-=================================================================
-Total params: 193
-Trainable params: 143
-Non-trainable params: 50
-\endverbatim
-

-Export the model to JSON:
-
-model_simple.to_json()
-

-
-'{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": false, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_4", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}'
-

-Load into model architecture table:
-
-SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
-$$
-{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": false, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_4", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
-$$
-::json,  -- JSON blob
-                               NULL,                  -- Weights
-                               'Maria',               -- Name
-                               'A transfer model'     -- Descr
-);
-

-Fetch the weights from a previous MADlib run. (Normally
-these would be downloaded from a source that trained
-the same model architecture on a related dataset.)
-
-UPDATE model_arch_library
-SET model_weights = iris_model.model_weights
-FROM iris_model
-WHERE model_arch_library.model_id = 2;
-

-Now train the model using the transfer model and the pre-trained weights:
-
-DROP TABLE IF EXISTS iris_model, iris_model_summary;
-SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
-                               'iris_model',          -- model output table
-                               'model_arch_library',  -- model arch table
-                                2,                    -- model arch id
-                                $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
-                                $$ batch_size=5, epochs=3 $$,  -- fit_params
-                                10                    -- num_iterations
-                              );
-SELECT * FROM iris_model_summary;
-

-
--[ RECORD 1 ]-------------+--------------------------------------------------------------------------
-source_table              | iris_train_packed
-model                     | iris_model
-dependent_varname         | class_text
-independent_varname       | attributes
-model_arch_table          | model_arch_library
-model_id                  | 2
-compile_params            |  loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']
-fit_params                |  batch_size=5, epochs=3
-num_iterations            | 10
-validation_table          |
-metrics_compute_frequency | 10
-name                      |
-description               |
-model_type                | madlib_keras
-model_size                | 0.7900390625
-start_training_time       | 2019-06-05 21:01:03.998422
-end_training_time         | 2019-06-05 21:01:13.525838
-metrics_elapsed_time      | {9.52741599082947}
-madlib_version            | 1.17.0
-num_classes               | 3
-class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
-dependent_vartype         | character varying
-normalizing_const         | 1
-metrics_type              | {accuracy}
-training_metrics_final    | 0.975000023842
-training_loss_final       | 0.245171800256
-training_metrics          | {0.975000023841858}
-training_loss             | {0.245171800255775}
-validation_metrics_final  |
-validation_loss_final     |
-validation_metrics        |
-validation_loss           |
-metrics_iters             | {10}
-

-
-@anchor notes
-@par Notes
-
-1. Refer to the deep learning section of the Apache MADlib
-wiki [5] for important information including supported libraries
-and versions.
-
-2. Classification is currently supported, not regression.
-
-3. Reminder about the distinction between warm start and transfer learning.  Warm start uses model
-state (weights) from the model output table from a previous training run -
-set the 'warm_start' parameter to TRUE in the fit function.
-Transfer learning uses initial model state (weights) stored in the 'model_arch_table' - in this case set the
-'warm_start' parameter to FALSE in the fit function.
-
-@anchor background
-@par Technical Background
-
-For an introduction to deep learning foundations, including MLP and CNN,
-refer to [6].
-
-This module trains a single large model across the database cluster
-using the bulk synchronous parallel (BSP) approach, with model averaging [7].
-
-On the effect of database cluster size: as the database cluster size increases, the per iteration
-loss will be higher since the model only sees 1/n of the data, where n is the number of segments.
-However, each iteration runs faster than single node because it is only traversing 1/n of the data.
-For highly non-convex solution spaces, convergence behavior may diminish as cluster size increases.
-Ensure that each segment has sufficient volume of data and examples of each class value.
-
-Alternatively, to train multiple models at the same time for model
-architecture search or hyperparameter tuning, you can
-use Model Selection,
-which does not do model averaging and hence may have better covergence efficiency.
-
-@anchor literature
-@literature
-
-@anchor mlp-lit-1
-[1] https://keras.io/
-
-[2] https://www.tensorflow.org/
-
-[3] "Neural Networks for Machine Learning", Lectures 6a and 6b on mini-batch gradient descent,
-Geoffrey Hinton with Nitish Srivastava and Kevin Swersky,
-http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
-
-[4] https://keras.io/models/model/
-
-[5] Deep learning section of Apache MADlib wiki, https://cwiki.apache.org/confluence/display/MADLIB/Deep+Learning
-
-[6] Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.
-
-[7] "Resource-Efficient and Reproducible Model Selection on Deep Learning Systems," Supun Nakandala,
-Yuhao Zhang, and Arun Kumar, Technical Report, Computer Science and Engineering, University of California,
-San Diego https://adalabucsd.github.io/papers/TR_2019_Cerebro.pdf.
-
-[8] Greenplum Database server configuration parameters https://gpdb.docs.pivotal.io/latest/ref_guide/config_params/guc-list.html
-
-@anchor related
-@par Related Topics
-
-File madlib_keras.sql_in documenting the training, evaluate and predict functions.
-
-*/
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit(
-    source_table            VARCHAR,
-    model                   VARCHAR,
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    compile_params          VARCHAR,
-    fit_params              VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR,
-    metrics_compute_frequency  INTEGER,
-    warm_start              BOOLEAN,
-    name                    VARCHAR,
-    description             VARCHAR
-) RETURNS VOID AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras')
-    from utilities.control import SetGUC
-    with AOControl(False):
-        with SetGUC("plan_cache_mode", "force_generic_plan"):
-            madlib_keras.fit(**globals())
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit(
-    source_table            VARCHAR,
-    model                   VARCHAR,
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    compile_params          VARCHAR,
-    fit_params              VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR,
-    metrics_compute_frequency  INTEGER,
-    warm_start              BOOLEAN,
-    name                    VARCHAR
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit(
-    source_table            VARCHAR,
-    model                   VARCHAR,
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    compile_params          VARCHAR,
-    fit_params              VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR,
-    metrics_compute_frequency  INTEGER,
-    warm_start              BOOLEAN
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit(
-    source_table            VARCHAR,
-    model                   VARCHAR,
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    compile_params          VARCHAR,
-    fit_params              VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR,
-    metrics_compute_frequency  INTEGER
-) RETURNS VOID AS $$
-SELECT MADLIB_SCHEMA.madlib_keras_fit($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, NULL, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-    m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit(
-    source_table            VARCHAR,
-    model                   VARCHAR,
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    compile_params          VARCHAR,
-    fit_params              VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit($1, $2, $3, $4, $5, $6, $7, $8, $9, NULL, NULL, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit(
-    source_table            VARCHAR,
-    model                   VARCHAR,
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    compile_params          VARCHAR,
-    fit_params              VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit($1, $2, $3, $4, $5, $6, $7, $8, NULL, NULL, NULL, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit(
-    source_table            VARCHAR,
-    model                   VARCHAR,
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    compile_params          VARCHAR,
-    fit_params              VARCHAR,
-    num_iterations          INTEGER
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit($1, $2, $3, $4, $5, $6, $7, FALSE, NULL, NULL, NULL, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.fit_transition(
-    state                       BYTEA,
-    dependent_var               BYTEA,
-    independent_var             BYTEA,
-    dependent_var_shape         INTEGER[],
-    independent_var_shape       INTEGER[],
-    model_architecture          TEXT,
-    compile_params              TEXT,
-    fit_params                  TEXT,
-    dist_key                    INTEGER,
-    dist_key_mapping            INTEGER[],
-    current_seg_id              INTEGER,
-    segments_per_host           INTEGER,
-    images_per_seg              INTEGER[],
-    use_gpus                    BOOLEAN,
-    accessible_gpus_for_seg                INTEGER[],
-    prev_serialized_weights     BYTEA,
-    is_final_iteration          BOOLEAN
-) RETURNS BYTEA AS $$
-PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras')
-    return madlib_keras.fit_transition(**globals())
-$$ LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.fit_merge(
-    state1          BYTEA,
-    state2          BYTEA
-) RETURNS BYTEA AS $$
-PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras')
-    return madlib_keras.fit_merge(**globals())
-$$ LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.fit_final(
-    state BYTEA
-) RETURNS BYTEA AS $$
-PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras')
-    return madlib_keras.fit_final(**globals())
-$$ LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.fit_step(
-    BYTEA,
-    BYTEA,
-    TEXT,
-    TEXT,
-    TEXT,
-    TEXT,
-    TEXT,
-    INTEGER,
-    INTEGER[],
-    INTEGER,
-    INTEGER,
-    INTEGER[],
-    BOOLEAN,
-    INTEGER[],
-    BYTEA,
-    BOOLEAN);
-CREATE AGGREGATE MADLIB_SCHEMA.fit_step(
-    /* dep_var */                BYTEA,
-    /* ind_var */                BYTEA,
-    /* dep_var_shape */          INTEGER[],
-    /* ind_var_shape */          INTEGER[],
-    /* model_architecture */     TEXT,
-    /* compile_params */         TEXT,
-    /* fit_params */             TEXT,
-    /* dist_key */               INTEGER,
-    /* dist_key_mapping */       INTEGER[],
-    /* current_seg_id */         INTEGER,
-    /* segments_per_host */      INTEGER,
-    /* images_per_seg */         INTEGER[],
-    /* use_gpus  */              BOOLEAN,
-    /* segments_per_host  */     INTEGER[],
-    /* serialized_weights */     BYTEA,
-    /* is_final_iteration */     BOOLEAN
-)(
-    STYPE=BYTEA,
-    SFUNC=MADLIB_SCHEMA.fit_transition,
-    m4_ifdef(`__POSTGRESQL__', `', `prefunc=MADLIB_SCHEMA.fit_merge,')
-    FINALFUNC=MADLIB_SCHEMA.fit_final
-);
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict(
-    model_table             VARCHAR,
-    test_table              VARCHAR,
-    id_col                  VARCHAR,
-    independent_varname     VARCHAR,
-    output_table            VARCHAR,
-    pred_type               VARCHAR,
-    use_gpus                BOOLEAN,
-    mst_key                 INTEGER
-) RETURNS VOID AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_predict')
-    from utilities.control import SetGUC
-    with AOControl(False):
-        with SetGUC("plan_cache_mode", "force_generic_plan"):
-            madlib_keras_predict.Predict(schema_madlib,
-                   model_table,
-                   test_table,
-                   id_col,
-                   independent_varname,
-                   output_table,
-                   pred_type,
-                   use_gpus,
-                   mst_key)
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict(
-    model_table             VARCHAR,
-    test_table              VARCHAR,
-    id_col                  VARCHAR,
-    independent_varname     VARCHAR,
-    output_table            VARCHAR,
-    pred_type               VARCHAR,
-    use_gpus                BOOLEAN
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_predict($1, $2, $3, $4, $5, $6, $7, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict(
-    model_table             VARCHAR,
-    test_table              VARCHAR,
-    id_col                  VARCHAR,
-    independent_varname     VARCHAR,
-    output_table            VARCHAR,
-    pred_type               VARCHAR
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_predict($1, $2, $3, $4, $5, $6, FALSE, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict(
-    model_table             VARCHAR,
-    test_table              VARCHAR,
-    id_col                  VARCHAR,
-    independent_varname     VARCHAR,
-    output_table            VARCHAR
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_predict($1, $2, $3, $4, $5, NULL, FALSE, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_keras_predict(
-    independent_var    REAL[],
-    model_architecture TEXT,
-    model_weights      BYTEA,
-    is_response        BOOLEAN,
-    normalizing_const  DOUBLE PRECISION,
-    current_seg_id     INTEGER,
-    seg_ids            INTEGER[],
-    images_per_seg     INTEGER[],
-    use_gpus           BOOLEAN,
-    gpus_per_host      INTEGER,
-    segments_per_host  INTEGER
-) RETURNS DOUBLE PRECISION[] AS $$
-    PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras_predict')
-    return madlib_keras_predict.internal_keras_predict(**globals())
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
--------------------------------------------------------------------------------
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    test_table              VARCHAR,
-    id_col                  VARCHAR,
-    independent_varname     VARCHAR,
-    output_table            VARCHAR,
-    pred_type               VARCHAR,
-    use_gpus                BOOLEAN,
-    class_values            TEXT[],
-    normalizing_const       DOUBLE PRECISION
-) RETURNS VOID AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_predict')
-    from utilities.control import SetGUC
-    with AOControl(False):
-        with SetGUC("plan_cache_mode", "force_generic_plan"):
-            madlib_keras_predict.PredictBYOM(**globals())
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    test_table              VARCHAR,
-    id_col                  VARCHAR,
-    independent_varname     VARCHAR,
-    output_table            VARCHAR,
-    pred_type               VARCHAR,
-    use_gpus                BOOLEAN,
-    class_values            TEXT[]
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_predict_byom($1, $2, $3, $4, $5, $6, $7, $8, $9, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    test_table              VARCHAR,
-    id_col                  VARCHAR,
-    independent_varname     VARCHAR,
-    output_table            VARCHAR,
-    pred_type               VARCHAR,
-    use_gpus                BOOLEAN
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_predict_byom($1, $2, $3, $4, $5, $6, $7, $8, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    test_table              VARCHAR,
-    id_col                  VARCHAR,
-    independent_varname     VARCHAR,
-    output_table            VARCHAR,
-    pred_type               VARCHAR
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_predict_byom($1, $2, $3, $4, $5, $6, $7, NULL, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict_byom(
-    model_arch_table        VARCHAR,
-    model_id                INTEGER,
-    test_table              VARCHAR,
-    id_col                  VARCHAR,
-    independent_varname     VARCHAR,
-    output_table            VARCHAR
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_predict_byom($1, $2, $3, $4, $5, $6, NULL, NULL, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
--------------------------------------------------------------------------------
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_evaluate(
-    model_table             VARCHAR,
-    test_table              VARCHAR,
-    output_table            VARCHAR,
-    use_gpus                BOOLEAN,
-    mst_key                 INTEGER
-) RETURNS VOID AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras')
-    from utilities.control import SetGUC
-    with AOControl(False):
-        with SetGUC("plan_cache_mode", "force_generic_plan"):
-            madlib_keras.evaluate(**globals())
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_evaluate(
-    model_table             VARCHAR,
-    test_table              VARCHAR,
-    output_table            VARCHAR,
-    use_gpus                BOOLEAN
-) RETURNS VOID AS $$
-  SELECT MADLIB_SCHEMA.madlib_keras_evaluate($1, $2, $3, $4, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_evaluate(
-    model_table             VARCHAR,
-    test_table              VARCHAR,
-    output_table            VARCHAR
-) RETURNS VOID AS $$
-  SELECT MADLIB_SCHEMA.madlib_keras_evaluate($1, $2, $3, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_keras_eval_transition(
-    state                              REAL[3],
-    dependent_var                      BYTEA,
-    independent_var                    BYTEA,
-    dependent_var_shape                INTEGER[],
-    independent_var_shape              INTEGER[],
-    model_architecture                 TEXT,
-    serialized_weights                 BYTEA,
-    compile_params                     TEXT,
-    dist_key                           INTEGER,
-    dist_key_mapping                   INTEGER[],
-    current_seg_id                     INTEGER,
-    segments_per_host                  INTEGER,
-    images_per_seg                     INTEGER[],
-    use_gpus                           BOOLEAN,
-    accessible_gpus_for_seg                       INTEGER[],
-    is_final_iteration                 BOOLEAN
-) RETURNS REAL[3] AS $$
-PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras')
-    return madlib_keras.internal_keras_eval_transition(**globals())
-$$ LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_keras_eval_merge(
-    state1          REAL[3],
-    state2          REAL[3]
-) RETURNS REAL[3] AS $$
-PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras')
-    return madlib_keras.internal_keras_eval_merge(**globals())
-$$ LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_keras_eval_final(
-    state REAL[3]
-) RETURNS REAL[2] AS $$
-PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras')
-    return madlib_keras.internal_keras_eval_final(**globals())
-$$ LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.internal_keras_evaluate(
-                                       BYTEA,
-                                       BYTEA,
-                                       INTEGER[],
-                                       INTEGER[],
-                                       TEXT,
-                                       BYTEA,
-                                       TEXT,
-                                       INTEGER,
-                                       INTEGER[],
-                                       INTEGER,
-                                       INTEGER,
-                                       INTEGER[],
-                                       BOOLEAN,
-                                       INTEGER[],
-                                       BOOLEAN);
-
-CREATE AGGREGATE MADLIB_SCHEMA.internal_keras_evaluate(
-    /* dependent_var */             BYTEA,
-    /* independent_var */           BYTEA,
-    /* dependent_var_shape */       INTEGER[],
-    /* independent_var_shape */     INTEGER[],
-    /* model_architecture */        TEXT,
-    /* model_weights */             BYTEA,
-    /* compile_params */            TEXT,
-    /* dist_key */                  INTEGER,
-    /* dist_key_mapping */          INTEGER[],
-    /* current_seg_id */            INTEGER,
-    /* segments_per_host */         INTEGER,
-    /* images_per_seg*/             INTEGER[],
-    /* use_gpus */                  BOOLEAN,
-    /* accessible_gpus_for_seg */              INTEGER[],
-    /* is_final_iteration */        BOOLEAN
-)(
-    STYPE=REAL[3],
-    INITCOND='{0,0,0}',
-    SFUNC=MADLIB_SCHEMA.internal_keras_eval_transition,
-    m4_ifdef(`__POSTGRESQL__', `', `prefunc=MADLIB_SCHEMA.internal_keras_eval_merge,')
-    FINALFUNC=MADLIB_SCHEMA.internal_keras_eval_final
-);
-
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit(
-     message VARCHAR
-) RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras')
-    with AOControl(False):
-        return madlib_keras.fit_help(**globals())
-$$ LANGUAGE plpythonu IMMUTABLE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit()
-RETURNS VARCHAR AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit('');
-$$ LANGUAGE sql IMMUTABLE
-m4_ifdef(`\_\_HAS_FUNCTION_PROPERTIES\_\_', `CONTAINS SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_evaluate(
-     message VARCHAR
-) RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras')
-    with AOControl(False):
-        return madlib_keras.evaluate_help(**globals())
-$$ LANGUAGE plpythonu IMMUTABLE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_evaluate()
-RETURNS VARCHAR AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_evaluate('');
-$$ LANGUAGE sql IMMUTABLE
-m4_ifdef(`\_\_HAS_FUNCTION_PROPERTIES\_\_', `CONTAINS SQL', `');
-
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict(
-     message VARCHAR
-) RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_predict')
-    with AOControl(False):
-        return madlib_keras_predict.predict_help(**globals())
-$$ LANGUAGE plpythonu IMMUTABLE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_predict()
-RETURNS VARCHAR AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_predict('');
-$$ LANGUAGE sql IMMUTABLE
-m4_ifdef(`\_\_HAS_FUNCTION_PROPERTIES\_\_', `CONTAINS SQL', `');
-
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.py_in
deleted file mode 100644
index 9de9774..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.py_in
+++ /dev/null
@@ -1,671 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import plpy
-import time
-import sys
-
-from keras.models import *
-from madlib_keras import compute_loss_and_metrics
-from madlib_keras import get_initial_weights
-from madlib_keras import get_model_arch_weights
-from madlib_keras import get_source_summary_table_dict
-from madlib_keras import should_compute_metrics_this_iter
-from madlib_keras_helper import *
-from madlib_keras_model_selection import ModelSelectionSchema
-from madlib_keras_validator import *
-from madlib_keras_wrapper import *
-
-from utilities.control import MinWarning
-from utilities.control import OptimizerControl
-from utilities.control import SetGUC
-from utilities.utilities import add_postfix
-from utilities.utilities import is_platform_gp6_or_up
-from utilities.utilities import unique_string
-from utilities.utilities import rotate
-from utilities.utilities import madlib_version
-from utilities.utilities import is_platform_pg
-from utilities.utilities import get_seg_number
-from utilities.utilities import get_segments_per_host
-from utilities.utilities import rename_table
-
-import json
-from collections import defaultdict
-import random
-import datetime
-mb_dep_var_col = MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL
-mb_indep_var_col = MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL
-dist_key_col = DISTRIBUTION_KEY_COLNAME
-
-"""
-FitMultipleModel: This class implements the Model Hopper technique for
-training multiple models in parallel. The goal of this function is to train
-multiple different models on the same data with different parameters.
-The main advantage of this method over running the existing fit function in a
-loop is avoiding inaccuracies caused by the model averaging. The basic idea of
-model hopper is simple. Let's assume that there are n segments and c*n model
-configurations. We begin with distributing these configs to segments. After
-that, each segment trains their c models on the data they have locally for one
-iteration. Once we have these models, we move them to a different segment
-(hopping) as well as receive a different set of models. Once we have the new
-models, we use our segments data to refine them (similar to the warm start
-functionality). Once every model hops through every segment, we consider an
-iteration complete.
-
-This method ensures that we don't have to average any model and the
-loss&accuracy is very close to the ideal case, where all of the data is in one
-segment.
-
-Note that this function is disabled for Postgres.
-"""
-
-@MinWarning("warning")
-class FitMultipleModel():
-    def __init__(self, schema_madlib, source_table, model_output_table,
-                 model_selection_table, num_iterations,
-                 use_gpus=False, validation_table=None,
-                 metrics_compute_frequency=None, warm_start=False, name="",
-                 description="", **kwargs):
-        # set the random seed for visit order/scheduling
-        random.seed(1)
-        if is_platform_pg():
-            plpy.error(
-                "DL: Multiple model training is not supported on PostgreSQL.")
-        self.source_table = source_table
-        self.validation_table = validation_table
-        self.model_selection_table = model_selection_table
-        if self.model_selection_table:
-            self.model_selection_summary_table = add_postfix(self.model_selection_table, '_summary')
-
-        self.num_iterations = num_iterations
-        self.metrics_compute_frequency = metrics_compute_frequency
-        self.name = name
-        self.description = description
-        self.module_name = 'madlib_keras_fit_multiple_model'
-        self.schema_madlib = schema_madlib
-        self.version = madlib_version(self.schema_madlib)
-        self.mst_key_col = ModelSelectionSchema.MST_KEY
-        self.model_id_col = ModelSelectionSchema.MODEL_ID
-        self.compile_params_col = ModelSelectionSchema.COMPILE_PARAMS
-        self.fit_params_col = ModelSelectionSchema.FIT_PARAMS
-        self.model_arch_table_col = ModelSelectionSchema.MODEL_ARCH_TABLE
-        self.model_weights_col = ModelArchSchema.MODEL_WEIGHTS
-        self.model_arch_col = ModelArchSchema.MODEL_ARCH
-        self.train_mst_metric_eval_time = defaultdict(list)
-        self.train_mst_loss = defaultdict(list)
-        self.train_mst_metric = defaultdict(list)
-        self.info_str = ""
-        self.dep_shape_col = add_postfix(mb_dep_var_col, "_shape")
-        self.ind_shape_col = add_postfix(mb_indep_var_col, "_shape")
-        self.use_gpus = use_gpus
-        self.segments_per_host = get_segments_per_host()
-        if self.use_gpus:
-            self.accessible_gpus_for_seg = get_accessible_gpus_for_seg(
-                self.schema_madlib, self.segments_per_host, self.module_name)
-        else:
-            self.accessible_gpus_for_seg = get_seg_number()*[0]
-
-        self.original_model_output_table = model_output_table
-        if self.original_model_output_table:
-            self.model_info_table = add_postfix(self.original_model_output_table, '_info')
-            self.model_summary_table = add_postfix(
-                self.original_model_output_table, '_summary')
-
-        self.model_output_table = self.original_model_output_table
-
-        """
-        For warm start, we need to copy the model output table to a temp table
-        because we call truncate on the model output table while training.
-        If the query gets aborted, we need to make sure that the user passed
-        model output table can be recovered.
-        """
-        self.warm_start = bool(warm_start)
-        self.warm_start_msts = []
-        if self.warm_start:
-            self.model_output_table = unique_string('initial_model')
-
-        self.fit_validator_train = FitMultipleInputValidator(
-            self.source_table, self.validation_table, self.original_model_output_table,
-            self.model_selection_table, self.model_selection_summary_table,
-            mb_dep_var_col, mb_indep_var_col, self.num_iterations,
-            self.model_info_table, self.mst_key_col, self.model_arch_table_col,
-            self.metrics_compute_frequency, self.warm_start, self.use_gpus,
-            self.accessible_gpus_for_seg)
-        if self.metrics_compute_frequency is None:
-            self.metrics_compute_frequency = num_iterations
-
-
-        self.msts = self.fit_validator_train.msts
-        self.model_arch_table = self.fit_validator_train.model_arch_table
-        self.metrics_iters = []
-
-        original_cuda_env = None
-        if CUDA_VISIBLE_DEVICES_KEY in os.environ:
-            original_cuda_env = os.environ[CUDA_VISIBLE_DEVICES_KEY]
-
-        self.dist_key_mapping, self.images_per_seg_train = \
-            get_image_count_per_seg_for_minibatched_data_from_db(
-                self.source_table)
-
-        if self.validation_table:
-            self.valid_mst_metric_eval_time = defaultdict(list)
-            self.valid_mst_loss = defaultdict(list)
-            self.valid_mst_metric = defaultdict(list)
-            self.dist_key_mapping_valid, self.images_per_seg_valid = \
-                get_image_count_per_seg_for_minibatched_data_from_db(
-                    self.validation_table)
-        self.mst_weights_tbl = unique_string(desp='mst_weights')
-        self.mst_current_schedule_tbl = unique_string(desp='mst_current_schedule')
-
-        self.dist_keys = query_dist_keys(self.source_table, dist_key_col)
-        if len(self.msts) < len(self.dist_keys):
-            self.msts_for_schedule = self.msts + [None] * \
-                                     (len(self.dist_keys) - len(self.msts))
-        else:
-            self.msts_for_schedule = self.msts
-        random.shuffle(self.msts_for_schedule)
-        self.grand_schedule = self.generate_schedule(self.msts_for_schedule)
-        self.gp_segment_id_col = '0' if is_platform_pg() else GP_SEGMENT_ID_COLNAME
-        self.unlogged_table = "UNLOGGED" if is_platform_gp6_or_up() else ''
-
-        if self.warm_start:
-            self.create_model_output_table_warm_start()
-        else:
-            self.create_model_output_table()
-
-        self.weights_to_update_tbl = unique_string(desp='weights_to_update')
-        self.fit_multiple_model()
-
-        # Update and cleanup metadata tables
-        self.insert_info_table()
-        self.create_model_summary_table()
-        if self.warm_start:
-            self.cleanup_for_warm_start()
-        reset_cuda_env(original_cuda_env)
-
-    def fit_multiple_model(self):
-        # WARNING: set orca off to prevent unwanted redistribution
-        with OptimizerControl(False):
-            self.start_training_time = datetime.datetime.now()
-            self.metrics_elapsed_start_time = time.time()
-            self.train_multiple_model()
-            self.end_training_time = datetime.datetime.now()
-
-    def cleanup_for_warm_start(self):
-        """
-        1. drop original model table
-        2. rename temp to original
-        :return:
-        """
-        drop_query = "DROP TABLE IF EXISTS {}".format(
-            self.original_model_output_table)
-        plpy.execute(drop_query)
-        rename_table(self.schema_madlib, self.model_output_table,
-                     self.original_model_output_table)
-
-    def train_multiple_model(self):
-        total_msts = len(self.msts_for_schedule)
-        for iter in range(1, self.num_iterations+1):
-            for mst_idx in range(total_msts):
-                mst_row = [self.grand_schedule[dist_key][mst_idx]
-                           for dist_key in self.dist_keys]
-                self.create_mst_schedule_table(mst_row)
-                self.is_final_training_call = (iter == self.num_iterations and mst_idx == total_msts-1)
-                if mst_idx == 0:
-                    start_iteration = time.time()
-                self.run_training(mst_idx)
-                if mst_idx == (total_msts - 1):
-                    end_iteration = time.time()
-                    self.info_str = "\tTime for training in iteration " \
-                                    "{0}: {1} sec\n".format(iter,
-                                                            end_iteration -
-                                                            start_iteration)
-            if should_compute_metrics_this_iter(iter,
-                                                self.metrics_compute_frequency,
-                                                self.num_iterations):
-                self.metrics_iters.append(iter)
-                self.info_str += "\tTraining set after iteration {0}:".format(iter)
-                self.evaluate_model(iter, self.source_table, True)
-                if self.validation_table:
-                    self.evaluate_model(iter, self.validation_table, False)
-            plpy.info("\n"+self.info_str)
-
-    def evaluate_model(self, epoch, table, is_train):
-        if is_train:
-            mst_metric_eval_time = self.train_mst_metric_eval_time
-            mst_loss = self.train_mst_loss
-            mst_metric = self.train_mst_metric
-            seg_ids = self.dist_key_mapping
-            images_per_seg = self.images_per_seg_train
-        else:
-            mst_metric_eval_time = self.valid_mst_metric_eval_time
-            mst_loss = self.valid_mst_loss
-            mst_metric = self.valid_mst_metric
-            seg_ids = self.dist_key_mapping_valid
-            images_per_seg = self.images_per_seg_valid
-            self.info_str += "\n\tValidation set after iteration {0}:".format(epoch)
-        for mst in self.msts:
-            weights = query_weights(self.model_output_table, self.model_weights_col,
-                                    self.mst_key_col, mst[self.mst_key_col])
-            model_arch, _ = get_model_arch_weights(self.model_arch_table, mst[self.model_id_col])
-            _, metric, loss = compute_loss_and_metrics(
-                self.schema_madlib, table, "$madlib${0}$madlib$".format(
-                    mst[self.compile_params_col]),
-                model_arch,
-                weights,
-                self.use_gpus,
-                self.accessible_gpus_for_seg,
-                seg_ids,
-                images_per_seg,
-                [], [], epoch, True)
-            mst_metric_eval_time[mst[self.mst_key_col]] \
-                .append(time.time() - self.metrics_elapsed_start_time)
-            mst_loss[mst[self.mst_key_col]].append(loss)
-            mst_metric[mst[self.mst_key_col]].append(metric)
-            self.info_str += "\n\tmst_key={0}: metric={1}, loss={2}".format(mst[self.mst_key_col], metric, loss)
-
-    def generate_schedule(self, msts):
-        """ Generate the schedule for models hopping to segments """
-        grand_schedule = {}
-        for index, dist_key in enumerate(self.dist_keys):
-            grand_schedule[dist_key] = rotate(msts, index)
-        return grand_schedule
-
-    def create_mst_schedule_table(self, mst_row):
-        mst_temp_query = """
-                         CREATE {self.unlogged_table} TABLE {self.mst_current_schedule_tbl}
-                                ({self.model_id_col} INTEGER,
-                                 {self.compile_params_col} VARCHAR,
-                                 {self.fit_params_col} VARCHAR,
-                                 {dist_key_col} INTEGER,
-                                 {self.mst_key_col} INTEGER)
-                         """.format(dist_key_col=dist_key_col, **locals())
-        plpy.execute(mst_temp_query)
-        for mst, dist_key in zip(mst_row, self.dist_keys):
-            if mst:
-                model_id = mst[self.model_id_col]
-                compile_params = mst[self.compile_params_col]
-                fit_params = mst[self.fit_params_col]
-                mst_key = mst[self.mst_key_col]
-            else:
-                model_id = "NULL"
-                compile_params = "NULL"
-                fit_params = "NULL"
-                mst_key = "NULL"
-            mst_insert_query = """
-                               INSERT INTO {self.mst_current_schedule_tbl}
-                                   VALUES ({model_id},
-                                           $madlib${compile_params}$madlib$,
-                                           $madlib${fit_params}$madlib$,
-                                           {dist_key},
-                                           {mst_key})
-                                """.format(**locals())
-            plpy.execute(mst_insert_query)
-
-
-    def create_model_output_table(self):
-        output_table_create_query = """
-                                    CREATE TABLE {self.model_output_table}
-                                    ({self.mst_key_col} INTEGER PRIMARY KEY,
-                                     {self.model_weights_col} BYTEA,
-                                     {self.model_arch_col} JSON)
-                                    """.format(self=self)
-        plpy.execute(output_table_create_query)
-        self.initialize_model_output_and_info()
-
-    def create_model_output_table_warm_start(self):
-        """
-        For warm start, we need to copy the model output table to a temp table
-        because we call truncate on the model output table while training.
-        If the query gets aborted, we need to make sure that the user passed
-        model output table can be recovered.
-        """
-        plpy.execute("""
-            CREATE TABLE {self.model_output_table} (
-            LIKE {self.original_model_output_table} INCLUDING indexes);
-            """.format(self=self))
-
-        plpy.execute("""INSERT INTO {self.model_output_table}
-            SELECT * FROM {self.original_model_output_table};
-            """.format(self=self))
-
-        plpy.execute(""" DELETE FROM {self.model_output_table}
-                WHERE {self.mst_key_col} NOT IN (
-                    SELECT {self.mst_key_col} FROM {self.model_selection_table})
-                """.format(self=self))
-        self.warm_start_msts = plpy.execute(
-            """ SELECT array_agg({0}) AS a FROM {1}
-            """.format(self.mst_key_col, self.model_output_table))[0]['a']
-        plpy.execute("DROP TABLE {0}".format(self.model_info_table))
-        self.initialize_model_output_and_info()
-
-    def initialize_model_output_and_info(self):
-        info_table_create_query = """
-                                  CREATE TABLE {self.model_info_table}
-                                  ({self.mst_key_col} INTEGER PRIMARY KEY,
-                                   {self.model_id_col} INTEGER,
-                                   {self.compile_params_col} TEXT,
-                                   {self.fit_params_col} TEXT,
-                                   model_type TEXT,
-                                   model_size DOUBLE PRECISION,
-                                   metrics_elapsed_time DOUBLE PRECISION[],
-                                   metrics_type TEXT[],
-                                   training_metrics_final DOUBLE PRECISION,
-                                   training_loss_final DOUBLE PRECISION,
-                                   training_metrics DOUBLE PRECISION[],
-                                   training_loss DOUBLE PRECISION[],
-                                   validation_metrics_final DOUBLE PRECISION,
-                                   validation_loss_final DOUBLE PRECISION,
-                                   validation_metrics DOUBLE PRECISION[],
-                                   validation_loss DOUBLE PRECISION[])
-                                       """.format(self=self)
-
-        plpy.execute(info_table_create_query)
-        for mst in self.msts:
-            model_arch, model_weights = get_model_arch_weights(self.model_arch_table,
-                                                               mst[self.model_id_col])
-
-
-            # If warm start is enabled, weights from transfer learning cannot be
-            # used, even if a particular model doesn't have warm start weights.
-            if self.warm_start:
-                model_weights = None
-                mst_filter = """
-                            WHERE {mst_col}={mst_key}
-                        """.format(
-                    mst_col=self.mst_key_col,
-                    mst_key=mst['mst_key']
-                )
-
-            else:
-                mst_filter = ''
-
-            serialized_weights = get_initial_weights(self.model_output_table,
-                                                     model_arch,
-                                                     model_weights,
-                                                     mst['mst_key'] in self.warm_start_msts,
-                                                     self.use_gpus,
-                                                     self.accessible_gpus_for_seg,
-                                                     mst_filter
-                                                     )
-            model_size = sys.getsizeof(serialized_weights) / 1024.0
-
-            metrics_list = get_metrics_from_compile_param(
-                mst[self.compile_params_col])
-            is_metrics_specified = True if metrics_list else False
-            metrics_type = 'ARRAY{0}'.format(
-                metrics_list) if is_metrics_specified else 'NULL'
-
-            info_table_insert_query = """
-                            INSERT INTO {self.model_info_table}({self.mst_key_col},
-                                        {self.model_id_col}, {self.compile_params_col},
-                                        {self.fit_params_col}, model_type, model_size,
-                                        metrics_type)
-                                VALUES ({mst_key_val}, {model_id},
-                                        $madlib${compile_params}$madlib$,
-                                        $madlib${fit_params}$madlib$, '{model_type}',
-                                        {model_size}, {metrics_type})
-                        """.format(self=self,
-                                   mst_key_val=mst[self.mst_key_col],
-                                   model_id=mst[self.model_id_col],
-                                   compile_params=mst[self.compile_params_col],
-                                   fit_params=mst[self.fit_params_col],
-                                   model_type='madlib_keras',
-                                   model_size=model_size,
-                                   metrics_type=metrics_type)
-            plpy.execute(info_table_insert_query)
-
-            if not mst['mst_key'] in self.warm_start_msts:
-                output_table_insert_query = """
-                                    INSERT INTO {self.model_output_table}(
-                                        {self.mst_key_col}, {self.model_weights_col},
-                                        {self.model_arch_col})
-                                    VALUES ({mst_key}, $1, $2)
-                                       """.format(self=self,
-                                                  mst_key=mst[self.mst_key_col])
-                output_table_insert_query_prepared = plpy.prepare(
-                    output_table_insert_query, ["bytea", "json"])
-                plpy.execute(output_table_insert_query_prepared, [
-                             serialized_weights, model_arch])
-
-    def create_model_summary_table(self):
-        if self.warm_start:
-            plpy.execute("DROP TABLE {0}".format(self.model_summary_table))
-        src_summary_dict = get_source_summary_table_dict(self.fit_validator_train)
-        class_values = src_summary_dict['class_values']
-        class_values_type = src_summary_dict['class_values_type']
-        dep_vartype = src_summary_dict['dep_vartype']
-        dependent_varname = \
-            src_summary_dict['dependent_varname_in_source_table']
-        independent_varname = \
-            src_summary_dict['independent_varname_in_source_table']
-        norm_const = src_summary_dict['norm_const']
-        self.validation_table = 'NULL' if self.validation_table is None \
-            else '$MAD${0}$MAD$'.format(self.validation_table)
-        if class_values is None:
-            num_classes = 'NULL'
-        else:
-            num_classes = len(class_values)
-        name = 'NULL' if self.name is None else '$MAD${0}$MAD$'.format(self.name)
-        descr = 'NULL' if self.description is None else '$MAD${0}$MAD$'.format(self.description)
-        metrics_iters = self.metrics_iters if self.metrics_iters else 'NULL'
-        class_values_colname = CLASS_VALUES_COLNAME
-        dependent_vartype_colname = DEPENDENT_VARTYPE_COLNAME
-        normalizing_const_colname = NORMALIZING_CONST_COLNAME
-        float32_sql_type = FLOAT32_SQL_TYPE
-        create_query = plpy.prepare("""
-                CREATE TABLE {self.model_summary_table} AS
-                SELECT
-                    $MAD${self.source_table}$MAD$::TEXT AS source_table,
-                    {self.validation_table}::TEXT AS validation_table,
-                    $MAD${self.model_output_table}$MAD$::TEXT AS model,
-                    $MAD${self.model_info_table}$MAD$::TEXT AS model_info,
-                    $MAD${dependent_varname}$MAD$::TEXT AS dependent_varname,
-                    $MAD${independent_varname}$MAD$::TEXT AS independent_varname,
-                    $MAD${self.model_arch_table}$MAD$::TEXT AS model_arch_table,
-                    {self.num_iterations}::INTEGER AS num_iterations,
-                    {self.metrics_compute_frequency}::INTEGER AS metrics_compute_frequency,
-                    {self.warm_start} AS warm_start,
-                    {name}::TEXT AS name,
-                    {descr}::TEXT AS description,
-                    '{self.start_training_time}'::TIMESTAMP AS start_training_time,
-                    '{self.end_training_time}'::TIMESTAMP AS end_training_time,
-                    '{self.version}'::TEXT AS madlib_version,
-                    {num_classes}::INTEGER AS num_classes,
-                    $1 AS {class_values_colname},
-                    $MAD${dep_vartype}$MAD$::TEXT AS {dependent_vartype_colname},
-                    {norm_const}::{float32_sql_type} AS {normalizing_const_colname},
-                    ARRAY{metrics_iters}::INTEGER[] AS metrics_iters
-            """.format(**locals()), [class_values_type])
-        plpy.execute(create_query, [class_values])
-
-    def update_info_table(self, mst, is_train):
-        mst_key = mst[self.mst_key_col]
-        metrics, metrics_final, metrics_elapsed_time = \
-            "NULL", "NULL", "NULL"
-        if is_train:
-            mst_metric = self.train_mst_metric
-            mst_metric_eval_time = self.train_mst_metric_eval_time
-            mst_loss = self.train_mst_loss
-        else:
-            mst_metric = self.valid_mst_metric
-            mst_metric_eval_time = self.valid_mst_metric_eval_time
-            mst_loss = self.valid_mst_loss
-
-        if mst_key in mst_metric:
-            metrics = mst_metric[mst_key]
-            metrics_final = metrics[-1]
-            metrics_elapsed_time = mst_metric_eval_time[mst_key]
-            metrics = "ARRAY{}".format(metrics)
-            metrics_elapsed_time = "ARRAY{}".format(metrics_elapsed_time)
-        loss = mst_loss[mst_key]
-        loss_final = loss[-1]
-        loss = "ARRAY{}".format(loss)
-        if is_train:
-            update_query = """
-                           UPDATE {self.model_info_table} SET
-                           training_metrics_final = {metrics_final},
-                           training_loss_final = {loss_final},
-                           metrics_elapsed_time = {metrics_elapsed_time},
-                           training_metrics = {metrics},
-                           training_loss = {loss}
-                           WHERE {self.mst_key_col} = {mst_key}
-                           """.format(**locals())
-        else:
-            update_query = """
-                           UPDATE {self.model_info_table} SET
-                           validation_metrics_final = {metrics_final},
-                           validation_loss_final = {loss_final},
-                           metrics_elapsed_time = {metrics_elapsed_time},
-                           validation_metrics = {metrics},
-                           validation_loss = {loss}
-                           WHERE {self.mst_key_col} = {mst_key}
-                           """.format(**locals())
-        plpy.execute(update_query)
-
-    def insert_info_table(self):
-        for mst in self.msts:
-            self.update_info_table(mst, True)
-            if self.validation_table:
-                self.update_info_table(mst, False)
-
-    def run_training(self, mst_idx):
-        # NOTE: In the DL module, we want to avoid CREATING TEMP tables
-        # (creates a slice which stays until the session is disconnected)
-        # or minimize writing queries that generate plans with Motions (creating
-        # multiple slices on segments).
-        # This is mainly to avoid any GPU memory allocation failures. Since GPU
-        # memory allocation is tied to the process where it is initialized, failures
-        # may occur when a newly created slice(process) tries allocating GPU memory
-        # which is already allocated by a previously created slice(process).
-        # Therefore we want to have queries that do not add motions and all the
-        # sub-queries running Keras/tensorflow operations reuse the same slice(process)
-        # that was used for initializing GPU memory.
-        use_gpus = self.use_gpus if self.use_gpus else False
-        mst_weights_query = """
-            CREATE {self.unlogged_table} TABLE {self.mst_weights_tbl} AS
-                SELECT mst_tbl.*, wgh_tbl.{self.model_weights_col},
-                       model_arch_tbl.{self.model_arch_col}
-                FROM
-                    {self.mst_current_schedule_tbl} mst_tbl
-                    LEFT JOIN {self.model_output_table} wgh_tbl
-                    ON mst_tbl.{self.mst_key_col} = wgh_tbl.{self.mst_key_col}
-                        LEFT JOIN {self.model_arch_table} model_arch_tbl
-                        ON mst_tbl.{self.model_id_col} = model_arch_tbl.{self.model_id_col}
-                DISTRIBUTED BY ({dist_key_col})
-        """.format(dist_key_col=dist_key_col,
-                   **locals())
-        plpy.execute(mst_weights_query)
-        use_gpus = self.use_gpus if self.use_gpus else False
-        uda_query = """
-            CREATE {self.unlogged_table} TABLE {self.weights_to_update_tbl} AS
-            SELECT {self.schema_madlib}.fit_step_multiple_model({mb_dep_var_col},
-                {mb_indep_var_col},
-                {self.dep_shape_col},
-                {self.ind_shape_col},
-                {self.mst_weights_tbl}.{self.model_arch_col}::TEXT,
-                {self.mst_weights_tbl}.{self.compile_params_col}::TEXT,
-                {self.mst_weights_tbl}.{self.fit_params_col}::TEXT,
-                src.{dist_key_col},
-                ARRAY{self.dist_key_mapping},
-                src.{self.gp_segment_id_col},
-                {self.segments_per_host},
-                ARRAY{self.images_per_seg_train},
-                {use_gpus}::BOOLEAN,
-                ARRAY{self.accessible_gpus_for_seg},
-                {self.mst_weights_tbl}.{self.model_weights_col}::BYTEA,
-                {is_final_iteration}::BOOLEAN
-                )::BYTEA AS {self.model_weights_col},
-                {self.mst_weights_tbl}.{self.mst_key_col} AS {self.mst_key_col}
-                ,src.{dist_key_col} AS {dist_key_col}
-            FROM {self.source_table} src JOIN {self.mst_weights_tbl}
-                USING ({dist_key_col})
-            WHERE {self.mst_weights_tbl}.{self.mst_key_col} IS NOT NULL
-            GROUP BY src.{dist_key_col}, {self.mst_weights_tbl}.{self.mst_key_col}
-            DISTRIBUTED BY({dist_key_col})
-            """.format(mb_dep_var_col=mb_dep_var_col,
-                       mb_indep_var_col=mb_indep_var_col,
-                       is_final_iteration=True,
-                       dist_key_col=dist_key_col,
-                       use_gpus=use_gpus,
-                       self=self
-                       )
-        plpy.execute(uda_query)
-
-        update_query = """
-            UPDATE {self.model_output_table}
-            SET {self.model_weights_col} = {self.weights_to_update_tbl}.{self.model_weights_col}
-            FROM {self.weights_to_update_tbl}
-            WHERE {self.model_output_table}.{self.mst_key_col} = {self.weights_to_update_tbl}.{self.mst_key_col}
-        """.format(self=self)
-        plpy.execute(update_query)
-
-        self.truncate_and_drop_tables()
-
-    def truncate_and_drop_tables(self):
-        """
-        Context: UPDATE statements in postgres are not in-place replacements but
-        the row to be updated is marked for deletion(note that the disk space for
-        this row doesn't get released until vaccuum is called) and a new row in
-        inserted.
-
-        This function will clear out the disk space used by the model_output_table
-        and also drop all the other intermediate tables.
-        If available, set the `` guc so that the truncate command can release the
-        disk space. The disk space will be released immediately and hence the
-        model_output table won't grow in size with each UPDATE statement.
-
-        Without this guc, the disk space won't be released and each
-        call to the UPDATE statement will keep adding to the disk space. The disk
-        space will only be released when the query is completed.
-
-        The guc can cause data loss if not used properly. Since truncate will
-        actually clear the disk space immediately, there is no way to recover to
-        the state before truncate was called on that table. So this guc should only
-        be set for intermediate tables and never for tables created outside the
-        scope of the fit_multiple udf.
-
-        Workflow
-        1. Create temp table from model table (including the indexes)
-        2. truncate the model table to release disk space
-        3. rename temp table to model table so that it can be reused for the next
-        hop
-        :return:
-        """
-
-        with SetGUC("dev_opt_unsafe_truncate_in_subtransaction", "on"):
-            temp_model_table = unique_string('updated_model')
-            unlogged_table = self.unlogged_table if not self.is_final_training_call else ''
-            plpy.execute("""
-            CREATE {unlogged_table} TABLE {temp_model_table} ( LIKE {self.model_output_table}
-            INCLUDING indexes);""".format(temp_model_table=temp_model_table,
-                                          unlogged_table=unlogged_table,
-                                          self=self))
-            plpy.execute("""
-            INSERT INTO {temp_model_table} SELECT * FROM {self.model_output_table};
-            TRUNCATE TABLE {self.model_output_table};
-            DROP TABLE {self.model_output_table};
-            """.format(temp_model_table=temp_model_table, self=self))
-            rename_table(self.schema_madlib, temp_model_table,
-                         self.model_output_table)
-            plpy.execute("""
-            TRUNCATE TABLE {self.mst_weights_tbl}, {self.mst_current_schedule_tbl},
-            {self.weights_to_update_tbl};
-            DROP TABLE IF EXISTS {self.mst_weights_tbl}, {self.mst_current_schedule_tbl},
-            {self.weights_to_update_tbl};""".format(self=self))
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.sql_in b/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.sql_in
deleted file mode 100644
index 8d68385..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_fit_multiple_model.sql_in
+++ /dev/null
@@ -1,1555 +0,0 @@
-/* ----------------------------------------------------------------------- *//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *
- * @file madlib_keras_model_selection.sql_in
- *
- * @brief SQL functions for model hopper distributed training
- * @date August 2019
- *
- *
- *//* ----------------------------------------------------------------------- */
-
-m4_include(`SQLCommon.m4')
-
-/**
-@addtogroup grp_keras_run_model_selection
-
-@brief Explore network architectures and hyperparameters by training many models a time.
-
-
Contents

-
-\warning  This MADlib method is still in early stage development.
-Interface and implementation are subject to change. 
-
-This module allows you to explore network architectures and
-hyperparameters by training many models a time across the
-database cluster.  The aim is to support efficient empirical comparison of multiple
-training configurations. This process is called model selection,
-and the implementation here is based on a parallel execution strategy
-called model hopper parallelism (MOP) [1,2].
-
-Models are designed in Keras [3], which is a high-level neural
-network API written in Python.  It can run
-on top of different backends and the one that is currently
-supported by MADlib is TensorFlow [4].
-
-The main use case is image classification
-using sequential models, which are made up of a
-linear stack of layers.  This includes multilayer perceptrons (MLPs)
-and convolutional neural networks (CNNs).  Regression is not
-currently supported.
-
-Before doing model selection in MADlib you will need to run
-the mini-batch preprocessor, and create a table with the various models
-and hyperparameters to try.
-
-You can mini-batch the training and evaluation datasets by using the
-Preprocessor
-for Images which is a utility that prepares image data for
-use by models that support mini-batch as an optimization option.
-This is a one-time operation and you would only
-need to re-run the preprocessor if your input data has changed.
-The advantage of using mini-batching is that it
-can perform better than stochastic gradient descent
-because it uses more than one training example at a time,
-typically resulting faster and smoother convergence [5].
-The input preprocessor also sets the distribution rules
-for the training data.  For example, you may only want
-to train models on segments that reside on hosts that are GPU enabled.
-
-You can set up the models and hyperparameters to try with the
-Setup
-Model Selection utility to define the unique combinations
-of model architectures, compile and fit parameters.
-
-@note If 'madlib_keras_fit_multiple_model()' is running on GPDB 5 and some versions
-of GPDB 6, the database will
-keep adding to the disk space (in proportion to model size) and will only
-release the disk space once the fit multiple query has completed execution.
-This is not the case for GPDB 6.5.0+ where disk space is released during the
-fit multiple query.
-
-@note CUDA GPU memory cannot be released until the process holding it is terminated.
-When a MADlib deep learning function is called with GPUs, Greenplum internally
-creates a process (called a slice) which calls TensorFlow to do the computation.
-This process holds the GPU memory until one of the following two things happen:
-query finishes and user logs out of the Postgres client/session; or,
-query finishes and user waits for the timeout set by gp_vmem_idle_resource_timeout.
-The default value for this timeout is 18 sec [8].  So the recommendation is:
-log out/reconnect to the session after every GPU query; or
-wait for gp_vmem_idle_resource_timeout before you run another GPU query (you can
-also set it to a lower value).
-
-@anchor keras_fit
-@par Fit
-The fit (training) function has the following format:
-
-
-madlib_keras_fit_multiple_model(
-    source_table,
-    model_output_table,
-    model_selection_table,
-    num_iterations,
-    use_gpus,
-    validation_table,
-    metrics_compute_frequency,
-    warm_start,
-    name,
-    description
-    )
-

-
-\b Arguments
-

-  
source_table

-  
TEXT. Name of the table containing the training data.
-  This is the name of the output
-  table from the image preprocessor.  Independent
-  and dependent variables are specified in the preprocessor
-  step which is why you do not need to explictly state
-  them here as part of the fit function.

-
-  
model_output_table

-  
TEXT. Name of the output table containing the
-  multiple models created.
-  @note pg_temp is not allowed as an output table schema for fit multiple.
-  Details of output tables are shown below.
-  

-
-  
model_selection_table

-  
TEXT. Name of the table containing model selection parameters to be tried.
-  Here we mean both hyper-parameter tuning and model architecture search.
-  

-
-  
num_iterations

-  
INTEGER.  Number of iterations to train.
-
-    @note
-    This parameter is different than the number of passes over the dataset,
-    which is commonly referred to as the number of epochs.  Since MADlib operates
-    in a distributed system, the number of
-    epochs is actually equal to this parameter 'num_iterations' X 'epochs' as
-    specified in the Keras fit parameter.
-  

-
-  
use_gpus (optional)

-  
BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs
-  are to be used for training the neural network.  Set to TRUE to use GPUs.
-
-    @note
-    This parameter must not conflict with how the distribution rules are set in
-    the preprocessor function.  For example, if you set a distribution rule to use
-    certain segments on hosts that do not have GPUs attached, you will get an error
-    if you set ‘use_gpus’ to TRUE.  Also, we have seen some memory related issues
-    when segments share GPU resources.
-    For example, if you have 1 GPU per segment host and your cluster has 4
-    segments per segment host, it means that all 4
-    segments will share the same
-    GPU on each host. The current recommended
-    configuration is 1 GPU per segment.
-  

-
-  
validation_table (optional)

-  
TEXT, default: none. Name of the table containing
-  the validation dataset.
-  Note that the validation dataset must be preprocessed
-  in the same way as the training dataset, so this
-  is the name of the output
-  table from running the image preprocessor on the validation dataset.
-  Using a validation dataset can mean a
-  longer training time, depending on its size.
-  This can be controlled using the 'metrics_compute_frequency'
-  parameter described below.

-
-  
metrics_compute_frequency (optional)

-  
INTEGER, default: once at the end of training
-  after 'num_iterations'.  Frequency to compute per-iteration
-  metrics for the training dataset and validation dataset
-  (if specified).  There can be considerable cost to
-  computing metrics every iteration, especially if the
-  training dataset is large.  This parameter is a way of
-  controlling the frequency of those computations.
-  For example, if you specify 5, then metrics will be computed
-  every 5 iterations as well as at the end of training
-  after 'num_iterations'.  If you use the default,
-  metrics will be computed only
-  once after 'num_iterations' have completed.
-  

-
-  
warm_start (optional)

-  
BOOLEAN, default: FALSE.
-    Initalize weights with the coefficients
-    from the last call to the fit
-    function. If set to TRUE, weights will be
-    initialized from the model table
-    generated by the previous training run.
-
-    @note
-    The warm start feature works based on the name of the
-    model output table from a previous training run.
-    When using warm start, do not drop the model output table
-    or the model output summary table
-    before calling the fit function, since these are needed to obtain the
-    weights from the previous run.
-    If you are not using warm start, the model output table
-    and the model output table summary must be dropped in
-    the usual way before calling the training function.
-  

-
-  
name (optional)

-  
TEXT, default: NULL.
-    Free text string to identify a name, if desired.
-  

-
-  
description (optional)

-  
TEXT, default: NULL.
-    Free text string to provide a description, if desired.
-  

-

-
-Output tables
-

-    The model output table produced by fit contains the following columns.
-    There is one row per model as per the rows in the 'model_selection_table':
-    
idGives the 'id' for each prediction, corresponding to each row from the 'test_table'.
estimated_dependent_var
-        (For pred_type='response') Estimated class for classification. If
-        the 'class_values' parameter is passed in as NULL, then we assume that the class
-        labels are [0,1,2...,n-1] where n-1 is the number of classes in the model
-        architecture.
-        
prob_CLASS
-         (For pred_type='prob' for classification)
-         Probability of a given class.
-         If 'class_values' is passed in as NULL, we create one column called
-         'prob' which is an array of probabilities for each class.
-         If 'class_values' is not NULL, then there will be one
-         column for each class.
-        

-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-    
mst_keyINTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters,
-        as defined in the 'model_selection_table'.
model_weightsBYTEA8. Byte array containing the weights of the neural net.
model_archTEXT. A JSON representation of the model architecture
-        used in training.

-
-    An info table named \_info is also created, which has the following columns.
-    There is one row per model as per the rows in the 'model_selection_table':
-    
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-
-    
mst_keyINTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters,
-        as defined in the 'model_selection_table'.
model_idINTEGER. ID that defines model in the 'model_arch_table'.
compile_paramsCompile parameters passed to Keras.
fit_paramsFit parameters passed to Keras.
model_typeGeneral identifier for type of model trained.
-        Currently says 'madlib_keras'.
model_sizeSize of the model in KB.  Models are stored in
-        'bytea' data format which is used for binary strings
-        in PostgreSQL type databases.
metrics_elapsed_time Array of elapsed time for metric computations as
-        per the 'metrics_compute_frequency' parameter.
-        Useful for drawing a curve showing loss, accuracy or
-        other metrics as a function of time.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of elapsed time for every 5th
-        iteration, plus the last iteration.
metrics_typeMetric specified in the 'compile_params'.
training_metrics_finalFinal value of the training
-        metric after all iterations have completed.
-        The metric reported is the one
-        specified in the 'metrics_type' parameter.
training_loss_finalFinal value of the training loss after all
-        iterations have completed.
training_metricsArray of training metrics as
-        per the 'metrics_compute_frequency' parameter.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of metrics for every 5th
-        iteration, plus the last iteration.
training_lossArray of training losses as
-        per the 'metrics_compute_frequency' parameter.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of losses for every 5th
-        iteration, plus the last iteration.
validation_metrics_finalFinal value of the validation
-        metric after all iterations have completed.
-        The metric reported is the one
-        specified in the 'metrics_type' parameter.
validation_loss_finalFinal value of the validation loss after all
-        iterations have completed.
validation_metricsArray of validation metrics as
-        per the 'metrics_compute_frequency' parameter.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of metrics for every 5th
-        iteration, plus the last iteration.
validation_lossArray of validation losses as
-        per the 'metrics_compute_frequency' parameter.
-        For example, if 'metrics_compute_frequency=5'
-        this would be an array of losses for every 5th
-        iteration, plus the last iteration.

-
-    A summary table named \_summary is also created, which has the following columns:
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-    
-        
-        
-    
-   
source_tableSource table used for training.
validation_tableName of the table containing
-        the validation dataset (if specified).
modelName of the output table containing
-        the model for each model selection tuple.
model_infoName of the output table containing
-        the model performance and other info for
-        each model selection tuple.
dependent_varnameDependent variable column from the original
-        source table in the image preprocessing step.
independent_varnameIndependent variables column from the original
-        source table in the image preprocessing step.
model_arch_tableName of the table containing
-        the model architecture and (optionally) the
-        initial model weights.
num_iterationsNumber of iterations of training completed.
metrics_compute_frequencyFrequency that per-iteration metrics are computed
-        for the training dataset and validation
-        datasets.
warm_startIndicates whether warm start used or not.
nameName of the training run (free text).
descriptionDescription of the training run (free text).
start_training_timeTimestamp for start of training.
end_training_timeTimestamp for end of training.
madlib_versionVersion of MADlib used.
num_classesCount of distinct classes values used.
class_valuesArray of actual class values used.
dependent_vartypeData type of the dependent variable.
normalizing_constantNormalizing constant used from the
-        image preprocessing step.
metrics_itersArray indicating the iterations for which
-        metrics are calculated, as derived from the
-        parameters 'num_iterations' and 'metrics_compute_frequency'.
-        For example, if 'num_iterations=5'
-        and 'metrics_compute_frequency=2', then 'metrics_iters' value
-        would be {2,4,5} indicating that metrics were computed
-        at iterations 2, 4 and 5 (at the end).
-        If 'num_iterations=5'
-        and 'metrics_compute_frequency=1', then 'metrics_iters' value
-        would be {1,2,3,4,5} indicating that metrics were computed
-        at every iteration.

-
-@anchor keras_evaluate
-@par Evaluate
-The evaluation function has the following format:
-
-
-madlib_keras_evaluate(
-    model_table,
-    test_table,
-    output_table,
-    use_gpus
-    )
-

-
-\b Arguments
-

-
-
model_table

-  
TEXT. Name of the table containing the model
-  to use for validation.
-  

-
-  
test_table

-  
TEXT. Name of the table containing the evaluation dataset.
-  Note that test/validation data must be preprocessed in the same
-  way as the training dataset, so
-  this is the name of the output
-  table from the image preprocessor.  Independent
-  and dependent variables are specified in the preprocessor
-  step which is why you do not need to explictly state
-  them here as part of the fit function.

-
-  
output_table

-  
TEXT. Name of table that validation output will be
-  written to. Table contains:

-    
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-
-  
use_gpus (optional)

-  
BOOLEAN, default: FALSE (i.e., CPU). Determines whether GPUs
-  are to be used for training the neural network.  Set to TRUE to use GPUs.
-
-    @note
-    This parameter must not conflict with how the distribution rules are set in
-    the preprocessor function.  For example, if you set a distribution rule to use
-    certain segments on hosts that do not have GPUs attached, you will get an error
-    if you set ‘use_gpus’ to TRUE.  Also, we have seen some memory related issues
-    when segments share GPU resources.
-    For example, if you have 1 GPU per segment host and your cluster has 4
-    segments per segment host, it means that all 4
-    segments will share the same
-    GPU on each host. The current recommended
-    configuration is 1 GPU per segment.
-  

-
-
-@anchor keras_predict
-@par Predict
-The prediction function has the following format:
-
-madlib_keras_predict(
-    model_table,
-    test_table,
-    id_col,
-    independent_varname,
-    output_table,
-    pred_type,
-    use_gpus
-    )
-

-
-\b Arguments
-

-
-
model_table

-  
TEXT. Name of the table containing the model
-  to use for prediction.
-  

-
-  
test_table

-  
TEXT. Name of the table containing the dataset to
-  predict on.  Note that test data is not preprocessed (unlike
-  fit and evaluate) so put one test image per row for prediction.
-  Also see the comment below for the 'independent_varname' parameter
-  regarding normalization.
-
-  

-
-  
id_col

-  
TEXT. Name of the id column in the test data table.
-  

-
-  
independent_varname

-  
TEXT. Column with independent variables in the test table.
-  If a 'normalizing_const' is specified when preprocessing the
-  training dataset, this same normalization will be applied to
-  the independent variables used in predict.
-  

-
-  
output_table

-  
TEXT. Name of the table that prediction output will be
-  written to. Table contains:

-    
lossLoss value on evaluation dataset.
metricMetric value on evaluation dataset, where 'metrics_type'
-        below identifies the type of metric.
metrics_typeType of metric used that was used in the training step.

-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-
-  
pred_type (optional)

-  
TEXT, default: 'response'. The type of output
-  desired, where 'response' gives the actual prediction
-  and 'prob' gives the probability value for each class.
-  

-
-  
use_gpus (optional)

-  
BOOLEAN, default: FALSE (i.e., CPU). Determines
-  whether GPUs are to be used for prediction/inference.
-  Set to TRUE to use GPUs.
-
-    @note
-    The prediction function uses the whole cluster.  If you are using GPUs, it
-    requires that GPUs are attached to all hosts, and that there are the same number
-    of GPUs on each host (homogeneous cluster).  This is different from the fit()
-    and evaluate() functions that support GPUs on only some of the hosts (heterogeneous cluster).
-    Therefore, if you have GPUs only on some of the hosts, or an uneven numbers of GPUs per host, then
-    set this parameter to FALSE to use CPUs.
-  

-
-
-@anchor example
-@par Examples
-
-@note
-Deep learning works best on very large datasets,
-but that is not convenient for a quick introduction
-to the syntax. So in this example we use an MLP on the well
-known iris data set from https://archive.ics.uci.edu/ml/datasets/iris.
-For more realistic examples with images please refer
-to the deep learning notebooks
-at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts.
-
-
Classification

-
--#  Create an input data set.
-
-DROP TABLE IF EXISTS iris_data;
-CREATE TABLE iris_data(
-    id serial,
-    attributes numeric[],
-    class_text varchar
-);
-INSERT INTO iris_data(id, attributes, class_text) VALUES
-(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),
-(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),
-(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),
-(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),
-(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),
-(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),
-(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),
-(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),
-(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),
-(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
-(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),
-(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),
-(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),
-(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),
-(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),
-(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),
-(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),
-(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),
-(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),
-(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),
-(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),
-(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),
-(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),
-(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),
-(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),
-(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),
-(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),
-(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),
-(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),
-(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),
-(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),
-(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),
-(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),
-(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),
-(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
-(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),
-(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),
-(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
-(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),
-(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),
-(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),
-(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),
-(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),
-(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),
-(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),
-(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),
-(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),
-(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),
-(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),
-(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),
-(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),
-(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),
-(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),
-(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),
-(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),
-(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),
-(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),
-(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),
-(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),
-(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),
-(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),
-(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),
-(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),
-(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),
-(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),
-(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),
-(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),
-(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),
-(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),
-(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),
-(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),
-(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),
-(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),
-(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),
-(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),
-(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),
-(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),
-(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),
-(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),
-(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),
-(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),
-(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),
-(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),
-(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),
-(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),
-(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),
-(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),
-(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),
-(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),
-(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),
-(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),
-(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),
-(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),
-(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),
-(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),
-(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),
-(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),
-(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),
-(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),
-(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),
-(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),
-(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
-(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),
-(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),
-(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),
-(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),
-(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),
-(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),
-(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),
-(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),
-(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),
-(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),
-(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),
-(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),
-(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),
-(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),
-(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),
-(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),
-(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),
-(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),
-(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),
-(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),
-(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),
-(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),
-(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),
-(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),
-(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),
-(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),
-(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),
-(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),
-(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),
-(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),
-(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),
-(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),
-(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),
-(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),
-(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),
-(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),
-(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),
-(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),
-(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),
-(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),
-(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
-(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),
-(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),
-(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),
-(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),
-(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),
-(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),
-(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');
-

-Create a test/validation dataset from the training data:
-
-DROP TABLE IF EXISTS iris_train, iris_test;
--- Set seed so results are reproducible
-SELECT setseed(0);
-SELECT madlib.train_test_split('iris_data',     -- Source table
-                               'iris',          -- Output table root name
-                                0.8,            -- Train proportion
-                                NULL,           -- Test proportion (0.2)
-                                NULL,           -- Strata definition
-                                NULL,           -- Output all columns
-                                NULL,           -- Sample without replacement
-                                TRUE            -- Separate output tables
-                              );
-SELECT COUNT(*) FROM iris_train;
-

-
- count
-------+
-   120
-

-
--# Call the preprocessor for deep learning.  For the training dataset:
-
-\\x on
-DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;
-SELECT madlib.training_preprocessor_dl('iris_train',         -- Source table
-                                       'iris_train_packed',  -- Output table
-                                       'class_text',         -- Dependent variable
-                                       'attributes'          -- Independent variable
-                                        );
-SELECT * FROM iris_train_packed_summary;
-

-
--[ RECORD 1 ]-------+---------------------------------------------
-source_table        | iris_train
-output_table        | iris_train_packed
-dependent_varname   | class_text
-independent_varname | attributes
-dependent_vartype   | character varying
-class_values        | {Iris-setosa,Iris-versicolor,Iris-virginica}
-buffer_size         | 60
-normalizing_const   | 1.0
-num_classes         | 3
-

-For the validation dataset:
-
-DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;
-SELECT madlib.validation_preprocessor_dl('iris_test',          -- Source table
-                                         'iris_test_packed',   -- Output table
-                                         'class_text',         -- Dependent variable
-                                         'attributes',         -- Independent variable
-                                         'iris_train_packed'   -- From training preprocessor step
-                                          );
-SELECT * FROM iris_test_packed_summary;
-

-
--[ RECORD 1 ]-------+---------------------------------------------
-source_table        | iris_test
-output_table        | iris_test_packed
-dependent_varname   | class_text
-independent_varname | attributes
-dependent_vartype   | character varying
-class_values        | {Iris-setosa,Iris-versicolor,Iris-virginica}
-buffer_size         | 15
-normalizing_const   | 1.0
-num_classes         | 3
-

-
--# Define and load model architecture.  Use Keras to define
-the model architecture with 1 hidden layer:
-
-import keras
-from keras.models import Sequential
-from keras.layers import Dense
-model1 = Sequential()
-model1.add(Dense(10, activation='relu', input_shape=(4,)))
-model1.add(Dense(10, activation='relu'))
-model1.add(Dense(3, activation='softmax'))
-model1.summary()
-\verbatim
-
-_________________________________________________________________
-Layer (type)                 Output Shape              Param #
-=================================================================
-dense_1 (Dense)              (None, 10)                50
-_________________________________________________________________
-dense_2 (Dense)              (None, 10)                110
-_________________________________________________________________
-dense_3 (Dense)              (None, 3)                 33
-=================================================================
-Total params: 193
-Trainable params: 193
-Non-trainable params: 0
-\endverbatim
-

-Export the model to JSON:
-
-model1.to_json()
-

-
-'{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}'
-

-Define model architecture with 2 hidden layers:
-
-model2 = Sequential()
-model2.add(Dense(10, activation='relu', input_shape=(4,)))
-model2.add(Dense(10, activation='relu'))
-model2.add(Dense(10, activation='relu'))
-model2.add(Dense(3, activation='softmax'))
-model2.summary()
-\verbatim
-
-Layer (type)                 Output Shape              Param #
-=================================================================
-dense_4 (Dense)              (None, 10)                50
-_________________________________________________________________
-dense_5 (Dense)              (None, 10)                110
-_________________________________________________________________
-dense_6 (Dense)              (None, 10)                110
-_________________________________________________________________
-dense_7 (Dense)              (None, 3)                 33
-=================================================================
-Total params: 303
-Trainable params: 303
-Non-trainable params: 0
-\endverbatim
-

-Export the model to JSON:
-
-model2.to_json()
-

-
-'{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_4", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_5", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_6", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_7", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}'
-

-Load into model architecture table:
-
-DROP TABLE IF EXISTS model_arch_library;
-SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
-$$
-{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
-$$
-::json,         -- JSON blob
-                               NULL,                  -- Weights
-                               'Sophie',              -- Name
-                               'MLP with 1 hidden layer'       -- Descr
-);
-SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
-$$
-{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_4", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_5", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_6", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_7", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
-$$
-::json,         -- JSON blob
-                               NULL,                  -- Weights
-                               'Maria',               -- Name
-                               'MLP with 2 hidden layers'       -- Descr
-);
-

--# Define model selection tuples and load.  Select the model(s) from the model architecture
-table that you want to run, along with the compile and fit parameters. Combinations will be
-created for the set of model selection parameters will be loaded:
-
-DROP TABLE IF EXISTS mst_table, mst_table_summary;
-SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table
-                                         'mst_table',          -- model selection table output
-                                          ARRAY[1,2],              -- model ids from model architecture table
-                                          ARRAY[                   -- compile params
-                                              $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,
-                                              $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,
-                                              $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$
-                                          ],
-                                          ARRAY[                    -- fit params
-                                              $$batch_size=4,epochs=1$$,
-                                              $$batch_size=8,epochs=1$$
-                                          ]
-                                         );
-SELECT * FROM mst_table ORDER BY mst_key;
-

-
- mst_key | model_id |                                 compile_params                                  |      fit_params
----------+----------+---------------------------------------------------------------------------------+-----------------------
-       1 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1
-       2 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1
-       3 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1
-       4 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1
-       5 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1
-       6 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1
-       7 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1
-       8 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1
-       9 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1
-      10 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1
-      11 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1
-      12 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1
-(12 rows)
-

-This is the name of the model architecture table that corresponds to the model selection table:
-
-SELECT * FROM mst_table_summary;
-

-
-  model_arch_table
---------------------+
- model_arch_library
-

-
--# Train multiple models.
-
-DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;
-SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed',    -- source_table
-                                              'iris_multi_model',     -- model_output_table
-                                              'mst_table',            -- model_selection_table
-                                              10,                     -- num_iterations
-                                              FALSE                   -- use gpus
-                                             );
-

-View the model summary:
-
-SELECT * FROM iris_multi_model_summary;
-

-
-source_table              | iris_train_packed
-validation_table          |
-model                     | iris_multi_model
-model_info                | iris_multi_model_info
-dependent_varname         | class_text
-independent_varname       | attributes
-model_arch_table          | model_arch_library
-num_iterations            | 10
-metrics_compute_frequency | 10
-warm_start                | f
-name                      |
-description               |
-start_training_time       | 2019-12-16 18:54:33.826414
-end_training_time         | 2019-12-16 18:56:19.106321
-madlib_version            | 1.17.0
-num_classes               | 3
-class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
-dependent_vartype         | character varying
-normalizing_const         | 1
-metrics_iters             | {10}
-

-View results for each model:
-
-SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;
-

-
- mst_key | model_id |                                 compile_params                                  |      fit_params       |  model_type  |  model_size  | metrics_elapsed_time | metrics_type | training_metrics_final | training_loss_final |  training_metrics   |    training_loss    | validation_metrics_final | validation_loss_final | validation_metrics | validation_loss
----------+----------+---------------------------------------------------------------------------------+-----------------------+--------------+--------------+----------------------+--------------+------------------------+---------------------+---------------------+---------------------+--------------------------+-----------------------+--------------------+-----------------
-       9 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {119.42963886261}    | {accuracy}   |         0.983333349228 |       0.07286978513 | {0.983333349227905} | {0.072869785130024} |                          |                       |                    |
-      10 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {118.485460996628}   | {accuracy}   |         0.975000023842 |     0.0798489004374 | {0.975000023841858} | {0.079848900437355} |                          |                       |                    |
-       4 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {118.707404851913}   | {accuracy}   |         0.975000023842 |      0.143356323242 | {0.975000023841858} | {0.143356323242188} |                          |                       |                    |
-      11 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {118.224883794785}   | {accuracy}   |         0.958333313465 |      0.636615753174 | {0.958333313465118} | {0.636615753173828} |                          |                       |                    |
-       2 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {117.732690811157}   | {accuracy}   |         0.925000011921 |      0.161811202765 | {0.925000011920929} | {0.161811202764511} |                          |                       |                    |
-       5 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {120.357484817505}   | {accuracy}   |         0.833333313465 |        0.5542948246 | {0.833333313465118} | {0.55429482460022}  |                          |                       |                    |
-       3 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {118.928852796555}   | {accuracy}   |         0.824999988079 |      0.301002770662 | {0.824999988079071} | {0.301002770662308} |                          |                       |                    |
-       6 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {120.566634893417}   | {accuracy}   |         0.816666662693 |      0.875298440456 | {0.816666662693024} | {0.87529844045639}  |                          |                       |                    |
-      12 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {119.182703018188}   | {accuracy}   |         0.774999976158 |      0.785651266575 | {0.774999976158142} | {0.78565126657486}  |                          |                       |                    |
-       1 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {119.643137931824}   | {accuracy}   |         0.508333325386 |      0.762569189072 | {0.508333325386047} | {0.762569189071655} |                          |                       |                    |
-       7 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {120.15305685997}    | {accuracy}   |         0.333333343267 |       1.09794270992 | {0.333333343267441} | {1.09794270992279}  |                          |                       |                    |
-       8 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {119.911739826202}   | {accuracy}   |         0.333333343267 |       1.10344016552 | {0.333333343267441} | {1.10344016551971}  |                          |                       |                    |
-(12 rows)
-

-
--# Evaluate.  Now run evaluate using models we built above:
-
-DROP TABLE IF EXISTS iris_validate;
-SELECT madlib.madlib_keras_evaluate('iris_multi_model',  -- model
-                                    'iris_test_packed',  -- test table
-                                    'iris_validate',     -- output table
-                                     NULL,               -- use gpus
-                                     3                   -- mst_key to use
-                                   );
-SELECT * FROM iris_validate;
-

-
-       loss        |      metric       | metrics_type
--------------------+-------------------+--------------
- 0.103803977370262 | 0.966666638851166 | {accuracy}
-

-
--# Predict.  Now predict using one of the models we built. We will use the validation data set
-for prediction as well, which is not usual but serves to show the syntax.
-The prediction is in the estimated_class_text column:
-
-DROP TABLE IF EXISTS iris_predict;
-SELECT madlib.madlib_keras_predict('iris_multi_model', -- model
-                                   'iris_test',        -- test_table
-                                   'id',               -- id column
-                                   'attributes',       -- independent var
-                                   'iris_predict',     -- output table
-                                    'response',        -- prediction type
-                                    FALSE,             -- use gpus
-                                    3                  -- mst_key to use
-                                   );
-SELECT * FROM iris_predict ORDER BY id;
-

-
- id  | estimated_class_text
------+----------------------
-   9 | Iris-setosa
-  18 | Iris-setosa
-  22 | Iris-setosa
-  26 | Iris-setosa
-  35 | Iris-setosa
-  38 | Iris-setosa
-  42 | Iris-setosa
-  43 | Iris-setosa
-  45 | Iris-setosa
-  46 | Iris-setosa
-  50 | Iris-setosa
-  53 | Iris-versicolor
-  60 | Iris-versicolor
-  68 | Iris-versicolor
-  77 | Iris-versicolor
-  78 | Iris-versicolor
-  79 | Iris-versicolor
-  81 | Iris-versicolor
-  82 | Iris-versicolor
-  85 | Iris-virginica
-  95 | Iris-versicolor
-  97 | Iris-versicolor
-  98 | Iris-versicolor
- 113 | Iris-virginica
- 117 | Iris-virginica
- 118 | Iris-virginica
- 127 | Iris-virginica
- 136 | Iris-virginica
- 143 | Iris-virginica
- 145 | Iris-virginica
-(30 rows)
-

-Count missclassifications:
-
-SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id)
-WHERE iris_predict.estimated_class_text != iris_test.class_text;
-

-
- count
--------+
-     1
-

-Percent missclassifications:
-
-SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from
-    (select iris_test.class_text as actual, iris_predict.estimated_class_text as estimated
-     from iris_predict inner join iris_test
-     on iris_test.id=iris_predict.id) q
-WHERE q.actual=q.estimated;
-

-
- test_accuracy_percent
------------------------+
-                 96.67
-

-
-
Classification with Other Parameters

-
--# Validation dataset.  Now use a validation dataset
-and compute metrics every 3rd iteration using
-the 'metrics_compute_frequency' parameter. This can
-help reduce run time if you do not need metrics
-computed at every iteration.
-
-DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;
-SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed',    -- source_table
-                                              'iris_multi_model',     -- model_output_table
-                                              'mst_table',            -- model_selection_table
-                                               10,                     -- num_iterations
-                                               FALSE,                 -- use gpus
-                                              'iris_test_packed',     -- validation dataset
-                                               3,                     -- metrics compute frequency
-                                               FALSE,                 -- warm start
-                                              'Sophie L.',            -- name
-                                              'Model selection for iris dataset'  -- description
-                                             );
-

-View the model summary:
-
-SELECT * FROM iris_multi_model_summary;
-

-
-source_table              | iris_train_packed
-validation_table          | iris_test_packed
-model                     | iris_multi_model
-model_info                | iris_multi_model_info
-dependent_varname         | class_text
-independent_varname       | attributes
-model_arch_table          | model_arch_library
-num_iterations            | 10
-metrics_compute_frequency | 3
-warm_start                | f
-name                      | Sophie L.
-description               | Model selection for iris dataset
-start_training_time       | 2019-12-16 19:28:16.219137
-end_training_time         | 2019-12-16 19:30:19.238692
-madlib_version            | 1.17.0
-num_classes               | 3
-class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
-dependent_vartype         | character varying
-normalizing_const         | 1
-metrics_iters             | {3,6,9,10}
-

-View results for each model:
-
-SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;
-

-
- mst_key | model_id |                                 compile_params                                  |      fit_params       |  model_type  |  model_size  |                         metrics_elapsed_time                          | metrics_type | training_metrics_final | training_loss_final |                             training_metrics                              |                               training_loss                               | validation_metrics_final | validation_loss_final |                            validation_metrics                             |                              validation_loss
----------+----------+---------------------------------------------------------------------------------+-----------------------+--------------+--------------+-----------------------------------------------------------------------+--------------+------------------------+---------------------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+--------------------------+-----------------------+---------------------------------------------------------------------------+---------------------------------------------------------------------------
-       4 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {37.0420558452606,78.2046208381653,116.242669820786,134.287139892578} | {accuracy}   |         0.975000023842 |      0.165132269263 | {0.75,0.958333313465118,0.958333313465118,0.975000023841858}              | {0.618549585342407,0.319452553987503,0.223872095346451,0.165132269263268} |           0.966666638851 |        0.213689729571 | {0.733333349227905,0.933333337306976,0.933333337306976,0.966666638851166} | {0.683791160583496,0.370491921901703,0.255890935659409,0.213689729571342}
-       2 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {36.3931469917297,77.5780539512634,115.430645942688,133.599857807159} | {accuracy}   |         0.966666638851 |      0.277698725462 | {0.591666638851166,0.966666638851166,0.666666686534882,0.966666638851166} | {0.634598553180695,0.334936827421188,0.615665555000305,0.27769872546196}  |           0.966666638851 |         0.34405490756 | {0.5,0.966666638851166,0.566666662693024,0.966666638851166}               | {0.643225967884064,0.41021603345871,0.805291295051575,0.344054907560349}
-      10 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {36.8482949733734,78.0155048370361,115.83317399025,134.079672813416}  | {accuracy}   |         0.958333313465 |      0.122385449708 | {0.883333325386047,0.941666662693024,0.858333349227905,0.958333313465118} | {0.291894346475601,0.146935686469078,0.270052850246429,0.122385449707508} |           0.933333337307 |        0.181496843696 | {0.766666650772095,0.866666674613953,0.899999976158142,0.933333337306976} | {0.395013928413391,0.245234906673431,0.301119148731232,0.181496843695641}
-       3 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {37.2318170070648,78.3925468921661,116.45490694046,134.491376876831}  | {accuracy}   |         0.941666662693 |      0.193545326591 | {0.966666638851166,0.941666662693024,0.941666662693024,0.941666662693024} | {0.39665362238884,0.213271111249924,0.190151125192642,0.193545326590538}  |           0.933333337307 |        0.151459023356 | {1,0.966666638851166,0.933333337306976,0.933333337306976}                 | {0.464315593242645,0.198051139712334,0.138570576906204,0.151459023356438}
-       9 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {37.6678929328918,78.820240020752,116.939878940582,134.959810972214}  | {accuracy}   |         0.925000011921 |      0.192344605923 | {0.824999988079071,0.774999976158142,0.966666638851166,0.925000011920929} | {0.434513121843338,0.326292037963867,0.131333693861961,0.192344605922699} |           0.899999976158 |        0.209528595209 | {0.800000011920929,0.766666650772095,0.966666638851166,0.899999976158142} | {0.52033931016922,0.344535797834396,0.170280396938324,0.209528595209122}
-       8 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {38.0689258575439,79.4995639324188,117.36315202713,135.380483865738}  | {accuracy}   |         0.866666674614 |      0.390509605408 | {0.691666662693024,0.691666662693024,0.633333325386047,0.866666674613953} | {0.490214675664902,0.444783747196198,0.627961099147797,0.390509605407715} |           0.933333337307 |        0.376114845276 | {0.566666662693024,0.566666662693024,0.533333361148834,0.933333337306976} | {0.575542628765106,0.54660427570343,0.785183191299438,0.376114845275879}
-       5 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {38.474328994751,79.9709329605103,117.766183853149,135.803887844086}  | {accuracy}   |         0.841666638851 |      0.576696753502 | {0.616666674613953,0.699999988079071,0.758333325386047,0.841666638851166} | {0.90448260307312,0.750164151191711,0.616493880748749,0.576696753501892}  |           0.899999976158 |        0.631914675236 | {0.666666686534882,0.699999988079071,0.733333349227905,0.899999976158142} | {0.871200919151306,0.780709445476532,0.665971457958221,0.631914675235748}
-      11 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {36.6214678287506,77.7987759113312,115.631717920303,133.83836388588}  | {accuracy}   |         0.758333325386 |      0.881635427475 | {0.308333337306976,0.316666662693024,0.75,0.758333325386047}              | {1.12997460365295,1.02749967575073,0.923768699169159,0.881635427474976}   |           0.766666650772 |        0.878168046474 | {0.433333337306976,0.433333337306976,0.766666650772095,0.766666650772095} | {1.07487094402313,0.974115014076233,0.916269063949585,0.878168046474457}
-       7 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {38.2849600315094,79.7524738311768,117.580325841904,135.606695890427} | {accuracy}   |         0.691666662693 |      0.444524824619 | {0.908333361148834,0.391666680574417,0.691666662693024,0.691666662693024} | {0.335082054138184,2.02327847480774,0.444351017475128,0.444524824619293}  |           0.566666662693 |        0.539750337601 | {0.800000011920929,0.266666680574417,0.566666662693024,0.566666662693024} | {0.433189332485199,2.3276960849762,0.534160375595093,0.539750337600708}
-       6 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {38.6593668460846,80.1789360046387,117.957875013351,135.995815992355} | {accuracy}   |         0.683333337307 |      0.841839790344 | {0.316666662693024,0.366666674613953,0.666666686534882,0.683333337306976} | {1.07646071910858,0.963329672813416,0.87216705083847,0.841839790344238}   |           0.666666686535 |        0.840192914009 | {0.433333337306976,0.533333361148834,0.666666686534882,0.666666686534882} | {1.02845978736877,0.941896677017212,0.861787617206573,0.840192914009094}
-       1 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {37.8553328514099,79.2480089664459,117.139881849289,135.155915975571} | {accuracy}   |         0.358333319426 |       1.11013436317 | {0.358333319425583,0.333333343267441,0.333333343267441,0.358333319425583} | {1.10554325580597,1.11694586277008,1.09756696224213,1.11013436317444}     |           0.233333334327 |         1.17629003525 | {0.233333334326744,0.333333343267441,0.333333343267441,0.233333334326744} | {1.16081762313843,1.14324629306793,1.11625325679779,1.1762900352478}
-      12 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {37.4500079154968,78.6058378219604,116.700626850128,134.72905087471}  | {accuracy}   |         0.308333337307 |       1.06241953373 | {0.150000005960464,0.333333343267441,0.333333343267441,0.308333337306976} | {1.13338851928711,1.09694564342499,1.07030868530273,1.06241953372955}     |           0.433333337307 |         1.03659796715 | {0.16666667163372,0.333333343267441,0.433333337306976,0.433333337306976}  | {1.06262242794037,1.07252764701843,1.05843663215637,1.03659796714783}
-(12 rows)
-

-
--# Predict probabilities for each class:
-
-DROP TABLE IF EXISTS iris_predict;
-SELECT madlib.madlib_keras_predict('iris_multi_model', -- model
-                                   'iris_test',        -- test_table
-                                   'id',               -- id column
-                                   'attributes',       -- independent var
-                                   'iris_predict',     -- output table
-                                    'prob',            -- prediction type
-                                    FALSE,             -- use gpus
-                                    3                  -- mst_key to use
-                                   );
-SELECT * FROM iris_predict ORDER BY id;
-

-
- id  | prob_Iris-setosa | prob_Iris-versicolor | prob_Iris-virginica
------+------------------+----------------------+---------------------
-   9 |       0.99931216 |        0.00068789057 |       6.2587335e-10
-  18 |       0.99984336 |        0.00015656587 |        7.969957e-12
-  22 |        0.9998497 |        0.00015029701 |       6.4133347e-12
-  26 |        0.9995004 |        0.00049964694 |       2.2795305e-10
-  35 |       0.99964666 |        0.00035332117 |       9.4490485e-11
-  38 |       0.99964666 |        0.00035332117 |       9.4490485e-11
-  42 |        0.9985154 |         0.0014845316 |        5.293262e-09
-  43 |       0.99964476 |         0.0003552362 |        9.701174e-11
-  45 |        0.9997311 |        0.00026883607 |        3.076166e-11
-  46 |        0.9995486 |        0.00045140853 |       1.6814435e-10
-  50 |        0.9997856 |        0.00021441824 |       2.1316622e-11
-  53 |     9.837335e-06 |           0.97109175 |         0.028898403
-  60 |    0.00014028326 |           0.96552837 |         0.034331344
-  68 |    0.00087942625 |            0.9883348 |         0.010785843
-  77 |      6.08114e-06 |           0.94356424 |         0.056429718
-  78 |     7.116364e-07 |            0.8596206 |          0.14037873
-  79 |    1.3918722e-05 |           0.94052655 |          0.05945957
-  81 |    0.00045687397 |            0.9794796 |         0.020063542
-  82 |     0.0015463434 |           0.98768973 |         0.010763981
-  85 |    1.0929693e-05 |           0.87866926 |         0.121319845
-  95 |    6.3600986e-05 |           0.95264935 |         0.047287125
-  97 |    0.00020298029 |             0.981617 |         0.018180028
-  98 |    0.00019721613 |           0.98902065 |          0.01078211
- 113 |    1.0388683e-09 |           0.23626474 |           0.7637353
- 117 |     4.598902e-09 |           0.25669694 |           0.7433031
- 118 |    3.7139156e-11 |           0.13193987 |           0.8680601
- 127 |    2.1297862e-07 |             0.670349 |          0.32965073
- 136 |    7.1760774e-12 |           0.07074605 |            0.929254
- 143 |    1.2568385e-09 |          0.113820426 |           0.8861796
- 145 |      6.17019e-11 |          0.117578305 |          0.88242173
-(30 rows)
-

-
--# Warm start.  Next, use the warm_start parameter
-to continue learning, using the coefficients from
-the run above. Note that we don't drop the
-model table or model summary table:
-
-SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed',    -- source_table
-                                              'iris_multi_model',     -- model_output_table
-                                              'mst_table',            -- model_selection_table
-                                               3,                     -- num_iterations
-                                               FALSE,                 -- use gpus
-                                              'iris_test_packed',     -- validation dataset
-                                               1,                     -- metrics compute frequency
-                                               TRUE,                  -- warm start
-                                              'Sophie L.',            -- name
-                                              'Simple MLP for iris dataset'  -- description
-                                             );
-SELECT * FROM iris_multi_model_summary;
-

-
-source_table              | iris_train_packed
-validation_table          | iris_test_packed
-model                     | iris_multi_model
-model_info                | iris_multi_model_info
-dependent_varname         | class_text
-independent_varname       | attributes
-model_arch_table          | model_arch_library
-num_iterations            | 3
-metrics_compute_frequency | 1
-warm_start                | t
-name                      | Sophie L.
-description               | Simple MLP for iris dataset
-start_training_time       | 2019-12-16 20:07:41.488587
-end_training_time         | 2019-12-16 20:08:27.20651
-madlib_version            | 1.17.0
-num_classes               | 3
-class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
-dependent_vartype         | character varying
-normalizing_const         | 1
-metrics_iters             | {1,2,3}
-

-View results for each model:
-
-SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;
-

-
- mst_key | model_id |                                 compile_params                                  |      fit_params       |  model_type  |  model_size  |                 metrics_elapsed_time                 | metrics_type | training_metrics_final | training_loss_final |                    training_metrics                     |                      training_loss                       | validation_metrics_final | validation_loss_final |                   validation_metrics                    |                     validation_loss
----------+----------+---------------------------------------------------------------------------------+-----------------------+--------------+--------------+------------------------------------------------------+--------------+------------------------+---------------------+---------------------------------------------------------+----------------------------------------------------------+--------------------------+-----------------------+---------------------------------------------------------+---------------------------------------------------------
-       5 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {19.451014995575,37.2563629150391,54.7182998657227}  | {accuracy}   |         0.975000023842 |      0.490673750639 | {0.958333313465118,0.691666662693024,0.975000023841858} | {0.541427075862885,0.517450392246246,0.490673750638962}  |           0.933333337307 |        0.557333409786 | {0.933333337306976,0.666666686534882,0.933333337306976} | {0.60710871219635,0.570206344127655,0.557333409786224}
-       9 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 0.7900390625 | {18.2973220348358,36.3793680667877,54.0178129673004} | {accuracy}   |         0.966666638851 |     0.0894369781017 | {0.966666638851166,0.966666638851166,0.966666638851166} | {0.133233144879341,0.111788973212242,0.0894369781017303} |           0.899999976158 |        0.195293620229 | {0.933333337306976,0.966666638851166,0.899999976158142} | {0.156044512987137,0.132803827524185,0.195293620228767}
-       4 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {17.6080539226532,35.6788699626923,53.3836889266968} | {accuracy}   |         0.966666638851 |      0.147051945329 | {0.908333361148834,0.958333313465118,0.966666638851166} | {0.225205257534981,0.168186634778976,0.147051945328712}  |           0.866666674614 |        0.250319689512 | {0.899999976158142,0.933333337306976,0.866666674613953} | {0.23467344045639,0.182851999998093,0.250319689512253}
-       8 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {18.7529940605164,36.8255958557129,54.3704080581665} | {accuracy}   |         0.966666638851 |      0.244641214609 | {0.691666662693024,0.891666650772095,0.966666638851166} | {0.939713299274445,0.462556451559067,0.244641214609146}  |           0.966666638851 |        0.298279434443 | {0.566666662693024,0.966666638851166,0.966666638851166} | {1.30671143531799,0.412235885858536,0.29827943444252}
-      10 |        2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {17.4004180431366,35.4556438922882,53.1877279281616} | {accuracy}   |         0.958333313465 |      0.123381219804 | {0.949999988079071,0.766666650772095,0.958333313465118} | {0.0919980704784393,0.576169073581696,0.123381219804287} |           0.933333337307 |        0.203262642026 | {0.866666674613953,0.766666650772095,0.933333337306976} | {0.199721112847328,0.959742486476898,0.203262642025948}
-       3 |        1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {17.81547498703,35.8978669643402,53.5737180709839}   | {accuracy}   |         0.933333337307 |      0.150664463639 | {0.941666662693024,0.925000011920929,0.933333337306976} | {0.117781177163124,0.163000836968422,0.150664463639259}  |           0.833333313465 |        0.365329563618 | {0.866666674613953,0.833333313465118,0.833333313465118} | {0.249404579401016,0.375173389911652,0.365329563617706}
-       6 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {19.686233997345,37.4543249607086,54.8708770275116}  | {accuracy}   |         0.858333349228 |      0.743227303028 | {0.675000011920929,0.708333313465118,0.858333349227905} | {0.808507084846497,0.774080872535706,0.743227303028107}  |           0.966666638851 |        0.770158529282 | {0.666666686534882,0.666666686534882,0.966666638851166} | {0.808504283428192,0.791898012161255,0.770158529281616}
-      11 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {17.1583528518677,35.0312390327454,52.96133685112}   | {accuracy}   |         0.816666662693 |      0.739802956581 | {0.774999976158142,0.816666662693024,0.816666662693024} | {0.83727890253067,0.792884111404419,0.739802956581116}   |           0.800000011921 |        0.758302807808 | {0.766666650772095,0.800000011920929,0.800000011920929} | {0.837629973888397,0.801746726036072,0.758302807807922}
-       2 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1 | madlib_keras | 1.2197265625 | {16.9146749973297,34.794900894165,52.7328250408173}  | {accuracy}   |         0.808333337307 |      0.303489625454 | {0.683333337306976,0.966666638851166,0.808333337306976} | {1.05107569694519,0.189959138631821,0.303489625453949}   |           0.866666674614 |        0.285375326872 | {0.666666686534882,0.966666638851166,0.866666674613953} | {1.01942157745361,0.238933652639389,0.285375326871872}
-      12 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1 | madlib_keras | 0.7900390625 | {18.0590150356293,36.1394078731537,53.7930529117584} | {accuracy}   |         0.699999988079 |       1.02253305912 | {0.550000011920929,0.691666662693024,0.699999988079071} | {1.0493084192276,1.03803598880768,1.02253305912018}      |           0.666666686535 |         1.02013540268 | {0.633333325386047,0.600000023841858,0.666666686534882} | {1.03952574729919,1.03439521789551,1.02013540267944}
-       7 |        2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {19.2141709327698,37.0566499233246,54.5629329681396} | {accuracy}   |         0.691666662693 |      0.448221176863 | {0.691666662693024,0.691666662693024,0.691666662693024} | {0.447027385234833,0.444605946540833,0.448221176862717}  |           0.566666662693 |        0.555035352707 | {0.566666662693024,0.566666662693024,0.566666662693024} | {0.551217257976532,0.540408432483673,0.555035352706909}
-       1 |        1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1 | madlib_keras | 1.2197265625 | {18.501914024353,36.5938439369202,54.194118976593}   | {accuracy}   |         0.358333319426 |       1.09730923176 | {0.333333343267441,0.333333343267441,0.358333319425583} | {1.09999334812164,1.10405397415161,1.09730923175812}     |           0.233333334327 |         1.12532019615 | {0.333333343267441,0.333333343267441,0.233333334326744} | {1.12446486949921,1.13782525062561,1.12532019615173}
-(12 rows)
-

-Note that the loss and accuracy values pick up from where the previous run left off.
-
-@anchor notes
-@par Notes
-
-1. Refer to the deep learning section of the Apache MADlib
-wiki [6] for important information including supported libraries
-and versions.
-
-2. Classification is currently supported, not regression.
-
-3. Reminder about the distinction between warm start and transfer learning.  Warm start uses model
-state (weights) from the model output table from a previous training run -
-set the 'warm_start' parameter to TRUE in the fit function.
-Transfer learning uses initial model state (weights) stored in the 'model_arch_table' - in this case set the
-'warm_start' parameter to FALSE in the fit function.
-
-4. Here are some more details on how warm start works.  These details are mostly applicable when implementing autoML algorithms on top of MADlib's model selection.  In short, the 'model_selection_table' dictates which models get trained and output to the 'model_output_table' and associated summary and info tables.  When 'warm_start' is TRUE, models are built for each 'mst_key' in the 'model_selection_table'.  If there are prior runs for an 'mst_key' then the weights from that run will be used.  If there are no prior runs for an 'mst_key' then random initialization will be used.  For example, let's say we start with 'mst_keys' of 1, 2, 3, and 4 in the 'model_selection_table'. We run fit once to get model and info tables for 1, 2, 3, and 4.  Then we modify the 'model_selection_table' as part of an autoML scheme, in which we remove the 'mst_key' for 1 and add a new 'mst_key' for 5.  Next we run fit with warm start.  The result will be models created for 'mst_keys' of 2, 3, 4, and 5.  Warm start will be used for 2, 3, and 4 (using prior run) and random initialization will be used for 5 (no prior run).  The 'mst_key' of 1 will be dropped.
-
-5. The 'num_iterations' parameter and the Keras fit parameter 'epochs' can substantially affect accuracy and run-time.
-In general, increasing the number of 'epochs' for a fixed 'num_iterations' will speed up training, but may result
-in lower accuracy.  It's best to do some experimentation to find out what works for your models and dataset.
-
-@anchor background
-@par Technical Background
-
-For an introduction to deep learning foundations, including MLP and CNN,
-refer to [7].
-
-This module trains many models a time across the database cluster in order
-to explore network architectures and hyperparameters.  It uses model hopper
-parallelism (MOP) and has high convergence efficiency since it does not do
-model averaging [2].
-
-On the effect of database cluster size: as the database cluster size increases,
-it will be faster to train a set of models, as long as you have at
-least as many model selection tuples as segments. This is because model state is "hopped" from
-segment to segment and training takes place in parallel [1,2]. If you have fewer model
-selection tuples to train than segments, then some
-segments may not be busy 100% of the time so speedup will not necessarily increase
-on a larger cluster. Inference (predict) is an embarrassingly parallel operation so
-inference runtimes will be proportionally faster as the number of segments increases.
-
-@anchor literature
-@literature
-
-@anchor mlp-lit-1
-[1] "Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems,"
-Supun Nakandala, Yuhao Zhang, and Arun Kumar, ACM SIGMOD 2019 DEEM Workshop,
-https://adalabucsd.github.io/papers/2019_Cerebro_DEEM.pdf
-
-[2] "Resource-Efficient and Reproducible Model Selection on Deep Learning Systems,"
-Supun Nakandala, Yuhao Zhang, and Arun Kumar, Technical Report, Computer Science and
-Engineering, University of California, San Diego
-https://adalabucsd.github.io/papers/TR_2019_Cerebro.pdf
-
-[3] https://keras.io/
-
-[4] https://www.tensorflow.org/
-
-[5] "Neural Networks for Machine Learning", Lectures 6a and 6b on mini-batch gradient descent,
-Geoffrey Hinton with Nitish Srivastava and Kevin Swersky,
-http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
-
-[6] Deep learning section of Apache MADlib wiki https://cwiki.apache.org/confluence/display/MADLIB/Deep+Learning
-
-[7] Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.
-
-[8] Greenplum Database server configuration parameters https://gpdb.docs.pivotal.io/latest/ref_guide/config_params/guc-list.html
-
-@anchor related
-@par Related Topics
-
-File madlib_keras_fit_multiple_model.sql_in documents training, evaluate and predict functions.
-
-*/
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit_multiple_model(
-    source_table            VARCHAR,
-    model_output_table      VARCHAR,
-    model_selection_table   VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR,
-    metrics_compute_frequency  INTEGER,
-    warm_start              BOOLEAN,
-    name                    VARCHAR,
-    description             VARCHAR
-) RETURNS VOID AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_fit_multiple_model')
-    from utilities.control import SetGUC
-    with AOControl(False):
-        with SetGUC("plan_cache_mode", "force_generic_plan"):
-            fit_obj = madlib_keras_fit_multiple_model.FitMultipleModel(**globals())
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit_multiple_model(
-    source_table            VARCHAR,
-    model_output_table      VARCHAR,
-    model_selection_table   VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR,
-    metrics_compute_frequency  INTEGER,
-    warm_start              BOOLEAN,
-    name                    VARCHAR
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit_multiple_model($1, $2, $3, $4, $5, $6, $7, $8, $9, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit_multiple_model(
-    source_table            VARCHAR,
-    model_output_table      VARCHAR,
-    model_selection_table   VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR,
-    metrics_compute_frequency  INTEGER,
-    warm_start              BOOLEAN
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit_multiple_model($1, $2, $3, $4, $5, $6, $7, $8, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit_multiple_model(
-    source_table            VARCHAR,
-    model_output_table      VARCHAR,
-    model_selection_table   VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR,
-    metrics_compute_frequency  INTEGER
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit_multiple_model($1, $2, $3, $4, $5, $6, $7, FALSE, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit_multiple_model(
-    source_table            VARCHAR,
-    model_output_table      VARCHAR,
-    model_selection_table   VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN,
-    validation_table        VARCHAR
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit_multiple_model($1, $2, $3, $4, $5, $6, NULL, FALSE, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit_multiple_model(
-    source_table            VARCHAR,
-    model_output_table      VARCHAR,
-    model_selection_table   VARCHAR,
-    num_iterations          INTEGER,
-    use_gpus                BOOLEAN
-) RETURNS VOID AS $$
-    SELECT MADLIB_SCHEMA.madlib_keras_fit_multiple_model($1, $2, $3, $4, $5, NULL, NULL, FALSE, NULL, NULL);
-$$ LANGUAGE sql VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.fit_transition_multiple_model(
-    state                      BYTEA,
-    dependent_var              BYTEA,
-    independent_var            BYTEA,
-    dependent_var_shape        INTEGER[],
-    independent_var_shape      INTEGER[],
-    model_architecture         TEXT,
-    compile_params             TEXT,
-    fit_params                 TEXT,
-    dist_key                   INTEGER,
-    dist_key_mapping           INTEGER[],
-    current_seg_id             INTEGER,
-    segments_per_host          INTEGER,
-    images_per_seg             INTEGER[],
-    use_gpus                   BOOLEAN,
-    accessible_gpus_for_seg               INTEGER[],
-    prev_serialized_weights    BYTEA,
-    is_final_iteration         BOOLEAN
-) RETURNS BYTEA AS $$
-PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras')
-    return madlib_keras.fit_transition(is_multiple_model = True, **globals())
-$$ LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.fit_step_multiple_model(
-    BYTEA,
-    BYTEA,
-    INTEGER[],
-    INTEGER[],
-    TEXT,
-    TEXT,
-    TEXT,
-    INTEGER,
-    INTEGER[],
-    INTEGER,
-    INTEGER,
-    INTEGER[],
-    BOOLEAN,
-    INTEGER[],
-    BYTEA,
-    BOOLEAN);
-CREATE AGGREGATE MADLIB_SCHEMA.fit_step_multiple_model(
-    /* dependent_var */              BYTEA,
-    /* independent_var */            BYTEA,
-    /* dependent_var_shape */        INTEGER[],
-    /* independent_var_shape */      INTEGER[],
-    /* model_architecture */         TEXT,
-    /* compile_params */             TEXT,
-    /* fit_params */                 TEXT,
-    /* dist_key */                   INTEGER,
-    /* dist_key_mapping */           INTEGER[],
-    /* current_seg_id */             INTEGER,
-    /* segments_per_host */          INTEGER,
-    /* images_per_seg */             INTEGER[],
-    /* use_gpus */                   BOOLEAN,
-    /* accessible_gpus_for_seg */               INTEGER[],
-    /* prev_serialized_weights */    BYTEA,
-    /* is_final_iteration */         BOOLEAN
-)(
-    STYPE=BYTEA,
-    SFUNC=MADLIB_SCHEMA.fit_transition_multiple_model
-);
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_gpu_info.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_gpu_info.py_in
deleted file mode 100644
index 4eec8a7..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_gpu_info.py_in
+++ /dev/null
@@ -1,256 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-"""
-@file madlib_keras_gpu_info.py_in
-
-@brief GPU configuration helper function
-
-@namespace madlib_keras_gpu_info
-"""
-
-import os
-import subprocess
-
-import plpy
-from utilities.utilities import is_platform_pg
-from utilities.utilities import unique_string
-from utilities.validate_args import output_tbl_valid
-
-class OutputInfoSchema:
-    TEMP_INFO_TABLE = unique_string(desp='gpu_info')
-    SEG_ID_COL = 'gp_seg_id'
-    GPU_DESCR_COL = 'gpu_descr'
-
-
-class Source:
-    NVIDIA = 'nvidia'
-    TENSORFLOW = 'tensorflow'
-
-
-class GPUInfoFunctions:
-    @staticmethod
-    def get_gpu_info_from_nvidia():
-        """
-        This function will run only on segment(s). Make sure not to run any non
-        select plpy execute.
-        :return: list of gpu descriptions as returned by nvidia-smi -L.
-        """
-        try:
-            return subprocess.check_output(["nvidia-smi", "-L"]).splitlines()
-        except Exception:  # Handle case when nvidia-smi -L fails
-            return []
-
-    @staticmethod
-    def get_gpu_info_from_tensorflow():
-        """
-        This function will run only on segment(s). Make sure not to run any non
-        select plpy execute.
-        :return: list of gpu descriptions as returned by tensorflow
-        """
-        current_working_dir = os.path.dirname(os.path.realpath(__file__))
-        gpus = subprocess.check_output(["python", "gpu_info_from_tf.py"],
-                                       cwd=current_working_dir).splitlines()
-        return gpus
-
-
-def gpu_configuration(schema_madlib, output_table, source):
-    """
-    :return: List of gpus along with their hostname in the format
-        GPDB
-        gpu_descr         |       hostname
-        ------------------+--------------------------
-        NVIDIA Tesla P100 | host1
-        NVIDIA Tesla P100 | host1
-        Super Duper GPU   | host2
-        Super Duper GPU   | host2
-        1. We use gp_dist_random to run either the nvidia smi UDF or the tensorflow UDF
-            on all the hosts. (see gpu_info_nvidia/gpu_info_tensorflow)
-        2. Also we do not need to run the tf/nvidia UDF on all the segments,
-            just one segment per host. That's why we group the output of
-            gp_segment_configuration by hostname and get the min segment from
-            each host.
-        3. To get the hostname along with the gpu description, we have to join
-            the output of nvidia/tf UDF with gp_segment_configuration and filter
-            out the following
-            * master
-            * mirror segments
-            * empty/null gpu description
-        Workflow for gpdb
-        1. Run query to get min seg ids on each host. This is so that we can run
-        the gpu UDF on just one segment per host.
-        2. Create a table by running the tf/nvidia UDF on the segment ids returned
-        from the previous step. Note that this table will only contain the output
-        of the UDF and the segment id itself. This table does not contain hostnames
-        3. To get the hostname associated with the segment id, we need to join the
-        table created in step with gp_segment_configuration.
-        It's important to note that we can merge all these 3 queries into one but
-        the problem with that is that a redistribution happens before running the UDF
-        which means the UDF does not run on the segments that we pass in to the query.
-        To avoid this, we broke down the query into 3 parts so that the UDF is always
-        run on the intended segments.
-
-        POSTGRES
-        gpu_descr         |       hostname
-        ------------------+--------------------------
-        NVIDIA Tesla P100 | localhost
-    """
-    module_name = 'madlib_keras_gpu_info'
-    output_tbl_valid(output_table, module_name)
-
-    if not source:
-        source = Source.TENSORFLOW
-    source = source.lower()
-    if source != Source.TENSORFLOW and source != Source.NVIDIA:
-        plpy.error("DL: source has to be one of {0} or {1}".format(
-            Source.TENSORFLOW, Source.NVIDIA))
-
-    gpu_fn_name = 'gpu_info_{0}'.format(source)
-    if is_platform_pg():
-        gpu_for_postgres(schema_madlib, output_table, gpu_fn_name)
-    else:
-        gpu_for_gpdb(schema_madlib, output_table, gpu_fn_name)
-
-
-def gpu_for_postgres(schema_madlib, output_table, gpu_fn_name):
-    gpu_info_query = """
-    CREATE TABLE {0} AS
-    SELECT 'localhost' as hostname, {1} from (SELECT unnest({2}.{3}()) AS {1}) s1
-    where {1} is NOT NULL AND {1} != ''
-        """.format(output_table, OutputInfoSchema.GPU_DESCR_COL,
-                   schema_madlib, gpu_fn_name)
-    plpy.execute(gpu_info_query)
-
-
-def gpu_for_gpdb(schema_madlib, output_table, gpu_fn_name):
-    min_seg_on_each_host = get_min_seg_ids_on_each_host()
-
-    create_gpu_info_table_without_hostname(schema_madlib, gpu_fn_name,
-                                           min_seg_on_each_host)
-
-    create_gpu_info_table_with_hostname(output_table)
-
-    plpy.execute("DROP TABLE IF EXISTS {0}".format(OutputInfoSchema.TEMP_INFO_TABLE))
-
-
-def get_min_seg_ids_on_each_host():
-    """
-    Run query to get min seg ids on each host. This is so that we can run
-    the gpu UDF on just one segment per host.
-    :return: List of min seg id per host
-    """
-    min_seg_id_alias = 'min_seg_id'
-    min_seg_query = """
-    SELECT {min_seg_id_alias} FROM 
-    (select hostname, min(content) AS {min_seg_id_alias} 
-    FROM gp_segment_configuration WHERE content != -1 AND role='p' 
-    GROUP BY hostname) min_seg_id_subquery
-    """.format(**locals())
-    min_seg_on_each_host = plpy.execute(min_seg_query)
-
-    min_seg_on_each_host = ','.join([str(seg[min_seg_id_alias])
-                                     for seg in min_seg_on_each_host])
-    return min_seg_on_each_host
-
-
-def create_gpu_info_table_without_hostname(schema_madlib, gpu_fn_name,
-                                           min_seg_on_each_host):
-    """ output_table,
-    Create a table by running the tf/nvidia UDF on the segment ids returned
-    from the previous step. Note that this table will only contain the output
-    of the UDF and the segment id itself. This table does not contain hostnames
-    :param schema_madlib:
-    :param gpu_fn_name:
-    :param min_seg_on_each_host:
-    """
-    gpu_info_per_host_query = """
-    CREATE TABLE {0} AS SELECT gp_segment_id AS {1}, {2}.{3}()
-    AS {4} FROM gp_dist_random('gp_id') WHERE gp_segment_id IN ({5})
-    """.format(OutputInfoSchema.TEMP_INFO_TABLE,
-               OutputInfoSchema.SEG_ID_COL,
-               schema_madlib, gpu_fn_name,
-               OutputInfoSchema.GPU_DESCR_COL,
-               min_seg_on_each_host)
-    plpy.execute(gpu_info_per_host_query)
-
-
-def create_gpu_info_table_with_hostname(output_table):
-    """
-    Create the final output table that contains the hostname and the gpu description.
-    To create this table, we need to join the table created in
-    create_gpu_info_table_without_hostname with gp_segment_configuration.
-    """
-    final_join_query = """
-    CREATE TABLE {0} AS
-    SELECT hostname, {1} FROM
-    (
-    SELECT hostname, unnest({1}) AS {1} FROM {2}
-    JOIN
-    gp_segment_configuration ON {3}=content WHERE content != -1 AND role='p'
-    ) s1
-    WHERE {1} != '' AND {1} is NOT NULL ORDER BY 1,2;
-    """.format(output_table, OutputInfoSchema.GPU_DESCR_COL,
-               OutputInfoSchema.TEMP_INFO_TABLE,
-               OutputInfoSchema.SEG_ID_COL)
-    plpy.execute(final_join_query)
-
-
-def gpu_configuration_help(schema_madlib):
-    """
-        Help function for gpu configuration
-
-        Args:
-            @param schema_madlib
-
-        Returns:
-            String. Help/usage information
-        """
-
-    help_string = """
-    Utility function to report number and type of GPUs on the database cluster.
-    -----------------------------------------------------------------------
-                                USAGE
-    -----------------------------------------------------------------------
-     SELECT {schema_madlib}.gpu_configuration(
-        output_table,            -- Name of the output table to write out the 
-                                    GPU information.  
-        source                   -- Default: 'tensorflow'. Source for determining
-                                    GPU configuration.
-                                    Using 'tensorflow' returns a description based
-                                    on what TensorFlow reports.
-                                    Using 'nvidia' returns a description based 
-                                    on what the Nvidia Systems Management Interface
-                                    (nvidia-smi) reports [1].  
-                                    Note that MADlib and Keras will use the TensorFlow
-                                    information; the lower level nvidia-smi info 
-                                    is provided for convenience.
-        )
-    );
-
-    -----------------------------------------------------------------------
-                                OUTPUT
-    -----------------------------------------------------------------------
-    The output table ('output_table' above) contains the following columns:
-
-    hostname:   Name of the host machine in the cluster.
-                Does not include master or mirrors.  For PostgreSQL this will
-                always return 'localhost'.
-    gpu_descr:  String reported by TensorFlow or nvidia-smi.
-    """
-
-    return help_string.format(schema_madlib=schema_madlib)
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_gpu_info.sql_in b/src/ports/postgres/modules/deep_learning/madlib_keras_gpu_info.sql_in
deleted file mode 100644
index d2418e4..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_gpu_info.sql_in
+++ /dev/null
@@ -1,279 +0,0 @@
-/* ----------------------------------------------------------------------- *//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *
- * @file madlib_keras_gpu_info.sql_in
- *
- * @brief SQL functions for GPU configuration.
- * @date Nov 2019
- *
- *
- *//* ----------------------------------------------------------------------- */
-
-m4_include(`SQLCommon.m4')
-
-/**
-@addtogroup grp_gpu_configuration
-
-@brief Utility function to report number and type of GPUs in the database cluster.
-
-\warning  This MADlib method is still in early stage development.
-Interface and implementation are subject to change. 
-
-
Contents

-
-This utility function reports the number and type of GPUs
-attached to hosts on the database cluster.
-This can be useful when determining which segments to use for
-training deep neural nets.  For example, for economic reasons
-you may wish to set up a heterogeneous clusters with GPUs only
-on some of the hosts, not all of them. This utility
-can help you identify where the GPUS are and direct the compute
-to those locations only in subsequent training and evaluation steps.
-
-@anchor get_gpu_config
-@par GPU Confuguration
-
-
-gpu_configuration( output_table,
-                   source
-	               )
-

-\b Arguments
-

-  
output_table

-  
TEXT. Name of the output table to write out the GPU information.
-  

-
-  
source (optional)

-  
TEXT, default: 'tensorflow'. Source for determining GPU configuration.
-  Using 'tensorflow' returns a description based on what TensorFlow reports.
-  Using 'nvidia' returns a description based on what the Nvidia Systems
-  Management Interface (nvidia-smi) reports [1].  Note that MADlib and Keras will use the
-  TensorFlow information; the lower level nvidia-smi info is provided for convenience.
-
-  @note
-  If the command 'nvidia-smi -L' returns an error, we do not pass through
-  the error message, but instead will show no GPUs for that host in the output table.
-  You may want to run nvidia-smi from the command line to see error
-  and informational messages.
-
-  

-

-
-Output
-

-    The output table contains the following:
-    
idGives the 'id' for each prediction, corresponding to each row from the test_table.
estimated_COL_NAME
-        (For pred_type='response') The estimated class
-         for classification, where
-         COL_NAME is the name of the column to be
-         predicted from test data.
-        
prob_CLASS
-        (For pred_type='prob' for classification) The
-        probability of a given class.
-        There will be one column for each class
-        in the training data.
-        

-      
-        
-        
-      
-      
-        
-        
-      
-    
hostnameTEXT. Name of the host machine in the cluster.
-        Does not include master or mirrors.  For PostgreSQL
-        this will always return 'localhost'.
-        
gpu_descrTEXT. String reported by TensorFlow or nvidia-smi.
-        The formats are different and shown in the examples below.
-        

-

-
-@anchor example
-@par Examples
-
--# Get GPU configuration as per TensorFlow:
-
-DROP TABLE IF EXISTS host_gpu_mapping_tf;
-SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');
-SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;
-

-
- hostname |                                        gpu_descr
-----------+------------------------------------------------------------------------------------------
- phoenix0 | device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
- phoenix0 | device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
- phoenix0 | device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0
- phoenix0 | device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0
- phoenix1 | device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
- phoenix1 | device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
- phoenix3 | device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
- phoenix3 | device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
- phoenix3 | device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0
- phoenix3 | device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0
- phoenix4 | device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
- phoenix4 | device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
- phoenix4 | device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0
- phoenix4 | device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0
-(14 rows)
-

-In this heterogeneous cluster there are 4 GPUs attached to hosts 0, 3 and 4.  There are 2 GPUs
-attached to host 1 and no GPUs attached to host 2.
-
--# Get GPU configuration as per nvidia-smi:
-
-DROP TABLE IF EXISTS host_gpu_mapping_nvidia;
-SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_nvidia', -- output table
-                                       'nvidia'                   -- source for GPU info
-                                       );
-SELECT * FROM host_gpu_mapping_nvidia ORDER BY hostname, gpu_descr;
-

-
- hostname |                                  gpu_descr
-----------+------------------------------------------------------------------------------
- phoenix0 | GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-f2ccc77e-2501-f6ee-4754-069dda256fb2)
- phoenix0 | GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-b1fc40ca-c7c6-bc86-f20f-6e9a62cda3f8)
- phoenix0 | GPU 2: Tesla P100-PCIE-16GB (UUID: GPU-d93bb21b-96f9-7c1d-3bab-cdd92b7bbc9d)
- phoenix0 | GPU 3: Tesla P100-PCIE-16GB (UUID: GPU-2d79c4a8-479e-2f33-39f8-3ba80b63f830)
- phoenix1 | GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-0af6bb1e-5b5b-4988-ad3a-a917e9584702)
- phoenix1 | GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-d824c976-a8aa-ef26-a13c-9a9a7fe86bfd)
- phoenix3 | GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-3681d0b6-1ec6-0453-fd81-29d88e549cd9)
- phoenix3 | GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-d4b1f2e7-b238-ac9a-bbfe-918adeb69472)
- phoenix3 | GPU 2: Tesla P100-PCIE-16GB (UUID: GPU-42a32ef1-a60c-e599-c8cf-0e669111ab6f)
- phoenix3 | GPU 3: Tesla P100-PCIE-16GB (UUID: GPU-1cce09c4-6856-8031-be0b-8e8bbf9a10f3)
- phoenix4 | GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-a71bdc18-fdd5-ba25-617e-19b23cc8e827)
- phoenix4 | GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-f9d13688-7fe6-a029-24d1-985a5659f18f)
- phoenix4 | GPU 2: Tesla P100-PCIE-16GB (UUID: GPU-06a7f54b-c07a-e87a-20d6-09bd99b19531)
- phoenix4 | GPU 3: Tesla P100-PCIE-16GB (UUID: GPU-af3b32f3-8bd9-cb75-a8fb-25253b9da926)
-(14 rows)
-

-
--# To get a fuller picture at the segment level, combine with the Greenplum catalog
-table 'gp_segment_configuration' which contains information about
-segment instance configuration [2].  Here is an example of this table
-filtering out master and mirrors:
-
-SELECT * FROM gp_segment_configuration WHERE role='p' AND content>=0 ORDER BY hostname, dbid;
-

-
- dbid | content | role | preferred_role | mode | status | port  | hostname | address  | replication_port
-------+---------+------+----------------+------+--------+-------+----------+----------+------------------
-    2 |       0 | p    | p              | c    | u      | 40000 | phoenix0 | phoenix0 |            70000
-    3 |       1 | p    | p              | c    | u      | 40001 | phoenix0 | phoenix0 |            70001
-    4 |       2 | p    | p              | c    | u      | 40002 | phoenix0 | phoenix0 |            70002
-    5 |       3 | p    | p              | c    | u      | 40003 | phoenix0 | phoenix0 |            70003
-    6 |       4 | p    | p              | c    | u      | 40000 | phoenix1 | phoenix1 |            70000
-    7 |       5 | p    | p              | c    | u      | 40001 | phoenix1 | phoenix1 |            70001
-    8 |       6 | p    | p              | c    | u      | 40002 | phoenix1 | phoenix1 |            70002
-    9 |       7 | p    | p              | c    | u      | 40003 | phoenix1 | phoenix1 |            70003
-   10 |       8 | p    | p              | c    | u      | 40000 | phoenix2 | phoenix2 |            70000
-   11 |       9 | p    | p              | c    | u      | 40001 | phoenix2 | phoenix2 |            70001
-   12 |      10 | p    | p              | c    | u      | 40002 | phoenix2 | phoenix2 |            70002
-   13 |      11 | p    | p              | c    | u      | 40003 | phoenix2 | phoenix2 |            70003
-   14 |      12 | p    | p              | c    | u      | 40000 | phoenix3 | phoenix3 |            70000
-   15 |      13 | p    | p              | c    | u      | 40001 | phoenix3 | phoenix3 |            70001
-   16 |      14 | p    | p              | c    | u      | 40002 | phoenix3 | phoenix3 |            70002
-   17 |      15 | p    | p              | c    | u      | 40003 | phoenix3 | phoenix3 |            70003
-   18 |      16 | p    | p              | c    | u      | 40000 | phoenix4 | phoenix4 |            70000
-   19 |      17 | p    | p              | c    | u      | 40001 | phoenix4 | phoenix4 |            70001
-   20 |      18 | p    | p              | c    | u      | 40002 | phoenix4 | phoenix4 |            70002
-   21 |      19 | p    | p              | c    | u      | 40003 | phoenix4 | phoenix4 |            70003
-(20 rows)
-

-Now join this table with the GPU resources table to create a table containing a
-list of all segments on hosts with GPUs attached:
-
-DROP TABLE IF EXISTS segments_to_use;
-CREATE TABLE segments_to_use AS
-  SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)
-  WHERE role='p' AND content>=0;
-SELECT * FROM segments_to_use ORDER BY hostname, dbid;
-

-
- dbid | hostname
-------+----------
-    2 | phoenix0
-    3 | phoenix0
-    4 | phoenix0
-    5 | phoenix0
-    6 | phoenix1
-    7 | phoenix1
-    8 | phoenix1
-    9 | phoenix1
-   14 | phoenix3
-   15 | phoenix3
-   16 | phoenix3
-   17 | phoenix3
-   18 | phoenix4
-   19 | phoenix4
-   20 | phoenix4
-   21 | phoenix4
-(16 rows)
-

-
-@anchor references
-@par References
-
-[1] Nvidia Systems Management Interface (nvidia-smi) https://developer.nvidia.com/nvidia-system-management-interface
-
-[2] Greenplum 'gp_segment_configuration' table https://gpdb.docs.pivotal.io/latest/ref_guide/system_catalogs/gp_segment_configuration.html
-
-@anchor related
-@par Related Topics
-
-See madlib_keras_gpu_info.sql_in
-
-*/
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_info_tensorflow() RETURNS TEXT[] as
-$$
-    PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras_gpu_info')
-    return madlib_keras_gpu_info.GPUInfoFunctions.get_gpu_info_from_tensorflow()
-$$ LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_info_nvidia() RETURNS TEXT[] as
-$$
-    PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras_gpu_info')
-    return madlib_keras_gpu_info.GPUInfoFunctions.get_gpu_info_from_nvidia()
-$$ LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_configuration(output_table text, source text)
-RETURNS VOID AS
-$$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_gpu_info')
-    from utilities.control import MinWarning
-    with AOControl(False) and MinWarning("error"):
-        madlib_keras_gpu_info.gpu_configuration(schema_madlib, output_table, source)
-$$
-LANGUAGE plpythonu
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_configuration(output_table text)
-RETURNS VOID AS
-$$
-    SELECT MADLIB_SCHEMA.gpu_configuration($1, NULL);
-$$
-LANGUAGE sql;
-
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_configuration()
-    RETURNS VARCHAR AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_gpu_info')
-    return madlib_keras_gpu_info.gpu_configuration_help(schema_madlib)
-$$ LANGUAGE plpythonu IMMUTABLE
-    m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in
deleted file mode 100644
index 5be078b..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_helper.py_in
+++ /dev/null
@@ -1,316 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import numpy as np
-from model_arch_info import ModelArchSchema
-from utilities.utilities import add_postfix
-from utilities.utilities import unique_string
-from utilities.utilities import is_platform_pg
-from utilities.validate_args import table_exists
-from madlib_keras_gpu_info import GPUInfoFunctions
-import plpy
-
-
-############### Constants used in other deep learning files #########
-# Name of columns in model summary table.
-CLASS_VALUES_COLNAME = "class_values"
-NORMALIZING_CONST_COLNAME = "normalizing_const"
-COMPILE_PARAMS_COLNAME = "compile_params"
-DEPENDENT_VARNAME_COLNAME = "dependent_varname"
-DEPENDENT_VARTYPE_COLNAME = "dependent_vartype"
-INDEPENDENT_VARNAME_COLNAME = "independent_varname"
-MODEL_ARCH_TABLE_COLNAME = "model_arch_table"
-MODEL_ID_COLNAME = ModelArchSchema.MODEL_ID
-MODEL_WEIGHTS_COLNAME = "model_weights"
-METRIC_TYPE_COLNAME = "metrics_type"
-
-# Name of independent, dependent and distribution key colnames in batched table.
-# These are readonly variables, do not modify.
-# MADLIB-1300 Adding these variables for DL only at this time.
-MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL = "dependent_var"
-MINIBATCH_OUTPUT_INDEPENDENT_COLNAME_DL = "independent_var"
-DISTRIBUTION_KEY_COLNAME = "__dist_key__"
-## sql variable types
-FLOAT32_SQL_TYPE = 'REAL'
-SMALLINT_SQL_TYPE = 'SMALLINT'
-
-DEFAULT_NORMALIZING_CONST = 1.0
-GP_SEGMENT_ID_COLNAME = "gp_segment_id"
-INTERNAL_GPU_CONFIG = '__internal_gpu_config__'
-
-#####################################################################
-
-# Prepend a dimension to np arrays using expand_dims.
-def expand_input_dims(input_data):
-    input_data = np.array(input_data, dtype=np.float32)
-    input_data = np.expand_dims(input_data, axis=0)
-    return input_data
-
-def np_array_float32(var, var_shape):
-    arr = np.frombuffer(var, dtype=np.float32)
-    arr.shape = var_shape
-    return arr
-
-def np_array_int16(var, var_shape):
-    arr = np.frombuffer(var, dtype=np.int16)
-    arr.shape = var_shape
-    return arr
-
-def strip_trailing_nulls_from_class_values(class_values):
-    """
-        class_values is a list of unique class levels in training data. This
-        could have multiple Nones in it, and this function strips out all the
-        Nones that occur after the first element in the list.
-        Examples:
-            1) input class_values = ['cat', 'dog']
-               output class_values = ['cat', 'dog']
-
-            2) input class_values = [None, 'cat', 'dog']
-               output class_values = [None, 'cat', 'dog']
-
-            3) input class_values = [None, 'cat', 'dog', None, None]
-               output class_values = [None, 'cat', 'dog']
-
-            4) input class_values = ['cat', 'dog', None, None]
-               output class_values = ['cat', 'dog']
-
-            5) input class_values = [None, None]
-               output class_values = [None]
-        @args:
-            @param: class_values, list
-        @returns:
-            updated class_values list
-    """
-    num_of_valid_class_values = 0
-    if class_values is not None:
-        for ele in class_values:
-            if ele is None and num_of_valid_class_values > 0:
-                break
-            num_of_valid_class_values += 1
-        # Pass only the valid class_values for creating columns
-        class_values = class_values[:num_of_valid_class_values]
-    return class_values
-
-def get_image_count_per_seg_from_array(current_seg_id, images_per_seg):
-    """
-    Get the image count from the array containing all the images
-    per segment. Based on the platform, we find the index of the current segment.
-    This function is only called from inside the transition function.
-    """
-    if is_platform_pg():
-        total_images = images_per_seg[0]
-    else:
-        total_images = images_per_seg[current_seg_id]
-    return total_images
-
-def get_image_count_per_seg_for_minibatched_data_from_db(table_name):
-    """
-    Query the given minibatch formatted table and return the total rows per segment.
-    Since we cannot pass a dictionary to the keras fit step function we create
-    arrays out of the segment numbers and the rows per segment values.
-    This function assumes that the table is not empty and is minibatched which means
-    that it would have been distributed by __dist_key__.
-    :param table_name:
-    :return: Returns two arrays
-    1. An array containing all the segment numbers in ascending order
-    1. An array containing the total images on each of the segments in the
-    segment array.
-    """
-
-    mb_dep_var_col = MINIBATCH_OUTPUT_DEPENDENT_COLNAME_DL
-
-    shape_col = add_postfix(mb_dep_var_col, "_shape")
-
-    if is_platform_pg():
-        res = plpy.execute(
-            """ SELECT {0}::SMALLINT[] AS shape
-                FROM {1}
-            """.format(shape_col, table_name))
-        images_per_seg = [sum(r['shape'][0] for r in res)]
-        seg_ids = [0]
-    else:
-        # The number of images in the buffer is the first dimension in the shape.
-        # Using __dist_key__ instead of gp_segment_id: Since gp_segment_id is
-        # not the actual distribution key column, the optimizer/planner
-        # generates a plan with Redistribute Motion, creating multiple slices on
-        # each segment. For DL, since GPU memory allocation is tied to the process
-        # where it is initialized, we want to minimize creating any additional
-        # slices per segment. This is mainly to avoid any GPU memory allocation
-        # failures which can occur when a newly created slice(process) tries
-        # allocating GPU memory which is already allocated by a previously
-        # created slice(process).
-        # Since the minibatch_preprocessor evenly distributes the data with __dist_key__
-        # as the input table's distribution key, using this for calculating
-        # total images on each segment will avoid creating unnecessary slices(processes).
-        images_per_seg = plpy.execute(
-            """ SELECT {0}, sum({1}[1]) AS images_per_seg
-                FROM {2}
-                GROUP BY {0}
-            """.format(DISTRIBUTION_KEY_COLNAME, shape_col, table_name))
-        seg_ids = [int(each_segment[DISTRIBUTION_KEY_COLNAME])
-                   for each_segment in images_per_seg]
-        images_per_seg = [int(each_segment["images_per_seg"])
-                          for each_segment in images_per_seg]
-
-    return seg_ids, images_per_seg
-
-def get_image_count_per_seg_for_non_minibatched_data_from_db(table_name):
-    """
-    Query the given non minibatch formatted table and return the total rows per segment.
-    Since we cannot pass a dictionary to the keras fit step function we create arrays
-    out of the segment numbers and the rows per segment values.
-    This function assumes that the table is not empty.
-    :param table_name:
-    :return: gp segment id col name and two arrays
-    1. An array containing all the segment numbers in ascending order
-    2. An array containing the total rows for each of the segments in the
-    segment array
-    """
-    if is_platform_pg():
-        images_per_seg = plpy.execute(
-            """ SELECT count(*) AS images_per_seg
-                FROM {0}
-            """.format(table_name))
-        seg_ids = [0]
-        gp_segment_id_col = '0'
-    else:
-        # Compute total buffers on each segment
-        images_per_seg = plpy.execute(
-            """ SELECT {0}, count(*) AS images_per_seg
-                FROM {1}
-                GROUP BY {0}
-            """.format(GP_SEGMENT_ID_COLNAME, table_name))
-        seg_ids = [int(image[GP_SEGMENT_ID_COLNAME]) for image in images_per_seg]
-        gp_segment_id_col = '{0}.{1}'.format(table_name,GP_SEGMENT_ID_COLNAME)
-
-    images_per_seg = [int(image["images_per_seg"]) for image in images_per_seg]
-    return gp_segment_id_col, seg_ids, images_per_seg
-
-def parse_shape(shape):
-    # Parse the shape format given by the sql into an int array
-    # [1:10][1:32][1:3] -> [10, 32, 3]
-    # Split on :, discard the first one [1:],
-    # split each piece on ], take the first piece [0], convert to int
-    return [int(a.split(']')[0]) for a in shape.split(':')[1:]]
-
-
-def query_model_configs(model_selection_table, model_selection_summary_table,
-    mst_key_col, model_arch_table_col):
-    msts_query = """
-                 SELECT * FROM {model_selection_table}
-                 ORDER BY {mst_key_col}
-                 """.format(**locals())
-    model_arch_table_query = """
-                             SELECT {model_arch_table_col}
-                             FROM {model_selection_summary_table}
-                             """.format(**locals())
-    msts = list(plpy.execute(msts_query))
-    model_arch_table = plpy.execute(model_arch_table_query)[0][model_arch_table_col]
-    return msts, model_arch_table
-
-def query_dist_keys(source_table, dist_key_col):
-    """ Read distinct keys from the source table """
-    dist_key_query = """
-                     SELECT DISTINCT({dist_key_col}) FROM {source_table}
-                     ORDER BY {dist_key_col}
-                     """.format(dist_key_col=dist_key_col,
-                                source_table=source_table)
-    res = list(plpy.execute(dist_key_query))
-    res = [x[dist_key_col] for x in res]
-    return res
-
-def query_weights(model_output_table, model_weights_col, mst_key_col, mst_key):
-    mlp_weights_query = """
-                        SELECT {model_weights_col}, {mst_key_col}
-                        FROM {model_output_table}
-                        WHERE {mst_key_col} = {mst_key}
-                        """.format(**locals())
-
-    res = plpy.execute(mlp_weights_query)
-    return res[0][model_weights_col]
-
-def create_summary_view(module_name, model_table, mst_key):
-    tmp_view_summary = unique_string('tmp_view_summary')
-    model_summary_table = add_postfix(model_table, "_summary")
-    model_info_table = add_postfix(model_table, "_info")
-    if not (table_exists(model_summary_table) and
-            table_exists(model_info_table)):
-        plpy.error("{0}: Missing summary and/or info tables for {1}".format(
-            module_name, model_table))
-
-    res = plpy.execute("""
-        SELECT mst_key FROM {model_info_table} WHERE mst_key = {mst_key}
-        """.format(**locals()))
-    if len(res) < 1:
-        plpy.error("{0}: mst_key {1} does not exist in the info table".format(
-            module_name, mst_key))
-
-    plpy.execute("""
-        CREATE VIEW {tmp_view_summary} AS
-        SELECT *
-        FROM {model_summary_table}, {model_info_table}
-        WHERE mst_key = {mst_key}
-        """.format(**locals()))
-    return tmp_view_summary
-
-
-def get_accessible_gpus_for_seg(schema_madlib, segments_per_host, module_name):
-    if is_platform_pg():
-        gpus = GPUInfoFunctions.get_gpu_info_from_tensorflow()
-        if not gpus:
-            plpy.error("{0} error: No GPUs configured on host.".format(module_name))
-        return [len(gpus)]
-    else:
-        gpu_info_table = unique_string(desp = 'gpu_info')
-        gpu_table_query = """
-            SELECT {schema_madlib}.gpu_configuration('{gpu_info_table}')
-        """.format(**locals())
-        plpy.execute(gpu_table_query)
-        gpu_query = """
-            SELECT hostname, count(*) AS count FROM {gpu_info_table} GROUP BY hostname
-            """.format(**locals())
-        gpu_query_result = plpy.execute(gpu_query)
-        plpy.execute("DROP TABLE IF EXISTS {0}".format(gpu_info_table))
-        if not gpu_query_result:
-           plpy.error("{0} error: No GPUs configured on hosts.".format(module_name))
-
-        host_dict = {}
-        for i in gpu_query_result:
-            host_dict[i['hostname']] = int(i['count'])
-
-        seg_query = """
-            SELECT hostname, content AS segment_id
-            FROM gp_segment_configuration
-            WHERE content != -1 AND role = 'p'
-        """
-        seg_query_result = plpy.execute(seg_query)
-
-        accessible_gpus_for_seg = [0] * len(seg_query_result)
-        warning_flag = True
-        for i in seg_query_result:
-            if i['hostname'] in host_dict.keys():
-                accessible_gpus_for_seg[i['segment_id']] = host_dict[i['hostname']]
-            if 0 < accessible_gpus_for_seg[i['segment_id']] < segments_per_host and warning_flag:
-                plpy.warning(
-                    'The number of GPUs per segment host is less than the number of '
-                    'segments per segment host. When different segments share the '
-                    'same GPU, this may fail in some scenarios. The current '
-                    'recommended configuration is to have 1 GPU available per segment.')
-                warning_flag = False
-        return accessible_gpus_for_seg
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_model_selection.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_model_selection.py_in
deleted file mode 100644
index f3b02c7..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_model_selection.py_in
+++ /dev/null
@@ -1,192 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-
-import plpy
-from collections import OrderedDict
-from madlib_keras_validator import MstLoaderInputValidator
-from utilities.control import MinWarning
-from utilities.utilities import add_postfix
-from madlib_keras_wrapper import convert_string_of_args_to_dict
-from keras_model_arch_table import ModelArchSchema
-
-class ModelSelectionSchema:
-    MST_KEY = 'mst_key'
-    MODEL_ID = ModelArchSchema.MODEL_ID
-    MODEL_ARCH_TABLE = 'model_arch_table'
-    COMPILE_PARAMS = 'compile_params'
-    FIT_PARAMS = 'fit_params'
-    col_types = ('SERIAL', 'INTEGER', 'VARCHAR', 'VARCHAR')
-
-@MinWarning("warning")
-class MstLoader():
-    """The utility class for loading a model selection table with model parameters.
-
-    Currently just takes all combinations of input parameters passed. This
-    utility validates the inputs.
-
-    Attributes:
-        compile_params_list (list): The input list of compile params choices.
-        fit_params_list (list): The input list of fit params choices.
-        model_id_list (list): The input list of model id choices.
-        model_arch_table (str): The name of model architechure table.
-        model_selection_table (str): The name of the output mst table.
-        msts (list): The list of generated msts.
-
-    """
-
-    def __init__(self,
-                 model_arch_table,
-                 model_selection_table,
-                 model_id_list,
-                 compile_params_list,
-                 fit_params_list,
-                 **kwargs):
-
-        self.model_arch_table = model_arch_table
-        self.model_selection_table = model_selection_table
-        self.model_selection_summary_table = add_postfix(
-            model_selection_table, "_summary")
-        self.model_id_list = sorted(list(set(model_id_list)))
-        MstLoaderInputValidator(
-            model_arch_table=self.model_arch_table,
-            model_selection_table=self.model_selection_table,
-            model_selection_summary_table=self.model_selection_summary_table,
-            model_id_list=self.model_id_list,
-            compile_params_list=compile_params_list,
-            fit_params_list=fit_params_list
-        )
-        self.compile_params_list = self.params_preprocessed(
-            compile_params_list)
-        self.fit_params_list = self.params_preprocessed(fit_params_list)
-
-        self.msts = []
-
-        self.find_combinations()
-
-    def load(self):
-        """The entry point for loading the model selection table.
-        """
-        # All of the side effects happen in this function.
-        self.create_mst_table()
-        self.create_mst_summary_table()
-        self.insert_into_mst_table()
-
-    def params_preprocessed(self, list_strs):
-        """Preprocess the input lists. Eliminate white spaces and sort them.
-
-        Args:
-            list_strs (list): A list of strings.
-
-        Returns:
-            list: The preprocessed list of strings.
-        """
-
-        dict_dedup = {}
-        for string in list_strs:
-            d = convert_string_of_args_to_dict(string)
-            hash_tuple = tuple( '{0} = {1}'\
-            .format(x, d[x]) for x in sorted(d.keys()))
-            dict_dedup[hash_tuple] = string
-
-        return dict_dedup.values()
-
-    def find_combinations(self):
-        """Backtracking helper for generating the combinations.
-        """
-        param_grid = OrderedDict([
-            (ModelSelectionSchema.MODEL_ID, self.model_id_list),
-            (ModelSelectionSchema.COMPILE_PARAMS, self.compile_params_list),
-            (ModelSelectionSchema.FIT_PARAMS, self.fit_params_list)
-        ])
-
-        def find_combinations_helper(msts, p, i):
-            param_names = param_grid.keys()
-            if i < len(param_names):
-                for x in param_grid[param_names[i]]:
-                    p[param_names[i]] = x
-                    find_combinations_helper(msts, p, i + 1)
-            else:
-                msts.append(p.copy())
-        find_combinations_helper(self.msts, {}, 0)
-
-    def create_mst_table(self):
-        """Initialize the output mst table.
-        """
-        create_query = """
-                        CREATE TABLE {self.model_selection_table} (
-                            {mst_key} SERIAL,
-                            {model_id} INTEGER,
-                            {compile_params} VARCHAR,
-                            {fit_params} VARCHAR,
-                            unique ({model_id}, {compile_params}, {fit_params})
-                        );
-                       """.format(self=self,
-                                  mst_key=ModelSelectionSchema.MST_KEY,
-                                  model_id=ModelSelectionSchema.MODEL_ID,
-                                  compile_params=ModelSelectionSchema.COMPILE_PARAMS,
-                                  fit_params=ModelSelectionSchema.FIT_PARAMS)
-        with MinWarning('warning'):
-            plpy.execute(create_query)
-
-    def create_mst_summary_table(self):
-        """Initialize the output mst table.
-        """
-        create_query = """
-                        CREATE TABLE {self.model_selection_summary_table} (
-                            {model_arch_table} VARCHAR
-                        );
-                       """.format(self=self,
-                                  model_arch_table=ModelSelectionSchema.MODEL_ARCH_TABLE)
-        with MinWarning('warning'):
-            plpy.execute(create_query)
-
-    def insert_into_mst_table(self):
-        """Insert every thing in self.msts into the mst table.
-        """
-        for mst in self.msts:
-            model_id = mst[ModelSelectionSchema.MODEL_ID]
-            compile_params = mst[ModelSelectionSchema.COMPILE_PARAMS]
-            fit_params = mst[ModelSelectionSchema.FIT_PARAMS]
-            insert_query = """
-                            INSERT INTO
-                                {self.model_selection_table}(
-                                    {model_id_col},
-                                    {compile_params_col},
-                                    {fit_params_col}
-                                )
-                            VALUES (
-                                {model_id},
-                                $${compile_params}$$,
-                                $${fit_params}$$
-                            )
-                           """.format(model_id_col=ModelSelectionSchema.MODEL_ID,
-                                      compile_params_col=ModelSelectionSchema.COMPILE_PARAMS,
-                                      fit_params_col=ModelSelectionSchema.FIT_PARAMS,
-                                      **locals())
-            plpy.execute(insert_query)
-        insert_summary_query = """
-                        INSERT INTO
-                            {self.model_selection_summary_table}(
-                                {model_arch_table_name}
-                        )
-                        VALUES (
-                            $${self.model_arch_table}$$
-                        )
-                       """.format(model_arch_table_name=ModelSelectionSchema.MODEL_ARCH_TABLE,
-                                  **locals())
-        plpy.execute(insert_summary_query)
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_model_selection.sql_in b/src/ports/postgres/modules/deep_learning/madlib_keras_model_selection.sql_in
deleted file mode 100644
index c15757c..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_model_selection.sql_in
+++ /dev/null
@@ -1,437 +0,0 @@
-/* ----------------------------------------------------------------------- *//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *
- * @file madlib_keras_model_selection.sql_in
- *
- * @brief SQL functions for model hopper distributed training
- * @date August 2019
- *
- *
- *//* ----------------------------------------------------------------------- */
-
-m4_include(`SQLCommon.m4')
-/**
-@addtogroup grp_keras_setup_model_selection
-
-@brief Utility function to set up a model selection table for model architecture search
-and hyperparameter tuning.
-
-\warning  This MADlib method is still in early stage development.
-Interface and implementation are subject to change. 
-
-
Contents

-
-This utility function sets up a model selection table
-for use by the multiple model Keras fit feature of MADlib.
-By model selection we mean both hyperparameter tuning and
-model architecture search.  The table defines the unique combinations
-of model architectures, compile and fit parameters
-to run on a massively parallel processing database cluster.
-
-@anchor load_mst_table
-@par Load Model Selection Table
-
-
-load_model_selection_table(
-    model_arch_table,
-    model_selection_table,
-    model_id_list,
-    compile_params_list,
-    fit_params_list
-    )
-

-
-\b Arguments
-

-  
model_arch_table

-  
VARCHAR. Table containing model architectures and weights.
-  For more information on this table
-  refer to Load Model.
-  

-
-  
model_selection_table

-  
VARCHAR. Model selection table created by this utility.  A summary table
-  named _summary is also created.  Contents of both output
-  tables are described below.
-  

-
-  
model_id_list

-  
INTEGER[]. Array of model IDs from the 'model_arch_table' to be included
-  in the run combinations.  For hyperparameter search, this will typically be
-  one model ID.  For model architecture search, this will be the different model IDs
-  that you want to test.
-  

-
-  
compile_params_list

-  
VARCHAR[]. Array of compile parameters to be tested.  Each element
-  of the array should consist of a string of compile parameters
-  exactly as it is to be passed to Keras.
-  

-
-  
fit_params_list

-  
VARCHAR[].  Array of fit parameters to be tested.  Each element
-  of the array should consist of a string of fit parameters
-  exactly as it is to be passed to Keras.
-  

-
-

-
-Output table
-

-    The model selection output table contains the following columns:
-    
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-      
-        
-        
-      
-    
mst_keyINTEGER. ID that defines a unique tuple for
-        model architecture-compile parameters-fit parameters.
-        
model_idVARCHAR. Model architecture ID from the 'model_arch_table'.
-        
compile_paramsVARCHAR. Keras compile parameters.
-        
fit_paramsVARCHAR. Keras fit parameters.
-        

-    A summary table named _summary is
-    also created, which contains the following column:
-    
-      
-        
-        
-      
-    
model_arch_tableVARCHAR. Name of the model architecture table containing the
-        model architecture IDs.
-        

-

-
-@anchor example
-@par Examples
--# The model selection table works in conjunction with a model architecture table,
-so we first create a model architecture table with two different models.  Use Keras to define
-a model architecture with 1 hidden layer:
-
-import keras
-from keras.models import Sequential
-from keras.layers import Dense
-model1 = Sequential()
-model1.add(Dense(10, activation='relu', input_shape=(4,)))
-model1.add(Dense(10, activation='relu'))
-model1.add(Dense(3, activation='softmax'))
-model1.summary()
-\verbatim
-
-_________________________________________________________________
-Layer (type)                 Output Shape              Param #
-=================================================================
-dense_1 (Dense)              (None, 10)                50
-_________________________________________________________________
-dense_2 (Dense)              (None, 10)                110
-_________________________________________________________________
-dense_3 (Dense)              (None, 3)                 33
-=================================================================
-Total params: 193
-Trainable params: 193
-Non-trainable params: 0
-\endverbatim
-

-Export the model to JSON:
-
-model1.to_json()
-

-
-'{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}'
-

-Now use Keras to define
-a model architecture with 2 hidden layers:
-
-model2 = Sequential()
-model2.add(Dense(10, activation='relu', input_shape=(4,)))
-model2.add(Dense(10, activation='relu'))
-model2.add(Dense(10, activation='relu'))
-model2.add(Dense(3, activation='softmax'))
-model2.summary()
-\verbatim
-
-_________________________________________________________________
-Layer (type)                 Output Shape              Param #
-=================================================================
-dense_4 (Dense)              (None, 10)                50
-_________________________________________________________________
-dense_5 (Dense)              (None, 10)                110
-_________________________________________________________________
-dense_6 (Dense)              (None, 10)                110
-_________________________________________________________________
-dense_7 (Dense)              (None, 3)                 33
-=================================================================
-Total params: 303
-Trainable params: 303
-Non-trainable params: 0
-\endverbatim
-

-Export the model to JSON:
-
-model2.to_json()
-

-
-'{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_4", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_5", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_6", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_7", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}'
-

-
--# Load both models into the architecture table:
-
-DROP TABLE IF EXISTS model_arch_library;
-SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
-$$
-{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
-$$
-::json,         -- JSON blob
-                               NULL,                  -- Weights
-                               'Sophie',              -- Name
-                               'MLP with 1 hidden layer'       -- Descr
-);
-SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
-$$
-{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_4", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_5", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_6", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_7", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
-$$
-::json,         -- JSON blob
-                               NULL,                  -- Weights
-                               'Maria',               -- Name
-                               'MLP with 2 hidden layers'       -- Descr
-);
-SELECT model_id, name, description FROM model_arch_library ORDER BY model_id;
-

-
- model_id |  name  |       description
-----------+--------+--------------------------
-        1 | Sophie | MLP with 1 hidden layer
-        2 | Maria  | MLP with 2 hidden layers
-(2 rows)
-

-
--# Load model selection table.  Select the model(s) from the model
-architecture table that you want to run, along with the compile and
-fit parameters.  Unique combinations will be created:
-
-DROP TABLE IF EXISTS mst_table, mst_table_summary;
-SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table
-                                         'mst_table',          -- model selection table output
-                                          ARRAY[1,2],              -- model ids from model architecture table
-                                          ARRAY[                   -- compile params
-                                              $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,
-                                              $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,
-                                              $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$
-                                          ],
-                                          ARRAY[                    -- fit params
-                                              $$batch_size=4,epochs=1$$,
-                                              $$batch_size=8,epochs=1$$
-                                          ]
-                                         );
-SELECT * FROM mst_table ORDER BY mst_key;
-

-
- mst_key | model_id      |                                 compile_params                                  |      fit_params
----------+---------------+---------------------------------------------------------------------------------+-----------------------
-       1 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1
-       2 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1
-       3 |             1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1
-       4 |             1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1
-       5 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1
-       6 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1
-       7 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1
-       8 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1
-       9 |             2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1
-      10 |             2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1
-      11 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1
-      12 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1
-(12 rows)
-

-The name of the model architecture table is stored in the summary table:
-
-SELECT * FROM mst_table_summary;
-

-
-  model_arch_table
---------------------+
- model_arch_library
-(1 row)
-

-
--# Create model selection table manually.  If you would like to
-have more control over the set of model selection parameters
-to run, you can manually create the model selection table and
-the associated summary table. Both must be created since
-they are needed by the multiple model fit module.
-For example, let's say we don't want all combinations
-but only want batch_size=4 for model_id=1 and batch_size=8 for model_id=2:
-
-DROP TABLE IF EXISTS mst_table_manual;
-CREATE TABLE mst_table_manual(
-    mst_key serial,
-    model_id integer,
-    compile_params varchar,
-    fit_params varchar
-);
-INSERT INTO mst_table_manual(model_id, compile_params, fit_params) VALUES
-(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),
-(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),
-(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),
-(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'),
-(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'),
-(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=8,epochs=1');
-SELECT * FROM mst_table_manual ORDER BY mst_key;
-

-
- mst_key | model_id      |                                 compile_params                                  |      fit_params
----------+---------------+---------------------------------------------------------------------------------+-----------------------
-       1 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1
-       2 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']  | batch_size=4,epochs=1
-       3 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1
-       4 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1
-       5 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']  | batch_size=8,epochs=1
-       6 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1
-(6 rows)
-

-Create the summary table which must be named
-with the model selection output table appended by "_summary":
-
-DROP TABLE IF EXISTS mst_table_manual_summary;
-CREATE TABLE mst_table_manual_summary (
-    model_arch_table varchar
-);
-INSERT INTO mst_table_manual_summary(model_arch_table) VALUES
-('model_arch_library');
-SELECT * FROM mst_table_manual_summary;
-

-
-  model_arch_table
---------------------+
- model_arch_library
-(1 row)
-

-
--# Generate hyperparameters automatically.  You can use other
-libraries or methods to generate hyperparameters according
-to the tests that you want to run. For example, let's randomly
-generate batch size from powers of 2 and learning
-rate on a log scale.
-We use psycopg which is a PostgreSQL database adapter
-for the Python programming language.
-
-import numpy as np
-import psycopg2 as p2
-conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')
-#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')
-cur = conn.cursor()
-\#
-%sql DROP TABLE IF EXISTS mst_table_auto, mst_table_auto_summary;
-\#
-\#compile params
-learning_rate = np.random.permutation([0.1,0.01,0.001,0.0001])[:3]
-compile_param1 = "loss='categorical_crossentropy',optimizer='Adam(lr=" + str(learning_rate[0]) + ")',metrics=['accuracy']"
-compile_param2 = "loss='categorical_crossentropy',optimizer='Adam(lr=" + str(learning_rate[1]) + ")',metrics=['accuracy']"
-compile_param3 = "loss='categorical_crossentropy',optimizer='Adam(lr=" + str(learning_rate[2]) + ")',metrics=['accuracy']"
-compile_params = [compile_param1,compile_param2,compile_param3]
-\#
-\#fit params
-batch_size = np.random.permutation([4,8,16,32,64])[:2]
-fit_param1 = "batch_size=" + str(batch_size[0]) + ",epochs=1"
-fit_param2 = "batch_size=" + str(batch_size[1]) + ",epochs=1"
-fit_params = [fit_param1,fit_param2]
-\#
-query = "SELECT madlib.load_model_selection_table('model_arch_library', 'mst_table_auto', ARRAY[1,2], %s, %s);"
-\#
-cur.execute(query,[compile_params, fit_params])
-conn.commit()
-\#
-\# review model selection table
-%sql SELECT * FROM mst_table_auto ORDER BY mst_key;
-

-
- mst_key | model_id      |                                  compile_params                                  |      fit_params
----------+---------------+----------------------------------------------------------------------------------+-----------------------
-       1 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.0001)',metrics=['accuracy'] | batch_size=4,epochs=1
-       2 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.0001)',metrics=['accuracy'] | batch_size=8,epochs=1
-       3 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']    | batch_size=4,epochs=1
-       4 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']    | batch_size=8,epochs=1
-       5 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']  | batch_size=4,epochs=1
-       6 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']  | batch_size=8,epochs=1
-       7 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.0001)',metrics=['accuracy'] | batch_size=4,epochs=1
-       8 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.0001)',metrics=['accuracy'] | batch_size=8,epochs=1
-       9 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']    | batch_size=4,epochs=1
-      10 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']    | batch_size=8,epochs=1
-      11 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']  | batch_size=4,epochs=1
-      12 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']  | batch_size=8,epochs=1
-(12 rows)
-

-The name of the model architecture table is stored in the summary table:
-
-SELECT * FROM mst_table_auto_summary;
-

-
-  model_arch_table
---------------------+
- model_arch_library
-(1 row)
-

-
-@anchor notes
-@par Notes
-
-1. In this method, the same compile and fit parameters are applied to all model architectures
-when generating combinations.  However, you may wish to have different compile and fit parameters
-for each model.  To do so, call 'load_model_selection_table'
-multiple times - once for each model.  Then you can combine the resulting tables using UNION or other means.
-Note that the 'mst_key' must be unique so you will need to regenerate it in your final combined table.
-
-@anchor related
-@par Related Topics
-
-See keras_model_arch_table.sql_in
-
-*/
-
-CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_model_selection_table(
-    model_arch_table        VARCHAR,
-    model_selection_table   VARCHAR,
-    model_id_list           INTEGER[],
-    compile_params_list     VARCHAR[],
-    fit_params_list         VARCHAR[]
-) RETURNS VOID AS $$
-    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_model_selection')
-    with AOControl(False):
-        mst_loader = madlib_keras_model_selection.MstLoader(**globals())
-        mst_loader.load()
-$$ LANGUAGE plpythonu VOLATILE
-m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_predict.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_predict.py_in
deleted file mode 100644
index 412e63b..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_predict.py_in
+++ /dev/null
@@ -1,456 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import plpy
-
-import keras
-from keras import backend as K
-from keras.layers import *
-from keras.models import *
-from keras.optimizers import *
-
-from model_arch_info import *
-from madlib_keras_helper import *
-from madlib_keras_validator import *
-from predict_input_params import PredictParamsProcessor
-from utilities.control import MinWarning
-from utilities.utilities import _assert
-from utilities.utilities import add_postfix
-from utilities.utilities import create_cols_from_array_sql_string
-from utilities.utilities import get_segments_per_host
-from utilities.utilities import unique_string
-from utilities.validate_args import get_expr_type
-from utilities.validate_args import input_tbl_valid
-
-from madlib_keras_wrapper import *
-
-class BasePredict():
-    def __init__(self, schema_madlib, table_to_validate, test_table, id_col,
-                 independent_varname, output_table, pred_type, use_gpus, module_name):
-        self.schema_madlib = schema_madlib
-        self.table_to_validate = table_to_validate
-        self.test_table = test_table
-        self.id_col = id_col
-        self.independent_varname = independent_varname
-        self.output_table = output_table
-        self.pred_type = pred_type
-        self.module_name = module_name
-
-        self.segments_per_host = get_segments_per_host()
-        self.use_gpus = use_gpus if use_gpus else False
-        if self.use_gpus:
-            accessible_gpus_for_seg = get_accessible_gpus_for_seg(schema_madlib, self.segments_per_host, self.module_name)
-            _assert(len(set(accessible_gpus_for_seg)) == 1,
-                '{0}: Asymmetric gpu configurations are not supported'.format(self.module_name))
-            self.gpus_per_host = accessible_gpus_for_seg[0]
-        else:
-            self.gpus_per_host = 0
-
-        self._set_default_pred_type()
-
-    def _set_default_pred_type(self):
-        self.pred_type =  'response' if not self.pred_type else self.pred_type
-        self.is_response = True if self.pred_type == 'response' else False
-
-    def call_internal_keras(self):
-        if self.is_response:
-            pred_col_name = add_postfix("estimated_", self.dependent_varname)
-            pred_col_type = self.dependent_vartype
-        else:
-            pred_col_name = "prob"
-            pred_col_type = 'double precision'
-
-        intermediate_col = unique_string()
-        class_values = strip_trailing_nulls_from_class_values(self.class_values)
-
-        prediction_select_clause, create_table_columns = create_cols_from_array_sql_string(
-            class_values, intermediate_col, pred_col_name,
-            pred_col_type, self.is_response, self.module_name)
-        gp_segment_id_col, seg_ids_test, \
-        images_per_seg_test = get_image_count_per_seg_for_non_minibatched_data_from_db(
-            self.test_table)
-        segments_per_host = get_segments_per_host()
-
-        select_segmentid_comma = ""
-        group_by_clause = ""
-        join_cond_on_segmentid = ""
-        if not is_platform_pg():
-            select_segmentid_comma = "{self.test_table}.gp_segment_id AS gp_segment_id,".format(self=self)
-            group_by_clause = "GROUP BY {self.test_table}.gp_segment_id".format(self=self)
-            join_cond_on_segmentid = "{self.test_table}.gp_segment_id=min_ctid.gp_segment_id AND".format(self=self)
-
-        # Calling CREATE TABLE instead of CTAS, to ensure that the plan_cache_mode
-        # guc codepath is called when passing in the weights
-        plpy.execute("""
-            CREATE TABLE {self.output_table}
-            ({self.id_col} {self.id_col_type}, {create_table_columns})
-            """.format(self=self, create_table_columns=create_table_columns))
-        # Passing huge model weights to internal_keras_predict() for each row
-        # resulted in slowness of overall madlib_keras_predict().
-        # To avoid this, a CASE is added to pass the model weights only for
-        # the very first row(min(ctid)) that is fetched on each segment and NULL
-        # for the other rows.
-        predict_query = plpy.prepare("""
-            INSERT INTO {self.output_table}
-            SELECT {self.id_col}::{self.id_col_type}, {prediction_select_clause}
-            FROM (
-                SELECT {self.test_table}.{self.id_col},
-                       ({self.schema_madlib}.internal_keras_predict
-                           ({self.independent_varname},
-                            $1,
-                            CASE WHEN {self.test_table}.ctid = min_ctid.ctid THEN $2 ELSE NULL END,
-                            {self.is_response},
-                            {self.normalizing_const},
-                            {gp_segment_id_col},
-                            ARRAY{seg_ids_test},
-                            ARRAY{images_per_seg_test},
-                            {self.use_gpus},
-                            {self.gpus_per_host},
-                            {segments_per_host})
-                       ) AS {intermediate_col}
-                FROM {self.test_table}
-                LEFT JOIN
-                    (SELECT {select_segmentid_comma} MIN({self.test_table}.ctid) AS ctid
-                     FROM {self.test_table}
-                     {group_by_clause}) min_ctid
-                ON {join_cond_on_segmentid} {self.test_table}.ctid=min_ctid.ctid
-            ) q
-            """.format(self=self, prediction_select_clause=prediction_select_clause,
-                       seg_ids_test=seg_ids_test,
-                       images_per_seg_test=images_per_seg_test,
-                       gp_segment_id_col=gp_segment_id_col,
-                       segments_per_host=segments_per_host,
-                       intermediate_col=intermediate_col,
-                       select_segmentid_comma=select_segmentid_comma,
-                       group_by_clause=group_by_clause,
-                       join_cond_on_segmentid=join_cond_on_segmentid),
-                                     ["text", "bytea"])
-        plpy.execute(predict_query, [self.model_arch, self.model_weights])
-
-    def set_default_class_values(self, class_values):
-        self.class_values = class_values
-        if self.pred_type == 'prob':
-            return
-        if self.class_values is None:
-            num_classes = get_num_classes(self.model_arch)
-            self.class_values = range(0, num_classes)
-
-@MinWarning("warning")
-class Predict(BasePredict):
-    def __init__(self, schema_madlib, model_table,
-                 test_table, id_col, independent_varname,
-                 output_table, pred_type, use_gpus,
-                 mst_key, **kwargs):
-
-        self.module_name = 'madlib_keras_predict'
-        self.model_table = model_table
-        self.mst_key = mst_key
-        self.is_mult_model = mst_key is not None
-        if self.model_table:
-            self.model_summary_table = add_postfix(self.model_table, "_summary")
-
-        BasePredict.__init__(self, schema_madlib, model_table, test_table,
-                              id_col, independent_varname,
-                              output_table, pred_type,
-                              use_gpus, self.module_name)
-        param_proc = PredictParamsProcessor(self.model_table, self.module_name, self.mst_key)
-        if self.is_mult_model:
-            self.temp_summary_view = param_proc.model_summary_table
-            self.model_summary_table = self.temp_summary_view
-        self.dependent_vartype = param_proc.get_dependent_vartype()
-        self.model_weights = param_proc.get_model_weights()
-        self.model_arch = param_proc.get_model_arch()
-        class_values = param_proc.get_class_values()
-        self.set_default_class_values(class_values)
-        self.normalizing_const = param_proc.get_normalizing_const()
-        self.dependent_varname = param_proc.get_dependent_varname()
-
-        self.validate()
-        self.id_col_type = get_expr_type(self.id_col, self.test_table)
-        BasePredict.call_internal_keras(self)
-        if self.is_mult_model:
-            plpy.execute("DROP VIEW IF EXISTS {}".format(self.temp_summary_view))
-
-    def validate(self):
-        input_tbl_valid(self.model_table, self.module_name)
-        if self.is_mult_model and not columns_exist_in_table(self.model_table, ['mst_key']):
-            plpy.error("{self.module_name}: Single model should not pass mst_key".format(**locals()))
-        if not self.is_mult_model and columns_exist_in_table(self.model_table, ['mst_key']):
-            plpy.error("{self.module_name}: Multi-model needs to pass mst_key".format(**locals()))
-        InputValidator.validate_predict_evaluate_tables(
-            self.module_name, self.model_table, self.model_summary_table,
-            self.test_table, self.output_table, self.independent_varname)
-
-        InputValidator.validate_id_in_test_tbl(
-            self.module_name, self.test_table, self.id_col)
-
-        InputValidator.validate_class_values(
-            self.module_name, self.class_values, self.pred_type, self.model_arch)
-        input_shape = get_input_shape(self.model_arch)
-        InputValidator.validate_pred_type(
-            self.module_name, self.pred_type, self.class_values)
-        InputValidator.validate_input_shape(
-            self.test_table, self.independent_varname, input_shape, 1)
-
-@MinWarning("warning")
-class PredictBYOM(BasePredict):
-    def __init__(self, schema_madlib, model_arch_table, model_id,
-                 test_table, id_col, independent_varname, output_table,
-                 pred_type, use_gpus, class_values, normalizing_const,
-                 **kwargs):
-
-        self.module_name='madlib_keras_predict_byom'
-        self.model_arch_table = model_arch_table
-        self.model_id = model_id
-        self.class_values = class_values
-        self.normalizing_const = normalizing_const
-        self.dependent_varname = 'dependent_var'
-        BasePredict.__init__(self, schema_madlib, model_arch_table,
-                             test_table, id_col, independent_varname,
-                             output_table, pred_type, use_gpus, self.module_name)
-        if self.is_response:
-            self.dependent_vartype = 'text'
-        else:
-            self.dependent_vartype = 'double precision'
-        ## Set default values for norm const and class_values
-        # use_gpus and pred_type are defaulted in base_predict's init
-        self.normalizing_const = normalizing_const
-        if self.normalizing_const is None:
-            self.normalizing_const = DEFAULT_NORMALIZING_CONST
-        InputValidator.validate_predict_byom_tables(
-            self.module_name, self.model_arch_table, self.model_id,
-            self.test_table, self.id_col, self.output_table,
-            self.independent_varname)
-        self.validate_and_set_defaults()
-        self.id_col_type = get_expr_type(self.id_col, self.test_table)
-        BasePredict.call_internal_keras(self)
-
-    def validate_and_set_defaults(self):
-        # Set some defaults first and then validate and then set some more defaults
-        self.model_arch, self.model_weights = get_model_arch_weights(
-            self.model_arch_table, self.model_id)
-        # Assert model_weights and model_arch are not empty.
-        _assert(self.model_weights and self.model_arch,
-                "{0}: Model weights and architecture should not be NULL.".format(
-                    self.module_name))
-        self.set_default_class_values(self.class_values)
-
-        InputValidator.validate_pred_type(
-            self.module_name, self.pred_type, self.class_values)
-        InputValidator.validate_normalizing_const(
-            self.module_name, self.normalizing_const)
-        InputValidator.validate_class_values(
-            self.module_name, self.class_values, self.pred_type, self.model_arch)
-        InputValidator.validate_input_shape(
-            self.test_table, self.independent_varname,
-            get_input_shape(self.model_arch), 1)
-
-def internal_keras_predict(independent_var, model_architecture, model_weights,
-                           is_response, normalizing_const, current_seg_id, seg_ids,
-                           images_per_seg, use_gpus, gpus_per_host, segments_per_host,
-                           **kwargs):
-    SD = kwargs['SD']
-    model_key = 'segment_model_predict'
-    row_count_key = 'row_count'
-    try:
-        device_name = get_device_name_and_set_cuda_env( gpus_per_host, current_seg_id)
-        if model_key not in SD:
-            set_keras_session(device_name, gpus_per_host, segments_per_host)
-            model = model_from_json(model_architecture)
-            set_model_weights(model, model_weights)
-            SD[model_key] = model
-            SD[row_count_key] = 0
-        else:
-            model = SD[model_key]
-        SD[row_count_key] += 1
-
-        # Since the test data isn't mini-batched,
-        # we have to make sure that the test data np array has the same
-        # number of dimensions as input_shape. So we add a dimension to x.
-        independent_var = expand_input_dims(independent_var)
-        independent_var /= normalizing_const
-
-        if is_response:
-            with K.tf.device(device_name):
-                y_prob = model.predict(independent_var)
-                proba_argmax = y_prob.argmax(axis=-1)
-            # proba_argmax is a list with exactly one element in it. That element
-            # refers to the index containing the largest probability value in the
-            # output of Keras' predict function.
-            result = proba_argmax
-        else:
-            with K.tf.device(device_name):
-                probs = model.predict(independent_var)
-            # probs is a list containing a list of probability values, of all
-            # class levels. Since we are assuming each input is a single image,
-            # and not mini-batched, this list contains exactly one list in it,
-            # so return back the first list in probs.
-            result = probs[0]
-        total_images = get_image_count_per_seg_from_array(seg_ids.index(current_seg_id),
-                                                          images_per_seg)
-
-        if SD[row_count_key] == total_images:
-            SD.pop(model_key, None)
-            SD.pop(row_count_key, None)
-            clear_keras_session()
-        return result
-    except Exception as ex:
-        SD.pop(model_key, None)
-        SD.pop(row_count_key, None)
-        clear_keras_session()
-        plpy.error(ex)
-
-
-def predict_help(schema_madlib, message, **kwargs):
-    """
-    Help function for keras predict
-
-    Args:
-        @param schema_madlib
-        @param message: string, Help message string
-        @param kwargs
-
-    Returns:
-        String. Help/usage information
-    """
-    if not message:
-        help_string = """
------------------------------------------------------------------------
-                            SUMMARY
------------------------------------------------------------------------
-This function allows the user to predict using a madlib_keras_fit trained
-model.
-
-For more details on function usage:
-    SELECT {schema_madlib}.madlib_keras_predict('usage')
-            """
-    elif message in ['usage', 'help', '?']:
-        help_string = """
------------------------------------------------------------------------
-                            USAGE
------------------------------------------------------------------------
- SELECT {schema_madlib}.madlib_keras_predict(
-    model_table,    --  Name of the table containing the model
-    test_table,     --  Name of the table containing the evaluation dataset
-    id_col,         --  Name of the id column in the test data table
-    independent_varname,    --  Name of the column with independent
-                                variables in the test table
-    output_table,   --  Name of the output table
-    pred_type,      --  The type of the desired output
-    use_gpus,       --  Flag for enabling GPU support
-    mst_key         --  Identifier for the desired model out of multimodel
-                        training output
-    )
- );
-
------------------------------------------------------------------------
-                            OUTPUT
------------------------------------------------------------------------
-The output table ('output_table' above) contains the following columns:
-
-id:                 Gives the 'id' for each prediction, corresponding
-                    to each row from the test_table.
-estimated_COL_NAME: (For pred_type='response') The estimated class for
-                    classification, where COL_NAME is the name of the
-                    column to be predicted from test data.
-prob_CLASS:         (For pred_type='prob' for classification) The
-                    probability of a given class. There will be one
-                    column for each class in the training data.
-                    TODO change this
-"""
-    else:
-        help_string = "No such option. Use {schema_madlib}.madlib_keras_predict()"
-
-    return help_string.format(schema_madlib=schema_madlib)
-
-def predict_byom_help(schema_madlib, message, **kwargs):
-    """
-    Help function for keras predict
-
-    Args:
-        @param schema_madlib
-        @param message: string, Help message string
-        @param kwargs
-
-    Returns:
-        String. Help/usage information
-    """
-    if not message:
-        help_string = """
------------------------------------------------------------------------
-                            SUMMARY
------------------------------------------------------------------------
-This function allows the user to predict with their own pre trained model (note
-that this model doesn't have to be trained using MADlib.)
-
-For more details on function usage:
-    SELECT {schema_madlib}.madlib_keras_predict_byom('usage')
-            """
-    elif message in ['usage', 'help', '?']:
-        help_string = """
------------------------------------------------------------------------
-                            USAGE
------------------------------------------------------------------------
- SELECT {schema_madlib}.madlib_keras_predict_byom(
-    model_arch_table,       --  Name of the table containing the model architecture
-                                and the pre trained model weights
-    model_id,               --  This is the id in 'model_arch_table' containing the
-                                model architecture
-    test_table,             --  Name of the table containing the evaluation dataset
-    id_col,                 --  Name of the id column in the test data table
-    independent_varname,    --  Name of the column with independent
-                                variables in the test table
-    output_table,           --  Name of the output table
-    pred_type,              --  The type of the desired output
-    use_gpus,               --  Flag for enabling GPU support
-    class_values,           --  List of class labels that were used while training the
-                                model. If class_values is passed in as NULL, the output
-                                table will have a column named 'prob' which is an array
-                                of probabilities of all the classes.
-                                Otherwise if class_values is not NULL, then the output
-                                table will contain a column for each class/label from
-                                the training data
-    normalizing_const,      --  Normalizing constant used for standardizing arrays in
-                                independent_varname
-    )
- );
-
------------------------------------------------------------------------
-                            OUTPUT
------------------------------------------------------------------------
-The output table ('output_table' above) contains the following columns:
-
-id:                 Gives the 'id' for each prediction, corresponding
-                    to each row from the test_table.
-estimated_dependent_var: (For pred_type='response') The estimated class for
-                    classification. If class_values is passed in as NULL, then we
-                    assume that the class labels are [0,1,2...,n] where n in the
-                    num of classes in the model architecture.
-prob_CLASS:         (For pred_type='prob' for classification) The
-                    probability of a given class.
-                    If class_values is passed in as NULL, we create just one column
-                    called 'prob' which is an array of probabilites of all the classes
-                    Otherwise if class_values is not NULL, then there will be one
-                    column for each class in the training data.
-"""
-    else:
-        help_string = "No such option. Use {schema_madlib}.madlib_keras_predict_byom()"
-
-    return help_string.format(schema_madlib=schema_madlib)
-# ---------------------------------------------------------------------
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_serializer.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_serializer.py_in
deleted file mode 100644
index 6fa210c..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_serializer.py_in
+++ /dev/null
@@ -1,162 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-import numpy as np
-from utilities.utilities import _assert
-
-# TODO
-# 1. Current serializing logic
-    # serialized string -> byte string
-    # np.array(np.array(image_count).concatenate(weights_np_array)).tostring()
-    # Proposed logic
-    # image_count can be a separate value
-    # weights -> np.array(weights).tostring()
-    # combine these 2 into one string by a random splitter
-    # serialized string -> imagecount_splitter_weights
-# 2. combine the serialize_state_with_nd_weights and serialize_state_with_1d_weights
-    # into one function called serialize_state. This function can infer the shape
-    # of the model weights and then flatten if they are nd weights.
-# 3. Same as 2 for deserialize
-
-
-"""
-workflow
-1. Set initial weights in madlib keras fit function.
-2. Serialize these initial model weights as a byte string and pass it to keras step
-3. Deserialize the state passed from the previous step into a list of nd weights
-that will be passed on to model.set_weights()
-4. At the end of each buffer in fit transition, serialize the image count and
-the model weights into a bytestring that will be passed on to the fit merge function.
-5. In fit merge, deserialize the state as image and 1d np arrays. Do some averaging
-operations and serialize them again into a state which contains the image
-and the 1d state. same for fit final
-6. Return the final state from fit final to fit which will then be deserialized
-as 1d weights to be passed on to the evaluate function
-"""
-def get_serialized_1d_weights_from_state(state):
-    """
-    Output of this function is used to deserialize the output of each iteration
-    of madlib keras step UDA.
-
-    :param state: bytestring serialized model state containing image count
-    and weights
-    :return: model weights serialized as bytestring
-    """
-    _ , weights = deserialize_as_image_1d_weights(state)
-    return weights.tostring()
-
-def serialize_state_with_nd_weights(image_count, model_weights):
-    """
-    This function is called when the output of keras.get_weights() (list of nd
-    np arrays) has to be converted into a serialized model state.
-
-    :param image_count: float value
-    :param model_weights: a list of numpy arrays, what you get from
-        keras.get_weights()
-    :return: Image count and model weights serialized into a bytestring format
-
-    """
-    if model_weights is None:
-        return None
-    flattened_weights = [w.flatten() for w in model_weights]
-    state = [np.array([image_count])] + flattened_weights
-    state = np.concatenate(state)
-    return np.float32(state).tostring()
-
-
-def serialize_state_with_1d_weights(image_count, model_weights):
-    """
-    This function is called when the weights are to be passed to the keras fit
-    merge and final functions.
-
-    :param image_count: float value
-    :param model_weights: a single flattened numpy array containing all of the
-        weights
-    :return: Image count and model weights serialized into a bytestring format
-
-    """
-    if model_weights is None:
-        return None
-    merge_state = np.array([image_count])
-    merge_state = np.concatenate((merge_state, model_weights))
-    merge_state = np.float32(merge_state)
-    return merge_state.tostring()
-
-
-def deserialize_as_image_1d_weights(state):
-    """
-    This function is called when the model state needs to be deserialized in
-    the keras fit merge and final functions.
-
-    :param state: the stringified (serialized) state containing image_count and
-            model_weights
-    :return:
-        image_count: total buffer counts processed
-        model_weights: a single flattened numpy array containing all of the
-        weights
-    """
-    if not state:
-        return None
-    state = np.fromstring(state, dtype=np.float32)
-    return float(state[0]), state[1:]
-
-
-def serialize_nd_weights(model_weights):
-    """
-    This function is called for passing the initial model weights from the keras
-    fit function to the keras fit transition function.
-    :param model_weights: a list of numpy arrays, what you get from
-        keras.get_weights()
-    :return: Model weights serialized into a bytestring format
-    """
-    if model_weights is None:
-        return None
-    flattened_weights = [w.flatten() for w in model_weights]
-    flattened_weights = np.concatenate(flattened_weights)
-    return np.float32(flattened_weights).tostring()
-
-
-def deserialize_as_nd_weights(model_weights_serialized, model_shapes):
-    """
-    The output of this function is used to set keras model weights using the
-    function model.set_weights()
-    :param model_weights_serialized: bytestring containing model weights
-    :param model_shapes: list containing the shapes of each layer.
-    :return: list of nd numpy arrays containing all of the
-        weights
-    """
-    if not model_weights_serialized or not model_shapes:
-        return None
-
-    i, j, model_weights = 0, 0, []
-    model_weights_serialized = np.fromstring(model_weights_serialized, dtype=np.float32)
-
-    total_model_shape = \
-        sum([reduce(lambda x, y: x * y, ls) for ls in model_shapes])
-    total_weights_shape = model_weights_serialized.size
-    _assert(total_model_shape == total_weights_shape,
-            "Number of elements in model weights({0}) doesn't match model({1})."\
-                .format(total_weights_shape, total_model_shape))
-    while j < len(model_shapes):
-        next_pointer = i + reduce(lambda x, y: x * y, model_shapes[j])
-        weight_arr_portion = model_weights_serialized[i:next_pointer]
-        model_weights.append(np.array(weight_arr_portion).reshape(model_shapes[j]))
-        i, j = next_pointer, j + 1
-    return model_weights
-
-
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_validator.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_validator.py_in
deleted file mode 100644
index 11730cf..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_validator.py_in
+++ /dev/null
@@ -1,506 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import plpy
-from keras_model_arch_table import ModelArchSchema
-from model_arch_info import get_num_classes
-from madlib_keras_helper import CLASS_VALUES_COLNAME
-from madlib_keras_helper import COMPILE_PARAMS_COLNAME
-from madlib_keras_helper import DEPENDENT_VARNAME_COLNAME
-from madlib_keras_helper import DEPENDENT_VARTYPE_COLNAME
-from madlib_keras_helper import MODEL_ID_COLNAME
-from madlib_keras_helper import MODEL_ARCH_TABLE_COLNAME
-from madlib_keras_helper import MODEL_WEIGHTS_COLNAME
-from madlib_keras_helper import NORMALIZING_CONST_COLNAME
-from madlib_keras_helper import DISTRIBUTION_KEY_COLNAME
-from madlib_keras_helper import METRIC_TYPE_COLNAME
-from madlib_keras_helper import INTERNAL_GPU_CONFIG
-from madlib_keras_helper import query_model_configs
-
-from utilities.minibatch_validation import validate_bytea_var_for_minibatch
-from utilities.utilities import _assert
-from utilities.utilities import add_postfix
-from utilities.utilities import is_platform_pg
-from utilities.utilities import is_var_valid
-from utilities.validate_args import cols_in_tbl_valid
-from utilities.validate_args import columns_exist_in_table
-from utilities.validate_args import get_expr_type
-from utilities.validate_args import input_tbl_valid
-from utilities.validate_args import output_tbl_valid
-from madlib_keras_wrapper import parse_and_validate_fit_params
-from madlib_keras_wrapper import parse_and_validate_compile_params
-
-class InputValidator:
-    @staticmethod
-    def validate_predict_evaluate_tables(
-        module_name, model_table, model_summary_table, test_table, output_table,
-        independent_varname):
-        InputValidator._validate_model_weights_tbl(module_name, model_table)
-        InputValidator._validate_model_summary_tbl(
-            module_name, model_summary_table)
-        InputValidator._validate_test_tbl(
-            module_name, test_table, independent_varname)
-        output_tbl_valid(output_table, module_name)
-
-    @staticmethod
-    def validate_id_in_test_tbl(module_name, test_table, id_col):
-        _assert(is_var_valid(test_table, id_col),
-                "{module_name} error: invalid id column "
-                "('{id_col}') for test table ({table}).".format(
-                    module_name=module_name,
-                    id_col=id_col,
-                    table=test_table))
-
-    @staticmethod
-    def validate_predict_byom_tables(module_name, model_arch_table, model_id,
-                                     test_table, id_col, output_table,
-                                     independent_varname):
-        InputValidator.validate_model_arch_table(
-            module_name, model_arch_table, model_id)
-        InputValidator._validate_test_tbl(
-            module_name, test_table, independent_varname)
-        InputValidator.validate_id_in_test_tbl(module_name, test_table, id_col)
-
-        output_tbl_valid(output_table, module_name)
-
-
-    @staticmethod
-    def validate_pred_type(module_name, pred_type, class_values):
-        if not pred_type in ['prob', 'response']:
-            plpy.error("{0}: Invalid value for pred_type param ({1}). Must be "\
-                "either response or prob.".format(module_name, pred_type))
-
-    @staticmethod
-    def validate_input_shape(table, independent_varname, input_shape, offset,
-                             is_minibatched=False):
-        """
-        Validate if the input shape specified in model architecture is the same
-        as the shape of the image specified in the indepedent var of the input
-        table.
-        offset: This offset is the index of the start of the image array. We also
-        need to consider that sql array indexes start from 1
-        For ex if the image is of shape [32,32,3] and is minibatched, the image will
-        look like [10, 32, 32, 3]. The offset in this case is 1 (start the index at 1) +
-        1 (ignore the buffer size 10) = 2.
-        If the image is not batched then it will look like [32, 32 ,3] and the offset in
-        this case is 1 (start the index at 1).
-        """
-
-        if is_minibatched:
-            ind_shape_col = add_postfix(independent_varname, "_shape")
-            query = """
-                SELECT {ind_shape_col} AS shape
-                FROM {table}
-                LIMIT 1
-            """.format(**locals())
-            # This query will fail if an image in independent var does not have the
-            # same number of dimensions as the input_shape.
-            result = plpy.execute(query)[0]['shape']
-            result = result[1:]
-        else:
-            array_upper_query = ", ".join("array_upper({0}, {1}) AS n_{2}".format(
-                independent_varname, i+offset, i) for i in range(len(input_shape)))
-            query = """
-                SELECT {0}
-                FROM {1}
-                LIMIT 1
-            """.format(array_upper_query, table)
-
-            # This query will fail if an image in independent var does not have the
-            # same number of dimensions as the input_shape.
-            result = plpy.execute(query)[0]
-
-        _assert(len(result) == len(input_shape),
-            "model_keras error: The number of dimensions ({0}) of each image"
-            " in model architecture and {1} in {2} ({3}) do not match.".format(
-                len(input_shape), independent_varname, table, len(result)))
-
-        for i in range(len(input_shape)):
-            if is_minibatched:
-                key_name = i
-                input_shape_from_table = [result[j]
-                    for j in range(len(input_shape))]
-            else:
-                key_format = "n_{0}"
-                key_name = key_format.format(i)
-                input_shape_from_table = [result[key_format.format(j)]
-                    for j in range(len(input_shape))]
-
-            if result[key_name] != input_shape[i]:
-                # Construct the shape in independent varname to display
-                # meaningful error msg.
-                plpy.error("model_keras error: Input shape {0} in the model"
-                    " architecture does not match the input shape {1} of column"
-                    " {2} in table {3}.".format(
-                        input_shape, input_shape_from_table,
-                        independent_varname, table))
-
-    @staticmethod
-    def validate_model_arch_table(module_name, model_arch_table, model_id):
-        input_tbl_valid(model_arch_table, module_name)
-        _assert(model_id is not None,
-            "{0}: Invalid model architecture ID.".format(module_name))
-
-
-    @staticmethod
-    def validate_normalizing_const(module_name, normalizing_const):
-        _assert(normalizing_const > 0,
-                "{0} error: Normalizing constant has to be greater than 0.".
-                format(module_name))
-
-    @staticmethod
-    def validate_class_values(module_name, class_values, pred_type, model_arch):
-        if not class_values:
-            return
-        num_classes = len(class_values)
-        _assert(num_classes == get_num_classes(model_arch),
-                "{0}: The number of class values do not match the " \
-                "provided architecture.".format(module_name))
-        if pred_type == 'prob' and num_classes+1 >= 1600:
-            plpy.error({"{0}: The output will have {1} columns, exceeding the "\
-                " max number of columns that can be created (1600)".format(
-                    module_name, num_classes+1)})
-
-    @staticmethod
-    def validate_model_weights(module_name, model_arch, model_weights):
-        _assert(model_weights and model_arch,
-                "{0}: Model weights and architecture must be valid.".format(
-                    module_name))
-
-    @staticmethod
-    def _validate_model_weights_tbl(module_name, model_table):
-        _assert(is_var_valid(model_table, MODEL_WEIGHTS_COLNAME),
-                "{module_name} error: column '{model_weights}' "
-                "does not exist in model table '{table}'.".format(
-                    module_name=module_name,
-                    model_weights=MODEL_WEIGHTS_COLNAME,
-                    table=model_table))
-        _assert(is_var_valid(model_table, ModelArchSchema.MODEL_ARCH),
-                "{module_name} error: column '{model_arch}' "
-                "does not exist in model table '{table}'.".format(
-                    module_name=module_name,
-                    model_arch=ModelArchSchema.MODEL_ARCH,
-                    table=model_table))
-
-    @staticmethod
-    def _validate_test_tbl(module_name, test_table, independent_varname):
-        input_tbl_valid(test_table, module_name)
-        _assert(is_var_valid(test_table, independent_varname),
-                "{module_name} error: invalid independent_varname "
-                "('{independent_varname}') for test table "
-                "({table}).".format(
-                    module_name=module_name,
-                    independent_varname=independent_varname,
-                    table=test_table))
-
-    @staticmethod
-    def _validate_model_summary_tbl(module_name, model_summary_table):
-        input_tbl_valid(model_summary_table, module_name)
-        cols_to_check_for = [CLASS_VALUES_COLNAME,
-                             DEPENDENT_VARNAME_COLNAME,
-                             DEPENDENT_VARTYPE_COLNAME,
-                             MODEL_ID_COLNAME,
-                             MODEL_ARCH_TABLE_COLNAME,
-                             NORMALIZING_CONST_COLNAME,
-                             COMPILE_PARAMS_COLNAME,
-                             METRIC_TYPE_COLNAME]
-        _assert(columns_exist_in_table(
-            model_summary_table, cols_to_check_for),
-            "{0} error: One or more expected columns missing in model "
-            "summary table ('{1}'). The expected columns are {2}.".format(
-                module_name, model_summary_table, cols_to_check_for))
-
-    @staticmethod
-    def _validate_gpu_config(module_name, source_table, accessible_gpus_for_seg):
-
-        summary_table = add_postfix(source_table, "_summary")
-        gpu_config = plpy.execute(
-            "SELECT {0} FROM {1}".format(INTERNAL_GPU_CONFIG, summary_table)
-            )[0][INTERNAL_GPU_CONFIG]
-        if gpu_config == 'all_segments':
-            _assert(0 not in accessible_gpus_for_seg,
-                "{0} error: Host(s) are missing gpus.".format(module_name))
-        else:
-            for i in gpu_config:
-                _assert(accessible_gpus_for_seg[i] != 0,
-                    "{0} error: Segment {1} does not have gpu".format(module_name, i))
-
-class FitCommonValidator(object):
-    def __init__(self, source_table, validation_table, output_model_table,
-                 model_arch_table, model_id, dependent_varname,
-                 independent_varname, num_iterations,
-                 metrics_compute_frequency, warm_start,
-                 use_gpus, accessible_gpus_for_seg, module_name):
-        self.source_table = source_table
-        self.validation_table = validation_table
-        self.output_model_table = output_model_table
-        self.model_arch_table = model_arch_table
-        self.model_id = model_id
-        self.dependent_varname = dependent_varname
-        self.independent_varname = independent_varname
-        self.dep_shape_col = add_postfix(dependent_varname, "_shape")
-        self.ind_shape_col = add_postfix(independent_varname, "_shape")
-        self.metrics_compute_frequency = metrics_compute_frequency
-        self.warm_start = warm_start
-        self.num_iterations = num_iterations
-        self.source_summary_table = None
-        if self.source_table:
-            self.source_summary_table = add_postfix(
-                self.source_table, "_summary")
-        if self.output_model_table:
-            self.output_summary_model_table = add_postfix(
-                self.output_model_table, "_summary")
-        self.accessible_gpus_for_seg = accessible_gpus_for_seg
-        self.module_name = module_name
-        self._validate_common_args()
-        if use_gpus:
-            InputValidator._validate_gpu_config(self.module_name,
-                self.source_table, self.accessible_gpus_for_seg)
-
-    def _validate_common_args(self):
-        _assert(self.num_iterations > 0,
-            "{0}: Number of iterations cannot be < 1.".format(self.module_name))
-        _assert(self._is_valid_metrics_compute_frequency(),
-            "{0}: metrics_compute_frequency must be in the range (1 - {1}).".format(
-                self.module_name, self.num_iterations))
-        input_tbl_valid(self.source_table, self.module_name)
-        input_tbl_valid(self.source_summary_table, self.module_name,
-                        error_suffix_str="Please ensure that the source table ({0}) "
-                                         "has been preprocessed by "
-                                         "the image preprocessor.".format(self.source_table))
-        cols_in_tbl_valid(self.source_summary_table, [CLASS_VALUES_COLNAME,
-            NORMALIZING_CONST_COLNAME, DEPENDENT_VARTYPE_COLNAME,
-            'dependent_varname', 'independent_varname'], self.module_name)
-        if not is_platform_pg():
-            cols_in_tbl_valid(self.source_table, [DISTRIBUTION_KEY_COLNAME], self.module_name)
-
-        # Source table and validation tables must have the same schema
-        self._validate_input_table(self.source_table)
-        validate_bytea_var_for_minibatch(self.source_table,
-                                         self.dependent_varname)
-
-        self._validate_validation_table()
-        if self.warm_start:
-            input_tbl_valid(self.output_model_table, self.module_name)
-            input_tbl_valid(self.output_summary_model_table, self.module_name)
-        else:
-            output_tbl_valid(self.output_model_table, self.module_name)
-            output_tbl_valid(self.output_summary_model_table, self.module_name)
-
-    def _validate_input_table(self, table):
-        _assert(is_var_valid(table, self.independent_varname),
-                "{module_name}: invalid independent_varname "
-                "('{independent_varname}') for table ({table}). "
-                "Please ensure that the input table ({table}) "
-                "has been preprocessed by the image preprocessor.".format(
-                    module_name=self.module_name,
-                    independent_varname=self.independent_varname,
-                    table=table))
-
-        _assert(is_var_valid(table, self.dependent_varname),
-                "{module_name}: invalid dependent_varname "
-                "('{dependent_varname}') for table ({table}). "
-                "Please ensure that the input table ({table}) "
-                "has been preprocessed by the image preprocessor.".format(
-                    module_name=self.module_name,
-                    dependent_varname=self.dependent_varname,
-                    table=table))
-
-        _assert(is_var_valid(table, self.ind_shape_col),
-                "{module_name}: invalid independent_var_shape "
-                "('{ind_shape_col}') for table ({table}). "
-                "Please ensure that the input table ({table}) "
-                "has been preprocessed by the image preprocessor.".format(
-                    module_name=self.module_name,
-                    ind_shape_col=self.ind_shape_col,
-                    table=table))
-
-        _assert(is_var_valid(table, self.dep_shape_col),
-                "{module_name}: invalid dependent_var_shape "
-                "('{dep_shape_col}') for table ({table}). "
-                "Please ensure that the input table ({table}) "
-                "has been preprocessed by the image preprocessor.".format(
-                    module_name=self.module_name,
-                    dep_shape_col=self.dep_shape_col,
-                    table=table))
-
-        if not is_platform_pg():
-            _assert(is_var_valid(table, DISTRIBUTION_KEY_COLNAME),
-                    "{module_name}: missing distribution key "
-                    "('{dist_key_col}') for table ({table}). "
-                    "Please ensure that the input table ({table}) "
-                    "has been preprocessed by the image preprocessor.".format(
-                        module_name=self.module_name,
-                        dist_key_col=DISTRIBUTION_KEY_COLNAME,
-                        table=table))
-
-    def _is_valid_metrics_compute_frequency(self):
-        return self.metrics_compute_frequency is None or \
-               (self.metrics_compute_frequency >= 1 and \
-               self.metrics_compute_frequency <= self.num_iterations)
-
-    def _validate_validation_table(self):
-        if self.validation_table and self.validation_table.strip() != '':
-            input_tbl_valid(self.validation_table, self.module_name)
-            self._validate_input_table(self.validation_table)
-            dependent_vartype = get_expr_type(self.dependent_varname,
-                                              self.validation_table)
-            _assert(dependent_vartype == 'bytea',
-                    "Dependent variable column {0} in validation table {1} should be "
-                    "a bytea and also one hot encoded.".format(
-                        self.dependent_varname, self.validation_table))
-
-
-    def validate_input_shapes(self, input_shape):
-        InputValidator.validate_input_shape(self.source_table, self.independent_varname,
-                               input_shape, 2, True)
-        if self.validation_table:
-            InputValidator.validate_input_shape(
-                self.validation_table, self.independent_varname,
-                input_shape, 2, True)
-
-
-class FitInputValidator(FitCommonValidator):
-    def __init__(self, source_table, validation_table, output_model_table,
-                 model_arch_table, model_id, dependent_varname,
-                 independent_varname, num_iterations,
-                 metrics_compute_frequency, warm_start,
-                 use_gpus, accessible_gpus_for_seg):
-
-        self.module_name = 'madlib_keras_fit'
-        super(FitInputValidator, self).__init__(source_table,
-                                                validation_table,
-                                                output_model_table,
-                                                model_arch_table,
-                                                model_id,
-                                                dependent_varname,
-                                                independent_varname,
-                                                num_iterations,
-                                                metrics_compute_frequency,
-                                                warm_start,
-                                                use_gpus,
-                                                accessible_gpus_for_seg,
-                                                self.module_name)
-        InputValidator.validate_model_arch_table(self.module_name, self.model_arch_table,
-            self.model_id)
-
-class FitMultipleInputValidator(FitCommonValidator):
-    def __init__(self, source_table, validation_table, output_model_table,
-                 model_selection_table, model_selection_summary_table, dependent_varname,
-                 independent_varname, num_iterations, model_info_table, mst_key_col,
-                 model_arch_table_col, metrics_compute_frequency, warm_start,
-                 use_gpus, accessible_gpus_for_seg):
-
-        self.module_name = 'madlib_keras_fit_multiple'
-
-        input_tbl_valid(model_selection_table, self.module_name)
-        input_tbl_valid(model_selection_summary_table, self.module_name,
-                        error_suffix_str="Please ensure that the model selection table ({0}) "
-                                         "has been created by "
-                                         "load_model_selection_table().".format(
-                                            model_selection_table))
-        self.msts, self.model_arch_table = query_model_configs(
-            model_selection_table, model_selection_summary_table,
-            mst_key_col, model_arch_table_col)
-        if warm_start:
-            input_tbl_valid(model_info_table, self.module_name)
-        else:
-            output_tbl_valid(model_info_table, self.module_name)
-        super(FitMultipleInputValidator, self).__init__(source_table,
-                                                        validation_table,
-                                                        output_model_table,
-                                                        self.model_arch_table,
-                                                        None,
-                                                        dependent_varname,
-                                                        independent_varname,
-                                                        num_iterations,
-                                                        metrics_compute_frequency,
-                                                        warm_start,
-                                                        use_gpus,
-                                                        accessible_gpus_for_seg,
-                                                        self.module_name)
-
-class MstLoaderInputValidator():
-    def __init__(self,
-                 model_arch_table,
-                 model_selection_table,
-                 model_selection_summary_table,
-                 model_id_list,
-                 compile_params_list,
-                 fit_params_list
-                 ):
-        self.model_arch_table = model_arch_table
-        self.model_selection_table = model_selection_table
-        self.model_selection_summary_table = model_selection_summary_table
-        self.model_id_list = model_id_list
-        self.compile_params_list = compile_params_list
-        self.fit_params_list = fit_params_list
-        self.module_name = 'load_model_selection_table'
-        self._validate_input_args()
-
-    def _validate_input_args(self):
-        self._validate_input_output_tables()
-        self._validate_model_ids()
-        self._validate_compile_and_fit_params()
-
-    def _validate_model_ids(self):
-        model_id_str = '({0})'\
-            .format(','.join([str(x) for x in self.model_id_list]))
-        query = """
-            SELECT count(model_id)
-            FROM {self.model_arch_table}
-            WHERE model_id IN {model_id_str}
-        """.format(**locals())
-        res = int(plpy.execute(query)[0]['count'])
-        _assert(
-            res == len(self.model_id_list),
-            "{0}: One or more model_id of {1} not found in table {2}".format(
-                self.module_name,
-                model_id_str,
-                self.model_arch_table
-            )
-        )
-    def _validate_compile_and_fit_params(self):
-        if not self.fit_params_list:
-            plpy.error("fit_params_list cannot be NULL")
-        for fit_params in self.fit_params_list:
-            try:
-                res = parse_and_validate_fit_params(fit_params)
-            except Exception as e:
-                plpy.error(
-                    """Fit param check failed for: {0} \n
-                    {1}
-                    """.format(fit_params, str(e)))
-        if not self.compile_params_list:
-            plpy.error( "compile_params_list cannot be NULL")
-        for compile_params in self.compile_params_list:
-            try:
-                res = parse_and_validate_compile_params(compile_params)
-            except Exception as e:
-                plpy.error(
-                    """Compile param check failed for: {0} \n
-                    {1}
-                    """.format(compile_params, str(e)))
-
-    def _validate_input_output_tables(self):
-        input_tbl_valid(self.model_arch_table, self.module_name)
-        output_tbl_valid(self.model_selection_table, self.module_name)
-        output_tbl_valid(self.model_selection_summary_table, self.module_name)
-
-
diff --git a/src/ports/postgres/modules/deep_learning/madlib_keras_wrapper.py_in b/src/ports/postgres/modules/deep_learning/madlib_keras_wrapper.py_in
deleted file mode 100644
index 541d370..0000000
--- a/src/ports/postgres/modules/deep_learning/madlib_keras_wrapper.py_in
+++ /dev/null
@@ -1,333 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import ast
-import os
-import plpy
-from math import ceil
-
-from keras import backend as K
-from keras import utils as keras_utils
-from keras.optimizers import *
-
-import keras.optimizers as opt
-import keras.losses as losses
-
-import madlib_keras_serializer
-import madlib_keras_gpu_info
-from utilities.utilities import _assert
-from utilities.utilities import is_platform_pg
-
-CUDA_VISIBLE_DEVICES_KEY = 'CUDA_VISIBLE_DEVICES'
-#######################################################################
-########### Keras specific functions #####
-#######################################################################
-
-def set_cuda_env(value):
-    """
-    :param value: -1 to disable gpu
-    :return:
-    """
-    os.environ[CUDA_VISIBLE_DEVICES_KEY] = value
-
-def reset_cuda_env(value):
-    """
-    This function will reset the cuda env variable. This should only be called
-    if set_cuda_env was called previously.
-    :param value:
-    """
-    if value:
-        set_cuda_env(value)
-    else:
-        if CUDA_VISIBLE_DEVICES_KEY in os.environ:
-            del os.environ[CUDA_VISIBLE_DEVICES_KEY]
-
-def get_device_name_and_set_cuda_env(gpu_count, seg):
-
-    if gpu_count > 0:
-        device_name = '/gpu:0'
-        if is_platform_pg():
-            cuda_visible_dev = ','.join([str(i) for i in range(gpu_count)])
-        else:
-            cuda_visible_dev = str(seg % gpu_count)
-        set_cuda_env(cuda_visible_dev)
-    else: # cpu only
-        device_name = '/cpu:0'
-        set_cuda_env('-1')
-    return device_name
-
-def set_keras_session(device_name, gpu_count, segments_per_host):
-    with K.tf.device(device_name):
-        session = get_keras_session(device_name, gpu_count, segments_per_host)
-        K.set_session(session)
-
-def get_keras_session(device_name, gpu_count, segments_per_host):
-    config = K.tf.ConfigProto()
-    if gpu_count > 0:
-        memory_fraction = get_gpu_memory_fraction(gpu_count, segments_per_host)
-        config.gpu_options.allow_growth = False
-        config.gpu_options.per_process_gpu_memory_fraction = memory_fraction
-    session = tf.Session(config=config)
-    return session
-
-def clear_keras_session(sess = None):
-    if sess is None:
-        sess = K.get_session()
-    K.clear_session()
-    sess.close()
-
-
-def get_gpu_memory_fraction(gpu_count, segments_per_host):
-    """
-    We cap the gpu memory usage to 90% of the total available gpu memory.
-    This 90% is evenly distributed among the segments per gpu.
-    :param gpu_count:
-    :param segments_per_host:
-    :return:
-    """
-    return 0.9 / ceil(1.0 * segments_per_host / gpu_count)
-
-def get_model_shapes(model):
-    model_shapes = []
-    for a in model.get_weights():
-        model_shapes.append(a.shape)
-    return model_shapes
-
-def compile_and_set_weights(segment_model, compile_params, device_name,
-                            serialized_weights):
-    model_shapes = get_model_shapes(segment_model)
-    compile_model(segment_model, compile_params)
-    model_weights = madlib_keras_serializer.deserialize_as_nd_weights(
-        serialized_weights, model_shapes)
-    segment_model.set_weights(model_weights)
-
-# TODO: This can be refactored to be part of compile_and_set_weights(),
-# by making compile_params an optional param in that function. Doing that
-# now might create more merge conflicts with other JIRAs, so get to this later.
-def set_model_weights(segment_model, serialized_weights):
-    model_shapes = get_model_shapes(segment_model)
-    model_weights = madlib_keras_serializer.deserialize_as_nd_weights(
-        serialized_weights, model_shapes)
-    segment_model.set_weights(model_weights)
-
-"""
-Used to convert compile_params and fit_params to actual argument dictionaries
-"""
-
-def convert_string_of_args_to_dict(str_of_args):
-    """Uses parenthases matching algorithm to intelligently convert
-    a string with valid python code into an argument dictionary"""
-    stack = []
-    dual = {
-        '(' : ')',
-        '[' : ']',
-        '{' : '}',
-    }
-    result_str = ""
-    key_str = ""
-    compile_dict = {}
-    for char in str_of_args:
-        if char in dual.keys():
-            stack.append(char)
-            result_str += char
-        elif char in dual.values() and stack:
-            if dual[stack[-1]] == char:
-                stack.pop(-1)
-            result_str += char
-        elif not stack and char == "=":
-            key_str = result_str
-            result_str = ""
-        elif not stack and char == ",":
-            value_str = result_str
-            result_str = ""
-            compile_dict[key_str.strip()]=value_str.strip().strip('\'')
-        else:
-            result_str += char
-    value_str = result_str
-    compile_dict[key_str.strip()]=value_str.strip().strip('\'')
-    return compile_dict
-
-def get_metrics_from_compile_param(str_of_args):
-    compile_dict = convert_string_of_args_to_dict(str_of_args)
-    metrics = None
-    ckey = 'metrics'
-    if ckey in compile_dict:
-        try:
-            metrics = ast.literal_eval(compile_dict[ckey])
-        except ValueError:
-            plpy.error(("Invalid input value for parameter {0}, "
-                        "please refer to the documentation").format(ckey))
-    return metrics
-
-# Parse the compile parameters and the optimizer.
-def parse_and_validate_compile_params(str_of_args):
-    """
-    Args:
-        @param: str_of_args     The string of arguments given by the user
-    Returns:
-        opt_name:               Name of the optimizer
-        opt_args:               Arguments for the optimizer
-        compile_dict:           Dictionary of arguments for keras.compile
-    """
-    literal_eval_compile_params = ['metrics', 'loss_weights',
-                                   'weighted_metrics', 'sample_weight_mode']
-    accepted_compile_params = literal_eval_compile_params + ['optimizer', 'loss']
-
-    compile_dict = convert_string_of_args_to_dict(str_of_args)
-    compile_dict = validate_and_literal_eval_keys(compile_dict,
-                                                  literal_eval_compile_params,
-                                                  accepted_compile_params)
-    _assert('optimizer' in compile_dict, "optimizer is a required parameter for compile")
-    opt_name, opt_args = parse_optimizer(compile_dict)
-
-    _assert('loss' in compile_dict, "loss is a required parameter for compile")
-    validate_compile_param_types(compile_dict)
-    _validate_metrics(compile_dict)
-    return (opt_name, opt_args, compile_dict)
-
-def _validate_metrics(compile_dict):
-    _assert('metrics' not in compile_dict.keys() or
-        compile_dict['metrics'] is None or
-        type(compile_dict['metrics']) is list,
-        "wrong input type for compile parameter metrics: multi-output model"
-        "and user defined metrics are not supported yet, please pass a list")
-    if 'metrics' in compile_dict and compile_dict['metrics']:
-        unsupported_metrics_list = ['sparse_categorical_accuracy',
-            'sparse_categorical_crossentropy', 'top_k_categorical_accuracy',
-            'sparse_top_k_categorical_accuracy']
-        _assert(len(compile_dict['metrics']) == 1,
-            "Only one metric at a time is supported.")
-        _assert(compile_dict['metrics'][0] not in unsupported_metrics_list,
-            "Metric {0} is not supported.".format(compile_dict['metrics'][0]))
-
-# Parse the optimizer name and params.
-def parse_optimizer(compile_dict):
-    """
-    Args:
-        @param: compile_dict    Dictionary of arguments for keras.compile
-    Returns:
-        opt_name:               Name of the optimizer
-        opt_args:               Arguments for the optimizer
-    """
-    opt_split = compile_dict['optimizer'].split('(')
-    opt_name = opt_split[0]
-    optimizers = get_optimizers()
-    _assert(opt_name in optimizers,
-            "model_keras error: invalid optimizer name: {0}".format(opt_name))
-
-    # If we use only the optimizer name
-    if len(opt_split) == 1:
-        final_args = None
-    # If we use optimizer object with no params
-    elif opt_split[1] == ')':
-        final_args = None
-    # If we give parameters to the optimizer
-    else:
-        opt_params = opt_split[1][:-1]
-        opt_params_array = opt_params.split(',')
-        opt_params_clean = map(split_and_strip, opt_params_array)
-        key_value_params = { x[0] : x[1] for x in opt_params_clean}
-
-        final_args = {}
-        for key,value in key_value_params.iteritems():
-            if value == 'None':
-                final_args[key] = None
-            elif value == 'True' or value == 'False':
-                final_args[key] = bool(value)
-            else:
-                final_args[key] = float(value)
-    return (opt_name,final_args)
-
-
-# Parse the fit parameters into a dictionary.
-def parse_and_validate_fit_params(fit_param_str):
-
-    if fit_param_str:
-        fit_params_dict = convert_string_of_args_to_dict(fit_param_str)
-
-        literal_eval_fit_params = ['batch_size','epochs','verbose',
-                               'class_weight','initial_epoch','steps_per_epoch']
-        accepted_fit_params = literal_eval_fit_params + ['shuffle']
-
-        fit_params_dict = validate_and_literal_eval_keys(fit_params_dict,
-                                                         literal_eval_fit_params,
-                                                         accepted_fit_params)
-        if 'shuffle' in fit_params_dict:
-            shuffle_value = fit_params_dict['shuffle']
-            if shuffle_value == 'True' or shuffle_value == 'False':
-                fit_params_dict['shuffle'] = bool(shuffle_value)
-
-        return fit_params_dict
-    else:
-        return {}
-
-# Validate the keys of the given dictionary and run literal_eval on the
-# user-defined subset
-def validate_and_literal_eval_keys(keys_dict, literal_eval_list, accepted_list):
-    for ckey in keys_dict.keys():
-        _assert(ckey in accepted_list,
-            "{0} is not currently accepted as a parameter. ".format(ckey))
-        if ckey in literal_eval_list:
-            try:
-                keys_dict[ckey] = ast.literal_eval(keys_dict[ckey])
-            except ValueError:
-                plpy.error(("invalid input value for parameter {0}, "
-                            "please refer to the documentation").format(ckey))
-    return keys_dict
-
-# Split and strip the whitespace of key=value formatted strings
-def split_and_strip(x):
-    y = x.split('=')
-    return (y[0].strip(),y[1].strip())
-
-# Return the list of keras optimizers
-def get_optimizers():
-    optimizers = dict()
-    names = dir(opt)
-    for n in names:
-        optimizer = eval('opt.' + n)
-        if optimizer.__class__ == type and optimizer.__base__ == opt.Optimizer:
-            optimizers[n] = optimizer
-    return optimizers
-
-# Run the keras.compile with the given parameters
-def compile_model(model, compile_params):
-    optimizers = get_optimizers()
-    (opt_name,final_args,compile_dict) = parse_and_validate_compile_params(compile_params)
-    compile_dict['optimizer'] = optimizers[opt_name](**final_args) if final_args else opt_name
-    model.compile(**compile_dict)
-
-def validate_compile_param_types(compile_dict):
-    _assert('loss_weights' not in compile_dict.keys() or
-            compile_dict['loss_weights'] is None or
-            type(compile_dict['loss_weights']) is list or
-            type(compile_dict['loss_weights']) is dict,
-            "wrong input type for compile parameter loss_weights: only list "
-            "and dictionary are supported.")
-
-    _assert('weighted_metrics' not in compile_dict.keys() or
-            compile_dict['weighted_metrics'] is None or
-            type(compile_dict['weighted_metrics']) is list,
-            "wrong input type for compile parameter weighted_metrics: only list "
-            "is supported.")
-
-    _assert('sample_weight_mode' not in compile_dict.keys() or
-            compile_dict['sample_weight_mode'] is None or
-            compile_dict['sample_weight_mode'] == "temporal",
-            """compile parameter sample_weight_mode can only be "temporal" or None""")
diff --git a/src/ports/postgres/modules/deep_learning/model_arch_info.py_in b/src/ports/postgres/modules/deep_learning/model_arch_info.py_in
deleted file mode 100644
index 3a225cc..0000000
--- a/src/ports/postgres/modules/deep_learning/model_arch_info.py_in
+++ /dev/null
@@ -1,104 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-m4_changequote(`')
-
-import sys
-import json
-import plpy
-from keras_model_arch_table import ModelArchSchema
-
-def _get_layers(model_arch):
-    d = json.loads(model_arch)
-    config = d['config']
-    if type(config) == list:
-        return config  # In keras 2.1.x, all models are sequential
-    elif type(config) == dict and 'layers' in config:
-        layers = config['layers']
-        if type(layers) == list:
-            return config['layers']  # In keras 2.x, only sequential models are supported
-    plpy.error("Unable to read model architecture JSON.")
-
-def get_input_shape(model_arch):
-    arch_layers = _get_layers(model_arch)
-    if 'batch_input_shape' in arch_layers[0]['config']:
-        return arch_layers[0]['config']['batch_input_shape'][1:]
-    plpy.error('Unable to get input shape from model architecture.')
-
-def get_num_classes(model_arch):
-    """
-     We assume that the last dense layer in the model architecture contains the num_classes (units)
-     An example can be:
-     ```
-     ...
-     model.add(Flatten())
-     model.add(Dense(512))
-     model.add(Activation('relu'))
-     model.add(Dropout(0.5))
-     model.add(Dense(num_classes))
-     model.add(Activation('softmax'))
-     ```
-     where activation can be after the dense layer.
-    :param model_arch:
-    :return:
-    """
-    arch_layers = _get_layers(model_arch)
-    i = len(arch_layers) - 1
-    while i >= 0:
-        if 'units' in arch_layers[i]['config']:
-            return arch_layers[i]['config']['units']
-        i -= 1
-    plpy.error('Unable to get number of classes from model architecture.')
-
-def get_model_arch_layers_str(model_arch):
-    arch_layers = _get_layers(model_arch)
-    layers = "Model arch layers:\n"
-    first = True
-    for layer in arch_layers:
-        if first:
-            first = False
-        else:
-            layers += "   |\n"
-            layers += "   V\n"
-        class_name = layer['class_name']
-        config = layer['config']
-        if class_name == 'Dense':
-            layers += "{1}[{2}]\n".format(class_name, config['units'])
-        else:
-            layers += "{1}\n".format(class_name)
-    return layers
-
-def get_model_arch_weights(model_arch_table, model_id):
-
-    #assume validation is already called
-    model_arch_query = "SELECT {0}, {1} FROM {2} WHERE {3} = {4}".format(
-        ModelArchSchema.MODEL_ARCH, ModelArchSchema.MODEL_WEIGHTS,
-        model_arch_table, ModelArchSchema.MODEL_ID,
-        model_id)
-    model_arch_result = plpy.execute(model_arch_query)
-    if not model_arch_result:
-        plpy.error("no model arch found in table {0} with id {1}".format(
-            model_arch_table, model_id))
-
-    model_arch_result = model_arch_result[0]
-
-    model_arch = model_arch_result[ModelArchSchema.MODEL_ARCH]
-    model_weights = model_arch_result[ModelArchSchema.MODEL_WEIGHTS]
-
-    return model_arch, model_weights
diff --git a/src/ports/postgres/modules/deep_learning/predict_input_params.py_in b/src/ports/postgres/modules/deep_learning/predict_input_params.py_in
deleted file mode 100644
index fdab1e4..0000000
--- a/src/ports/postgres/modules/deep_learning/predict_input_params.py_in
+++ /dev/null
@@ -1,67 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import plpy
-from keras_model_arch_table import ModelArchSchema
-from utilities.utilities import add_postfix
-from utilities.validate_args import input_tbl_valid
-
-from madlib_keras_helper import CLASS_VALUES_COLNAME
-from madlib_keras_helper import DEPENDENT_VARNAME_COLNAME
-from madlib_keras_helper import DEPENDENT_VARTYPE_COLNAME
-from madlib_keras_helper import MODEL_WEIGHTS_COLNAME
-from madlib_keras_helper import NORMALIZING_CONST_COLNAME
-from madlib_keras_helper import create_summary_view
-
-class PredictParamsProcessor:
-    def __init__(self, model_table, module_name, mst_key):
-        self.module_name = module_name
-        self.model_table = model_table
-        self.mst_key = mst_key
-        self.model_summary_table = add_postfix(self.model_table, '_summary')
-        self.mult_where_clause = ""
-        if self.mst_key:
-            self.model_summary_table = create_summary_view(self.module_name,
-                                                           self.model_table,
-                                                           self.mst_key)
-            self.mult_where_clause = "WHERE mst_key = {0}".format(self.mst_key)
-        input_tbl_valid(self.model_summary_table, self.module_name)
-        self.model_summary_dict = self._get_dict_for_table(self.model_summary_table)
-        self.model_arch_dict = self._get_dict_for_table(self.model_table)
-
-    def _get_dict_for_table(self, table_name):
-        return plpy.execute("SELECT * FROM {0} {1}".format(table_name, self.mult_where_clause), 1)[0]
-
-    def get_class_values(self):
-        return self.model_summary_dict[CLASS_VALUES_COLNAME]
-
-    def get_dependent_varname(self):
-        return self.model_summary_dict[DEPENDENT_VARNAME_COLNAME]
-
-    def get_dependent_vartype(self):
-        return self.model_summary_dict[DEPENDENT_VARTYPE_COLNAME]
-
-    def get_model_arch(self):
-        return self.model_arch_dict[ModelArchSchema.MODEL_ARCH]
-
-    def get_model_weights(self):
-        return self.model_arch_dict[MODEL_WEIGHTS_COLNAME]
-
-    def get_normalizing_const(self):
-        return self.model_summary_dict[NORMALIZING_CONST_COLNAME]
diff --git a/src/ports/postgres/modules/deep_learning/test/input_data_preprocessor.sql_in b/src/ports/postgres/modules/deep_learning/test/input_data_preprocessor.sql_in
deleted file mode 100644
index 7c6c5c3..0000000
--- a/src/ports/postgres/modules/deep_learning/test/input_data_preprocessor.sql_in
+++ /dev/null
@@ -1,645 +0,0 @@
-/* ----------------------------------------------------------------------- *//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ----------------------------------------------------------------------- */
-m4_include(`SQLCommon.m4')
-m4_changequote(`')
-
-DROP TABLE IF EXISTS data_preprocessor_input;
-CREATE TABLE data_preprocessor_input(id serial, x double precision[], label TEXT);
-INSERT INTO data_preprocessor_input(x, label) VALUES
-(ARRAY[1,2,3,4,5,6], 'a'),
-(ARRAY[11,2,3,4,5,6], 'a'),
-(ARRAY[11,22,33,4,5,6], 'a'),
-(ARRAY[11,22,33,44,5,6], 'a'),
-(ARRAY[11,22,33,44,65,6], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,144,65,56], 'a'),
-(ARRAY[11,22,233,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], 'b'),
-(ARRAY[11,22,33,44,65,56], 'b'),
-(ARRAY[11,22,33,44,65,56], 'b');
-
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input',
-  'data_preprocessor_input_batch',
-  'id',
-  'x',
-  5);
-
--- Divide two numbers and round up to the nearest integer
-CREATE FUNCTION divide_roundup(numerator NUMERIC, denominator NUMERIC)
-RETURNS INTEGER AS
-$$
-    SELECT (ceil($1 / $2)::INTEGER);
-$$ LANGUAGE SQL;
-
--- num_buffers_calc() represents the num_buffers value that should be
---  calculated by the preprocessor.
--- For postgres, just need total rows / buffer_size rounded up.
--- For greenplum, we take that result, and round up to the nearest multiple
---   of num_segments.
-CREATE FUNCTION num_buffers_calc(rows_in_tbl INTEGER, buffer_size INTEGER)
-RETURNS INTEGER AS
-$$
-m4_ifdef(,
-    ,
-    
-)
-$$ LANGUAGE SQL;
-
---  num_buffers() represents the actual number of buffers expected to
---      be returned in the output table.
---   For postgres, this should always be the same as num_buffers_calc()
---      (as long as rows_in_tbl > 0, which should be validated elsewhere)
---   For greenplum, this can be less than num_buffers_calc() in
---     the special case where there is only one row per buffer.  In
---      that case, the buffers in the output table will be equal to
---      the number of rows in the input table.  This can only happen
---      if rows_in_tbl < num_segments and is the only case where the
---      number of buffers on each segment will not be exactly equal
-CREATE FUNCTION num_buffers(rows_in_tbl INTEGER, buffer_size INTEGER)
-RETURNS INTEGER AS
-$$
-    SELECT LEAST(num_buffers_calc($1, $2), $1);
-$$ LANGUAGE SQL;
-
-CREATE FUNCTION buffer_size(rows_in_tbl INTEGER, requested_buffer_size INTEGER)
-RETURNS INTEGER AS
-$$
-  SELECT divide_roundup($1, num_buffers($1, $2));
-$$ LANGUAGE SQL;
-
-SELECT assert(COUNT(*) = num_buffers(17, 5),
-    'Incorrect number of buffers in data_preprocessor_input_batch.')
-FROM data_preprocessor_input_batch;
-
-SELECT assert(independent_var_shape[2]=6, 'Incorrect image shape ' || independent_var_shape[2])
-FROM data_preprocessor_input_batch WHERE buffer_id=0;
-
-SELECT assert(independent_var_shape[1]=buffer_size, 'Incorrect buffer size ' || independent_var_shape[1])
-FROM (SELECT buffer_size(17, 5) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=0;
-
-SELECT assert(independent_var_shape[1]=buffer_size, 'Incorrect buffer size ' || independent_var_shape[1])
-FROM (SELECT buffer_size(17, 5) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=1;
-
-SELECT assert(independent_var_shape[1]=buffer_size, 'Incorrect buffer size ' || independent_var_shape[1])
-FROM (SELECT buffer_size(17, 5) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=2;
-
-SELECT assert(total_images = 17, 'Incorrect total number of images! Last buffer has incorrect size?')
-FROM (SELECT SUM(independent_var_shape[1]) AS total_images FROM data_preprocessor_input_batch) a;
-
-SELECT assert(octet_length(independent_var) = buffer_size*6*4, 'Incorrect buffer length ' || octet_length(independent_var)::TEXT)
-FROM (SELECT buffer_size(17, 5) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=0;
-
-
-DROP TABLE IF EXISTS validation_out, validation_out_summary;
-SELECT validation_preprocessor_dl(
-  'data_preprocessor_input',
-  'validation_out',
-  'id',
-  'x',
-  'data_preprocessor_input_batch',
-  5);
-
-SELECT assert(COUNT(*) = num_buffers(17, 5),
-    'Incorrect number of buffers in validation_out.')
-FROM validation_out;
-
-SELECT assert(independent_var_shape[2]=6, 'Incorrect image shape.')
-FROM data_preprocessor_input_batch WHERE buffer_id=0;
-
-SELECT assert(independent_var_shape[1]=buffer_size, 'Incorrect buffer size.')
-FROM (SELECT buffer_size(17, 5) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=1;
-
-SELECT assert(total_images = 17, 'Incorrect total number of images! Last buffer has incorrect size?')
-FROM (SELECT SUM(independent_var_shape[1]) AS total_images FROM data_preprocessor_input_batch) a;
-
-SELECT assert(octet_length(independent_var) = buffer_size*6*4, 'Incorrect buffer length')
-FROM (SELECT buffer_size(17, 5) buffer_size) a, validation_out WHERE buffer_id=0;
-
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input',
-  'data_preprocessor_input_batch',
-  'label',
-  'x');
-
--- Test data is evenly distributed across all segments (GPDB only)
-m4_ifdef(, , )
-
-DROP TABLE IF EXISTS data_preprocessor_input;
-CREATE TABLE data_preprocessor_input(id serial, x double precision[], y INTEGER, y1 BOOLEAN, y2 TEXT, y3 DOUBLE PRECISION, y4 DOUBLE PRECISION[], y5 INTEGER[]);
-INSERT INTO data_preprocessor_input(x, y, y1, y2, y3, y4, y5) VALUES
-(ARRAY[1,2,3,4,5,6], 4, TRUE, 'a', 4.0, ARRAY[1.0, 0.0], ARRAY[1,0]),
-(ARRAY[11,2,3,4,5,6], 3, TRUE, 'c', 4.2, ARRAY[0.0, 1.0], ARRAY[1,0]),
-(ARRAY[11,22,33,4,5,6], 8, TRUE, 'a', 4.0, ARRAY[0.0, 1.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,5,6], 2, FALSE, 'a', 4.2, ARRAY[0.0, 1.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,65,6], 5, TRUE, 'b', 4.0, ARRAY[0.0, 1.0], ARRAY[0,1]),
-(ARRAY[11,22,33,44,65,56], 6, TRUE, 'a', 5.0, ARRAY[1.0, 0.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,65,56], 2, TRUE, 'a', 4.0, ARRAY[1.0, 0.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,65,56], 10, TRUE, 'a', 4.0, ARRAY[1.0, 0.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,65,56], 3, TRUE, 'b', 4.0, ARRAY[1.0, 0.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,65,56], 7, FALSE, 'a', 5.0, ARRAY[1.0, 0.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,65,56], 6, TRUE, 'a', 4.0, ARRAY[0.0, 1.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,65,56], -6, TRUE, 'a', 4.0, ARRAY[1.0, 0.0], ARRAY[1,0]),
-(ARRAY[11,22,33,144,65,56], 9, TRUE, 'c', 4.0, ARRAY[0.0, 1.0], ARRAY[1,0]),
-(ARRAY[11,22,233,44,65,56], 0, TRUE, 'a', 5.0, ARRAY[1.0, 0.0], ARRAY[0,1]),
-(ARRAY[11,22,33,44,65,56], 12, TRUE, 'a', 4.0, ARRAY[1.0, 0.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,65,56], -3, FALSE, 'a', 4.2, ARRAY[1.0, 0.0], ARRAY[1,0]),
-(ARRAY[11,22,33,44,65,56], -1, TRUE, 'b', 4.0, ARRAY[0.0, 1.0], ARRAY[0,1]);
-
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input',
-  'data_preprocessor_input_batch',
-  'y',
-  'x',
-  4,
-  5,
-  16 -- num_classes
-  );
-
--- Test that indepdendent vars get divided by 5, by verifying min value goes from 1 to 0.2, and max value from 233 to 46.6
-SELECT assert(relative_error(MIN(x),0.2) < 0.00001, 'Independent var not normalized properly!') FROM (SELECT UNNEST(convert_bytea_to_real_array(independent_var)) as x FROM data_preprocessor_input_batch) a;
-SELECT assert(relative_error(MAX(x),46.6) < 0.00001, 'Independent var not normalized properly!') FROM (SELECT UNNEST(convert_bytea_to_real_array(independent_var)) as x FROM data_preprocessor_input_batch) a;
--- Test that 1-hot encoded array is of length 16 (num_classes)
-SELECT assert(dependent_var_shape[2] = 16, 'Incorrect one-hot encode dimension with num_classes') FROM
-  data_preprocessor_input_batch WHERE buffer_id = 0;
-
-SELECT assert(octet_length(independent_var) = buffer_size*6*4, 'Incorrect buffer length')
-FROM (SELECT buffer_size(17, 4) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=0;
-
--- Test summary table
-SELECT assert
-        (
-        source_table        = 'data_preprocessor_input' AND
-        output_table        = 'data_preprocessor_input_batch' AND
-        dependent_varname   = 'y' AND
-        independent_varname = 'x' AND
-        dependent_vartype   = 'integer' AND
-        class_values        = '{-6,-3,-1,0,2,3,4,5,6,7,8,9,10,12,NULL,NULL}' AND
-        summary.buffer_size = a.buffer_size AND  -- we sort the class values in python
-        normalizing_const   = 5 AND
-        pg_typeof(normalizing_const) = 'real'::regtype AND
-        num_classes         = 16 AND
-        distribution_rules  = 'all_segments',
-        'Summary Validation failed. Actual:' || __to_char(summary)
-        ) FROM (SELECT buffer_size(17, 4) buffer_size) a,
-          (SELECT * FROM data_preprocessor_input_batch_summary) summary;
-
---- Test output data type
-SELECT assert(pg_typeof(independent_var) = 'bytea'::regtype, 'Wrong independent_var type') FROM data_preprocessor_input_batch WHERE buffer_id = 0;
-SELECT assert(pg_typeof(dependent_var) = 'bytea'::regtype, 'Wrong dependent_var type') FROM data_preprocessor_input_batch WHERE buffer_id = 0;
-
--- Test for validation data where the input table has only a subset of
--- the classes compared to the original training data
--- (data_preprocessor_input_batch). The one hot encoding must be based
--- on class_values from data_preprocessor_input_batch_summary rather
--- than the class levels found in validation_input.
-DROP TABLE IF EXISTS validation_input;
-CREATE TABLE validation_input(id serial, x_new double precision[], y_new INTEGER, y1 BOOLEAN, y2 TEXT, y3 DOUBLE PRECISION, y4 DOUBLE PRECISION[], y5 INTEGER[]);
-INSERT INTO validation_input(x_new, y_new, y1, y2, y3, y4, y5) VALUES
-(ARRAY[1,2,3,4,5,6], 4, TRUE, 'a', 4.0, ARRAY[1.0, 0.0], ARRAY[1,0]);
-DROP TABLE IF EXISTS validation_out, validation_out_summary;
-SELECT validation_preprocessor_dl(
-  'validation_input',
-  'validation_out',
-  'y_new',
-  'x_new',
-  'data_preprocessor_input_batch');
--- Hard code 5.0 as the normalizing constant, based on the previous
--- query's input param, to test if normalization is correct.
-SELECT assert(abs(x_new[1]/5.0-(convert_bytea_to_real_array(independent_var))[1]) < 0.0000001, 'Incorrect normalizing in validation table.')
-FROM validation_input, validation_out;
--- Validate if one hot encoding is as expected.
-SELECT assert(convert_bytea_to_smallint_array(dependent_var) = '{0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0}', 'Incorrect one-hot encode dimension with num_classes') FROM
-  validation_out WHERE buffer_id = 0;
-
--- Test summary table
-SELECT assert
-        (
-        source_table        = 'validation_input' AND
-        output_table        = 'validation_out' AND
-        dependent_varname   = 'y_new' AND
-        independent_varname = 'x_new' AND
-        dependent_vartype   = 'integer' AND
-        class_values        = '{-6,-3,-1,0,2,3,4,5,6,7,8,9,10,12,NULL,NULL}' AND
-        buffer_size         = 1 AND  -- we sort the class values in python
-        normalizing_const   = 5 AND
-        pg_typeof(normalizing_const) = 'real'::regtype AND
-        num_classes         = 16,
-        'Summary Validation failed. Actual:' || __to_char(summary)
-        ) from (select * from validation_out_summary) summary;
-
--- Test one-hot encoding for dependent_var
--- test boolean type
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input',
-  'data_preprocessor_input_batch',
-  'y1',
-  'x',
-  4,
-  5);
-SELECT assert(pg_typeof(dependent_var) = 'bytea'::regtype, 'One-hot encode doesn''t convert into integer array format') FROM data_preprocessor_input_batch WHERE buffer_id = 0;
-SELECT assert(dependent_var_shape[2] = 2, 'Incorrect one-hot encode dimension') FROM
-   data_preprocessor_input_batch WHERE buffer_id = 0;
-
-SELECT assert(octet_length(independent_var) = buffer_size*6*4, 'Incorrect buffer length')
-FROM (SELECT buffer_size(17, 4) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=0;
-
-SELECT assert(SUM(y) = 1, 'Incorrect one-hot encode format') FROM (SELECT buffer_id, UNNEST((convert_bytea_to_smallint_array(dependent_var))[1:2]) as y FROM data_preprocessor_input_batch) a WHERE buffer_id = 0;
-SELECT assert (dependent_vartype   = 'boolean' AND
-               class_values        = '{f,t}' AND
-               num_classes         = 2,
-               'Summary Validation failed. Actual:' || __to_char(summary)
-              ) from (select * from data_preprocessor_input_batch_summary) summary;
-
--- Test to assert the output summary table for validation has the correct
--- num_classes and class_values
-DROP TABLE IF EXISTS validation_input;
-CREATE TABLE validation_input(id serial, x_new double precision[], y INTEGER, y_new BOOLEAN, y2 TEXT, y3 DOUBLE PRECISION, y4 DOUBLE PRECISION[], y5 INTEGER[]);
-INSERT INTO validation_input(x_new, y, y_new, y2, y3, y4, y5) VALUES
-(ARRAY[1,2,3,4,5,6], 4, TRUE, 'a', 4.0, ARRAY[1.0, 0.0], ARRAY[1,0]);
-DROP TABLE IF EXISTS validation_out, validation_out_summary;
-SELECT validation_preprocessor_dl(
-  'validation_input',
-  'validation_out',
-  'y_new',
-  'x_new',
-  'data_preprocessor_input_batch');
-
-SELECT assert (dependent_vartype   = 'boolean' AND
-               class_values        = '{f,t}' AND
-               num_classes         = 2,
-               'Summary Validation failed. Actual:' || __to_char(summary)
-              ) from (select * from validation_out_summary) summary;
--- test text type
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input',
-  'data_preprocessor_input_batch',
-  'y2',
-  'x',
-  4,
-  5);
-SELECT assert(pg_typeof(dependent_var) = 'bytea'::regtype, 'One-hot encode doesn''t convert into integer array format') FROM data_preprocessor_input_batch WHERE buffer_id = 0;
-SELECT assert(dependent_var_shape[2] = 3, 'Incorrect one-hot encode dimension') FROM
-   data_preprocessor_input_batch WHERE buffer_id = 0;
-
-SELECT assert(octet_length(independent_var) = buffer_size*6*4, 'Incorrect buffer length')
-FROM (SELECT buffer_size(17, 4) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=0;
-
-SELECT assert(SUM(y) = 1, 'Incorrect one-hot encode format') FROM (SELECT buffer_id, UNNEST((convert_bytea_to_smallint_array(dependent_var))[1:3]) as y FROM data_preprocessor_input_batch) a WHERE buffer_id = 0;
-SELECT assert (dependent_vartype   = 'text' AND
-               class_values        = '{a,b,c}' AND
-               num_classes         = 3,
-               'Summary Validation failed. Actual:' || __to_char(summary)
-              ) from (select * from data_preprocessor_input_batch_summary) summary;
-
--- data_preprocessor_input_batch was created for boolean dependent var,
--- trying to create validation data using this table for reference, but
--- passing a non-boolean dep var should error out.
-DROP TABLE IF EXISTS validation_out, validation_out_summary;
-select assert(trap_error($TRAP$SELECT validation_preprocessor_dl(
-  'validation_input',
-  'validation_out',
-  'y_new',
-  'x_new',
-  'data_preprocessor_input_batch');$TRAP$) = 1,
-    'Passing boolean dep var while expecting text dep var should error out.');
-
-
--- test double precision type
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input',
-  'data_preprocessor_input_batch',
-  'y3',
-  'x',
-  4,
-  5);
-SELECT assert(pg_typeof(dependent_var) = 'bytea'::regtype, 'One-hot encode doesn''t convert into integer array format') FROM data_preprocessor_input_batch WHERE buffer_id = 0;
-SELECT assert(dependent_var_shape[2] = 3, 'Incorrect one-hot encode dimension') FROM
-  data_preprocessor_input_batch WHERE buffer_id = 0;
-SELECT assert(octet_length(independent_var) = buffer_size*6*4, 'Incorrect buffer length')
-FROM (SELECT buffer_size(17, 4) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=0;
-SELECT assert(SUM(y) = 1, 'Incorrect one-hot encode format') FROM (SELECT buffer_id, UNNEST((convert_bytea_to_smallint_array(dependent_var))[1:3]) as y FROM data_preprocessor_input_batch) a WHERE buffer_id = 0;
-SELECT assert (dependent_vartype   = 'double precision' AND
-               class_values        = '{4.0,4.2,5.0}' AND
-               num_classes         = 3,
-               'Summary Validation failed. Actual:' || __to_char(summary)
-              ) from (select * from data_preprocessor_input_batch_summary) summary;
-
--- test double precision array type
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input',
-  'data_preprocessor_input_batch',
-  'y4',
-  'x',
-  4,
-  5);
-SELECT assert(pg_typeof(dependent_var) = 'bytea'::regtype, 'One-hot encode doesn''t convert into integer array format') FROM data_preprocessor_input_batch WHERE buffer_id = 0;
-SELECT assert(dependent_var_shape[2] = 2, 'Incorrect one-hot encode dimension') FROM
-  data_preprocessor_input_batch WHERE buffer_id = 0;
-
-SELECT assert(octet_length(independent_var) = buffer_size*6*4, 'Incorrect buffer length')
-FROM (SELECT buffer_size(17, 4) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=0;
-
-SELECT assert(relative_error(SUM(y), SUM(y4)) < 0.000001, 'Incorrect one-hot encode value') FROM (SELECT UNNEST(convert_bytea_to_smallint_array(dependent_var)) AS y FROM data_preprocessor_input_batch) a, (SELECT UNNEST(y4) as y4 FROM data_preprocessor_input) b;
-SELECT assert (dependent_vartype   = 'double precision[]' AND
-               class_values        IS NULL AND
-               num_classes         IS NULL,
-               'Summary Validation failed. Actual:' || __to_char(summary)
-              ) from (select * from data_preprocessor_input_batch_summary) summary;
-
-DROP TABLE IF EXISTS validation_out, validation_out_summary;
-SELECT validation_preprocessor_dl(
-  'validation_input',
-  'validation_out',
-  'y4',
-  'x_new',
-  'data_preprocessor_input_batch');
-
-SELECT assert(convert_bytea_to_smallint_array(dependent_var) = '{1,0}' AND dependent_var_shape[2] = 2, 'Incorrect one-hot encoding for already encoded dep var') FROM
-  validation_out WHERE buffer_id = 0;
-
--- test integer array type
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input',
-  'data_preprocessor_input_batch',
-  'y5',
-  'x',
-  4,
-  5);
-SELECT assert(pg_typeof(dependent_var) = 'bytea'::regtype, 'One-hot encode doesn''t convert into integer array format') FROM data_preprocessor_input_batch WHERE buffer_id = 0;
-SELECT assert(dependent_var_shape[2] = 2, 'Incorrect one-hot encode dimension') FROM
-  data_preprocessor_input_batch WHERE buffer_id = 0;
-
-SELECT assert(octet_length(independent_var) = buffer_size*6*4, 'Incorrect buffer length')
-FROM (SELECT buffer_size(17, 4) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=0;
-
-SELECT assert(relative_error(SUM(y), SUM(y5)) < 0.000001, 'Incorrect one-hot encode value') FROM (SELECT UNNEST(convert_bytea_to_smallint_array(dependent_var)) AS y FROM data_preprocessor_input_batch) a, (SELECT UNNEST(y5) as y5 FROM data_preprocessor_input) b;
-SELECT assert (dependent_vartype   = 'integer[]' AND
-               class_values        IS NULL AND
-               num_classes         IS NULL,
-               'Summary Validation failed. Actual:' || __to_char(summary)
-              ) from (select * from data_preprocessor_input_batch_summary) summary;
-
--- Test cases with NULL in class values
-DROP TABLE IF EXISTS data_preprocessor_input_null;
-CREATE TABLE data_preprocessor_input_null(id serial, x double precision[], label TEXT);
-INSERT INTO data_preprocessor_input_null(x, label) VALUES
-(ARRAY[1,2,3,4,5,6], 'a'),
-(ARRAY[11,2,3,4,5,6], 'a'),
-(ARRAY[11,22,33,4,5,6], NULL),
-(ARRAY[11,22,33,44,5,6], 'a'),
-(ARRAY[11,22,33,44,65,6], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], NULL),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,44,65,56], NULL),
-(ARRAY[11,22,33,44,65,56], 'a'),
-(ARRAY[11,22,33,144,65,56], 'b'),
-(ARRAY[11,22,233,44,65,56], 'b'),
-(ARRAY[11,22,33,44,65,56], 'b'),
-(ARRAY[11,22,33,44,65,56], 'b'),
-(ARRAY[11,22,33,44,65,56], NULL);
-
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input_null',
-  'data_preprocessor_input_batch',
-  'label',
-  'x',
-  4,
-  5,
-  5 -- num_classes
-  );
--- Test summary table if class_values has NULL as a legitimate
--- class label, and also two other NULLs because num_classes=5
--- but table has only 3 distinct class labels (including NULL)
-SELECT assert
-        (
-        class_values        = '{NULL,a,b,NULL,NULL}',
-        'Summary Validation failed with NULL data. Actual:' || __to_char(summary)
-        ) from (select * from data_preprocessor_input_batch_summary) summary;
-
-SELECT assert(dependent_var_shape[2] = 5, 'Incorrect one-hot encode dimension') FROM
-  data_preprocessor_input_batch WHERE buffer_id = 0;
-
-SELECT assert(octet_length(independent_var) = buffer_size*6*4, 'Incorrect buffer length')
-FROM (SELECT buffer_size(17, 4) buffer_size) a, data_preprocessor_input_batch WHERE buffer_id=0;
-
--- The same tests, but for validation.
-DROP TABLE IF EXISTS data_preprocessor_input_validation_null;
-CREATE TABLE data_preprocessor_input_validation_null(id serial, x double precision[], label TEXT);
-INSERT INTO data_preprocessor_input_validation_null(x, label) VALUES
-(ARRAY[1,2,3,4,5,6], 'a'),
-(ARRAY[11,2,3,4,5,6], 'b'),
-(ARRAY[11,2,3,4,5,6], 'b'),
-(ARRAY[111,22,33,4,5,6], NULL);
-DROP TABLE IF EXISTS validation_out_batch, validation_out_batch_summary;
-SELECT validation_preprocessor_dl(
-  'data_preprocessor_input_validation_null',
-  'validation_out_batch',
-  'label',
-  'x',
-  'data_preprocessor_input_batch',
-  1
-  );
-
--- Test summary table if class_values has NULL as a legitimate
--- class label, and also two other NULLs because num_classes=5
--- but table has only 3 distinct class labels (including NULL)
-SELECT assert
-        (
-        class_values        = '{NULL,a,b,NULL,NULL}',
-        'Summary Validation failed with NULL data. Actual:' || __to_char(summary)
-        ) from (select * from validation_out_batch_summary) summary;
-
--- Validate one hot encoding for specific row is correct
-SELECT assert(convert_bytea_to_smallint_array(dependent_var) = '{0,1,0,0,0}' AND dependent_var_shape[2] =5, 'Incorrect normalizing in validation table.')
-FROM data_preprocessor_input_validation_null, validation_out_batch
-WHERE x[1]=1 AND abs((convert_bytea_to_real_array(independent_var))[1] - 0.2::REAL) < 0.00001;
--- Assert one-hot encoding for NULL label
-SELECT assert(convert_bytea_to_smallint_array(dependent_var) = '{1,0,0,0,0}' AND dependent_var_shape[2] =5, 'Incorrect normalizing in validation table.')
-FROM data_preprocessor_input_validation_null, validation_out_batch
-WHERE x[1]=111 AND abs((convert_bytea_to_real_array(independent_var))[1] - 22.2::REAL) < 0.00001;
-
--- Test the content of 1-hot encoded dep var when NULL is the
--- class label.
-DROP TABLE IF EXISTS data_preprocessor_input_null;
-CREATE TABLE data_preprocessor_input_null(id serial, x double precision[], label TEXT);
-INSERT INTO data_preprocessor_input_null(x, label) VALUES
-(ARRAY[11,22,33,4,5,6], NULL);
-
-DROP TABLE IF EXISTS data_preprocessor_input_batch, data_preprocessor_input_batch_summary;
-SELECT training_preprocessor_dl(
-  'data_preprocessor_input_null',
-  'data_preprocessor_input_batch',
-  'label',
-  'x',
-  4,
-  5,
-  3 -- num_classes
-  );
-
--- class_values must be '{NULL,NULL,NULL}' where the first NULL
--- is for the class label seen in data, and the other two NULLs
--- are added as num_classes=3.
-SELECT assert
-        (
-        class_values        = '{NULL,NULL,NULL}',
-        'Summary Validation failed with NULL data. Actual:' || __to_char(summary)
-        ) from (select * from data_preprocessor_input_batch_summary) summary;
-
-SELECT assert(dependent_var_shape[2] = 3, 'Incorrect one-hot encode dimension') FROM
-  data_preprocessor_input_batch WHERE buffer_id = 0;
-SELECT assert(octet_length(independent_var) = 24, 'Incorrect buffer length')
-FROM data_preprocessor_input_batch WHERE buffer_id=0;
--- NULL is treated as a class label, so it should show '1' for the
--- first index
-SELECT assert(convert_bytea_to_smallint_array(dependent_var) = '{1,0,0}', 'Incorrect one-hot encode dimension with NULL data') FROM
-  data_preprocessor_input_batch WHERE buffer_id = 0;
-
--- The same tests for validation.
-DROP TABLE IF EXISTS validation_out_batch, validation_out_batch_summary;
-SELECT validation_preprocessor_dl(
-  'data_preprocessor_input_null',
-  'validation_out_batch',
-  'label',
-  'x',
-  'data_preprocessor_input_batch',
-  4
-  );
-
--- class_values must be '{NULL,NULL,NULL}' where the first NULL
--- is for the class label seen in data, and the other two NULLs
--- are added as num_classes=3.
-SELECT assert
-        (
-        class_values        = '{NULL,NULL,NULL}',
-        'Summary Validation failed with NULL data. Actual:' || __to_char(summary)
-        ) from (select * from validation_out_batch_summary) summary;
-
-SELECT assert(dependent_var_shape[2] = 3, 'Incorrect one-hot encode dimension') FROM
-  validation_out_batch WHERE buffer_id = 0;
-SELECT assert(octet_length(independent_var) = 24, 'Incorrect buffer length')
-FROM data_preprocessor_input_batch WHERE buffer_id=0;
--- NULL is treated as a class label, so it should show '1' for the
--- first index
-SELECT assert(convert_bytea_to_smallint_array(dependent_var) = '{1,0,0}', 'Incorrect one-hot encode dimension with NULL data') FROM
-  validation_out_batch WHERE buffer_id = 0;
-
--- Test if validation class values is not a subset of training data class values.
-DROP TABLE IF EXISTS data_preprocessor_input_validation_null;
-CREATE TABLE data_preprocessor_input_validation_null(id serial, x double precision[], label TEXT);
-INSERT INTO data_preprocessor_input_validation_null(x, label) VALUES
-(ARRAY[11,2,3,4,5,6], 'bbbb');
-DROP TABLE IF EXISTS validation_out_batch, validation_out_batch_summary;
-select assert(trap_error($TRAP$SELECT validation_preprocessor_dl(
-  'data_preprocessor_input_validation_null',
-  'validation_out_batch',
-  'label',
-  'x',
-  'data_preprocessor_input_batch',
-  1);$TRAP$) = 1,
-    'validation class values should be a subset of training data class values.');
diff --git a/src/ports/postgres/modules/deep_learning/test/keras_model_arch_table.ic.sql_in b/src/ports/postgres/modules/deep_learning/test/keras_model_arch_table.ic.sql_in
deleted file mode 100644
index 8d82a43..0000000
--- a/src/ports/postgres/modules/deep_learning/test/keras_model_arch_table.ic.sql_in
+++ /dev/null
@@ -1,31 +0,0 @@
-/* -----------------------------------------------------------------------
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- * ----------------------------------------------------------------------- */
-
-/* -----------------------------------------------------------------------------
- * Test Keras Model Arch Table helper functions
- * -------------------------------------------------------------------------- */
-
-SELECT load_keras_model('test239587_keras_model_arch_table','{"a" : 1, "b" : 2, "c" : [4,5,6] }');
-SELECT load_keras_model('test239587_keras_model_arch_table','{"config" : [1,2,3]}');
-SELECT load_keras_model('test239587_keras_model_arch_table','{"config" : [8,4,0]}');
-SELECT delete_keras_model('test239587_keras_model_arch_table',2);
-SELECT delete_keras_model('test239587_keras_model_arch_table',3);
-SELECT delete_keras_model('test239587_keras_model_arch_table',1);
diff --git a/src/ports/postgres/modules/deep_learning/test/keras_model_arch_table.sql_in b/src/ports/postgres/modules/deep_learning/test/keras_model_arch_table.sql_in
deleted file mode 100644
index 1f2009b..0000000
--- a/src/ports/postgres/modules/deep_learning/test/keras_model_arch_table.sql_in
+++ /dev/null
@@ -1,154 +0,0 @@
-/* -----------------------------------------------------------------------
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- * ----------------------------------------------------------------------- */
-
-/* -----------------------------------------------------------------------------
- * Test Keras Model Arch Table helper functions
- * -------------------------------------------------------------------------- */
-
-
-/* Test successful model creation where no table exists */
-DROP TABLE IF EXISTS test_keras_model_arch_table;
-SELECT load_keras_model('test_keras_model_arch_table', '{"a" : 1, "b" : 2, "c" : [4,5,6] }');
-
-SELECT assert(UPPER(atttypid::regtype::TEXT) = 'INTEGER', 'model_id column should be INTEGER type')
-    FROM pg_attribute WHERE attrelid = 'test_keras_model_arch_table'::regclass
-        AND attname = 'model_id';
-SELECT assert(UPPER(atttypid::regtype::TEXT) = 'JSON', 'model_arch column should be JSON type' ) FROM pg_attribute WHERE attrelid = 'test_keras_model_arch_table'::regclass
-        AND attname = 'model_arch';
-SELECT assert(UPPER(atttypid::regtype::TEXT) = 'BYTEA',
-    'model_weights column should be bytea type')
-    FROM pg_attribute WHERE attrelid = 'test_keras_model_arch_table'::regclass
-        AND attname = 'model_weights';
-
-/*  model id should be 1 */
-SELECT assert(model_id = 1, 'Wrong model_id written by load_keras_model')
-    FROM test_keras_model_arch_table;
-
-/* model arch should be valid json, with all fields accessible with json operators */
-SELECT assert((model_arch->>'a') = '1', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table;
-SELECT assert((model_arch->>'b') = '2', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table;
-SELECT assert((model_arch->'c')->>0 = '4', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table;
-SELECT assert((model_arch->'c')->>1 = '5', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table;
-SELECT assert((model_arch->'c')->>2 = '6', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table;
-/* model_weights should be set to null, since this is not a warm start */
-SELECT assert(model_weights IS NULL, 'model_weights should be NULL after load_keras_model() called.') FROM test_keras_model_arch_table;
-
-
-/* Test model creation where valid table exists */
-SELECT load_keras_model('test_keras_model_arch_table', '{"config" : [1,2,3]}');
-SELECT load_keras_model('test_keras_model_arch_table', '{"config" : [8,4,0]}');
-SELECT assert(model_arch->'config'->>0 = '1', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table WHERE model_id = 2;
-SELECT assert(model_arch->'config'->>1 = '2', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table WHERE model_id = 2;
-SELECT assert(model_arch->'config'->>2 = '3', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table WHERE model_id = 2;
-SELECT assert(model_arch->'config'->>0 = '8', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table WHERE model_id = 3;
-SELECT assert(model_arch->'config'->>1 = '4', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table WHERE model_id = 3;
-SELECT assert(model_arch->'config'->>2 = '0', 'Cannot parse model_arch json in model table.')
-    FROM test_keras_model_arch_table WHERE model_id = 3;
-
-/* Test deletion where valid table exists */
-SELECT delete_keras_model('test_keras_model_arch_table', 2);
-SELECT assert(COUNT(model_id) = 0, 'model id 2 should have been deleted!')
-    FROM test_keras_model_arch_table WHERE model_id = 2;
-SELECT delete_keras_model('test_keras_model_arch_table', 3);
-SELECT assert(COUNT(model_id) = 0, 'model id 3 should have been deleted!')
-    FROM test_keras_model_arch_table WHERE model_id = 3;
-      /* Delete a second time, to make sure nothing weird happens.
-       *  It should archrt to the user that the model_id wasn't found but not
-       *  raise an exception or change anything. */
-SELECT delete_keras_model('test_keras_model_arch_table', 1);
-SELECT assert(trap_error($$SELECT * from test_keras_model_arch_table$$) = 1,
-              'Table test_keras_model_arch_table should have been deleted.');
-
-SELECT load_keras_model('test_keras_model_arch_table', '{"config" : [1,2,3]}');
-DELETE FROM test_keras_model_arch_table;
-
-/* Test deletion where invalid table exists */
-SELECT load_keras_model('test_keras_model_arch_table', '{"config" : [1,2,3]}');
-ALTER TABLE test_keras_model_arch_table DROP COLUMN model_id;
-
-/* Test deletion where empty table exists */
-select assert(trap_error($$SELECT delete_keras_model('test_keras_model_arch_table', 3)$$) = 1,
-    'Deleting a model in an empty table should generate an exception.');
-
-SELECT assert(trap_error($$SELECT delete_keras_model('test_keras_model_arch_table', 1)$$) = 1,
-    'Deleting an invalid table should generate an exception.');
-
-SELECT assert(trap_error($$SELECT load_keras_model('test_keras_model_arch_table', '{"config" : 1}')$$) = 1, 'Passing an invalid table to load_keras_model() should raise exception.');
-
-/* Test deletion where no table exists */
-DROP TABLE IF EXISTS test_keras_model_arch_table;
-SELECT assert(trap_error($$SELECT delete_keras_model('test_keras_model_arch_table', 3)$$) = 1,
-              'Deleting a non-existent table should raise exception.');
-
-DROP TABLE IF EXISTS test_keras_model_arch_table;
-SELECT load_keras_model('test_keras_model_arch_table', '{"config" : [1,2,3]}', 'dummy weights'::bytea);
-SELECT load_keras_model('test_keras_model_arch_table', '{"config" : [1,2,3]}', NULL, 'my name', 'my desc');
-
-/* Test model weights */
-SELECT assert(model_weights = 'dummy weights', 'Incorrect model_weights in the model arch table.')
-FROM test_keras_model_arch_table WHERE model_id = 1;
-SELECT assert(model_weights IS NULL, 'model_weights is not NULL')
-FROM test_keras_model_arch_table WHERE model_id = 2;
-
-/* Test name and description */
-SELECT assert(name IS NULL AND description IS NULL, 'Name or description is not NULL.')
-FROM test_keras_model_arch_table WHERE model_id = 1;
-SELECT assert(name = 'my name' AND description = 'my desc', 'Incorrect name or description in the model arch table.')
-FROM test_keras_model_arch_table WHERE model_id = 2;
-
-
---------------------------- Test calling the UDF from python ---------------------------------
-CREATE OR REPLACE FUNCTION create_model_arch_transfer_learning() RETURNS VOID AS $$
-from keras.layers import *
-from keras import Sequential
-import numpy as np
-import plpy
-
-model = Sequential()
-model.add(Conv2D(1, kernel_size=(1, 1), activation='relu', input_shape=(1,1,1,)))
-weights = model.get_weights()
-weights_flat = [ w.flatten() for w in weights ]
-weights1d = np.array([j for sub in weights_flat for j in sub])
-weights1d = np.ones_like(weights1d)
-weights_bytea = weights1d.tostring()
-
-load_query = plpy.prepare("""SELECT load_keras_model(
-                        'test_keras_model_arch_table',
-                        $1, $2)
-                    """, ['json','bytea'])
-plpy.execute(load_query, [model.to_json(), weights_bytea])
-$$ LANGUAGE plpythonu VOLATILE;
-
-DROP TABLE IF EXISTS test_keras_model_arch_table;
-SELECT create_model_arch_transfer_learning();
-
-select assert(model_weights = '\000\000\200?\000\000\200?', 'loading weights from udf failed')
-from test_keras_model_arch_table;
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_cifar.setup.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_cifar.setup.sql_in
deleted file mode 100644
index 7c9ad5e..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_cifar.setup.sql_in
+++ /dev/null
@@ -1,118 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
--------------------- CIFAR 10 test input tables -----------------
-
-DROP TABLE IF EXISTS cifar_10_sample;
-CREATE TABLE cifar_10_sample(id INTEGER, y SMALLINT, y_text TEXT, imgpath TEXT, x  REAL[]);
-COPY cifar_10_sample FROM STDIN DELIMITER '|';
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-\.
-
-DROP TABLE IF EXISTS cifar_10_sample_batched;
-DROP TABLE IF EXISTS cifar_10_sample_batched_summary;
-SELECT training_preprocessor_dl('cifar_10_sample','cifar_10_sample_batched','y','x', 1, 255);
-
-DROP TABLE IF EXISTS cifar_10_sample_val, cifar_10_sample_val_summary;
-SELECT validation_preprocessor_dl('cifar_10_sample','cifar_10_sample_val','y','x', 'cifar_10_sample_batched', 1);
---- NOTE:  In order to test fit_merge, we need at least 2 rows in the batched table (1 on each segment).
-
-DROP TABLE IF EXISTS cifar_10_sample_int_batched;
-DROP TABLE IF EXISTS cifar_10_sample_int_batched_summary;
-SELECT training_preprocessor_dl('cifar_10_sample','cifar_10_sample_int_batched','y','x', 2, 255, 5);
-
--- This table is for testing a different input shape (3, 32, 32) instead of (32, 32, 3).
--- Create a table with image shape 3, 32, 32
-drop table if exists cifar_10_sample_test_shape;
-create table cifar_10_sample_test_shape(id INTEGER, y SMALLINT, x  REAL[] );
-copy cifar_10_sample_test_shape from stdin delimiter '|';
-1|0|{{{248,248,250,245,245,246,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245},{247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245},{245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247},{248,248,250,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245},{247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245},{245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247,245,245,247},{249,249,251,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246},{248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246},{246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248},{249,249,251,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246},{248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246},{246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248},{249,249,251,245,245,247,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,249,246,246,249,246,246},{249,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248,246},{246,248,246,246,248,246,246,248,246,246,248,246,246,249,246,246,249,246,246,248,246,246,248,246,246,248,246,246,248,246,246,248},{249,249,251,245,245,247,246,246,248,247,246,249,247,246,248,246,246,247,245,246,248,246,246,251,247,245,252,247,245,251,247,245},{250,247,245,250,248,245,249,247,245,248,246,243,247,246,243,247,245,244,248,244,244,248,246,245,248,246,246,248,246,246,248,246},{246,248,246,246,248,246,246,248,246,246,248,246,245,249,246,246,249,245,246,249,245,246,249,246,246,247,246,246,246,246,246,247},{249,249,251,245,245,247,246,246,248,247,246,248,247,246,248,247,246,249,246,246,250,245,246,250,245,246,248,245,246,247,246,246},{247,246,246,247,247,246,247,246,246,247,245,244,245,245,244,245,244,244,247,244,243,247,245,245,248,246,246,248,246,246,248,246},{246,248,246,246,248,246,246,248,246,246,246,246,246,244,246,247,244,246,247,245,245,246,247,246,245,250,246,246,248,246,246,248},{249,249,251,245,245,247,246,246,248,244,245,245,242,243,246,244,243,248,246,244,247,247,247,246,248,250,247,247,249,247,246,247},{248,245,246,248,245,246,251,245,246,251,243,244,249,243,244,248,244,244,244,244,244,244,245,245,247,246,246,248,246,246,248,246},{246,248,246,246,248,246,246,248,246,246,247,246,247,245,246,246,244,247,245,243,249,247,246,246,245,249,246,245,251,246,245,250},{249,249,251,245,245,247,245,245,247,245,248,249,245,248,251,245,245,247,247,246,237,245,243,229,235,233,226,238,236,233,243,240},{241,248,245,249,246,245,253,246,245,253,245,244,252,245,245,251,245,246,243,245,246,241,246,246,246,246,245,249,246,246,249,246},{246,248,246,246,248,246,246,248,245,245,250,244,244,251,248,245,249,247,242,239,240,235,227,242,243,235,245,245,247,246,245,251},{249,249,251,244,244,246,233,233,235,187,192,193,161,166,169,160,161,157,208,207,179,230,227,188,215,210,179,215,209,184,231,224},{205,240,233,220,239,236,226,241,238,229,242,238,230,241,238,229,238,239,229,244,245,238,246,246,245,246,246,250,246,246,249,246},{246,248,247,246,248,247,246,248,245,245,250,246,247,252,240,238,232,222,216,193,202,198,164,206,209,180,241,242,235,246,245,250},{248,248,250,245,245,247,231,231,233,127,131,133,39,44,46,26,27,17,109,109,70,169,167,108,180,174,116,191,183,131,203,194},{147,184,175,133,145,139,102,149,144,106,150,146,108,151,148,114,186,187,169,240,241,234,245,246,245,246,245,250,246,246,251,245}},{{246,250,245,246,249,245,246,249,246,248,248,229,231,219,196,198,164,196,195,140,185,183,121,186,191,148,240,242,229,247,246,250},{249,249,251,244,244,246,244,244,246,233,232,234,174,173,172,114,113,100,112,112,85,112,110,70,112,108,59,125,119,67,151,143},{86,149,139,83,124,113,60,111,103,42,115,111,51,151,148,103,203,204,184,241,243,237,246,247,249,247,247,251,245,247,252,246},{246,251,248,247,249,240,243,241,216,219,204,196,197,157,195,200,138,197,199,132,178,178,118,208,210,176,246,245,244,245,245,250},{249,249,251,244,244,246,244,244,246,246,244,246,249,247,245,239,238,230,222,221,209,199,197,178,124,120,91,82,77,36,132,126},{71,171,164,102,171,162,100,143,137,68,147,143,80,194,191,145,201,204,180,211,214,204,227,229,226,236,238,235,245,243,239,249},{242,236,235,224,212,209,198,177,194,183,153,187,176,134,165,158,106,141,137,80,109,107,52,187,187,159,248,246,251,245,245,249},{248,248,250,244,244,246,244,244,246,245,244,244,242,242,241,243,243,242,250,249,249,253,251,251,189,185,176,91,87,61,96,92},{46,155,152,93,153,150,88,149,147,82,153,151,92,177,175,130,197,200,172,199,203,187,186,190,179,166,169,156,153,152,138,143},{133,117,115,96,73,123,102,69,145,124,86,132,108,74,90,68,36,80,68,26,100,98,47,213,212,190,249,247,251,244,245,248},{247,247,249,244,244,246,244,244,246,244,244,245,244,244,245,244,244,247,242,242,248,243,242,249,244,241,245,179,176,165,104,101},{73,82,80,40,89,90,43,101,102,53,108,107,60,120,118,78,130,134,105,144,149,131,161,165,150,154,159,140,118,124,106,87},{86,70,74,64,42,69,58,29,74,60,27,81,58,27,84,60,28,100,86,48,156,155,114,223,222,207,247,245,250,245,246,246},{247,247,249,244,243,246,244,244,246,243,244,247,242,245,247,242,245,247,242,245,247,243,245,247,244,244,248,241,240,240,219,216},{209,140,138,125,85,88,68,79,81,56,86,85,54,83,81,46,62,65,40,55,60,44,77,81,66,91,96,75,106,113,89,109},{112,90,104,100,78,88,87,63,72,72,45,70,62,26,94,79,29,142,132,87,149,149,121,205,203,195,246,244,250,244,245,244},{246,247,248,243,243,244,244,244,245,242,244,247,241,245,247,241,245,245,241,245,243,241,245,243,242,244,245,244,244,248,252,249},{254,212,210,213,185,186,184,160,161,153,159,159,140,107,107,79,61,64,42,49,53,38,41,45,31,38,42,25,38,41,18,58},{56,29,76,72,43,83,83,52,82,86,58,74,75,37,139,133,71,173,169,114,129,131,104,202,201,197,247,245,250,242,244,243},{245,247,245,242,243,242,242,244,243,242,244,245,242,243,245,242,243,245,242,244,244,242,243,244,242,243,245,242,243,246,242,242},{246,175,175,177,159,160,154,142,143,133,133,135,120,105,108,89,89,96,75,85,93,75,72,78,64,56,63,51,42,45,34,47},{47,26,55,55,18,67,65,21,79,74,38,74,68,30,117,113,58,176,174,122,138,137,108,177,178,176,247,247,250,243,244,245},{245,247,246,242,243,242,242,244,243,242,243,244,242,243,245,242,243,245,242,243,245,242,243,245,242,243,245,242,243,245,243,244},{245,211,213,212,210,215,207,178,181,171,133,134,122,108,107,94,89,97,80,76,88,69,77,89,69,80,91,72,63,69,51,68},{70,46,72,72,40,99,96,65,101,96,59,62,57,8,69,66,21,132,127,103,203,198,196,228,229,231,243,244,246,242,243,245},{245,246,248,241,243,244,242,243,245,242,243,245,242,243,245,242,243,245,242,243,245,242,243,245,242,243,245,242,243,245,241,242},{244,245,248,248,243,252,246,245,249,242,239,237,230,218,212,205,194,193,184,133,135,122,60,62,43,76,80,54,82,85,57,79},{81,55,117,117,97,201,199,188,171,168,147,95,92,54,130,128,99,212,209,204,245,241,252,245,245,248,242,243,245,241,242,244},{244,245,249,241,242,246,241,242,246,241,242,245,242,243,245,241,242,244,241,242,244,241,242,244,241,242,244,241,242,244,241,242},{244,239,242,243,235,244,242,240,244,241,240,238,235,192,184,181,182,170,169,152,140,136,53,42,30,68,57,38,93,90,66,139},{141,122,214,213,207,244,244,245,228,226,225,210,207,200,225,223,215,244,244,240,242,243,243,242,243,245,243,244,246,243,244,246},{243,244,249,241,241,246,241,242,247,241,242,245,241,242,243,241,242,244,241,242,244,241,242,244,241,242,244,241,242,244,241,242}},{{244,241,242,245,241,242,246,242,243,245,243,242,242,221,219,217,214,205,205,196,185,183,100,90,83,121,112,100,188,184,171,234},{232,228,245,245,249,238,242,244,243,245,248,248,245,253,244,243,247,240,243,241,240,244,239,242,243,244,242,243,245,242,243,245},{243,244,250,240,241,247,240,242,247,240,241,246,240,241,245,240,242,245,241,242,245,241,242,245,241,242,245,241,242,245,241,242},{245,241,242,245,242,241,247,242,241,246,242,243,245,246,247,248,248,248,248,246,245,244,209,209,207,225,225,221,247,246,244,244},{242,247,240,240,248,239,243,246,239,242,243,241,240,245,241,241,247,240,243,247,239,243,245,241,242,245,241,242,245,241,242,245},{241,244,251,238,241,247,238,241,248,238,241,248,239,241,248,239,241,248,239,242,248,239,241,247,240,241,246,240,241,246,241,242},{246,241,241,246,240,241,246,240,241,246,241,242,246,240,241,245,239,240,245,240,241,245,242,243,247,241,243,246,240,240,245,240},{240,246,241,241,247,240,241,246,240,242,246,241,242,246,241,242,247,241,242,247,241,242,247,241,242,247,241,242,247,241,242,247},{240,243,250,237,240,247,238,241,248,238,241,248,237,240,247,237,240,247,237,240,247,238,241,247,240,241,246,240,241,246,239,240},{245,239,240,245,240,241,246,240,241,246,239,240,245,239,241,246,240,241,246,240,241,247,239,240,245,240,241,246,241,242,247,240},{241,246,240,241,246,240,241,246,240,241,246,240,241,246,240,241,246,241,242,247,241,242,247,241,242,247,241,242,247,241,242,247},{239,242,249,237,240,247,238,240,247,237,240,247,236,239,246,236,239,246,236,239,246,237,240,246,238,241,246,237,241,245,237,240},{244,237,240,245,238,241,246,238,241,246,237,240,245,237,240,245,238,241,245,238,241,246,238,241,246,238,241,246,238,241,246,238},{241,246,238,241,246,238,241,246,238,241,246,238,241,245,238,241,246,239,242,247,239,242,247,239,242,247,239,242,247,239,242,247},{239,242,249,236,239,246,236,239,246,237,240,247,237,240,247,236,239,246,236,239,246,236,240,246,235,241,245,235,241,245,235,241},{245,235,241,245,235,241,245,235,241,245,235,241,245,235,241,245,235,241,245,235,241,245,235,241,245,234,241,244,235,241,245,235},{242,245,236,242,246,236,242,246,235,242,245,234,240,244,235,241,245,236,242,246,236,242,246,236,242,246,236,242,246,236,242,246},{239,242,249,236,239,245,236,239,246,236,239,246,236,239,246,236,239,246,236,239,246,235,240,245,233,241,244,233,241,244,233,241},{244,233,241,244,233,241,244,233,241,244,233,241,244,233,241,244,233,241,244,233,241,244,233,241,244,233,241,244,233,241,244,233},{241,244,234,242,245,235,243,246,234,242,245,233,241,244,234,242,245,235,243,246,235,243,246,235,243,246,235,243,246,235,243,246},{237,242,249,234,239,246,235,239,246,234,239,246,234,238,246,235,239,246,235,239,246,234,239,246,232,240,245,232,240,244,232,240},{244,232,240,244,232,240,244,232,240,244,232,240,244,232,240,244,232,240,244,232,240,245,233,240,245,233,241,245,233,240,245,232},{240,245,233,241,246,234,242,247,234,241,246,233,240,245,233,241,245,234,242,246,234,242,246,234,242,247,234,242,247,234,242,247},{235,242,250,232,238,246,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239},{247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,233,240,247,233,240,248,233,240,247,233},{240,247,233,240,248,233,240,248,233,240,247,232,239,247,232,239,247,232,239,247,232,239,247,233,240,248,233,240,248,233,240,248},{235,242,250,231,239,246,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239},{247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232},{239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,233,240,248},{235,242,250,232,238,246,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239},{247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,231,238,246,231,238,246,231},{238,246,231,238,246,231,238,246,232,239,247,232,239,247,232,239,247,232,239,247,232,239,247,231,238,246,232,239,246,232,239,247}}}
-\.
-
-DROP TABLE IF EXISTS cifar_10_sample_test_shape_batched;
-DROP TABLE IF EXISTS cifar_10_sample_test_shape_batched_summary;
-SELECT training_preprocessor_dl('cifar_10_sample_test_shape','cifar_10_sample_test_shape_batched','y','x', NULL, 255, 3);
-
-DROP TABLE IF EXISTS model_arch;
-SELECT load_keras_model('model_arch',
-  $${
-  "class_name": "Sequential",
-  "keras_version": "2.1.6",
-  "config": [{
-	"class_name": "Conv2D", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}},
-	"name": "conv2d_1",
-	"kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null,
-	"dtype": "float32", "activation": "relu", "trainable": true,
-	"data_format": "channels_last", "filters": 32, "padding": "valid",
-	"strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null,
-	"bias_initializer": {"class_name": "Zeros", "config": {}},
-	"batch_input_shape": [null, 32, 32, 3], "use_bias": true,
-	"activity_regularizer": null, "kernel_size": [3, 3]}},
-	{"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "data_format": "channels_last", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2]}},
-	{"class_name": "Dropout", "config": {"rate": 0.25, "noise_shape": null, "trainable": true, "seed": null, "name": "dropout_1"}},
-	{"class_name": "Flatten", "config": {"trainable": true, "name": "flatten_1", "data_format": "channels_last"}},
-	{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer":
-	{"class_name": "Zeros", "config": {}}, "units": 2, "use_bias": true, "activity_regularizer": null}
-	}], "backend": "tensorflow"}$$);
-
-SELECT load_keras_model('model_arch',
-  $${
-  "class_name": "Sequential",
-  "keras_version": "2.1.6",
-  "config": [{
-	"class_name": "Conv2D", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}},
-	"name": "conv2d_1",
-	"kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null,
-	"dtype": "float32", "activation": "relu", "trainable": true,
-	"data_format": "channels_last", "filters": 32, "padding": "valid",
-	"strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null,
-	"bias_initializer": {"class_name": "Zeros", "config": {}},
-	"batch_input_shape": [null, 32, 32, 3], "use_bias": true,
-	"activity_regularizer": null, "kernel_size": [3, 3]}},
-	{"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "data_format": "channels_last", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2]}},
-	{"class_name": "Dropout", "config": {"rate": 0.25, "noise_shape": null, "trainable": true, "seed": null, "name": "dropout_1"}},
-	{"class_name": "Flatten", "config": {"trainable": true, "name": "flatten_1", "data_format": "channels_last"}},
-	{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer":
-	{"class_name": "Zeros", "config": {}}, "units": 5, "use_bias": true, "activity_regularizer": null}
-	}], "backend": "tensorflow"}$$);
-
-SELECT load_keras_model('model_arch',
-  $${
-  "class_name": "Sequential",
-  "keras_version": "2.1.6",
-  "config": [{
-    "class_name": "Conv2D", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}},
-    "name": "conv2d_1",
-    "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null,
-    "dtype": "float32", "activation": "relu", "trainable": true,
-    "data_format": "channels_first", "filters": 32, "padding": "valid",
-    "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null,
-    "bias_initializer": {"class_name": "Zeros", "config": {}},
-    "batch_input_shape": [null, 3, 32, 32], "use_bias": true,
-    "activity_regularizer": null, "kernel_size": [3, 3]}},
-    {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "data_format": "channels_first", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2]}},
-    {"class_name": "Dropout", "config": {"rate": 0.25, "noise_shape": null, "trainable": true, "seed": null, "name": "dropout_1"}},
-    {"class_name": "Flatten", "config": {"trainable": true, "name": "flatten_1", "data_format": "channels_first"}},
-    {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer":
-    {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}
-    }], "backend": "tensorflow"}$$);
-
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_evaluate.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_evaluate.sql_in
deleted file mode 100644
index cdda44a..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_evaluate.sql_in
+++ /dev/null
@@ -1,117 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
-m4_include(`SQLCommon.m4')
-
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_cifar.setup.sql_in'
-)
-
--- -- Please do not break up the compile_params string
--- -- It might break the assertion
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_saved_out',
-    'model_arch',
-    1,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['mae']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3);
-
--- Test that evaluate works as expected:
-DROP TABLE IF EXISTS evaluate_out;
-SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val', 'evaluate_out', FALSE);
-
-SELECT assert(loss  >= 0 AND
-        metric  >= 0 AND
-        metrics_type = '{mae}', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
-FROM evaluate_out;
-
--- Test that passing NULL / None instead of 0 for gpus_per_host works
-DROP TABLE IF EXISTS evaluate_out;
-SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val', 'evaluate_out');
-SELECT assert(loss  >= 0 AND
-        metric  >= 0 AND
-        metrics_type = '{mae}', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
-FROM evaluate_out;
-
--- Test that evaluate errors out correctly if mst_key is given for non-multi model tables
-DROP TABLE IF EXISTS evaluate_out;
-SELECT assert(trap_error($TRAP$
-    SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val', 'evaluate_out', FALSE ,1);
-    $TRAP$) = 1, 'Should error out if mst_key is given for non-multi model tables');
-
--- Test that evaluate errors out correctly if model_arch field missing from fit output
-DROP TABLE IF EXISTS evaluate_out;
-ALTER TABLE keras_saved_out DROP COLUMN model_arch;
-SELECT assert(trap_error($TRAP$
-	SELECT madlib_keras_evaluate('keras_saved_out', 'cifar_10_sample_val', 'evaluate_out');
-	$TRAP$) = 1, 'Should error out if model_arch column is missing from model_table');
-
-
-m4_changequote(`')
-m4_ifdef(, , )
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_fit.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_fit.sql_in
deleted file mode 100644
index a35eb6b..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_fit.sql_in
+++ /dev/null
@@ -1,419 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_cifar.setup.sql_in'
-)
-
-m4_include(`SQLCommon.m4')
-
--- Please do not break up the compile_params string
--- It might break the assertion
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_saved_out',
-    'model_arch',
-    1,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['mae']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3,
-    NULL,
-    'cifar_10_sample_val');
-
-SELECT assert(
-        model_arch_table = 'model_arch' AND
-        model_id = 1 AND
-        model_type = 'madlib_keras' AND
-        start_training_time         < now() AND
-        end_training_time > start_training_time AND
-        source_table = 'cifar_10_sample_batched' AND
-        validation_table = 'cifar_10_sample_val' AND
-        model = 'keras_saved_out' AND
-        dependent_varname = 'y' AND
-        dependent_vartype = 'smallint' AND
-        independent_varname = 'x' AND
-        normalizing_const = 255.0 AND
-        pg_typeof(normalizing_const) = 'real'::regtype AND
-        name is NULL AND
-        description is NULL AND
-        model_size > 0 AND
-        madlib_version is NOT NULL AND
-        compile_params = $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['mae']$$::text AND
-        fit_params = $$ batch_size=2, epochs=1, verbose=0 $$::text AND
-        num_iterations = 3 AND
-        metrics_compute_frequency = 3 AND
-        num_classes = 2 AND
-        class_values = '{0,1}' AND
-        metrics_type = '{mae}' AND
-        training_metrics_final >= 0  AND
-        training_loss_final  >= 0  AND
-        array_upper(training_metrics, 1) = 1 AND
-        array_upper(training_loss, 1) = 1 AND
-        array_upper(metrics_elapsed_time, 1) = 1 AND
-        validation_metrics_final >= 0 AND
-        validation_loss_final  >= 0  AND
-        array_upper(validation_metrics, 1) = 1 AND
-        array_upper(validation_loss, 1) = 1 ,
-        'Keras model output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
-SELECT assert(
-        model_weights IS NOT NULL AND
-        model_arch IS NOT NULL, 'Keras model output validation failed. Actual:' || __to_char(k))
-FROM (SELECT * FROM keras_saved_out) k;
-
--- Verify number of iterations for which metrics and loss are computed
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_saved_out',
-    'model_arch',
-    1,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3,
-    NULL,
-    'cifar_10_sample_val',
-    2);
-SELECT assert(
-        num_iterations = 3 AND
-        metrics_compute_frequency = 2 AND
-        training_metrics_final >= 0  AND
-        training_loss_final  >= 0  AND
-        metrics_type = '{accuracy}' AND
-        array_upper(training_metrics, 1) = 2 AND
-        array_upper(training_loss, 1) = 2 AND
-        array_upper(metrics_elapsed_time, 1) = 2 AND
-        validation_metrics_final >= 0 AND
-        validation_loss_final  >= 0  AND
-        array_upper(validation_metrics, 1) = 2 AND
-        array_upper(validation_loss, 1) = 2 ,
-        'Keras model output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
--- Test for
-  -- Non null name and description columns
-	-- Null validation table
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_out',
-    'model_arch',
-    1,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    2,
-    NULL,
-    NULL,
-    1,
-    NULL,
-    'model name',
-    'model desc');
-
-SELECT assert(
-    source_table = 'cifar_10_sample_batched' AND
-    model = 'keras_out' AND
-    dependent_varname = 'y' AND
-    independent_varname = 'x' AND
-    model_arch_table = 'model_arch' AND
-    model_id = 1 AND
-    compile_params = $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text AND
-    fit_params = $$ batch_size=2, epochs=1, verbose=0 $$::text AND
-    num_iterations = 2 AND
-    validation_table is NULL AND
-    metrics_compute_frequency = 1 AND
-    name = 'model name' AND
-    description = 'model desc' AND
-    model_type = 'madlib_keras' AND
-    model_size > 0 AND
-    start_training_time         < now() AND
-    end_training_time > start_training_time AND
-    array_upper(metrics_elapsed_time, 1) = 2 AND
-    dependent_vartype = 'smallint' AND
-    madlib_version is NOT NULL AND
-    num_classes = 2 AND
-    class_values = '{0,1}' AND
-    metrics_type = '{accuracy}' AND
-    normalizing_const = 255.0 AND
-    training_metrics_final is not NULL AND
-    training_loss_final is not NULL AND
-    array_upper(training_metrics, 1) = 2 AND
-    array_upper(training_loss, 1) = 2 AND
-    validation_metrics_final is  NULL AND
-    validation_loss_final is  NULL AND
-    validation_metrics is NULL AND
-    validation_loss is NULL,
-    'Keras model output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM keras_out_summary) summary;
-
-SELECT assert(model_weights IS NOT NULL , 'Keras model output validation failed') FROM (SELECT * FROM keras_out) k;
-
--- Validate metrics=NULL works with fit
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-'cifar_10_sample_batched',
-'keras_saved_out',
-'model_arch',
-1,
-$$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy'$$::text,
-$$ batch_size=2, epochs=1, verbose=0 $$::text,
-1);
-
-SELECT assert(
-        metrics_type is NULL AND
-        training_metrics IS NULL AND
-        array_upper(training_loss, 1) = 1 AND
-        array_upper(metrics_elapsed_time, 1) = 1 AND
-        validation_metrics_final IS NULL AND
-        validation_loss_final  >= 0  AND
-        validation_metrics IS NULL AND
-        array_upper(validation_loss, 1) = 1,
-        'Keras model output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
--- Validate metrics=[] works with fit
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-'cifar_10_sample_batched',
-'keras_saved_out',
-'model_arch',
-1,
-$$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=[]$$::text,
-$$ batch_size=2, epochs=1, verbose=0 $$::text,
-1);
-
-SELECT assert(
-        metrics_type IS NULL AND
-        training_metrics IS NULL AND
-        array_upper(training_loss, 1) = 1 AND
-        array_upper(metrics_elapsed_time, 1) = 1 AND
-        validation_metrics_final IS NULL AND
-        validation_loss_final  >= 0  AND
-        validation_metrics IS NULL AND
-        array_upper(validation_loss, 1) = 1,
-        'Keras model output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
--- Compile and fit parameter tests
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_out',
-    'model_arch',
-    1,
-    $$ optimizer='SGD', loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    1,
-    NULL,
-    NULL,
-    NULL,
-    NULL, 'model name', 'model desc');
-
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_out',
-    'model_arch',
-    1,
-    $$ optimizer='Adam()', loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    1,
-    NULL,
-    NULL,
-    NULL,
-    NULL, 'model name', 'model desc');
-
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_out',
-    'model_arch',
-    1,
-    $$ optimizer=Adam(epsilon=None), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    1,
-    FALSE,
-    NULL,
-    NULL,
-    NULL, 'model name', 'model desc');
-
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_out',
-    'model_arch',
-    1,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), metrics=['accuracy'], loss_weights=[2], sample_weight_mode=None, loss='categorical_crossentropy' $$::text,
-    $$ epochs=10, verbose=0, shuffle=True, initial_epoch=1, steps_per_epoch=2 $$::text,
-    1,
-    NULL,
-    NULL,
-    NULL,
-    False, 'model name', 'model desc');
-
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_out',
-    'model_arch',
-    1,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), metrics=['accuracy'], loss_weights=[2], sample_weight_mode=None, loss='categorical_crossentropy' $$::text,
-    NULL,
-    1,
-    NULL,
-    NULL,
-    NULL,
-    False, 'model name', 'model desc');
-
--- -- negative test case for passing non numeric y to fit
--- induce failure by passing a non numeric column
-DROP TABLE IF EXISTS cifar_10_sample_val_failure;
-CREATE TABLE cifar_10_sample_val_failure AS SELECT * FROM cifar_10_sample_val;
-ALTER TABLE cifar_10_sample_val_failure rename dependent_var to dependent_var_original;
-ALTER TABLE cifar_10_sample_val_failure rename buffer_id to dependent_var;
-DROP TABLE IF EXISTS keras_out, keras_out_summary;
-SELECT assert(trap_error($TRAP$SELECT madlib_keras_fit(
-           'cifar_10_sample_batched',
-           'keras_out',
-           'model_arch',
-           1,
-           $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-           $$ batch_size=2, epochs=1, verbose=0 $$::text,
-           2,
-           NULL,
-          'cifar_10_sample_val_failure');$TRAP$) = 1,
-       'Passing y of type non numeric array to fit should error out.');
-
--- Tests with text class values:
--- Modify input data to have text classes, and mini-batch it.
--- Create a new table using the text based column for dep var.
-DROP TABLE IF EXISTS cifar_10_sample_text_batched;
-m4_changequote(`')
-CREATE TABLE cifar_10_sample_text_batched AS
-    SELECT buffer_id, independent_var, dependent_var,
-      independent_var_shape, dependent_var_shape
-      m4_ifdef(, , )
-    FROM cifar_10_sample_batched m4_ifdef(, , );
-
--- Insert a new row with NULL as the dependent var (one-hot encoded)
-UPDATE cifar_10_sample_text_batched
-	SET dependent_var = convert_array_to_bytea(ARRAY[0,0,1,0,0]::smallint[]) WHERE buffer_id=0;
-UPDATE cifar_10_sample_text_batched
-	SET dependent_var = convert_array_to_bytea(ARRAY[0,1,0,0,0]::smallint[]) WHERE buffer_id=1;
-INSERT INTO cifar_10_sample_text_batched(m4_ifdef(, , ) buffer_id, independent_var, dependent_var, independent_var_shape, dependent_var_shape)
-    SELECT m4_ifdef(, , ) 2 AS buffer_id, independent_var,
-        convert_array_to_bytea(ARRAY[0,1,0,0,0]::smallint[]) AS dependent_var,
-        independent_var_shape, dependent_var_shape
-    FROM cifar_10_sample_batched WHERE cifar_10_sample_batched.buffer_id=0;
-UPDATE cifar_10_sample_text_batched SET dependent_var_shape = ARRAY[1,5];
-
--- Create the necessary summary table for the batched input.
-DROP TABLE IF EXISTS cifar_10_sample_text_batched_summary;
-CREATE TABLE cifar_10_sample_text_batched_summary(
-    source_table text,
-    output_table text,
-    dependent_varname text,
-    independent_varname text,
-    dependent_vartype text,
-    class_values text[],
-    buffer_size integer,
-    normalizing_const numeric);
-INSERT INTO cifar_10_sample_text_batched_summary values (
-    'cifar_10_sample',
-    'cifar_10_sample_text_batched',
-    'y_text',
-    'x',
-    'text',
-    ARRAY[NULL,'cat','dog',NULL,NULL],
-    1,
-    255.0);
-
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_text_batched',
-    'keras_saved_out',
-    'model_arch',
-    2,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3);
--- Assert fit has correct class_values
-SELECT assert(
-    dependent_vartype = 'text' AND
-    class_values = '{NULL,cat,dog,NULL,NULL}',
-    'Keras model output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
--- Test with INTEGER class_values
--- with NULL as a valid class value
-INSERT INTO cifar_10_sample(id, x, y, imgpath)
-SELECT 3, x, NULL, '0/img3.jpg' FROM cifar_10_sample
-WHERE y = 1;
-INSERT INTO cifar_10_sample(id, x, y, imgpath)
-SELECT 4, x, 4, '0/img4.jpg' FROM cifar_10_sample
-WHERE y = 0;
-INSERT INTO cifar_10_sample(id, x, y, imgpath)
-SELECT 5, x, 5, '0/img5.jpg' FROM cifar_10_sample
-WHERE y = 1;
-
-DROP TABLE IF EXISTS cifar_10_sample_int_batched;
-DROP TABLE IF EXISTS cifar_10_sample_int_batched_summary;
-SELECT training_preprocessor_dl('cifar_10_sample','cifar_10_sample_int_batched','y','x', 2, 255, 5);
-
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_int_batched',
-    'keras_saved_out',
-    'model_arch',
-    2,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3);
-
--- Assert fit has correct class_values
-SELECT assert(
-    dependent_vartype = 'smallint' AND
-    class_values = '{NULL,0,1,4,5}',
-    'Keras model output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM keras_saved_out_summary) summary;
-
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_test_shape_batched',
-    'keras_saved_out',
-    'model_arch',
-    3,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3);
-
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-CREATE TABLE "special-char?" AS SELECT * FROM model_arch;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_test_shape_batched',
-    'keras_saved_out',
-    '"special-char?"',
-    3,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3);
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_gpu_info.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_gpu_info.sql_in
deleted file mode 100644
index a8376db..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_gpu_info.sql_in
+++ /dev/null
@@ -1,42 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
-SELECT assert(trap_error($$SELECT * from gpu_configuration('output_table', 'invalid_source')$$) = 1, 'Invalid source');
-
-DROP TABLE IF EXISTS output_table_tf;
-SELECT gpu_configuration('output_table_tf');
-SELECT * FROM output_table_tf;
-
-DROP TABLE IF EXISTS output_table_tf;
-SELECT gpu_configuration('output_table_tf', NULL);
-SELECT * FROM output_table_tf;
-
-DROP TABLE IF EXISTS output_table_nvidia;
-SELECT gpu_configuration('output_table_nvidia','NVIDIA');
-SELECT * FROM output_table_nvidia;
-
-DROP TABLE IF EXISTS output_table_tf;
-SELECT gpu_configuration('output_table_tf', 'tensorflow');
-SELECT * FROM output_table_tf;
-
-DROP TABLE IF EXISTS output_table_tf;
-SELECT * FROM gpu_configuration('output_table_tf');
-SELECT * FROM output_table_tf;
\ No newline at end of file
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_iris.setup.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_iris.setup.sql_in
deleted file mode 100644
index 389efa3..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_iris.setup.sql_in
+++ /dev/null
@@ -1,295 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
--------------------- IRIS test input tables -----------------
-
-DROP TABLE IF EXISTS iris_data;
-CREATE TABLE iris_data(
-    id serial,
-    attributes numeric[],
-    class_text varchar
-);
-INSERT INTO iris_data(id, attributes, class_text) VALUES
-(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),
-(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),
-(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),
-(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),
-(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),
-(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),
-(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),
-(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),
-(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),
-(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
-(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),
-(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),
-(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),
-(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),
-(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),
-(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),
-(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),
-(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),
-(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),
-(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),
-(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),
-(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),
-(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),
-(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),
-(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),
-(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),
-(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),
-(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),
-(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),
-(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),
-(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),
-(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),
-(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),
-(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),
-(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
-(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),
-(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),
-(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
-(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),
-(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),
-(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),
-(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),
-(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),
-(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),
-(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),
-(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),
-(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),
-(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),
-(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),
-(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),
-(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),
-(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),
-(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),
-(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),
-(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),
-(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),
-(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),
-(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),
-(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),
-(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),
-(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),
-(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),
-(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),
-(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),
-(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),
-(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),
-(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),
-(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),
-(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),
-(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),
-(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),
-(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),
-(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),
-(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),
-(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),
-(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),
-(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),
-(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),
-(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),
-(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),
-(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),
-(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),
-(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),
-(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),
-(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),
-(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),
-(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),
-(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),
-(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),
-(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),
-(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),
-(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),
-(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),
-(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),
-(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),
-(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),
-(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),
-(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),
-(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),
-(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),
-(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),
-(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
-(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),
-(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),
-(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),
-(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),
-(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),
-(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),
-(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),
-(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),
-(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),
-(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),
-(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),
-(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),
-(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),
-(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),
-(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),
-(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),
-(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),
-(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),
-(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),
-(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),
-(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),
-(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),
-(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),
-(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),
-(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),
-(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),
-(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),
-(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),
-(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),
-(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),
-(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),
-(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),
-(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),
-(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),
-(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),
-(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),
-(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),
-(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),
-(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),
-(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),
-(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
-(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),
-(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),
-(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),
-(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),
-(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),
-(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),
-(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');
-
-
-DROP TABLE IF EXISTS iris_data_one_hot_encoded;
-CREATE TABLE iris_data_one_hot_encoded as select id, attributes, ARRAY[class_text is not distinct from 'Iris-setosa', class_text is not distinct from 'Iris-versicolor', class_text is not distinct from 'Iris-virginica']::int[] as class_one_hot_encoded
-from iris_data;
-
-
-DROP TABLE IF EXISTS iris_data_packed, iris_data_packed_summary;
-SELECT training_preprocessor_dl('iris_data',         -- Source table
-                                'iris_data_packed',  -- Output table
-                                'class_text',        -- Dependent variable
-                                'attributes'         -- Independent variable
-                                );
-
-DROP TABLE IF EXISTS iris_data_one_hot_encoded_packed, iris_data_one_hot_encoded_packed_summary;
-SELECT training_preprocessor_dl('iris_data_one_hot_encoded',         -- Source table
-                                'iris_data_one_hot_encoded_packed',  -- Output table
-                                'class_one_hot_encoded',        -- Dependent variable
-                                'attributes'         -- Independent variable
-           );
-
-DROP TABLE IF EXISTS iris_data_val, iris_data_val_summary;
-SELECT validation_preprocessor_dl('iris_data',    -- Source table
-                                'iris_data_val',  -- Output table
-                                'class_text',     -- Dependent variable
-                                'attributes',     -- Independent variable
-                                'iris_data_packed'-- Training preprocessed table
-                                );
-
-DROP TABLE IF EXISTS iris_data_one_hot_encoded_val, iris_data_one_hot_encoded_val_summary;
-SELECT validation_preprocessor_dl('iris_data_one_hot_encoded',    -- Source table
-                                'iris_data_one_hot_encoded_val',  -- Output table
-                                'class_one_hot_encoded',        -- Dependent variable
-                                'attributes',         -- Independent variable
-                                'iris_data_one_hot_encoded_packed'    -- Training preprocessed table
-           );
-
-DROP TABLE IF EXISTS iris_model_arch;
--- NOTE: The seed is set to 0 for every layer.
-SELECT load_keras_model('iris_model_arch',  -- Output table,
-$$
-{
-"class_name": "Sequential",
-"keras_version": "2.1.6",
-"config":
-    [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling",
-    "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}},
-    "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null,
-    "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true,
-    "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
-    "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true,
-    "activity_regularizer": null}}, {"class_name": "Dense",
-    "config": {"kernel_initializer": {"class_name": "VarianceScaling",
-    "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}},
-    "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null,
-    "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null,
-    "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true,
-    "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer":
-    {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0,
-    "seed": 0, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null,
-    "bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
-    "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
-    "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}],
-    "backend": "tensorflow"}
-$$
-);
-
--- Duplicate the architecture, but note that trainable is set to FALSE.
--- This is to ensure we don't learn anything new, that would help us
--- deterministically assert the accuracy and loss after transfer learning
--- and warm start.
-SELECT load_keras_model('iris_model_arch',  -- Output table,
-$$
-{
-"class_name": "Sequential",
-"keras_version": "2.1.6",
-"config":
-    [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling",
-    "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}},
-    "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null,
-    "bias_constraint": null, "dtype": "float32", "activation": "relu",
-    "trainable": false,
-    "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
-    "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true,
-    "activity_regularizer": null}}, {"class_name": "Dense",
-    "config": {"kernel_initializer": {"class_name": "VarianceScaling",
-    "config": {"distribution": "uniform", "scale": 1.0, "seed": 0, "mode": "fan_avg"}},
-    "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null,
-    "bias_constraint": null, "activation": "relu",
-    "trainable": false,
-    "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
-    "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}},
-    {"class_name": "Dense", "config": {"kernel_initializer":
-    {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0,
-    "seed": 0, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null,
-    "bias_regularizer": null, "bias_constraint": null, "activation": "softmax",
-    "trainable": false,
-    "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros",
-    "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}],
-    "backend": "tensorflow"}
-$$
-);
-
-DROP TABLE IF EXISTS iris_train, iris_test;
--- Set seed so results are reproducible
-SELECT setseed(0);
-SELECT train_test_split('iris_data',     -- Source table
-                        'iris',          -- Output table root name
-                        0.8,            -- Train proportion
-                        NULL,           -- Test proportion (0.2)
-                        NULL,           -- Strata definition
-                        NULL,           -- Output all columns
-                        NULL,           -- Sample without replacement
-                        TRUE            -- Separate output tables
-                        );
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_model_averaging_e2e.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_model_averaging_e2e.sql_in
deleted file mode 100644
index 20e2332..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_model_averaging_e2e.sql_in
+++ /dev/null
@@ -1,139 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
-m4_include(`SQLCommon.m4')
-
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
-)
-
-DROP TABLE if exists pg_temp.iris_model, pg_temp.iris_model_summary;
-SELECT madlib_keras_fit(
-	'iris_data_packed',
-	'pg_temp.iris_model',
-	'iris_model_arch',
-	1,
-	$$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$,
-  $$batch_size=16, epochs=1$$,
-	3,
-	FALSE
-);
-SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;
-
-SELECT assert(
-        model_arch_table = 'iris_model_arch' AND
-        validation_table is NULL AND
-        source_table = 'iris_data_packed' AND
-        model = 'pg_temp.iris_model' AND
-        dependent_varname = 'class_text' AND
-        independent_varname = 'attributes' AND
-        madlib_version is NOT NULL AND
-        num_iterations = 3 AND
-        start_training_time < now() AND
-        end_training_time < now() AND
-        num_classes = 3 AND
-        class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND
-        dependent_vartype LIKE '%char%' AND
-        normalizing_const = 1,
-        'Keras Fit Multiple Output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM pg_temp.iris_model_summary) summary;
-
--- Run Predict
-DROP TABLE IF EXISTS pg_temp.iris_predict;
-SELECT madlib_keras_predict(
-    'pg_temp.iris_model',
-    'iris_data',
-    'id',
-    'attributes',
-    'pg_temp.iris_predict',
-    'prob',
-    FALSE);
-SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;
-
--- Run Evaluate
-DROP TABLE IF EXISTS pg_temp.evaluate_out;
-SELECT madlib_keras_evaluate(
-    'pg_temp.iris_model',
-    'iris_data_val',
-    'pg_temp.evaluate_out',
-    FALSE);
-
-SELECT assert(loss >= 0 AND
-        metric >= 0 AND
-        metrics_type = '{accuracy}', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
-FROM pg_temp.evaluate_out;
-SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;
-
--- Test for one-hot encoded user input data
-DROP TABLE if exists iris_model, iris_model_summary, iris_model_info;
-SELECT madlib_keras_fit(
-	'iris_data_one_hot_encoded_packed',
-	'iris_model',
-	'iris_model_arch',
-	1,
-	$$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$,
-  $$batch_size=16, epochs=1$$,
-	3,
-	FALSE
-);
-
-SELECT assert(
-        model_arch_table = 'iris_model_arch' AND
-        validation_table is NULL AND
-        source_table = 'iris_data_one_hot_encoded_packed' AND
-        model = 'iris_model' AND
-        dependent_varname = 'class_one_hot_encoded' AND
-        independent_varname = 'attributes' AND
-        madlib_version is NOT NULL AND
-        num_iterations = 3 AND
-        start_training_time < now() AND
-        end_training_time < now() AND
-        dependent_vartype = 'integer[]' AND
-        num_classes = NULL AND
-        class_values = NULL AND
-        normalizing_const = 1,
-        'Keras Fit Multiple Output Summary Validation failed when user passes in 1-hot encoded label vector. Actual:' || __to_char(summary))
-FROM (SELECT * FROM iris_model_summary) summary;
-
--- Run Predict
-DROP TABLE IF EXISTS iris_predict;
-SELECT madlib_keras_predict(
-    'iris_model',
-    'iris_data_one_hot_encoded',
-    'id',
-    'attributes',
-    'iris_predict',
-    'prob',
-    FALSE);
-
--- Run Evaluate
-DROP TABLE IF EXISTS evaluate_out;
-SELECT madlib_keras_evaluate(
-    'iris_model',
-    'iris_data_one_hot_encoded_val',
-    'evaluate_out',
-    FALSE);
-
-SELECT assert(loss >= 0 AND
-        metric >= 0 AND
-        metrics_type = '{accuracy}', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
-FROM evaluate_out;
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_model_selection.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_model_selection.sql_in
deleted file mode 100644
index e1dbe0c..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_model_selection.sql_in
+++ /dev/null
@@ -1,426 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
-m4_include(`SQLCommon.m4')
-
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
-)
-
--- MST table generation tests
--- Valid inputs should pass and yield 6 msts in the table
-DROP TABLE IF EXISTS mst_table, mst_table_summary;
-SELECT load_model_selection_table(
-    'iris_model_arch',
-    'mst_table',
-    ARRAY[1],
-    ARRAY[
-        $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,
-        $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,
-        $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$
-    ],
-    ARRAY[
-        $$batch_size=5,epochs=1$$,
-        $$batch_size=10,epochs=1$$
-    ]
-);
-SELECT assert(
-    COUNT(*)=6,
-    'The length of mst table does not match with the inputs'
-)
-FROM mst_table;
-
--- Test summary table output
-SELECT assert(
-    model_arch_table = 'iris_model_arch',
-    'Model selection output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM mst_table_summary) summary;
-
--- Invalid arguments must be errored out
-
-DROP TABLE IF EXISTS mst_table, mst_table_summary;
-SELECT assert(trap_error($TRAP$
-    SELECT load_model_selection_table(
-        'iris_model_arch',
-        'mst_table',
-        ARRAY[-1],
-        ARRAY[
-            $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$
-        ],
-        ARRAY[
-            $$batch_size=5,epochs=1$$
-        ]
-    );
-$TRAP$)=1, 'Should error out if model_id is not in the model arch table');
-
-DROP TABLE IF EXISTS mst_table, mst_table_summary;
-SELECT assert(trap_error($TRAP$
-    SELECT load_model_selection_table(
-        'iris_model_arch',
-        'mst_table',
-        ARRAY[1],
-        ARRAY[
-            $$foo='bar'$$
-        ],
-        ARRAY[
-            $$batch_size='bar'$$
-        ]
-    );
-$TRAP$)=1, 'Should error out if the provided parameters are not valid');
-
--- Must deduplicate, options with extra white spaces should not be considered
--- as distinct params.
-
-DROP TABLE IF EXISTS mst_table, mst_table_summary;
-SELECT load_model_selection_table(
-    'iris_model_arch',
-    'mst_table',
-    ARRAY[1],
-    ARRAY[
-        $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,
-        $$  loss='categorical_crossentropy', optimizer='Adam(lr=0.1)',metrics=['accuracy'] $$,
-        $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$
-    ],
-    ARRAY[
-        $$batch_size=5,epochs=1$$,
-        $$batch_size=10,epochs=1$$
-    ]
-);
-SELECT assert(
-    COUNT(*)=4,
-    'The length of mst table (' || COUNT(*) || ')does not match with the inputs due to deduplication failure'
-)
-FROM mst_table;
-
--- Must also handle duplicates where order of key/value pairs is re-arranged
-DROP TABLE IF EXISTS mst_table, mst_table_summary;
-SELECT load_model_selection_table(
-    'iris_model_arch',
-    'mst_table',
-    ARRAY[1],
-    ARRAY[
-        $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,
-        $$metrics=  ['accuracy'], loss='categorical_crossentropy', optimizer='Adam(lr=0.1)'$$,
-        $$loss='mse',optimizer='Adam(lr=0.001)', metrics=['accuracy']$$
-    ],
-    ARRAY[
-        $$batch_size=5,epochs=1$$,
-        $$epochs=1, batch_size=5$$
-    ]
-);
-SELECT assert(
-    COUNT(*)=2,
-    'The length of mst table (' || COUNT(*) || ') does not match with the inputs due to deduplication failure'
-)
-FROM mst_table;
-
-m4_changequote(`')
-m4_ifdef(, ,  0 AND
-        fit_params = $MAD$batch_size=50, epochs=1$MAD$::text AND
-        metrics_type = '{accuracy}' AND
-        training_metrics_final >= 0  AND
-        training_loss_final  >= 0  AND
-        array_upper(training_metrics, 1) = 1 AND
-        array_upper(training_loss, 1) = 1 AND
-        validation_metrics_final >= 0  AND
-        validation_loss_final  >= 0  AND
-        array_upper(validation_metrics, 1) = 1 AND
-        array_upper(validation_loss, 1) = 1 AND
-        array_upper(metrics_elapsed_time, 1) = 1,
-        'Keras Fit Multiple Output Info Validation failed. Actual:' || __to_char(info))
-FROM (SELECT * FROM iris_multiple_model_info) info;
-
-SELECT assert(cnt = 1,
-	'Keras Fit Multiple Output Info compile params validation failed. Actual:' || __to_char(info))
-FROM (SELECT count(*) cnt FROM iris_multiple_model_info
-WHERE compile_params = $MAD$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$MAD$::text) info;
-
-SELECT assert(cnt = 1,
-	'Keras Fit Multiple Output Info compile params validation failed. Actual:' || __to_char(info))
-FROM (SELECT count(*) cnt FROM iris_multiple_model_info
-WHERE compile_params = $MAD$loss='categorical_crossentropy', optimizer='Adam(lr=0.001)', metrics=['accuracy']$MAD$::text) info;
-
-SELECT assert(cnt = 1,
-	'Keras Fit Multiple Output Info compile params validation failed. Actual:' || __to_char(info))
-FROM (SELECT count(*) cnt FROM iris_multiple_model_info
-WHERE compile_params = $MAD$loss='categorical_crossentropy', optimizer='Adam(lr=0.0001)', metrics=['accuracy']$MAD$::text) info;
-
-SELECT assert(
-  training_loss[6]-training_loss[1] < 0.1 AND
-  training_metrics[6]-training_metrics[1] > -0.1,
-    'The loss and accuracy should have improved with more iterations.'
-)
-FROM iris_multiple_model_info
-WHERE compile_params like '%lr=0.001%';
-
--- Test when number of configs(1) is less than number of segments(3)
-DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
-SELECT madlib_keras_fit_multiple_model(
-	'iris_data_packed',
-	'iris_multiple_model',
-	'mst_table_1row',
-	3,
-	FALSE,
-	NULL,
-	1,
-	FALSE,
-	'multi_model_name',
-	'multi_model_descr'
-);
-
-SELECT assert(COUNT(*)=1, 'Info table must have exactly same rows as the number of msts.')
-FROM iris_multiple_model_info;
-
-SELECT assert(
-        model_id = 1 AND
-        model_type = 'madlib_keras' AND
-        model_size > 0 AND
-        fit_params = $MAD$batch_size=16, epochs=1$MAD$::text AND
-        metrics_type = '{accuracy}' AND
-        training_metrics_final >= 0  AND
-        training_loss_final  >= 0  AND
-        array_upper(training_metrics, 1) = 3 AND
-        array_upper(training_loss, 1) = 3 AND
-        array_upper(metrics_elapsed_time, 1) = 3,
-        'Keras Fit Multiple Output Info Validation failed. Actual:' || __to_char(info))
-FROM (SELECT * FROM iris_multiple_model_info) info;
-
-SELECT assert(metrics_elapsed_time[3] - metrics_elapsed_time[1] > 0,
-        'Keras Fit Multiple invalid elapsed time calculation.')
-FROM (SELECT * FROM iris_multiple_model_info) info;
-
-SELECT assert(
-        name = 'multi_model_name' AND
-        description = 'multi_model_descr' AND
-        metrics_compute_frequency = 1,
-        'Keras Fit Multiple Output Summary Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM iris_multiple_model_summary) summary;
-
-SELECT assert(cnt = 1,
-	'Keras Fit Multiple Output Info compile params validation failed. Actual:' || __to_char(info))
-FROM (SELECT count(*) cnt FROM iris_multiple_model_info
-WHERE compile_params = $MAD$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$MAD$::text) info;
-
--- Test when number of configs(4) larger than number of segments(3)
-DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
-SELECT madlib_keras_fit_multiple_model(
-	'iris_data_packed',
-	'iris_multiple_model',
-	'mst_table_4row',
-	3,
-	FALSE
-);
-
--- The default value of the guc 'dev_opt_unsafe_truncate_in_subtransaction' is 'off'
--- but we change it to 'on' in fit_multiple.py. Assert that the value is
--- reset after calling fit_multiple
-SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('dev_opt_unsafe_truncate_in_subtransaction', 'off') END;
-
-SELECT assert(COUNT(*)=4, 'Info table must have exactly same rows as the number of msts.')
-FROM iris_multiple_model_info;
-
-SELECT assert(
-        model_id = 1 AND
-        model_type = 'madlib_keras' AND
-        model_size > 0 AND
-        metrics_type = '{accuracy}' AND
-        training_metrics_final >= 0  AND
-        training_loss_final  >= 0  AND
-        array_upper(training_metrics, 1) = 1 AND
-        array_upper(training_loss, 1) = 1 AND
-        array_upper(metrics_elapsed_time, 1) = 1,
-        'Keras Fit Multiple Output Info Validation failed. Actual:' || __to_char(info))
-FROM (SELECT * FROM iris_multiple_model_info) info;
-
-SELECT assert(cnt = 1,
-	'Keras Fit Multiple Output Info compile params validation failed. Actual:' || __to_char(info))
-FROM (SELECT count(*) cnt FROM iris_multiple_model_info
-WHERE compile_params = $MAD$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$MAD$::text
-AND fit_params = $MAD$batch_size=32, epochs=1$MAD$::text) info;
-
--- Test when class values have NULL values
-UPDATE iris_data_packed_summary SET class_values = ARRAY['Iris-setosa','Iris-versicolor',NULL];
-DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
-SELECT madlib_keras_fit_multiple_model(
-	'iris_data_packed',
-	'iris_multiple_model',
-	'mst_table_1row',
-	1,
-	FALSE,
-	NULL,
-	1,
-	FALSE
-);
-
-SELECT assert(
-        num_classes = 3 AND
-        class_values = '{Iris-setosa,Iris-versicolor,NULL}',
-        'Keras Fit Multiple num_clases and class values Validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM iris_multiple_model_summary) summary;
-
-------------- Test for schema qualified output table and input table -----------------------
-CREATE SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__;
-CREATE TABLE __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_data_packed as select * from iris_data_packed;
-CREATE TABLE __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_data_packed_summary as select * from iris_data_packed_summary;
-
--- do not drop the output table created in the previous test
-SELECT count(*) from iris_multiple_model;
-SELECT madlib_keras_fit_multiple_model(
-	'__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_data_packed',
-	'__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model',
-	'mst_table_1row',
-	1,
-	FALSE,
-	NULL,
-	1,
-	FALSE
-);
-SELECT count(*) from __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model;
-SELECT assert(
-        num_classes = 3 AND
-        class_values = '{Iris-setosa,Iris-versicolor,NULL}',
-        'Keras Fit Multiple validation failed. Actual:' || __to_char(summary))
-FROM (SELECT * FROM __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_summary) summary;
-
-DROP SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__ CASCADE;
-!>)
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_model_selection_e2e.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_model_selection_e2e.sql_in
deleted file mode 100644
index d738f48..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_model_selection_e2e.sql_in
+++ /dev/null
@@ -1,165 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
-m4_include(`SQLCommon.m4')
-
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
-)
-
-m4_changequote(`')
-m4_ifdef(, , = 0 AND
-        metric >= 0 AND
-        metrics_type = '{accuracy}', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
-FROM pg_temp.evaluate_out;
-
--- Test for one-hot encoded user input data
-DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info;
-SELECT madlib_keras_fit_multiple_model(
-	'iris_data_one_hot_encoded_packed',
-	'iris_multiple_model',
-	'pg_temp.mst_table',
-	3,
-	FALSE
-);
-
-SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;
-
-SELECT assert(
-        model_arch_table = 'iris_model_arch' AND
-        validation_table is NULL AND
-        model_info = 'iris_multiple_model_info' AND
-        source_table = 'iris_data_one_hot_encoded_packed' AND
-        model = 'iris_multiple_model' AND
-        dependent_varname = 'class_one_hot_encoded' AND
-        independent_varname = 'attributes' AND
-        madlib_version is NOT NULL AND
-        num_iterations = 3 AND
-        start_training_time < now() AND
-        end_training_time < now() AND
-        dependent_vartype = 'integer[]' AND
-        num_classes = NULL AND
-        class_values = NULL AND
-        normalizing_const = 1,
-        'Keras Fit Multiple Output Summary Validation failed when user passes in 1-hot encoded label vector. Actual:' || __to_char(summary))
-FROM (SELECT * FROM iris_multiple_model_summary) summary;
-
--- Run Predict
-DROP TABLE IF EXISTS iris_predict;
-SELECT madlib_keras_predict(
-    'iris_multiple_model',
-    'iris_data_one_hot_encoded',
-    'id',
-    'attributes',
-    'iris_predict',
-    'prob',
-    NULL,
-    1);
-SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;
-
--- Run Evaluate
-DROP TABLE IF EXISTS evaluate_out;
-SELECT madlib_keras_evaluate(
-    'iris_multiple_model',
-    'iris_data_one_hot_encoded_val',
-    'evaluate_out',
-    NULL,
-    1);
-SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END;
-
-SELECT assert(loss >= 0 AND
-        metric >= 0 AND
-        metrics_type = '{accuracy}', 'Evaluate output validation failed.  Actual:' || __to_char(evaluate_out))
-FROM evaluate_out;
-
-DROP SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__ CASCADE;
-!>)
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict.sql_in
deleted file mode 100644
index 5a5b362..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict.sql_in
+++ /dev/null
@@ -1,403 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
-m4_include(`SQLCommon.m4')
-
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_cifar.setup.sql_in'
-)
-
--- Please do not break up the compile_params string
--- It might break the assertion
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_batched',
-    'keras_saved_out',
-    'model_arch',
-    1,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3);
-
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample',
-    'id',
-    'x',
-    'cifar10_predict',
-    NULL,
-    FALSE);
-
--- Validate that prediction output table exists and has correct schema
-SELECT assert(UPPER(pg_typeof(id)::TEXT) = 'INTEGER', 'id column should be INTEGER type')
-    FROM cifar10_predict;
-
-SELECT assert(UPPER(pg_typeof(estimated_y)::TEXT) =
-    'SMALLINT', 'prediction column should be SMALLINT type')
-    FROM cifar10_predict;
-
--- Validate correct number of rows returned.
-SELECT assert(COUNT(*)=2, 'Output table of madlib_keras_predict should have two rows')
-FROM cifar10_predict;
-
--- First test that all values are in set of class values; if this breaks, it's definitely a problem.
-SELECT assert(estimated_y IN (0,1),
-    'Predicted value not in set of defined class values for model')
-FROM cifar10_predict;
-
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT assert(trap_error($TRAP$SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample_batched',
-    'buffer_id',
-    'independent_var',
-    'cifar10_predict',
-    NULL,
-    FALSE);$TRAP$) = 1,
-    'Passing batched image table to predict should error out.');
-
--- Test with pred_type=prob
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample',
-    'id',
-    'x',
-    'cifar10_predict',
-    'prob',
-    FALSE);
-
-SELECT assert(UPPER(pg_typeof(prob_0)::TEXT) =
-    'DOUBLE PRECISION', 'column prob_0 should be double precision type')
-    FROM  cifar10_predict;
-
-SELECT assert(UPPER(pg_typeof(prob_1)::TEXT) =
-    'DOUBLE PRECISION', 'column prob_1 should be double precision type')
-    FROM  cifar10_predict;
-
-SELECT assert(COUNT(*)=3, 'Predict out table must have exactly three cols.')
-FROM pg_attribute
-WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
-
--- Tests with text class values:
--- Create a new table using the text based column for dep var.
-DROP TABLE IF EXISTS cifar_10_sample_text_batched;
-m4_changequote(`')
-CREATE TABLE cifar_10_sample_text_batched AS
-    SELECT buffer_id, independent_var, dependent_var,
-      independent_var_shape, dependent_var_shape
-      m4_ifdef(, , )
-    FROM cifar_10_sample_batched m4_ifdef(, , );
-
--- Insert a new row with NULL as the dependent var (one-hot encoded)
-UPDATE cifar_10_sample_text_batched
-	SET dependent_var = convert_array_to_bytea(ARRAY[0,0,1,0,0]::smallint[]) WHERE buffer_id=0;
-UPDATE cifar_10_sample_text_batched
-	SET dependent_var = convert_array_to_bytea(ARRAY[0,1,0,0,0]::smallint[]) WHERE buffer_id=1;
-INSERT INTO cifar_10_sample_text_batched(m4_ifdef(, , ) buffer_id, independent_var, dependent_var, independent_var_shape, dependent_var_shape)
-    SELECT m4_ifdef(, , ) 2 AS buffer_id, independent_var,
-        convert_array_to_bytea(ARRAY[0,1,0,0,0]::smallint[]) AS dependent_var,
-        independent_var_shape, dependent_var_shape
-    FROM cifar_10_sample_batched WHERE cifar_10_sample_batched.buffer_id=0;
-UPDATE cifar_10_sample_text_batched SET dependent_var_shape = ARRAY[1,5];
-m4_changequote(,)
-
--- Create the necessary summary table for the batched input.
-DROP TABLE IF EXISTS cifar_10_sample_text_batched_summary;
-CREATE TABLE cifar_10_sample_text_batched_summary(
-    source_table text,
-    output_table text,
-    dependent_varname text,
-    independent_varname text,
-    dependent_vartype text,
-    class_values text[],
-    buffer_size integer,
-    normalizing_const numeric);
-INSERT INTO cifar_10_sample_text_batched_summary values (
-    'cifar_10_sample',
-    'cifar_10_sample_text_batched',
-    'y_text',
-    'x',
-    'text',
-    ARRAY[NULL,'cat','dog',NULL,NULL],
-    1,
-    255.0);
-
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_text_batched',
-    'keras_saved_out',
-    'model_arch',
-    2,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3);
-
--- Predict with pred_type=prob
-DROP TABLE IF EXISTS cifar_10_sample_text;
-CREATE TABLE cifar_10_sample_text AS
-    SELECT id, x, y_text
-    FROM cifar_10_sample;
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample_text',
-    'id',
-    'x',
-    'cifar10_predict',
-    'prob',
-    FALSE);
-
--- Validate the output datatype of newly created prediction columns
--- for prediction type = 'prob' and class_values 'TEXT' with NULL as a valid
--- class_values
-SELECT assert(UPPER(pg_typeof(prob_cat)::TEXT) =
-    'DOUBLE PRECISION', 'column prob_cat should be double precision type')
-FROM cifar10_predict;
-
-SELECT assert(UPPER(pg_typeof(prob_dog)::TEXT) =
-    'DOUBLE PRECISION', 'column prob_dog should be double precision type')
-FROM cifar10_predict;
-
-SELECT assert(UPPER(pg_typeof("prob_NULL")::TEXT) =
-    'DOUBLE PRECISION', 'column prob_NULL should be double precision type')
-FROM cifar10_predict;
-
--- Must have exactly 4 cols (3 for class_values and 1 for id)
-SELECT assert(COUNT(*)=4, 'Predict out table must have exactly four cols.')
-FROM pg_attribute
-WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
-
--- Predict with pred_type=response
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample_text',
-    'id',
-    'x',
-    'cifar10_predict',
-    'response',
-    FALSE);
-
--- Validate the output datatype of newly created prediction columns
--- for prediction type = 'response' and class_values 'TEXT' with NULL
--- as a valid class_values
-SELECT assert(UPPER(pg_typeof(estimated_y_text)::TEXT) =
-    'TEXT', 'prediction column should be TEXT type')
-FROM  cifar10_predict LIMIT 1;
-
--- Tests where the assumption is user has one-hot encoded, so class_values
--- in input summary table will be NULL.
-UPDATE keras_saved_out_summary SET class_values=NULL;
-
--- Predict with pred_type=prob
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample_text',
-    'id',
-    'x',
-    'cifar10_predict',
-    'prob',
-    FALSE);
-
--- Validate the output datatype of newly created prediction column
--- for prediction type = 'response' and class_value = NULL
--- Returns: Array of probabilities for user's one-hot encoded data
-SELECT assert(UPPER(pg_typeof(prob)::TEXT) =
-    'DOUBLE PRECISION[]', 'column prob should be double precision[] type')
-FROM cifar10_predict LIMIT 1;
-
--- Predict with pred_type=response
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample_text',
-    'id',
-    'x',
-    'cifar10_predict',
-    'response',
-    FALSE);
-
--- Validate the output datatype of newly created prediction column
--- for prediction type = 'response' and class_value = NULL
--- Returns: Index of class value in user's one-hot encoded data with
--- highest probability
-SELECT assert(UPPER(pg_typeof(estimated_y_text)::TEXT) =
-    'TEXT', 'column estimated_y_text should be text type')
-FROM cifar10_predict LIMIT 1;
-
--- Test predict with INTEGER class_values
--- with NULL as a valid class value
--- Update output_summary table to reflect
--- class_values {NULL,0,1,4,5} and dependent_vartype is SMALLINT
-UPDATE keras_saved_out_summary
-SET dependent_varname = 'y',
-    class_values = ARRAY[NULL,0,1,4,5]::INTEGER[],
-    dependent_vartype = 'smallint';
--- Predict with pred_type=prob
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample',
-    'id',
-    'x',
-    'cifar10_predict',
-    'prob',
-    FALSE);
-
--- Validate the output datatype of newly created prediction column
--- for prediction type = 'prob' and class_values 'INT' with NULL
--- as a valid class_values
-SELECT assert(UPPER(pg_typeof("prob_NULL")::TEXT) =
-    'DOUBLE PRECISION', 'column prob_NULL should be double precision type')
-FROM cifar10_predict;
-
--- Must have exactly 6 cols (5 for class_values and 1 for id)
-SELECT assert(COUNT(*)=6, 'Predict out table must have exactly six cols.')
-FROM pg_attribute
-WHERE attrelid='cifar10_predict'::regclass AND attnum>0;
-
--- Predict with pred_type=response
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample',
-    'id',
-    'x',
-    'cifar10_predict',
-    'response',
-    FALSE);
-
--- Validate the output datatype of newly created prediction column
--- for prediction type = 'response' and class_values 'TEXT' with NULL
--- as a valid class_values
--- Returns: class_value with highest probability
-SELECT assert(UPPER(pg_typeof(estimated_y)::TEXT) =
-    'SMALLINT', 'prediction column should be smallint type')
-FROM cifar10_predict;
-
--- Predict with correctly shaped data, must go thru.
--- Update output_summary table to reflect
--- class_values, num_classes and model_id for shaped data
-DROP TABLE IF EXISTS keras_saved_out, keras_saved_out_summary;
-SELECT madlib_keras_fit(
-    'cifar_10_sample_test_shape_batched',
-    'keras_saved_out',
-    'model_arch',
-    3,
-    $$ optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']$$::text,
-    $$ batch_size=2, epochs=1, verbose=0 $$::text,
-    3);
-
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample_test_shape',
-    'id',
-    'x',
-    'cifar10_predict',
-    'prob',
-    FALSE);
-
--- Prediction with incorrectly shaped data must error out.
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT assert(trap_error($TRAP$SELECT madlib_keras_predict(
-        'keras_saved_out',
-        'cifar_10_sample',
-        'id',
-        'x',
-        'cifar10_predict',
-        'prob',
-        FALSE);$TRAP$) = 1,
-    'Input shape is (32, 32, 3) but model was trained with (3, 32, 32). Should have failed.');
-
--- Test model_arch is retrieved from model data table and not model architecture
-DROP TABLE IF EXISTS model_arch;
-DROP TABLE IF EXISTS cifar10_predict;
-SELECT madlib_keras_predict(
-    'keras_saved_out',
-    'cifar_10_sample_test_shape',
-    'id',
-    'x',
-    'cifar10_predict',
-    'prob',
-    FALSE);
-
--- Test multi model
-
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
-)
-
-m4_changequote(`')
-m4_ifdef(, , )
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict_byom.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict_byom.sql_in
deleted file mode 100644
index 12dee6f..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_predict_byom.sql_in
+++ /dev/null
@@ -1,137 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
-)
-
-DROP TABLE IF EXISTS iris_model, iris_model_summary;
-SELECT madlib_keras_fit('iris_data_packed',   -- source table
-                        'iris_model',          -- model output table
-                        'iris_model_arch',  -- model arch table
-                         1,                    -- model arch id
-                         $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
-                         $$ batch_size=5, epochs=3 $$,  -- fit_params
-                         3);                    -- num_iterations
-
-DROP TABLE IF EXISTS iris_predict;
-SELECT madlib_keras_predict('iris_model', -- model
-                            'iris_test',  -- test_table
-                            'id',  -- id column
-                            'attributes', -- independent var
-                            'iris_predict'  -- output table
-                            );
-
--- Copy weights that were learnt from the previous run, for transfer
--- learning. Copy it now, because using warm_start will overwrite it.
-UPDATE iris_model_arch set model_weights = (select model_weights from iris_model)  WHERE model_id = 2;
-
--- class_values not NULL, pred_type is response
-DROP TABLE IF EXISTS iris_predict_byom;
-SELECT madlib_keras_predict_byom(
-                                 'iris_model_arch',
-                                 2,
-                                 'iris_test',
-                                 'id',
-                                 'attributes',
-                                 'iris_predict_byom',
-                                 'response',
-                                 NULL,
-                                 ARRAY['Iris-setosa', 'Iris-versicolor',
-                                  'Iris-virginica']
-                                 );
-
-SELECT assert(
-  p0.estimated_class_text = p1.estimated_dependent_var,
-  'Predict byom failure for non null class value and response pred_type.')
-FROM iris_predict AS p0,  iris_predict_byom AS p1
-WHERE p0.id=p1.id;
-SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
-	'Predict byom failure for non null class value and response pred_type.
-	 Expeceted estimated_dependent_var to be of type TEXT')
-FROM  iris_predict_byom LIMIT 1;
-
--- class_values NULL, pred_type is NULL (response)
-DROP TABLE IF EXISTS iris_predict_byom;
-SELECT madlib_keras_predict_byom(
-                                 'iris_model_arch',
-                                 2,
-                                 'iris_test',
-                                 'id',
-                                 'attributes',
-                                 'iris_predict_byom'
-                                 );
-SELECT assert(
-  p1.estimated_dependent_var IN ('0', '1', '2'),
-  'Predict byom failure for null class value and null pred_type.')
-FROM iris_predict_byom AS p1;
-SELECT assert(UPPER(pg_typeof(estimated_dependent_var)::TEXT) = 'TEXT',
-	'Predict byom failure for non null class value and response pred_type.
-	 Expeceted estimated_dependent_var to be of type TEXT')
-FROM  iris_predict_byom LIMIT 1;
-
--- class_values not NULL, pred_type is prob
-DROP TABLE IF EXISTS iris_predict_byom;
-SELECT madlib_keras_predict_byom(
-                                 'iris_model_arch',
-                                 2,
-                                 'iris_test',
-                                 'id',
-                                 'attributes',
-                                 'iris_predict_byom',
-                                 'prob',
-                                 NULL,
-                                 ARRAY['Iris-setosa', 'Iris-versicolor',
-                                  'Iris-virginica'],
-                                 1.0
-                                 );
-
-SELECT assert(
-  (p1."prob_Iris-setosa" + p1."prob_Iris-virginica" + p1."prob_Iris-versicolor") - 1 < 1e-6,
-    'Predict byom failure for non null class value and prob pred_type.')
-FROM iris_predict_byom AS p1;
-SELECT assert(UPPER(pg_typeof("prob_Iris-setosa")::TEXT) = 'DOUBLE PRECISION',
-	'Predict byom failure for non null class value and prob pred_type.
-	Expeceted "prob_Iris-setosa" to be of type DOUBLE PRECISION')
-FROM  iris_predict_byom LIMIT 1;
-
--- class_values NULL, pred_type is prob
-DROP TABLE IF EXISTS iris_predict_byom;
-SELECT madlib_keras_predict_byom(
-                                 'iris_model_arch',
-                                 2,
-                                 'iris_test',
-                                 'id',
-                                 'attributes',
-                                 'iris_predict_byom',
-                                 'prob',
-                                 NULL,
-                                 NULL
-                                 );
-SELECT assert(
-  (prob[1] + prob[2] + prob[3]) - 1 < 1e-6,
-    'Predict byom failure for null class value and prob pred_type.')
-FROM iris_predict_byom;
-SELECT assert(UPPER(pg_typeof(prob)::TEXT) = 'DOUBLE PRECISION[]',
-	'Predict byom failure for null class value and prob pred_type. Expeceted prob to
-	be of type DOUBLE PRECISION[]')
-FROM  iris_predict_byom LIMIT 1;
diff --git a/src/ports/postgres/modules/deep_learning/test/madlib_keras_transfer_learning.sql_in b/src/ports/postgres/modules/deep_learning/test/madlib_keras_transfer_learning.sql_in
deleted file mode 100644
index 43ad38b..0000000
--- a/src/ports/postgres/modules/deep_learning/test/madlib_keras_transfer_learning.sql_in
+++ /dev/null
@@ -1,355 +0,0 @@
-/* ---------------------------------------------------------------------*//**
- *
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- *
- *//* ---------------------------------------------------------------------*/
-
-m4_include(`SQLCommon.m4')
-
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
-)
--------------- Warm start test (along with schema qualified output table) -------------------------
-CREATE SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__;
-DROP TABLE IF EXISTS iris_model, iris_model_summary;
-SELECT madlib_keras_fit('iris_data_packed',   -- source table
-                        '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_model',          -- model output table
-                        'iris_model_arch',  -- model arch table
-                         1,                    -- model arch id
-                         $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
-                         $$ batch_size=5, epochs=3 $$,  -- fit_params
-                         5,                    -- num_iterations
-                         NULL, NULL,
-                         1 -- metrics_compute_frequency
-                        );
-
--- Test that our code is indeed learning something and not broken. The loss
--- from the first iteration should be less than the 5th, while the accuracy
--- must be greater.
-SELECT assert(
-  array_upper(training_loss, 1) = 5 AND
-  array_upper(training_metrics, 1) = 5,
-  'metrics compute frequency must be 1.')
-FROM __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_model_summary;
-
-SELECT assert(
-  training_loss[5]-training_loss[1] < 0.1 AND
-  training_metrics[5]-training_metrics[1] > -0.1,
-    'The loss and accuracy should have improved with more iterations.'
-)
-FROM __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_model_summary;
-
--- Make a copy of the loss and metrics array, to compare it with runs after
--- warm start and transfer learning.
-DROP TABLE IF EXISTS iris_model_first_run;
-CREATE TABLE iris_model_first_run AS
-SELECT training_loss_final, training_metrics_final
-FROM __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_model_summary;
-
--- Copy weights that were learnt from the previous run, for transfer
--- learning. Copy it now, because using warm_start will overwrite it.
-UPDATE iris_model_arch set model_weights = (select model_weights from __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_model)
-WHERE model_id = 2;
-
--- Warm start test
-SELECT madlib_keras_fit('iris_data_packed',   -- source table
-                       '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_model',          -- model output table
-                       'iris_model_arch',  -- model arch table
-                        2,                    -- model arch id
-                        $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
-                        $$ batch_size=5, epochs=3 $$,  -- fit_params
-                        2,                    -- num_iterations,
-                        NULL, NULL, 1,
-                        TRUE -- warm start
-                      );
-
-SELECT assert(
-  array_upper(training_loss, 1) = 2 AND
-  array_upper(training_metrics, 1) = 2,
-  'metrics compute frequency must be 1.')
-FROM __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_model_summary;
-
-SELECT assert(
-  abs(first.training_loss_final-second.training_loss[1]) < 1e-6 AND
-  abs(first.training_loss_final-second.training_loss[2]) < 1e-6 AND
-  abs(first.training_metrics_final-second.training_metrics[1]) < 1e-10 AND
-  abs(first.training_metrics_final-second.training_metrics[2]) < 1e-10,
-  'warm start test failed because training loss and metrics don''t match the expected value from the previous run of keras fit.')
-FROM iris_model_first_run AS first, __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_model_summary AS second;
-
-DROP SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__ CASCADE;
-
----------------- end Warm start test ----------------------------------------------------
-
----------------- Transfer learning test ----------------------------------------------------
-DROP TABLE IF EXISTS iris_model_transfer, iris_model_transfer_summary;
-SELECT madlib_keras_fit('iris_data_packed',   -- source table
-                       'iris_model_transfer',          -- model output table
-                       'iris_model_arch',  -- model arch table
-                        2,                    -- model arch id
-                        $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
-                        $$ batch_size=5, epochs=3 $$,  -- fit_params
-                        2,
-                        NULL, NULL, 1
-                      );
-
-SELECT assert(
-  array_upper(training_loss, 1) = 2 AND
-  array_upper(training_metrics, 1) = 2,
-  'metrics compute frequency must be 1.')
-FROM iris_model_transfer_summary;
-
-SELECT assert(
-  abs(first.training_loss_final-second.training_loss[1]) < 1e-6 AND
-  abs(first.training_loss_final-second.training_loss[2]) < 1e-6 AND
-  abs(first.training_metrics_final-second.training_metrics[1]) < 1e-10 AND
-  abs(first.training_metrics_final-second.training_metrics[2]) < 1e-10,
-  'Transfer learning test failed because training loss and metrics don''t match the expected value.')
-FROM iris_model_first_run AS first,
-iris_model_transfer_summary AS second;
-
--- Rerun the iris setup to discard the changes
-\i m4_regexp(MODULE_PATHNAME,
-             `\(.*\)libmadlib\.so',
-             `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in'
-)
-
-m4_changequote(`')
-m4_ifdef(, ,  1e-6,
-  'Transfer learning test failed because training loss and metrics don''t match the expected value.')
-FROM iris_model_first_run AS first, iris_multiple_model_info AS second
-WHERE first.mst_key = second.mst_key AND first.model_id = 1;
-
-!>)
-
diff --git a/src/ports/postgres/modules/deep_learning/test/unit_tests/plpy_mock.py_in b/src/ports/postgres/modules/deep_learning/test/unit_tests/plpy_mock.py_in
deleted file mode 100644
index dd18649..0000000
--- a/src/ports/postgres/modules/deep_learning/test/unit_tests/plpy_mock.py_in
+++ /dev/null
@@ -1,43 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-m4_changequote(`')
-def __init__(self):
-    pass
-
-def error(message):
-    raise PLPYException(message)
-
-def execute(query):
-    pass
-
-def warning(query):
-    pass
-
-def info(query):
-    print query
-
-
-class PLPYException(Exception):
-    def __init__(self, message):
-        super(PLPYException, self).__init__()
-        self.message = message
-
-    def __str__(self):
-        return repr(self.message)
diff --git a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_input_data_preprocessor.py_in b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_input_data_preprocessor.py_in
deleted file mode 100644
index d2e14cd..0000000
--- a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_input_data_preprocessor.py_in
+++ /dev/null
@@ -1,383 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import sys
-import numpy as np
-from os import path
-
-# Add modules to the pythonpath.
-sys.path.append(path.dirname(path.dirname(path.dirname(path.dirname(path.abspath(__file__))))))
-sys.path.append(path.dirname(path.dirname(path.dirname(path.abspath(__file__)))))
-
-import unittest
-from mock import *
-import plpy_mock as plpy
-
-m4_changequote(`')
-
-class InputPreProcessorDLTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy,
-            'utilities.mean_std_dev_calculator': Mock(),
-        }
-        # we need to use MagicMock() instead of Mock() for the plpy.execute mock
-        # to be able to iterate on the return value
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-
-        self.default_schema_madlib = "madlib"
-        self.default_source_table = "source"
-        self.default_output_table = "output"
-        self.default_dep_var = "depvar"
-        self.default_ind_var = "indvar"
-        self.default_buffer_size = 5
-        self.default_normalizing_const = 1.0
-        self.default_num_classes = None
-        self.default_distribution_rules = "all_segments"
-        self.default_module_name = "dummy"
-
-        import deep_learning.input_data_preprocessor
-        self.module = deep_learning.input_data_preprocessor
-        import utilities.minibatch_preprocessing
-        self.util_module = utilities.minibatch_preprocessing
-        import utilities.control
-        self.control_module = utilities.control
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        self.module.validate_module_input_params = Mock()
-        self.module.get_distinct_col_levels = Mock(return_value = [0,22,100])
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def test_input_preprocessor_dl_executes_query(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        self.control_module.OptimizerControl.__enter__ = Mock()
-        self.control_module.OptimizerControl.optimizer_control = True
-        self.control_module.OptimizerControl.optimizer_enabled = True
-        preprocessor_obj = self.module.InputDataPreprocessorDL(
-            self.default_schema_madlib,
-            "input",
-            "out",
-            self.default_dep_var,
-            self.default_ind_var,
-            self.default_buffer_size,
-            self.default_normalizing_const,
-            self.default_num_classes,
-            self.default_distribution_rules,
-            self.default_module_name)
-        preprocessor_obj.input_preprocessor_dl()
-
-    def test_input_preprocessor_null_buffer_size_executes_query(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        self.control_module.OptimizerControl.__enter__ = Mock()
-        self.control_module.OptimizerControl.optimizer_control = True
-        self.control_module.OptimizerControl.optimizer_enabled = True
-        preprocessor_obj = self.module.InputDataPreprocessorDL(
-            self.default_schema_madlib,
-            "input",
-            "out",
-            self.default_dep_var,
-            self.default_ind_var,
-            None,
-            self.default_normalizing_const,
-            self.default_num_classes,
-            self.default_distribution_rules,
-            self.default_module_name)
-        self.util_module.MiniBatchBufferSizeCalculator.calculate_default_buffer_size = Mock(return_value = 5)
-        preprocessor_obj.input_preprocessor_dl()
-
-    def test_input_preprocessor_multiple_dep_var_raises_exception(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        with self.assertRaises(plpy.PLPYException):
-            self.module.InputDataPreprocessorDL(
-                self.default_schema_madlib,
-                self.default_source_table,
-                self.default_output_table,
-                "y1,y2",
-                self.default_ind_var,
-                self.default_buffer_size,
-                self.default_normalizing_const,
-                self.default_num_classes,
-                self.default_distribution_rules,
-                self.default_module_name)
-
-    def test_input_preprocessor_multiple_indep_var_raises_exception(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        with self.assertRaises(plpy.PLPYException):
-            self.module.InputDataPreprocessorDL(
-                self.default_schema_madlib,
-                self.default_source_table,
-                self.default_output_table,
-                self.default_dep_var,
-                "x1,x2",
-                self.default_buffer_size,
-                self.default_normalizing_const,
-                self.default_num_classes,
-                self.default_distribution_rules,
-                self.default_module_name)
-
-    def test_input_preprocessor_buffer_size_zero_fails(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        with self.assertRaises(plpy.PLPYException):
-            self.module.InputDataPreprocessorDL(
-                self.default_schema_madlib,
-                self.default_source_table,
-                self.default_output_table,
-                self.default_dep_var,
-                self.default_ind_var,
-                0,
-                self.default_normalizing_const,
-                self.default_num_classes,
-                self.default_distribution_rules,
-                self.default_module_name)
-
-    def test_input_preprocessor_negative_buffer_size_fails(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        with self.assertRaises(plpy.PLPYException):
-            self.module.InputDataPreprocessorDL(self.default_schema_madlib,
-                                              self.default_source_table,
-                                              self.default_output_table,
-                                              self.default_dep_var,
-                                              self.default_ind_var,
-                                              -1,
-                                              self.default_normalizing_const,
-                                              self.default_num_classes,
-                                              self.default_distribution_rules,
-                                              self.default_module_name)
-
-    def test_input_preprocessor_invalid_indep_vartype_raises_exception(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer', 'integer[]'])
-        with self.assertRaises(plpy.PLPYException):
-            self.module.InputDataPreprocessorDL(self.default_schema_madlib,
-                                                self.default_source_table,
-                                                self.default_output_table,
-                                                self.default_dep_var,
-                                                self.default_ind_var,
-                                                self.default_buffer_size,
-                                                self.default_normalizing_const,
-                                                self.default_num_classes,
-                                                self.default_distribution_rules,
-                                                self.default_module_name)
-
-    def test_input_preprocessor_invalid_dep_vartype_raises_exception(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'text[]'])
-        with self.assertRaises(plpy.PLPYException):
-            self.module.InputDataPreprocessorDL(self.default_schema_madlib,
-                                                self.default_source_table,
-                                                self.default_output_table,
-                                                self.default_dep_var,
-                                                self.default_ind_var,
-                                                self.default_buffer_size,
-                                                self.default_normalizing_const,
-                                                self.default_num_classes,
-                                                self.default_distribution_rules,
-                                                self.default_module_name)
-
-    def test_input_preprocessor_normalizing_const_zero_fails(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        with self.assertRaises(plpy.PLPYException):
-            self.module.InputDataPreprocessorDL(self.default_schema_madlib,
-                                                self.default_source_table,
-                                                self.default_output_table,
-                                                self.default_dep_var,
-                                                self.default_ind_var,
-                                                self.default_buffer_size,
-                                                0,
-                                                self.default_num_classes,
-                                                self.default_distribution_rules,
-                                                self.default_module_name)
-
-    def test_input_preprocessor_negative_normalizing_const_fails(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        with self.assertRaises(plpy.PLPYException):
-            self.module.InputDataPreprocessorDL(self.default_schema_madlib,
-                                                self.default_source_table,
-                                                self.default_output_table,
-                                                self.default_dep_var,
-                                                self.default_ind_var,
-                                                self.default_buffer_size,
-                                                -1,
-                                                self.default_num_classes,
-                                                self.default_distribution_rules,
-                                                self.default_module_name)
-
-    def test_get_one_hot_encoded_dep_var_expr_null_val(self):
-        self.module.get_expr_type = Mock(side_effect = ['smallint[]', 'text'])
-        self.module.get_distinct_col_levels = Mock(return_value = ["NULL", "'a'"])
-        obj = self.module.InputDataPreprocessorDL(
-            self.default_schema_madlib,
-            self.default_source_table,
-            self.default_output_table,
-            self.default_dep_var,
-            self.default_ind_var,
-            self.default_buffer_size,
-            self.default_normalizing_const,
-            self.default_num_classes,
-            self.default_distribution_rules,
-            self.default_module_name)
-        obj.dependent_levels = ["NULL", "'a'"]
-        dep_var_array_expr = obj.get_one_hot_encoded_dep_var_expr()
-        self.assertEqual("array[({0}) is not distinct from null, " \
-            "({0}) is not distinct from 'a']::integer[]::smallint[]".
-                     format(self.default_dep_var),
-                     dep_var_array_expr.lower())
-
-    def test_get_one_hot_encoded_dep_var_expr_numeric_array_val(self):
-        self.module.get_expr_type = Mock(side_effect = ['smallint[]', 'integer[]'])
-        obj = self.module.InputDataPreprocessorDL(
-            self.default_schema_madlib,
-            self.default_source_table,
-            self.default_output_table,
-            self.default_dep_var,
-            self.default_ind_var,
-            self.default_buffer_size,
-            self.default_normalizing_const,
-            self.default_num_classes,
-            self.default_distribution_rules,
-            self.default_module_name)
-        dep_var_array_expr = obj.get_one_hot_encoded_dep_var_expr()
-        self.assertEqual("{0}::smallint[]".
-                     format(self.default_dep_var),
-                     dep_var_array_expr.lower())
-
-    def test_validate_num_classes_none(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'text'])
-        obj = self.module.InputDataPreprocessorDL(
-            self.default_schema_madlib,
-            self.default_source_table,
-            self.default_output_table,
-            self.default_dep_var,
-            self.default_ind_var,
-            self.default_buffer_size,
-            self.default_normalizing_const,
-            None,
-            self.default_distribution_rules,
-            self.default_module_name)
-        obj.dependent_levels = ["dummy"]
-        self.assertEqual(0, obj.padding_size)
-
-    def test_validate_num_classes_greater(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'text'])
-        self.module._get_dependent_levels = Mock(return_value = ["'a'", "'b'", "'c'"])
-        obj = self.module.TrainingDataPreprocessorDL(
-            self.default_schema_madlib,
-            self.default_source_table,
-            self.default_output_table,
-            self.default_dep_var,
-            self.default_ind_var,
-            self.default_buffer_size,
-            self.default_normalizing_const,
-            5,
-            self.default_distribution_rules)
-        obj._set_one_hot_encoding_variables()
-        self.assertEqual(2, obj.padding_size)
-
-    def test_validate_num_classes_lesser(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'text'])
-        self.module.dependent_levels = Mock(return_value = ["'a'", "'b'", "'c'"])
-        obj = self.module.TrainingDataPreprocessorDL(
-            self.default_schema_madlib,
-            self.default_source_table,
-            self.default_output_table,
-            self.default_dep_var,
-            self.default_ind_var,
-            self.default_buffer_size,
-            self.default_normalizing_const,
-            2,
-            self.default_distribution_rules)
-        with self.assertRaises(plpy.PLPYException):
-            obj._set_one_hot_encoding_variables()
-
-    def test_validate_distribution_table(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-
-        obj = self.module.InputDataPreprocessorDL(self.default_schema_madlib,
-            self.default_source_table,
-            self.default_output_table,
-            self.default_dep_var,
-            self.default_ind_var,
-            self.default_buffer_size,
-            self.default_normalizing_const,
-            self.default_num_classes,
-            self.default_distribution_rules,
-            self.default_module_name)
-        self.module.input_tbl_valid = Mock()
-        self.module.is_var_valid = Mock()
-        self.plpy_mock_execute.side_effect = [
-            [{'dbids': [2,3,4]}],
-            [{'dbids': [3,4], 'c1': 2, 'c2': 2}]
-            ]
-
-        obj._validate_distribution_table()
-
-    def test_validate_distribution_table_dup(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-
-        obj = self.module.InputDataPreprocessorDL(self.default_schema_madlib,
-            self.default_source_table,
-            self.default_output_table,
-            self.default_dep_var,
-            self.default_ind_var,
-            self.default_buffer_size,
-            self.default_normalizing_const,
-            self.default_num_classes,
-            self.default_distribution_rules,
-            self.default_module_name)
-        self.module.input_tbl_valid = Mock()
-        self.module.is_var_valid = Mock()
-        self.plpy_mock_execute.side_effect = [
-            [{'dbids': [2,3,4]}],
-            [{'dbids': [3,3], 'c1': 2, 'c2': 1}]
-            ]
-        with self.assertRaises(plpy.PLPYException) as error:
-            obj._validate_distribution_table()
-        self.assertIn('duplicate', str(error.exception).lower())
-
-    def test_validate_distribution_table_invalid(self):
-        self.module.get_expr_type = Mock(side_effect = ['integer[]', 'integer[]'])
-        obj = self.module.InputDataPreprocessorDL(self.default_schema_madlib,
-            self.default_source_table,
-            self.default_output_table,
-            self.default_dep_var,
-            self.default_ind_var,
-            self.default_buffer_size,
-            self.default_normalizing_const,
-            self.default_num_classes,
-            self.default_distribution_rules,
-            self.default_module_name)
-        self.module.input_tbl_valid = Mock()
-        self.module.is_var_valid = Mock()
-        self.plpy_mock_execute.side_effect = [
-            [{'dbids': [2,3,4]}],
-            [{'dbids': [3,30], 'c1': 2, 'c2': 2}]
-            ]
-        with self.assertRaises(plpy.PLPYException) as error:
-            obj._validate_distribution_table()
-        self.assertIn('invalid', str(error.exception).lower())
-
-
-if __name__ == '__main__':
-    unittest.main()
-
-# ---------------------------------------------------------------------
diff --git a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
deleted file mode 100644
index ed3e0da..0000000
--- a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras.py_in
+++ /dev/null
@@ -1,1681 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-m4_changequote(`')
-
-import sys
-import numpy as np
-import os
-from os import path
-# Add convex module to the pythonpath.
-sys.path.append(path.dirname(path.dirname(path.dirname(path.dirname(path.abspath(__file__))))))
-sys.path.append(path.dirname(path.dirname(path.dirname(path.abspath(__file__)))))
-
-import keras
-from keras.models import *
-from keras.layers import *
-import unittest
-from mock import *
-import plpy_mock as plpy
-
-# helper for multiplying array by int
-def mult(k,arr):
-    return [ k*a for a in arr ]
-
-class MadlibKerasFitTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        import madlib_keras
-        self.subject = madlib_keras
-
-        self.model = Sequential()
-        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
-                         input_shape=(1,1,1,), padding='same'))
-        self.model.add(Flatten())
-
-        self.compile_params = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']"
-        self.fit_params = "batch_size=1, epochs=1"
-        self.model_weights = [3,4,5,6]
-        self.serialized_weights = np.array(self.model_weights, dtype=np.float32
-                                           ).tostring()
-        self.dist_key_mapping = [0,1,2]
-        self.accessible_gpus_for_seg = [0]
-
-        self.independent_var_real = [[[[0.5]]]] * 10
-        self.dependent_var_int = [[0,1]] * 10
-
-        # Params as bytea
-        self.independent_var = np.array(self.independent_var_real, dtype=np.float32).tobytes()
-        self.dependent_var = np.array(self.dependent_var_int, dtype=np.int16).tobytes()
-
-        self.independent_var_shape = [10,1,1,1]
-        self.dependent_var_shape = [10,2]
-        # We test on segment 0, which has 3 buffers filled with 10 identical
-        #  images each, or 30 images total
-        self.total_images_per_seg = [3*len(self.dependent_var_int),20,40]
-
-        self.dummy_prev_weights = 'dummy weights'
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def _test_fit_transition_first_buffer_pass(self, is_platform_pg):
-        #TODO should we mock tensorflow's close_session and keras'
-        # clear_session instead of mocking the function `K.clear_session`
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
-        self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
-        starting_image_count = 0
-        ending_image_count = len(self.dependent_var_int)
-
-        # last iteration Call
-        previous_state = np.array(self.model_weights, dtype=np.float32)
-
-        k = {'SD': {}}
-
-        new_state = self.subject.fit_transition(
-            None, self.dependent_var, self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(), self.compile_params, self.fit_params, 0,
-            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, previous_state.tostring(), True, **k)
-
-        image_count = new_state
-        self.assertEqual(ending_image_count, image_count)
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # Clear session and sess.close must not get called for the first buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-        self.assertTrue(k['SD']['segment_model'])
-
-        # Non-last iteration Call
-        self.subject.K.set_session.reset_mock()
-        self.subject.K.clear_session.reset_mock()
-        previous_state = np.array(self.model_weights, dtype=np.float32)
-
-        k = {'SD' : {}}
-
-        new_state = self.subject.fit_transition(
-            None, self.dependent_var, self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(), self.compile_params, self.fit_params, 0,
-            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, previous_state.tostring(), False, **k)
-
-        image_count = new_state
-
-        self.assertEqual(ending_image_count, image_count)
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # Clear session and sess.close must not get called for the first buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-        self.assertTrue(k['SD']['segment_model'])
-
-    def test_fit_transition_multiple_model_first_buffer_pass(self):
-        #TODO should we mock tensorflow's close_session and keras'
-        # clear_session instead of mocking the function `K.clear_session`
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
-        starting_image_count = 0
-        ending_image_count = len(self.dependent_var_int)
-
-        previous_weights = np.array(self.model_weights, dtype=np.float32)
-
-        k = {'SD': {}}
-
-        new_state = self.subject.fit_transition(
-            None, self.dependent_var, self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(), self.compile_params, self.fit_params, 0,
-            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, previous_weights.tostring(), True, True, **k)
-
-        image_count = new_state
-        self.assertEqual(ending_image_count, image_count)
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # Clear session must not be called for the first buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-        self.assertTrue(k['SD']['segment_model'])
-
-    def _test_fit_transition_middle_buffer_pass(self, is_platform_pg):
-        #TODO should we mock tensorflow's close_session and keras'
-        # clear_session instead of mocking the function `K.clear_session`
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
-        self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
-
-        starting_image_count = len(self.dependent_var_int)
-        ending_image_count = starting_image_count + len(self.dependent_var_int)
-
-        # last iteration Call
-
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-        k = {'SD': {'segment_model': self.model, 'sess': Mock()}}
-
-        state = starting_image_count
-        new_state = self.subject.fit_transition(
-            state, self.dependent_var, self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(), None, self.fit_params, 0,
-            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, self.dummy_prev_weights, True, **k)
-
-        image_count = new_state
-        self.assertEqual(ending_image_count, image_count)
-
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # Clear session and sess.close must not get called for the middle buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-
-        # Non-last iteration Call
-        self.subject.K.set_session.reset_mock()
-        self.subject.K.clear_session.reset_mock()
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-        k = {'SD': {'segment_model': self.model, 'sess': Mock()}}
-
-        state = starting_image_count
-        new_state = self.subject.fit_transition(
-            state, self.dependent_var, self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(), None, self.fit_params, 0,
-            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, self.dummy_prev_weights, False, **k)
-
-        image_count = new_state
-        self.assertEqual(ending_image_count, image_count)
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # Clear session and sess.close must not get called for the middle buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-
-    def test_fit_transition_multiple_model_middle_buffer_pass(self):
-        #TODO should we mock tensorflow's close_session and keras'
-        # clear_session instead of mocking the function `K.clear_session`
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
-
-        starting_image_count = len(self.dependent_var_int)
-        ending_image_count = starting_image_count + len(self.dependent_var_int)
-
-        # last iteration Call
-
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-        k = {'SD': {'segment_model': self.model, 'sess': Mock()}}
-
-        state = starting_image_count
-        new_state = self.subject.fit_transition(
-            state, self.dependent_var, self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(), None, self.fit_params, 0,
-            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, self.dummy_prev_weights, True, True, **k)
-
-        image_count = new_state
-        self.assertEqual(ending_image_count, image_count)
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # Clear session and sess.close must not get called for the middle buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-
-    def _test_fit_transition_last_buffer_pass(self, is_platform_pg):
-        #TODO should we mock tensorflow's close_session and keras'
-        # clear_session instead of mocking the function `K.clear_session`
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
-        self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
-
-        starting_image_count = 2*len(self.dependent_var_int)
-        ending_image_count = starting_image_count + len(self.dependent_var_int)
-
-        # last iteration Call
-
-        multiplied_weights = mult(self.total_images_per_seg[0],self.model_weights)
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-        k = {'SD': {'segment_model' :self.model, 'sess': Mock()}}
-
-        state = starting_image_count
-        new_state = self.subject.fit_transition(
-            state, self.dependent_var, self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(), None, self.fit_params, 0,
-            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, self.dummy_prev_weights, True, **k)
-
-        state = np.fromstring(new_state, dtype=np.float32)
-        image_count = state[0]
-        weights = np.rint(state[1:]).astype(np.int)
-        self.assertEqual(ending_image_count, image_count)
-        # weights should be multiplied by final image count
-        self.assertTrue((multiplied_weights == weights).all())
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # Clear session and sess.close must get called for the last buffer in gpdb,
-        #  but not in postgres
-        self.assertEqual(1, self.subject.K.clear_session.call_count)
-
-        # Non-last iteration Call
-        self.subject.K.set_session.reset_mock()
-        self.subject.K.clear_session.reset_mock()
-
-        multiplied_weights = mult(self.total_images_per_seg[0],self.model_weights)
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-        k = {'SD': {'segment_model' :self.model, 'sess': Mock()}}
-
-        state = starting_image_count
-        new_state = self.subject.fit_transition(
-            state, self.dependent_var, self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(), None, self.fit_params, 0,
-            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, self.dummy_prev_weights, False, **k)
-
-        state = np.fromstring(new_state, dtype=np.float32)
-        image_count = state[0]
-        weights = np.rint(state[1:]).astype(np.int)
-        self.assertEqual(ending_image_count, image_count)
-        # weights should be multiplied by final image count
-        self.assertTrue((multiplied_weights == weights).all())
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # Clear session and sess.close must get called for the last buffer in gpdb,
-        #  but not in postgres
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-
-    def test_fit_transition_multiple_model_last_buffer_pass(self):
-        #TODO should we mock tensorflow's close_session and keras'
-        # clear_session instead of mocking the function `K.clear_session`
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
-
-        starting_image_count = 2*len(self.dependent_var_int)
-        ending_image_count = starting_image_count + len(self.dependent_var_int)
-
-        # last iteration Call
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-        k = {'SD': {'segment_model' :self.model, 'sess': Mock()}}
-
-        state = starting_image_count
-        new_state = self.subject.fit_transition(
-            state   , self.dependent_var, self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(), None, self.fit_params, 0,
-            self.dist_key_mapping, 0, 4, self.total_images_per_seg, False,
-            self.accessible_gpus_for_seg, self.dummy_prev_weights, True, True, **k)
-
-        state = np.fromstring(new_state, dtype=np.float32)
-        weights = np.rint(state[0:]).astype(np.int)
-
-        ## image count should not be added to the final state of
-        # fit multiple
-        self.assertEqual(len(self.model_weights), len(weights))
-
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # Clear session and sess.close must get called for the last buffer in gpdb,
-        #  but not in postgres
-        self.assertEqual(1, self.subject.K.clear_session.call_count)
-
-    def test_fit_transition_first_buffer_pass_pg(self):
-        self._test_fit_transition_first_buffer_pass(True)
-
-    def test_fit_transition_first_buffer_pass_gpdb(self):
-        self._test_fit_transition_first_buffer_pass(False)
-
-    def test_fit_transition_middle_buffer_pass_pg(self):
-        self._test_fit_transition_middle_buffer_pass(True)
-
-    def test_fit_transition_middle_buffer_pass_gpdb(self):
-        self._test_fit_transition_middle_buffer_pass(False)
-
-    def test_fit_transition_last_buffer_pass_pg(self):
-        self._test_fit_transition_last_buffer_pass(True)
-
-    def test_fit_transition_last_buffer_pass_gpdb(self):
-        self._test_fit_transition_last_buffer_pass(False)
-
-    def test_fit_transition_first_tuple_none_ind_var_dep_var(self):
-        k = {}
-        self.assertEqual('dummy_state',
-                         self.subject.fit_transition('dummy_state', [0], None,
-                                                     'noshape', 'noshape',
-                                                     'dummy_model_json', "foo", "bar",
-                                                     1, [0,1,2], 0, 4, [3,3,3], False,
-                                                     [0], 'dummy_prev_state', **k))
-        self.assertEqual('dummy_state',
-                         self.subject.fit_transition('dummy_state', None, [[0.5]],
-                                                     'noshape', 'noshape',
-                                                     'dummy_model_json', "foo", "bar",
-                                                     1, [0,1,2], 0, 4, [3,3,3], False,
-                                                     [0], 'dummy_prev_state', **k))
-        self.assertEqual('dummy_state',
-                         self.subject.fit_transition('dummy_state', None, None,
-                                                     'noshape', 'noshape',
-                                                     'dummy_model_json', "foo", "bar",
-                                                     1, [0,1,2], 0, 4, [3,3,3], False,
-                                                     [0], 'dummy_prev_state', **k))
-
-    def test_fit_merge(self):
-        image_count = self.total_images_per_seg[0]
-        state1 = [image_count]
-        state1.extend(mult(3,self.model_weights))
-        state1 = np.array(state1, dtype=np.float32)
-        state2 = [image_count+30]
-        state2.extend(mult(2,self.model_weights))
-        state2 = np.array(state2, dtype=np.float32)
-        merged_state = self.subject.fit_merge(state1.tostring(),state2.tostring())
-        state = np.fromstring(merged_state, dtype=np.float32)
-        image_count_total = state[0]
-        weights = np.rint(state[1:]).astype(np.int)
-
-        self.assertEqual( 2*image_count+30 , image_count_total )
-        self.assertTrue( (mult(5,self.model_weights) == weights).all())
-
-    def test_fit_merge_none_first(self):
-        image_count = self.total_images_per_seg[0]
-        input_state = [image_count]
-        input_state.extend(self.model_weights)
-        input_state = np.array(input_state, dtype=np.float32)
-        merged_state = self.subject.fit_merge(None, input_state.tostring())
-        state = np.fromstring(merged_state, dtype=np.float32)
-        image_count_total = state[0]
-        weights = np.rint(state[1:]).astype(np.int)
-
-        self.assertEqual(image_count, image_count_total)
-        self.assertTrue((self.model_weights == weights).all())
-
-    def test_fit_merge_none_second(self):
-        image_count = self.total_images_per_seg[0]
-        input_state = [image_count]
-        input_state.extend(self.model_weights)
-        input_state = np.array(input_state, dtype=np.float32)
-        merged_state = self.subject.fit_merge(input_state.tostring(), None)
-        state = np.fromstring(merged_state, dtype=np.float32)
-        image_count_total = state[0]
-        weights = np.rint(state[1:]).astype(np.int)
-
-        self.assertEqual(image_count, image_count_total)
-        self.assertTrue((self.model_weights == weights).all())
-
-    def test_fit_merge_both_none(self):
-        result = self.subject.fit_merge(None,None)
-        self.assertEqual(None, result)
-
-    def test_fit_final(self):
-        image_count = self.total_images_per_seg[0]
-        input_state = [image_count]
-        input_state.extend(mult(image_count,self.model_weights))
-        input_state = np.array(input_state, dtype=np.float32)
-
-        output_state = self.subject.fit_final(input_state.tostring())
-        output_state = np.fromstring(output_state, dtype=np.float32)
-        weights = np.rint(output_state).astype(np.int)
-
-        self.assertTrue((self.model_weights == weights).all())
-
-    def test_fit_final_none(self):
-        result = self.subject.fit_final(None)
-        self.assertEqual(result, None)
-
-    def test_fit_final_image_count_zero(self):
-        input_state = [0]
-        input_state.extend(self.model_weights)
-        input_state = np.array(input_state, dtype=np.float32)
-
-        with self.assertRaises(plpy.PLPYException):
-            result = self.subject.fit_final(input_state.tostring())
-
-    def test_should_compute_metrics_this_iter_last_iter_case1(self):
-        res = self.subject.should_compute_metrics_this_iter(5, 5, 5)
-        self.assertEqual(True, res)
-    def test_should_compute_metrics_this_iter_last_iter_case2(self):
-        res = self.subject.should_compute_metrics_this_iter(5, 3, 5)
-        self.assertEqual(True, res)
-    def test_should_compute_metrics_this_iter_before_interval(self):
-        res = self.subject.should_compute_metrics_this_iter(1, 3, 5)
-        self.assertEqual(False, res)
-    def test_should_compute_metrics_this_iter_after_interval(self):
-        res = self.subject.should_compute_metrics_this_iter(4, 3, 5)
-        self.assertEqual(False, res)
-    def test_should_compute_metrics_this_iter_at_interval(self):
-        res = self.subject.should_compute_metrics_this_iter(3, 3, 5)
-        self.assertEqual(True, res)
-    def test_should_compute_metrics_this_iter_every_iter(self):
-        res = self.subject.should_compute_metrics_this_iter(2, 1, 5)
-        self.assertEqual(True, res)
-
-
-class InternalKerasPredictTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        import madlib_keras_predict
-        self.subject = madlib_keras_predict
-
-        self.model = Sequential()
-        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
-                              input_shape=(1,1,1,), padding='same'))
-        self.model.add(Flatten())
-
-        self.all_seg_ids = [0,1,2]
-
-        self.independent_var = [[[240]]]
-        self.total_images_per_seg = [3,3,4]
-        self.subject.K.set_session = Mock()
-        self.subject.clear_keras_session = Mock()
-
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def test_predict_first_image_pass_gpdb(self):
-        self.subject.is_platform_pg = Mock(return_value = False)
-        model_weights = [1, 2, 3, 4]
-        serialized_weights = np.array(model_weights, dtype=np.float32).tostring()
-
-        k = {'SD': {}}
-        is_response = True
-        result = self.subject.internal_keras_predict(
-            self.independent_var, self.model.to_json(),
-            serialized_weights, is_response, 255, 0, self.all_seg_ids,
-            self.total_images_per_seg, False, 0, 4, **k)
-        self.assertEqual(1, len(result))
-        self.assertEqual(1,  k['SD']['row_count'])
-        self.assertEqual(True, 'segment_model_predict' in k['SD'])
-
-    def test_predict_middle_image_pass_gpdb(self):
-        self.subject.is_platform_pg = Mock(return_value = False)
-
-        k = {'SD': { 'row_count': 1}}
-        k['SD']['segment_model_predict'] = self.model
-        is_response = True
-        result = self.subject.internal_keras_predict(
-            self.independent_var, None, None, is_response, 255, 0,
-            self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
-        self.assertEqual(1, len(result))
-        self.assertEqual(2,  k['SD']['row_count'])
-        self.assertEqual(True, 'segment_model_predict' in k['SD'])
-
-    def test_predict_last_image_pass_gpdb(self):
-        self.subject.is_platform_pg = Mock(return_value = False)
-        self.model.add(Dense(3))
-
-        k = {'SD': { 'row_count': 2}}
-        k['SD']['segment_model_predict'] = self.model
-        is_response = True
-        result = self.subject.internal_keras_predict(
-            self.independent_var, None, None, is_response, 255, 0,
-            self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
-        self.assertEqual(1, len(result))
-        self.assertEqual(False, 'row_count' in k['SD'])
-        self.assertEqual(False, 'segment_model_predict' in k['SD'])
-
-        k = {'SD': { 'row_count': 2}}
-        k['SD']['segment_model_predict'] = self.model
-        is_response = False
-        result = self.subject.internal_keras_predict(
-            self.independent_var, None, None, is_response, 255, 0,
-            self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
-
-        # we except len(result) to be 3 because we have 3 dense layers in the
-        # architecture
-        self.assertEqual(3, len(result))
-        self.assertEqual(False, 'row_count' in k['SD'])
-        self.assertEqual(False, 'segment_model_predict' in k['SD'])
-
-
-    def test_predict_error_should_clear_sd(self):
-        self.subject.is_platform_pg = Mock(return_value = False)
-        self.model.add(Dense(3))
-
-        # inject error by passing 0 as the normalizing const so that we get a
-        # divide by zero error
-        normalizing_const = 0
-        k = {'SD':{}}
-        is_response = True
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.internal_keras_predict(
-                self.independent_var, None, None, is_response, normalizing_const,
-                0, self.all_seg_ids, self.total_images_per_seg, False, 0, 4, **k)
-        self.assertEqual(False, 'row_count' in k['SD'])
-        self.assertEqual(False, 'segment_model_predict' in k['SD'])
-
-
-class MadlibKerasPredictBYOMTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        self.num_classes = 5
-        self.model = Sequential()
-        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
-                              input_shape=(1,1,1,), padding='same'))
-        self.model.add(Dense(self.num_classes))
-
-        self.pred_type = 'prob'
-        self.use_gpus = False
-        self.class_values = ['foo', 'bar', 'baaz', 'foo2', 'bar2']
-        self.normalizing_const = 255.0
-
-        import madlib_keras_predict
-        self.module = madlib_keras_predict
-        self.module.get_model_arch_weights = Mock(return_value=(
-            self.model.to_json(), 'weights'))
-        self.module.InputValidator.validate_predict_byom_tables = Mock()
-        self.module.InputValidator.validate_input_shape = Mock()
-        self.module.BasePredict.call_internal_keras = Mock()
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def test_predictbyom_defaults_1(self):
-        self.module.get_accessible_gpus_for_seg = Mock(return_value = [2,2,2])
-        res = self.module.PredictBYOM('schema_madlib', 'model_arch_table',
-                                 'model_id', 'test_table', 'id_col',
-                                 'independent_varname', 'output_table', None,
-                                 True, None, None)
-        self.assertEqual('response', res.pred_type)
-        self.assertEqual(2, res.gpus_per_host)
-        self.assertEqual([0,1,2,3,4], res.class_values)
-        self.assertEqual(1.0, res.normalizing_const)
-        self.assertEqual('text', res.dependent_vartype)
-
-    def test_predictbyom_defaults_2(self):
-        res = self.module.PredictBYOM('schema_madlib', 'model_arch_table',
-                                       'model_id', 'test_table', 'id_col',
-                                       'independent_varname', 'output_table',
-                                       self.pred_type, self.use_gpus,
-                                       self.class_values, self.normalizing_const)
-        self.assertEqual('prob', res.pred_type)
-        self.assertEqual(0, res.gpus_per_host)
-        self.assertEqual(['foo', 'bar', 'baaz', 'foo2', 'bar2'], res.class_values)
-        self.assertEqual(255.0, res.normalizing_const)
-        self.assertEqual('double precision', res.dependent_vartype)
-
-    def test_predictbyom_exception_invalid_params(self):
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.module.PredictBYOM('schema_madlib', 'model_arch_table',
-                                     'model_id', 'test_table', 'id_col',
-                                     'independent_varname', 'output_table',
-                                     'invalid_pred_type', self.use_gpus,
-                                     self.class_values, self.normalizing_const)
-        self.assertIn('invalid_pred_type', str(error.exception))
-
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.module.PredictBYOM('schema_madlib', 'model_arch_table',
-                                     'model_id', 'test_table', 'id_col',
-                                     'independent_varname', 'output_table',
-                                     self.pred_type, self.use_gpus,
-                                     ["foo", "bar", "baaz"], self.normalizing_const)
-        self.assertIn('class values', str(error.exception).lower())
-
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.module.PredictBYOM('schema_madlib', 'model_arch_table',
-                                     'model_id', 'test_table', 'id_col',
-                                     'independent_varname', 'output_table',
-                                     self.pred_type, self.use_gpus,
-                                     self.class_values, 0)
-        self.assertIn('normalizing const', str(error.exception).lower())
-
-
-class MadlibKerasWrapperTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-
-        import madlib_keras_wrapper
-        self.subject = madlib_keras_wrapper
-
-        self.use_gpus = False
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def test_get_gpu_memory_fraction(self):
-
-        gpus_per_host = 4
-        segments_per_host = 4
-        result = self.subject.get_gpu_memory_fraction(gpus_per_host, segments_per_host)
-        self.assertEqual(result, 0.9)
-
-        gpus_per_host = 10
-        segments_per_host = 4
-        result = self.subject.get_gpu_memory_fraction(gpus_per_host, segments_per_host)
-        self.assertEqual(result, 0.9)
-
-        gpus_per_host = 2
-        segments_per_host = 6
-        result = self.subject.get_gpu_memory_fraction(gpus_per_host, segments_per_host)
-        self.assertEqual(result, 0.3)
-
-        gpus_per_host = 1
-        segments_per_host = 4
-        result = self.subject.get_gpu_memory_fraction(gpus_per_host, segments_per_host)
-        self.assertEqual(result, 0.225)
-
-    def test_get_device_name_and_set_cuda_env_postgres(self):
-        self.subject.is_platform_pg = Mock(return_value = True)
-
-        seg_id = -1
-        gpus_per_host = 3
-
-        self.assertEqual('/gpu:0', self.subject.get_device_name_and_set_cuda_env(
-            gpus_per_host, seg_id ))
-        self.assertEqual('0,1,2', os.environ['CUDA_VISIBLE_DEVICES'])
-
-        gpus_per_host = 0
-        self.assertEqual('/cpu:0', self.subject.get_device_name_and_set_cuda_env(
-            gpus_per_host, seg_id ))
-        self.assertEqual('-1', os.environ['CUDA_VISIBLE_DEVICES'])
-
-    def test_get_device_name_and_set_cuda_env_gpdb(self):
-        self.subject.is_platform_pg = Mock(return_value = False)
-
-        seg_id=3
-        gpus_per_host=2
-        self.assertEqual('/gpu:0', self.subject.get_device_name_and_set_cuda_env(
-            gpus_per_host, seg_id))
-        self.assertEqual('1', os.environ['CUDA_VISIBLE_DEVICES'])
-
-        gpus_per_host=0
-        self.assertEqual('/cpu:0', self.subject.get_device_name_and_set_cuda_env(
-            gpus_per_host, seg_id))
-        self.assertEqual('-1', os.environ['CUDA_VISIBLE_DEVICES'])
-
-
-    def test_split_and_strip(self):
-        self.assertEqual(('a','b'), self.subject.split_and_strip(' a = b '))
-
-    def test_parse_optimizer(self):
-        opt_name = 'SGD'
-        final_args = {'lr':0.01, 'decay':1e-6, 'nesterov':True}
-        compile_dict = {}
-        compile_dict['optimizer']='SGD(lr=0.01, decay=1e-6, nesterov=True)'
-        result_name, result_params = self.subject.parse_optimizer(compile_dict)
-
-        self.assertEqual(result_name, opt_name)
-        self.assertDictEqual(result_params, final_args)
-
-    def test_validate_and_literal_eval_keys(self):
-        test_dict = {'batch_size':'2', 'epochs':'1', 'verbose':'0'}
-        target_dict = {'batch_size':2, 'epochs':1, 'verbose':0}
-        literal_eval_fit_params = ['batch_size','epochs','verbose','shuffle',
-                           'class_weight','initial_epoch','steps_per_epoch']
-        accepted_fit_params = literal_eval_fit_params + ['shuffle']
-        result_params = self.subject.validate_and_literal_eval_keys(
-                            test_dict,
-                            literal_eval_fit_params,
-                            accepted_fit_params)
-        self.assertDictEqual(result_params, target_dict)
-
-    def test_parse_and_validate_fit_params(self):
-        result = {'batch_size':2, 'epochs':1, 'verbose':0}
-        self.assertDictEqual(result, self.subject.parse_and_validate_fit_params('batch_size=2, epochs=1, verbose=0'))
-
-    def test_parse_and_validate_fit_params(self):
-        test_str = "batch_size=2, epochs=1, verbose=0"
-        fit_dict = {'batch_size':2, 'epochs':1, 'verbose':0}
-        result_params = self.subject.parse_and_validate_fit_params(test_str)
-        self.assertDictEqual(result_params, fit_dict)
-
-        test_str = "batch_size=2, epochs=1, verbose=0, shuffle = 'batch'"
-        fit_dict = {'batch_size':2, 'epochs':1, 'verbose':0, 'shuffle':'batch'}
-        result_params = self.subject.parse_and_validate_fit_params(test_str)
-        self.assertDictEqual(result_params, fit_dict)
-
-        test_str = "batch_size=2, epochs=1, verbose=0, shuffle = True"
-        fit_dict = {'batch_size':2, 'epochs':1, 'verbose':0, 'shuffle':True}
-        result_params = self.subject.parse_and_validate_fit_params(test_str)
-        self.assertDictEqual(result_params, fit_dict)
-
-        test_str = ""
-        fit_dict = {}
-        result_params = self.subject.parse_and_validate_fit_params(test_str)
-        self.assertDictEqual(result_params, fit_dict)
-
-    def test_parse_and_validate_fit_params_duplicate_params(self):
-        test_str = "batch_size=1, batch_size=10, verbose=0"
-        fit_dict = {'batch_size':10, 'verbose':0}
-        result_params = self.subject.parse_and_validate_fit_params(test_str)
-        self.assertDictEqual(result_params, fit_dict)
-
-    def test_parse_and_validate_fit_params_invalid_fit_param_fail(self):
-        test_str = "does_not_exist=2, epochs=1, verbose=0"
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.parse_and_validate_fit_params(test_str)
-
-        self.assertIn('not currently accepted', str(error.exception))
-
-        test_str = "batch_size=not_lit_eval(1), epochs=1, verbose=0"
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.parse_and_validate_fit_params(test_str)
-
-        self.assertIn('invalid input value', str(error.exception))
-
-    def test_parse_and_validate_compile_params(self):
-        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), " \
-                   "loss='categorical_crossentropy', metrics=['accuracy']"
-        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
-                        'metrics':['accuracy'], 'loss':'categorical_crossentropy'}
-        opt_name,opt_args,result_params = self.subject.parse_and_validate_compile_params(test_str)
-        self.assertDictEqual(result_params, compile_dict)
-
-        # Test without the metrics
-        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), " \
-                   "loss='categorical_crossentropy'"
-        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
-                        'loss':'categorical_crossentropy'}
-        opt_name,opt_args,result_params = self.subject.parse_and_validate_compile_params(test_str)
-        self.assertDictEqual(result_params, compile_dict)
-
-    def test_validate_metrics_None_pass(self):
-        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
-                        'metrics':['accuracy'], 'loss':'categorical_crossentropy'}
-        self.subject._validate_metrics(compile_dict)
-
-    def test_validate_metrics_empty_pass(self):
-        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
-                        'metrics':[], 'loss':'categorical_crossentropy'}
-        self.subject._validate_metrics(compile_dict)
-
-    def test_validate_metrics_two_params_fail(self):
-        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
-                        'metrics':['accuracy','mae'], 'loss':'categorical_crossentropy'}
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject._validate_metrics(compile_dict)
-
-    def test_validate_metrics_one_unsupported_fail(self):
-        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
-                        'metrics':['sparse_categorical_accuracy'], 'loss':'categorical_crossentropy'}
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject._validate_metrics(compile_dict)
-
-    def test_validate_metrics_two_unsupported_fail(self):
-        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
-                        'metrics':['sparse_categorical_accuracy', 'sparse_categorical_crossentropy'], 'loss':'categorical_crossentropy'}
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject._validate_metrics(compile_dict)
-
-    def test_validate_metrics_one_supported_one_unsupported_fail(self):
-        compile_dict = {'optimizer':'SGD(lr=0.01, decay=1e-6, nesterov=True)',
-                        'metrics':['accuracy', 'sparse_categorical_crossentropy'], 'loss':'categorical_crossentropy'}
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject._validate_metrics(compile_dict)
-
-    def test_parse_and_validate_compile_params_default_optimizer_pass(self):
-        test_str = "optimizer='SGD', loss='categorical_crossentropy'"
-        _,_,result_dict = self.subject.parse_and_validate_compile_params(test_str)
-        self.assertEqual('SGD', result_dict['optimizer'])
-
-        test_str = "optimizer='sgd()', loss='categorical_crossentropy'"
-        _,_,result_dict = self.subject.parse_and_validate_compile_params(test_str)
-        self.assertEqual('sgd()', result_dict['optimizer'])
-
-    def test_parse_and_validate_compile_params_duplicate_loss(self):
-        test_str = "optimizer='SGD', loss='loss1', metrics=['accuracy'], loss='loss2'"
-        _,_,result_dict = self.subject.parse_and_validate_compile_params(test_str)
-        self.assertEqual('loss2', result_dict['loss'])
-
-    def test_parse_and_validate_compile_params_no_optimizer_fail(self):
-        test_str = "loss='categorical_crossentropy', metrics=['accuracy']"
-
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_compile_params(test_str)
-
-    def test_parse_and_validate_compile_params_no_loss_fail(self):
-        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), " \
-                   "metrics=['accuracy']"
-
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_compile_params(test_str)
-
-    def test_parse_and_validate_compile_params_dict_metrics_fail(self):
-        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), " \
-                   "loss='categorical_crossentropy', metrics={'0':'accuracy'}"
-
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_compile_params(test_str)
-
-    def test_parse_and_validate_compile_params_tensor_loss_weights_fail(self):
-
-        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True)," \
-                   " loss='categorical_crossentropy', metrics=['accuracy']," \
-                   " loss_weights = keras.layers.Input(shape=(32,))"
-
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_compile_params(test_str)
-
-    def test_parse_and_validate_compile_params_list_dict_sample_weight_mode_fail(self):
-        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True)," \
-                   " loss='categorical_crossentropy', metrics=['accuracy']," \
-                   " sample_weight_mode = [0,1]"
-
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_compile_params(test_str)
-
-        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True)," \
-                   " loss='categorical_crossentropy', metrics=['accuracy']," \
-                   " sample_weight_mode = {'0':'1'}"
-
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_compile_params(test_str)
-
-
-    def test_parse_and_validate_compile_params_target_tensors_fail(self):
-        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True)," \
-                   " loss='categorical_crossentropy', metrics=['accuracy']," \
-                   " target_tensors = keras.layers.Input(shape=(32,))"
-
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_compile_params(test_str)
-
-    def test_parse_and_validate_fit_params_callbacks_fail(self):
-
-        test_str = "batch_size=2, epochs=1, verbose=0, callbacks=keras.callbacks.Callback()"
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_fit_params(test_str)
-
-    def test_parse_and_validate_fit_params_validation_split_fail(self):
-
-        test_str = "batch_size=2, epochs=1, verbose=0, validation_split=0.1"
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_fit_params(test_str)
-
-    def test_parse_and_validate_fit_params_validation_data_fail(self):
-
-        test_str = "batch_size=2, epochs=1, verbose=0, validation_data=(1,2,3)"
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_fit_params(test_str)
-
-    def test_parse_and_validate_fit_params_sample_weight_fail(self):
-
-        test_str = "batch_size=2, epochs=1, verbose=0, sample_weight=np.array([1,2,3])"
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_fit_params(test_str)
-
-    def test_parse_and_validate_fit_params_validation_steps_fail(self):
-
-        test_str = "batch_size=2, epochs=1, verbose=0, validation_steps=1"
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_fit_params(test_str)
-
-    def test_parse_and_validate_fit_params_validation_freq_fail(self):
-        test_str = "batch_size=2, epochs=1, verbose=0, validation_freq=1"
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_fit_params(test_str)
-
-        test_str = "batch_size=2, epochs=1, verbose=0, validation_freq=[1]"
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.parse_and_validate_fit_params(test_str)
-
-    def test_parse_and_validate_compile_params_syntax_error_fail(self):
-        #missing end parentheses
-        test_str = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True"
-        with self.assertRaises(ValueError) as error:
-            self.subject.parse_and_validate_compile_params(test_str)
-        self.assertIn('could not convert string to float', str(error.exception))
-
-        #missing beginning parentheses
-        test_str = "optimizer=SGDlr=0.01, decay=1e-6, nesterov=True)," \
-                   " loss='categorical_crossentropy'"
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.parse_and_validate_compile_params(test_str)
-        self.assertIn('not currently accepted', str(error.exception))
-
-        #missing comma
-        test_str = "optimizer=SGD(lr=0.01 decay=1e-6, nesterov=True)," \
-                   " loss='categorical_crossentropy'"
-        with self.assertRaises(ValueError) as error:
-            self.subject.parse_and_validate_compile_params(test_str)
-        self.assertIn('invalid literal for float', str(error.exception))
-
-    def test_parse_and_validate_fit_params_invalid_optimizer_fail(self):
-        test_str = "optimizer='SGD1', loss='categorical_crossentropy'"
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.parse_and_validate_compile_params(test_str)
-        self.assertIn('invalid optimizer', str(error.exception))
-
-
-class MadlibKerasFitCommonValidatorTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        import madlib_keras_validator
-        self.subject = madlib_keras_validator
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-
-    def test_is_valid_metrics_compute_frequency_True_None(self):
-        self.subject.FitCommonValidator._validate_common_args = Mock()
-        obj = self.subject.FitCommonValidator(
-            'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
-            'dep_varname', 'independent_varname', 5, None, False, False, [0],
-            'module_name')
-        self.assertEqual(True, obj._is_valid_metrics_compute_frequency())
-
-    def test_is_valid_metrics_compute_frequency_True_num(self):
-        self.subject.FitCommonValidator._validate_common_args = Mock()
-        obj = self.subject.FitCommonValidator(
-            'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
-            'dep_varname', 'independent_varname', 5, 3, False, False, [0],
-            'module_name')
-        self.assertEqual(True, obj._is_valid_metrics_compute_frequency())
-
-    def test_is_valid_metrics_compute_frequency_False_zero(self):
-        self.subject.FitCommonValidator._validate_common_args = Mock()
-        obj = self.subject.FitCommonValidator(
-            'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
-            'dep_varname', 'independent_varname', 5, 0, False, False, [0],
-            'module_name')
-        self.assertEqual(False, obj._is_valid_metrics_compute_frequency())
-
-    def test_is_valid_metrics_compute_frequency_False_greater(self):
-        self.subject.FitCommonValidator._validate_common_args = Mock()
-        obj = self.subject.FitCommonValidator(
-            'test_table', 'val_table', 'model_table', 'model_arch_table', 2,
-            'dep_varname', 'independent_varname', 5, 6, False, False, [0],
-            'module_name')
-        self.assertEqual(False, obj._is_valid_metrics_compute_frequency())
-
-
-class InputValidatorTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        import madlib_keras_validator
-        self.module = madlib_keras_validator
-        self.subject = self.module.InputValidator
-
-        self.module_name = 'module'
-        self.test_table = 'test_table'
-        self.model_table = 'model_table'
-        self.id_col = 'id_col'
-        self.ind_var = 'ind_var'
-        self.model_arch_table = 'model_arch_table'
-        self.model_id = 2
-        self.num_classes = 1598
-        self.model = Sequential()
-        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
-                              input_shape=(1,1,1,), padding='same'))
-        self.model.add(Dense(self.num_classes))
-        self.classes = ['train', 'boat', 'car', 'airplane']
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def test_validate_pred_type_invalid_pred_type(self):
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.validate_pred_type(
-                self.module_name, 'invalid_pred_type', ['cat', 'dog'])
-        self.assertIn('type', str(error.exception).lower())
-
-    def test_validate_class_values_greater_than_1600_class_values(self):
-        self.model.add(Dense(1599))
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.validate_class_values(
-                self.module_name, range(1599), 'prob', self.model.to_json())
-        self.assertIn('1600', str(error.exception))
-
-    def test_validate_class_values_valid_class_values_prob(self):
-        self.subject.validate_class_values(
-            self.module_name, range(self.num_classes), 'prob', self.model.to_json())
-        self.subject.validate_class_values(
-            self.module_name, None, 'prob', self.model.to_json())
-
-    def test_validate_class_values_valid_pred_type_valid_class_values_response(self):
-        self.subject.validate_class_values(
-            self.module_name, range(self.num_classes), 'response', self.model.to_json())
-        self.subject.validate_class_values(
-            self.module_name, None, 'response', self.model.to_json())
-
-    def test_validate_input_shape_shapes_do_not_match(self):
-        # minibatched data
-        self.plpy_mock_execute.return_value = [{'shape': [1,3,32,32]}]
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.validate_input_shape(
-                self.test_table, self.ind_var, [32,32,3], 2, True)
-        # non-minibatched data
-        self.plpy_mock_execute.return_value = [{'n_0': 1,'n_1': 32,'n_2': 32,'n_3': 3}]
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.validate_input_shape(
-                self.test_table, self.ind_var, [32,32,3], 1)
-        self.plpy_mock_execute.return_value = [{'n_0': 1,'n_1': 3}]
-        with self.assertRaises(plpy.PLPYException):
-            self.subject.validate_input_shape(
-                self.test_table, self.ind_var, [3,32], 1)
-
-    def test_validate_input_shape_shapes_match(self):
-        # minibatched data
-        self.plpy_mock_execute.return_value = [{'shape': [1,32,32,3]}]
-        self.subject.validate_input_shape(
-            self.test_table, self.ind_var, [32,32,3], 2, True)
-        # non-minibatched data
-        self.plpy_mock_execute.return_value = [{'n_0': 32,'n_1': 32,'n_2': 3}]
-        self.subject.validate_input_shape(
-            self.test_table, self.ind_var, [32,32,3], 1)
-
-    def test_validate_model_arch_table_none_values(self):
-        with self.assertRaises(plpy.PLPYException) as error:
-            obj = self.subject.validate_model_arch_table(
-                self.module_name, None, self.model_id)
-        self.assertIn('null', str(error.exception).lower())
-
-        self.module.input_tbl_valid = Mock()
-        with self.assertRaises(plpy.PLPYException) as error:
-            obj = self.subject.validate_model_arch_table(
-                self.module_name, self.model_arch_table, None)
-        self.assertIn('id', str(error.exception).lower())
-
-    def test_validate_gpu_config_with_gpu_all_segments(self):
-
-        self.plpy_mock_execute.return_value = [{'__internal_gpu_config__': 'all_segments'}]
-        obj = self.subject._validate_gpu_config(self.module_name, 'foo', [1])
-
-    def test_validate_gpu_config_no_gpu_all_segments(self):
-
-        self.plpy_mock_execute.return_value = [{'__internal_gpu_config__': 'all_segments'}]
-        with self.assertRaises(plpy.PLPYException) as error:
-            obj = self.subject._validate_gpu_config(self.module_name, 'foo', [0])
-        self.assertIn('missing gpus', str(error.exception).lower())
-
-    def test_validate_gpu_config_with_gpu_valid_seg_list(self):
-
-        self.plpy_mock_execute.return_value = [{'__internal_gpu_config__': [0,1]}]
-        obj = self.subject._validate_gpu_config(self.module_name, 'foo', [1,1,0,1])
-
-    def test_validate_gpu_config_with_gpu_invalid_seg_list(self):
-
-        self.plpy_mock_execute.return_value = [{'__internal_gpu_config__': [0,1]}]
-        with self.assertRaises(plpy.PLPYException) as error:
-            obj = self.subject._validate_gpu_config(self.module_name, 'foo', [1,0,0,1])
-        self.assertIn('does not have gpu', str(error.exception).lower())
-
-class MadlibSerializerTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        import madlib_keras_serializer
-        self.subject = madlib_keras_serializer
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def test_deserialize_image_1d_weights_null_state_returns_none(self):
-        self.assertEqual(None, self.subject.deserialize_as_image_1d_weights(None))
-
-    def test_deserialize_image_1d_weights_returns_not_none(self):
-        dummy_state = np.array([2,3,4,5,6], dtype=np.float32)
-        res = self.subject.deserialize_as_image_1d_weights(dummy_state.tostring())
-        self.assertEqual(2, res[0])
-        self.assertEqual([3,4,5,6], res[1].tolist())
-
-    def test_deserialize_nd_weights_null_input_returns_none(self):
-        dummy_state = np.array([0,1,2,3,4,5,6], dtype=np.float32)
-        self.assertEqual(None, self.subject.deserialize_as_nd_weights(dummy_state.tostring(), None))
-        self.assertEqual(None, self.subject.deserialize_as_nd_weights(None, [1, 2, 3]))
-        self.assertEqual(None, self.subject.deserialize_as_nd_weights(None, None))
-
-    def test_deserialize_nd_weights_valid_input_returns_not_none(self):
-        dummy_model_weights = np.array([3,4,5], dtype=np.float32)
-        dummy_model_shape = [(2, 1, 1, 1), (1,)]
-        res = self.subject.deserialize_as_nd_weights(dummy_model_weights.tostring(),
-                                                     dummy_model_shape)
-        self.assertEqual([[[[3.0]]], [[[4.0]]]], res[0].tolist())
-        self.assertEqual([5], res[1].tolist())
-
-    def test_deserialize_nd_weights_invalid_input_fails(self):
-        # pass an invalid state with missing model weights
-        invalid_model_weights = np.array([1,2], dtype=np.float32)
-        dummy_model_shape = [(2, 1, 1, 1), (1,)]
-
-        # we expect raise exception because we cannot reshape
-        # model weights of size 0 into shape (2,2,3,1)
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.deserialize_as_nd_weights(invalid_model_weights.tostring(),
-                                                   dummy_model_shape)
-
-        invalid_model_weights = np.array([1,2,3,4], dtype=np.float32)
-        dummy_model_shape = [(2, 2, 3, 1), (1,)]
-        # we expect raise exception because we cannot reshape
-        # model weights of size 2 into shape (2,2,3,1)
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.deserialize_as_nd_weights(invalid_model_weights.tostring(),
-                                                   dummy_model_shape)
-
-        invalid_model_weights = np.array([0,1,2,3,4], dtype=np.float32)
-        dummy_model_shape = [(2, 1), (1,)]
-        # we expect to raise exception because we cannot reshape
-        # model weights of size 2 into shape (1,)
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.deserialize_as_nd_weights(invalid_model_weights.tostring(),
-                                                   dummy_model_shape)
-
-    def test_serialize_image_nd_weights_none_weights_returns_none(self):
-        res = self.subject.serialize_state_with_nd_weights(0, None)
-        self.assertEqual(None , res)
-
-    def test_serialize_image_nd_weights_valid_output(self):
-        res = self.subject.serialize_state_with_nd_weights(0, [np.array([1, 3]),
-                                                               np.array([4,5])])
-        self.assertEqual(np.array([0,1,3,4,5], dtype=np.float32).tostring(),
-                         res)
-
-    def test_serialize_image_1d_weights_none_weights_returns_none(self):
-        res = self.subject.serialize_state_with_1d_weights(0, None)
-        self.assertEqual(None , res)
-
-    def test_serialize_image_1d_weights_valid_output(self):
-        res = self.subject.serialize_state_with_1d_weights(0, np.array([1, 3, 4, 5]))
-        self.assertEqual(np.array([0,1,3,4,5], dtype=np.float32).tostring(),
-                         res)
-
-
-class MadlibKerasHelperTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        import madlib_keras_helper
-        self.subject = madlib_keras_helper
-        self.input_data = [32, 32, 3]
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def test_expand_input_dims(self):
-        self.assertEqual(np.array(self.input_data).shape, (3,))
-        res = self.subject.expand_input_dims(self.input_data)
-        self.assertEqual(res.shape, (1, 3))
-
-    def test_strip_trailing_nulls_from_class_values(self):
-        self.assertEqual(['cat', 'dog'],
-                         self.subject.strip_trailing_nulls_from_class_values(
-                ['cat', 'dog']))
-        self.assertEqual([None, 'cat', 'dog'],
-                         self.subject.strip_trailing_nulls_from_class_values(
-                [None, 'cat', 'dog']))
-        self.assertEqual([None, 'cat', 'dog'],
-                         self.subject.strip_trailing_nulls_from_class_values(
-                [None, 'cat', 'dog', None, None]))
-        self.assertEqual(['cat', 'dog'],
-                         self.subject.strip_trailing_nulls_from_class_values(
-                ['cat', 'dog', None, None]))
-        self.assertEqual([None],
-                         self.subject.strip_trailing_nulls_from_class_values(
-                [None, None]))
-
-    def test_get_gpus_per_one_seg_gpu_gpdb(self):
-
-        self.subject.is_platform_pg = Mock(return_value = False)
-
-        self.plpy_mock_execute.side_effect = \
-            [ [],
-            [ {'hostname': 'mdw0', 'count' : 1}],
-            [],
-            [ {'hostname': 'mdw0', 'segment_id' : 0},
-              {'hostname': 'mdw1', 'segment_id' : 1},
-              {'hostname': 'mdw2', 'segment_id' : 2}
-            ]]
-
-        self.assertEqual([1,0,0], self.subject.get_accessible_gpus_for_seg(
-            'schema_madlib', 2, 'foo'))
-
-    def test_get_gpus_per_mult_seg_gpu_gpdb(self):
-
-        self.subject.is_platform_pg = Mock(return_value = False)
-
-        self.plpy_mock_execute.side_effect = \
-            [[],
-            [ {'hostname': 'mdw0', 'count' : 1}],
-            [],
-            [ {'hostname': 'mdw0', 'segment_id' : 0},
-              {'hostname': 'mdw0', 'segment_id' : 1},
-              {'hostname': 'mdw1', 'segment_id' : 2},
-              {'hostname': 'mdw1', 'segment_id' : 3}
-            ]]
-
-        self.assertEqual([1,1,0,0], self.subject.get_accessible_gpus_for_seg(
-            'schema_madlib', 2, 'foo'))
-
-    def test_get_gpus_per_no_gpu_gpdb(self):
-
-        self.subject.is_platform_pg = Mock(return_value = False)
-
-        self.plpy_mock_execute.side_effect = [[],[],[]]
-        with self.assertRaises(plpy.PLPYException) as error:
-            self.subject.get_accessible_gpus_for_seg('schema_madlib', 2, 'foo')
-        self.assertIn('no gpus configured on hosts', str(error.exception).lower())
-
-class MadlibKerasEvaluationTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        import madlib_keras
-        self.subject = madlib_keras
-
-        self.model = Sequential()
-        self.model.add(Conv2D(2, kernel_size=(1, 1), activation='relu',
-                         input_shape=(1,1,1,), padding='same'))
-        self.model.add(Flatten())
-
-        self.compile_params = "optimizer=SGD(lr=0.01, decay=1e-6, nesterov=True), loss='categorical_crossentropy', metrics=['accuracy']"
-        self.model_weights = [3,4,5,6]
-        self.serialized_weights = np.array(self.model_weights, dtype='float32'
-                                           ).tostring()
-        self.loss = 0.5947071313858032
-        self.accuracy = 1.0
-        self.dist_key_mapping = [0,1,2]
-        self.accessible_gpus_for_seg = [0]
-
-        #self.model.evaluate = Mock(return_value = [self.loss, self.accuracy])
-
-        self.independent_var_real = [[[[0.5]]]] * 10
-        self.dependent_var_int = [[0,1]] * 10
-
-        # Params as bytea
-        self.independent_var = np.array(self.independent_var_real, dtype=np.float32).tobytes()
-        self.dependent_var = np.array(self.dependent_var_int, dtype=np.int16).tobytes()
-
-        self.independent_var_shape = [10,1,1,1]
-        self.dependent_var_shape = [10,2]
-        # We test on segment 0, which has 3 buffers filled with 10 identical
-        #  images each, or 30 images total
-        self.total_images_per_seg = [3*len(self.dependent_var_int),20,40]
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def _test_internal_keras_eval_transition_first_buffer(self, is_platform_pg):
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
-        self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
-        starting_image_count = 0
-        ending_image_count = len(self.dependent_var_int)
-
-        # last iteration call
-
-        k = {'SD' : {}}
-        state = [0,0,0]
-        new_state = self.subject.internal_keras_eval_transition(
-            state, self.dependent_var , self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(),
-            self.serialized_weights, self.compile_params, 0,
-            self.dist_key_mapping, 0, 4,
-            self.total_images_per_seg, False, self.accessible_gpus_for_seg, True, **k)
-
-        agg_loss, agg_accuracy, image_count = new_state
-
-        self.assertEqual(ending_image_count, image_count)
-        # Call set_session once for gpdb (but not for postgres)
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # loss and accuracy should be unchanged
-        self.assertAlmostEqual(self.loss * image_count, agg_loss, 4)
-        self.assertAlmostEqual(self.accuracy * image_count, agg_accuracy, 4)
-        # Clear session and sess.close must not get called for the first buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-        self.assertTrue(k['SD']['segment_model'])
-
-        # Non-final call
-
-        self.subject.K.set_session.reset_mock()
-        self.subject.K.clear_session.reset_mock()
-        k = {'SD' : {}}
-        state = [0,0,0]
-
-        new_state = self.subject.internal_keras_eval_transition(
-            state, self.dependent_var , self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(),
-            self.serialized_weights, self.compile_params, 0,
-            self.dist_key_mapping, 0, 4,
-            self.total_images_per_seg, False, self.accessible_gpus_for_seg, False, **k)
-        agg_loss, agg_accuracy, image_count = new_state
-
-        self.assertEqual(ending_image_count, image_count)
-        # set_session must not get called for the first buffer
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # loss and accuracy should be unchanged
-        self.assertAlmostEqual(self.loss * image_count, agg_loss, 4)
-        self.assertAlmostEqual(self.accuracy * image_count, agg_accuracy, 4)
-        # Clear session and sess.close must not get called for the first buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-        self.assertTrue(k['SD']['segment_model'])
-
-    def _test_internal_keras_eval_transition_middle_buffer(self, is_platform_pg):
-        #TODO should we mock tensorflow's close_session and keras'
-        # clear_session instead of mocking the function `K.clear_session`
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
-        self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
-
-        starting_image_count = len(self.dependent_var_int)
-        ending_image_count = starting_image_count + len(self.dependent_var_int)
-
-        # last iteration call
-
-        k = {'SD' : {}}
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-
-        state = [self.loss * starting_image_count, self.accuracy * starting_image_count, starting_image_count]
-        k['SD']['segment_model'] = self.model
-        k['SD']['sess'] = Mock()
-
-        new_state = self.subject.internal_keras_eval_transition(
-            state, self.dependent_var , self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(),
-            'dummy_model_weights', None, 0,
-            self.dist_key_mapping, 0, 4,
-            self.total_images_per_seg, False, self.accessible_gpus_for_seg, True, **k)
-
-        agg_loss, agg_accuracy, image_count = new_state
-
-        self.assertEqual(ending_image_count, image_count)
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-         # loss and accuracy should be unchanged
-        self.assertAlmostEqual(self.loss * ending_image_count, agg_loss, 4)
-        self.assertAlmostEqual(self.accuracy * ending_image_count, agg_accuracy, 4)
-        # Clear session and sess.close must not get called for the middle buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-
-        # Non-last iteration call
-
-        self.subject.K.set_session.reset_mock()
-        self.subject.K.clear_session.reset_mock()
-        k = {'SD' : {}}
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-
-        state = [self.loss * starting_image_count, self.accuracy * starting_image_count, starting_image_count]
-        k['SD']['segment_model'] = self.model
-        k['SD']['sess'] = Mock()
-
-        new_state = self.subject.internal_keras_eval_transition(
-            state, self.dependent_var , self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(),
-            'dummy_model_weights', None, 0,
-            self.dist_key_mapping, 0, 4,
-            self.total_images_per_seg, False, self.accessible_gpus_for_seg, False, **k)
-        agg_loss, agg_accuracy, image_count = new_state
-
-        self.assertEqual(ending_image_count, image_count)
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-         # loss and accuracy should be unchanged
-        self.assertAlmostEqual(self.loss * ending_image_count, agg_loss, 4)
-        self.assertAlmostEqual(self.accuracy * ending_image_count, agg_accuracy, 4)
-        # Clear session and sess.close must not get called for the middle buffer
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-
-    def _test_internal_keras_eval_transition_last_buffer(self, is_platform_pg):
-        #TODO should we mock tensorflow's close_session and keras'
-        # clear_session instead of mocking the function `K.clear_session`
-        self.subject.K.set_session = Mock()
-        self.subject.K.clear_session = Mock()
-        self.subject.is_platform_pg = Mock(return_value = is_platform_pg)
-
-        starting_image_count = 2*len(self.dependent_var_int)
-        ending_image_count = starting_image_count + len(self.dependent_var_int)
-
-        k = {'SD' : {}}
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-
-        state = [self.loss * starting_image_count,
-                 self.accuracy * starting_image_count,
-                 starting_image_count]
-
-        k['SD']['segment_model'] = self.model
-        k['SD']['sess'] = Mock()
-
-        new_state = self.subject.internal_keras_eval_transition(
-            state, self.dependent_var , self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(),
-            'dummy_model_weights', None, 0,
-            self.dist_key_mapping, 0, 4,
-            self.total_images_per_seg, False, self.accessible_gpus_for_seg, True, **k)
-        agg_loss, agg_accuracy, image_count = new_state
-
-        self.assertEqual(ending_image_count, image_count)
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # loss and accuracy should be unchanged
-        self.assertAlmostEqual(self.loss * ending_image_count, agg_loss, 4)
-        self.assertAlmostEqual(self.accuracy * ending_image_count, agg_accuracy, 4)
-        # Clear session and sess.close must get called for the last buffer in gpdb,
-        #  but not in postgres
-        self.assertEqual(1, self.subject.K.clear_session.call_count)
-
-        # Non-final call
-
-        self.subject.K.set_session.reset_mock()
-        self.subject.K.clear_session.reset_mock()
-        k = {'SD' : {}}
-
-        self.subject.compile_and_set_weights(self.model, self.compile_params,
-                                             '/cpu:0', self.serialized_weights)
-
-        state = [self.loss * starting_image_count,
-                 self.accuracy * starting_image_count,
-                 starting_image_count]
-
-        k['SD']['segment_model'] = self.model
-        k['SD']['sess'] = Mock()
-
-        new_state = self.subject.internal_keras_eval_transition(
-            state, self.dependent_var , self.independent_var,
-            self.dependent_var_shape, self.independent_var_shape,
-            self.model.to_json(),
-            'dummy_model_weights', None, 0,
-            self.dist_key_mapping, 0, 4,
-            self.total_images_per_seg, False, self.accessible_gpus_for_seg, False, **k)
-
-        agg_loss, agg_accuracy, image_count = new_state
-
-        self.assertEqual(ending_image_count, image_count)
-        # set_session is always called
-        self.assertEqual(1, self.subject.K.set_session.call_count)
-        # loss and accuracy should be unchanged
-        self.assertAlmostEqual(self.loss * ending_image_count, agg_loss, 4)
-        self.assertAlmostEqual(self.accuracy * ending_image_count, agg_accuracy, 4)
-        # Clear session and sess.close must not get called in non-final iterations
-        self.assertEqual(0, self.subject.K.clear_session.call_count)
-
-    def test_internal_keras_eval_transition_first_buffer_pg(self):
-        self._test_internal_keras_eval_transition_first_buffer(True)
-
-    def test_internal_keras_eval_transition_first_buffer_gpdb(self):
-        self._test_internal_keras_eval_transition_first_buffer(False)
-
-    def test_internal_keras_eval_transition_middle_buffer_pg(self):
-        self._test_internal_keras_eval_transition_middle_buffer(True)
-
-    def test_internal_keras_eval_transition_middle_buffer_gpdb(self):
-        self._test_internal_keras_eval_transition_middle_buffer(False)
-
-    def test_internal_keras_eval_transition_last_buffer_pg(self):
-        self._test_internal_keras_eval_transition_last_buffer(True)
-
-    def test_internal_keras_eval_transition_last_buffer_gpdb(self):
-        self._test_internal_keras_eval_transition_last_buffer(False)
-
-    def test_internal_keras_eval_merge(self):
-        image_count = self.total_images_per_seg[0]
-        state1 = [3.0*self.loss, 3.0*self.accuracy, image_count]
-        state1 = state1
-        state2 = [2.0*self.loss, 2.0*self.accuracy, image_count+30]
-        state2 = state2
-        merged_state = self.subject.internal_keras_eval_merge(state1,state2)
-        agg_loss = merged_state[0]
-        agg_accuracy = merged_state[1]
-        image_count_total = merged_state[2]
-
-        self.assertEqual( 2*image_count+30 , image_count_total )
-        self.assertAlmostEqual( 5.0*self.loss, agg_loss, 2)
-        self.assertAlmostEqual( 5.0*self.accuracy, agg_accuracy, 2)
-
-    def test_internal_keras_eval_merge_none_first(self):
-        image_count = self.total_images_per_seg[0]
-        input_state = [self.loss, self.accuracy, image_count]
-        merged_state = self.subject.internal_keras_eval_merge(None, input_state)
-        agg_loss = merged_state[0]
-        agg_accuracy = merged_state[1]
-        image_count_total = merged_state[2]
-
-        self.assertEqual(image_count, image_count_total)
-        self.assertAlmostEqual(self.loss, agg_loss, 2)
-        self.assertAlmostEqual(self.accuracy, agg_accuracy, 2)
-
-    def test_internal_keras_eval_merge_none_second(self):
-        image_count = self.total_images_per_seg[0]
-        input_state = [self.loss, self.accuracy, image_count]
-        merged_state = self.subject.internal_keras_eval_merge(input_state, None)
-        agg_loss = merged_state[0]
-        agg_accuracy = merged_state[1]
-        image_count_total = merged_state[2]
-
-        self.assertEqual(image_count, image_count_total)
-        self.assertAlmostEqual(self.loss, agg_loss, 2)
-        self.assertAlmostEqual(self.accuracy, agg_accuracy, 2)
-
-    def test_internal_keras_eval_merge_both_none(self):
-        result = self.subject.internal_keras_eval_merge(None,None)
-        self.assertEqual(None, result)
-
-    def test_internal_keras_eval_final(self):
-        image_count = self.total_images_per_seg[0]
-        input_state = [image_count*self.loss, image_count*self.accuracy, image_count]
-
-        output_state = self.subject.internal_keras_eval_final(input_state)
-        self.assertEqual(len(output_state), 2)
-        agg_loss = output_state[0]
-        agg_accuracy = output_state[1]
-
-        self.assertAlmostEqual(self.loss, agg_loss,2)
-        self.assertAlmostEqual(self.accuracy, agg_accuracy,2)
-
-    def internal_keras_eval_final_none(self):
-        result = self.subject.internal_keras_eval_final(None)
-        self.assertEqual(result, None)
-
-    def test_internal_keras_eval_final_image_count_zero(self):
-        input_state = [0, 0, 0]
-
-        with self.assertRaises(plpy.PLPYException):
-            result = self.subject.internal_keras_eval_final(input_state)
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-if __name__ == '__main__':
-    unittest.main()
-# ---------------------------------------------------------------------
diff --git a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras_gpu_info.py_in b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras_gpu_info.py_in
deleted file mode 100644
index cc885f9..0000000
--- a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras_gpu_info.py_in
+++ /dev/null
@@ -1,88 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-m4_changequote(`')
-
-import sys
-import numpy as np
-import os
-from os import path
-# Add dl module to the pythonpath.
-sys.path.append(path.dirname(path.dirname(path.dirname(path.dirname(path.abspath(__file__))))))
-sys.path.append(path.dirname(path.dirname(path.dirname(path.abspath(__file__)))))
-
-import unittest
-from mock import *
-import plpy_mock as plpy
-
-
-
-class GPUInfoTestCase(unittest.TestCase):
-    def setUp(self):
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        import madlib_keras_gpu_info
-        self.module = madlib_keras_gpu_info
-        self.module.output_tbl_valid = Mock()
-        self.table_name = 'does_not_matter'
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-    def test_gpu_configuration_invalid_source(self):
-        with self.assertRaises(plpy.PLPYException):
-            self.module.gpu_configuration('schema', self.table_name, 'invalid_source')
-
-    def test_gpu_configuration_none_source(self):
-        self.module.gpu_configuration('schema', self.table_name, None)
-        self.assertIn('tensorflow', str(self.plpy_mock_execute.call_args_list))
-        self.assertNotIn('nvidia', str(self.plpy_mock_execute.call_args_list))
-
-    def test_gpu_configuration_tensorflow(self):
-        self.module.gpu_configuration('schema', self.table_name, 'tensorflow')
-        self.assertIn('tensorflow', str(self.plpy_mock_execute.call_args_list))
-        self.assertNotIn('nvidia', str(self.plpy_mock_execute.call_args_list))
-
-    def test_gpu_configuration_nvidia(self):
-        self.module.gpu_configuration('schema', self.table_name, 'nvidia')
-        self.assertIn('nvidia', str(self.plpy_mock_execute.call_args_list))
-        self.assertNotIn('tensorflow', str(self.plpy_mock_execute.call_args_list))
-
-    def test_gpu_configuration_tensorflow_uppercase(self):
-        self.module.gpu_configuration('schema', self.table_name, 'TENSORFLOW')
-        self.assertIn('tensorflow', str(self.plpy_mock_execute.call_args_list))
-        self.assertNotIn('nvidia', str(self.plpy_mock_execute.call_args_list))
-
-    def test_gpu_configuration_nvidia_mixedcase(self):
-        self.module.gpu_configuration('schema', self.table_name, 'NVidIa')
-        self.assertIn('nvidia', str(self.plpy_mock_execute.call_args_list))
-        self.assertNotIn('tensorflow', str(self.plpy_mock_execute.call_args_list))
-
-
-if __name__ == '__main__':
-    unittest.main()
-# ---------------------------------------------------------------------
diff --git a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras_model_selection_table.py_in b/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras_model_selection_table.py_in
deleted file mode 100644
index 57e08a5..0000000
--- a/src/ports/postgres/modules/deep_learning/test/unit_tests/test_madlib_keras_model_selection_table.py_in
+++ /dev/null
@@ -1,140 +0,0 @@
-# coding=utf-8
-#
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-m4_changequote(`')
-
-import sys
-import os
-from os import path
-# Add convex module to the pythonpath.
-sys.path.append(path.dirname(path.dirname(path.dirname(path.dirname(path.abspath(__file__))))))
-sys.path.append(path.dirname(path.dirname(path.dirname(path.abspath(__file__)))))
-
-import keras
-import unittest
-from mock import *
-import plpy_mock as plpy
-
-class LoadModelSelectionTableTestCase(unittest.TestCase):
-    def setUp(self):
-        # The side effects of this class(writing to the output table) are not
-        # tested here. They are tested in dev-check.
-        self.plpy_mock = Mock(spec='error')
-        patches = {
-            'plpy': plpy
-        }
-
-        self.plpy_mock_execute = MagicMock()
-        plpy.execute = self.plpy_mock_execute
-
-        self.module_patcher = patch.dict('sys.modules', patches)
-        self.module_patcher.start()
-        import deep_learning.madlib_keras_model_selection
-        self.module = deep_learning.madlib_keras_model_selection
-        self.module.MstLoaderInputValidator._validate_input_args = \
-            MagicMock()
-
-        self.subject = self.module.MstLoader
-        self.model_selection_table = 'mst_table'
-        self.model_arch_table = 'model_arch_library'
-        self.model_id_list = [1]
-        self.compile_params_list = [
-            """
-                loss='categorical_crossentropy',
-                optimizer='Adam(lr=0.1)',
-                metrics=['accuracy']
-            """,
-            """
-                loss='categorical_crossentropy',
-                optimizer='Adam(lr=0.01)',
-                metrics=['accuracy']
-            """,
-            """
-                loss='categorical_crossentropy',
-                optimizer='Adam(lr=0.001)',
-                metrics=['accuracy']
-            """
-        ]
-        self.fit_params_list = [
-            "batch_size=5,epochs=1",
-            "batch_size=10,epochs=1"
-        ]
-
-    def test_mst_table_dimension(self):
-        generate_mst = self.subject(
-            self.model_selection_table,
-            self.model_arch_table,
-            self.model_id_list,
-            self.compile_params_list,
-            self.fit_params_list
-        )
-        self.assertEqual(6, len(generate_mst.msts))
-
-    def test_invalid_input_args(self):
-        self.module.MstLoaderInputValidator \
-            ._validate_input_args \
-            .side_effect = plpy.PLPYException('Invalid input args')
-        with self.assertRaises(plpy.PLPYException):
-            generate_mst = self.subject(
-                self.model_selection_table,
-                self.model_arch_table,
-                self.model_id_list,
-                self.compile_params_list,
-                self.fit_params_list
-            )
-
-    def test_duplicate_params(self):
-        self.model_id_list = [1, 1, 2]
-        self.compile_params_list = [
-            """
-                loss='categorical_crossentropy',
-                optimizer='Adam(lr=0.1)',
-                metrics=['accuracy']
-            """,
-            """
-                loss='categorical_crossentropy',
-                optimizer='Adam(lr=0.1)',
-                metrics=['accuracy']
-            """,
-            """
-                loss='categorical_crossentropy',
-                optimizer='Adam(lr=0.001)',
-                metrics=['accuracy']
-            """
-        ]
-        self.fit_params_list = [
-            "batch_size= 5,epochs=1",
-            "epochs=1 ,batch_size=5",
-            "batch_size=10,epochs =1"
-        ]
-        generate_mst = self.subject(
-            self.model_selection_table,
-            self.model_arch_table,
-            self.model_id_list,
-            self.compile_params_list,
-            self.fit_params_list
-        )
-        self.assertEqual(8, len(generate_mst.msts))
-
-    def tearDown(self):
-        self.module_patcher.stop()
-
-if __name__ == '__main__':
-    unittest.main()
-# ---------------------------------------------------------------------
diff --git a/src/ports/postgres/modules/elastic_net/elastic_net.py_in b/src/ports/postgres/modules/elastic_net/elastic_net.py_in
index c6dec53..43c05e1 100644
--- a/src/ports/postgres/modules/elastic_net/elastic_net.py_in
+++ b/src/ports/postgres/modules/elastic_net/elastic_net.py_in
@@ -282,7 +282,7 @@ def elastic_net_help(schema_madlib, family_or_optimizer=None, **kwargs):
 def elastic_net_train(schema_madlib, source_table, model_table, dependent_varname,
                       independent_varname, regress_family, alpha, lambda_value,
                       standardize, grouping_col, optimizer,
-                      optimizer_params, excluded, max_iter, tolerance,
+                      optimizer_params, exclude, max_iter, tolerance,
                       **kwargs):
     """
     A wrapper for all variants of elastic net regularization.
@@ -350,7 +350,7 @@ def _internal_elastic_net_train(
         schema_madlib, source_table, model_table, dependent_varname,
         independent_varname, grouping_col,
         regress_family, alpha, lambda_value,
-        standardize, optimizer, optimizer_params, excluded,
+        standardize, optimizer, optimizer_params, exclude,
         max_iter, tolerance, **kwargs):
 
     tbl_summary = add_postfix(model_table, "_summary")
@@ -358,7 +358,7 @@ def _internal_elastic_net_train(
     # handle all special cases of independent_varname
     independent_varname, outstr_array = analyze_input_str(
         schema_madlib, source_table,
-        independent_varname, dependent_varname, excluded)
+        independent_varname, dependent_varname, exclude)
 
     # get the grouping info
     reserved_cols =['regress_family', 'coef_all',
@@ -495,7 +495,7 @@ def _check_args(tbl_source, col_ind_var, col_dep_var):
 
 
 def analyze_input_str(schema_madlib, tbl_source,
-                      col_ind_var, col_dep_var, excluded):
+                      col_ind_var, col_dep_var, exclude):
     """
     Make input strings and output strings compatible with functions
 
@@ -511,7 +511,7 @@ def analyze_input_str(schema_madlib, tbl_source,
         col_types_dict = dict(get_cols_and_types(tbl_source))
         cols = col_types_dict.keys()
 
-        s = _string_to_array(excluded) if excluded is not None else []
+        s = _string_to_array(exclude) if exclude is not None else []
 
         for each_col in cols:
             if each_col not in s and each_col != col_dep_var:
@@ -537,7 +537,7 @@ def analyze_input_str(schema_madlib, tbl_source,
     if columns_exist_in_table(tbl_source, [col_ind_var], schema_madlib):
         # if the input is a column name and not an expression
         return analyze_single_input_str(schema_madlib, tbl_source,
-                                        col_ind_var, excluded)
+                                        col_ind_var, exclude)
     else:
         # if input is an expression resulting in an array output
         matched = is_string_formatted_as_array_expression(col_ind_var)
@@ -562,7 +562,7 @@ def analyze_input_str(schema_madlib, tbl_source,
 
 
 def analyze_single_input_str(schema_madlib, tbl_source, col_ind_var,
-                             excluded=None):
+                             exclude=None):
     """
     Args:
         @param schema_madlib: string, Name of schema where MADlib is installed
@@ -588,8 +588,8 @@ def analyze_single_input_str(schema_madlib, tbl_source, col_ind_var,
             """.format(tbl_source=tbl_source,
                        col_ind_var=col_ind_var))[0]["dimension"]
 
-        if excluded is not None:
-            s = _string_to_array(excluded)
+        if exclude is not None:
+            s = _string_to_array(exclude)
             invalid_excluded = """
                                Elastic Net error: When the independent variable is
                                an array column, excluded values can only be indices
diff --git a/src/ports/postgres/modules/elastic_net/elastic_net.sql_in b/src/ports/postgres/modules/elastic_net/elastic_net.sql_in
index c1aaebf..2358b4e 100644
--- a/src/ports/postgres/modules/elastic_net/elastic_net.sql_in
+++ b/src/ports/postgres/modules/elastic_net/elastic_net.sql_in
@@ -895,7 +895,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (
     grouping_col        TEXT,
     optimizer           TEXT,
     optimizer_params    TEXT,
-    excluded            TEXT,
+    exclude             TEXT,
     max_iter            INTEGER,
     tolerance           DOUBLE PRECISION
 ) RETURNS VOID AS $$
@@ -905,7 +905,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (
             schema_madlib, tbl_source, tbl_result, col_dep_var,
             col_ind_var, regress_family, alpha, lambda_value,
             standardize, grouping_col, optimizer, optimizer_params,
-            excluded, max_iter, tolerance)
+            exclude, max_iter, tolerance)
         $$ LANGUAGE plpythonu
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
 
@@ -923,7 +923,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (
     grouping_columns    TEXT,
     optimizer           TEXT,
     optimizer_params    TEXT,
-    excluded            TEXT,
+    exclude             TEXT,
     max_iter            INTEGER
 ) RETURNS VOID AS $$
 BEGIN
@@ -945,7 +945,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_train (
     grouping_columns    TEXT,
     optimizer           TEXT,
     optimizer_params    TEXT,
-    excluded            TEXT
+    exclude             TEXT
 ) RETURNS VOID AS $$
 BEGIN
     PERFORM MADLIB_SCHEMA.elastic_net_train($1, $2, $3, $4, $5, $6, $7, $8,
@@ -1157,7 +1157,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_gaussian_predict (
     ind_var         DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', '__elastic_net_gaussian_predict'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1176,7 +1176,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_binomial_predict (
     ind_var         DOUBLE PRECISION[]
 ) RETURNS BOOLEAN AS
 'MODULE_PATHNAME', '__elastic_net_binomial_predict'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1195,7 +1195,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.elastic_net_binomial_prob (
     ind_var         DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', '__elastic_net_binomial_prob'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1207,7 +1207,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__elastic_net_binomial_loglikelihood (
     ind_var         DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', '__elastic_net_binomial_loglikelihood'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1240,7 +1240,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_igd_transition (
     step_decay          DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'gaussian_igd_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1250,7 +1250,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_igd_merge (
     state2              DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'gaussian_igd_merge'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1259,7 +1259,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_igd_final (
     state               DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'gaussian_igd_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /*
@@ -1337,7 +1337,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_igd_state_diff (
     state2          DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', '__gaussian_igd_state_diff'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1349,7 +1349,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_igd_result (
     tolerance       DOUBLE PRECISION
 ) RETURNS MADLIB_SCHEMA.__elastic_net_result AS
 'MODULE_PATHNAME', '__gaussian_igd_result'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1372,7 +1372,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_fista_transition (
     random_stepsize     INTEGER
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'gaussian_fista_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1382,7 +1382,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_fista_merge (
     state2              DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'gaussian_fista_merge'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1391,7 +1391,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_fista_final (
     state               DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'gaussian_fista_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /*
@@ -1439,7 +1439,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_fista_state_diff (
     state2          DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', '__gaussian_fista_state_diff'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1448,7 +1448,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__gaussian_fista_result (
     in_state        DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__elastic_net_result AS
 'MODULE_PATHNAME', '__gaussian_fista_result'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1472,7 +1472,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_igd_transition (
     step_decay          DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'binomial_igd_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1482,7 +1482,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_igd_merge (
     state2              DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'binomial_igd_merge'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1491,7 +1491,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_igd_final (
     state               DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'binomial_igd_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /*
@@ -1569,7 +1569,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_igd_state_diff (
     state2          DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', '__binomial_igd_state_diff'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1581,7 +1581,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_igd_result (
     tolerance       DOUBLE PRECISION
 ) RETURNS MADLIB_SCHEMA.__elastic_net_result AS
 'MODULE_PATHNAME', '__binomial_igd_result'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1604,7 +1604,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_fista_transition (
     random_stepsize     INTEGER
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'binomial_fista_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1614,7 +1614,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_fista_merge (
     state2              DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'binomial_fista_merge'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1623,7 +1623,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_fista_final (
     state               DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'binomial_fista_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /*
@@ -1671,7 +1671,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_fista_state_diff (
     state2          DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', '__binomial_fista_state_diff'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --
@@ -1680,7 +1680,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__binomial_fista_result (
     in_state        DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__elastic_net_result AS
 'MODULE_PATHNAME', '__binomial_fista_result'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
diff --git a/src/ports/postgres/modules/deep_learning/__init__.py_in b/src/ports/postgres/modules/facebook_prophet/__init__.py_in
similarity index 100%
rename from src/ports/postgres/modules/deep_learning/__init__.py_in
rename to src/ports/postgres/modules/facebook_prophet/__init__.py_in
diff --git a/src/ports/postgres/modules/facebook_prophet/prophet_alg.py_in b/src/ports/postgres/modules/facebook_prophet/prophet_alg.py_in
new file mode 100644
index 0000000..a1ea25e
--- /dev/null
+++ b/src/ports/postgres/modules/facebook_prophet/prophet_alg.py_in
@@ -0,0 +1,349 @@
+# -*- coding:utf-8 -*-
+# Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+#
+# openGauss is licensed under Mulan PSL v2.
+# You can use this software according to the terms
+# and conditions of the Mulan PSL v2.
+# You may obtain a copy of Mulan PSL v2 at:
+#
+#          http://license.coscl.org.cn/MulanPSL2
+#
+# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS,
+# WITHOUT WARRANTIES OF ANY KIND,
+# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+# See the Mulan PSL v2 for more details.
+# ----------------------------------------------------------------------------
+
+# ----------------------------------------------------------------------
+# Timeseries predict use facebook module prophet.
+# ----------------------------------------------------------------------
+import plpy
+import cPickle as pickle
+
+from utilities.validate_args import table_exists
+from utilities.validate_args import table_is_empty
+from utilities.validate_args import columns_exist_in_table
+from utilities.validate_args import get_expr_type
+from utilities.validate_args import get_cols
+from utilities.utilities import _assert
+from utilities.control import MinWarning
+
+
+def prophet_fit(schema_madlib, input_table, model_table, column_map, params_str, verbose, **kwargs):
+    """
+        Args:
+        @param input_table,                 -- name of input table
+        @param model_table,                 -- name of output table
+        @param column_map,                  -- map relation of column
+        @param params_str,                  -- names of paraters for prophet
+        @param verbose                      -- verbose or not
+    """
+    with MinWarning('warning'):
+        _validate_args(schema_madlib, input_table, model_table, params_str)
+
+    sql = """
+          DROP TABLE IF EXISTS {model_table};
+          CREATE TABLE {model_table} m4_ifdef(`__GSDBMPP__', `DISTRIBUTE BY HASH(id)', `') 
+          AS
+          (
+              SELECT
+                    1 as id,
+                    (mdl_results).y_type, 
+                    (mdl_results).model,
+                    (mdl_results).params
+              FROM (
+                    SELECT
+                          {schema_madlib}.__get_fbmodel_as_bytea__(
+                          '{input_table}',
+                          $${column_map}$$,
+                          $${params_str}$$,
+                          {verbose}
+                    ) as mdl_results
+               )q
+           );
+          """.format(
+                     schema_madlib=schema_madlib,
+                     model_table=model_table,
+                     input_table=input_table,
+                     column_map=column_map,
+                     params_str=params_str,
+                     verbose=verbose
+              )
+    
+    if verbose:
+        plpy.info(sql) 
+    plpy.execute(sql)
+   
+    return "Prophet_fit over and model is saved in {model_table}".format(model_table=model_table)
+
+
+# ------------------------------------------------------------------------------
+# -- prophet predict function ------------------------------------------------------------
+# ------------------------------------------------------------------------------
+def prophet_predict(schema_madlib, model_table, output_table, periods, freq, cap, floor, include_history, verbose, **kwargs):
+    """
+    Args:
+    @param model_table,                  -- name of model table
+    @param output_table,                 -- name of output table
+    @param periods,                      -- value of predict length
+    @param freq,                         -- frequency of predict value
+    @param cap,                          -- max value of predict value(logistic)
+    @param floor,                        -- min value of predict value(logistic)
+    @param inlcude_history
+    """
+    with MinWarning('warning'):
+        _validate_predict(schema_madlib, model_table, output_table, periods)
+    
+    sql = """select y_type, model from {model_table};""".format(model_table=model_table)
+    if verbose:
+        plpy.info(sql)
+    result = plpy.execute(sql)
+    y_type = result[0]['y_type']
+    
+    model = result[0]['model']
+    model = pickle.loads(model)
+
+    if freq:
+        future = model.make_future_dataframe(periods=periods, freq=freq, include_history=include_history)
+    else:
+        future = model.make_future_dataframe(periods=periods, include_history=include_history)
+    if cap is not None:
+        future['cap'] = cap
+    if floor is not None:
+        future['floor'] = floor
+    forecast = model.predict(future)
+   
+    columns = forecast.columns
+    column_list, value_list = [], []
+    for column in columns:
+        if column == 'ds':
+	    column_list.append('{column} date'.format(column=column))
+            value_list.append("'{{{column}}}'".format(column=column))
+        else:
+            column_list.append('{column} {y_type}'.format(column=column, y_type=y_type))
+            value_list.append('{{{column}}}'.format(column=column))
+    column_information = ', '.join(column_list)
+    value_information = ', '.join(value_list)
+    plpy.info("column_information is: " + str(column_information))    
+    plpy.info("value_information is: " + str(value_information))    
+ 
+    sql = """
+          DROP TABLE IF EXISTS {output_table};
+          CREATE TABLE {output_table} ({column_information})
+          """.format(
+                     output_table=output_table,
+                     column_information=column_information
+                    )
+    if verbose:
+        plpy.info(sql)
+    plpy.execute(sql)
+    
+
+    sql = """INSERT INTO {output_table} VALUES ({value_information});""".format(output_table=output_table,
+                                                                                value_information=value_information)
+    plpy.info("insert sql is {sql}".format(sql=sql))
+    predict_length = len(forecast['ds'])
+    for i in range(predict_length):
+        plpy.execute(
+                     sql.format(**{col:forecast[col][i] for col in columns})
+    )        
+
+    return "prophet predict over and result is saved in {output_table}".format(output_table=output_table)
+
+
+# ------------------------------------------------------------------------------
+# -- validate args  ------------------------------------------------------------
+# ------------------------------------------------------------------------------
+def _validate_args(schema_madlib, input_table, model_table, params_str):
+    """
+    @brief validate the arguments
+    """
+    # 1, check input_table: name.
+    _assert(input_table and input_table.strip().lower() not in ('null', ''),
+            "Prophet error: Invalid data table name!")
+    _assert(table_exists(input_table),
+            "Prophet error: Data table does not exist!")
+    _assert(not table_is_empty(input_table),
+            "Prophet error: Data table is empty!")
+
+    # 3, check output table.
+    _assert(model_table is not None and model_table.strip().lower() not in ('null', ''),
+            "Prophet error: Invalid output table name!")
+    _assert(not table_exists(model_table, only_first_schema=True),
+            "Output table name already exists. Drop the table before calling the function.")
+
+    # 4, check params_str
+    if params_str != '':
+        # let prophet to check the parameter
+        pass
+
+
+def _validate_predict(schema_madlib, model_table, output_table, periods):
+    """
+    @brief validate the predict arguments
+    """
+    # 1, check input_table: name.
+    _assert(model_table and model_table.strip().lower() not in ('null', ''),
+            "Prophet error: Invalid model table name!")
+    _assert(table_exists(model_table),
+            "Prophet error: model table does not exist!")
+    _assert(not table_is_empty(model_table),
+            "Prophet error: Data table is empty!")
+
+    # 3, check output table.
+    _assert(output_table is not None and output_table.strip().lower() not in ('null', ''),
+            "Prophet error: Invalid output_table table name!")
+    _assert(not table_exists(output_table, only_first_schema=True),
+            "Output table name already exists. Drop the table before calling the function.")
+
+    # 4, check periods
+    _assert(isinstance(periods, int) and periods > 0, "Prophet error: Invalid parameter 'periods'")
+
+
+# ------------------------------------------------------------------------------
+# -- Online help function ------------------------------------------------------
+# ------------------------------------------------------------------------------
+def prophet_fit_help_message(schema_madlib, message, **kwargs):
+    """ Help message for Prophet_fit 
+
+    @brief
+    Args:
+        @param schema_madlib string, Name of the schema madlib
+        @param message string, Help message indicator
+
+    Returns:
+        String. Contain the help message string
+    """
+    if not message:
+        help_string="""
+        -----------------------------------------------------------------------
+                                    SUMMARY
+        -----------------------------------------------------------------------
+        facebook prophet API timeseries predict.
+
+        For more details on function usage:
+            SELECT {schema_madlib}.prophet_fit('usage')
+        """
+    elif message in ['usage', 'help', '?']:
+        help_string="""
+        -----------------------------------------------------------------------
+                                        USAGE
+        -----------------------------------------------------------------------
+        SELECT {schema_madlib}.prophet_fit(
+            input_table  TEXT,   -- in, Data table name
+            model_table  TEXT,   -- out, save model and features
+            column_map   TEXT,   -- map relation of column : 'ds', 'y', 'floor', 'cap'
+            params_str   TEXT,   -- prophet parameters
+            verbose      BOOLEAN    -- Boolean, whether to print more info, default is False
+         );
+
+        -----------------------------------------------------------------------
+                                   parameters
+        -----------------------------------------------------------------------
+        below is optional parameters:
+
+        column_map:
+            Prophet has specified column name as follows:
+                'ds': column of date
+                'y': column of predict value
+                'cap': upper limit of predict value
+                'floor': lower limit of predict value
+            
+            so in order to simplify the usage of prophet, we allow user to provide a map relationship between 
+        specified column name and corresponding column name, for example:
+            date  | value 
+         ------------------
+         2000-1-1 |  100 
+         2000-1-2 |  120
+         2000-1-3 |  130
+        
+        in this case, we should provide a map like {{'ds': 'date', 'y': 'value'}}
+        if user do not provide map relationship, then use the default {{'ds': 'ds', 'y': 'y'}}, if you want to use 'cap' and 'floor', 
+        you should definite it in column_map, otherwise will not use.         
+
+        params_str:
+            For example, if the input is:
+                $${{'growth': 'linear', 'changepoints': ['2000-01-01', '2000-02-02'], 'yearly_seasonality': True}}$$
+
+        a full input query:
+            select {schema_madlib}.prophet_fit('inputTable', 'output_modelTable', $${{'ds':'ds', 'y': 'y', 'cap': 'cap'}}$$,  
+            $${{'growth': 'linear', 'changepoints': ['2000-01-01', '2000-02-02'], 'yearly_seasonality': True, 'holidays': ['playoffs']}}$$, True);
+
+        -----------------------------------------------------------------------
+                                        OUTPUT
+        -----------------------------------------------------------------------
+        The output table ('model_table' above) has the following columns:
+            id                -- index
+            model             -- binary model
+                
+        note: the type of ds_column must be DATE
+
+        """
+    else:
+        help_string="No such option. Use {schema_madlib}.prophet_fit()"
+
+    return help_string.format(schema_madlib=schema_madlib)
+
+
+def prophet_predict_help_message(schema_madlib, message, **kwargs):
+    """ Help message for Prophet_predict
+
+    @brief
+    Args:
+        @param schema_madlib string, Name of the schema madlib
+        @param message string, Help message indicator
+
+    Returns:
+        String. Contain the help message string
+    """
+    if not message:
+        help_string="""
+        -----------------------------------------------------------------------
+                                    SUMMARY
+        -----------------------------------------------------------------------
+        facebook prophet API timeseries predict.
+
+        For more details on function usage:
+            SELECT {schema_madlib}.prophet_predict('usage')
+        """
+    elif message in ['usage', 'help', '?']:
+        help_string="""
+        -----------------------------------------------------------------------
+                                        USAGE
+        -----------------------------------------------------------------------
+        SELECT {schema_madlib}.prophet_predict(
+            model_table       TEXT,                    -- in, model table name
+            output_table      TEXT,                    -- out, save predict result
+            periods           INTEGER,                 -- the number of periods to forecast forward.
+            freq              TEXT,                    -- 'day', 'week', 'month', 'quarter', 'year', 1(1 sec), 60(1 minute) or 3600(1 hour)
+            cap               DOUBLE PRECISION,        -- the max value in future predict(logistic mode)
+            floor             DOUBLE PRECISION,        -- the min value in future predict(logistic mode)
+            include_history   BOOLEAN,                 -- to include the historical dates in the data frame for predictions.
+            verbose           BOOLEAN                  -- whether to print more info, default is False
+         );
+
+        -----------------------------------------------------------------------
+                                   parameters
+        -----------------------------------------------------------------------
+        below is optional parameters:
+
+        periods:
+            periods >= 1
+        cap, floor:
+            if growth is 'logistic', user should provide 'cap' or 'floor', if do not use it, just set NULL.
+        
+         example: select {schema_madlib}.prophet_predict('modelTable', 'output_Table', 1000, '10T', 10, NULL, False, False);
+         example: select {schema_madlib}.prophet_predict('modelTable', 'output_Table', 1000, '10T', NULL, NULL, False, False);
+
+
+        -----------------------------------------------------------------------
+                                        OUTPUT
+        -----------------------------------------------------------------------
+        The output table include all result of prophet_predict
+        """
+    else:
+        help_string="No such option. Use {schema_madlib}.prophet_fit()"
+
+    return help_string.format(schema_madlib=schema_madlib)
+
diff --git a/src/ports/postgres/modules/facebook_prophet/prophet_alg.sql_in b/src/ports/postgres/modules/facebook_prophet/prophet_alg.sql_in
new file mode 100644
index 0000000..9bace08
--- /dev/null
+++ b/src/ports/postgres/modules/facebook_prophet/prophet_alg.sql_in
@@ -0,0 +1,186 @@
+/*
+ * Portions Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+ *
+ * openGauss is licensed under Mulan PSL v2.
+ * You can use this software according to the terms and conditions of the Mulan PSL v2.
+ * You may obtain a copy of Mulan PSL v2 at:
+ *
+ *          http://license.coscl.org.cn/MulanPSL2
+ *
+ * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
+ * EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+ * MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+ * See the Mulan PSL v2 for more details.
+ * -------------------------------------------------------------------------
+ */
+
+-------------------------------------------------------------------------------------------------------------------------------
+-- Build facebook Prophet models in-database - -
+-------------------------------------------------------------------------------------------------------------------------------
+--
+-----------
+-- Note: --
+-----------
+-- 1) Porphet uses facebook-API. However we're limited by the maximum
+--    field-size in Greenplum/Postgres which is currently 1 GB.
+-- 2) If your dataset is much larger (> 1 GB), it is strongly recommended that you use MADlib's models so that
+--    training & scoring will happen in-parallel on all nodes.
+-------------------------------------------------------------------------------------------------------------------------------
+
+m4_include(`SQLCommon.m4')
+
+----------------------------------------------------------------------------------------------
+------ Help messages
+----------------------------------------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_fit(
+    message    TEXT
+) RETURNS TEXT AS $XX$
+    PythonFunction(facebook_prophet, prophet_alg, prophet_fit_help_message)
+$XX$ LANGUAGE plpythonu IMMUTABLE;
+-------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_fit()
+RETURNS TEXT AS $XX$
+    SELECT MADLIB_SCHEMA.prophet_fit(''::TEXT);
+$XX$
+LANGUAGE sql IMMUTABLE;
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_predict(
+    message    TEXT
+) RETURNS TEXT AS $XX$
+    PythonFunction(facebook_prophet, prophet_alg, prophet_predict_help_message)
+$XX$ LANGUAGE plpythonu IMMUTABLE;
+-------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_predict()
+RETURNS TEXT AS $XX$
+    SELECT MADLIB_SCHEMA.prophet_predict(''::TEXT);
+$XX$
+LANGUAGE sql IMMUTABLE;
+
+
+----------------------------------------------------------------------------------------------
+------ prophet fit
+----------------------------------------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_fit(
+    input_table  TEXT,                -- in, dataset table
+    model_table  TEXT,                -- out, save model and features
+    column_map   TEXT,                -- map relation of column {'ds' 'y' 'floor' 'cap'}
+    params_str   TEXT,                -- prophet parameters
+    verbose      BOOLEAN
+)
+RETURNS TEXT AS $$
+PythonFunction(facebook_prophet, prophet_alg, prophet_fit)
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_fit(
+    input_table  TEXT,
+    model_table  TEXT,
+    column_map   TEXT,
+    params_str   TEXT
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.prophet_fit($1, $2, $3, $4, False);
+$$ LANGUAGE sql;
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_fit(
+    input_table  TEXT,
+    model_table  TEXT,
+    column_map   TEXT
+
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.prophet_fit($1, $2, $3, NULL);
+$$ LANGUAGE sql;
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_fit(
+    input_table  TEXT,
+    model_table  TEXT
+
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.prophet_fit($1, $2, NULL);
+$$ LANGUAGE sql;
+
+
+--def prophet_predict(schema_madlib, model_table, output_table, periods, verbose, **kwargs):
+
+
+----------------------------------------------------------------------------------------------
+------ prophet predict
+----------------------------------------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_predict(
+    model_table      TEXT,                -- in, dataset table
+    output_table     TEXT,                -- out, save model and features
+    periods          INTEGER,                 -- predict length
+    freq             TEXT,                -- frequency of input_dataset
+    cap              DOUBLE PRECISION,               -- max value of predict value(logistic)
+    floor            DOUBLE PRECISION,               -- min value of predict value(logistic)
+    include_history  BOOLEAN,                -- include history dataset in output_table
+    verbose          BOOLEAN                 -- log or not
+)
+RETURNS TEXT AS $$
+PythonFunction(facebook_prophet, prophet_alg, prophet_predict)
+$$ LANGUAGE plpythonu;
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_predict(
+    model_table      TEXT,                -- in, dataset table
+    output_table     TEXT,                -- out, save model and features
+    periods          INTEGER,                 -- predict length
+    freq             TEXT,                -- frequency of input_dataset
+    cap              DOUBLE PRECISION,               -- max value of predict value(logistic)
+    floor            DOUBLE PRECISION,               -- min value of predict value(logistic)
+    include_history  BOOLEAN                 -- include history dataset in output_table
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.prophet_predict($1, $2, $3, $4, $5, $6, $7, False);
+$$ LANGUAGE sql;
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_predict(
+    model_table      TEXT,                -- in, dataset table
+    output_table     TEXT,                -- out, save model and features
+    periods          INTEGER,                 -- predict length
+    freq             TEXT,                 -- frequency of input_dataset
+    cap              DOUBLE PRECISION,                 -- max value of predict value(logistic)
+    floor            DOUBLE PRECISION                 -- min value of predict value(logistic)
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.prophet_predict($1, $2, $3, $4, $5, $6, False);
+$$ LANGUAGE sql;
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_predict(
+    model_table      TEXT,                -- in, dataset table
+    output_table     TEXT,                -- out, save model and features
+    periods          INTEGER,                 -- predict length
+    freq             TEXT,                 -- frequency of input_dataset
+    cap              DOUBLE PRECISION                 -- max value of predict value(logistic)
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.prophet_predict($1, $2, $3, $4, $5, NULL);
+$$ LANGUAGE sql;
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_predict(
+    model_table      TEXT,                -- in, dataset table
+    output_table     TEXT,                -- out, save model and features
+    periods          INTEGER,                 -- predict length
+    freq             TEXT                 -- frequency of input_dataset
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.prophet_predict($1, $2, $3, $4, NULL);
+$$ LANGUAGE sql;
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.prophet_predict(
+    model_table      TEXT,                -- in, dataset table
+    output_table     TEXT,                -- out, save model and features
+    periods          INTEGER                  -- predict length
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.prophet_predict($1, $2, $3, NULL);
+$$ LANGUAGE sql;
+
+
diff --git a/src/ports/postgres/modules/facebook_prophet/prophet_utilities.py_in b/src/ports/postgres/modules/facebook_prophet/prophet_utilities.py_in
new file mode 100644
index 0000000..2eaba82
--- /dev/null
+++ b/src/ports/postgres/modules/facebook_prophet/prophet_utilities.py_in
@@ -0,0 +1,107 @@
+# -*- coding:utf-8 -*-
+# Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+#
+# openGauss is licensed under Mulan PSL v2.
+# You can use this software according to the terms
+# and conditions of the Mulan PSL v2.
+# You may obtain a copy of Mulan PSL v2 at:
+#
+#          http://license.coscl.org.cn/MulanPSL2
+#
+# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS,
+# WITHOUT WARRANTIES OF ANY KIND,
+# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+# See the Mulan PSL v2 for more details.
+# ----------------------------------------------------------------------------
+
+import re
+import ast
+import plpy
+
+import pandas as pd
+import cPickle as pickle 
+from fbprophet import Prophet
+
+from utilities.validate_args import get_expr_type
+
+
+support_type_list = ('TINYINT', 'SMALLINT', 'INTEGER', 'BINARY_INTEGER', 'BIGINT',
+                    'NUMERIC', 'DECIMAL',
+                    'SMALLSERIAL', 'SERIAL', 'BIGSERIAL',
+                    'REAL', 'FLOAT4', 'DOUBLE PRECISION', 'FLOAT8', 'FLOAT', 'BINARY_DOUBLE', 'DEC',
+                    'BOOLEAN')
+
+
+def get_fbmodel(schema_madlib, input_table, column_map, params_str, verbose, **kwargs):
+    """
+        train prophet model and return as bytea
+        Args:
+        @param input_table,              -- name of input table
+        @param column_map,               -- map relationship of specified name of column and current name
+        @param params_str,               -- paramater of prophet fit function
+        @param verbose                   -- display log
+    """
+    if params_str is None or params_str is "" or ":" not in params_str:
+        params_str = "{}"
+        plpy.info("prophet_fit() params_str type is: " + params_str)
+
+    if column_map is None or column_map is "" or ":" not in column_map:
+        column_map = "{'ds': 'ds', 'y': 'y'}"
+        plpy.info("Prophet_fit() column_map relation is: " + column_map)
+
+    params = ast.literal_eval(re.sub("[\\t]", "", params_str).strip())
+    column_map_dict = ast.literal_eval(re.sub("[\\t]", "", column_map).strip())
+
+    y_type = check_input_type(schema_madlib, input_table, column_map_dict)
+
+    if 'holidays' in params.keys():
+        holidays = pd.DataFrame()
+        holiday_tables = params['holidays']
+        sql = """select * from {holiday_table}"""
+        for holiday_table in holiday_tables:
+            sql = sql.format(holiday_table=holiday_table)
+            if verbose:
+                plpy.info(sql)
+            result = plpy.execute(sql)
+            holiday_df = pd.DataFrame.from_records(result)
+            holidays = pd.concat((holidays, holiday_df))
+        params['holidays'] = holidays
+
+    real_columns = ', '.join([value for value in column_map_dict.values()])
+    sql = """select {real_columns} from {input_table};""".format(input_table=input_table, real_columns=real_columns)
+    if verbose:
+        plpy.info(sql)
+    
+    result = plpy.execute(sql)
+    df = pd.DataFrame.from_records(result)
+
+    # change column name to accord with prophet requirement
+    for s_name, c_name in column_map_dict.items():
+        if s_name != c_name:
+            df.rename(columns={c_name: s_name}, inplace=True)
+    model = Prophet(**params)
+    model.fit(df)
+
+    return (y_type, pickle.dumps(model), str(params))
+
+
+# ------------------------------------------------------------------------------
+# -- validate column type of input table  ------------------------------------------------------------
+# ------------------------------------------------------------------------------
+def check_input_type(schema_madlib, input_table, column_map_dict):
+    """
+    func: check the column type of input_table
+    """
+    for s_col, c_col in column_map_dict.items():
+        col_type = get_expr_type(c_col, input_table)
+        if s_col == 'ds':
+            if 'time' not in col_type.lower() and 'date' not in col_type.lower():
+                plpy.error("prophet Error: the type of {column} must be DATE".format(column=c_col))
+        else:
+            if col_type.upper() not in support_type_list:
+                plpy.error("prophet Error: the type of {column} is not supported".format(column=c_col))
+    
+    y_type = get_expr_type(column_map_dict['y'], input_table) 
+
+    return y_type
diff --git a/src/ports/postgres/modules/facebook_prophet/prophet_utilities.sql_in b/src/ports/postgres/modules/facebook_prophet/prophet_utilities.sql_in
new file mode 100644
index 0000000..4a132b4
--- /dev/null
+++ b/src/ports/postgres/modules/facebook_prophet/prophet_utilities.sql_in
@@ -0,0 +1,54 @@
+/*
+ * Portions Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+ *
+ * openGauss is licensed under Mulan PSL v2.
+ * You can use this software according to the terms and conditions of the Mulan PSL v2.
+ * You may obtain a copy of Mulan PSL v2 at:
+ *
+ *          http://license.coscl.org.cn/MulanPSL2
+ *
+ * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
+ * EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+ * MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+ * See the Mulan PSL v2 for more details.
+ * -------------------------------------------------------------------------
+ */
+
+-------------------------------------------------------------------------------------------------------------------------------
+-- Build facebook Prophet models in-database - -
+-------------------------------------------------------------------------------------------------------------------------------
+--
+-----------
+-- Note: --
+-----------
+-- 1) Porphet uses facebook-API. However we're limited by the maximum
+--    field-size in Greenplum/Postgres which is currently 1 GB.
+-- 2) If your dataset is much larger (> 1 GB), it is strongly recommended that you use MADlib's models so that
+--    training & scoring will happen in-parallel on all nodes.
+-------------------------------------------------------------------------------------------------------------------------------
+
+m4_include(`SQLCommon.m4')
+
+
+------------------------------------------------
+-- Type for internal train results
+------------------------------------------------
+DROP TYPE IF EXISTS MADLIB_SCHEMA.prophet_fit_results_type cascade;
+CREATE TYPE MADLIB_SCHEMA.prophet_fit_results_type AS(
+    y_type           TEXT,                  -- save X
+    model            bytea,                -- dump model
+    params           TEXT                  -- parameters used
+);
+
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__get_fbmodel_as_bytea__(
+    input_table  TEXT,                 -- dataset table
+    column_map   TEXT,                 -- map relation of column {'ds' 'y' 'floor' 'cap'}
+    params_str   TEXT,                 -- xgboost parameters
+    verbose      BOOLEAN
+)
+RETURNS MADLIB_SCHEMA.prophet_fit_results_type AS $$
+PythonFunction(facebook_prophet, prophet_utilities, get_fbmodel)
+$$ LANGUAGE plpythonu;
+
diff --git a/src/ports/postgres/modules/facebook_prophet/test/prophet_test.sql_in b/src/ports/postgres/modules/facebook_prophet/test/prophet_test.sql_in
new file mode 100644
index 0000000..37b8c88
--- /dev/null
+++ b/src/ports/postgres/modules/facebook_prophet/test/prophet_test.sql_in
@@ -0,0 +1,1522 @@
+/*
+* Test case of facebook-prophet
+*/
+
+m4_include(`SQLCommon.m4')
+
+select MADLIB_SCHEMA.prophet_fit();
+select MADLIB_SCHEMA.prophet_fit('usage');
+
+DROP TABLE IF EXISTS ts_data;
+CREATE TABLE ts_data (date date, value float);
+
+
+INSERT INTO ts_data (date, value) VALUES ('2016-11-29 21:20:00', 5.592186415908717);INSERT INTO ts_data (date, value) VALUES ('2016-11-29 21:30:00', 5.213219014779573);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-29 21:40:00', 5.349837267365468);INSERT INTO ts_data (date, value) VALUES ('2016-11-29 21:50:00', 5.348009861982742);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-29 22:00:00', 5.141486962759949);INSERT INTO ts_data (date, value) VALUES ('2016-11-29 22:10:00', 5.831837644472339);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-29 22:20:00', 5.586104738236943);INSERT INTO ts_data (date, value) VALUES ('2016-11-29 22:30:00', 5.424038616617589);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-29 22:40:00', 5.3953055294925);INSERT INTO ts_data (date, value) VALUES ('2016-11-29 22:50:00', 5.08062402180196);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-29 23:00:00', 5.216144429037251);INSERT INTO ts_data (date, value) VALUES ('2016-11-29 23:10:00', 5.89720541594696);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-29 23:20:00', 5.903379069573093);INSERT INTO ts_data (date, value) VALUES ('2016-11-29 23:30:00', 5.108443063954224);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-29 23:40:00', 5.829479550836606);INSERT INTO ts_data (date, value) VALUES ('2016-11-29 23:50:00', 5.983691151745764);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 00:00:00', 5.911012559550056);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 00:10:00', 5.267628873207424);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 00:20:00', 5.440281847906313);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 00:30:00', 5.056752106326658);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 00:40:00', 5.57695918188404);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 00:50:00', 5.708959203703136);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 01:00:00', 5.837530980321919);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 01:10:00', 5.397656757637807);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 01:20:00', 5.80272939233592);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 01:30:00', 5.05404686767896);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 01:40:00', 5.599526207554924);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 01:50:00', 5.624649182696436);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 02:00:00', 5.630874726351715);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 02:10:00', 5.885412123046953);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 02:20:00', 5.7281711367203965);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 02:30:00', 5.367035247234945);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 02:40:00', 5.647728469736866);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 02:50:00', 5.422412855958562);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 03:00:00', 5.084810422982988);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 03:10:00', 5.045434995298759);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 03:20:00', 5.903595563281863);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 03:30:00', 5.8464706036749226);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 03:40:00', 5.423780748843331);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 03:50:00', 5.698599390020942);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 04:00:00', 5.620828657830448);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 04:10:00', 5.853793611596745);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 04:20:00', 5.1160341760917465);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 04:30:00', 5.760091737386397);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 04:40:00', 5.547488495151962);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 04:50:00', 5.074259195264116);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 05:00:00', 5.767149486416109);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 05:10:00', 5.5412954545856525);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 05:20:00', 5.671416922868077);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 05:30:00', 5.214281328444059);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 05:40:00', 5.704061227110594);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 05:50:00', 5.981904654103126);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 06:00:00', 5.811672256762379);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 06:10:00', 5.636629064171528);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 06:20:00', 5.222889294671177);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 06:30:00', 5.7944970498250745);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 06:40:00', 5.301092523899824);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 06:50:00', 5.378620727476704);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 07:00:00', 5.832205512624812);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 07:10:00', 5.1940020098503785);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 07:20:00', 5.2801063135872415);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 07:30:00', 5.333104829752228);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 07:40:00', 5.845933760530428);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 07:50:00', 5.932520038392692);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 08:00:00', 5.64679296474034);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 08:10:00', 5.209244473298587);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 08:20:00', 5.379575393446499);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 08:30:00', 5.551810996707294);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 08:40:00', 5.98125647954102);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 08:50:00', 5.352159243180884);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 09:00:00', 5.98297448186513);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 09:10:00', 5.05840496515969);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 09:20:00', 5.071467132599093);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 09:30:00', 5.508013094801815);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 09:40:00', 5.593629750578956);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 09:50:00', 5.0073554230453405);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 10:00:00', 5.08721378732741);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 10:10:00', 5.659885556354787);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 10:20:00', 5.422996896584455);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 10:30:00', 5.430291377769443);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 10:40:00', 5.661941353506331);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 10:50:00', 5.222650073421543);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 11:00:00', 5.422560019488827);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 11:10:00', 5.2976478671030405);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 11:20:00', 5.584338761217304);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 11:30:00', 5.028576011172816);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 11:40:00', 5.247034856187782);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 11:50:00', 5.229344731201686);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 12:00:00', 5.471182531178972);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 12:10:00', 5.0949254922245055);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 12:20:00', 5.683636089958879);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 12:30:00', 5.437006581310596);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 12:40:00', 5.245413304985197);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 12:50:00', 5.522111051119028);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 13:00:00', 5.005599900215963);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 13:10:00', 5.53702445367031);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 13:20:00', 5.5766246582094405);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 13:30:00', 5.258581727612666);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 13:40:00', 5.464358328063363);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 13:50:00', 5.91011805664996);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 14:00:00', 10.88382409798199);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 14:10:00', 10.555427090136963);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 14:20:00', 10.2695546171855);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 14:30:00', 10.96434493248081);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 14:40:00', 10.005061878030595);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 14:50:00', 10.053893326397173);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 15:00:00', 10.207155270283303);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 15:10:00', 10.217068622058886);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 15:20:00', 10.2800096297076);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 15:30:00', 10.084258019727397);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 15:40:00', 10.870604507956386);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 15:50:00', 10.139519698694448);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 16:00:00', 10.966851433652065);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 16:10:00', 10.208452839253603);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 16:20:00', 10.677742584132282);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 16:30:00', 10.20991792799135);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 16:40:00', 10.243176260100649);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 16:50:00', 10.24725814854844);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 17:00:00', 10.76947018545776);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 17:10:00', 10.637158701289039);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 17:20:00', 10.531066872723857);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 17:30:00', 10.699064458359702);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 17:40:00', 10.857099725561811);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 17:50:00', 10.947585602968706);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 18:00:00', 10.072388396154338);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 18:10:00', 10.269329386566012);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 18:20:00', 10.100058130861127);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 18:30:00', 10.630305282558199);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 18:40:00', 10.276992479817986);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 18:50:00', 10.823962547747168);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 19:00:00', 10.938196297447686);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 19:10:00', 10.751352407863825);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 19:20:00', 10.55267731888166);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 19:30:00', 10.95764805135116);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 19:40:00', 10.319600419308607);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 19:50:00', 10.894597987994707);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 20:00:00', 10.561011949231316);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 20:10:00', 10.558376344084758);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 20:20:00', 10.54238813342208);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 20:30:00', 10.353533152566689);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 20:40:00', 10.94670826781977);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 20:50:00', 10.867785908808944);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 21:00:00', 10.743813978598242);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 21:10:00', 10.553624735869224);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 21:20:00', 10.48483757931147);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 21:30:00', 10.842613332127966);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 21:40:00', 10.381878265543788);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 21:50:00', 10.044634020757982);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 22:00:00', 10.606953617509358);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 22:10:00', 10.63043787557256);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 22:20:00', 10.558000303473458);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 22:30:00', 10.119618312591788);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 22:40:00', 10.420324078784883);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 22:50:00', 10.831853130351744);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 23:00:00', 10.383033393248413);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 23:10:00', 10.630842365846464);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 23:20:00', 10.076453778579774);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 23:30:00', 10.228368345858518);
+INSERT INTO ts_data (date, value) VALUES ('2016-11-30 23:40:00', 10.636636225999974);INSERT INTO ts_data (date, value) VALUES ('2016-11-30 23:50:00', 10.809387112125291);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 00:00:00', 10.555836272306514);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 00:10:00', 10.17361788915921);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 00:20:00', 10.935490280962807);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 00:30:00', 10.336876569080902);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 00:40:00', 10.315564948016368);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 00:50:00', 10.0503251516345);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 01:00:00', 10.686498994079804);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 01:10:00', 10.488270765930583);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 01:20:00', 10.382238780237266);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 01:30:00', 10.664282558119293);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 01:40:00', 10.482673868507927);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 01:50:00', 10.690901645112259);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 02:00:00', 10.808960869817462);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 02:10:00', 10.892103395445046);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 02:20:00', 10.889652181902528);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 02:30:00', 10.087235381673107);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 02:40:00', 10.354527252749081);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 02:50:00', 10.704626831711339);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 03:00:00', 10.653290677771649);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 03:10:00', 10.527049427857113);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 03:20:00', 10.193508167768368);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 03:30:00', 10.192935509184334);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-01 03:40:00', 10.807842757555397);INSERT INTO ts_data (date, value) VALUES ('2016-12-01 03:50:00', 10.224608561871785);
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+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 10:40:00', 145.52161260399842);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 10:50:00', 145.96643519774298);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 11:00:00', 145.82570031315652);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 11:10:00', 145.25411620623473);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 11:20:00', 145.01084066406216);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 11:30:00', 145.765867810619);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 11:40:00', 145.89444272758473);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 11:50:00', 145.77793331580872);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 12:00:00', 145.44588175007783);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 12:10:00', 145.49194633436278);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 12:20:00', 145.30017295055927);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 12:30:00', 145.41543052738336);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 12:40:00', 145.8074991727321);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 12:50:00', 145.12258075325624);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 13:00:00', 145.72481690483806);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 13:10:00', 145.7129788820173);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 13:20:00', 145.2621944801272);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 13:30:00', 145.8563327369375);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 13:40:00', 145.11773145469112);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 13:50:00', 145.94891778385843);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 14:00:00', 145.34019456698357);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 14:10:00', 145.65932709875548);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 14:20:00', 145.43751530410034);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 14:30:00', 145.0593460373174);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 14:40:00', 145.88811740693234);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 14:50:00', 145.90368066651718);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 15:00:00', 145.95884728201386);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 15:10:00', 145.06150896281636);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 15:20:00', 145.8622261033162);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 15:30:00', 145.83755664275444);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 15:40:00', 145.43972995819828);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 15:50:00', 145.73981228170072);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 16:00:00', 145.12681461732356);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 16:10:00', 145.012754868371);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 16:20:00', 145.0505322143747);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 16:30:00', 145.88282679938504);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 16:40:00', 145.00081151155172);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 16:50:00', 145.43198725370831);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 17:00:00', 145.82429510444118);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 17:10:00', 145.9498559447054);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 17:20:00', 145.97480554095102);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 17:30:00', 145.4446798624002);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 17:40:00', 145.2216477801595);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 17:50:00', 145.37234684968098);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 18:00:00', 145.0455193928619);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 18:10:00', 145.57137581029124);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 18:20:00', 145.97361696560858);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 18:30:00', 145.415751650966);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 18:40:00', 145.08356782962744);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 18:50:00', 145.88023605798838);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 19:00:00', 145.40523929500432);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 19:10:00', 145.3418164246789);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 19:20:00', 145.59691692201514);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 19:30:00', 145.4793346319479);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 19:40:00', 145.64966501406303);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 19:50:00', 145.50216746294907);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 20:00:00', 145.91907700356387);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 20:10:00', 145.87403548263796);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 20:20:00', 145.46946986838512);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 20:30:00', 145.4998155317617);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 20:40:00', 145.27447045101775);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 20:50:00', 145.52002992975224);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 21:00:00', 145.91042830526916);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 21:10:00', 145.5409829527971);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 21:20:00', 145.63202944838724);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 21:30:00', 145.5344594614299);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 21:40:00', 145.41555312632602);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 21:50:00', 145.13658569771513);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 22:00:00', 145.8566582624446);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 22:10:00', 145.81325233346422);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 22:20:00', 145.78274734773234);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 22:30:00', 145.0640730008673);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 22:40:00', 145.80812656708284);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 22:50:00', 145.87468388022162);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 23:00:00', 145.67072629238953);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 23:10:00', 145.88096610600363);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 23:20:00', 145.09472335198168);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 23:30:00', 145.6638566197089);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-19 23:40:00', 145.08440919685958);INSERT INTO ts_data (date, value) VALUES ('2016-12-19 23:50:00', 145.44861743475232);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 00:00:00', 145.89047384558398);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 00:10:00', 145.27914766231697);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 00:20:00', 145.69446146340147);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 00:30:00', 145.93709895795772);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 00:40:00', 150.95389038965732);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 00:50:00', 150.4695271916118);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 01:00:00', 150.71143300502908);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 01:10:00', 150.06902295783496);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 01:20:00', 150.94789146885924);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 01:30:00', 150.02560775759667);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 01:40:00', 150.46498881080538);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 01:50:00', 150.0295339298049);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 02:00:00', 150.9363084671751);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 02:10:00', 150.49309095688454);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 02:20:00', 150.42234840916416);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 02:30:00', 150.60626264187394);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 02:40:00', 150.29938954041432);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 02:50:00', 150.40643772440583);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 03:00:00', 150.29224512392312);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 03:10:00', 150.2138297054897);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 03:20:00', 150.74630354105142);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 03:30:00', 150.6907079934565);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 03:40:00', 150.03038540811488);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 03:50:00', 150.21958513347758);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 04:00:00', 150.17823386638824);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 04:10:00', 150.50507478083375);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 04:20:00', 150.79508618235164);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 04:30:00', 150.13488288529962);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 04:40:00', 150.1130873537007);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 04:50:00', 150.43758652726063);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 05:00:00', 150.5954318958787);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 05:10:00', 150.63272477805202);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 05:20:00', 150.85457355613744);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 05:30:00', 150.21404408605937);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 05:40:00', 150.34826903440288);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 05:50:00', 150.1586167071405);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 06:00:00', 150.21425811542989);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 06:10:00', 150.0486196281555);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 06:20:00', 150.33931288773363);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 06:30:00', 150.1331451778826);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 06:40:00', 150.3179776855876);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 06:50:00', 150.6026645961371);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 07:00:00', 150.63000440845244);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 07:10:00', 150.26746307195071);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 07:20:00', 150.30597385822765);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 07:30:00', 150.71097930825889);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 07:40:00', 150.2119519323343);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 07:50:00', 150.95334503876776);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 08:00:00', 150.21115641274213);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 08:10:00', 150.4672349421686);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 08:20:00', 150.35080350412545);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 08:30:00', 150.4910943472531);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 08:40:00', 150.6225395747);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 08:50:00', 150.89177789608738);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 09:00:00', 150.5461716161229);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 09:10:00', 150.63226578869234);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 09:20:00', 150.85689962949857);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 09:30:00', 150.9448742016535);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 09:40:00', 150.92408268138675);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 09:50:00', 150.73421350377888);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 10:00:00', 150.3034589100252);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 10:10:00', 150.7147463476092);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 10:20:00', 150.4921060710854);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 10:30:00', 150.0829502967536);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 10:40:00', 150.81063367796378);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 10:50:00', 150.36401854193718);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 11:00:00', 150.35446335237458);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 11:10:00', 150.23659343990658);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 11:20:00', 150.9489373250949);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 11:30:00', 150.72168461362784);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 11:40:00', 150.98344616973552);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 11:50:00', 150.90325970412428);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 12:00:00', 150.6322081841063);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 12:10:00', 150.07288034629926);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 12:20:00', 150.2769904157787);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 12:30:00', 150.45689512291858);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 12:40:00', 150.8623556168554);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 12:50:00', 150.65527174054034);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 13:00:00', 150.4105151231106);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 13:10:00', 150.3992746961309);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 13:20:00', 150.2225099991635);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 13:30:00', 150.6253682325025);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 13:40:00', 150.15362327609586);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 13:50:00', 150.45290266139548);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 14:00:00', 150.80716539465936);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 14:10:00', 150.53582530927787);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 14:20:00', 150.03001506957418);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 14:30:00', 150.29875278657747);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 14:40:00', 150.0375390473902);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 14:50:00', 150.30909323458138);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 15:00:00', 150.9957959535933);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 15:10:00', 150.5915194444149);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 15:20:00', 150.37621528200074);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 15:30:00', 150.93192625156226);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 15:40:00', 150.40413833750725);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 15:50:00', 150.49369775455642);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 16:00:00', 150.17480087886327);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 16:10:00', 150.30585649477317);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 16:20:00', 150.44827829973516);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 16:30:00', 150.1010045943311);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 16:40:00', 150.778458080293);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 16:50:00', 150.91295092252244);
+INSERT INTO ts_data (date, value) VALUES ('2016-12-20 17:00:00', 150.09800267705262);INSERT INTO ts_data (date, value) VALUES ('2016-12-20 17:10:00', 150.28707660560792);
+
+
+DROP TABLE IF EXISTS prophet_model;
+SELECT MADLIB_SCHEMA.prophet_fit('ts_data', 'prophet_model', $${'ds':'date', 'y': 'value'}$$, $${'growth': 'linear' , 'changepoints': ['2016-11-30 00:20:00', '2016-11-30 05:40:00']}$$, True);
+SELECT ASSERT(count(*) = 1, 'prophet: prophet parameter counts error') FROM prophet_model;
+
+DROP TABLE IF EXISTS prophet_output;
+SELECT MADLIB_SCHEMA.prophet_predict('prophet_model', 'prophet_output', 1008, '10T', NULL, NULL, False);
+
diff --git a/src/ports/postgres/modules/facebook_prophet/test/prophet_test2.sql_in b/src/ports/postgres/modules/facebook_prophet/test/prophet_test2.sql_in
new file mode 100644
index 0000000..3d02aa2
--- /dev/null
+++ b/src/ports/postgres/modules/facebook_prophet/test/prophet_test2.sql_in
@@ -0,0 +1,99 @@
+/*
+* Test case of facebook-prophet
+*/
+
+m4_include(`SQLCommon.m4')
+
+select MADLIB_SCHEMA.prophet_fit();
+select MADLIB_SCHEMA.prophet_fit('usage');
+
+DROP TABLE IF EXISTS ts_data2;
+CREATE TABLE ts_data2 (date date, value float);
+
+
+INSERT INTO ts_data2 (date, value) VALUES ('1949-01-01', 112);INSERT INTO ts_data2 (date, value) VALUES ('1949-02-01', 118);
+INSERT INTO ts_data2 (date, value) VALUES ('1949-03-01', 132);INSERT INTO ts_data2 (date, value) VALUES ('1949-04-01', 129);
+INSERT INTO ts_data2 (date, value) VALUES ('1949-05-01', 121);INSERT INTO ts_data2 (date, value) VALUES ('1949-06-01', 135);
+INSERT INTO ts_data2 (date, value) VALUES ('1949-07-01', 148);INSERT INTO ts_data2 (date, value) VALUES ('1949-08-01', 148);
+INSERT INTO ts_data2 (date, value) VALUES ('1949-09-01', 136);INSERT INTO ts_data2 (date, value) VALUES ('1949-10-01', 119);
+INSERT INTO ts_data2 (date, value) VALUES ('1949-11-01', 104);INSERT INTO ts_data2 (date, value) VALUES ('1949-12-01', 118);
+INSERT INTO ts_data2 (date, value) VALUES ('1950-01-01', 115);INSERT INTO ts_data2 (date, value) VALUES ('1950-02-01', 126);
+INSERT INTO ts_data2 (date, value) VALUES ('1950-03-01', 141);INSERT INTO ts_data2 (date, value) VALUES ('1950-04-01', 135);
+INSERT INTO ts_data2 (date, value) VALUES ('1950-05-01', 125);INSERT INTO ts_data2 (date, value) VALUES ('1950-06-01', 149);
+INSERT INTO ts_data2 (date, value) VALUES ('1950-07-01', 170);INSERT INTO ts_data2 (date, value) VALUES ('1950-08-01', 170);
+INSERT INTO ts_data2 (date, value) VALUES ('1950-09-01', 158);INSERT INTO ts_data2 (date, value) VALUES ('1950-10-01', 133);
+INSERT INTO ts_data2 (date, value) VALUES ('1950-11-01', 114);INSERT INTO ts_data2 (date, value) VALUES ('1950-12-01', 140);
+INSERT INTO ts_data2 (date, value) VALUES ('1951-01-01', 145);INSERT INTO ts_data2 (date, value) VALUES ('1951-02-01', 150);
+INSERT INTO ts_data2 (date, value) VALUES ('1951-03-01', 178);INSERT INTO ts_data2 (date, value) VALUES ('1951-04-01', 163);
+INSERT INTO ts_data2 (date, value) VALUES ('1951-05-01', 172);INSERT INTO ts_data2 (date, value) VALUES ('1951-06-01', 178);
+INSERT INTO ts_data2 (date, value) VALUES ('1951-07-01', 199);INSERT INTO ts_data2 (date, value) VALUES ('1951-08-01', 199);
+INSERT INTO ts_data2 (date, value) VALUES ('1951-09-01', 184);INSERT INTO ts_data2 (date, value) VALUES ('1951-10-01', 162);
+INSERT INTO ts_data2 (date, value) VALUES ('1951-11-01', 146);INSERT INTO ts_data2 (date, value) VALUES ('1951-12-01', 166);
+INSERT INTO ts_data2 (date, value) VALUES ('1952-01-01', 171);INSERT INTO ts_data2 (date, value) VALUES ('1952-02-01', 180);
+INSERT INTO ts_data2 (date, value) VALUES ('1952-03-01', 193);INSERT INTO ts_data2 (date, value) VALUES ('1952-04-01', 181);
+INSERT INTO ts_data2 (date, value) VALUES ('1952-05-01', 183);INSERT INTO ts_data2 (date, value) VALUES ('1952-06-01', 218);
+INSERT INTO ts_data2 (date, value) VALUES ('1952-07-01', 230);INSERT INTO ts_data2 (date, value) VALUES ('1952-08-01', 242);
+INSERT INTO ts_data2 (date, value) VALUES ('1952-09-01', 209);INSERT INTO ts_data2 (date, value) VALUES ('1952-10-01', 191);
+INSERT INTO ts_data2 (date, value) VALUES ('1952-11-01', 172);INSERT INTO ts_data2 (date, value) VALUES ('1952-12-01', 194);
+INSERT INTO ts_data2 (date, value) VALUES ('1953-01-01', 196);INSERT INTO ts_data2 (date, value) VALUES ('1953-02-01', 196);
+INSERT INTO ts_data2 (date, value) VALUES ('1953-03-01', 236);INSERT INTO ts_data2 (date, value) VALUES ('1953-04-01', 235);
+INSERT INTO ts_data2 (date, value) VALUES ('1953-05-01', 229);INSERT INTO ts_data2 (date, value) VALUES ('1953-06-01', 243);
+INSERT INTO ts_data2 (date, value) VALUES ('1953-07-01', 264);INSERT INTO ts_data2 (date, value) VALUES ('1953-08-01', 272);
+INSERT INTO ts_data2 (date, value) VALUES ('1953-09-01', 237);INSERT INTO ts_data2 (date, value) VALUES ('1953-10-01', 211);
+INSERT INTO ts_data2 (date, value) VALUES ('1953-11-01', 180);INSERT INTO ts_data2 (date, value) VALUES ('1953-12-01', 201);
+INSERT INTO ts_data2 (date, value) VALUES ('1954-01-01', 204);INSERT INTO ts_data2 (date, value) VALUES ('1954-02-01', 188);
+INSERT INTO ts_data2 (date, value) VALUES ('1954-03-01', 235);INSERT INTO ts_data2 (date, value) VALUES ('1954-04-01', 227);
+INSERT INTO ts_data2 (date, value) VALUES ('1954-05-01', 234);INSERT INTO ts_data2 (date, value) VALUES ('1954-06-01', 264);
+INSERT INTO ts_data2 (date, value) VALUES ('1954-07-01', 302);INSERT INTO ts_data2 (date, value) VALUES ('1954-08-01', 293);
+INSERT INTO ts_data2 (date, value) VALUES ('1954-09-01', 259);INSERT INTO ts_data2 (date, value) VALUES ('1954-10-01', 229);
+INSERT INTO ts_data2 (date, value) VALUES ('1954-11-01', 203);INSERT INTO ts_data2 (date, value) VALUES ('1954-12-01', 229);
+INSERT INTO ts_data2 (date, value) VALUES ('1955-01-01', 242);INSERT INTO ts_data2 (date, value) VALUES ('1955-02-01', 233);
+INSERT INTO ts_data2 (date, value) VALUES ('1955-03-01', 267);INSERT INTO ts_data2 (date, value) VALUES ('1955-04-01', 269);
+INSERT INTO ts_data2 (date, value) VALUES ('1955-05-01', 270);INSERT INTO ts_data2 (date, value) VALUES ('1955-06-01', 315);
+INSERT INTO ts_data2 (date, value) VALUES ('1955-07-01', 364);INSERT INTO ts_data2 (date, value) VALUES ('1955-08-01', 347);
+INSERT INTO ts_data2 (date, value) VALUES ('1955-09-01', 312);INSERT INTO ts_data2 (date, value) VALUES ('1955-10-01', 274);
+INSERT INTO ts_data2 (date, value) VALUES ('1955-11-01', 237);INSERT INTO ts_data2 (date, value) VALUES ('1955-12-01', 278);
+INSERT INTO ts_data2 (date, value) VALUES ('1956-01-01', 284);INSERT INTO ts_data2 (date, value) VALUES ('1956-02-01', 277);
+INSERT INTO ts_data2 (date, value) VALUES ('1956-03-01', 317);INSERT INTO ts_data2 (date, value) VALUES ('1956-04-01', 313);
+INSERT INTO ts_data2 (date, value) VALUES ('1956-05-01', 318);INSERT INTO ts_data2 (date, value) VALUES ('1956-06-01', 374);
+INSERT INTO ts_data2 (date, value) VALUES ('1956-07-01', 413);INSERT INTO ts_data2 (date, value) VALUES ('1956-08-01', 405);
+INSERT INTO ts_data2 (date, value) VALUES ('1956-09-01', 355);INSERT INTO ts_data2 (date, value) VALUES ('1956-10-01', 306);
+INSERT INTO ts_data2 (date, value) VALUES ('1956-11-01', 271);INSERT INTO ts_data2 (date, value) VALUES ('1956-12-01', 306);
+INSERT INTO ts_data2 (date, value) VALUES ('1957-01-01', 315);INSERT INTO ts_data2 (date, value) VALUES ('1957-02-01', 301);
+INSERT INTO ts_data2 (date, value) VALUES ('1957-03-01', 356);INSERT INTO ts_data2 (date, value) VALUES ('1957-04-01', 348);
+INSERT INTO ts_data2 (date, value) VALUES ('1957-05-01', 355);INSERT INTO ts_data2 (date, value) VALUES ('1957-06-01', 422);
+INSERT INTO ts_data2 (date, value) VALUES ('1957-07-01', 465);INSERT INTO ts_data2 (date, value) VALUES ('1957-08-01', 467);
+INSERT INTO ts_data2 (date, value) VALUES ('1957-09-01', 404);INSERT INTO ts_data2 (date, value) VALUES ('1957-10-01', 347);
+INSERT INTO ts_data2 (date, value) VALUES ('1957-11-01', 305);INSERT INTO ts_data2 (date, value) VALUES ('1957-12-01', 336);
+INSERT INTO ts_data2 (date, value) VALUES ('1958-01-01', 340);INSERT INTO ts_data2 (date, value) VALUES ('1958-02-01', 318);
+INSERT INTO ts_data2 (date, value) VALUES ('1958-03-01', 362);INSERT INTO ts_data2 (date, value) VALUES ('1958-04-01', 348);
+INSERT INTO ts_data2 (date, value) VALUES ('1958-05-01', 363);INSERT INTO ts_data2 (date, value) VALUES ('1958-06-01', 435);
+INSERT INTO ts_data2 (date, value) VALUES ('1958-07-01', 491);INSERT INTO ts_data2 (date, value) VALUES ('1958-08-01', 505);
+INSERT INTO ts_data2 (date, value) VALUES ('1958-09-01', 404);INSERT INTO ts_data2 (date, value) VALUES ('1958-10-01', 359);
+INSERT INTO ts_data2 (date, value) VALUES ('1958-11-01', 310);INSERT INTO ts_data2 (date, value) VALUES ('1958-12-01', 337);
+INSERT INTO ts_data2 (date, value) VALUES ('1959-01-01', 360);INSERT INTO ts_data2 (date, value) VALUES ('1959-02-01', 342);
+INSERT INTO ts_data2 (date, value) VALUES ('1959-03-01', 406);INSERT INTO ts_data2 (date, value) VALUES ('1959-04-01', 396);
+INSERT INTO ts_data2 (date, value) VALUES ('1959-05-01', 420);INSERT INTO ts_data2 (date, value) VALUES ('1959-06-01', 472);
+INSERT INTO ts_data2 (date, value) VALUES ('1959-07-01', 548);INSERT INTO ts_data2 (date, value) VALUES ('1959-08-01', 559);
+INSERT INTO ts_data2 (date, value) VALUES ('1959-09-01', 463);INSERT INTO ts_data2 (date, value) VALUES ('1959-10-01', 407);
+INSERT INTO ts_data2 (date, value) VALUES ('1959-11-01', 362);INSERT INTO ts_data2 (date, value) VALUES ('1959-12-01', 405);
+INSERT INTO ts_data2 (date, value) VALUES ('1960-01-01', 417);INSERT INTO ts_data2 (date, value) VALUES ('1960-02-01', 391);
+INSERT INTO ts_data2 (date, value) VALUES ('1960-03-01', 419);INSERT INTO ts_data2 (date, value) VALUES ('1960-04-01', 461);
+INSERT INTO ts_data2 (date, value) VALUES ('1960-05-01', 472);INSERT INTO ts_data2 (date, value) VALUES ('1960-06-01', 535);
+INSERT INTO ts_data2 (date, value) VALUES ('1960-07-01', 622);INSERT INTO ts_data2 (date, value) VALUES ('1960-08-01', 606);
+INSERT INTO ts_data2 (date, value) VALUES ('1960-09-01', 508);INSERT INTO ts_data2 (date, value) VALUES ('1960-10-01', 461);
+INSERT INTO ts_data2 (date, value) VALUES ('1960-11-01', 390);INSERT INTO ts_data2 (date, value) VALUES ('1960-12-01', 432);
+
+
+ALTER TABLE ts_data2 ADD COLUMN cap FLOAT;
+ALTER TABLE ts_data2 ADD COLUMN floor FLOAT;
+UPDATE ts_data2 SET cap=600;
+UPDATE ts_data2 SET floor=0;
+
+DROP TABLE IF EXISTS prophet_model2;
+SELECT MADLIB_SCHEMA.prophet_fit('ts_data2', 'prophet_model2', $${'ds':'date', 'y': 'value', 'cap': 'cap', 'floor': 'floor'}$$, $${'growth': 'logistic'}$$, True);
+SELECT ASSERT(count(*) = 1, 'prophet: prophet parameter counts error') FROM prophet_model2;
+
+DROP TABLE IF EXISTS prophet_output;
+SELECT MADLIB_SCHEMA.prophet_predict('prophet_model2', 'prophet_output2', 10, '1D', 600, 0, False);
+
diff --git a/src/ports/postgres/modules/gbdt/__init__.py_in b/src/ports/postgres/modules/gbdt/__init__.py_in
new file mode 100644
index 0000000..e69de29
diff --git a/src/ports/postgres/modules/gbdt/gbdt.py_in b/src/ports/postgres/modules/gbdt/gbdt.py_in
new file mode 100644
index 0000000..4004c65
--- /dev/null
+++ b/src/ports/postgres/modules/gbdt/gbdt.py_in
@@ -0,0 +1,306 @@
+# -*- coding:utf-8 -*-
+# Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+#
+# openGauss is licensed under Mulan PSL v2.
+# You can use this software according to the terms
+# and conditions of the Mulan PSL v2.
+# You may obtain a copy of Mulan PSL v2 at:
+#
+#          http://license.coscl.org.cn/MulanPSL2
+#
+# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS,
+# WITHOUT WARRANTIES OF ANY KIND,
+# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+# See the Mulan PSL v2 for more details.
+# ----------------------------------------------------------------------------
+
+import numpy as np
+import plpy
+from math import sqrt, ceil
+
+from utilities.control import MinWarning
+from utilities.utilities import _assert
+from utilities.utilities import extract_keyvalue_params
+from utilities.validate_args import get_expr_type
+
+from recursive_partitioning.decision_tree import _tree_train_using_bins
+from recursive_partitioning.decision_tree import _get_features_to_use
+from recursive_partitioning.decision_tree import _classify_features
+from recursive_partitioning.decision_tree import _get_filter_str
+from recursive_partitioning.decision_tree import _get_bins
+from recursive_partitioning.decision_tree import get_feature_str
+
+
+
+def gdbt_mean_square_error(y_pred, y):
+    y_pred = np.reshape(y_pred,[-1])
+    y = np.reshape(y,[-1])
+    return 0.5 * np.square(y_pred - y) / y.shape[0]
+
+def create_model_table(schema_madlib,output_table_name,tree_state, bins, tree_iter):
+    head = ''
+    if tree_iter == 0:
+        head = """CREATE TABLE {output_table_name} AS""".format(output_table_name = output_table_name)
+    else:
+        head = "INSERT INTO " + output_table_name
+
+    depth = (tree_state['pruned_depth'] if 'pruned_depth' in tree_state
+             else tree_state['tree_depth'])
+
+    sql = head + """ SELECT {tree_iter} as iteration, 
+            $1 AS tree,
+            $2 as cat_levels_in_text,
+            $3 as cat_n_levels,
+            {depth} as tree_depth
+            """.format(tree_iter = tree_iter, depth=depth)
+
+    sql_plan = plpy.prepare(sql, ['{0}.bytea8'.format(schema_madlib), 'text[]','integer[]'])
+    plpy.execute(sql_plan,[tree_state['tree_state'],bins['cat_origin'],bins['cat_n']])
+    return None
+
+def create_summary_table(output_table, null_proxy, cat_f, con_f, learning_rate, is_classification, predict_dt_prob, num_trees, source_table):
+    summary_table_name = output_table + '_summary'
+    null_proxy_str="NULL" if null_proxy is None else "'{0}'".format(null_proxy)
+    cat_f = ','.join(cat_f)
+    con_f = ','.join(con_f)
+    create_sql = """CREATE TABLE {summary_table_name} AS SELECT 
+                    'GBDT'::TEXT as method,
+                    '{cat_f}'::text     AS cat_features,
+                    '{con_f}'::text     AS con_features,
+                    '{source_table}'::text AS source_table,
+                    '{output_table}'::text AS model_table,
+                    {null_proxy_str}::text AS null_proxy,
+                    {learning_rate}::double precision AS learning_rate,
+                    {is_classification}::BOOLEAN AS is_classification,
+                    '{predict_dt_prob}'::text  AS predict_dt_prob,
+                    {num_trees}::INTEGER AS num_trees
+                """.format(**locals())
+    plpy.execute(create_sql)
+
+def create_test_table(test_result_table, id, y_pred):
+    plpy.execute( """CREATE TABLE {test_result_table} AS SELECT 
+            unnest(ARRAY{id}) as id,
+            unnest(ARRAY{y_pred}) as test_prediction
+        """.format(**locals()))
+
+def tree_fit(schema_madlib, bins, training_table_name, cat_features, con_features,
+                boolean_cats, num_bins,weights,
+                dep, min_split, min_bucket,
+                max_tree_depth, filter_null, dep_n_levels,
+                split_criterion,
+                num_random_features,
+                max_n_surr,null_proxy,verbose_mode=False,**kwargs):
+
+    msg_level = "notice" if verbose_mode else "warning"
+
+    with MinWarning(msg_level):
+        tree = _tree_train_using_bins(
+            schema_madlib, bins, training_table_name, cat_features, con_features,
+            boolean_cats, num_bins, weights, dep, min_split,
+            min_bucket, max_tree_depth, filter_null, dep_n_levels,
+            False, split_criterion, True,
+            num_random_features, max_n_surr, null_proxy)
+
+        tree['grp_key'] = ' '
+    return tree
+        
+def tree_gradient_predict(schema_madlib, model_table,iteration, id_col_name, cat_features_str,
+                            con_features_str, is_classification, predict_dt_prob, source_table):
+    id = []
+    res = []
+
+    if not is_classification or (predict_dt_prob == "response"):
+        sql = """ SELECT 
+                {id_col_name} as id,
+                {schema_madlib}._predict_dt_response(
+                tree, 
+                {cat_features_str}::INTEGER[],
+                {con_features_str}::DOUBLE PRECISION[]) as gradient_prediction
+            FROM (
+                SELECT * FROM {model_table} 
+                WHERE iteration = {iteration}) as m,
+                {source_table}""".format(**locals())
+    else :
+        sql = """ SELECT 
+                {id_col_name} as id,
+                {schema_madlib}._predict_dt_prob(
+                tree, 
+                {cat_features_str}::INTEGER[],
+                {con_features_str}::DOUBLE PRECISION[]) as gradient_prediction
+            FROM (
+                SELECT * FROM {model_table} 
+                WHERE iteration = {iteration}) as m,
+                {source_table}""".format(**locals())
+    rows = plpy.execute(sql)
+    for r in rows:
+        id.append(r['id'])
+        res.append(r['gradient_prediction'])   
+    return id , res
+
+def _get_gradient(y, y_pred, id, iteration, y_table_name):
+    # 1.calculate the loss
+    compute_loss = gdbt_mean_square_error(y_pred, y)
+
+    # 2.calculate the gradient
+    cal_gradient = (np.gradient(compute_loss)).tolist()
+
+    # 3.add column called gradient to source_table
+    if iteration == 1:
+        addCol_sql = """alter table {y_table_name} add gradient double precision""".format(**locals())
+        plpy.execute(addCol_sql)
+    
+    update_sql = """UPDATE {y_table_name} SET gradient = temp_gradient FROM (SELECT unnest(ARRAY{cal_gradient}) as temp_gradient,
+                    unnest(ARRAY{id}) as temp_id)q WHERE  id = temp_id""".format(**locals())
+    plpy.execute(update_sql)
+    return cal_gradient
+
+def gbdt_fit(schema_madlib,training_table_name, output_table_name,
+        id_col_name, dependent_variable, list_of_features,
+        list_of_features_to_exclude, weights,
+        num_trees, num_random_features,
+        max_tree_depth, min_split, min_bucket, num_bins,
+        null_handling_params, is_classification,
+        predict_dt_prob = None, learning_rate = None, 
+        verbose=False, **kwargs):
+
+    # 1. default paremeters
+    num_trees = 100 if num_trees is None else num_trees
+    weights = '1' if not weights or not weights.strip() else weights.strip()
+    max_tree_depth = 10 if max_tree_depth is None else max_tree_depth
+    min_split = 20 if min_split is None and min_bucket is None else min_split
+    min_bucket = min_split // 3 if not min_bucket else min_bucket
+    min_split = min_bucket * 3 if not min_split else min_split
+    num_bins = 100 if num_bins is None else num_bins
+    learning_rate = 0.01 if learning_rate is None else learning_rate
+    if predict_dt_prob not in ["response", "prob", None]:
+        plpy.error("""
+                invalid choice: '{predict_dt_prob}' (choose from 'response', 'prob')
+                """.format(predict_dt_prob= predict_dt_prob))
+    predict_dt_prob = "response" if predict_dt_prob is None else predict_dt_prob
+
+    null_handling_dict = extract_keyvalue_params(
+        null_handling_params,
+        dict(max_surrogates=int, null_as_category=bool),
+        dict(max_surrogates=0, null_as_category=False))
+    max_n_surr = null_handling_dict['max_surrogates']
+    null_as_category = null_handling_dict['null_as_category']
+    null_proxy = "__NULL__" if null_as_category else None
+    if null_as_category:
+        # can't have two ways of handling tuples with NULL values
+        max_n_surr = 0
+    _assert(max_n_surr >= 0,
+            "Maximum number of surrogates ({0}) should be non-negative".
+            format(max_n_surr))
+
+    # preprocess arguments
+    # expand "*" syntax and exclude some features
+    features = _get_features_to_use(schema_madlib,
+            training_table_name, list_of_features, list_of_features_to_exclude,
+            id_col_name, '1', dependent_variable, None)
+            
+    split_criterion = 'mse'
+    if num_random_features is None:
+        n_all_features = len(features)
+        num_random_features = int(sqrt(n_all_features) if is_classification else ceil(float(n_all_features) / 3))
+
+    _assert(0 < num_random_features <= len(features),
+        "GBDT error: Number of features to be selected "
+        "is more than the actual number of features.")
+
+    all_cols_types = dict([(f, get_expr_type(f, training_table_name))
+                    for f in features])
+    cat_features, ordered_cat_features, boolean_cats, con_features = \
+        _classify_features(all_cols_types, features)
+
+    
+    filter_null = _get_filter_str(dependent_variable, None)
+
+    n_rows = plpy.execute("SELECT count(*) FROM {0} WHERE {1}".format(training_table_name, filter_null))[0]['count']
+    dep_n_levels = 1
+
+    bins = _get_bins(schema_madlib, training_table_name,
+            cat_features, ordered_cat_features,
+            con_features, num_bins, dependent_variable,
+            boolean_cats, n_rows, False,
+            dep_n_levels, filter_null, null_proxy)
+    bins['grp_key_cat'] = ['']
+    cat_features = bins['cat_features']
+
+    tree = tree_fit(schema_madlib, bins, training_table_name, cat_features, con_features,
+                boolean_cats, num_bins,weights,
+                dependent_variable, min_split, min_bucket,
+                max_tree_depth, filter_null, dep_n_levels,
+                split_criterion,
+                num_random_features,
+                max_n_surr,null_proxy)
+    create_model_table(schema_madlib, output_table_name,tree, bins, 0)
+
+    cat_features_str, con_features_str = get_feature_str(schema_madlib,
+                                                         training_table_name,
+                                                         cat_features,
+                                                         con_features,
+                                                         "m.cat_levels_in_text", "m.cat_n_levels",
+                                                         null_proxy)
+
+    id, y_pred = tree_gradient_predict(schema_madlib,output_table_name,0, id_col_name, cat_features_str,
+    con_features_str,is_classification,predict_dt_prob, training_table_name)
+
+    y_res = plpy.execute("""SELECT {dependent_variable} AS y FROM {training_table_name}""".format(**locals()))
+    y = []
+    for row in y_res:
+        y.append(row['y'])
+
+    for sample_id in range(1, num_trees):
+        gradient = _get_gradient(y , y_pred, id, sample_id, training_table_name)
+        
+        tree = tree_fit (schema_madlib, bins, training_table_name, cat_features, con_features,
+                boolean_cats, num_bins, weights, 
+                'gradient', min_split, min_bucket,
+                max_tree_depth, filter_null, dep_n_levels,
+                split_criterion,
+                num_random_features,
+                max_n_surr,null_proxy)
+
+        create_model_table(schema_madlib, output_table_name,tree, bins, sample_id)
+        id, y_temp = tree_gradient_predict(schema_madlib,output_table_name,sample_id, id_col_name, cat_features_str,
+        con_features_str,is_classification, predict_dt_prob,training_table_name)
+        y_pred -= np.multiply(learning_rate, y_temp)
+    
+    plpy.execute("""ALTER TABLE {training_table_name} DROP COLUMN IF EXISTS gradient CASCADE
+                """.format(training_table_name=training_table_name))
+                            
+    create_summary_table(output_table_name, null_proxy, bins['cat_features'], 
+                        bins['con_features'], learning_rate, is_classification, predict_dt_prob, 
+                        num_trees, training_table_name)
+
+
+
+def gbdt_predict(schema_madlib, test_table_name, model_table_name, output_table_name, id_col_name, **kwargs):
+
+    num_tree = plpy.execute("""SELECT COUNT(*) AS count FROM {model_table_name}""".format(**locals()))[0]['count']
+    if num_tree == 0:
+        plpy.error("The GBDT-method has no trees")
+    
+
+    elements = plpy.execute("""SELECT * FROM {model_table_name}_summary""".format(**locals()))[0]
+    cat_features = [] if not elements['cat_features'] else elements['cat_features'].split(',')
+    con_features = [] if not elements['con_features'] else elements['con_features'].split(',')
+    null_proxy = elements['null_proxy']
+    learning_rate = elements['learning_rate']
+    is_classification = elements['is_classification']
+    predict_dt_prob = elements['predict_dt_prob']
+
+    cat_features_str, con_features_str = get_feature_str(
+        schema_madlib, test_table_name, cat_features, con_features, "m.cat_levels_in_text", "m.cat_n_levels", null_proxy)
+    
+    id, y_pred = tree_gradient_predict(schema_madlib,model_table_name ,0, id_col_name, cat_features_str,
+        con_features_str,is_classification,predict_dt_prob,test_table_name)
+
+    for i in range(1, num_tree):
+        _, gradient = tree_gradient_predict(schema_madlib,model_table_name , i, id_col_name, cat_features_str,
+        con_features_str,is_classification,predict_dt_prob,test_table_name)
+        y_pred -= np.multiply(learning_rate, gradient)
+    if is_classification:
+        y_pred = np.around(y_pred)
+    create_test_table(output_table_name, id, y_pred.tolist())
\ No newline at end of file
diff --git a/src/ports/postgres/modules/gbdt/gbdt.sql_in b/src/ports/postgres/modules/gbdt/gbdt.sql_in
new file mode 100644
index 0000000..70da99c
--- /dev/null
+++ b/src/ports/postgres/modules/gbdt/gbdt.sql_in
@@ -0,0 +1,332 @@
+/*
+ * Portions Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+ *
+ * openGauss is licensed under Mulan PSL v2.
+ * You can use this software according to the terms and conditions of the Mulan PSL v2.
+ * You may obtain a copy of Mulan PSL v2 at:
+ *
+ *          http://license.coscl.org.cn/MulanPSL2
+ *
+ * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
+ * EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+ * MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+ * See the Mulan PSL v2 for more details.
+ * -------------------------------------------------------------------------
+ */
+
+m4_include(`SQLCommon.m4')
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER,
+    min_split                   INTEGER,
+    min_bucket                  INTEGER,
+    num_bins                    INTEGER,
+    null_handling_params        TEXT,
+    is_classification           BOOLEAN,
+    predict_dt_prob             TEXT,
+    learning_rate               DOUBLE PRECISION,
+    verbose                     BOOLEAN,
+    sample_ratio                DOUBLE PRECISION
+)
+RETURNS VOID AS $$
+PythonFunction(gbdt, gbdt, gbdt_fit)
+$$ LANGUAGE plpythonu VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER,
+    min_split                   INTEGER,
+    min_bucket                  INTEGER,
+    num_bins                    INTEGER,
+    null_handling_params        TEXT,
+    is_classification           BOOLEAN,
+    predict_dt_prob             TEXT,
+    learning_rate               DOUBLE PRECISION,
+    verbose                     BOOLEAN
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 
+                                        $11, $12, $13, $14, $15, $16, $17, $18, NULL);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER,
+    min_split                   INTEGER,
+    min_bucket                  INTEGER,
+    num_bins                    INTEGER,
+    null_handling_params        TEXT,
+    is_classification           BOOLEAN,
+    predict_dt_prob             TEXT,
+    learning_rate               DOUBLE PRECISION
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 
+                                        $11, $12, $13, $14, $15, $16, $17, FALSE::BOOLEAN);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER,
+    min_split                   INTEGER,
+    min_bucket                  INTEGER,
+    num_bins                    INTEGER,
+    null_handling_params        TEXT,
+    is_classification           BOOLEAN,
+    predict_dt_prob             TEXT
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 
+                                        $11, $12, $13, $14, $15, $16, NULL);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER,
+    min_split                   INTEGER,
+    min_bucket                  INTEGER,
+    num_bins                    INTEGER,
+    null_handling_params        TEXT,
+    is_classification           BOOLEAN
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 
+                                        $11, $12, $13, $14, $15, NULL);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER,
+    min_split                   INTEGER,
+    min_bucket                  INTEGER,
+    num_bins                    INTEGER,
+    null_handling_params        TEXT
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 
+                                        $11, $12, $13, $14, FALSE);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER,
+    min_split                   INTEGER,
+    min_bucket                  INTEGER,
+    num_bins                    INTEGER
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 
+                                        $11, $12, $13, NULL::TEXT);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER,
+    min_split                   INTEGER,
+    min_bucket                  INTEGER
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 
+                                        $11, $12, 100::INTEGER);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER,
+    min_split                   INTEGER
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 
+                                        $11, ($11/3)::INTEGER);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER,
+    max_tree_depth              INTEGER
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 
+                                        20::INTEGER);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER,
+    num_random_features         INTEGER
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, $9, 1::INTEGER);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT,
+    num_trees                   INTEGER
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, $8, NULL::INTEGER);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT,
+    weights                     TEXT
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, $7, 30::INTEGER);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT,
+    list_of_features_to_exclude TEXT
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, $6, NULL::TEXT);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_train(
+    training_table_name         TEXT,
+    output_table_name           TEXT,
+    id_col_name                 TEXT,
+    dependent_variable          TEXT,
+    list_of_features            TEXT
+)
+RETURNS VOID AS $$
+    SELECT MADLIB_SCHEMA.gbdt_train($1, $2, $3, $4, $5, NULL::TEXT);
+$$ LANGUAGE sql VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
+
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gbdt_predict(
+    test_table_name        TEXT,
+    model_table_name       TEXT,
+    output_table_name      TEXT,
+    id_col_name            TEXT
+) RETURNS VOID AS $$
+PythonFunction(gbdt, gbdt, gbdt_predict)
+$$ LANGUAGE plpythonu VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
\ No newline at end of file
diff --git a/src/ports/postgres/modules/gbdt/test/gbdt.sql b/src/ports/postgres/modules/gbdt/test/gbdt.sql
new file mode 100644
index 0000000..1bdbd28
--- /dev/null
+++ b/src/ports/postgres/modules/gbdt/test/gbdt.sql
@@ -0,0 +1,59 @@
+DROP TABLE IF EXISTS dt_golf CASCADE;
+DROP TABLE IF EXISTS train_output,train_output_summary;
+CREATE TABLE dt_golf (
+    id integer NOT NULL,
+    "OUTLOOK" text,
+    temperature double precision,
+    humidity double precision,
+    "Cont_features" double precision[],
+    cat_features text[],
+    windy boolean,
+    class integer
+) m4_ifdef(`__COLUMNS__', `WITH(ORIENTATION=COLUMN)', `');
+
+INSERT INTO dt_golf (id,"OUTLOOK",temperature,humidity,"Cont_features",cat_features, windy,class) VALUES
+(1, 'sunny', 85, 85,ARRAY[85, 85], ARRAY['a', 'b'], false, 0),
+(2, 'sunny', 80, 90, ARRAY[80, 90], ARRAY['a', 'b'], true, 0),
+(3, 'overcast', 83, 78, ARRAY[83, 78], ARRAY['a', 'b'], false, 1),
+(4, 'rain', 70, NULL, ARRAY[70, 96], ARRAY['a', 'b'], false, 1),
+(5, 'rain', 68, 80, ARRAY[68, 80], ARRAY['a', 'b'], false, 1),
+(6, 'rain', NULL, 70, ARRAY[65, 70], ARRAY['a', 'b'], true, 0),
+(7, 'overcast', 64, 65, ARRAY[64, 65], ARRAY['c', 'b'], NULL , 1),
+(8, 'sunny', 72, 95, ARRAY[72, 95], ARRAY['a', 'b'], false, 0),
+(9, 'sunny', 69, 70, ARRAY[69, 70], ARRAY['a', 'b'], false, 1),
+(10, 'rain', 75, 80, ARRAY[75, 80], ARRAY['a', 'b'], false, 1),
+(11, 'sunny', 75, 70, ARRAY[75, 70], ARRAY['a', 'd'], true, 1),
+(12, 'overcast', 72, 90, ARRAY[72, 90], ARRAY['c', 'b'], NULL, 1),
+(13, 'overcast', 81, 75, ARRAY[81, 75], ARRAY['a', 'b'], false, 1),
+(15, NULL, 81, 75, ARRAY[81, 75], ARRAY['a', 'b'], false, 1),
+(16, 'overcast', NULL, 75, ARRAY[81, 75], ARRAY['a', 'd'], false,1),
+(14, 'rain', 71, 80, ARRAY[71, 80], ARRAY['c', 'b'], true, 0);
+
+-------------------------------------------------------------------------
+-- classification without grouping
+ select madlib.gbdt_train('dt_golf',         -- source table
+                  'train_output',    -- output model table
+                  'id'  ,            -- id column
+                  'class',           -- response
+                  'windy, "Cont_features"[1]',   -- features
+                  NULL,        -- exclude columns
+	  NULL,
+                  NULL,        -- no grouping
+                  30,                -- num of trees
+                  2,                 -- num of random features
+                  TRUE,    -- importance
+                  1,       -- num_permutations
+                  10,       -- max depth
+                  1,        -- min split
+                  1,        -- min bucket
+                  8,        -- number of bins per continuous variable
+                  'max_surrogates=0',
+	              'response',
+	              0.001,
+                  FALSE,
+	              0.5
+);
+
+CREATE TABLE dt_golf2 as SELECT * from dt_golf;
+DROP TABLE IF EXISTS test_output;
+select madlib.gbdt_predict('dt_golf2','train_output','test_output','id');
diff --git a/src/ports/postgres/modules/glm/glm.sql_in b/src/ports/postgres/modules/glm/glm.sql_in
index 377dfea..4ebc6eb 100644
--- a/src/ports/postgres/modules/glm/glm.sql_in
+++ b/src/ports/postgres/modules/glm/glm.sql_in
@@ -649,7 +649,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_merge_states(
         state2 MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_merge_states'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -658,7 +658,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_final(
         state MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -671,7 +671,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_poisson_log_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_poisson_log_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -699,7 +699,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_poisson_identity_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_poisson_identity_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -727,7 +727,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_poisson_sqrt_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_poisson_sqrt_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -755,7 +755,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_gaussian_identity_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_gaussian_identity_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -783,7 +783,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_gaussian_log_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_gaussian_log_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -811,7 +811,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_gaussian_inverse_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_gaussian_inverse_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -838,7 +838,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_gamma_log_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_gamma_log_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -866,7 +866,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_gamma_inverse_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_gamma_inverse_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -893,7 +893,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_gamma_identity_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_gamma_identity_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -921,7 +921,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_binomial_probit_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_binomial_probit_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------
@@ -949,7 +949,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_inverse_gaussian_identity_transit
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_inverse_gaussian_identity_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -961,7 +961,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_binomial_logit_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_binomial_logit_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------
@@ -986,7 +986,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_result_z_stats(
         /*+ state */ MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.__glm_result_type
 AS 'MODULE_PATHNAME', 'glm_result_z_stats'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1014,7 +1014,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_inverse_gaussian_log_transition(
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_inverse_gaussian_log_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1042,7 +1042,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_inverse_gaussian_inverse_transiti
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_inverse_gaussian_inverse_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1070,7 +1070,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_inverse_gaussian_sqr_inverse_tran
         MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'glm_inverse_gaussian_sqr_inverse_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1095,7 +1095,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_result_z_stats(
         /*+ state */ MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.__glm_result_type
 AS 'MODULE_PATHNAME', 'glm_result_z_stats'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1104,7 +1104,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_result_t_stats(
         /*+ state */ MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.__glm_result_type
 AS 'MODULE_PATHNAME', 'glm_result_t_stats'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1114,7 +1114,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__glm_loglik_diff(
         /*+ state2 */ MADLIB_SCHEMA.bytea8)
 RETURNS double precision
 AS 'MODULE_PATHNAME', 'glm_loglik_diff'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1212,7 +1212,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.glm_predict(
     link            TEXT
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME', 'glm_predict'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1236,7 +1236,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.glm_predict_binomial(
     link            TEXT
 ) RETURNS BOOLEAN
 AS 'MODULE_PATHNAME', 'glm_predict_binomial'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1260,7 +1260,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.glm_predict_poisson(
     link            TEXT
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME', 'glm_predict_poisson'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
diff --git a/src/ports/postgres/modules/glm/multiresponseglm.sql_in b/src/ports/postgres/modules/glm/multiresponseglm.sql_in
index c810305..46de254 100644
--- a/src/ports/postgres/modules/glm/multiresponseglm.sql_in
+++ b/src/ports/postgres/modules/glm/multiresponseglm.sql_in
@@ -556,7 +556,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__multinom_merge_states(
         state2 MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'multi_response_glm_merge_states'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -565,7 +565,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__multinom_final(
         state MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'multi_response_glm_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -579,7 +579,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__multinom_logit_transition(
         smallint)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'multi_response_glm_multinom_logit_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -605,7 +605,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__multinom_result(
         /*+ state */ MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.__multinom_result_type
 AS 'MODULE_PATHNAME', 'multi_response_glm_result_z_stats'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -615,7 +615,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__multinom_loglik_diff(
         /*+ state2 */ MADLIB_SCHEMA.bytea8)
 RETURNS double precision
 AS 'MODULE_PATHNAME', 'multi_response_glm_loglik_diff'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
diff --git a/src/ports/postgres/modules/glm/ordinal.sql_in b/src/ports/postgres/modules/glm/ordinal.sql_in
index 7cda294..366f69c 100644
--- a/src/ports/postgres/modules/glm/ordinal.sql_in
+++ b/src/ports/postgres/modules/glm/ordinal.sql_in
@@ -492,7 +492,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__ordinal_merge_states(
         state2 MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'ordinal_merge_states'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -501,7 +501,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__ordinal_final(
         state MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'ordinal_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -514,7 +514,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__ordinal_logit_transition(
         smallint)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'ordinal_logit_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -544,7 +544,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__ordinal_probit_transition(
         smallint)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME', 'ordinal_probit_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -570,7 +570,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__ordinal_result(
         /*+ state */ MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.__ordinal_result_type
 AS 'MODULE_PATHNAME', 'ordinal_result'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -580,7 +580,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__ordinal_loglik_diff(
         /*+ state2 */ MADLIB_SCHEMA.bytea8)
 RETURNS double precision
 AS 'MODULE_PATHNAME', 'ordinal_loglik_diff'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
diff --git a/src/ports/postgres/modules/glm/test/multinom.sql_in b/src/ports/postgres/modules/glm/test/multinom.sql_in
index 936aa08..9ca1a1f 100644
--- a/src/ports/postgres/modules/glm/test/multinom.sql_in
+++ b/src/ports/postgres/modules/glm/test/multinom.sql_in
@@ -39,7 +39,8 @@ CREATE TABLE multinom_test (
     feat1 INTEGER,
     feat2 INTEGER,
     cat INTEGER,
-    g CHAR
+    g CHAR,
+    i serial
 );
 
 INSERT INTO multinom_test(feat1, feat2, cat, g) VALUES
@@ -274,7 +275,7 @@ WHERE category = '1';
 
 -- for prediction
 -- adding ID column for prediction
-ALTER TABLE multinom_test ADD COLUMN i SERIAL;
+-- ALTER TABLE multinom_test ADD COLUMN i SERIAL;
 
 DROP TABLE IF EXISTS mglm_predict;
 SELECT multinom_predict(
diff --git a/src/ports/postgres/modules/kmeans/kmeans.sql_in b/src/ports/postgres/modules/kmeans/kmeans.sql_in
index 9e36a92..afa9a4d 100644
--- a/src/ports/postgres/modules/kmeans/kmeans.sql_in
+++ b/src/ports/postgres/modules/kmeans/kmeans.sql_in
@@ -996,7 +996,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_execute_using_kmeans_args(
 ) RETURNS VOID
 VOLATILE
 CALLED ON NULL INPUT
-LANGUAGE c
+LANGUAGE c NOT FENCED
 AS 'MODULE_PATHNAME', 'exec_sql_using'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
@@ -1202,22 +1202,22 @@ BEGIN
 
     -- Handle PG11 pg_proc table changes
     IF (SELECT MADLIB_SCHEMA.is_pg_major_version_less_than(11) = TRUE) THEN
-      IF (SELECT prorettype != 'DOUBLE PRECISION'::regtype OR proisagg = TRUE
+      IF (SELECT prorettype != 'DOUBLE PRECISION'::regtype OR proisagg = 't'
           FROM pg_proc WHERE oid = proc_fn_dist) THEN
           RAISE EXCEPTION 'Kmeans error: Distance function has wrong signature or is not a simple function.';
       END IF;
       proc_agg_centroid := agg_centroid || '(DOUBLE PRECISION[])';
-      IF (SELECT prorettype != 'DOUBLE PRECISION[]'::regtype OR proisagg = FALSE
+      IF (SELECT prorettype != 'DOUBLE PRECISION[]'::regtype OR proisagg != 't'
           FROM pg_proc WHERE oid = proc_agg_centroid) THEN
           RAISE EXCEPTION 'Kmeans error: Mean aggregate has wrong signature or is not an aggregate.';
       END IF;
     ELSE
-      IF (SELECT prorettype != 'DOUBLE PRECISION'::regtype OR prokind = 'a'
+      IF (SELECT prorettype != 'DOUBLE PRECISION'::regtype OR proisagg = 't'
           FROM pg_proc WHERE oid = proc_fn_dist) THEN
           RAISE EXCEPTION 'Kmeans error: Distance function has wrong signature or is not a simple function.';
       END IF;
       proc_agg_centroid := agg_centroid || '(DOUBLE PRECISION[])';
-      IF (SELECT prorettype != 'DOUBLE PRECISION[]'::regtype OR prokind != 'a'
+      IF (SELECT prorettype != 'DOUBLE PRECISION[]'::regtype OR proisagg != 't'
           FROM pg_proc WHERE oid = proc_agg_centroid) THEN
           RAISE EXCEPTION 'Kmeans error: Mean aggregate has wrong signature or is not an aggregate.';
       END IF;
@@ -1255,8 +1255,8 @@ BEGIN
     -- PostgreSQL 8.4:
     -- http://www.postgresql.org/docs/8.4/static/plpgsql-statements.html#PLPGSQL-STATEMENTS-EXECUTING-DYN
     -- We therefore have to emulate.
+    DROP TABLE IF EXISTS pg_temp._madlib_kmeans_args;
     PERFORM MADLIB_SCHEMA.internal_execute_using_kmeans_args($sql$
-        DROP TABLE IF EXISTS pg_temp._madlib_kmeans_args;
         CREATE TABLE pg_temp._madlib_kmeans_args AS
         SELECT
             $1 AS initial_centroids,
@@ -1354,7 +1354,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_execute_using_kmeanspp_seeding
 ) RETURNS VOID
 VOLATILE
 CALLED ON NULL INPUT
-LANGUAGE c
+LANGUAGE c NOT FENCED
 AS 'MODULE_PATHNAME', 'exec_sql_using'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
@@ -1414,7 +1414,7 @@ BEGIN
 
     proc_fn_dist := fn_dist || '(DOUBLE PRECISION[], DOUBLE PRECISION[])';
     IF (SELECT MADLIB_SCHEMA.is_pg_major_version_less_than(11) = TRUE) THEN
-      IF (SELECT prorettype != 'DOUBLE PRECISION'::regtype OR proisagg = TRUE
+      IF (SELECT prorettype != 'DOUBLE PRECISION'::regtype OR prokind = 'a'
           FROM pg_proc WHERE oid = proc_fn_dist) THEN
           RAISE EXCEPTION 'Kmeans error: Distance function has wrong signature or is not a simple function.';
       END IF;
@@ -1463,8 +1463,8 @@ BEGIN
     -- PostgreSQL 8.4:
     -- http://www.postgresql.org/docs/8.4/static/plpgsql-statements.html#PLPGSQL-STATEMENTS-EXECUTING-DYN
     -- We therefore have to emulate.
+    DROP TABLE IF EXISTS pg_temp._madlib_kmeanspp_args;
     PERFORM MADLIB_SCHEMA.internal_execute_using_kmeanspp_seeding_args($sql$
-        DROP TABLE IF EXISTS pg_temp._madlib_kmeanspp_args;
         CREATE TEMPORARY TABLE _madlib_kmeanspp_args AS
         SELECT
             $1 AS k,
@@ -1685,7 +1685,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_execute_using_kmeans_random_se
 ) RETURNS VOID
 VOLATILE
 CALLED ON NULL INPUT
-LANGUAGE c
+LANGUAGE c NOT FENCED
 AS 'MODULE_PATHNAME', 'exec_sql_using'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
@@ -1745,8 +1745,8 @@ BEGIN
     -- PostgreSQL 8.4:
     -- http://www.postgresql.org/docs/8.4/static/plpgsql-statements.html#PLPGSQL-STATEMENTS-EXECUTING-DYN
     -- We therefore have to emulate.
+    DROP TABLE IF EXISTS pg_temp._madlib_kmeans_random_args;
     PERFORM MADLIB_SCHEMA.internal_execute_using_kmeans_random_seeding_args($sql$
-        DROP TABLE IF EXISTS pg_temp._madlib_kmeans_random_args;
         CREATE TEMPORARY TABLE _madlib_kmeans_random_args AS
         SELECT $1 AS k, $2 AS initial_centroids;
         $sql$,
@@ -1924,7 +1924,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_execute_using_kmeans_args(
 ) RETURNS MADLIB_SCHEMA.kmeans_result
 VOLATILE
 CALLED ON NULL INPUT
-LANGUAGE c
+LANGUAGE c NOT FENCED
 AS 'MODULE_PATHNAME', 'exec_sql_using'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
@@ -2060,7 +2060,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_execute_using_silhouette_args(
 ) RETURNS DOUBLE PRECISION
 STABLE
 CALLED ON NULL INPUT
-LANGUAGE c
+LANGUAGE c NOT FENCED
 AS 'MODULE_PATHNAME', 'exec_sql_using'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
@@ -2117,7 +2117,7 @@ BEGIN
     proc_fn_dist := fn_dist
         || '(DOUBLE PRECISION[], DOUBLE PRECISION[])';
     IF (SELECT MADLIB_SCHEMA.is_pg_major_version_less_than(11) = TRUE) THEN
-      IF (SELECT prorettype != 'DOUBLE PRECISION'::regtype OR proisagg = TRUE
+      IF (SELECT prorettype != 'DOUBLE PRECISION'::regtype OR prokind = 'a'
           FROM pg_proc WHERE oid = proc_fn_dist) THEN
           RAISE EXCEPTION 'Kmeans error: Distance function has wrong signature or is not a simple function.';
       END IF;
diff --git a/src/ports/postgres/modules/knn/knn.py_in b/src/ports/postgres/modules/knn/knn.py_in
index eb2150e..1f5f055 100644
--- a/src/ports/postgres/modules/knn/knn.py_in
+++ b/src/ports/postgres/modules/knn/knn.py_in
@@ -122,7 +122,7 @@ def knn_validate_src(schema_madlib, point_source, point_column_name, point_id,
 
     if fn_dist:
         fn_dist = fn_dist.lower().strip()
-        profunc = ("proisagg = TRUE"
+        profunc = ("prokind = 'a'"
                    if is_pg_major_version_less_than(schema_madlib, 11)
                    else "prokind = 'a'")
 
@@ -180,12 +180,13 @@ def build_kd_tree(schema_madlib, source_table, output_table, point_column_name,
             for curr_leaf in range(pow(2,curr_level)):
                 clause = clauses[clause_counter]
                 cutoff_sql = """
-                    SELECT percentile_disc(0.5)
-                           WITHIN GROUP (
+                    SELECT {schema_madlib}.percentile_disc(array_agg(cutoff), 0.5) AS cutoff
+                    FROM (
+                            SELECT {point_column_name}[{curr_feature}] AS cutoff
+                            FROM {source_table} 
+                            WHERE {clause} and cutoff is not null
                             ORDER BY ({point_column_name})[{curr_feature}]
-                           ) AS cutoff
-                    FROM {source_table}
-                    WHERE {clause}
+                        ) 
                     """.format(**locals())
 
                 cutoff = plpy.execute(cutoff_sql)[0]['cutoff']
diff --git a/src/ports/postgres/modules/knn/test/knn.sql_in b/src/ports/postgres/modules/knn/test/knn.sql_in
index e4cd578..63a9883 100644
--- a/src/ports/postgres/modules/knn/test/knn.sql_in
+++ b/src/ports/postgres/modules/knn/test/knn.sql_in
@@ -143,18 +143,18 @@ SELECT assert(array_agg(prediction::numeric ORDER BY id)='{1,1,0.040872859187601
 
 
 DROP TABLE if exists madlib_knn_result_classification;
-SELECT knn('knn_train_data','data[1:1]||data[2:2]','id','label','knn_test_data','data[1:1]||data[2:2]','id','madlib_knn_result_classification',3,False,'MADLIB_SCHEMA.squared_dist_norm2', True);
+SELECT knn('knn_train_data','data[1,1]||data[2,2]','id','label','knn_test_data','data[1,1]||data[2,2]','id','madlib_knn_result_classification',3,False,'MADLIB_SCHEMA.squared_dist_norm2', True);
 SELECT assert(array_agg(prediction::numeric ORDER BY id)='{1,1,0,1,0,0}', 'Wrong output in classification') FROM madlib_knn_result_classification;
 
 DROP TABLE if exists madlib_knn_result_regression;
-SELECT knn('knn_train_data_reg','data[1:1]||data[2:2]','id','label','knn_test_data','data[1:1]||data[2:2]','id','madlib_knn_result_regression',3,False,'MADLIB_SCHEMA.squared_dist_norm2', True);
+SELECT knn('knn_train_data_reg','data[1,1]||data[2,2]','id','label','knn_test_data','data[1,1]||data[2,2]','id','madlib_knn_result_regression',3,False,'MADLIB_SCHEMA.squared_dist_norm2', True);
 SELECT assert(array_agg(prediction::numeric ORDER BY id)='{1,1,0.0408728591876018,1,0,0}', 'Wrong output in regression') FROM madlib_knn_result_regression;
 
 
 
 
 DROP TABLE if exists madlib_knn_result_classification;
-SELECT knn('knn_train_data_expr','ARRAY[data1,data2]','id','label','knn_test_data','data[1:1]||data[2:2]','id','madlib_knn_result_classification',3,False,'MADLIB_SCHEMA.squared_dist_norm2', True);
+SELECT knn('knn_train_data_expr','ARRAY[data1,data2]','id','label','knn_test_data','data[1,1]||data[2,2]','id','madlib_knn_result_classification',3,False,'MADLIB_SCHEMA.squared_dist_norm2', True);
 SELECT assert(array_agg(prediction::numeric ORDER BY id)='{1,1,0,1,0,0}', 'Wrong output in classification') FROM madlib_knn_result_classification;
 
 
diff --git a/src/ports/postgres/modules/lda/lda.py_in b/src/ports/postgres/modules/lda/lda.py_in
index 29c9d85..c34ced5 100644
--- a/src/ports/postgres/modules/lda/lda.py_in
+++ b/src/ports/postgres/modules/lda/lda.py_in
@@ -229,7 +229,7 @@ class LDATrainer:
                         {schema_madlib}.__lda_count_topic_agg(
                             words,
                             counts,
-                            doc_topic[{topic_num} + 1:array_upper(doc_topic, 1)],
+                            doc_topic[{topic_num} + 1,array_upper(doc_topic, 1)],
                             {voc_size},
                             {topic_num}
                         ) AS model
@@ -253,7 +253,7 @@ class LDATrainer:
                     {schema_madlib}.__lda_count_topic_agg(
                         words,
                         counts,
-                        doc_topic[{topic_num} + 1:array_upper(doc_topic, 1)],
+                        doc_topic[{topic_num} + 1,array_upper(doc_topic, 1)],
                         {voc_size},
                         {topic_num}
                     ) AS model
@@ -319,8 +319,8 @@ class LDATrainer:
         plpy.execute("""
             INSERT INTO {output_data_table}
             SELECT
-                docid, wordcount, words, counts, doc_topic[1:{topic_num}] topic_count,
-                doc_topic[{topic_num} + 1:array_upper(doc_topic,1)] topic_assignment
+                docid, wordcount, words, counts, doc_topic[1,{topic_num}] topic_count,
+                doc_topic[{topic_num} + 1,array_upper(doc_topic,1)] topic_assignment
             FROM
                 {work_table_final}
             """.format(output_data_table=self.output_data_table,
@@ -448,8 +448,8 @@ class LDAPredictor:
         plpy.execute("""
             INSERT INTO {doc_topic}
             SELECT
-                docid, wordcount, words, counts, doc_topic[1:{topic_num}] topic_count,
-                doc_topic[{topic_num} + 1:array_upper(doc_topic,1)] topic_assignment
+                docid, wordcount, words, counts, doc_topic[1,{topic_num}] topic_count,
+                doc_topic[{topic_num} + 1,array_upper(doc_topic,1)] topic_assignment
             FROM {work_table_out}
             """.format(doc_topic=self.doc_topic,
                        topic_num=self.topic_num,
@@ -1116,7 +1116,7 @@ def _validate_data_table(data_table, voc_size):
 
     rv = plpy.execute("""
         SELECT
-            count(wordid) size,
+            count(wordid) AS size,
             min(wordid) min_wordid,
             max(wordid) max_wordid
         FROM
diff --git a/src/ports/postgres/modules/lda/lda.sql_in b/src/ports/postgres/modules/lda/lda.sql_in
index 814b0ae..a0910ca 100644
--- a/src/ports/postgres/modules/lda/lda.sql_in
+++ b/src/ports/postgres/modules/lda/lda.sql_in
@@ -1264,7 +1264,7 @@ MADLIB_SCHEMA.__lda_random_assign
 )
 RETURNS INT4[]
 AS 'MODULE_PATHNAME', 'lda_random_assign'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1302,7 +1302,7 @@ MADLIB_SCHEMA.__lda_gibbs_sample
 )
 RETURNS INT4[]
 AS 'MODULE_PATHNAME', 'lda_gibbs_sample'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1330,7 +1330,7 @@ MADLIB_SCHEMA.__lda_count_topic_sfunc
 )
 RETURNS INT8[]
 AS 'MODULE_PATHNAME', 'lda_count_topic_sfunc'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1348,7 +1348,7 @@ MADLIB_SCHEMA.__lda_count_topic_prefunc
 )
 RETURNS INT8[]
 AS 'MODULE_PATHNAME', 'lda_count_topic_prefunc'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1436,7 +1436,7 @@ MADLIB_SCHEMA.__lda_perplexity_sfunc
 )
 RETURNS INT8[]
 AS 'MODULE_PATHNAME', 'lda_perplexity_sfunc'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1453,7 +1453,7 @@ MADLIB_SCHEMA.__lda_perplexity_prefunc
 )
 RETURNS INT8[]
 AS 'MODULE_PATHNAME', 'lda_perplexity_prefunc'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1468,7 +1468,7 @@ MADLIB_SCHEMA.__lda_perplexity_ffunc
 )
 RETURNS FLOAT8
 AS 'MODULE_PATHNAME', 'lda_perplexity_ffunc'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /*
@@ -1526,7 +1526,7 @@ MADLIB_SCHEMA.__lda_check_count_ceiling
 )
 RETURNS INT4[]
 AS 'MODULE_PATHNAME', 'lda_check_count_ceiling'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1543,7 +1543,7 @@ MADLIB_SCHEMA.__lda_util_unnest
 )
 RETURNS SETOF INT4[]
 AS 'MODULE_PATHNAME', 'lda_unnest'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -1555,7 +1555,7 @@ MADLIB_SCHEMA.__lda_util_unnest_transpose
 )
 RETURNS SETOF INT4[]
 AS 'MODULE_PATHNAME', 'lda_unnest_transpose'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1570,7 +1570,7 @@ MADLIB_SCHEMA.__lda_util_transpose
 )
 RETURNS INT8[][]
 AS 'MODULE_PATHNAME', 'lda_transpose'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1587,7 +1587,7 @@ MADLIB_SCHEMA.__lda_util_norm_with_smoothing
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'l1_norm_with_smoothing'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1694,6 +1694,6 @@ MADLIB_SCHEMA.lda_parse_model
 )
 RETURNS MADLIB_SCHEMA._pivotalr_lda_model
 AS 'MODULE_PATHNAME'
-LANGUAGE c STRICT
+LANGUAGE c STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
diff --git a/src/ports/postgres/modules/lda/test/lda.sql_in b/src/ports/postgres/modules/lda/test/lda.sql_in
index 96d5f5f..0dc3b9a 100644
--- a/src/ports/postgres/modules/lda/test/lda.sql_in
+++ b/src/ports/postgres/modules/lda/test/lda.sql_in
@@ -276,7 +276,7 @@ CREATE OR REPLACE FUNCTION validate_lda_output() RETURNS integer AS $$
         -- Loop through the output of lda_get_word_topic_count() and compare the results with the helper table word_topic_mapping
         FOR word_topic_count_row IN select * from word_topic_count ORDER BY wordid LOOP
             word_topic_count_topic_count := ARRAY[word_topic_count_row.topic_count];
-            word_topic_mapping_topic_count := word_topic_mapping_array[word_topic_count_row.wordid+1:word_topic_count_row.wordid+1]; -- Here arrayX[i:i] means the ith 1d array of a 2d array
+            word_topic_mapping_topic_count := word_topic_mapping_array[word_topic_count_row.wordid+1,word_topic_count_row.wordid+1]; -- Here arrayX[i:i] means the ith 1d array of a 2d array
 
             IF (word_topic_mapping_topic_count != word_topic_count_topic_count) THEN
                 RAISE EXCEPTION 'Topic assignment for wordid % does not match. word_topic_mapping: % word_topic_count: %',word_topic_count_row.wordid, word_topic_mapping_topic_count, word_topic_count_topic_count;
diff --git a/src/ports/postgres/modules/linalg/linalg.sql_in b/src/ports/postgres/modules/linalg/linalg.sql_in
index 3c74451..7570ae1 100644
--- a/src/ports/postgres/modules/linalg/linalg.sql_in
+++ b/src/ports/postgres/modules/linalg/linalg.sql_in
@@ -227,7 +227,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.norm1(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -242,7 +242,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.norm2(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -259,7 +259,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dist_inf_norm(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -278,7 +278,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dist_pnorm(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -295,7 +295,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dist_norm1(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -312,7 +312,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dist_norm2(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 
@@ -331,7 +331,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.cosine_similarity(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 
@@ -350,7 +350,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.squared_dist_norm2(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -368,7 +368,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dist_angle(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -387,7 +387,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dist_tanimoto(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 
@@ -405,7 +405,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dist_jaccard(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /*
@@ -424,7 +424,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._closest_column(
     dist_dn     TEXT
 ) RETURNS MADLIB_SCHEMA.closest_column_result AS
     'MODULE_PATHNAME', 'closest_column'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 
@@ -508,7 +508,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._closest_columns(
     dist_dn     TEXT
 ) RETURNS MADLIB_SCHEMA.closest_columns_result AS
     'MODULE_PATHNAME', 'closest_columns'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.closest_columns(
@@ -541,7 +541,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.avg_vector_transition(
     x DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
-IMMUTABLE
+IMMUTABLE NOT FENCED
 CALLED ON NULL INPUT
 AS 'MODULE_PATHNAME'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
@@ -552,7 +552,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.avg_vector_merge(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -561,7 +561,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.avg_vector_final(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -590,7 +590,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normalized_avg_vector_transition(
     x DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
-IMMUTABLE
+IMMUTABLE NOT FENCED
 CALLED ON NULL INPUT
 AS 'MODULE_PATHNAME'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
@@ -600,7 +600,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normalized_avg_vector_final(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -632,7 +632,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.matrix_agg_transition(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -641,7 +641,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.matrix_agg_final(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -676,7 +676,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.matrix_column(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -690,7 +690,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.deconstruct_2d_array(
     in_array   double precision[]
 ) RETURNS SETOF record
 AS 'MODULE_PATHNAME', 'deconstruct_2d_array'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 ------------------------------
@@ -703,7 +703,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__deconstruct_lower_triangle(
     in_array   double precision[]
 ) RETURNS SETOF record
 AS 'MODULE_PATHNAME', 'deconstruct_lower_triangle'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 ------------------- Created for In-Memory Iteration Controller -----------
@@ -716,7 +716,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_to_1d(
     in_array   DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'array_to_1d'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -729,7 +729,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_to_2d(
     in_array    DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'array_to_2d'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -744,7 +744,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.index_2d_array(
 )
 RETURNS double precision[]
 AS 'MODULE_PATHNAME', 'get_row_from_2d_array'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -759,7 +759,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.get_row(
 )
 RETURNS double precision[]
 AS 'MODULE_PATHNAME', 'get_row_from_2d_array'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -774,7 +774,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.get_col(
 )
 RETURNS double precision[]
 AS 'MODULE_PATHNAME', 'get_col_from_2d_array'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 m4_changequote(, )
diff --git a/src/ports/postgres/modules/linalg/matrix_ops.py_in b/src/ports/postgres/modules/linalg/matrix_ops.py_in
index bdbb502..b03d26d 100644
--- a/src/ports/postgres/modules/linalg/matrix_ops.py_in
+++ b/src/ports/postgres/modules/linalg/matrix_ops.py_in
@@ -2570,10 +2570,12 @@ def matrix_cholesky(schema_madlib, matrix_in, in_args,
         plpy.execute('DROP TABLE IF EXISTS %s' % matrix_p_temp)
 
     matrix_l_temp = "pg_temp." + unique_string() if is_output_sparse else matrix_l
+    dim_start = dim[0] + 1
+    dim_end = dim[0] * 2
     plpy.execute("""
         CREATE TABLE {matrix_l_temp} AS
         SELECT row_id AS {out_args[row]},
-               row_vec[{dim[0]} + 1 : {dim[0]} * 2] AS {out_args[val]}
+               row_vec[{dim_start}:{dim_end}] AS {out_args[val]}
         FROM {matrix_temp}
         """.format(**locals()))
     if is_output_sparse:
@@ -2581,10 +2583,12 @@ def matrix_cholesky(schema_madlib, matrix_in, in_args,
         plpy.execute('DROP TABLE IF EXISTS %s' % matrix_l_temp)
 
     matrix_d_temp = "pg_temp." + unique_string() if is_output_sparse else matrix_d
+    dim_start = dim[0] * 2 + 1
+    dim_end = dim[0] * 3
     plpy.execute("""
         CREATE TABLE {matrix_d_temp} AS
         SELECT row_id AS {out_args[row]},
-               row_vec[{dim[0]} * 2 + 1: {dim[0]} * 3] AS {out_args[val]}
+               row_vec[{dim_start}:{dim_end}] AS {out_args[val]}
         FROM {matrix_temp}
         """.format(**locals()))
     if is_output_sparse:
@@ -2673,10 +2677,12 @@ def matrix_qr(schema_madlib, matrix_in, in_args, matrix_out_prefix, out_args):
         plpy.execute('DROP TABLE IF EXISTS %s' % matrix_q_temp)
 
     matrix_r_temp = "pg_temp." + unique_string() if is_output_sparse else matrix_r
+    dim_start = dim[0] + 1
+    dim_end = dim[0] + dim[1]
     plpy.execute("""
         CREATE TABLE {matrix_r_temp} AS
         SELECT row_id as {out_args[row]},
-               row_vec[{dim[0]} + 1 : {dim[0]} + {dim[1]}] AS {out_args[val]}
+               row_vec[{dim_start}:{dim_end}] AS {out_args[val]}
         FROM {matrix_temp}
         """.format(**locals()))
     if is_output_sparse:
@@ -2829,22 +2835,28 @@ def matrix_lu(schema_madlib, matrix_in, in_args,
                row_vec[1:{dim[0]}] AS {out_args[val]}
         FROM {matrix_temp} WHERE row_id <= {dim[0]}
         """.format(**locals()))
+    dim_start = 2 * dim[0] + dim[1] + 1
+    dim_end = 2 * dim[0] + 2 * dim[1]
     plpy.execute("""
         CREATE TABLE {matrix_temp_names[q]} AS
         SELECT row_id AS {out_args[row]},
-               row_vec[2 * {dim[0]} + {dim[1]} + 1:2 * {dim[0]} + 2 * {dim[1]}] AS {out_args[val]}
+               row_vec[{dim_start} : {dim_end}] AS {out_args[val]}
         FROM {matrix_temp} WHERE row_id <= {dim[1]}
         """.format(**locals()))
+    dim_start = dim[0] + 1
+    dim_end = 2 * dim[0]
     plpy.execute("""
         CREATE TABLE {matrix_temp_names[l]} AS
         SELECT row_id AS {out_args[row]},
-               row_vec[{dim[0]} + 1 : 2 * {dim[0]}] AS {out_args[val]}
+               row_vec[{dim_start} : {dim_end}] AS {out_args[val]}
         FROM {matrix_temp} WHERE row_id <= {dim[0]}
         """.format(**locals()))
+    dim_start = 2 * dim[0] + 1
+    dim_end = 2 * dim[0] + dim[1]
     plpy.execute("""
         CREATE TABLE {matrix_temp_names[u]} AS
         SELECT row_id AS {out_args[row]},
-               row_vec[2 * {dim[0]} + 1 : 2 * {dim[0]} + {dim[1]}] AS {out_args[val]}
+               row_vec[{dim_start} : {dim_end}] AS {out_args[val]}
         FROM {matrix_temp} WHERE row_id <= {dim[0]}
         """.format(**locals()))
 
diff --git a/src/ports/postgres/modules/linalg/matrix_ops.sql_in b/src/ports/postgres/modules/linalg/matrix_ops.sql_in
index 6e33f46..06eb944 100644
--- a/src/ports/postgres/modules/linalg/matrix_ops.sql_in
+++ b/src/ports/postgres/modules/linalg/matrix_ops.sql_in
@@ -3000,7 +3000,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_vec_mult_in_mem(
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'matrix_vec_mult_in_mem_2d'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /*
@@ -3015,7 +3015,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_vec_mult_in_mem(
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'matrix_vec_mult_in_mem_1d'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -3031,7 +3031,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__row_fold(
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'row_fold'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -3045,7 +3045,7 @@ MADLIB_SCHEMA.__bernoulli_vector
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'bernoulli_vector'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -3068,7 +3068,7 @@ MADLIB_SCHEMA.__uniform_vector
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'uniform_vector'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -3093,7 +3093,7 @@ MADLIB_SCHEMA.__normal_vector
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'normal_vector'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -3116,7 +3116,7 @@ MADLIB_SCHEMA.__rand_vector
 )
 RETURNS INTEGER[]
 AS 'MODULE_PATHNAME', 'rand_vector'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -3128,7 +3128,7 @@ MADLIB_SCHEMA.__rand_block
 )
 RETURNS INTEGER[]
 AS 'MODULE_PATHNAME', 'rand_block'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -3149,7 +3149,7 @@ MADLIB_SCHEMA.__matrix_row_split
 )
 RETURNS SETOF FLOAT8[]
 AS 'MODULE_PATHNAME', 'row_split'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -3162,7 +3162,7 @@ MADLIB_SCHEMA.__matrix_densify_sfunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'matrix_densify_sfunc'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS
@@ -3222,7 +3222,7 @@ MADLIB_SCHEMA.__matrix_blockize_sfunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'matrix_blockize_sfunc'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS
@@ -3255,7 +3255,7 @@ MADLIB_SCHEMA.__matrix_unblockize_sfunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'matrix_unblockize_sfunc'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS
@@ -3287,7 +3287,7 @@ MADLIB_SCHEMA.matrix_mem_mult
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'matrix_mem_mult'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -3310,7 +3310,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
 -- )
 -- RETURNS FLOAT8[]
 -- AS 'MODULE_PATHNAME', 'matrix_vec_mem_mult'
--- $$ LANGUAGE C;
+-- $$ LANGUAGE C NOT FENCED;
 
 
 CREATE OR REPLACE FUNCTION
@@ -3320,7 +3320,7 @@ MADLIB_SCHEMA.matrix_mem_trans
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'matrix_mem_trans'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -3332,7 +3332,7 @@ MADLIB_SCHEMA.__matrix_mem_sum_sfunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'matrix_mem_sum_sfunc'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS
@@ -3427,7 +3427,7 @@ MADLIB_SCHEMA.__matrix_unnest_block
 )
 RETURNS SETOF FLOAT8[]
 AS 'MODULE_PATHNAME', 'unnest_block'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 -------------------------------------------------------------------------
 
@@ -3439,7 +3439,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_compose_dense_transition(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_compose_dense_transition'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3453,7 +3453,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_compose_sparse_transition(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_compose_sparse_transition'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3463,7 +3463,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_compose_merge(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_compose_merge'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3472,7 +3472,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_inv_final(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_inv'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3481,7 +3481,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_pinv_final(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_pinv'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3490,7 +3490,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_eigen_final(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_eigen'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3499,7 +3499,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_cholesky_final(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_cholesky'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3508,7 +3508,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_qr_final(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_qr'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3517,7 +3517,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_rank_final(
 ) RETURNS INT8
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_rank'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3526,7 +3526,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_lu_final(
 ) RETURNS DOUBLE PRECISION[]
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_lu'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
@@ -3535,7 +3535,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__matrix_nuclear_norm_final(
 ) RETURNS DOUBLE PRECISION
 LANGUAGE c
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 AS 'MODULE_PATHNAME', 'matrix_nuclear_norm'
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
diff --git a/src/ports/postgres/modules/linalg/pivotalr_arrayops.sql_in b/src/ports/postgres/modules/linalg/pivotalr_arrayops.sql_in
index ff569d8..0f069ee 100644
--- a/src/ports/postgres/modules/linalg/pivotalr_arrayops.sql_in
+++ b/src/ports/postgres/modules/linalg/pivotalr_arrayops.sql_in
@@ -6,7 +6,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__pivotalr_crossprod_transition(
     right_arr           DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', '__pivotalr_crossprod_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__pivotalr_crossprod_merge(
@@ -14,7 +14,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__pivotalr_crossprod_merge(
     right_state         DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', '__pivotalr_crossprod_merge'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.crossprod(DOUBLE PRECISION[], DOUBLE PRECISION[]);
@@ -36,7 +36,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__pivotalr_crossprod_sym_transition(
     arr                 DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', '__pivotalr_crossprod_sym_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.crossprod_sym(DOUBLE PRECISION[]);
@@ -57,14 +57,14 @@ CREATE AGGREGATE MADLIB_SCHEMA.crossprod_sym(
 --     arr                 DOUBLE PRECISION[]
 -- ) RETURNS DOUBLE PRECISION[] AS
 --     'MODULE_PATHNAME', '__pivotalr_array_mean_transition'
--- LANGUAGE C IMMUTABLE;
+-- LANGUAGE C IMMUTABLE NOT FENCED;
 
 -- CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__pivotalr_array_sum_merge(
 --     left_state          DOUBLE PRECISION[],
 --     right_state         DOUBLE PRECISION[]
 -- ) RETURNS DOUBLE PRECISION[] AS
 --     'MODULE_PATHNAME', '__pivotalr_array_mean_merge'
--- LANGUAGE C IMMUTABLE;
+-- LANGUAGE C IMMUTABLE NOT FENCED;
 
 -- CREATE AGGREGATE MADLIB_SCHEMA.pivotalr_array_sum(
 --     /* vector */        DOUBLE PRECISION[]
diff --git a/src/ports/postgres/modules/linalg/svd.sql_in b/src/ports/postgres/modules/linalg/svd.sql_in
index 50313c5..a7115ac 100644
--- a/src/ports/postgres/modules/linalg/svd.sql_in
+++ b/src/ports/postgres/modules/linalg/svd.sql_in
@@ -677,7 +677,7 @@ MADLIB_SCHEMA.__svd_unit_vector
 -- RETURNS MADLIB_SCHEMA.__svd_unit_vector_result
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'svd_unit_vector'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP TYPE IF EXISTS MADLIB_SCHEMA.__svd_lanczos_result CASCADE;
@@ -705,7 +705,7 @@ MADLIB_SCHEMA.__svd_lanczos_sfunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'svd_lanczos_sfunc'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------------------------------------------
@@ -723,7 +723,7 @@ MADLIB_SCHEMA.__svd_block_lanczos_sfunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'svd_block_lanczos_sfunc'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------------------------------------------
@@ -741,7 +741,7 @@ MADLIB_SCHEMA.__svd_sparse_lanczos_sfunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'svd_sparse_lanczos_sfunc'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -752,7 +752,7 @@ MADLIB_SCHEMA.__svd_lanczos_prefunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'svd_lanczos_prefunc'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------------------------------------------
@@ -860,7 +860,7 @@ MADLIB_SCHEMA.__svd_lanczos_pvec
 )
 RETURNS MADLIB_SCHEMA.__svd_lanczos_result
 AS 'MODULE_PATHNAME', 'svd_lanczos_pvec'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -872,7 +872,7 @@ MADLIB_SCHEMA.__svd_lanczos_qvec
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'svd_lanczos_qvec'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------------------------------------------
@@ -887,7 +887,7 @@ MADLIB_SCHEMA.__svd_gram_schmidt_orthogonalize_sfunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'svd_gram_schmidt_orthogonalize_sfunc'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION
@@ -898,7 +898,7 @@ MADLIB_SCHEMA.__svd_gram_schmidt_orthogonalize_prefunc
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'svd_gram_schmidt_orthogonalize_prefunc'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -- This function will also do the normalization
@@ -909,7 +909,7 @@ MADLIB_SCHEMA.__svd_gram_schmidt_orthogonalize_ffunc
 )
 RETURNS MADLIB_SCHEMA.__svd_lanczos_result
 AS 'MODULE_PATHNAME', 'svd_gram_schmidt_orthogonalize_ffunc'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS
@@ -1003,7 +1003,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__svd_decompose_bidiag(
 )
 RETURNS MADLIB_SCHEMA.__svd_bidiagonal_matrix_result
 AS 'MODULE_PATHNAME', 'svd_decompose_bidiag'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -----------------------------------------------------------------------
@@ -1022,7 +1022,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__svd_vec_mult_matrix(
 )
 RETURNS FLOAT8[]
 AS 'MODULE_PATHNAME', 'svd_vec_mult_matrix'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP TYPE IF EXISTS MADLIB_SCHEMA.__svd_vec_mat_mult_result CASCADE;
@@ -1042,7 +1042,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__svd_vec_trans_mult_matrix_internal(
 )
 RETURNS SETOF MADLIB_SCHEMA.__svd_vec_mat_mult_result
 AS 'MODULE_PATHNAME', 'svd_vec_trans_mult_matrix'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --Multiplication of P/Q vectors with Left/Right Singular Matrix of B
diff --git a/src/ports/postgres/modules/linalg/test/linalg.sql_in b/src/ports/postgres/modules/linalg/test/linalg.sql_in
index 4777d8d..b20bd44 100644
--- a/src/ports/postgres/modules/linalg/test/linalg.sql_in
+++ b/src/ports/postgres/modules/linalg/test/linalg.sql_in
@@ -127,7 +127,7 @@ SELECT assert(
         'Incorrect closest column.'
 ) FROM (
     SELECT
-        squared_dist_norm2(matrix[column_id + lb:column_id + lb], x) AS dist1,
+        squared_dist_norm2(matrix[column_id + lb,column_id + lb], x) AS dist1,
         distance AS dist2,
         least AS dist3,
         *
diff --git a/src/ports/postgres/modules/linalg/test/matrix_ops.sql_in b/src/ports/postgres/modules/linalg/test/matrix_ops.sql_in
index 8a7eb95..234373f 100644
--- a/src/ports/postgres/modules/linalg/test/matrix_ops.sql_in
+++ b/src/ports/postgres/modules/linalg/test/matrix_ops.sql_in
@@ -216,3 +216,22 @@ FROM (
         SELECT array_to_1d(__matrix_sparse_inverse(3, 3, row_id - 1, col_id - 1, val)) as result
         FROM random_sparse_matrix
     ) q;
+
+CREATE TABLE "mat_A" (
+        row_id integer,
+        row_vec integer[]
+);
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (1, '{9,6,5,8,5,6,6,3,10,8}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (2, '{8,2,2,6,6,10,2,1,9,9}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (3, '{3,9,9,9,8,6,3,9,5,6}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (4, '{6,4,2,2,2,7,8,8,0,7}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (5, '{6,8,9,9,4,6,9,5,7,7}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (6, '{4,10,7,3,9,5,9,2,3,4}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (7, '{8,10,7,10,1,9,7,9,8,7}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (8, '{7,4,5,6,2,8,1,1,4,8}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (9, '{8,8,8,5,2,6,9,1,8,3}');
+INSERT INTO "mat_A" (row_id, row_vec) VALUES (10, '{4,6,3,2,6,4,1,2,3,8}');
+
+SELECT madlib.matrix_cholesky('"mat_A"', 'row=row_id, val=row_vec', 'matrix_out_prefix1');
+SELECT madlib.matrix_qr('"mat_A"', 'row=row_id, val=row_vec', 'matrix_out_prefix2');
+SELECT madlib.matrix_lu('"mat_A"', 'row=row_id, val=row_vec', 'matrix_out_prefix3');
diff --git a/src/ports/postgres/modules/linear_systems/dense_linear_systems.sql_in b/src/ports/postgres/modules/linear_systems/dense_linear_systems.sql_in
index 15886f7..b1f2777 100644
--- a/src/ports/postgres/modules/linear_systems/dense_linear_systems.sql_in
+++ b/src/ports/postgres/modules/linear_systems/dense_linear_systems.sql_in
@@ -249,7 +249,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dense_residual_norm_transition(
     x       DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dense_residual_norm_merge_states(
@@ -257,14 +257,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dense_residual_norm_merge_states(
     state2 MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dense_residual_norm_final(
     state MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.residual_norm_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -315,7 +315,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dense_direct_linear_system_transition(
     algorithm INTEGER)
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dense_direct_linear_system_merge_states(
@@ -323,14 +323,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dense_direct_linear_system_merge_states
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.dense_direct_linear_system_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.dense_linear_solver_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
diff --git a/src/ports/postgres/modules/linear_systems/sparse_linear_systems.sql_in b/src/ports/postgres/modules/linear_systems/sparse_linear_systems.sql_in
index a61a48e..a9ef39a 100644
--- a/src/ports/postgres/modules/linear_systems/sparse_linear_systems.sql_in
+++ b/src/ports/postgres/modules/linear_systems/sparse_linear_systems.sql_in
@@ -362,7 +362,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.sparse_inmem_iterative_linear_system_tr
     termToler   DOUBLE PRECISION)
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.sparse_inmem_iterative_linear_system_merge_states(
@@ -370,14 +370,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.sparse_inmem_iterative_linear_system_me
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.sparse_inmem_iterative_linear_system_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.sparse_linear_solver_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -456,7 +456,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.sparse_direct_linear_system_transition(
     algorithm INTEGER)
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.sparse_direct_linear_system_merge_states(
@@ -464,7 +464,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.sparse_direct_linear_system_merge_state
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -472,7 +472,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.sparse_direct_linear_system_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.sparse_linear_solver_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
diff --git a/src/ports/postgres/modules/pca/pca.py_in b/src/ports/postgres/modules/pca/pca.py_in
index cab8e7f..3882f24 100644
--- a/src/ports/postgres/modules/pca/pca.py_in
+++ b/src/ports/postgres/modules/pca/pca.py_in
@@ -166,9 +166,9 @@ def pca_wrap(schema_madlib, source_table, pc_table, row_id,
 
         # declare variables whose values will be different for each group, if
         # grouping_cols is specified
-        grouping_where_clause = ''
-        sparse_where_condition = ''
-        select_grouping_cols = ''
+        grouping_where_clause = ' '
+        sparse_where_condition = ' '
+        select_grouping_cols = ' '
         temp_table_columns = ''
         result_summary_table_temp = ''
         # For Dense matrix format only:
diff --git a/src/ports/postgres/modules/pca/pca_project.py_in b/src/ports/postgres/modules/pca/pca_project.py_in
index d205044..64e97fd 100644
--- a/src/ports/postgres/modules/pca/pca_project.py_in
+++ b/src/ports/postgres/modules/pca/pca_project.py_in
@@ -416,10 +416,10 @@ def pca_project_wrap(schema_madlib, source_table, pc_table, out_table,
 
     # declare variables whose values will be different for each group, if
     # grouping_cols is specified
-    grouping_where_clause = ''
-    sparse_where_condition = ''
-    select_grouping_cols = ''
-    grouping_cols_values = ''
+    grouping_where_clause = ' '
+    sparse_where_condition = ' '
+    select_grouping_cols = ' '
+    grouping_cols_values = ' '
     result_summary_table_temp = ''
     other_columns_in_pc_table = [col for col in get_cols(pc_table) if col not in grouping_cols_list]
     temp_pc_table_columns = ', '.join(other_columns_in_pc_table)
diff --git a/src/ports/postgres/modules/prob/prob.sql_in b/src/ports/postgres/modules/prob/prob.sql_in
index 5322c29..88dfe63 100644
--- a/src/ports/postgres/modules/prob/prob.sql_in
+++ b/src/ports/postgres/modules/prob/prob.sql_in
@@ -144,7 +144,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.bernoulli_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -162,9 +162,20 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.bernoulli_pmf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.bernoulli_pmf(
+    x DOUBLE PRECISION,
+    sp DOUBLE PRECISION
+) RETURNS VOID AS $$
+DECLARE
+BEGIN
+    RAISE EXCEPTION 'The first element must be INT';
+END
+$$ LANGUAGE plpgsql IMMUTABLE STRICT
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
 /**
  * @brief Bernoulli quantile function
  *
@@ -178,7 +189,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.bernoulli_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -198,7 +209,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.beta_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -218,7 +229,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.beta_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -238,7 +249,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.beta_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -259,7 +270,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.binomial_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -279,7 +290,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.binomial_pmf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -301,7 +312,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.binomial_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -322,7 +333,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.cauchy_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -342,7 +353,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.cauchy_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -362,7 +373,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.cauchy_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -380,7 +391,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi_squared_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -398,7 +409,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi_squared_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -416,7 +427,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi_squared_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -434,7 +445,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.exponential_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -452,7 +463,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.exponential_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -470,7 +481,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.exponential_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -491,7 +502,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.extreme_value_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -511,7 +522,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.extreme_value_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -531,7 +542,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.extreme_value_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -551,7 +562,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.fisher_f_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -571,7 +582,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.fisher_f_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -591,7 +602,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.fisher_f_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -612,7 +623,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gamma_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -632,7 +643,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gamma_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -652,7 +663,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gamma_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -669,7 +680,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.geometric_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -687,9 +698,20 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.geometric_pmf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.geometric_pmf(
+    x DOUBLE PRECISION,
+    sp DOUBLE PRECISION
+) RETURNS VOID AS $$
+DECLARE
+BEGIN
+    RAISE EXCEPTION 'The first element must be INT';
+END
+$$ LANGUAGE plpgsql IMMUTABLE STRICT
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
 /**
  * @brief Geometric quantile function
  *
@@ -707,7 +729,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.geometric_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -735,7 +757,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.hypergeometric_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -760,7 +782,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.hypergeometric_pmf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -786,7 +808,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.hypergeometric_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -807,7 +829,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.inverse_gamma_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -827,7 +849,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.inverse_gamma_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -847,7 +869,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.inverse_gamma_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -865,7 +887,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.kolmogorov_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -885,7 +907,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.laplace_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -905,7 +927,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.laplace_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -925,7 +947,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.laplace_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -945,7 +967,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.logistic_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -965,7 +987,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.logistic_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -985,7 +1007,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.logistic_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1006,7 +1028,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lognormal_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1026,7 +1048,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lognormal_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1046,7 +1068,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.lognormal_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1067,7 +1089,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.negative_binomial_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1088,7 +1110,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.negative_binomial_pmf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1111,7 +1133,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.negative_binomial_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1134,7 +1156,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_beta_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1157,7 +1179,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_beta_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1180,7 +1202,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_beta_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1201,7 +1223,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_chi_squared_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1221,7 +1243,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_chi_squared_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1241,7 +1263,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_chi_squared_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1264,7 +1286,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_f_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1286,7 +1308,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_f_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1308,7 +1330,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_f_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1329,7 +1351,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_t_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1349,7 +1371,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_t_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1369,7 +1391,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.non_central_t_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1389,7 +1411,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normal_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normal_cdf(
@@ -1428,7 +1450,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normal_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normal_pdf(
@@ -1469,7 +1491,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normal_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normal_quantile(
@@ -1511,7 +1533,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pareto_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1531,7 +1553,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pareto_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1551,7 +1573,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.pareto_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1569,7 +1591,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.poisson_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1586,9 +1608,20 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.poisson_pmf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.poisson_pmf(
+    x DOUBLE PRECISION,
+    sp DOUBLE PRECISION
+) RETURNS VOID AS $$
+DECLARE
+BEGIN
+    RAISE EXCEPTION 'The first element must be INT';
+END
+$$ LANGUAGE plpgsql IMMUTABLE STRICT
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
 /**
  * @brief Poisson quantile function
  *
@@ -1605,7 +1638,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.poisson_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1623,7 +1656,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.rayleigh_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1640,7 +1673,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.rayleigh_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1657,7 +1690,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.rayleigh_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1675,7 +1708,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.students_t_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1693,7 +1726,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.students_t_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1711,7 +1744,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.students_t_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1733,7 +1766,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.triangular_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1754,7 +1787,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.triangular_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1775,7 +1808,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.triangular_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1795,7 +1828,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.uniform_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1814,7 +1847,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.uniform_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1833,7 +1866,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.uniform_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1854,7 +1887,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.weibull_cdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1874,7 +1907,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.weibull_pdf(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1894,5 +1927,5 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.weibull_quantile(
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
diff --git a/src/ports/postgres/modules/recursive_partitioning/decision_tree.py_in b/src/ports/postgres/modules/recursive_partitioning/decision_tree.py_in
index 69a54f3..2cffe99 100644
--- a/src/ports/postgres/modules/recursive_partitioning/decision_tree.py_in
+++ b/src/ports/postgres/modules/recursive_partitioning/decision_tree.py_in
@@ -398,7 +398,7 @@ def _build_tree(schema_madlib, is_classification, split_criterion,
               FROM {1}
           """.format(grouping_array_str, cp_table)
     grp_cp_values = plpy.execute(sql)
-    grp_key_to_cp = dict((row['grp_key'], row['cp_val']) for row in grp_cp_values)
+    grp_key_to_cp = dict((""if row['grp_key'] is None else row['grp_key'], row['cp_val']) for row in grp_cp_values)
 
     with MinWarning(msg_level):
         plpy.notice("Building tree for cross validation")
@@ -2144,7 +2144,7 @@ def _xvalidate(schema_madlib, tree_states, training_table_name, output_table_nam
     validation_result = plpy.execute(validation_result_query)
     plpy.notice("finished validation_result_query, validation_result = " + str(list(validation_result)))
 
-    grp_key_to_best_cp = dict((row['grp_key'], row['cp']) for row in validation_result)
+    grp_key_to_best_cp = dict((""if row['grp_key'] is None else row['grp_key'], row['cp']) for row in validation_result)
 
     # 4) update tree_states to have the best cp cross-validated
     for tree in tree_states:
diff --git a/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in b/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in
index 04f7b82..613d7e6 100644
--- a/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in
+++ b/src/ports/postgres/modules/recursive_partitioning/decision_tree.sql_in
@@ -1659,14 +1659,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._dst_compute_con_splits_transition(
     num_splits      SMALLINT
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME', 'dst_compute_con_splits_transition'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._dst_compute_con_splits_final(
     state           MADLIB_SCHEMA.bytea8
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME', 'dst_compute_con_splits_final'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS MADLIB_SCHEMA._dst_compute_con_splits(
@@ -1694,7 +1694,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._dst_compute_entropy_transition(
     num_dep_var     integer  -- constant for the state size
 ) RETURNS integer[] AS
     'MODULE_PATHNAME', 'dst_compute_entropy_transition'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1703,7 +1703,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._dst_compute_entropy_merge(
     state2          integer[]
 ) RETURNS integer[] AS
     'MODULE_PATHNAME', 'dst_compute_entropy_merge'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1711,7 +1711,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._dst_compute_entropy_final(
     state           integer[]
 ) RETURNS double precision AS
     'MODULE_PATHNAME', 'dst_compute_entropy_final'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1738,7 +1738,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._map_catlevel_to_int(
     null_as_category            BOOLEAN     -- flag to check if NULL is treated as a separate category
 ) RETURNS INTEGER[] AS
     'MODULE_PATHNAME', 'map_catlevel_to_int'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 ------------------------------------------------------------
 
@@ -1749,7 +1749,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._initialize_decision_tree(
     max_n_surr          SMALLINT
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME', 'initialize_decision_tree'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1766,7 +1766,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._compute_leaf_stats_transition(
     weights_as_rows        BOOLEAN
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME', 'compute_leaf_stats_transition'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1775,7 +1775,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._compute_leaf_stats_merge(
     state2          MADLIB_SCHEMA.BYTEA8
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME', 'compute_leaf_stats_merge'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -- One step in the iteration
@@ -1827,7 +1827,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._dt_apply(
     num_random_features INTEGER
 ) RETURNS MADLIB_SCHEMA._tree_result_type AS
     'MODULE_PATHNAME', 'dt_apply'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------------------------------------------------
@@ -1842,7 +1842,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._compute_surr_stats_transition(
     dup_count              INTEGER
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME', 'compute_surr_stats_transition'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS MADLIB_SCHEMA._compute_surr_stats(
@@ -1874,7 +1874,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._dt_surr_apply(
     con_splits      MADLIB_SCHEMA.BYTEA8
 ) RETURNS MADLIB_SCHEMA.BYTEA8 AS
     'MODULE_PATHNAME', 'dt_surr_apply'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -------------------------------------------------------------------------
@@ -1896,7 +1896,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._print_decision_tree(
     tree            MADLIB_SCHEMA.BYTEA8
 ) RETURNS MADLIB_SCHEMA._flattened_tree AS
     'MODULE_PATHNAME', 'print_decision_tree'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 -------------------------------------------------------------------------
 
@@ -1906,7 +1906,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._compute_var_importance(
     n_con_features  INTEGER
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'compute_variable_importance'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 -------------------------------------------------------------------------
 
@@ -1916,7 +1916,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._predict_dt_response(
     con_features    DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION AS
     'MODULE_PATHNAME', 'predict_dt_response'
-LANGUAGE C VOLATILE
+LANGUAGE C VOLATILE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 -------------------------------------------------------------------------
 
@@ -1926,7 +1926,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._predict_dt_prob(
     con_features    DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'predict_dt_prob'
-LANGUAGE C VOLATILE
+LANGUAGE C VOLATILE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------
@@ -2119,7 +2119,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._display_decision_tree(
   verbose            BOOLEAN
 )  RETURNS TEXT
 AS 'MODULE_PATHNAME', 'display_decision_tree'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._display_decision_tree(
@@ -2143,7 +2143,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._display_decision_tree_surrogate(
   cat_n_levels       INTEGER[]
 )  RETURNS TEXT
 AS 'MODULE_PATHNAME', 'display_decision_tree_surrogate'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._display_text_decision_tree(
@@ -2155,7 +2155,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._display_text_decision_tree(
   dependent_levels   TEXT[]
 ) RETURNS TEXT AS
     'MODULE_PATHNAME', 'display_text_tree'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 ------------------------------------------------------------
 
@@ -2422,7 +2422,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._prune_and_cplist(
     compute_cp_list     BOOLEAN
 ) RETURNS MADLIB_SCHEMA._prune_result_type AS
     'MODULE_PATHNAME', 'prune_and_cplist'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------
@@ -2433,7 +2433,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._convert_to_rpart_format(
     n_cats          INTEGER
 ) RETURNS DOUBLE PRECISION[][] AS
     'MODULE_PATHNAME', 'convert_to_rpart_format'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------
@@ -2444,7 +2444,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._get_split_thresholds(
     n_cats          integer
 ) RETURNS double precision[][] AS
     'MODULE_PATHNAME', 'get_split_thresholds'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 -------------------------------------------------------------------------
 
diff --git a/src/ports/postgres/modules/recursive_partitioning/random_forest.py_in b/src/ports/postgres/modules/recursive_partitioning/random_forest.py_in
index fae91bd..209b86a 100644
--- a/src/ports/postgres/modules/recursive_partitioning/random_forest.py_in
+++ b/src/ports/postgres/modules/recursive_partitioning/random_forest.py_in
@@ -333,7 +333,7 @@ def forest_train(
                 if grouping_cols is None:
                     sql_grp_key_to_grp_cols = """
                             CREATE TABLE {grp_key_to_grp_cols} AS
-                            SELECT ''::text AS grp_key, 1 AS gid
+                            SELECT ' '::text AS grp_key, 1 AS gid
                             """.format(**locals())
                     plpy.notice("sql_grp_key_to_grp_cols:\n" + sql_grp_key_to_grp_cols)
                     plpy.execute(sql_grp_key_to_grp_cols)
@@ -504,13 +504,13 @@ def forest_train(
                             is_classification, split_criterion, True,
                             num_random_features, max_n_surr, null_proxy)
 
-                        tree['grp_key'] = ''
+                        tree['grp_key'] = ' '
                         if importance:
                             tree.update(_compute_var_importance(
                                 schema_madlib, tree,
                                 len(cat_features), len(con_features)))
                         tree_states = [tree]
-                        tree_terminated = {'': tree['finished']}
+                        tree_terminated = {' ': tree['finished']}
                     else:
                         tree_states = _tree_train_grps_using_bins(
                             schema_madlib, bins, src_view, cat_features, con_features,
@@ -837,7 +837,7 @@ def get_tree(schema_madlib, model_table, gid, sample_id,
                         SELECT {0}._display_decision_tree(
                             $1, $2, $3, $4, $5, $6, '{1}', {2}
                             ) as display_tree
-                        """.format(schema_madlib, "", verbose)
+                        """.format(schema_madlib, " ", verbose)
             else:
                 sql_display = """
                         SELECT {0}._display_text_decision_tree(
diff --git a/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in b/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in
index 888388c..ae79129 100644
--- a/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in
+++ b/src/ports/postgres/modules/recursive_partitioning/random_forest.sql_in
@@ -1562,7 +1562,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._get_bin_value_by_index(
     bin_index           INTEGER
 ) RETURNS DOUBLE PRECISION AS
     'MODULE_PATHNAME', 'get_bin_value_by_index'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__',`NO SQL', `');
 -----------------------------------------------------------
 
@@ -1572,7 +1572,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._get_bin_index_by_value(
     feature_index       INTEGER
 ) RETURNS integer AS
     'MODULE_PATHNAME', 'get_bin_index_by_value'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__',`NO SQL', `');
 ------------------------------------------------------------
 
@@ -1581,7 +1581,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._get_bin_indices_by_values(
     con_splits          MADLIB_SCHEMA.bytea8
 ) RETURNS integer[] AS
     'MODULE_PATHNAME', 'get_bin_indices_by_values'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__',`NO SQL', `');
 ------------------------------------------------------------
 
@@ -2058,7 +2058,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._convert_to_random_forest_format(
     model           MADLIB_SCHEMA.bytea8
 ) RETURNS DOUBLE PRECISION[][] AS
     'MODULE_PATHNAME', 'convert_to_random_forest_format'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -- Helper functions for variable importance
@@ -2073,7 +2073,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._rf_cat_imp_score(
     cat_feature_distributions   DOUBLE PRECISION[][]
 ) RETURNS DOUBLE PRECISION[][] AS
     'MODULE_PATHNAME', 'rf_cat_imp_score'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._rf_con_imp_score(
@@ -2087,7 +2087,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._rf_con_imp_score(
     con_index_distrbutions  DOUBLE PRECISION[][]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'rf_con_imp_score'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -2096,5 +2096,5 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.normalize_sum_array(
     target_sum  DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'normalize_sum_array'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
diff --git a/src/ports/postgres/modules/regress/clustered_variance.sql_in b/src/ports/postgres/modules/regress/clustered_variance.sql_in
index f05630d..2f7345b 100644
--- a/src/ports/postgres/modules/regress/clustered_variance.sql_in
+++ b/src/ports/postgres/modules/regress/clustered_variance.sql_in
@@ -514,7 +514,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_err_lin_transition (
     coef DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -524,7 +524,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_err_lin_merge (
     state2 MADLIB_SCHEMA.bytea8
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -533,7 +533,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_err_lin_final (
     state MADLIB_SCHEMA.bytea8
 ) RETURNS MADLIB_SCHEMA.__clustered_agg_result AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -568,7 +568,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_lin_compute_stats (
     numRows             INTEGER
 ) RETURNS MADLIB_SCHEMA.__clustered_lin_result AS
     'MODULE_PATHNAME', 'clustered_lin_compute_stats'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -753,7 +753,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_err_log_transition (
     coef DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -763,7 +763,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_err_log_merge (
     state2 MADLIB_SCHEMA.bytea8
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -772,7 +772,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_err_log_final (
     state MADLIB_SCHEMA.bytea8
 ) RETURNS MADLIB_SCHEMA.__clustered_agg_result AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -807,7 +807,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_log_compute_stats (
     numRows             INTEGER
 ) RETURNS MADLIB_SCHEMA.__clustered_log_result AS
     'MODULE_PATHNAME', 'clustered_log_compute_stats'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -925,7 +925,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_err_mlog_transition (
     ref_cat INTEGER
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -935,7 +935,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_err_mlog_merge (
     state2 MADLIB_SCHEMA.bytea8
 ) RETURNS MADLIB_SCHEMA.bytea8 AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -944,7 +944,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_err_mlog_final (
     state MADLIB_SCHEMA.bytea8
 ) RETURNS MADLIB_SCHEMA.__clustered_agg_result AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -982,7 +982,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__clustered_mlog_compute_stats (
     numRows             INTEGER
 ) RETURNS MADLIB_SCHEMA.__clustered_mlog_result AS
     'MODULE_PATHNAME', 'clustered_mlog_compute_stats'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -- DEPRECATED NOTICE -----------------------------------------------------------
diff --git a/src/ports/postgres/modules/regress/linear.sql_in b/src/ports/postgres/modules/regress/linear.sql_in
index afc14d5..b827c43 100644
--- a/src/ports/postgres/modules/regress/linear.sql_in
+++ b/src/ports/postgres/modules/regress/linear.sql_in
@@ -646,7 +646,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.linregr_transition(
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -656,14 +656,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.linregr_merge_states(
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.linregr_final(
     state MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.linregr_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -- hetero
@@ -675,7 +675,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.hetero_linregr_transition(
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -685,7 +685,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.hetero_linregr_merge_states(
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -694,7 +694,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.hetero_linregr_final(
     state MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.heteroskedasticity_test_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 ---------------------------------------------------------------------------
 
@@ -810,7 +810,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.linregr_predict(
     col_ind_var     DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME', 'array_dot'
-LANGUAGE c IMMUTABLE STRICT;
+LANGUAGE c IMMUTABLE STRICT NOT FENCED;
 
 -- Help messages -------------------------------------------------------
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.linregr_predict(
diff --git a/src/ports/postgres/modules/regress/logistic.sql_in b/src/ports/postgres/modules/regress/logistic.sql_in
index e15b416..5fc9ad1 100644
--- a/src/ports/postgres/modules/regress/logistic.sql_in
+++ b/src/ports/postgres/modules/regress/logistic.sql_in
@@ -623,7 +623,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_cg_step_transition(
     DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'logregr_cg_step_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -635,7 +635,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_irls_step_transition(
     DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'logregr_irls_step_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -647,7 +647,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_igd_step_transition(
     DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'logregr_igd_step_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -657,7 +657,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_cg_step_merge_states(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'logregr_cg_step_merge_states'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE NOT FENCED STRICT
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -667,7 +667,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_irls_step_merge_states(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'logregr_irls_step_merge_states'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE NOT FENCED STRICT
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -677,7 +677,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_igd_step_merge_states(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'logregr_igd_step_merge_states'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE NOT FENCED STRICT
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -686,7 +686,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_cg_step_final(
     state DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'logregr_cg_step_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE NOT FENCED STRICT
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -695,7 +695,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_irls_step_final(
     state DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'logregr_irls_step_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE NOT FENCED STRICT
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -704,7 +704,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_igd_step_final(
     state DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'logregr_igd_step_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE NOT FENCED STRICT
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -776,7 +776,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_cg_step_distance(
     /*+ state2 */ DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', 'internal_logregr_cg_step_distance'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -785,7 +785,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_cg_result(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.__logregr_result AS
 'MODULE_PATHNAME', 'internal_logregr_cg_result'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -795,7 +795,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_irls_step_distance(
     /*+ state2 */ DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', 'internal_logregr_irls_step_distance'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -804,7 +804,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_irls_result(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.__logregr_result AS
 'MODULE_PATHNAME', 'internal_logregr_irls_result'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -814,7 +814,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_igd_step_distance(
     /*+ state2 */ DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME', 'internal_logregr_igd_step_distance'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -823,7 +823,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__logregr_igd_result(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.__logregr_result AS
 'MODULE_PATHNAME', 'internal_logregr_igd_result'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------------------
@@ -1044,7 +1044,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.logregr_predict(
     col_ind_var     DOUBLE PRECISION[]
 ) RETURNS BOOLEAN
 AS 'MODULE_PATHNAME', 'logregr_predict'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -- Help messages -------------------------------------------------------
@@ -1081,7 +1081,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.logregr_predict_prob(
     col_ind_var     DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME', 'logregr_predict_prob'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -- Help messages -------------------------------------------------------
diff --git a/src/ports/postgres/modules/regress/marginal.sql_in b/src/ports/postgres/modules/regress/marginal.sql_in
index a19424e..0b459af 100644
--- a/src/ports/postgres/modules/regress/marginal.sql_in
+++ b/src/ports/postgres/modules/regress/marginal.sql_in
@@ -510,7 +510,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.marginal_logregr_step_transition(
     coef  DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -520,7 +520,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.marginal_logregr_step_merge_states(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -529,7 +529,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.marginal_logregr_step_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.marginal_logregr_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -785,7 +785,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mlogregr_marginal_step_transition(
     coef            DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mlogregr_marginal_step_merge_states(
@@ -793,7 +793,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mlogregr_marginal_step_merge_states(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -801,7 +801,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mlogregr_marginal_step_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.marginal_mlogregr_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1207,7 +1207,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__sub_array(
     index_array INTEGER[]             -- The array containing the index
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -----------------------------------------------------------------------
@@ -1233,7 +1233,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_linregr_int_transition(
     derivative            DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'margins_linregr_int_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1243,7 +1243,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_linregr_int_merge(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'margins_linregr_int_merge'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1252,7 +1252,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_linregr_int_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.margins_result
 AS 'MODULE_PATHNAME', 'margins_linregr_int_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1299,7 +1299,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_logregr_int_transition(
     x_unset               DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'margins_logregr_int_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1309,7 +1309,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_logregr_int_merge(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'margins_logregr_int_merge'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1318,7 +1318,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_logregr_int_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.margins_result
 AS 'MODULE_PATHNAME', 'margins_logregr_int_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1375,7 +1375,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_mlogregr_int_transition(
     )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'margins_mlogregr_int_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1385,7 +1385,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_mlogregr_int_merge(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'margins_mlogregr_int_merge'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1394,7 +1394,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_mlogregr_int_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.margins_result
 AS 'MODULE_PATHNAME', 'margins_mlogregr_int_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1450,7 +1450,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_coxph_int_transition(
     )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'margins_coxph_int_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1460,7 +1460,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_coxph_int_merge(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'margins_coxph_int_merge'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1469,7 +1469,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_coxph_int_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.margins_result
 AS 'MODULE_PATHNAME', 'margins_coxph_int_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------------
@@ -1479,7 +1479,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__margins_compute_stats(
     std_err DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.margins_result
 AS 'MODULE_PATHNAME', 'margins_compute_stats'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
diff --git a/src/ports/postgres/modules/regress/multilogistic.sql_in b/src/ports/postgres/modules/regress/multilogistic.sql_in
index a4acf60..1f4f92b 100644
--- a/src/ports/postgres/modules/regress/multilogistic.sql_in
+++ b/src/ports/postgres/modules/regress/multilogistic.sql_in
@@ -523,7 +523,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__mlogregr_irls_step_transition
 )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -534,7 +534,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__mlogregr_irls_step_merge_states
 )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -544,7 +544,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__mlogregr_irls_step_final
 )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -575,14 +575,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__internal_mlogregr_irls_step_distance(
     /*+ state2 */ DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__internal_mlogregr_irls_result(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.mlogregr_result AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -590,7 +590,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__internal_mlogregr_summary_results(
     /*+ state */ DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.mlogregr_summary_result AS
 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 /**
@@ -1071,7 +1071,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__mlogregr_format(
     ref_category    INTEGER
 ) RETURNS SETOF MADLIB_SCHEMA.__mlogregr_cat_coef
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------
@@ -1084,7 +1084,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__mlogregr_predict_prob(
     col_ind_var     DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'mlogregr_predict_prob'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------
@@ -1097,7 +1097,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__mlogregr_predict_response(
     col_ind_var     DOUBLE PRECISION[]
 ) RETURNS INTEGER AS
     'MODULE_PATHNAME', 'mlogregr_predict_response'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 -------------------------------------------------------------------------
 
diff --git a/src/ports/postgres/modules/regress/robust.sql_in b/src/ports/postgres/modules/regress/robust.sql_in
index 7964eb0..c5ecb7b 100644
--- a/src/ports/postgres/modules/regress/robust.sql_in
+++ b/src/ports/postgres/modules/regress/robust.sql_in
@@ -531,7 +531,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.robust_linregr_transition(
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------
@@ -542,7 +542,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.robust_linregr_merge_states(
 RETURNS MADLIB_SCHEMA.bytea8
 AS 'MODULE_PATHNAME'
 LANGUAGE C
-IMMUTABLE STRICT
+IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------
@@ -551,7 +551,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.robust_linregr_final(
     state MADLIB_SCHEMA.bytea8)
 RETURNS MADLIB_SCHEMA.robust_linregr_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------
@@ -646,7 +646,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.robust_logregr_step_transition(
     coef DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------
@@ -656,7 +656,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.robust_logregr_step_merge_states(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------
@@ -665,7 +665,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.robust_logregr_step_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.robust_logregr_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------
@@ -864,7 +864,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mlogregr_robust_step_transition
 )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------
@@ -876,7 +876,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mlogregr_robust_step_merge_states
 )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------
@@ -887,7 +887,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mlogregr_robust_step_final
 )
 RETURNS MADLIB_SCHEMA.robust_mlogregr_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ---------------------------------
diff --git a/src/ports/postgres/modules/sample/sample.sql_in b/src/ports/postgres/modules/sample/sample.sql_in
index 8f8a56f..3652f4c 100644
--- a/src/ports/postgres/modules/sample/sample.sql_in
+++ b/src/ports/postgres/modules/sample/sample.sql_in
@@ -69,7 +69,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.weighted_sample_transition_int64(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 VOLATILE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -80,7 +80,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.weighted_sample_merge_int64(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 VOLATILE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -90,7 +90,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.weighted_sample_final_int64(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -128,7 +128,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.weighted_sample_transition_vector(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 VOLATILE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -139,7 +139,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.weighted_sample_merge_vector(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 VOLATILE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -149,7 +149,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.weighted_sample_final_vector(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 IMMUTABLE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -173,7 +173,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.poisson_random(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 VOLATILE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 ---------------------------------------------------------------------------
 
@@ -183,7 +183,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gamma_random(
 AS 'MODULE_PATHNAME'
 LANGUAGE C
 VOLATILE
-STRICT
+STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 ---------------------------------------------------------------------------
 
@@ -191,5 +191,5 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.index_weighted_sample(
     double precision[]
 ) RETURNS integer
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
diff --git a/src/ports/postgres/modules/sample/stratified_sample.py_in b/src/ports/postgres/modules/sample/stratified_sample.py_in
index 90d12d5..7fe88f0 100644
--- a/src/ports/postgres/modules/sample/stratified_sample.py_in
+++ b/src/ports/postgres/modules/sample/stratified_sample.py_in
@@ -88,9 +88,9 @@ def stratified_sample(schema_madlib, source_table, output_table, proportion,
 
                 # Find the cut-off label for the given proportion
                 sql2 = """ CREATE TEMP TABLE {perc} AS (
-                    SELECT {grouping_cols}, percentile_disc({proportion})
-                        WITHIN GROUP (ORDER BY __samp_out_label) AS __samp_out_label
-                    FROM {label} GROUP BY {grouping_cols})""".format(**locals())
+                    SELECT {grouping_cols},
+                    {schema_madlib}.percentile_disc(array_agg(__samp_out_label),{proportion}) AS __samp_out_label
+                    FROM {label} where __samp_out_label is not null GROUP BY {grouping_cols})""".format(**locals())
                 plpy.execute(sql2)
 
                 # Select every record that has a label under the threshold
diff --git a/src/ports/postgres/modules/stats/clustered_variance_coxph.sql_in b/src/ports/postgres/modules/stats/clustered_variance_coxph.sql_in
index 58d6e8e..4475071 100644
--- a/src/ports/postgres/modules/stats/clustered_variance_coxph.sql_in
+++ b/src/ports/postgres/modules/stats/clustered_variance_coxph.sql_in
@@ -55,7 +55,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_a_b_transition(
     /* H values */  DOUBLE PRECISION[],
     /* S */         DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -65,7 +65,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_a_b_merge(
     /* left state */    DOUBLE PRECISION[],
     /* right state */   DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -81,7 +81,7 @@ CREATE TYPE MADLIB_SCHEMA.__coxph_a_b_result AS(
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_a_b_final(
     /* right state */   DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__coxph_a_b_result AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -117,7 +117,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_compute_w(
     A       DOUBLE PRECISION,
     B       DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ----------------------------------------------------------------------
@@ -136,7 +136,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_compute_clustered_stats(
     hessian DOUBLE PRECISION[],
     A       DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__coxph_cl_var_result AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
diff --git a/src/ports/postgres/modules/stats/correlation.sql_in b/src/ports/postgres/modules/stats/correlation.sql_in
index 4932385..b9b6f9d 100644
--- a/src/ports/postgres/modules/stats/correlation.sql_in
+++ b/src/ports/postgres/modules/stats/correlation.sql_in
@@ -396,7 +396,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.correlation_transition(
     mean        double precision[]
 ) RETURNS double precision[] AS
     'MODULE_PATHNAME', 'correlation_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.correlation_merge(
@@ -404,14 +404,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.correlation_merge(
     right_state double precision[]
 ) RETURNS double precision[] AS
     'MODULE_PATHNAME', 'correlation_merge_states'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.correlation_final(
     state       double precision[]
 ) RETURNS double precision[] AS
     'MODULE_PATHNAME', 'correlation_final'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.correlation_agg(
diff --git a/src/ports/postgres/modules/stats/cox_prop_hazards.sql_in b/src/ports/postgres/modules/stats/cox_prop_hazards.sql_in
index e8faa01..b3b70b6 100644
--- a/src/ports/postgres/modules/stats/cox_prop_hazards.sql_in
+++ b/src/ports/postgres/modules/stats/cox_prop_hazards.sql_in
@@ -589,7 +589,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_avg_transition(
     use_abs         BOOLEAN
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_avg_merge(
@@ -597,14 +597,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_avg_merge(
     right           DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.array_avg_final(
     state           DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.array_avg(
@@ -811,7 +811,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._coxph_predict_resp(
     pred_type           TEXT
 ) RETURNS DOUBLE PRECISION
 AS 'MODULE_PATHNAME', 'coxph_predict_resp'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -821,7 +821,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._coxph_predict_terms(
     mean_ind_var        DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'coxph_predict_terms'
-LANGUAGE C STRICT IMMUTABLE
+LANGUAGE C STRICT IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -858,7 +858,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._split_transition(
     /* num_splits */        INTEGER
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'split_transition'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 -------------
@@ -868,7 +868,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._split_merge(
     /* right state */   DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'split_merge'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------
@@ -877,7 +877,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._split_final(
     /* final state */   DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'split_final'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------
@@ -908,7 +908,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA._compute_grpid(
     reverse     BOOLEAN
 ) RETURNS INTEGER AS
     'MODULE_PATHNAME', 'compute_grpid'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------
@@ -948,7 +948,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.compute_coxph_result(
     stds    DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.coxph_result AS
     'MODULE_PATHNAME', 'compute_coxph_result'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------
@@ -968,7 +968,7 @@ MADLIB_SCHEMA.coxph_improved_step_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.coxph_step_result AS
     'MODULE_PATHNAME', 'coxph_improved_step_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -984,7 +984,7 @@ MADLIB_SCHEMA.coxph_improved_step_transition(
 )
 RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'coxph_improved_step_transition'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------
@@ -1035,7 +1035,7 @@ DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.coxph_improved_strata_step_inner(
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_step_inner_final(
     state DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ----------------------------------------------------------------------
@@ -1044,7 +1044,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_step_outer_transition(
     /*+ state1 */ DOUBLE PRECISION[],
     /*+ state2 */ DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------------------------------
@@ -1071,7 +1071,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_improved_strata_step_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.coxph_step_result AS
     'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 ------------------------------------
@@ -1149,7 +1149,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__zph_transition(
     /*+  coef */   DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'zph_transition'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1159,7 +1159,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__zph_merge(
     /*+  right_state */  DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'zph_merge'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1167,7 +1167,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__zph_final(
     /*+  left_state */  DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'zph_final'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1199,7 +1199,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__coxph_scale_resid(
     resid   DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'coxph_scale_resid'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1213,7 +1213,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__array_elem_corr_transition(
     /*+  y */      DOUBLE PRECISION)
 RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'array_elem_corr_transition'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1223,7 +1223,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__array_elem_corr_merge(
     /*+  right_state */  DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'array_elem_corr_merge'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1231,7 +1231,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__array_elem_corr_final(
     /*+  state */  DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[] AS
 'MODULE_PATHNAME', 'array_elem_corr_final'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1265,7 +1265,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__coxph_resid_stat_transition(
     /*+ m        */ INTEGER)
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'coxph_resid_stat_transition'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1275,7 +1275,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__coxph_resid_stat_merge(
     /*+ state2   */ DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'coxph_resid_stat_merge'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1292,7 +1292,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__coxph_resid_stat_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.__cox_resid_stat_result
 AS 'MODULE_PATHNAME', 'coxph_resid_stat_final'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1479,7 +1479,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__array_element_min(
 )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'array_element_min'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -1503,7 +1503,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__array_element_max(
 )
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME', 'array_element_max'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
diff --git a/src/ports/postgres/modules/stats/distribution.sql_in b/src/ports/postgres/modules/stats/distribution.sql_in
index 30ee3cd..a60ccb8 100644
--- a/src/ports/postgres/modules/stats/distribution.sql_in
+++ b/src/ports/postgres/modules/stats/distribution.sql_in
@@ -14,14 +14,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.vectorized_distribution_transition(
     levels          integer[]
 ) RETURNS double precision[][] AS
     'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__',`NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.vectorized_distribution_final(
     state   double precision[][]
 ) RETURNS double precision[][] AS
     'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__',`NO SQL', `');
 
 DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.vectorized_distribution_agg(
@@ -45,14 +45,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.discrete_distribution_transition(
     level           integer
 ) RETURNS double precision[] AS
     'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE
+LANGUAGE c IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__',`NO SQL', `');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.discrete_distribution_final(
     state   double precision[]
 ) RETURNS double precision[] AS
     'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE c IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__',`NO SQL', `');
 
 DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.discrete_distribution_agg(
diff --git a/src/ports/postgres/modules/stats/hypothesis_tests.sql_in b/src/ports/postgres/modules/stats/hypothesis_tests.sql_in
index 61dbb89..ba43a03 100644
--- a/src/ports/postgres/modules/stats/hypothesis_tests.sql_in
+++ b/src/ports/postgres/modules/stats/hypothesis_tests.sql_in
@@ -572,7 +572,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.t_test_one_transition(
     value DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.t_test_merge_states(
@@ -580,7 +580,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.t_test_merge_states(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 
@@ -588,7 +588,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.t_test_one_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.t_test_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 DROP TYPE IF EXISTS MADLIB_SCHEMA.f_test_result CASCADE;
@@ -604,7 +604,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.f_test_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.f_test_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 /**
@@ -664,7 +664,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.t_test_two_transition(
     "value" DOUBLE PRECISION)
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 
@@ -672,7 +672,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.t_test_two_pooled_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.t_test_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 /**
  * @brief Perform two-sample pooled (i.e., equal variances) Student t-test
@@ -739,7 +739,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.t_test_two_unpooled_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.t_test_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 /**
  * @brief Perform unpooled (i.e., unequal variances) t-test (also known as
@@ -861,7 +861,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi2_gof_test_transition(
     df BIGINT
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi2_gof_test_transition(
@@ -870,7 +870,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi2_gof_test_transition(
     expected DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi2_gof_test_transition(
@@ -878,7 +878,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi2_gof_test_transition(
     observed BIGINT
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi2_gof_test_merge_states(
@@ -886,7 +886,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi2_gof_test_merge_states(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 DROP TYPE IF EXISTS MADLIB_SCHEMA.chi2_test_result CASCADE;
@@ -902,7 +902,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.chi2_gof_test_final(
     state DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.chi2_test_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 /**
@@ -1009,7 +1009,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.ks_test_transition(
     "numSecond" BIGINT
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 DROP TYPE IF EXISTS MADLIB_SCHEMA.ks_test_result CASCADE;
@@ -1023,7 +1023,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.ks_test_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.ks_test_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 /**
@@ -1097,7 +1097,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mw_test_transition(
     "value" DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 DROP TYPE IF EXISTS MADLIB_SCHEMA.mw_test_result CASCADE;
@@ -1112,7 +1112,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.mw_test_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.mw_test_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 /**
  * @brief Perform Mann-Whitney test
@@ -1184,7 +1184,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.wsr_test_transition(
     "precision" DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.wsr_test_transition(
@@ -1192,7 +1192,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.wsr_test_transition(
     value DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 
@@ -1211,7 +1211,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.wsr_test_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.wsr_test_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 /**
  * @brief Perform Wilcoxon-Signed-Rank test
@@ -1336,7 +1336,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.one_way_anova_transition(
     value DOUBLE PRECISION)
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.one_way_anova_merge_states(
@@ -1344,14 +1344,14 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.one_way_anova_merge_states(
     state2 DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[]
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.one_way_anova_final(
     state DOUBLE PRECISION[])
 RETURNS MADLIB_SCHEMA.one_way_anova_result
 AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(, , );
 /**
  * @brief Perform one-way analysis of variance
diff --git a/src/ports/postgres/modules/stats/robust_variance_coxph.sql_in b/src/ports/postgres/modules/stats/robust_variance_coxph.sql_in
index 5bc58f7..710a649 100644
--- a/src/ports/postgres/modules/stats/robust_variance_coxph.sql_in
+++ b/src/ports/postgres/modules/stats/robust_variance_coxph.sql_in
@@ -60,7 +60,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.rb_coxph_step_transition(
     /* H values */  DOUBLE PRECISION[],
     /* S */         DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -69,7 +69,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.rb_coxph_step_final(
     /* state */     DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__rb_coxph_result AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------
@@ -106,7 +106,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_h_s_transition(
     /* x */         DOUBLE PRECISION[],
     /* coef */      DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------
@@ -115,7 +115,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_h_s_merge(
     /*+  left_state */      DOUBLE PRECISION[],
     /*+  right_state */     DOUBLE PRECISION[])
 RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------
@@ -123,7 +123,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.coxph_h_s_final(
     /* state */     DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__rb_coxph_hs_result AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------
@@ -148,7 +148,7 @@ CREATE AGGREGATE MADLIB_SCHEMA.coxph_h_s(
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.rb_coxph_strata_step_final(
     /* state */     DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------
@@ -185,7 +185,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.rb_sum_strata_transition(
     in_state1       DOUBLE PRECISION[],
     in_state2       DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------
@@ -193,7 +193,7 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.rb_sum_strata_final(
     in_state        DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__rb_coxph_result AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 --------------------------------
diff --git a/src/ports/postgres/modules/summary/Summarizer.py_in b/src/ports/postgres/modules/summary/Summarizer.py_in
index 5790972..a4030d3 100644
--- a/src/ports/postgres/modules/summary/Summarizer.py_in
+++ b/src/ports/postgres/modules/summary/Summarizer.py_in
@@ -169,17 +169,19 @@ class Summarizer:
             return "NULL"
 
         def mfv_type(get_count, c):
-            slicing = ('0:0', '1:1')[get_count]
+            slicing = ('0,0', '1,1')[get_count]
             mfv_method = ('mfvsketch_top_histogram',
                           'mfvsketch_quick_histogram')[self._get_mfv_quick]
+            dim_end = self._how_many_mfv - 1
             return """
                     array_to_string(({schema_madlib}.{mfv_method}(
-                                {column},{topk}))[0:{topk}-1][{slice}],
+                                {column},{topk}))[0,{dim_end}][{slice}],
                                 '{delimiter}')""".format(
                 schema_madlib=self._schema_madlib,
                 mfv_method=mfv_method,
                 column=c['attname'],
                 topk=self._how_many_mfv,
+                dim_end=dim_end,
                 slice=slicing,
                 delimiter=self._delimiter)
         # ------ End of Helper sub-functions  ------
diff --git a/src/ports/postgres/modules/svm/svm.sql_in b/src/ports/postgres/modules/svm/svm.sql_in
index ba05e86..8a538aa 100644
--- a/src/ports/postgres/modules/svm/svm.sql_in
+++ b/src/ports/postgres/modules/svm/svm.sql_in
@@ -1155,20 +1155,20 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.linear_svm_igd_transition(
         tuple_weight    double precision
 )
 RETURNS double precision[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.linear_svm_igd_merge(
         state1 double precision[],
         state2 double precision[])
 RETURNS double precision[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.linear_svm_igd_final(
         state double precision[])
 RETURNS double precision[] AS 'MODULE_PATHNAME'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 /**
@@ -1213,13 +1213,13 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_linear_svm_igd_distance(
     /*+ state1 */ double precision[],
     /*+ state2 */ double precision[])
 RETURNS double precision AS 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.internal_linear_svm_igd_result(
     /*+ state */ double precision[])
 RETURNS MADLIB_SCHEMA.linear_svm_result AS 'MODULE_PATHNAME'
-LANGUAGE c IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL');
 
 
diff --git a/src/ports/postgres/modules/tsa/arima.py_in b/src/ports/postgres/modules/tsa/arima.py_in
index 6ae7588..2dfea47 100644
--- a/src/ports/postgres/modules/tsa/arima.py_in
+++ b/src/ports/postgres/modules/tsa/arima.py_in
@@ -202,7 +202,7 @@ def arima_train (schema_madlib, input_table, output_table,
     # Also add an auxiliary array column which contains p previous time
     # series values.
     # tbl_ts is the time series table we are going to use. For convenience,
-    # we fix the column names: tid, distid and tvals for time, distribution
+    # we fix the column names: timeid, distid and tvals for time, distribution
     # id and an array of current value and p previous values.
     # if the data has been differenced, we only need to do adjustment
     if d > 0:
@@ -621,16 +621,16 @@ def _redist_data(schema_madlib, input_table, timestamp_col,
     dist_table = unique_string() + "redist__"
 
     # Redistribute the data
-    # 
+    # 
     plpy.execute(
         """
         create temp table {dist_table} as
             select
-                ((tid - 1) / {chunk_size})::integer + 1 as distid,
-                tid, origin_tid, tval
+                ((timeid - 1) / {chunk_size})::integer + 1 as distid,
+                timeid, origin_tid, tval
             from (
                 select
-                    row_number() over (order by {timestamp_col}) as tid,
+                    row_number() over (order by {timestamp_col}) as timeid,
                     {timestamp_col} as origin_tid,
                     {timeseries_col} as tval
                 from
@@ -650,10 +650,10 @@ def _redist_data(schema_madlib, input_table, timestamp_col,
             """
             insert into {dist_table}
                 select
-                    o.distid + 1, tid, NULL, tval
+                    o.distid + 1, timeid, NULL, tval
                 from (
                     select
-                        distid, max(tid) maxid
+                        distid, max(timeid) maxid
                     from
                         {dist_table}
                     where
@@ -662,24 +662,24 @@ def _redist_data(schema_madlib, input_table, timestamp_col,
                 ) q1, {dist_table} o
                 where
                     q1.distid = o.distid and
-                    q1.maxid - o.tid < {d}
+                    q1.maxid - o.timeid < {d}
             """.format(dist_table=dist_table, d=d))
 
     output_table = unique_string() + "redist_agg__"
     # Aggregate the chunk into an array
     # Note that each aggregated chunk except for the first one
-    # has additonal p or d elements and tid is 1 for the first
-    # chunk and is the actual starting tid for the other chunks
+    # has additonal p or d elements and timeid is 1 for the first
+    # chunk and is the actual starting timeid for the other chunks
     # - the corresponding tval is the pth (0 based) element in
     # tvals
-    # Note that both distid and tid starts from 1
+    # Note that both distid and timeid starts from 1
     plpy.execute(
         """
         create temp table {output_table} as
         select
             distid,
-            (distid - 1) * {chunk_size} + 1 as tid,
-            array_agg(tval order by tid) as tvals
+            (distid - 1) * {chunk_size} + 1 as timeid,
+            array_agg(tval order by timeid) as tvals
         from
             {dist_table}
         group by
@@ -722,14 +722,14 @@ def _diff (schema_madlib, input_table, timestamp_col, timeseries_col, d,
         create temp table {output_table} as
             select
                 distid::integer,
-                case when tid = 1 then 1::integer
-                     else (tid - {d})::integer
-                end as tid,
+                case when timeid = 1 then 1::integer
+                     else (timeid - {d})::integer
+                end as timeid,
                 tvals
             from
             (
                 select
-                    distid, tid,
+                    distid, timeid,
                     {schema_madlib}.__arima_diff(tvals, {d}) tvals
                 from
                     {input_table}
@@ -759,7 +759,7 @@ def _adjust_diff_data(schema_madlib, diff_table, p):
         """
         create temp table {output_table} as
             select
-                t1.distid::integer, t1.tid::integer,
+                t1.distid::integer, t1.timeid::integer,
                 {schema_madlib}.__arima_adjust(t1.distid, t1.tvals, t2.tvals, {p}) as tvals
             from
                 {diff_table} as t1, {diff_table} as t2
@@ -816,7 +816,7 @@ def _create_resid_tbl (schema_madlib, tbl_ts, p, d, q, phi,
     if q != 0:
         theta_str = "array[" + ",".join(str(i) for i in theta) + "]"
 
-    # Joining on tid is really slow on large data
+    # Joining on timeid is really slow on large data
     # (more than 100 times slower).
     # This is why we use join on distid, which gives almost the
     # same performance as that of the original code.
@@ -1099,7 +1099,7 @@ def _compute_statistics (schema_madlib, tbl_ts, p, q, phi, theta,
 
     Args:
         @param schema_madlib - MADlib schema name
-        @param tbl_ts - time series table name, it has two columns: tid
+        @param tbl_ts - time series table name, it has two columns: timeid
                         and tvals, which is the p previous values plus the
                         current value ordered by time.
         @param p - integer, AR order
@@ -1129,7 +1129,7 @@ def _compute_statistics (schema_madlib, tbl_ts, p, q, phi, theta,
                     '{theta}'::double precision[],
                     {mean},
                     {delta}
-                    order by tid
+                    order by timeid
                 ) as f
             from {tbl_ts}
         ) s
diff --git a/src/ports/postgres/modules/tsa/arima.sql_in b/src/ports/postgres/modules/tsa/arima.sql_in
index 48f0abd..5769ca3 100644
--- a/src/ports/postgres/modules/tsa/arima.sql_in
+++ b/src/ports/postgres/modules/tsa/arima.sql_in
@@ -641,7 +641,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_residual
     prez    DOUBLE PRECISION[]
 )
 RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME', 'arima_residual'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -652,7 +652,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_diff
     d       INTEGER
 )
 RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME', 'arima_diff'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -666,7 +666,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_adjust
     p             INTEGER
 )
 RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME', 'arima_adjust'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -680,7 +680,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_lm_delta
     u   DOUBLE PRECISION      -- Weight of gradient-descent step
 )
 RETURNS DOUBLE PRECISION[] AS 'MODULE_PATHNAME', 'arima_lm_delta'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -713,7 +713,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_lm
 )
 RETURNS MADLIB_SCHEMA.__arima_lm_result
 AS 'MODULE_PATHNAME', 'arima_lm'
-LANGUAGE C
+LANGUAGE C NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -728,7 +728,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_lm_result_sfunc (
     z2          DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'arima_lm_result_sfunc'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -739,7 +739,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_lm_result_pfunc (
     state2      DOUBLE PRECISION[]
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'arima_lm_result_pfunc'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -759,7 +759,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_lm_result_ffunc (
     state_data  DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__arima_lm_sum_result AS
     'MODULE_PATHNAME', 'arima_lm_result_ffunc'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -799,7 +799,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_lm_stat_sfunc (
     delta           DOUBLE PRECISION
 ) RETURNS DOUBLE PRECISION[] AS
     'MODULE_PATHNAME', 'arima_lm_stat_sfunc'
-LANGUAGE C IMMUTABLE
+LANGUAGE C IMMUTABLE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
@@ -818,7 +818,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__arima_lm_stat_ffunc (
     state_data      DOUBLE PRECISION[]
 ) RETURNS MADLIB_SCHEMA.__arima_lm_stat_result AS
     'MODULE_PATHNAME', 'arima_lm_stat_ffunc'
-LANGUAGE C IMMUTABLE STRICT
+LANGUAGE C IMMUTABLE STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
diff --git a/src/ports/postgres/modules/utilities/control.py_in b/src/ports/postgres/modules/utilities/control.py_in
index 32f2f59..3965e50 100644
--- a/src/ports/postgres/modules/utilities/control.py_in
+++ b/src/ports/postgres/modules/utilities/control.py_in
@@ -234,7 +234,7 @@ class AOControl(ContextDecorator):
     def __init__(self, enable=False):
         self.to_enable = enable
         self.was_ao_enabled = False
-        self.guc_exists = True
+        self.guc_exists = False
         self.storage_options_dict = dict()
 
     def _parse_gp_default_storage_options(self, gp_default_storage_options_str):
diff --git a/src/ports/postgres/modules/utilities/in_mem_group_control.py_in b/src/ports/postgres/modules/utilities/in_mem_group_control.py_in
index 86f0c17..be28ea0 100644
--- a/src/ports/postgres/modules/utilities/in_mem_group_control.py_in
+++ b/src/ports/postgres/modules/utilities/in_mem_group_control.py_in
@@ -106,10 +106,14 @@ class BaseState(object):
         if col_grp_state == '':
             self._is_none = True
             for s in ret_states:
+                if s[col_grp_key] is None:
+                    s[col_grp_key] = ' '
                 self._state[s[col_grp_key]] = None
             return failed_grp_keys
         for t in ret_states:
             _grp_key, _grp_state = t[col_grp_key], t[col_grp_state]
+            if _grp_key is None:
+                _grp_key = ' '
             if _grp_state is None:
                 failed_grp_keys.append(_grp_key)
             else:
@@ -204,6 +208,7 @@ class GroupIterationController:
                 col_n_tuples: Name of the column containing count of tuples.
                               Set to a unique string if not defined
         """
+        NULL_list = ['Null', 'NULL','None','NONE']
         self.schema_madlib = arg_dict['schema_madlib']
         self.in_with = False
         self.iteration = -1
@@ -222,6 +227,8 @@ class GroupIterationController:
                           if arg_dict["grouping_col"] is None
                           else arg_dict["grouping_col"]),
         )
+        if self.kwargs['grouping_str'] in NULL_list:
+            self.kwargs['grouping_str'] = "' '"
         self.grp_to_n_tuples = {}
         self.failed_grp_keys = []
 
@@ -287,6 +294,7 @@ class GroupIterationController:
                          else "{grouping_col}").format(**self.kwargs)
             groupby_str = ("{col_grp_null}" if self.is_group_null
                            else "{grouping_col}").format(**self.kwargs)
+
             plpy.execute("""
                 DROP TABLE IF EXISTS {rel_state};
                 CREATE TEMPORARY TABLE {rel_state} AS (
@@ -384,6 +392,7 @@ class GroupIterationController:
             select_rel_state=group_param.select_rel_state,
             select_n_tuples=group_param.select_n_tuples,
             **self.kwargs)
+        
         insert_plan = plpy.prepare(insert_sql,
                                    ["text[]", group_param.grouped_state_type,
                                     "text[]", "bigint[]"])
diff --git a/src/ports/postgres/modules/utilities/minibatch_preprocessing.py_in b/src/ports/postgres/modules/utilities/minibatch_preprocessing.py_in
index c25463c..9346d4a 100644
--- a/src/ports/postgres/modules/utilities/minibatch_preprocessing.py_in
+++ b/src/ports/postgres/modules/utilities/minibatch_preprocessing.py_in
@@ -284,8 +284,8 @@ class MiniBatchPreProcessor:
                    {self.schema_madlib}.matrix_agg({dep_colname}) as {dep_colname},
                    {self.schema_madlib}.matrix_agg({ind_colname}) as {ind_colname}
             FROM (
-                SELECT (row_number() OVER ({partition_by} ORDER BY random()) - 1)
-                        / {buffer_size}
+                SELECT trunc((row_number() OVER ({partition_by} ORDER BY random()) - 1)
+                        / {buffer_size})
                             as {row_id}, *
                 FROM (
                     {standardize_query}
@@ -474,7 +474,7 @@ class MiniBatchBufferSizeCalculator:
            default_buffer_size to int, because it will be used to calculate the
            row id of the packed input. The query looks like this
 
-           SELECT (row_number() OVER (ORDER BY random()) - 1) / {buffer_size}
+           SELECT (row_number() OVER (ORDER BY random())) / {buffer_size}
 
            This calculation has to return an int for which buffer_size has
            to be an int
diff --git a/src/ports/postgres/modules/utilities/path.sql_in b/src/ports/postgres/modules/utilities/path.sql_in
index 6c102eb..6ab554a 100644
--- a/src/ports/postgres/modules/utilities/path.sql_in
+++ b/src/ports/postgres/modules/utilities/path.sql_in
@@ -551,7 +551,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.path_pattern_match
     overlapping_patterns      BOOLEAN
 ) RETURNS MADLIB_SCHEMA.path_match_result
 AS 'MODULE_PATHNAME', 'path_pattern_match'
-LANGUAGE C STRICT
+LANGUAGE C STRICT NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');
 
 
diff --git a/src/ports/postgres/modules/utilities/pivot.py_in b/src/ports/postgres/modules/utilities/pivot.py_in
index 1b1a5d9..48a8376 100644
--- a/src/ports/postgres/modules/utilities/pivot.py_in
+++ b/src/ports/postgres/modules/utilities/pivot.py_in
@@ -305,8 +305,7 @@ def pivot(schema_madlib, source_table, output_table, index, pivot_cols,
                     # Aggregate over each pivot column, while filtering all NULL
                     #  values created by previous query.
                     sub_pivot_str_sel = _fill_value_wrapper(
-                        "{agg}({p_name}) "
-                        "   FILTER (WHERE {p_name} IS NOT NULL)".
+                        "{agg}(CASE WHEN {p_name} IS NOT NULL THEN {p_name} END )".
                         format(agg=agg, p_name=p_name))
                     if not is_array_output:
                         # keep spaces around the 'AS'
diff --git a/src/ports/postgres/modules/utilities/test/minibatch_preprocessing.sql_in b/src/ports/postgres/modules/utilities/test/minibatch_preprocessing.sql_in
index b6b2996..3f7d0f1 100644
--- a/src/ports/postgres/modules/utilities/test/minibatch_preprocessing.sql_in
+++ b/src/ports/postgres/modules/utilities/test/minibatch_preprocessing.sql_in
@@ -139,7 +139,7 @@ SELECT assert
         ) from (select * from minibatch_preprocessing_out_summary) summary;
 
 -- grouping with the same dataset
-\set expected_grouping_row_count '\'' 1,1,2 '\''
+\set expected_grouping_row_count '''1,1,2'''
 DROP TABLE IF EXISTS minibatch_preprocessing_out, minibatch_preprocessing_out_standardization, minibatch_preprocessing_out_summary;
 SELECT minibatch_preprocessor('minibatch_preprocessing_input', 'minibatch_preprocessing_out',  'length>0.2',  'ARRAY[diameter,height,whole,shucked,viscera,shell]', 'rings', 4);
 SELECT assert
@@ -231,8 +231,8 @@ INSERT INTO minibatch_preprocessing_input(x1,x2,y) VALUES
 SELECT minibatch_preprocessor('minibatch_preprocessing_input', 'minibatch_preprocessing_out', 'y', 'ARRAY[x1,x2]', NULL, 2);
 
 -- since the order is not deterministic, we assert for all possible orders
-\set expected_normalized_independent_var1 '\'' {{-1, -1},{1, 1}} '\''
-\set expected_normalized_independent_var2 '\'' {{1, 1},{-1, -1}} '\''
+\set expected_normalized_independent_var1 '''{{-1,-1},{1,1}}'''
+\set expected_normalized_independent_var2 '''{{1,1},{-1,-1}}'''
 
 SELECT assert
 (
diff --git a/src/ports/postgres/modules/utilities/test/pivot.sql_in b/src/ports/postgres/modules/utilities/test/pivot.sql_in
index fb034e7..695805a 100644
--- a/src/ports/postgres/modules/utilities/test/pivot.sql_in
+++ b/src/ports/postgres/modules/utilities/test/pivot.sql_in
@@ -48,8 +48,8 @@ SELECT assert(val_avg_piv_20 = 3, 'Wrong output in pivoting') FROM pivout WHERE
 DROP VIEW IF EXISTS pivset_ext;
 CREATE VIEW pivset_ext AS
     SELECT *,
-    COALESCE(id + ("PIVSET".val / 3), 0) AS id2,
-    COALESCE(piv + ("PIVSET".val / 3), 0) AS piv2,
+    COALESCE(id + floor("PIVSET".val / 3), 0)::int AS id2,
+    COALESCE(piv + floor("PIVSET".val / 3), 0)::int AS piv2,
     COALESCE(val + 10, 0) AS val2
    FROM "PIVSET";
 SELECT id,id2,piv,piv2,val,val2 FROM pivset_ext
diff --git a/src/ports/postgres/modules/utilities/test/sessionize.sql_in b/src/ports/postgres/modules/utilities/test/sessionize.sql_in
index 808970c..1d948f3 100644
--- a/src/ports/postgres/modules/utilities/test/sessionize.sql_in
+++ b/src/ports/postgres/modules/utilities/test/sessionize.sql_in
@@ -75,7 +75,7 @@ INSERT INTO eventlog_installchk VALUES
 (to_timestamp(NULL, 'MM/DD/YYYY HH:MI:SS'), 103711, NULL, 'WINE', 0, 1, 't'),
 (to_timestamp('04/15/2016 02:17:00', 'MM/DD/YYYY HH:MI:SS'), 103711, 1, 'BEER', 0, 1, 't'),
 (to_timestamp('04/15/2016 02:21:00', 'MM/DD/YYYY HH:MI:SS'), 103711, 2, 'LANDING', 0, 1, 't'),
-(to_timestamp('04/15/2016 02:31:0.05', 'MM/DD/YYYY HH:MI:SS'), 103711, 3, 'WINE', 0, 1, 't');
+(to_timestamp('04/15/2016 02:31:0.05', 'MM/DD/YYYY HH:MI:SS.FF'), 103711, 3, 'WINE', 0, 1, 't');
 
 
 SELECT sessionize(
diff --git a/src/ports/postgres/modules/utilities/test/transform_vec_cols.sql_in b/src/ports/postgres/modules/utilities/test/transform_vec_cols.sql_in
index b43b39f..860d20d 100644
--- a/src/ports/postgres/modules/utilities/test/transform_vec_cols.sql_in
+++ b/src/ports/postgres/modules/utilities/test/transform_vec_cols.sql_in
@@ -397,7 +397,7 @@ SELECT vec2cols(
     'out_table2',
     'feature_vector'
 );
-SELECT assert((select data_type from information_schema.columns where table_name = 'out_table2' limit 1) = 'double precision', 'Incorrect type casting of features for cols2vec');
+SELECT assert((select data_type from information_schema.columns where table_name = 'out_table2' limit 1) = 'numeric', 'Incorrect type casting of features for cols2vec');
 
 DROP TABLE IF EXISTS out_table, out_table_summary, out_table2;
 SELECT cols2vec(
diff --git a/src/ports/postgres/modules/utilities/utilities.py_in b/src/ports/postgres/modules/utilities/utilities.py_in
index 577235e..6941926 100644
--- a/src/ports/postgres/modules/utilities/utilities.py_in
+++ b/src/ports/postgres/modules/utilities/utilities.py_in
@@ -611,6 +611,8 @@ def set_client_min_messages(new_level):
 
 def is_pg_major_version_less_than(schema_madlib, compare_version, **kwargs):
     version = plpy.execute("select version()")[0]["version"]
+    if "openGauss" in version:
+        return True
     regex = re.compile('PostgreSQL\s*([0-9]+)([0-9.beta]+)', re.IGNORECASE)
     version = regex.findall(version)
     if len(version) > 0 and int(version[0][0]) < compare_version:
diff --git a/src/ports/postgres/modules/utilities/utilities.sql_in b/src/ports/postgres/modules/utilities/utilities.sql_in
index bbf861d..2d938e6 100644
--- a/src/ports/postgres/modules/utilities/utilities.sql_in
+++ b/src/ports/postgres/modules/utilities/utilities.sql_in
@@ -265,11 +265,11 @@ m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `READS SQL DATA', `');
  * >PostgreSQL documentation. For portability, MADlib code should not make
  * use of such "features" directly, but only use isnan() instead.
  *
- * @param number
- * @returns \c TRUE if \c number is \c NaN, \c FALSE otherwise
+ * @param number1
+ * @returns \c TRUE if \c number1 is \c NaN, \c FALSE otherwise
  */
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.isnan(
-    number DOUBLE PRECISION
+    number1 DOUBLE PRECISION
 ) RETURNS BOOLEAN
 AS $$
     SELECT $1 = 'NaN'::DOUBLE PRECISION;
@@ -313,7 +313,7 @@ CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.noop()
 RETURNS VOID
 AS 'MODULE_PATHNAME'
 LANGUAGE c
-VOLATILE
+VOLATILE NOT FENCED
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
 
 ------------------------------------------------------------------------
@@ -434,7 +434,7 @@ $$m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__to_char(val anyelement)
 RETURNS TEXT
 AS 'MODULE_PATHNAME', '__to_text'
-LANGUAGE C STRICT IMMUTABLE;
+LANGUAGE C STRICT IMMUTABLE NOT FENCED;
 
 ------------------------------------------------------------------------
 
diff --git a/src/ports/postgres/modules/utilities/validate_args.py_in b/src/ports/postgres/modules/utilities/validate_args.py_in
index a21be58..6f2f3bd 100644
--- a/src/ports/postgres/modules/utilities/validate_args.py_in
+++ b/src/ports/postgres/modules/utilities/validate_args.py_in
@@ -215,10 +215,11 @@ def get_first_schema(table_name):
 def drop_tables(table_list):
     """
         Drop tables specified in table_list.
+        Notice: Both TEMP and non-TEMP objects maybe in the table_list at the same time.
     """
-    drop_str = ', '.join(table_list)
-    if drop_str:
-        plpy.execute("DROP TABLE IF EXISTS {0}".format(drop_str))
+    if len(table_list) > 0:
+        for table in table_list:
+            plpy.execute("DROP TABLE IF EXISTS {0}".format(table))
 
 
 def table_is_empty(tbl, filter_str=None):
diff --git a/src/ports/postgres/modules/xgboost_gs/__init__.py_in b/src/ports/postgres/modules/xgboost_gs/__init__.py_in
new file mode 100644
index 0000000..e69de29
diff --git a/src/ports/postgres/modules/xgboost_gs/test/xgboost_sk_classifi.sql_in b/src/ports/postgres/modules/xgboost_gs/test/xgboost_sk_classifi.sql_in
new file mode 100644
index 0000000..4446481
--- /dev/null
+++ b/src/ports/postgres/modules/xgboost_gs/test/xgboost_sk_classifi.sql_in
@@ -0,0 +1,257 @@
+/*
+ * Test case for sklearn-api xgboost.
+ */
+
+m4_include(`SQLCommon.m4')
+
+select MADLIB_SCHEMA.xgboost_sk_Classifier();
+select MADLIB_SCHEMA.xgboost_sk_Classifier('usage');
+
+DROP TABLE IF EXISTS iris;
+create table iris (id serial, a float, d float, c float, b float, label int);
+
+INSERT into iris (a, b, c, d, label) values (5.1, 3.5, 1.4, 0.2, 0);INSERT into iris (a, b, c, d, label) values (4.9, 3.0, 1.4, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (4.7, 3.2, 1.3, 0.2, 0);INSERT into iris (a, b, c, d, label) values (4.6, 3.1, 1.5, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (5.0, 3.6, 1.4, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.4, 3.9, 1.7, 0.4, 0);
+INSERT into iris (a, b, c, d, label) values (4.6, 3.4, 1.4, 0.3, 0);INSERT into iris (a, b, c, d, label) values (5.0, 3.4, 1.5, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (4.4, 2.9, 1.4, 0.2, 0);INSERT into iris (a, b, c, d, label) values (4.9, 3.1, 1.5, 0.1, 0);
+INSERT into iris (a, b, c, d, label) values (5.4, 3.7, 1.5, 0.2, 0);INSERT into iris (a, b, c, d, label) values (4.8, 3.4, 1.6, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (4.8, 3.0, 1.4, 0.1, 0);INSERT into iris (a, b, c, d, label) values (4.3, 3.0, 1.1, 0.1, 0);
+INSERT into iris (a, b, c, d, label) values (5.8, 4.0, 1.2, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.7, 4.4, 1.5, 0.4, 0);
+INSERT into iris (a, b, c, d, label) values (5.4, 3.9, 1.3, 0.4, 0);INSERT into iris (a, b, c, d, label) values (5.1, 3.5, 1.4, 0.3, 0);
+INSERT into iris (a, b, c, d, label) values (5.7, 3.8, 1.7, 0.3, 0);INSERT into iris (a, b, c, d, label) values (5.1, 3.8, 1.5, 0.3, 0);
+INSERT into iris (a, b, c, d, label) values (5.4, 3.4, 1.7, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.1, 3.7, 1.5, 0.4, 0);
+INSERT into iris (a, b, c, d, label) values (4.6, 3.6, 1.0, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.1, 3.3, 1.7, 0.5, 0);
+INSERT into iris (a, b, c, d, label) values (4.8, 3.4, 1.9, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.0, 3.0, 1.6, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (5.0, 3.4, 1.6, 0.4, 0);INSERT into iris (a, b, c, d, label) values (5.2, 3.5, 1.5, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (5.2, 3.4, 1.4, 0.2, 0);INSERT into iris (a, b, c, d, label) values (4.7, 3.2, 1.6, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (4.8, 3.1, 1.6, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.4, 3.4, 1.5, 0.4, 0);
+INSERT into iris (a, b, c, d, label) values (5.2, 4.1, 1.5, 0.1, 0);INSERT into iris (a, b, c, d, label) values (5.5, 4.2, 1.4, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (4.9, 3.1, 1.5, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.0, 3.2, 1.2, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (5.5, 3.5, 1.3, 0.2, 0);INSERT into iris (a, b, c, d, label) values (4.9, 3.6, 1.4, 0.1, 0);
+INSERT into iris (a, b, c, d, label) values (4.4, 3.0, 1.3, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.1, 3.4, 1.5, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (5.0, 3.5, 1.3, 0.3, 0);INSERT into iris (a, b, c, d, label) values (4.5, 2.3, 1.3, 0.3, 0);
+INSERT into iris (a, b, c, d, label) values (4.4, 3.2, 1.3, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.0, 3.5, 1.6, 0.6, 0);
+INSERT into iris (a, b, c, d, label) values (5.1, 3.8, 1.9, 0.4, 0);INSERT into iris (a, b, c, d, label) values (4.8, 3.0, 1.4, 0.3, 0);
+INSERT into iris (a, b, c, d, label) values (5.1, 3.8, 1.6, 0.2, 0);INSERT into iris (a, b, c, d, label) values (4.6, 3.2, 1.4, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (5.3, 3.7, 1.5, 0.2, 0);INSERT into iris (a, b, c, d, label) values (5.0, 3.3, 1.4, 0.2, 0);
+INSERT into iris (a, b, c, d, label) values (7.0, 3.2, 4.7, 1.4, 1);INSERT into iris (a, b, c, d, label) values (6.4, 3.2, 4.5, 1.5, 1);
+INSERT into iris (a, b, c, d, label) values (6.9, 3.1, 4.9, 1.5, 1);INSERT into iris (a, b, c, d, label) values (5.5, 2.3, 4.0, 1.3, 1);
+INSERT into iris (a, b, c, d, label) values (6.5, 2.8, 4.6, 1.5, 1);INSERT into iris (a, b, c, d, label) values (5.7, 2.8, 4.5, 1.3, 1);
+INSERT into iris (a, b, c, d, label) values (6.3, 3.3, 4.7, 1.6, 1);INSERT into iris (a, b, c, d, label) values (4.9, 2.4, 3.3, 1.0, 1);
+INSERT into iris (a, b, c, d, label) values (6.6, 2.9, 4.6, 1.3, 1);INSERT into iris (a, b, c, d, label) values (5.2, 2.7, 3.9, 1.4, 1);
+INSERT into iris (a, b, c, d, label) values (5.0, 2.0, 3.5, 1.0, 1);INSERT into iris (a, b, c, d, label) values (5.9, 3.0, 4.2, 1.5, 1);
+INSERT into iris (a, b, c, d, label) values (6.0, 2.2, 4.0, 1.0, 1);INSERT into iris (a, b, c, d, label) values (6.1, 2.9, 4.7, 1.4, 1);
+INSERT into iris (a, b, c, d, label) values (5.6, 2.9, 3.6, 1.3, 1);INSERT into iris (a, b, c, d, label) values (6.7, 3.1, 4.4, 1.4, 1);
+INSERT into iris (a, b, c, d, label) values (5.6, 3.0, 4.5, 1.5, 1);INSERT into iris (a, b, c, d, label) values (5.8, 2.7, 4.1, 1.0, 1);
+INSERT into iris (a, b, c, d, label) values (6.2, 2.2, 4.5, 1.5, 1);INSERT into iris (a, b, c, d, label) values (5.6, 2.5, 3.9, 1.1, 1);
+INSERT into iris (a, b, c, d, label) values (5.9, 3.2, 4.8, 1.8, 1);INSERT into iris (a, b, c, d, label) values (6.1, 2.8, 4.0, 1.3, 1);
+INSERT into iris (a, b, c, d, label) values (6.3, 2.5, 4.9, 1.5, 1);INSERT into iris (a, b, c, d, label) values (6.1, 2.8, 4.7, 1.2, 1);
+INSERT into iris (a, b, c, d, label) values (6.4, 2.9, 4.3, 1.3, 1);INSERT into iris (a, b, c, d, label) values (6.6, 3.0, 4.4, 1.4, 1);
+INSERT into iris (a, b, c, d, label) values (6.8, 2.8, 4.8, 1.4, 1);INSERT into iris (a, b, c, d, label) values (6.7, 3.0, 5.0, 1.7, 1);
+INSERT into iris (a, b, c, d, label) values (6.0, 2.9, 4.5, 1.5, 1);INSERT into iris (a, b, c, d, label) values (5.7, 2.6, 3.5, 1.0, 1);
+INSERT into iris (a, b, c, d, label) values (5.5, 2.4, 3.8, 1.1, 1);INSERT into iris (a, b, c, d, label) values (5.5, 2.4, 3.7, 1.0, 1);
+INSERT into iris (a, b, c, d, label) values (5.8, 2.7, 3.9, 1.2, 1);INSERT into iris (a, b, c, d, label) values (6.0, 2.7, 5.1, 1.6, 1);
+INSERT into iris (a, b, c, d, label) values (5.4, 3.0, 4.5, 1.5, 1);INSERT into iris (a, b, c, d, label) values (6.0, 3.4, 4.5, 1.6, 1);
+INSERT into iris (a, b, c, d, label) values (6.7, 3.1, 4.7, 1.5, 1);INSERT into iris (a, b, c, d, label) values (6.3, 2.3, 4.4, 1.3, 1);
+INSERT into iris (a, b, c, d, label) values (5.6, 3.0, 4.1, 1.3, 1);INSERT into iris (a, b, c, d, label) values (5.5, 2.5, 4.0, 1.3, 1);
+INSERT into iris (a, b, c, d, label) values (5.5, 2.6, 4.4, 1.2, 1);INSERT into iris (a, b, c, d, label) values (6.1, 3.0, 4.6, 1.4, 1);
+INSERT into iris (a, b, c, d, label) values (5.8, 2.6, 4.0, 1.2, 1);INSERT into iris (a, b, c, d, label) values (5.0, 2.3, 3.3, 1.0, 1);
+INSERT into iris (a, b, c, d, label) values (5.6, 2.7, 4.2, 1.3, 1);INSERT into iris (a, b, c, d, label) values (5.7, 3.0, 4.2, 1.2, 1);
+INSERT into iris (a, b, c, d, label) values (5.7, 2.9, 4.2, 1.3, 1);INSERT into iris (a, b, c, d, label) values (6.2, 2.9, 4.3, 1.3, 1);
+INSERT into iris (a, b, c, d, label) values (5.1, 2.5, 3.0, 1.1, 1);INSERT into iris (a, b, c, d, label) values (5.7, 2.8, 4.1, 1.3, 1);
+INSERT into iris (a, b, c, d, label) values (6.3, 3.3, 6.0, 2.5, 2);INSERT into iris (a, b, c, d, label) values (5.8, 2.7, 5.1, 1.9, 2);
+INSERT into iris (a, b, c, d, label) values (7.1, 3.0, 5.9, 2.1, 2);INSERT into iris (a, b, c, d, label) values (6.3, 2.9, 5.6, 1.8, 2);
+INSERT into iris (a, b, c, d, label) values (6.5, 3.0, 5.8, 2.2, 2);INSERT into iris (a, b, c, d, label) values (7.6, 3.0, 6.6, 2.1, 2);
+INSERT into iris (a, b, c, d, label) values (4.9, 2.5, 4.5, 1.7, 2);INSERT into iris (a, b, c, d, label) values (7.3, 2.9, 6.3, 1.8, 2);
+INSERT into iris (a, b, c, d, label) values (6.7, 2.5, 5.8, 1.8, 2);INSERT into iris (a, b, c, d, label) values (7.2, 3.6, 6.1, 2.5, 2);
+INSERT into iris (a, b, c, d, label) values (6.5, 3.2, 5.1, 2.0, 2);INSERT into iris (a, b, c, d, label) values (6.4, 2.7, 5.3, 1.9, 2);
+INSERT into iris (a, b, c, d, label) values (6.8, 3.0, 5.5, 2.1, 2);INSERT into iris (a, b, c, d, label) values (5.7, 2.5, 5.0, 2.0, 2);
+INSERT into iris (a, b, c, d, label) values (5.8, 2.8, 5.1, 2.4, 2);INSERT into iris (a, b, c, d, label) values (6.4, 3.2, 5.3, 2.3, 2);
+INSERT into iris (a, b, c, d, label) values (6.5, 3.0, 5.5, 1.8, 2);INSERT into iris (a, b, c, d, label) values (7.7, 3.8, 6.7, 2.2, 2);
+INSERT into iris (a, b, c, d, label) values (7.7, 2.6, 6.9, 2.3, 2);INSERT into iris (a, b, c, d, label) values (6.0, 2.2, 5.0, 1.5, 2);
+INSERT into iris (a, b, c, d, label) values (6.9, 3.2, 5.7, 2.3, 2);INSERT into iris (a, b, c, d, label) values (5.6, 2.8, 4.9, 2.0, 2);
+INSERT into iris (a, b, c, d, label) values (7.7, 2.8, 6.7, 2.0, 2);INSERT into iris (a, b, c, d, label) values (6.3, 2.7, 4.9, 1.8, 2);
+INSERT into iris (a, b, c, d, label) values (6.7, 3.3, 5.7, 2.1, 2);INSERT into iris (a, b, c, d, label) values (7.2, 3.2, 6.0, 1.8, 2);
+INSERT into iris (a, b, c, d, label) values (6.2, 2.8, 4.8, 1.8, 2);INSERT into iris (a, b, c, d, label) values (6.1, 3.0, 4.9, 1.8, 2);
+INSERT into iris (a, b, c, d, label) values (6.4, 2.8, 5.6, 2.1, 2);INSERT into iris (a, b, c, d, label) values (7.2, 3.0, 5.8, 1.6, 2);
+INSERT into iris (a, b, c, d, label) values (7.4, 2.8, 6.1, 1.9, 2);INSERT into iris (a, b, c, d, label) values (7.9, 3.8, 6.4, 2.0, 2);
+INSERT into iris (a, b, c, d, label) values (6.4, 2.8, 5.6, 2.2, 2);INSERT into iris (a, b, c, d, label) values (6.3, 2.8, 5.1, 1.5, 2);
+INSERT into iris (a, b, c, d, label) values (6.1, 2.6, 5.6, 1.4, 2);INSERT into iris (a, b, c, d, label) values (7.7, 3.0, 6.1, 2.3, 2);
+INSERT into iris (a, b, c, d, label) values (6.3, 3.4, 5.6, 2.4, 2);INSERT into iris (a, b, c, d, label) values (6.4, 3.1, 5.5, 1.8, 2);
+INSERT into iris (a, b, c, d, label) values (6.0, 3.0, 4.8, 1.8, 2);INSERT into iris (a, b, c, d, label) values (6.9, 3.1, 5.4, 2.1, 2);
+INSERT into iris (a, b, c, d, label) values (6.7, 3.1, 5.6, 2.4, 2);INSERT into iris (a, b, c, d, label) values (6.9, 3.1, 5.1, 2.3, 2);
+INSERT into iris (a, b, c, d, label) values (5.8, 2.7, 5.1, 1.9, 2);INSERT into iris (a, b, c, d, label) values (6.8, 3.2, 5.9, 2.3, 2);
+INSERT into iris (a, b, c, d, label) values (6.7, 3.3, 5.7, 2.5, 2);INSERT into iris (a, b, c, d, label) values (6.7, 3.0, 5.2, 2.3, 2);
+INSERT into iris (a, b, c, d, label) values (6.3, 2.5, 5.0, 1.9, 2);INSERT into iris (a, b, c, d, label) values (6.5, 3.0, 5.2, 2.0, 2);
+INSERT into iris (a, b, c, d, label) values (6.2, 3.4, 5.4, 2.3, 2);INSERT into iris (a, b, c, d, label) values (5.9, 3.0, 5.1, 1.8, 2);
+
+drop table if exists iris_model_xgbc;
+select MADLIB_SCHEMA.xgboost_sk_Classifier('iris', 'iris_model_xgbc', 'id', 'label', 'a,b,c,d', NULL, $${'booster': ['gbtree'], 'eta': (0.1, 0.9), 'max_depth': (5,1), 'objective': ('multi:softmax',)}$$);
+
+select id, train_timestamp, source_table, y_type, metrics, features, params from iris_model_xgbc;
+
+select assert(count(*) = 4, 'xgboost: xgboost parameter counts error') from iris_model_xgbc;
+
+select assert(metrics = 'evalution is OFF, no metrics', 'xgboost: xgboost metrics error') from iris_model_xgbc where id = 1;
+
+----------------
+drop table if exists iris_model_xgbc;
+select MADLIB_SCHEMA.xgboost_sk_Classifier('iris', 'iris_model_xgbc', 'id', 'label', 'a,b,c,d', NULL, $${'booster': ['gbtree'], 'eta': (0.1, 0.9), 'max_depth': (5,1), 'objective': ('multi:softmax',)}$$, TRUE);
+
+select id, train_timestamp, source_table, y_type, metrics, features, params from iris_model_xgbc;
+select assert(count(*) = 4, 'xgboost: xgboost parameter counts error') from iris_model_xgbc;
+select assert(metrics <> 'evalution is OFF, no metrics', 'xgboost: xgboost metrics error') from iris_model_xgbc where id = 1;
+
+-----------
+drop table if exists iris_xgbc_out;
+select MADLIB_SCHEMA.xgboost_sk_predict('iris', 'iris_model_xgbc', 'iris_xgbc_out', 'id');
+
+drop table if exists iris_model_xgbc;
+select xgboost_sk_Classifier('iris', 'iris_model_xgbc', 'id', 'label', '*', NULL, $${'booster': ('gbtree',), 'eta': [0.1, 0.9], 'max_depth': (5,1), 'objective': ['multi:softmax']}$$);
+
+SELECT assert(count(*) = 4, 'xgboost: Xgboost model output has incorrect number of rows')
+FROM (select train_timestamp, source_table, y_type, metrics, features, params, id from iris_model_xgbc) q;
+
+drop table if exists iris_xgbc_out;
+select MADLIB_SCHEMA.xgboost_sk_predict('iris', 'iris_model_xgbc', 'iris_xgbc_out', 'id');
+
+drop table if exists temp_iris;
+create table temp_iris as select t1.id, prediction, label from iris as t1, iris_xgbc_out as t2 where t1.id = t2.id order by t1.id;
+
+select assert(count(*) >135, 'xgboost: xgboost prediction incorrect')
+from (select * from temp_iris where prediction = label) q;
+
+
+-----------------------------------------------------------  binaryl classification ----------------------------------------------
+
+drop table if exists xgbctable;
+create table xgbctable(id serial, "X" float, Y float, label int);
+
+insert into xgbctable("X", y, label) values (1.349880, 3.503074, 0);insert into xgbctable("X", y, label) values (3.605580, 4.285024, 0);
+insert into xgbctable("X", y, label) values (2.537560, 5.671145, 0);insert into xgbctable("X", y, label) values (2.596382, 4.219879, 0);
+insert into xgbctable("X", y, label) values (3.043375, 3.425613, 0);insert into xgbctable("X", y, label) values (2.277542, 2.532572, 0);
+insert into xgbctable("X", y, label) values (4.036647, 3.891047, 0);insert into xgbctable("X", y, label) values (3.814163, 3.737898, 0);
+insert into xgbctable("X", y, label) values (2.674975, 4.340797, 0);insert into xgbctable("X", y, label) values (1.694112, 4.533308, 0);
+insert into xgbctable("X", y, label) values (2.623954, 4.337177, 0);insert into xgbctable("X", y, label) values (3.412750, 6.994476, 0);
+insert into xgbctable("X", y, label) values (3.562286, 3.827778, 0);insert into xgbctable("X", y, label) values (3.278937, 4.116019, 0);
+insert into xgbctable("X", y, label) values (2.691412, 3.482750, 0);insert into xgbctable("X", y, label) values (1.947223, 3.734410, 0);
+insert into xgbctable("X", y, label) values (1.376026, 3.232049, 0);insert into xgbctable("X", y, label) values (1.499600, 7.429014, 0);
+insert into xgbctable("X", y, label) values (3.971720, 4.967350, 0);insert into xgbctable("X", y, label) values (2.984960, 3.780627, 0);
+insert into xgbctable("X", y, label) values (3.493389, 3.693761, 0);insert into xgbctable("X", y, label) values (3.653253, 4.156218, 0);
+insert into xgbctable("X", y, label) values (2.552306, 3.436848, 0);insert into xgbctable("X", y, label) values (3.395667, 2.731429, 0);
+insert into xgbctable("X", y, label) values (4.356895, 3.604831, 0);insert into xgbctable("X", y, label) values (2.703829, 2.192765, 0);
+insert into xgbctable("X", y, label) values (2.507021, 4.329131, 0);insert into xgbctable("X", y, label) values (5.437925, 4.798840, 0);
+insert into xgbctable("X", y, label) values (4.275079, 4.472619, 0);insert into xgbctable("X", y, label) values (3.829493, 5.539909, 0);
+insert into xgbctable("X", y, label) values (2.148925, 4.254559, 0);insert into xgbctable("X", y, label) values (2.798221, 3.916840, 0);
+insert into xgbctable("X", y, label) values (3.388155, 3.060772, 0);insert into xgbctable("X", y, label) values (2.741393, 2.502708, 0);
+insert into xgbctable("X", y, label) values (3.930997, 3.435667, 0);insert into xgbctable("X", y, label) values (2.641057, 4.070123, 0);
+insert into xgbctable("X", y, label) values (1.631679, 5.890544, 0);insert into xgbctable("X", y, label) values (3.857039, 3.486820, 0);
+insert into xgbctable("X", y, label) values (4.009723, 5.215680, 0);insert into xgbctable("X", y, label) values (4.815413, 3.742883, 0);
+insert into xgbctable("X", y, label) values (2.010829, 3.793627, 0);insert into xgbctable("X", y, label) values (3.803213, 4.966541, 0);
+insert into xgbctable("X", y, label) values (3.074533, 3.769696, 0);insert into xgbctable("X", y, label) values (3.305067, 3.941329, 0);
+insert into xgbctable("X", y, label) values (4.255057, 2.714015, 0);insert into xgbctable("X", y, label) values (1.990719, 3.748694, 0);
+insert into xgbctable("X", y, label) values (3.271186, 1.932663, 0);insert into xgbctable("X", y, label) values (5.712751, 4.925150, 0);
+insert into xgbctable("X", y, label) values (4.436121, 1.918319, 0);insert into xgbctable("X", y, label) values (1.467938, 3.670151, 0);
+insert into xgbctable("X", y, label) values (2.986207, 4.793286, 0);insert into xgbctable("X", y, label) values (2.599137, 2.807718, 0);
+insert into xgbctable("X", y, label) values (3.108875, 2.852789, 0);insert into xgbctable("X", y, label) values (3.031777, 4.673723, 0);
+insert into xgbctable("X", y, label) values (3.262225, 2.773082, 0);insert into xgbctable("X", y, label) values (2.022650, 4.607023, 0);
+insert into xgbctable("X", y, label) values (3.147877, 3.796113, 0);insert into xgbctable("X", y, label) values (3.111487, 3.129382, 0);
+insert into xgbctable("X", y, label) values (2.982505, 4.189220, 0);insert into xgbctable("X", y, label) values (2.928811, 4.102743, 0);
+insert into xgbctable("X", y, label) values (3.121620, 5.194476, 0);insert into xgbctable("X", y, label) values (3.520649, 3.870720, 0);
+insert into xgbctable("X", y, label) values (1.329847, 4.796737, 0);insert into xgbctable("X", y, label) values (4.355449, 3.361799, 0);
+insert into xgbctable("X", y, label) values (3.125785, 4.588572, 0);insert into xgbctable("X", y, label) values (2.465354, 3.974886, 0);
+insert into xgbctable("X", y, label) values (4.031982, 3.994294, 0);insert into xgbctable("X", y, label) values (2.649743, 4.096057, 0);
+insert into xgbctable("X", y, label) values (3.613181, 3.801199, 0);insert into xgbctable("X", y, label) values (6.107873, 4.124081, 0);
+insert into xgbctable("X", y, label) values (3.574605, 4.029697, 0);insert into xgbctable("X", y, label) values (2.426873, 2.782740, 0);
+insert into xgbctable("X", y, label) values (2.159410, 3.899774, 0);insert into xgbctable("X", y, label) values (2.625650, 2.297274, 0);
+insert into xgbctable("X", y, label) values (3.069545, 4.923757, 0);insert into xgbctable("X", y, label) values (2.950428, 2.858376, 0);
+insert into xgbctable("X", y, label) values (3.601556, 2.587292, 0);insert into xgbctable("X", y, label) values (3.639818, 4.304443, 0);
+insert into xgbctable("X", y, label) values (2.759995, 2.999176, 0);insert into xgbctable("X", y, label) values (1.826029, 3.564573, 0);
+insert into xgbctable("X", y, label) values (4.016062, 3.730916, 0);insert into xgbctable("X", y, label) values (2.464892, 3.112348, 0);
+insert into xgbctable("X", y, label) values (4.325916, 5.596980, 0);insert into xgbctable("X", y, label) values (2.131952, 4.396857, 0);
+insert into xgbctable("X", y, label) values (3.473718, 4.673927, 0);insert into xgbctable("X", y, label) values (3.711892, 4.908759, 0);
+insert into xgbctable("X", y, label) values (3.700971, 3.462295, 0);insert into xgbctable("X", y, label) values (3.125731, 5.828338, 0);
+insert into xgbctable("X", y, label) values (3.957516, 5.104233, 0);insert into xgbctable("X", y, label) values (4.658009, 5.078593, 0);
+insert into xgbctable("X", y, label) values (2.576429, 3.274793, 0);insert into xgbctable("X", y, label) values (3.160974, 3.466810, 0);
+insert into xgbctable("X", y, label) values (2.613970, 2.838497, 0);insert into xgbctable("X", y, label) values (2.385694, 3.749695, 0);
+insert into xgbctable("X", y, label) values (2.640589, 2.259807, 0);insert into xgbctable("X", y, label) values (4.589973, 2.776422, 0);
+insert into xgbctable("X", y, label) values (1.875313, 3.571599, 0);insert into xgbctable("X", y, label) values (3.472463, 5.251267, 0);
+insert into xgbctable("X", y, label) values (2.638684, 4.845147, 0);insert into xgbctable("X", y, label) values (3.405707, 2.662543, 0);
+
+insert into xgbctable("X", y, label) values (3.092827, -0.757907, 1);insert into xgbctable("X", y, label) values (1.982449, 0.781236, 1);
+insert into xgbctable("X", y, label) values (1.880089, 3.638690, 1);insert into xgbctable("X", y, label) values (-0.143234, -0.823097, 1);
+insert into xgbctable("X", y, label) values (1.913393, 0.717394, 1);insert into xgbctable("X", y, label) values (-0.406319, 1.495522, 1);
+insert into xgbctable("X", y, label) values (1.982796, -0.043737, 1);insert into xgbctable("X", y, label) values (2.001371, 2.005737, 1);
+insert into xgbctable("X", y, label) values (1.683994, 2.722670, 1);insert into xgbctable("X", y, label) values (1.201483, -0.544765, 1);
+insert into xgbctable("X", y, label) values (1.054981, 0.857019, 1);insert into xgbctable("X", y, label) values (-1.137237, 0.769533, 1);
+insert into xgbctable("X", y, label) values (0.526662, -0.076017, 1);insert into xgbctable("X", y, label) values (0.431452, 1.018968, 1);
+insert into xgbctable("X", y, label) values (0.911789, -1.003597, 1);insert into xgbctable("X", y, label) values (0.271256, 0.749327, 1);
+insert into xgbctable("X", y, label) values (-0.141832, -1.478830, 1);insert into xgbctable("X", y, label) values (0.508604, 0.648365, 1);
+insert into xgbctable("X", y, label) values (1.903264, 0.034425, 1);insert into xgbctable("X", y, label) values (0.788896, 0.615347, 1);
+insert into xgbctable("X", y, label) values (3.350388, -0.238897, 1);insert into xgbctable("X", y, label) values (-0.632393, 0.605239, 1);
+insert into xgbctable("X", y, label) values (1.170017, 0.536877, 1);insert into xgbctable("X", y, label) values (1.381837, 0.453970, 1);
+insert into xgbctable("X", y, label) values (0.238530, 0.234563, 1);insert into xgbctable("X", y, label) values (-0.619639, -0.739849, 1);
+insert into xgbctable("X", y, label) values (1.782465, 2.802568, 1);insert into xgbctable("X", y, label) values (-1.690702, 0.989325, 1);
+insert into xgbctable("X", y, label) values (0.109266, 2.755468, 1);insert into xgbctable("X", y, label) values (1.670044, 1.392086, 1);
+insert into xgbctable("X", y, label) values (2.461095, -0.625544, 1);insert into xgbctable("X", y, label) values (1.580424, 1.225731, 1);
+insert into xgbctable("X", y, label) values (0.949291, 0.709720, 1);insert into xgbctable("X", y, label) values (1.919173, 0.810358, 1);
+insert into xgbctable("X", y, label) values (1.086786, 2.770693, 1);insert into xgbctable("X", y, label) values (0.955707, 1.763802, 1);
+insert into xgbctable("X", y, label) values (1.689414, 1.062906, 1);insert into xgbctable("X", y, label) values (1.438960, 1.282794, 1);
+insert into xgbctable("X", y, label) values (0.499648, 1.425253, 1);insert into xgbctable("X", y, label) values (0.712802, -0.080972, 1);
+insert into xgbctable("X", y, label) values (1.501847, 1.318616, 1);insert into xgbctable("X", y, label) values (-0.718307, 0.644610, 1);
+insert into xgbctable("X", y, label) values (-0.259394, 1.004450, 1);insert into xgbctable("X", y, label) values (1.822353, 1.461093, 1);
+insert into xgbctable("X", y, label) values (0.065569, 2.134341, 1);insert into xgbctable("X", y, label) values (1.137744, 1.431270, 1);
+insert into xgbctable("X", y, label) values (1.613366, -0.399205, 1);insert into xgbctable("X", y, label) values (2.780135, 0.523116, 1);
+insert into xgbctable("X", y, label) values (1.641254, 0.189915, 1);insert into xgbctable("X", y, label) values (0.224779, 0.320547, 1);
+insert into xgbctable("X", y, label) values (1.509881, 0.488949, 1);insert into xgbctable("X", y, label) values (2.742344, 1.078382, 1);
+insert into xgbctable("X", y, label) values (0.015037, 0.906546, 1);insert into xgbctable("X", y, label) values (0.790595, 0.721075, 1);
+insert into xgbctable("X", y, label) values (0.296409, 0.922898, 1);insert into xgbctable("X", y, label) values (1.116124, 2.510543, 1);
+insert into xgbctable("X", y, label) values (1.104642, 1.043124, 1);insert into xgbctable("X", y, label) values (1.261002, 1.275877, 1);
+insert into xgbctable("X", y, label) values (0.995230, 1.131128, 1);insert into xgbctable("X", y, label) values (0.500961, 1.053070, 1);
+insert into xgbctable("X", y, label) values (0.753316, 1.587285, 1);insert into xgbctable("X", y, label) values (1.415908, 1.080478, 1);
+insert into xgbctable("X", y, label) values (1.391515, 0.915593, 1);insert into xgbctable("X", y, label) values (1.309082, 0.667459, 1);
+insert into xgbctable("X", y, label) values (0.622153, 2.885750, 1);insert into xgbctable("X", y, label) values (2.499712, 1.917895, 1);
+insert into xgbctable("X", y, label) values (0.754728, 1.182577, 1);insert into xgbctable("X", y, label) values (1.653910, -0.448352, 1);
+insert into xgbctable("X", y, label) values (2.263763, -0.022151, 1);insert into xgbctable("X", y, label) values (-0.289203, 2.267931, 1);
+insert into xgbctable("X", y, label) values (2.117514, 0.852814, 1);insert into xgbctable("X", y, label) values (0.682405, 0.043468, 1);
+insert into xgbctable("X", y, label) values (1.304387, 1.649660, 1);insert into xgbctable("X", y, label) values (2.775033, 0.608014, 1);
+insert into xgbctable("X", y, label) values (0.695554, 1.631088, 1);insert into xgbctable("X", y, label) values (1.736803, 0.258182, 1);
+insert into xgbctable("X", y, label) values (1.369698, 1.281675, 1);insert into xgbctable("X", y, label) values (-0.461257, 2.632165, 1);
+insert into xgbctable("X", y, label) values (1.672385, 0.965322, 1);insert into xgbctable("X", y, label) values (0.527026, 0.849661, 1);
+insert into xgbctable("X", y, label) values (1.583187, 0.823039, 1);insert into xgbctable("X", y, label) values (1.217844, 1.465343, 1);
+insert into xgbctable("X", y, label) values (-0.190087, -0.190168, 1);insert into xgbctable("X", y, label) values (0.939329, -0.837490, 1);
+insert into xgbctable("X", y, label) values (0.421842, -1.023412, 1);insert into xgbctable("X", y, label) values (1.015774, 0.422640, 1);
+insert into xgbctable("X", y, label) values (0.036181, -1.399000, 1);insert into xgbctable("X", y, label) values (1.001890, 1.183682, 1);
+insert into xgbctable("X", y, label) values (3.219715, 1.811829, 1);insert into xgbctable("X", y, label) values (2.270468, 1.717835, 1);
+insert into xgbctable("X", y, label) values (0.354725, 0.918786, 1);insert into xgbctable("X", y, label) values (3.065704, 0.358424, 1);
+insert into xgbctable("X", y, label) values (-0.609859, 0.152997, 1);insert into xgbctable("X", y, label) values (0.476704, 0.071017, 1);
+insert into xgbctable("X", y, label) values (0.494553, 3.201871, 1);insert into xgbctable("X", y, label) values (3.430162, -0.834660, 1);
+insert into xgbctable("X", y, label) values (2.542903, 1.831189, 1);insert into xgbctable("X", y, label) values (1.973389, 0.079485, 1);
+insert into xgbctable("X", y, label) values (1.309454, 0.648461, 1);insert into xgbctable("X", y, label) values (0.841486, -0.177773, 1);
+
+drop table if exists xgbctable_model;
+select MADLIB_SCHEMA.xgboost_sk_Classifier('xgbctable', 'xgbctable_model', 'id', 'label', '"X", Y', NULL, $${'booster': ('gbtree',), 'eta': (0.1), 'max_depth': 5, 'objective': ('binary:logistic',)}$$, TRUE);
+
+select id, train_timestamp, source_table, y_type, metrics, features, params from xgbctable_model;
+select assert(count(*) = 1, 'xgboost: xgboost parameter counts error') from xgbctable_model;
+
+drop table if exists xgbctable_model;
+select MADLIB_SCHEMA.xgboost_sk_Classifier('xgbctable', 'xgbctable_model', 'id', 'label', '*', 'id, label', $${'booster': ['gbtree'], 'eta': 0.1, 'max_depth': (5), 'objective': ['binary:logistic']}$$);
+select assert(metrics = 'evalution is OFF, no metrics', 'xgboost: evalution error') from xgbctable_model;
+
+
+drop table if exists xgbctable_output;
+select MADLIB_SCHEMA.xgboost_sk_predict('xgbctable', 'xgbctable_model', 'xgbctable_output', 'id', TRUE);
+
+drop table if exists xgbctable_output_summary;
+create table xgbctable_output_summary as select t1.id, prediction, label from xgbctable_output as t1, xgbctable as t2 where t1.id = t2.id order by t1.id;
+
+drop table if exists xgbctable_aur;
+select MADLIB_SCHEMA.area_under_roc('xgbctable_output_summary', 'xgbctable_aur', 'prediction', 'label');
+select * from xgbctable_aur;
+select assert(area_under_roc > 0.9, 'xgbctable AUR error') from xgbctable_aur;
+
+drop table if exists xgbctable_cm;
+select MADLIB_SCHEMA.confusion_matrix('xgbctable_output_summary', 'xgbctable_cm', 'prediction', 'label');
+select * from xgbctable_cm;
diff --git a/src/ports/postgres/modules/xgboost_gs/test/xgboost_sk_regress.sql_in b/src/ports/postgres/modules/xgboost_gs/test/xgboost_sk_regress.sql_in
new file mode 100644
index 0000000..99e216a
--- /dev/null
+++ b/src/ports/postgres/modules/xgboost_gs/test/xgboost_sk_regress.sql_in
@@ -0,0 +1,554 @@
+/*
+ * Test case for sklearn-api xgboost.
+ */
+
+m4_include(`SQLCommon.m4')
+
+select xgboost_sk_regressor('usage');
+select xgboost_sk_regressor('help');
+select xgboost_sk_regressor('?');
+select xgboost_sk_regressor('');
+
+DROP TABLE IF EXISTS boston;
+CREATE TABLE boston (
+id integer NOT NULL, crim double precision, zn float8, Indus double precision,
+chas double precision, nox float, rm float4, "Age" int,
+dis DECIMAL(4), rad double precision, tax double precision, ptratio float,
+b double precision, LstaT double precision, target float);
+
+INSERT INTO boston VALUES (2, 0.027310000000000001, 0, 7.0700000000000003, 0, 0.46899999999999997, 6.4210000000000003, 78.900000000000006, 4.9671000000000003, 2, 242, 17.800000000000001, 396.89999999999998, 9.1400000000000006, 21.600000000000001);
+INSERT INTO boston VALUES (6, 0.029850000000000002, 0, 2.1800000000000002, 0, 0.45800000000000002, 6.4299999999999997, 58.700000000000003, 6.0621999999999998, 3, 222, 18.699999999999999, 394.12, 5.21, 28.699999999999999);
+INSERT INTO boston VALUES (10, 0.17004, 12.5, 7.8700000000000001, 0, 0.52400000000000002, 6.0039999999999996, 85.900000000000006, 6.5921000000000003, 5, 311, 15.199999999999999, 386.70999999999998, 17.100000000000001, 18.899999999999999);
+INSERT INTO boston VALUES (14, 0.62975999999999999, 0, 8.1400000000000006, 0, 0.53800000000000003, 5.9489999999999998, 61.799999999999997, 4.7074999999999996, 4, 307, 21, 396.89999999999998, 8.2599999999999998, 20.399999999999999);
+INSERT INTO boston VALUES (18, 0.78420000000000001, 0, 8.1400000000000006, 0, 0.53800000000000003, 5.9900000000000002, 81.700000000000003, 4.2579000000000002, 4, 307, 21, 386.75, 14.67, 17.5);
+INSERT INTO boston VALUES (22, 0.85204000000000002, 0, 8.1400000000000006, 0, 0.53800000000000003, 5.9649999999999999, 89.200000000000003, 4.0122999999999998, 4, 307, 21, 392.52999999999997, 13.83, 19.600000000000001);
+INSERT INTO boston VALUES (26, 0.84053999999999995, 0, 8.1400000000000006, 0, 0.53800000000000003, 5.5990000000000002, 85.700000000000003, 4.4546000000000001, 4, 307, 21, 303.42000000000002, 16.510000000000002, 13.9);
+INSERT INTO boston VALUES (30, 1.0024500000000001, 0, 8.1400000000000006, 0, 0.53800000000000003, 6.6740000000000004, 87.299999999999997, 4.2389999999999999, 4, 307, 21, 380.23000000000002, 11.98, 21);
+INSERT INTO boston VALUES (34, 1.1517200000000001, 0, 8.1400000000000006, 0, 0.53800000000000003, 5.7009999999999996, 95, 3.7871999999999999, 4, 307, 21, 358.76999999999998, 18.350000000000001, 13.1);
+INSERT INTO boston VALUES (38, 0.080140000000000003, 0, 5.96, 0, 0.499, 5.8499999999999996, 41.5, 3.9342000000000001, 5, 279, 19.199999999999999, 396.89999999999998, 8.7699999999999996, 21);
+INSERT INTO boston VALUES (42, 0.12744, 0, 6.9100000000000001, 0, 0.44800000000000001, 6.7699999999999996, 2.8999999999999999, 5.7209000000000003, 3, 233, 17.899999999999999, 385.41000000000003, 4.8399999999999999, 26.600000000000001);
+INSERT INTO boston VALUES (46, 0.17141999999999999, 0, 6.9100000000000001, 0, 0.44800000000000001, 5.6820000000000004, 33.799999999999997, 5.1003999999999996, 3, 233, 17.899999999999999, 396.89999999999998, 10.210000000000001, 19.300000000000001);
+INSERT INTO boston VALUES (50, 0.21976999999999999, 0, 6.9100000000000001, 0, 0.44800000000000001, 5.6020000000000003, 62, 6.0876999999999999, 3, 233, 17.899999999999999, 396.89999999999998, 16.199999999999999, 19.399999999999999);
+INSERT INTO boston VALUES (54, 0.04981, 21, 5.6399999999999997, 0, 0.439, 5.9980000000000002, 21.399999999999999, 6.8147000000000002, 4, 243, 16.800000000000001, 396.89999999999998, 8.4299999999999997, 23.399999999999999);
+INSERT INTO boston VALUES (58, 0.014319999999999999, 100, 1.3200000000000001, 0, 0.41099999999999998, 6.8159999999999998, 40.5, 8.3247999999999998, 5, 256, 15.1, 392.89999999999998, 3.9500000000000002, 31.600000000000001);
+INSERT INTO boston VALUES (62, 0.17171, 25, 5.1299999999999999, 0, 0.45300000000000001, 5.9660000000000002, 93.400000000000006, 6.8185000000000002, 8, 284, 19.699999999999999, 378.07999999999998, 14.44, 16);
+INSERT INTO boston VALUES (66, 0.035839999999999997, 80, 3.3700000000000001, 0, 0.39800000000000002, 6.29, 17.800000000000001, 6.6115000000000004, 4, 337, 16.100000000000001, 396.89999999999998, 4.6699999999999999, 23.5);
+INSERT INTO boston VALUES (70, 0.12816, 12.5, 6.0700000000000003, 0, 0.40899999999999997, 5.8849999999999998, 33, 6.4980000000000002, 4, 345, 18.899999999999999, 396.89999999999998, 8.7899999999999991, 20.899999999999999);
+INSERT INTO boston VALUES (74, 0.19539000000000001, 0, 10.81, 0, 0.41299999999999998, 6.2450000000000001, 6.2000000000000002, 5.2873000000000001, 4, 305, 19.199999999999999, 377.17000000000002, 7.54, 23.399999999999999);
+INSERT INTO boston VALUES (78, 0.087069999999999995, 0, 12.83, 0, 0.437, 6.1399999999999997, 45.799999999999997, 4.0904999999999996, 5, 398, 18.699999999999999, 386.95999999999998, 10.27, 20.800000000000001);
+INSERT INTO boston VALUES (82, 0.04462, 25, 4.8600000000000003, 0, 0.42599999999999999, 6.6189999999999998, 70.400000000000006, 5.4006999999999996, 4, 281, 19, 395.63, 7.2199999999999998, 23.899999999999999);
+INSERT INTO boston VALUES (86, 0.057349999999999998, 0, 4.4900000000000002, 0, 0.44900000000000001, 6.6299999999999999, 56.100000000000001, 4.4377000000000004, 3, 247, 18.5, 392.30000000000001, 6.5300000000000002, 26.600000000000001);
+INSERT INTO boston VALUES (90, 0.053019999999999998, 0, 3.4100000000000001, 0, 0.48899999999999999, 7.0789999999999997, 63.100000000000001, 3.4144999999999999, 2, 270, 17.800000000000001, 396.06, 5.7000000000000002, 28.699999999999999);
+INSERT INTO boston VALUES (94, 0.028750000000000001, 28, 15.039999999999999, 0, 0.46400000000000002, 6.2110000000000003, 28.899999999999999, 3.6659000000000002, 4, 270, 18.199999999999999, 396.32999999999998, 6.21, 25);
+INSERT INTO boston VALUES (98, 0.12083000000000001, 0, 2.8900000000000001, 0, 0.44500000000000001, 8.0690000000000008, 76, 3.4952000000000001, 2, 276, 18, 396.89999999999998, 4.21, 38.700000000000003);
+INSERT INTO boston VALUES (102, 0.11432, 0, 8.5600000000000005, 0, 0.52000000000000002, 6.7809999999999997, 71.299999999999997, 2.8561000000000001, 5, 384, 20.899999999999999, 395.57999999999998, 7.6699999999999999, 26.5);
+INSERT INTO boston VALUES (106, 0.13261999999999999, 0, 8.5600000000000005, 0, 0.52000000000000002, 5.851, 96.700000000000003, 2.1069, 5, 384, 20.899999999999999, 394.05000000000001, 16.469999999999999, 19.5);
+INSERT INTO boston VALUES (110, 0.26362999999999998, 0, 8.5600000000000005, 0, 0.52000000000000002, 6.2290000000000001, 91.200000000000003, 2.5451000000000001, 5, 384, 20.899999999999999, 391.23000000000002, 15.550000000000001, 19.399999999999999);
+INSERT INTO boston VALUES (114, 0.22212000000000001, 0, 10.01, 0, 0.54700000000000004, 6.0919999999999996, 95.400000000000006, 2.548, 6, 432, 17.800000000000001, 396.89999999999998, 17.09, 18.699999999999999);
+INSERT INTO boston VALUES (118, 0.15098, 0, 10.01, 0, 0.54700000000000004, 6.0209999999999999, 82.599999999999994, 2.7473999999999998, 6, 432, 17.800000000000001, 394.50999999999999, 10.300000000000001, 19.199999999999999);
+INSERT INTO boston VALUES (122, 0.071650000000000005, 0, 25.649999999999999, 0, 0.58099999999999996, 6.0039999999999996, 84.099999999999994, 2.1974, 2, 188, 19.100000000000001, 377.67000000000002, 14.27, 20.300000000000001);
+INSERT INTO boston VALUES (126, 0.16902, 0, 25.649999999999999, 0, 0.58099999999999996, 5.9859999999999998, 88.400000000000006, 1.9928999999999999, 2, 188, 19.100000000000001, 385.01999999999998, 14.81, 21.399999999999999);
+INSERT INTO boston VALUES (130, 0.88124999999999998, 0, 21.890000000000001, 0, 0.624, 5.6369999999999996, 94.700000000000003, 1.9799, 4, 437, 21.199999999999999, 396.89999999999998, 18.34, 14.300000000000001);
+INSERT INTO boston VALUES (134, 0.32982, 0, 21.890000000000001, 0, 0.624, 5.8220000000000001, 95.400000000000006, 2.4699, 4, 437, 21.199999999999999, 388.69, 15.029999999999999, 18.399999999999999);
+INSERT INTO boston VALUES (138, 0.35232999999999998, 0, 21.890000000000001, 0, 0.624, 6.4539999999999997, 98.400000000000006, 1.8498000000000001, 4, 437, 21.199999999999999, 394.07999999999998, 14.59, 17.100000000000001);
+INSERT INTO boston VALUES (142, 1.6286400000000001, 0, 21.890000000000001, 0, 0.624, 5.0190000000000001, 100, 1.4394, 4, 437, 21.199999999999999, 396.89999999999998, 34.409999999999997, 14.4);
+INSERT INTO boston VALUES (146, 2.37934, 0, 19.579999999999998, 0, 0.871, 6.1299999999999999, 100, 1.4191, 5, 403, 14.699999999999999, 172.91, 27.800000000000001, 13.800000000000001);
+INSERT INTO boston VALUES (150, 2.7339699999999998, 0, 19.579999999999998, 0, 0.871, 5.5970000000000004, 94.900000000000006, 1.5257000000000001, 5, 403, 14.699999999999999, 351.85000000000002, 21.449999999999999, 15.4);
+INSERT INTO boston VALUES (154, 2.1491799999999999, 0, 19.579999999999998, 0, 0.871, 5.7089999999999996, 98.5, 1.6232, 5, 403, 14.699999999999999, 261.94999999999999, 15.789999999999999, 19.399999999999999);
+INSERT INTO boston VALUES (158, 1.2235799999999999, 0, 19.579999999999998, 0, 0.60499999999999998, 6.9429999999999996, 97.400000000000006, 1.8773, 5, 403, 14.699999999999999, 363.43000000000001, 4.5899999999999999, 41.299999999999997);
+INSERT INTO boston VALUES (162, 1.46336, 0, 19.579999999999998, 0, 0.60499999999999998, 7.4889999999999999, 90.799999999999997, 1.9709000000000001, 5, 403, 14.699999999999999, 374.43000000000001, 1.73, 50);
+INSERT INTO boston VALUES (166, 2.9239999999999999, 0, 19.579999999999998, 0, 0.60499999999999998, 6.101, 93, 2.2833999999999999, 5, 403, 14.699999999999999, 240.16, 9.8100000000000005, 25);
+INSERT INTO boston VALUES (170, 2.4495300000000002, 0, 19.579999999999998, 0, 0.60499999999999998, 6.4020000000000001, 95.200000000000003, 2.2625000000000002, 5, 403, 14.699999999999999, 330.04000000000002, 11.32, 22.300000000000001);
+INSERT INTO boston VALUES (174, 0.09178, 0, 4.0499999999999998, 0, 0.51000000000000001, 6.4160000000000004, 84.099999999999994, 2.6463000000000001, 5, 296, 16.600000000000001, 395.5, 9.0399999999999991, 23.600000000000001);
+INSERT INTO boston VALUES (178, 0.05425, 0, 4.0499999999999998, 0, 0.51000000000000001, 6.3150000000000004, 73.400000000000006, 3.3174999999999999, 5, 296, 16.600000000000001, 395.60000000000002, 6.29, 24.600000000000001);
+INSERT INTO boston VALUES (182, 0.068879999999999997, 0, 2.46, 0, 0.48799999999999999, 6.1440000000000001, 62.200000000000003, 2.5979000000000001, 3, 193, 17.800000000000001, 396.89999999999998, 9.4499999999999993, 36.200000000000003);
+INSERT INTO boston VALUES (186, 0.060470000000000003, 0, 2.46, 0, 0.48799999999999999, 6.1529999999999996, 68.799999999999997, 3.2797000000000001, 3, 193, 17.800000000000001, 387.11000000000001, 13.15, 29.600000000000001);
+INSERT INTO boston VALUES (190, 0.083699999999999997, 45, 3.4399999999999999, 0, 0.437, 7.1849999999999996, 38.899999999999999, 4.5667, 5, 398, 15.199999999999999, 396.89999999999998, 5.3899999999999997, 34.899999999999999);
+INSERT INTO boston VALUES (194, 0.021870000000000001, 60, 2.9300000000000002, 0, 0.40100000000000002, 6.7999999999999998, 9.9000000000000004, 6.2195999999999998, 1, 265, 15.6, 393.37, 5.0300000000000002, 31.100000000000001);
+INSERT INTO boston VALUES (198, 0.04666, 80, 1.52, 0, 0.40400000000000003, 7.1070000000000002, 36.600000000000001, 7.3090000000000002, 2, 329, 12.6, 354.31, 8.6099999999999994, 30.300000000000001);
+INSERT INTO boston VALUES (202, 0.034450000000000001, 82.5, 2.0299999999999998, 0, 0.41499999999999998, 6.1619999999999999, 38.399999999999999, 6.2699999999999996, 2, 348, 14.699999999999999, 393.76999999999998, 7.4299999999999997, 24.100000000000001);
+INSERT INTO boston VALUES (206, 0.13642000000000001, 0, 10.59, 0, 0.48899999999999999, 5.891, 22.300000000000001, 3.9453999999999998, 4, 277, 18.600000000000001, 396.89999999999998, 10.869999999999999, 22.600000000000001);
+INSERT INTO boston VALUES (210, 0.43570999999999999, 0, 10.59, 1, 0.48899999999999999, 5.3440000000000003, 100, 3.875, 4, 277, 18.600000000000001, 396.89999999999998, 23.09, 20);
+INSERT INTO boston VALUES (214, 0.14052000000000001, 0, 10.59, 0, 0.48899999999999999, 6.375, 32.299999999999997, 3.9453999999999998, 4, 277, 18.600000000000001, 385.81, 9.3800000000000008, 28.100000000000001);
+INSERT INTO boston VALUES (218, 0.070129999999999998, 0, 13.890000000000001, 0, 0.55000000000000004, 6.6420000000000003, 85.099999999999994, 3.4211, 5, 276, 16.399999999999999, 392.77999999999997, 9.6899999999999995, 28.699999999999999);
+INSERT INTO boston VALUES (222, 0.40771000000000002, 0, 6.2000000000000002, 1, 0.50700000000000001, 6.1639999999999997, 91.299999999999997, 3.048, 8, 307, 17.399999999999999, 395.24000000000001, 21.460000000000001, 21.699999999999999);
+INSERT INTO boston VALUES (226, 0.52693000000000001, 0, 6.2000000000000002, 0, 0.504, 8.7249999999999996, 83, 2.8944000000000001, 8, 307, 17.399999999999999, 382, 4.6299999999999999, 50);
+INSERT INTO boston VALUES (230, 0.44178000000000001, 0, 6.2000000000000002, 0, 0.504, 6.5519999999999996, 21.399999999999999, 3.3751000000000002, 8, 307, 17.399999999999999, 380.33999999999997, 3.7599999999999998, 31.5);
+INSERT INTO boston VALUES (1, 0.0063200000000000001, 18, 2.3100000000000001, 0, 0.53800000000000003, 6.5750000000000002, 65.200000000000003, 4.0899999999999999, 1, 296, 15.300000000000001, 396.89999999999998, 4.9800000000000004, 24);
+INSERT INTO boston VALUES (5, 0.06905, 0, 2.1800000000000002, 0, 0.45800000000000002, 7.1470000000000002, 54.200000000000003, 6.0621999999999998, 3, 222, 18.699999999999999, 396.89999999999998, 5.3300000000000001, 36.200000000000003);
+INSERT INTO boston VALUES (9, 0.21124000000000001, 12.5, 7.8700000000000001, 0, 0.52400000000000002, 5.6310000000000002, 100, 6.0820999999999996, 5, 311, 15.199999999999999, 386.63, 29.93, 16.5);
+INSERT INTO boston VALUES (13, 0.093780000000000002, 12.5, 7.8700000000000001, 0, 0.52400000000000002, 5.8890000000000002, 39, 5.4508999999999999, 5, 311, 15.199999999999999, 390.5, 15.710000000000001, 21.699999999999999);
+INSERT INTO boston VALUES (17, 1.05393, 0, 8.1400000000000006, 0, 0.53800000000000003, 5.9349999999999996, 29.300000000000001, 4.4985999999999997, 4, 307, 21, 386.85000000000002, 6.5800000000000001, 23.100000000000001);
+INSERT INTO boston VALUES (21, 1.25179, 0, 8.1400000000000006, 0, 0.53800000000000003, 5.5700000000000003, 98.099999999999994, 3.7978999999999998, 4, 307, 21, 376.56999999999999, 21.02, 13.6);
+INSERT INTO boston VALUES (25, 0.75026000000000004, 0, 8.1400000000000006, 0, 0.53800000000000003, 5.9240000000000004, 94.099999999999994, 4.3996000000000004, 4, 307, 21, 394.32999999999998, 16.300000000000001, 15.6);
+INSERT INTO boston VALUES (29, 0.77298999999999995, 0, 8.1400000000000006, 0, 0.53800000000000003, 6.4950000000000001, 94.400000000000006, 4.4546999999999999, 4, 307, 21, 387.94, 12.800000000000001, 18.399999999999999);
+INSERT INTO boston VALUES (33, 1.3879900000000001, 0, 8.1400000000000006, 0, 0.53800000000000003, 5.9500000000000002, 82, 3.9900000000000002, 4, 307, 21, 232.59999999999999, 27.710000000000001, 13.199999999999999);
+INSERT INTO boston VALUES (37, 0.097439999999999999, 0, 5.96, 0, 0.499, 5.8410000000000002, 61.399999999999999, 3.3778999999999999, 5, 279, 19.199999999999999, 377.56, 11.41, 20);
+INSERT INTO boston VALUES (41, 0.033590000000000002, 75, 2.9500000000000002, 0, 0.42799999999999999, 7.024, 15.800000000000001, 5.4010999999999996, 3, 252, 18.300000000000001, 395.62, 1.98, 34.899999999999999);
+INSERT INTO boston VALUES (45, 0.12268999999999999, 0, 6.9100000000000001, 0, 0.44800000000000001, 6.069, 40, 5.7209000000000003, 3, 233, 17.899999999999999, 389.38999999999999, 9.5500000000000007, 21.199999999999999);
+INSERT INTO boston VALUES (49, 0.25386999999999998, 0, 6.9100000000000001, 0, 0.44800000000000001, 5.399, 95.299999999999997, 5.8700000000000001, 3, 233, 17.899999999999999, 396.89999999999998, 30.809999999999999, 14.4);
+INSERT INTO boston VALUES (53, 0.053600000000000002, 21, 5.6399999999999997, 0, 0.439, 6.5110000000000001, 21.100000000000001, 6.8147000000000002, 4, 243, 16.800000000000001, 396.89999999999998, 5.2800000000000002, 25);
+INSERT INTO boston VALUES (57, 0.020549999999999999, 85, 0.73999999999999999, 0, 0.40999999999999998, 6.383, 35.700000000000003, 9.1875999999999998, 2, 313, 17.300000000000001, 396.89999999999998, 5.7699999999999996, 24.699999999999999);
+INSERT INTO boston VALUES (61, 0.14932000000000001, 25, 5.1299999999999999, 0, 0.45300000000000001, 5.7409999999999997, 66.200000000000003, 7.2253999999999996, 8, 284, 19.699999999999999, 395.11000000000001, 13.15, 18.699999999999999);
+INSERT INTO boston VALUES (65, 0.01951, 17.5, 1.3799999999999999, 0, 0.41610000000000003, 7.1040000000000001, 59.5, 9.2228999999999992, 3, 216, 18.600000000000001, 393.24000000000001, 8.0500000000000007, 33);
+INSERT INTO boston VALUES (69, 0.13553999999999999, 12.5, 6.0700000000000003, 0, 0.40899999999999997, 5.5940000000000003, 36.799999999999997, 6.4980000000000002, 4, 345, 18.899999999999999, 396.89999999999998, 13.09, 17.399999999999999);
+INSERT INTO boston VALUES (73, 0.091639999999999999, 0, 10.81, 0, 0.41299999999999998, 6.0650000000000004, 7.7999999999999998, 5.2873000000000001, 4, 305, 19.199999999999999, 390.91000000000003, 5.5199999999999996, 22.800000000000001);
+INSERT INTO boston VALUES (77, 0.10153, 0, 12.83, 0, 0.437, 6.2789999999999999, 74.5, 4.0522, 5, 398, 18.699999999999999, 373.66000000000003, 11.970000000000001, 20);
+INSERT INTO boston VALUES (81, 0.04113, 25, 4.8600000000000003, 0, 0.42599999999999999, 6.7270000000000003, 33.5, 5.4006999999999996, 4, 281, 19, 396.89999999999998, 5.29, 28);
+INSERT INTO boston VALUES (85, 0.050590000000000003, 0, 4.4900000000000002, 0, 0.44900000000000001, 6.3890000000000002, 48, 4.7793999999999999, 3, 247, 18.5, 396.89999999999998, 9.6199999999999992, 23.899999999999999);
+INSERT INTO boston VALUES (89, 0.056599999999999998, 0, 3.4100000000000001, 0, 0.48899999999999999, 7.0069999999999997, 86.299999999999997, 3.4217, 2, 270, 17.800000000000001, 396.89999999999998, 5.5, 23.600000000000001);
+INSERT INTO boston VALUES (93, 0.042029999999999998, 28, 15.039999999999999, 0, 0.46400000000000002, 6.4420000000000002, 53.600000000000001, 3.6659000000000002, 4, 270, 18.199999999999999, 395.00999999999999, 8.1600000000000001, 22.899999999999999);
+INSERT INTO boston VALUES (97, 0.11504, 0, 2.8900000000000001, 0, 0.44500000000000001, 6.1630000000000003, 69.599999999999994, 3.4952000000000001, 2, 276, 18, 391.82999999999998, 11.34, 21.399999999999999);
+INSERT INTO boston VALUES (101, 0.14865999999999999, 0, 8.5600000000000005, 0, 0.52000000000000002, 6.7270000000000003, 79.900000000000006, 2.7778, 5, 384, 20.899999999999999, 394.75999999999999, 9.4199999999999999, 27.5);
+INSERT INTO boston VALUES (105, 0.1396, 0, 8.5600000000000005, 0, 0.52000000000000002, 6.1669999999999998, 90, 2.4209999999999998, 5, 384, 20.899999999999999, 392.69, 12.33, 20.100000000000001);
+INSERT INTO boston VALUES (109, 0.12801999999999999, 0, 8.5600000000000005, 0, 0.52000000000000002, 6.4740000000000002, 97.099999999999994, 2.4329000000000001, 5, 384, 20.899999999999999, 395.24000000000001, 12.27, 19.800000000000001);
+INSERT INTO boston VALUES (113, 0.12329, 0, 10.01, 0, 0.54700000000000004, 5.9130000000000003, 92.900000000000006, 2.3534000000000002, 6, 432, 17.800000000000001, 394.94999999999999, 16.210000000000001, 18.800000000000001);
+INSERT INTO boston VALUES (117, 0.13158, 0, 10.01, 0, 0.54700000000000004, 6.1760000000000002, 72.5, 2.7301000000000002, 6, 432, 17.800000000000001, 393.30000000000001, 12.039999999999999, 21.199999999999999);
+INSERT INTO boston VALUES (121, 0.068989999999999996, 0, 25.649999999999999, 0, 0.58099999999999996, 5.8700000000000001, 69.700000000000003, 2.2576999999999998, 2, 188, 19.100000000000001, 389.14999999999998, 14.369999999999999, 22);
+INSERT INTO boston VALUES (125, 0.098489999999999994, 0, 25.649999999999999, 0, 0.58099999999999996, 5.8789999999999996, 95.799999999999997, 2.0063, 2, 188, 19.100000000000001, 379.38, 17.579999999999998, 18.800000000000001);
+INSERT INTO boston VALUES (129, 0.32543, 0, 21.890000000000001, 0, 0.624, 6.431, 98.799999999999997, 1.8125, 4, 437, 21.199999999999999, 396.89999999999998, 15.390000000000001, 18);
+INSERT INTO boston VALUES (133, 0.59004999999999996, 0, 21.890000000000001, 0, 0.624, 6.3719999999999999, 97.900000000000006, 2.3273999999999999, 4, 437, 21.199999999999999, 385.75999999999999, 11.119999999999999, 23);
+INSERT INTO boston VALUES (137, 0.32263999999999998, 0, 21.890000000000001, 0, 0.624, 5.9420000000000002, 93.5, 1.9669000000000001, 4, 437, 21.199999999999999, 378.25, 16.899999999999999, 17.399999999999999);
+INSERT INTO boston VALUES (141, 0.29089999999999999, 0, 21.890000000000001, 0, 0.624, 6.1740000000000004, 93.599999999999994, 1.6119000000000001, 4, 437, 21.199999999999999, 388.07999999999998, 24.16, 14);
+INSERT INTO boston VALUES (145, 2.7797399999999999, 0, 19.579999999999998, 0, 0.871, 4.9029999999999996, 97.799999999999997, 1.3459000000000001, 5, 403, 14.699999999999999, 396.89999999999998, 29.289999999999999, 11.800000000000001);
+INSERT INTO boston VALUES (149, 2.3309899999999999, 0, 19.579999999999998, 0, 0.871, 5.1859999999999999, 93.799999999999997, 1.5296000000000001, 5, 403, 14.699999999999999, 356.99000000000001, 28.32, 17.800000000000001);
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+INSERT INTO boston VALUES (472, 4.0384099999999998, 0, 18.100000000000001, 0, 0.53200000000000003, 6.2290000000000001, 90.700000000000003, 3.0992999999999999, 24, 666, 20.199999999999999, 395.32999999999998, 12.869999999999999, 19.600000000000001);
+INSERT INTO boston VALUES (476, 6.3931199999999997, 0, 18.100000000000001, 0, 0.58399999999999996, 6.1619999999999999, 97.400000000000006, 2.206, 24, 666, 20.199999999999999, 302.75999999999999, 24.100000000000001, 13.300000000000001);
+INSERT INTO boston VALUES (480, 14.3337, 0, 18.100000000000001, 0, 0.61399999999999999, 6.2290000000000001, 88, 1.9512, 24, 666, 20.199999999999999, 383.31999999999999, 13.109999999999999, 21.399999999999999);
+INSERT INTO boston VALUES (484, 2.8183799999999999, 0, 18.100000000000001, 0, 0.53200000000000003, 5.7619999999999996, 40.299999999999997, 4.0983000000000001, 24, 666, 20.199999999999999, 392.92000000000002, 10.42, 21.800000000000001);
+INSERT INTO boston VALUES (488, 4.8356700000000004, 0, 18.100000000000001, 0, 0.58299999999999996, 5.9050000000000002, 53.200000000000003, 3.1522999999999999, 24, 666, 20.199999999999999, 388.22000000000003, 11.449999999999999, 20.600000000000001);
+INSERT INTO boston VALUES (492, 0.10574, 0, 27.739999999999998, 0, 0.60899999999999999, 5.9829999999999997, 98.799999999999997, 1.8681000000000001, 4, 711, 20.100000000000001, 390.11000000000001, 18.07, 13.6);
+INSERT INTO boston VALUES (496, 0.17899000000000001, 0, 9.6899999999999995, 0, 0.58499999999999996, 5.6699999999999999, 28.800000000000001, 2.7986, 6, 391, 19.199999999999999, 393.29000000000002, 17.600000000000001, 23.100000000000001);
+INSERT INTO boston VALUES (500, 0.17782999999999999, 0, 9.6899999999999995, 0, 0.58499999999999996, 5.569, 73.5, 2.3999000000000001, 6, 391, 19.199999999999999, 395.76999999999998, 15.1, 17.5);
+INSERT INTO boston VALUES (504, 0.060760000000000002, 0, 11.93, 0, 0.57299999999999995, 6.976, 91, 2.1675, 1, 273, 21, 396.89999999999998, 5.6399999999999997, 23.899999999999999);
+INSERT INTO boston VALUES (471, 4.3487900000000002, 0, 18.100000000000001, 0, 0.57999999999999996, 6.1669999999999998, 84, 3.0333999999999999, 24, 666, 20.199999999999999, 396.89999999999998, 16.289999999999999, 19.899999999999999);
+INSERT INTO boston VALUES (475, 8.05579, 0, 18.100000000000001, 0, 0.58399999999999996, 5.4269999999999996, 95.400000000000006, 2.4298000000000002, 24, 666, 20.199999999999999, 352.57999999999998, 18.140000000000001, 13.800000000000001);
+INSERT INTO boston VALUES (479, 10.233000000000001, 0, 18.100000000000001, 0, 0.61399999999999999, 6.1849999999999996, 96.700000000000003, 2.1705000000000001, 24, 666, 20.199999999999999, 379.69999999999999, 18.030000000000001, 14.6);
+INSERT INTO boston VALUES (483, 5.73116, 0, 18.100000000000001, 0, 0.53200000000000003, 7.0609999999999999, 77, 3.4106000000000001, 24, 666, 20.199999999999999, 395.27999999999997, 7.0099999999999998, 25);
+INSERT INTO boston VALUES (487, 5.6917499999999999, 0, 18.100000000000001, 0, 0.58299999999999996, 6.1139999999999999, 79.799999999999997, 3.5459000000000001, 24, 666, 20.199999999999999, 392.68000000000001, 14.98, 19.100000000000001);
+INSERT INTO boston VALUES (491, 0.20746000000000001, 0, 27.739999999999998, 0, 0.60899999999999999, 5.093, 98, 1.8226, 4, 711, 20.100000000000001, 318.43000000000001, 29.68, 8.0999999999999996);
+INSERT INTO boston VALUES (495, 0.27956999999999999, 0, 9.6899999999999995, 0, 0.58499999999999996, 5.9260000000000002, 42.600000000000001, 2.3816999999999999, 6, 391, 19.199999999999999, 396.89999999999998, 13.59, 24.5);
+INSERT INTO boston VALUES (499, 0.23912, 0, 9.6899999999999995, 0, 0.58499999999999996, 6.0190000000000001, 65.299999999999997, 2.4091, 6, 391, 19.199999999999999, 396.89999999999998, 12.92, 21.199999999999999);
+INSERT INTO boston VALUES (503, 0.045269999999999998, 0, 11.93, 0, 0.57299999999999995, 6.1200000000000001, 76.700000000000003, 2.2875000000000001, 1, 273, 21, 396.89999999999998, 9.0800000000000001, 20.600000000000001);
+
+
+-------------- data insert finish -------------
+
+select MADLIB_SCHEMA.xgboost_sk_Regressor('boston', 'boston_model_xgbr', 'id', 'target', 'crim, zn, Indus, chas, nox, rm, "Age"', NULL, $${'booster': ['gbtree'], 'eta': (0.1), 'max_depth': (5,6,7), 'objective':['reg:linear']}$$, TRUE, TRUE);
+
+select count(*) from boston_model_xgbr;
+select id, train_timestamp, source_table, y_type, metrics, features, params from boston_model_xgbr;
+
+SELECT assert(count(*) = 3, 'xgboost: Xgboost model output has incorrect number of rows')
+FROM (select * from boston_model_xgbr) q;
+
+drop table boston_model_xgbr;
+select MADLIB_SCHEMA.xgboost_sk_Regressor('boston', 'boston_model_xgbr', 'id', 'target', 'crim, zn, Indus, chas, nox, rm, "Age"', NULL, 
+    $${'booster': ['gbtree'], 'eta': (0.1), 'max_depth': (5,6,7), 'objective':['reg:linear']}$$);
+
+select id, train_timestamp, source_table, y_type, metrics, features, params from boston_model_xgbr;
+select assert(metrics = 'evalution is OFF, no metrics', 'xgboost metrics error') from boston_model_xgbr;
+
+drop table if exists boston_model_xgbr3;
+create table boston_model_xgbr3 as select * from boston_model_xgbr where id=3;
+
+select MADLIB_SCHEMA.xgboost_sk_predict('boston', 'boston_model_xgbr', 'boston_output_xgbr', 'id');
+
+drop table if exists boston_xgb_results;
+create table boston_xgb_results as select t1.id, prediction, target from boston_output_xgbr as t1, boston as t2 order by t1.id;
+
+select mean_squared_error('boston_xgb_results', 'table_out','prediction', 'target');
+
+select * from table_out;
\ No newline at end of file
diff --git a/src/ports/postgres/modules/xgboost_gs/test/xgboost_test2.sql_in b/src/ports/postgres/modules/xgboost_gs/test/xgboost_test2.sql_in
new file mode 100644
index 0000000..b3e3a09
--- /dev/null
+++ b/src/ports/postgres/modules/xgboost_gs/test/xgboost_test2.sql_in
@@ -0,0 +1,464 @@
+
+DROP TABLE IF EXISTS telecom;
+CREATE TABLE telecom(
+"customerID" serial, gender VARCHAR(8), "SeniorCitizen" BOOLEAN, "Partner" BOOLEAN, "Dependents" BOOLEAN,
+tenure INT, "PhoneService" VARCHAR(20), "MultipleLines" VARCHAR(20), "InternetService" VARCHAR(20), "OnlineSecurity" VARCHAR(20),
+"OnlineBackup" VARCHAR(20), "DeviceProtection" VARCHAR(20), "TechSupport" VARCHAR(20), "StreamingTV" VARCHAR(20), "StreamingMovies" VARCHAR(20),
+"Contract" VARCHAR(30), "PaperlessBilling" BOOLEAN, "PaymentMethod" VARCHAR(40), "MonthlyCharges" FLOAT DEFAULT 0, "TotalCharges" FLOAT DEFAULT 0, "Churn" INT DEFAULT 0);
+
+COPY telecom (gender, "SeniorCitizen", "Partner", "Dependents", tenure, "PhoneService", "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup", "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies", "Contract", "PaperlessBilling", "PaymentMethod", "MonthlyCharges", "TotalCharges", "Churn") FROM STDIN DELIMITER ',';
+Male,0,0,0,2,1,0,DSL,1,1,0,0,0,0,Month-to-month,1,Mailed check,53.85,108.15,1
+Male,0,0,0,45,0,0 phone service,DSL,1,0,1,1,0,0,One year,0,Bank transfer (automatic),42.3,1840.75,0
+Female,0,0,0,2,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,70.7,151.65,1
+Female,0,0,0,8,1,1,Fiber optic,0,0,1,0,1,1,Month-to-month,1,Electronic check,99.65,820.5,1
+Male,0,0,1,22,1,1,Fiber optic,0,1,0,0,1,0,Month-to-month,1,Credit card (automatic),89.1,1949.4,0
+Female,0,0,0,10,0,0 phone service,DSL,1,0,0,0,0,0,Month-to-month,0,Mailed check,29.75,301.9,0
+Female,0,1,0,28,1,1,Fiber optic,0,0,1,1,1,1,Month-to-month,1,Electronic check,104.8,3046.05,1
+Male,0,0,1,62,1,0,DSL,1,1,0,0,0,0,One year,0,Bank transfer (automatic),56.15,3487.95,0
+Male,0,1,1,13,1,0,DSL,1,0,0,0,0,0,Month-to-month,1,Mailed check,49.95,587.45,0
+Male,0,0,0,16,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Credit card (automatic),18.95,326.8,0
+Male,0,1,0,58,1,1,Fiber optic,0,0,1,0,1,1,One year,0,Credit card (automatic),100.35,5681.1,0
+Male,0,0,0,49,1,1,Fiber optic,0,1,1,0,1,1,Month-to-month,1,Bank transfer (automatic),103.7,5036.3,1
+Male,0,0,0,25,1,0,Fiber optic,1,0,1,1,1,1,Month-to-month,1,Electronic check,105.5,2686.05,0
+Female,0,1,1,69,1,1,Fiber optic,1,1,1,1,1,1,Two year,0,Credit card (automatic),113.25,7895.15,0
+Female,0,0,0,52,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,20.65,1022.95,0
+Male,0,0,1,71,1,1,Fiber optic,1,0,1,0,1,1,Two year,0,Bank transfer (automatic),106.7,7382.25,0
+Female,0,1,1,10,1,0,DSL,0,0,1,1,0,0,Month-to-month,0,Credit card (automatic),55.2,528.35,1
+Female,0,0,0,21,1,0,Fiber optic,0,1,1,0,0,1,Month-to-month,1,Electronic check,90.05,1862.9,0
+Male,1,0,0,1,0,0 phone service,DSL,0,0,1,0,0,1,Month-to-month,1,Electronic check,39.65,39.65,1
+Male,0,1,0,12,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Bank transfer (automatic),19.8,202.25,0
+Male,0,0,0,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,20.15,20.15,1
+Female,0,1,0,58,1,1,DSL,0,1,0,1,0,0,Two year,1,Credit card (automatic),59.9,3505.1,0
+Male,0,1,1,49,1,0,DSL,1,1,0,1,0,0,Month-to-month,0,Credit card (automatic),59.6,2970.3,0
+Female,0,0,0,30,1,0,DSL,1,1,0,0,0,0,Month-to-month,1,Bank transfer (automatic),55.3,1530.6,0
+Male,0,1,1,47,1,1,Fiber optic,0,1,0,0,1,1,Month-to-month,1,Electronic check,99.35,4749.15,1
+Male,0,1,1,1,0,0 phone service,DSL,0,1,0,0,0,0,Month-to-month,0,Electronic check,30.2,30.2,1
+Male,0,1,0,72,1,1,DSL,1,1,1,1,1,1,Two year,1,Credit card (automatic),90.25,6369.45,0
+Female,0,0,1,17,1,0,DSL,0,0,0,0,1,1,Month-to-month,1,Mailed check,64.7,1093.1,1
+Female,1,1,0,71,1,1,Fiber optic,1,1,1,1,0,0,Two year,1,Credit card (automatic),96.35,6766.95,0
+Male,1,1,0,2,1,0,Fiber optic,0,0,1,0,1,1,Month-to-month,1,Credit card (automatic),95.5,181.65,0
+Female,0,1,1,27,1,0,DSL,1,1,1,1,0,0,One year,0,Mailed check,66.15,1874.45,0
+Male,0,0,0,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Bank transfer (automatic),20.2,20.2,0
+Male,1,0,0,1,1,0,DSL,0,0,0,0,0,0,Month-to-month,0,Bank transfer (automatic),45.25,45.25,0
+Female,0,1,1,72,1,1,Fiber optic,1,1,0,1,1,0,Two year,0,Bank transfer (automatic),99.9,7251.7,0
+Male,0,0,0,5,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,69.7,316.9,1
+Female,0,0,0,46,1,0,Fiber optic,0,0,1,0,0,0,Month-to-month,1,Credit card (automatic),74.8,3548.3,0
+Male,0,0,0,34,1,1,Fiber optic,0,1,1,0,1,1,Month-to-month,1,Electronic check,106.35,3549.25,1
+Female,0,0,0,11,1,1,Fiber optic,0,0,1,0,1,1,Month-to-month,1,Bank transfer (automatic),97.85,1105.4,1
+Male,0,1,1,10,1,0,DSL,0,1,0,0,0,0,One year,0,Mailed check,49.55,475.7,0
+Female,0,1,1,70,1,1,DSL,1,1,0,0,1,0,Two year,1,Credit card (automatic),69.2,4872.35,0
+Female,0,1,1,17,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,20.75,418.25,0
+Female,0,0,0,63,1,1,DSL,1,1,1,1,1,0,Two year,1,Credit card (automatic),79.85,4861.45,0
+Female,0,1,0,13,1,1,DSL,1,1,0,1,1,0,Month-to-month,1,Electronic check,76.2,981.45,0
+Female,0,0,0,49,1,1,Fiber optic,0,0,0,0,0,1,Month-to-month,1,Electronic check,84.5,3906.7,0
+Male,0,0,0,2,1,0,DSL,0,1,0,0,0,0,Month-to-month,0,Mailed check,49.25,97,0
+Female,0,0,0,2,1,0,Fiber optic,0,0,0,0,1,0,Month-to-month,1,Electronic check,80.65,144.15,1
+Male,0,0,0,52,1,1,DSL,1,0,0,1,1,1,Two year,1,Credit card (automatic),79.75,4217.8,0
+Female,0,1,1,69,1,1,DSL,1,0,1,1,0,0,Two year,1,Credit card (automatic),64.15,4254.1,0
+Female,1,0,0,43,1,1,Fiber optic,0,1,0,0,1,0,Month-to-month,1,Electronic check,90.25,3838.75,0
+Female,0,0,0,15,1,0,Fiber optic,1,1,0,0,1,1,Month-to-month,1,Credit card (automatic),99.1,1426.4,1
+Female,1,1,0,25,1,1,DSL,1,0,0,1,1,0,Month-to-month,1,Credit card (automatic),69.5,1752.65,0
+Female,1,1,0,8,1,1,Fiber optic,0,1,0,0,0,0,Month-to-month,1,Credit card (automatic),80.65,633.3,1
+Female,1,1,1,60,1,0,DSL,1,1,1,1,0,1,One year,1,Credit card (automatic),74.85,4456.35,0
+Male,1,0,0,18,1,1,Fiber optic,0,0,0,0,1,1,Month-to-month,1,Electronic check,95.45,1752.55,1
+Female,0,1,1,63,1,1,Fiber optic,1,0,0,0,1,1,One year,1,Credit card (automatic),99.65,6311.2,0
+Male,1,1,1,66,1,1,Fiber optic,0,1,1,1,1,1,One year,1,Electronic check,108.45,7076.35,0
+Female,0,1,1,34,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Credit card (automatic),24.95,894.3,0
+Female,0,0,0,72,1,1,Fiber optic,0,0,1,1,1,1,Two year,1,Credit card (automatic),107.5,7853.7,0
+Female,0,1,0,47,1,1,Fiber optic,0,0,1,0,1,1,Month-to-month,1,Electronic check,100.5,4707.1,0
+Male,0,0,0,60,1,1,Fiber optic,0,1,0,0,1,0,Month-to-month,1,Bank transfer (automatic),89.9,5450.7,0
+Male,0,1,0,72,0,0 phone service,DSL,1,1,1,0,0,0,Two year,0,Bank transfer (automatic),42.1,2962,0
+Female,0,1,1,18,1,0,DSL,0,0,1,1,0,0,One year,1,Credit card (automatic),54.4,957.1,0
+Female,0,0,0,9,1,1,Fiber optic,0,0,0,0,1,1,Month-to-month,0,Electronic check,94.4,857.25,1
+Female,0,0,0,3,1,0,DSL,0,1,0,1,1,1,Month-to-month,1,Electronic check,75.3,244.1,0
+Male,0,1,0,47,1,1,Fiber optic,0,1,0,0,0,0,One year,1,Electronic check,78.9,3650.35,0
+Female,0,0,0,31,1,0,DSL,0,1,1,1,1,1,Two year,0,Mailed check,79.2,2497.2,0
+Female,0,1,1,50,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Bank transfer (automatic),20.15,930.9,0
+Male,0,0,0,10,1,0,Fiber optic,1,0,1,0,0,0,Month-to-month,1,Mailed check,79.85,887.35,0
+Male,0,0,0,1,1,0,DSL,0,0,0,1,0,0,Month-to-month,0,Mailed check,49.05,49.05,0
+Female,0,1,1,52,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,1,Electronic check,20.4,1090.65,0
+Male,1,1,1,64,1,1,Fiber optic,1,0,1,1,1,1,Two year,1,Bank transfer (automatic),111.6,7099,0
+Male,0,1,1,62,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Bank transfer (automatic),24.25,1424.6,0
+Female,0,0,1,3,1,0,DSL,1,0,0,1,0,1,Month-to-month,1,Bank transfer (automatic),64.5,177.4,0
+Female,1,0,0,56,1,1,Fiber optic,1,1,1,0,1,1,One year,0,Electronic check,110.5,6139.5,0
+Female,0,0,0,46,1,0,DSL,0,0,0,0,0,1,One year,0,Credit card (automatic),55.65,2688.85,0
+Female,0,1,1,8,1,0,DSL,1,1,0,0,0,0,Month-to-month,0,Mailed check,54.65,482.25,0
+Male,1,0,0,30,1,0,DSL,1,1,0,0,1,1,Month-to-month,1,Electronic check,74.75,2111.3,0
+Female,0,1,1,45,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,1,Credit card (automatic),25.9,1216.6,0
+Female,0,0,1,1,1,0,Fiber optic,0,0,0,0,1,0,Month-to-month,1,Electronic check,79.35,79.35,1
+Female,0,1,1,11,0,0 phone service,DSL,1,0,0,0,1,1,Month-to-month,0,Electronic check,50.55,565.35,0
+Female,0,1,0,7,1,0,Fiber optic,0,0,1,0,0,0,Month-to-month,1,Bank transfer (automatic),75.15,496.9,1
+Female,0,0,0,42,1,0,Fiber optic,0,1,1,1,1,1,Month-to-month,1,Bank transfer (automatic),103.8,4327.5,0
+Female,0,1,0,49,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Bank transfer (automatic),20.15,973.35,0
+Male,0,0,0,9,1,1,Fiber optic,0,1,0,0,1,1,Month-to-month,1,Credit card (automatic),99.3,918.75,0
+Female,0,1,0,35,1,0,DSL,1,0,0,0,1,0,One year,1,Bank transfer (automatic),62.15,2215.45,0
+Female,0,1,1,48,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Bank transfer (automatic),20.65,1057,0
+Female,0,1,1,46,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Mailed check,19.95,927.1,0
+Male,0,1,0,29,0,0 phone service,DSL,0,0,0,0,1,0,Month-to-month,0,Mailed check,33.75,1009.25,0
+Male,0,1,1,30,1,0,Fiber optic,0,1,1,0,0,0,Month-to-month,0,Bank transfer (automatic),82.05,2570.2,0
+Male,1,0,0,1,1,0,Fiber optic,0,0,0,1,0,0,Month-to-month,0,Electronic check,74.7,74.7,0
+Male,0,1,1,66,1,1,DSL,1,0,1,1,1,1,Two year,1,Mailed check,84,5714.25,0
+Female,0,0,0,65,1,1,Fiber optic,1,1,1,0,1,1,Month-to-month,1,Credit card (automatic),111.05,7107,0
+Male,0,0,0,72,1,1,Fiber optic,0,1,1,0,1,1,Two year,1,Bank transfer (automatic),100.9,7459.05,0
+Female,0,0,0,12,1,1,Fiber optic,1,0,0,0,0,0,Month-to-month,1,Electronic check,78.95,927.35,1
+Male,0,1,1,71,1,1,DSL,1,1,0,1,0,0,One year,1,Credit card (automatic),66.85,4748.7,0
+Male,0,0,0,5,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,21.05,113.85,1
+Male,0,0,0,52,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Bank transfer (automatic),21,1107.2,0
+Female,1,1,0,25,1,0,Fiber optic,0,1,1,0,1,1,Month-to-month,1,Electronic check,98.5,2514.5,1
+Male,0,0,0,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,20.2,20.2,0
+Female,0,1,1,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Electronic check,19.45,19.45,0
+Male,0,0,0,38,1,1,Fiber optic,0,0,1,1,1,0,One year,0,Bank transfer (automatic),95,3605.6,0
+Female,1,1,0,66,0,0 phone service,DSL,0,1,1,0,1,0,One year,0,Bank transfer (automatic),45.55,3027.25,0
+Male,0,1,0,68,1,1,Fiber optic,0,1,1,1,1,1,Two year,1,Bank transfer (automatic),110,7611.85,1
+Male,0,0,0,5,0,0 phone service,DSL,0,0,0,0,0,0,Month-to-month,0,Mailed check,24.3,100.2,0
+Female,0,1,1,72,1,1,Fiber optic,1,1,0,0,1,1,One year,1,Electronic check,104.15,7303.05,0
+Female,0,0,0,32,0,0 phone service,DSL,1,0,0,0,0,0,One year,0,Mailed check,30.15,927.65,0
+Male,0,0,0,43,1,1,Fiber optic,0,0,0,0,1,1,One year,0,Mailed check,94.35,3921.3,0
+Male,0,1,1,72,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Credit card (automatic),19.4,1363.25,0
+Male,0,1,0,55,1,1,Fiber optic,1,1,0,0,1,0,Month-to-month,1,Electronic check,96.75,5238.9,1
+Female,0,0,0,52,1,0,DSL,1,0,1,1,0,0,One year,0,Credit card (automatic),57.95,3042.25,0
+Female,0,0,0,43,1,0,Fiber optic,0,1,1,0,1,0,Month-to-month,1,Electronic check,91.65,3954.1,0
+Female,1,1,0,37,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,76.5,2868.15,1
+Female,0,1,1,64,0,0 phone service,DSL,0,1,0,1,1,1,Two year,1,Electronic check,54.6,3423.5,0
+Male,0,1,1,3,1,0,Fiber optic,1,1,0,0,1,0,Month-to-month,0,Electronic check,89.85,248.4,0
+Female,0,0,0,36,0,0 phone service,DSL,1,0,0,0,0,0,Two year,0,Bank transfer (automatic),31.05,1126.35,0
+Female,0,1,1,10,1,1,Fiber optic,1,0,0,0,1,1,Month-to-month,1,Electronic check,100.25,1064.65,1
+Female,0,0,0,41,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Mailed check,20.65,835.15,0
+Male,0,1,1,27,1,1,Fiber optic,0,1,0,1,0,0,Month-to-month,1,Credit card (automatic),85.2,2151.6,0
+Female,0,1,1,56,1,1,Fiber optic,0,0,1,0,1,1,One year,1,Credit card (automatic),99.8,5515.45,0
+Female,0,0,0,6,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,20.7,112.75,0
+Male,0,0,0,3,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,74.4,229.55,1
+Female,0,1,1,7,1,0,DSL,1,0,0,0,0,0,Month-to-month,0,Mailed check,50.7,350.35,0
+Female,0,1,1,4,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,20.85,62.9,0
+Male,0,0,0,33,1,0,Fiber optic,1,0,0,1,1,0,Two year,1,Electronic check,88.95,3027.65,0
+Female,1,0,0,27,1,1,Fiber optic,0,0,1,0,0,0,Month-to-month,1,Electronic check,78.05,2135.5,1
+Male,0,1,0,72,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Bank transfer (automatic),23.55,1723.95,0
+Male,0,0,0,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,19.75,19.75,0
+Male,1,0,0,71,0,0 phone service,DSL,1,1,0,0,1,1,One year,1,Electronic check,56.45,3985.35,0
+Female,0,0,0,13,1,1,Fiber optic,0,0,0,0,0,1,Month-to-month,1,Electronic check,85.95,1215.65,0
+Female,0,1,1,25,0,0 phone service,DSL,1,1,1,0,1,1,Month-to-month,1,Credit card (automatic),58.6,1502.65,1
+Male,0,0,0,67,1,0,DSL,0,0,0,1,0,0,Two year,0,Bank transfer (automatic),50.55,3260.1,0
+Male,0,0,0,1,0,0 phone service,DSL,0,0,0,0,0,1,Month-to-month,1,Electronic check,35.45,35.45,1
+Male,0,0,0,2,1,0,DSL,0,0,0,0,0,0,Month-to-month,0,Electronic check,44.35,81.25,1
+Female,0,0,0,43,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Credit card (automatic),25.7,1188.2,0
+Female,0,0,0,23,1,0,Fiber optic,1,0,0,0,0,0,Month-to-month,1,Bank transfer (automatic),75,1778.5,0
+Female,0,1,1,64,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Mailed check,20.2,1277.75,0
+Male,0,0,1,57,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Mailed check,19.6,1170.55,0
+Female,1,1,0,1,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Mailed check,70.45,70.45,1
+Female,1,1,1,72,1,1,DSL,1,1,1,1,1,1,Two year,1,Credit card (automatic),88.05,6425.65,0
+Female,0,0,0,8,1,1,DSL,1,0,0,1,0,1,Month-to-month,0,Electronic check,71.15,563.65,1
+Female,0,1,0,61,1,1,Fiber optic,0,1,0,0,1,1,Two year,1,Bank transfer (automatic),101.05,5971.25,0
+Male,0,0,0,64,1,1,Fiber optic,1,1,0,0,0,0,One year,1,Credit card (automatic),84.3,5289.05,0
+Male,1,1,0,71,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Bank transfer (automatic),23.95,1756.2,0
+Female,0,1,1,65,1,1,Fiber optic,1,1,0,1,1,0,Two year,1,Credit card (automatic),99.05,6416.7,0
+Male,0,0,0,3,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,19.6,61.35,0
+Male,0,0,0,1,1,0,DSL,0,0,0,0,0,0,Month-to-month,1,Electronic check,45.65,45.65,1
+Male,0,0,1,30,1,0,DSL,0,1,1,0,1,0,One year,0,Credit card (automatic),64.5,1929.95,0
+Male,0,1,1,15,1,0,DSL,0,1,1,1,0,1,Month-to-month,1,Mailed check,69.5,1071.4,0
+Female,0,1,1,8,1,0,DSL,0,0,0,1,1,1,Month-to-month,1,Electronic check,68.55,564.35,0
+Male,0,0,0,7,1,1,Fiber optic,0,0,0,0,1,1,Month-to-month,0,Mailed check,95,655.5,1
+Female,0,1,1,70,1,1,Fiber optic,1,1,1,0,1,1,One year,1,Credit card (automatic),108.15,7930.55,0
+Male,0,1,1,62,1,0,DSL,1,1,1,1,1,1,Two year,0,Electronic check,86.1,5215.25,0
+Female,0,1,1,6,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,19.7,113.5,0
+Female,0,1,1,14,1,0,Fiber optic,0,0,0,0,0,1,Month-to-month,1,Bank transfer (automatic),80.9,1152.8,0
+Female,0,0,0,22,1,1,Fiber optic,0,0,0,0,1,0,Month-to-month,1,Bank transfer (automatic),84.15,1821.95,0
+Male,0,1,1,22,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,20.15,419.9,0
+Male,0,1,1,16,1,0,DSL,1,0,1,0,1,0,Two year,0,Mailed check,64.25,1024,0
+Male,0,0,0,10,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Bank transfer (automatic),25.7,251.6,0
+Female,0,0,1,13,1,0,DSL,1,1,0,0,0,0,Month-to-month,1,Electronic check,56,764.55,0
+Female,0,1,0,20,1,1,Fiber optic,1,0,0,0,0,0,Month-to-month,0,Electronic check,82.4,1592.35,1
+Female,0,0,0,2,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,69.7,135.2,0
+Male,0,0,0,53,1,1,DSL,0,1,1,1,0,1,Two year,1,Bank transfer (automatic),73.9,3958.25,0
+Female,0,1,1,11,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,20.6,233.9,0
+Male,0,1,0,69,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Mailed check,19.9,1363.45,0
+Female,0,0,0,4,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Bank transfer (automatic),70.9,273,1
+Male,0,1,0,72,1,1,DSL,1,1,1,1,1,1,Two year,1,Bank transfer (automatic),89.05,6254.45,0
+Male,1,1,0,58,0,0 phone service,DSL,0,1,1,0,0,1,Month-to-month,1,Electronic check,45.3,2651.2,1
+Female,0,1,1,16,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Electronic check,20.4,321.4,0
+Male,0,1,0,43,1,0,Fiber optic,1,0,1,1,0,0,One year,1,Bank transfer (automatic),84.25,3539.25,0
+Female,0,1,0,2,1,1,Fiber optic,0,0,1,1,1,1,Month-to-month,1,Electronic check,104.4,242.8,1
+Male,0,1,0,14,1,0,DSL,0,1,1,1,1,1,Two year,0,Mailed check,81.95,1181.75,0
+Female,0,1,0,53,1,0,Fiber optic,0,0,1,0,1,1,One year,1,Bank transfer (automatic),94.85,5000.2,1
+Female,0,0,0,32,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Bank transfer (automatic),20.55,654.55,0
+Female,0,1,0,34,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Electronic check,24.7,780.2,0
+Female,1,0,0,15,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Mailed check,74.45,1145.7,1
+Female,1,0,0,7,1,0,Fiber optic,1,0,0,0,0,0,Month-to-month,0,Bank transfer (automatic),76.45,503.6,1
+Female,0,1,1,15,1,1,Fiber optic,0,1,1,0,1,1,Month-to-month,1,Bank transfer (automatic),105.35,1559.25,0
+Male,0,1,0,61,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,1,Credit card (automatic),20.55,1252,0
+Female,0,0,0,1,0,0 phone service,DSL,0,1,0,0,0,0,Month-to-month,0,Bank transfer (automatic),29.95,29.95,1
+Female,0,0,0,1,1,0,DSL,0,0,0,0,0,0,Month-to-month,0,Mailed check,45.3,45.3,0
+Male,0,0,0,8,1,1,Fiber optic,0,0,0,0,0,1,Month-to-month,0,Bank transfer (automatic),84.5,662.65,1
+Male,0,1,1,33,1,0,DSL,1,0,1,0,1,1,One year,0,Credit card (automatic),74.75,2453.3,0
+Female,0,0,0,13,1,0,Fiber optic,0,1,1,0,0,0,Month-to-month,1,Electronic check,79.25,1111.65,1
+Female,0,1,0,1,0,0 phone service,DSL,0,0,0,0,0,0,Month-to-month,1,Electronic check,24.8,24.8,1
+Male,0,0,0,20,1,0,DSL,0,1,0,0,0,0,Month-to-month,1,Mailed check,51.8,1023.85,0
+Male,0,0,0,3,0,0 phone service,DSL,0,0,0,1,0,0,Month-to-month,0,Electronic check,30.4,82.15,0
+Female,0,0,0,13,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Electronic check,19.65,244.8,0
+Female,0,1,0,40,1,1,DSL,0,1,0,0,0,0,Month-to-month,0,Credit card (automatic),56.6,2379.1,0
+Male,0,1,1,43,1,1,DSL,1,0,0,1,0,1,One year,1,Credit card (automatic),71.9,3173.35,0
+Male,0,1,0,6,1,0,Fiber optic,0,1,1,0,0,1,Month-to-month,1,Electronic check,91,531,1
+Female,0,1,1,69,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Bank transfer (automatic),19.75,1375.4,0
+Female,0,1,1,72,1,1,Fiber optic,1,1,1,0,1,1,Two year,0,Credit card (automatic),109.7,8129.3,0
+Male,0,1,1,59,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Credit card (automatic),19.3,1192.7,0
+Female,0,1,0,20,1,0,Fiber optic,1,1,0,1,1,0,Month-to-month,1,Electronic check,96.55,1901.65,0
+Male,0,1,1,24,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Bank transfer (automatic),24.1,587.4,0
+Male,0,0,0,59,1,1,Fiber optic,0,1,1,1,1,1,Month-to-month,1,Electronic check,111.35,6519.75,0
+Male,0,1,1,72,1,1,Fiber optic,0,1,1,1,1,1,Two year,1,Bank transfer (automatic),112.25,8041.65,0
+Male,0,0,1,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,20.75,20.75,0
+Female,0,1,0,27,1,0,Fiber optic,1,0,0,1,1,1,One year,1,Credit card (automatic),101.9,2681.15,0
+Female,0,0,0,14,1,0,Fiber optic,0,1,0,1,0,0,Month-to-month,1,Credit card (automatic),80.05,1112.3,0
+Male,0,1,1,71,1,1,Fiber optic,1,0,1,0,1,1,Two year,0,Electronic check,105.55,7405.5,0
+Male,0,0,1,13,1,0,Fiber optic,0,0,0,0,1,0,Month-to-month,1,Credit card (automatic),78.3,1033.95,0
+Male,0,0,0,44,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,0,Credit card (automatic),68.85,2958.95,0
+Female,0,0,0,33,1,0,Fiber optic,0,1,0,1,0,0,One year,1,Bank transfer (automatic),79.95,2684.85,0
+Male,0,1,1,72,0,0 phone service,DSL,1,1,1,1,0,1,Two year,1,Credit card (automatic),55.45,4179.2,0
+Male,0,0,0,1,1,0,Fiber optic,0,0,0,0,0,1,Month-to-month,0,Electronic check,79.9,79.9,1
+Female,0,0,0,19,1,0,Fiber optic,1,0,1,1,1,1,Month-to-month,1,Electronic check,106.6,1934.45,1
+Male,0,1,0,64,1,0,Fiber optic,0,1,1,1,1,1,One year,1,Credit card (automatic),102.45,6654.1,0
+Male,0,0,0,2,1,0,DSL,0,0,0,0,0,0,Month-to-month,1,Credit card (automatic),46,84.5,1
+Female,0,0,0,1,0,0 phone service,DSL,0,0,0,0,0,0,Month-to-month,0,Electronic check,25.25,25.25,0
+Male,0,0,1,61,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Mailed check,19.75,1124.2,0
+Female,0,1,1,29,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Electronic check,20,540.05,0
+Male,1,1,0,23,1,1,Fiber optic,0,0,0,0,0,1,Month-to-month,0,Electronic check,86.8,1975.85,0
+Female,0,1,0,57,0,0 phone service,DSL,1,1,1,0,1,1,Month-to-month,1,Bank transfer (automatic),58.75,3437.45,0
+Male,0,1,1,72,0,0 phone service,DSL,1,1,1,1,0,0,Two year,0,Credit card (automatic),45.25,3139.8,0
+Male,0,1,0,66,0,0 phone service,DSL,1,1,1,1,1,0,Two year,1,Bank transfer (automatic),56.6,3789.2,0
+Male,0,1,1,65,1,1,DSL,0,1,1,1,1,1,Two year,1,Bank transfer (automatic),84.2,5324.5,0
+Female,0,0,0,8,1,0,Fiber optic,0,0,0,0,1,0,Month-to-month,1,Bank transfer (automatic),80,624.6,0
+Female,0,0,0,4,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,70.15,268.35,1
+Female,0,1,0,71,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Bank transfer (automatic),24.75,1836.9,0
+Male,0,1,1,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,1,Electronic check,20.2,20.2,0
+Male,0,1,0,4,1,0,DSL,0,0,0,1,0,0,Month-to-month,0,Mailed check,50.05,179.35,1
+Female,0,0,0,12,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,1,Bank transfer (automatic),19.35,219.35,0
+Male,0,0,0,24,0,0 phone service,DSL,1,0,0,0,1,1,One year,1,Mailed check,50.6,1288.75,0
+Female,0,1,1,31,1,0,DSL,1,0,1,1,1,1,One year,1,Mailed check,81.15,2545.75,0
+Female,0,1,0,1,1,0,DSL,0,0,0,0,1,0,Month-to-month,0,Mailed check,55.2,55.2,1
+Male,0,0,0,30,1,1,Fiber optic,0,0,0,1,0,1,Month-to-month,1,Credit card (automatic),89.9,2723.15,0
+Female,0,1,1,47,1,1,DSL,1,1,0,1,1,1,Two year,1,Credit card (automatic),85.3,4107.25,0
+Male,0,0,0,54,1,1,Fiber optic,1,1,0,1,1,1,Two year,1,Credit card (automatic),108,5760.65,0
+Male,0,0,0,50,1,0,Fiber optic,1,1,0,1,0,1,Month-to-month,1,Credit card (automatic),93.5,4747.5,0
+Male,0,0,0,1,1,0,Fiber optic,1,0,0,0,0,1,Month-to-month,1,Mailed check,84.6,84.6,1
+Female,0,0,0,72,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Credit card (automatic),20.25,1566.9,0
+Female,0,0,0,29,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Electronic check,25.15,702,0
+Male,0,0,0,2,1,1,DSL,0,1,0,0,0,0,Month-to-month,1,Mailed check,54.4,114.1,1
+Female,0,0,0,10,0,0 phone service,DSL,0,0,0,1,0,0,Two year,1,Mailed check,29.6,299.05,0
+Male,0,1,0,18,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,73.15,1305.95,0
+Female,1,0,0,11,1,0,Fiber optic,0,1,0,0,1,1,Month-to-month,1,Electronic check,95,1120.3,1
+Male,0,0,0,16,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Mailed check,19.75,284.35,0
+Female,0,0,0,72,1,1,DSL,0,1,1,1,1,1,Two year,0,Bank transfer (automatic),86.6,6350.5,0
+Male,0,0,0,72,1,1,Fiber optic,0,1,1,1,1,1,Two year,0,Bank transfer (automatic),109.2,7878.3,0
+Female,0,1,1,41,1,0,DSL,1,0,0,1,1,1,One year,1,Credit card (automatic),74.7,3187.65,0
+Female,1,1,0,65,1,0,Fiber optic,1,1,1,0,0,1,Month-to-month,0,Electronic check,94.4,6126.15,0
+Female,1,0,0,13,1,0,DSL,0,1,1,0,0,0,Month-to-month,1,Electronic check,54.8,731.3,0
+Male,1,0,0,4,1,0,Fiber optic,1,0,0,0,0,0,Month-to-month,0,Electronic check,75.35,273.4,0
+Male,0,0,0,41,1,1,DSL,0,0,1,0,0,1,One year,0,Mailed check,65,2531.8,0
+Male,1,0,0,15,1,0,Fiber optic,0,1,0,0,0,0,Month-to-month,1,Electronic check,74.4,1074.3,1
+Male,0,0,0,1,1,0,DSL,1,0,0,0,0,0,Month-to-month,1,Mailed check,48.55,48.55,1
+Male,0,0,0,42,1,1,Fiber optic,1,1,1,0,0,1,One year,1,Electronic check,99,4298.45,0
+Female,0,1,0,51,1,0,Fiber optic,1,1,1,0,1,0,One year,1,Electronic check,93.5,4619.55,0
+Female,0,1,1,2,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,0,Electronic check,70.4,147.15,1
+Male,1,1,0,1,0,0 phone service,DSL,0,1,0,0,1,0,Month-to-month,0,Electronic check,40.2,40.2,1
+Male,0,0,1,32,1,1,DSL,0,1,1,1,1,1,One year,0,Credit card (automatic),83.7,2633.3,0
+Male,0,0,0,10,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,1,Mailed check,19.85,193.05,0
+Female,0,1,1,67,0,0 phone service,DSL,0,1,1,1,1,1,Two year,1,Electronic check,59.55,4103.9,0
+Female,0,1,1,61,1,1,Fiber optic,1,1,1,1,1,1,Two year,0,Credit card (automatic),115.1,7008.15,0
+Male,0,0,0,50,1,1,Fiber optic,1,1,1,1,1,1,One year,0,Credit card (automatic),114.35,5791.1,0
+Female,0,1,0,2,1,0,DSL,0,0,0,0,0,0,Month-to-month,1,Credit card (automatic),44.6,80.55,1
+Female,1,1,0,29,0,0 phone service,DSL,1,0,1,0,0,1,Month-to-month,1,Electronic check,45,1228.65,0
+Male,1,0,0,3,0,0 phone service,DSL,0,0,1,0,0,1,Month-to-month,1,Electronic check,41.15,132.2,1
+Male,1,0,0,13,1,1,Fiber optic,0,1,1,0,1,1,Month-to-month,1,Electronic check,106.9,1364.3,1
+Male,1,1,0,57,1,1,Fiber optic,0,1,0,0,0,1,Month-to-month,1,Electronic check,89.85,4925.35,0
+Female,0,0,0,31,0,0 phone service,DSL,1,1,0,1,0,1,Two year,1,Bank transfer (automatic),49.85,1520.1,0
+Female,0,1,0,45,1,1,Fiber optic,1,1,1,1,1,1,One year,0,Mailed check,113.3,5032.25,0
+Female,0,1,1,61,1,1,DSL,1,1,1,1,1,1,Two year,0,Mailed check,88.1,5526.75,0
+Male,0,0,0,50,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Bank transfer (automatic),24.9,1195.25,0
+Female,1,0,0,19,1,0,Fiber optic,1,1,0,1,1,1,Month-to-month,1,Mailed check,105,2007.25,0
+Male,0,0,0,59,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Mailed check,19.35,1099.6,1
+Female,0,1,0,71,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Credit card (automatic),24.25,1732.95,0
+Female,1,0,0,16,1,0,Fiber optic,0,0,0,1,1,1,Month-to-month,1,Electronic check,94.45,1511.2,1
+Male,0,1,0,57,1,0,DSL,1,1,0,1,0,0,Two year,1,Electronic check,59.75,3450.15,0
+Male,0,0,0,1,0,0 phone service,DSL,0,0,0,0,0,0,Month-to-month,0,Bank transfer (automatic),24.8,24.8,1
+Male,1,1,0,20,1,1,Fiber optic,0,1,0,1,1,1,Month-to-month,0,Electronic check,107.05,2172.05,0
+Male,0,0,0,1,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,0,Mailed check,70.6,70.6,1
+Male,0,1,0,5,1,1,Fiber optic,0,1,0,1,0,0,Month-to-month,1,Electronic check,85.4,401.1,1
+Female,0,1,0,52,1,1,Fiber optic,0,1,1,0,1,1,Month-to-month,1,Electronic check,105.05,5624.85,1
+Male,1,1,0,21,1,0,DSL,1,1,0,0,0,1,Month-to-month,1,Bank transfer (automatic),64.95,1339.8,0
+Male,0,0,0,14,0,0 phone service,DSL,0,0,1,1,1,1,Month-to-month,1,Electronic check,55,771.95,0
+Female,0,0,0,5,1,0,DSL,0,1,0,0,0,0,Month-to-month,0,Bank transfer (automatic),50.55,244.75,0
+Female,0,0,0,6,1,0,DSL,1,1,0,0,0,0,Month-to-month,0,Mailed check,55.15,322.9,0
+Female,0,0,0,10,1,0,DSL,0,1,0,0,0,0,Month-to-month,0,Electronic check,51.2,498.25,0
+Female,0,0,1,1,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,25.4,25.4,0
+Female,0,0,0,68,1,1,DSL,1,0,0,0,0,0,Month-to-month,0,Mailed check,54.45,3687.75,0
+Male,0,1,1,18,1,0,Fiber optic,0,0,1,0,1,1,Month-to-month,0,Mailed check,95.15,1779.95,1
+Female,0,0,0,22,1,1,Fiber optic,0,0,0,0,0,0,One year,1,Credit card (automatic),76,1783.6,0
+Female,0,0,0,20,1,0,DSL,0,0,0,0,0,0,Month-to-month,1,Electronic check,44.35,927.15,0
+Male,0,1,0,1,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,0,Electronic check,70,70,1
+Male,1,0,0,8,1,0,Fiber optic,0,1,0,0,0,0,Month-to-month,1,Electronic check,74.5,606.55,1
+Male,0,0,0,10,1,0,DSL,0,0,0,0,0,0,Month-to-month,1,Credit card (automatic),44.85,435.4,1
+Male,1,0,0,24,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,76.1,1712.7,1
+Male,0,0,0,35,1,0,DSL,1,1,1,0,0,0,One year,0,Mailed check,61.2,2021.2,0
+Male,0,1,1,23,1,0,Fiber optic,0,1,1,1,0,0,One year,0,Mailed check,86.8,1940.8,0
+Female,1,0,0,6,1,0,Fiber optic,0,0,0,0,1,1,Month-to-month,1,Electronic check,89.35,567.8,0
+Female,0,0,0,12,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,19.7,220.35,0
+Male,0,0,0,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,20.25,20.25,0
+Female,0,1,1,71,1,0,Fiber optic,0,0,1,0,0,0,Two year,0,Electronic check,76.05,5436.45,0
+Male,1,1,0,35,1,1,Fiber optic,0,0,1,0,1,1,Month-to-month,1,Electronic check,100.8,3437.5,0
+Male,0,1,1,40,1,1,DSL,0,0,1,0,1,1,Month-to-month,1,Electronic check,74.55,3015.75,0
+Female,0,0,1,1,1,0,Fiber optic,1,0,0,0,0,0,Month-to-month,1,Mailed check,73.6,73.6,1
+Male,0,0,0,23,1,0,DSL,0,0,0,0,1,1,One year,1,Bank transfer (automatic),64.9,1509.8,0
+Female,1,0,0,4,1,1,Fiber optic,0,0,0,0,1,1,Month-to-month,1,Electronic check,95.45,396.1,1
+Male,0,0,0,4,1,0,Fiber optic,0,0,1,1,0,1,Month-to-month,1,Electronic check,90.4,356.65,0
+Female,0,1,1,68,0,0 phone service,DSL,0,1,1,1,1,1,Two year,0,Credit card (automatic),60.3,4109,0
+Male,0,0,1,38,1,1,Fiber optic,0,1,0,0,0,0,Month-to-month,1,Mailed check,81.85,3141.7,0
+Male,0,1,1,52,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Mailed check,24.8,1229.1,0
+Male,1,1,0,32,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,74.9,2303.35,1
+Male,0,1,1,29,1,0,DSL,0,0,1,1,1,1,Two year,0,Mailed check,75.55,2054.4,0
+Male,1,1,1,38,1,1,Fiber optic,0,1,0,0,1,1,Month-to-month,0,Electronic check,101.15,3741.85,0
+Male,0,0,1,48,1,1,DSL,1,0,1,0,1,1,One year,1,Credit card (automatic),78.75,3682.45,0
+Female,0,0,0,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,1,Bank transfer (automatic),19.25,19.25,0
+Female,0,0,0,22,1,0,Fiber optic,1,0,0,1,0,1,Month-to-month,1,Electronic check,89.05,1886.25,0
+Female,0,0,0,43,1,1,Fiber optic,1,1,1,1,1,1,Two year,0,Credit card (automatic),115.05,4895.1,0
+Female,1,1,0,5,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,69.35,341.6,0
+Male,0,0,0,5,1,0,Fiber optic,0,0,0,0,1,0,Month-to-month,1,Electronic check,80.6,415.55,1
+Male,0,1,1,51,1,1,Fiber optic,1,1,0,1,1,1,One year,0,Credit card (automatic),110.05,5686.4,0
+Male,0,0,0,71,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Credit card (automatic),19.9,1355.1,0
+Female,0,1,1,38,1,0,DSL,1,1,0,1,1,1,One year,0,Credit card (automatic),80.3,3058.65,1
+Male,1,1,0,24,1,0,Fiber optic,0,0,1,0,1,1,Month-to-month,1,Electronic check,93.15,2231.05,1
+Male,0,1,0,35,1,1,Fiber optic,0,0,0,1,1,0,Month-to-month,1,Credit card (automatic),91.5,3236.35,0
+Female,1,0,0,54,1,1,DSL,1,1,0,1,1,1,One year,0,Bank transfer (automatic),82.45,4350.1,1
+Female,0,1,1,72,0,0 phone service,DSL,0,1,1,1,1,1,Two year,1,Electronic check,60,4264,0
+Male,0,0,0,1,1,0,DSL,0,0,0,0,0,0,Month-to-month,1,Electronic check,44.8,44.8,0
+Female,0,1,1,9,1,0,DSL,1,0,0,0,0,0,One year,0,Mailed check,48.6,422.3,0
+Male,0,0,0,69,0,0 phone service,DSL,1,1,0,1,1,1,Two year,1,Bank transfer (automatic),60.05,4176.7,0
+Female,0,1,0,52,1,1,Fiber optic,0,1,0,1,1,1,Month-to-month,1,Credit card (automatic),102.7,5138.1,0
+Female,1,1,0,11,1,0,Fiber optic,0,0,0,1,1,0,Month-to-month,1,Electronic check,82.9,880.05,0
+Female,1,0,0,2,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,70.35,139.05,1
+Female,1,1,1,28,0,0 phone service,DSL,0,0,0,0,1,0,Month-to-month,1,Electronic check,35.9,973.65,0
+Female,1,0,0,17,1,1,Fiber optic,0,0,1,0,0,0,Month-to-month,1,Credit card (automatic),82.65,1470.05,0
+Female,0,1,1,35,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Mailed check,19.85,739.35,0
+Female,0,0,0,8,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,1,Mailed check,19.2,161.95,0
+Male,0,0,0,46,1,1,Fiber optic,0,1,1,0,1,0,One year,1,Credit card (automatic),94.9,4422.95,0
+Female,0,0,0,7,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,73.85,511.25,1
+Female,0,0,0,2,1,1,Fiber optic,0,0,1,0,0,0,Month-to-month,0,Mailed check,80.6,155.8,1
+Male,0,1,1,68,1,1,DSL,0,1,1,1,0,1,One year,1,Bank transfer (automatic),75.8,5293.95,1
+Female,0,0,0,43,1,1,Fiber optic,0,1,1,0,1,1,Two year,1,Electronic check,104.6,4759.85,1
+Female,0,0,0,68,1,1,DSL,1,1,1,1,1,1,Two year,1,Electronic check,88.15,6148.45,0
+Female,0,0,0,36,1,0,Fiber optic,0,1,0,0,1,1,Month-to-month,1,Credit card (automatic),94.8,3565.65,0
+Male,0,1,0,63,1,1,Fiber optic,0,1,1,0,1,1,Month-to-month,1,Electronic check,103.4,6603,1
+Female,1,0,0,32,1,0,DSL,0,1,0,1,0,0,Month-to-month,0,Bank transfer (automatic),54.65,1830.1,0
+Female,0,1,0,71,1,1,DSL,1,1,1,1,1,1,Two year,0,Credit card (automatic),85.75,6223.8,0
+Female,0,1,1,66,1,1,DSL,1,1,1,0,0,0,Month-to-month,1,Bank transfer (automatic),67.45,4508.65,0
+Male,0,0,0,63,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,20.5,1328.15,0
+Female,0,0,1,41,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,1,Credit card (automatic),20.25,865,0
+Female,1,0,0,1,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,72.1,72.1,0
+Female,0,0,0,2,1,0,Fiber optic,0,0,0,0,1,1,Month-to-month,1,Electronic check,90.4,168.2,1
+Female,0,1,0,70,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,0,Bank transfer (automatic),19.45,1303.5,0
+Female,0,0,0,23,0,0 phone service,DSL,0,0,0,0,1,1,Month-to-month,1,Electronic check,44.95,996.85,0
+Female,1,1,0,64,1,0,Fiber optic,1,1,1,0,0,1,One year,0,Bank transfer (automatic),97,6430.9,0
+Male,0,1,1,37,1,1,DSL,1,0,1,1,0,0,Two year,1,Credit card (automatic),62.8,2278.75,0
+Male,0,0,1,17,1,0,DSL,0,0,0,0,0,0,One year,0,Mailed check,44.6,681.4,0
+Female,1,0,0,7,1,0,Fiber optic,0,0,0,0,1,1,Month-to-month,1,Electronic check,89.15,574.35,0
+Male,0,1,1,4,1,1,Fiber optic,0,1,1,0,0,0,Month-to-month,1,Bank transfer (automatic),84.8,371.9,1
+Female,1,0,0,21,0,0 phone service,DSL,1,0,0,0,1,0,Month-to-month,1,Electronic check,41.9,840.1,1
+Female,0,0,0,10,1,0,Fiber optic,0,0,0,0,0,1,Month-to-month,1,Bank transfer (automatic),80.25,846,1
+Female,1,0,0,16,1,1,DSL,1,0,0,0,0,0,Month-to-month,1,Electronic check,54.1,889,0
+Male,0,1,0,64,1,1,Fiber optic,0,1,0,1,1,1,Two year,1,Electronic check,105.25,6823.4,0
+Male,1,1,0,27,0,0 phone service,DSL,1,0,0,0,0,0,Month-to-month,0,Credit card (automatic),30.75,805.1,1
+Male,0,1,1,42,1,1,Fiber optic,1,0,0,0,1,1,One year,0,Electronic check,97.1,4016.75,0
+Male,0,0,1,5,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,20.2,83.75,0
+Female,0,0,0,41,1,0,Fiber optic,0,1,0,1,1,1,Two year,1,Bank transfer (automatic),98.8,3959.15,0
+Male,0,1,0,58,1,0,DSL,0,0,1,0,0,0,One year,0,Credit card (automatic),50.3,2878.55,0
+Female,0,0,0,47,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Mailed check,20.55,945.7,0
+Male,0,0,0,18,1,0,Fiber optic,0,0,0,1,0,0,Month-to-month,1,Bank transfer (automatic),75.9,1373.05,0
+Male,0,0,0,5,1,0,Fiber optic,0,0,1,0,1,1,Month-to-month,0,Bank transfer (automatic),96.5,492.55,1
+Male,0,1,0,23,1,0,DSL,1,0,0,0,0,1,Month-to-month,1,Bank transfer (automatic),59.95,1406,0
+Female,0,0,0,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,19.15,19.15,0
+Male,0,1,0,71,1,1,Fiber optic,0,0,1,0,1,1,One year,1,Credit card (automatic),98.65,6962.85,0
+Male,0,1,1,72,1,1,Fiber optic,1,1,1,1,1,1,Two year,1,Credit card (automatic),112.6,8126.65,0
+Male,0,1,1,33,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,20.6,690.25,0
+Male,0,0,0,2,1,1,Fiber optic,0,0,0,0,1,0,Month-to-month,1,Electronic check,85.65,181.5,1
+Male,0,0,1,24,0,0 phone service,DSL,1,0,0,1,0,0,Two year,0,Mailed check,35.75,830.8,0
+Female,0,1,1,56,1,1,Fiber optic,0,1,1,1,0,1,One year,1,Credit card (automatic),99.75,5608.4,0
+Male,0,0,0,37,1,0,Fiber optic,0,1,0,0,1,1,Month-to-month,1,Credit card (automatic),96.1,3646.8,0
+Female,0,0,0,43,1,1,DSL,0,1,1,1,1,1,One year,0,Credit card (automatic),85.1,3662.25,0
+Male,0,0,0,1,0,0 phone service,DSL,0,0,0,0,0,0,Month-to-month,0,Mailed check,25.35,25.35,0
+Female,0,1,0,25,1,1,Fiber optic,0,0,1,1,1,1,Month-to-month,1,Bank transfer (automatic),104.95,2566.5,1
+Female,0,0,0,61,1,1,DSL,1,1,1,1,1,1,Two year,0,Electronic check,89.65,5308.7,0
+Male,0,0,0,17,1,1,Fiber optic,0,0,1,1,0,0,One year,0,Electronic check,86.75,1410.25,0
+Female,0,1,0,41,1,0,Fiber optic,0,1,1,1,0,0,One year,1,Bank transfer (automatic),86.2,3339.05,0
+Male,0,0,0,1,1,0,DSL,0,1,0,0,0,0,Month-to-month,0,Mailed check,50.65,50.65,1
+Male,1,1,1,72,0,0 phone service,DSL,1,1,1,1,1,1,Two year,1,Bank transfer (automatic),64.8,4732.35,0
+Male,0,0,0,1,1,1,Fiber optic,0,1,0,0,0,1,Month-to-month,1,Credit card (automatic),90.85,90.85,1
+Male,0,0,0,48,1,1,Fiber optic,0,1,1,0,1,1,Two year,1,Bank transfer (automatic),108.1,5067.45,0
+Male,1,1,0,11,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,19.95,214.75,1
+Female,1,1,0,55,1,1,Fiber optic,0,1,1,0,0,0,Month-to-month,1,Electronic check,85.45,4874.7,1
+Female,0,0,0,42,0,0 phone service,DSL,1,1,1,1,0,1,One year,1,Electronic check,54.75,2348.45,0
+Male,0,0,0,44,1,1,Fiber optic,0,0,1,0,0,1,Month-to-month,1,Mailed check,90.4,4063,0
+Male,0,0,1,1,1,0,DSL,0,0,0,0,0,0,Month-to-month,0,Electronic check,44,44,0
+Female,0,0,0,27,1,1,Fiber optic,0,0,0,0,1,1,Month-to-month,1,Bank transfer (automatic),95.6,2595.25,0
+Male,1,1,0,27,1,1,DSL,0,1,1,1,1,1,Month-to-month,1,Bank transfer (automatic),84.8,2309.55,0
+Female,1,0,0,2,1,0,DSL,0,0,0,0,0,0,Month-to-month,1,Electronic check,44.3,89.3,0
+Female,0,0,0,19,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,0,Mailed check,19.9,367.55,0
+Female,1,1,0,42,1,1,Fiber optic,0,0,0,0,1,1,Month-to-month,1,Electronic check,95.05,3944.5,0
+Female,0,0,0,66,1,1,DSL,1,1,1,1,1,1,Two year,1,Credit card (automatic),90.05,5965.95,0
+Male,1,1,0,33,1,1,Fiber optic,1,0,1,1,1,1,Month-to-month,1,Bank transfer (automatic),109.9,3694.7,0
+Female,0,0,0,34,1,0,Fiber optic,0,1,0,0,0,0,Month-to-month,1,Credit card (automatic),73.95,2524.45,1
+Male,1,0,0,33,0,0 phone service,DSL,0,0,1,1,1,1,Month-to-month,1,Electronic check,54.6,1803.7,0
+Female,0,1,1,23,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,20.05,415.1,0
+Female,0,0,0,32,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Bank transfer (automatic),19.75,624.15,0
+Female,0,0,0,11,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Credit card (automatic),20.05,237.7,0
+Male,0,1,1,69,1,1,Fiber optic,0,0,1,0,1,1,Month-to-month,1,Credit card (automatic),99.45,7007.6,1
+Female,0,1,0,68,1,0,DSL,1,0,0,1,0,0,One year,0,Bank transfer (automatic),55.9,3848.8,0
+Male,0,0,0,20,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Mailed check,19.7,419.4,0
+Male,0,0,0,72,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Credit card (automatic),19.8,1468.75,0
+Male,0,1,1,60,1,1,Fiber optic,1,0,1,0,1,0,One year,1,Bank transfer (automatic),95.4,5812,0
+Male,1,1,1,32,1,0,Fiber optic,0,1,0,0,1,1,Month-to-month,1,Mailed check,93.95,2861.45,0
+Female,0,0,0,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,1,Mailed check,19.9,19.9,1
+Male,0,0,0,1,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,1,Mailed check,19.6,19.6,1
+Male,1,0,0,3,1,1,Fiber optic,0,1,0,0,0,0,Month-to-month,1,Electronic check,81.35,233.7,1
+Female,0,1,0,46,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,1,Bank transfer (automatic),24.45,1066.15,0
+Female,0,1,0,29,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Electronic check,74.95,2149.05,0
+Male,0,0,0,51,1,1,Fiber optic,0,1,1,1,0,0,Month-to-month,1,Electronic check,87.35,4473,0
+Female,1,1,0,48,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Bank transfer (automatic),70.65,3545.05,0
+Female,0,1,1,16,1,0,Fiber optic,0,1,0,0,0,0,Month-to-month,0,Bank transfer (automatic),73.25,1195.75,0
+Male,0,1,0,70,1,1,Fiber optic,0,1,1,1,0,1,Two year,1,Bank transfer (automatic),98.7,6858.9,0
+Female,0,1,1,40,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Mailed check,24.8,1024.7,0
+Female,0,1,1,22,1,0,Fiber optic,0,1,0,0,0,1,Month-to-month,0,Electronic check,83.3,1845.9,1
+Female,1,0,0,1,1,0,Fiber optic,0,0,1,0,0,0,Month-to-month,1,Bank transfer (automatic),75.3,75.3,1
+Female,0,1,1,5,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,1,Mailed check,24.3,132.25,0
+Female,0,0,0,7,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Bank transfer (automatic),69.85,515.45,0
+Male,0,0,1,29,1,1,Fiber optic,0,1,1,1,0,1,One year,0,Credit card (automatic),100.55,2830.45,0
+Male,0,1,1,44,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,25.7,1110.5,0
+Female,0,0,0,10,0,0 phone service,DSL,1,1,1,0,0,0,Month-to-month,0,Bank transfer (automatic),40.7,449.3,0
+Female,1,1,0,55,1,1,DSL,0,0,0,0,0,0,Month-to-month,0,Bank transfer (automatic),51.65,2838.55,0
+Male,1,1,0,52,1,1,Fiber optic,0,1,1,0,1,1,One year,1,Bank transfer (automatic),105.1,5376.4,0
+Female,0,1,1,10,1,1,Fiber optic,0,0,0,0,0,1,Month-to-month,0,Electronic check,85.95,858.6,0
+Male,0,0,0,18,1,1,Fiber optic,0,0,0,0,0,0,Month-to-month,1,Bank transfer (automatic),75.6,1395.05,0
+Female,0,1,0,68,1,1,DSL,1,0,0,1,0,0,One year,0,Bank transfer (automatic),58.25,3975.7,0
+Female,0,1,1,61,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Month-to-month,1,Mailed check,19.4,1182.55,1
+Female,0,1,1,72,0,0 phone service,DSL,1,1,1,1,1,1,Two year,1,Credit card (automatic),65.2,4784.45,0
+Male,0,0,0,2,1,0,DSL,1,0,1,0,0,0,Month-to-month,0,Electronic check,53.45,119.5,0
+Male,0,0,0,12,1,0,DSL,0,0,0,0,0,0,Month-to-month,1,Bank transfer (automatic),45.4,518.9,1
+Male,0,1,0,41,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Electronic check,19.75,899.45,0
+Female,0,0,0,26,0,0 phone service,DSL,0,0,1,1,1,0,Month-to-month,1,Credit card (automatic),44.45,1183.8,0
+Male,0,1,0,36,1,0,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Bank transfer (automatic),20.85,720.05,0
+Male,0,1,1,72,1,1,Fiber optic,1,1,1,1,1,1,Two year,1,Credit card (automatic),114.05,8468.2,0
+Male,0,1,0,35,1,1,Fiber optic,0,0,0,1,0,1,Month-to-month,1,Bank transfer (automatic),89.85,3161.2,0
+Male,0,0,0,1,1,0,DSL,0,0,1,1,0,0,Month-to-month,0,Mailed check,55.05,55.05,0
+Female,0,1,1,16,1,1,Fiber optic,1,0,1,1,1,1,Month-to-month,1,Electronic check,112.95,1882.55,0
+Female,1,0,0,49,1,1,Fiber optic,1,0,0,0,1,1,Month-to-month,0,Credit card (automatic),101.55,5070.4,0
+Female,0,1,0,54,1,1,Fiber optic,1,1,1,1,1,1,One year,1,Mailed check,114.65,6049.5,0
+Female,0,0,1,18,1,1,DSL,1,0,0,0,0,1,Month-to-month,0,Electronic check,64.8,1166.7,0
+Female,0,1,0,36,1,0,Fiber optic,0,0,0,0,0,1,Month-to-month,0,Electronic check,80.4,2937.65,0
+Female,0,0,0,60,1,1,Fiber optic,0,1,1,0,1,1,Month-to-month,1,Electronic check,105.9,6396.45,1
+Female,0,0,0,1,1,0,Fiber optic,0,0,0,0,0,0,Month-to-month,0,Mailed check,69.55,69.55,1
+Female,0,1,1,52,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,Two year,1,Bank transfer (automatic),25.05,1270.25,0
+Male,0,1,1,8,1,0,Fiber optic,0,0,1,0,1,1,Month-to-month,1,Electronic check,94.75,759.55,0
+Male,0,1,1,72,1,0,Fiber optic,1,1,0,1,1,1,Two year,0,Bank transfer (automatic),105.5,7611.55,0
+Female,0,0,0,64,1,1,0,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,0 internet service,One year,0,Mailed check,24.7,1642.75,0
+\.
+
+
+select xgboost_sk_classifier('telecom', 'telecom_mode', '"customerID"', '"Churn"', '"MonthlyCharges", "TotalCharges"', NULL, $${'booster': ['gbtree'], 'eta': (0.1), 'max_depth': 5, 'objective': ['binary:logistic']}$$, TRUE);
+\d+ telecom_mode
+select xgboost_sk_predict('telecom', 'telecom_mode', 'outtable', '"customerID"', TRUE);
\ No newline at end of file
diff --git a/src/ports/postgres/modules/xgboost_gs/xgboost_sklearn.py_in b/src/ports/postgres/modules/xgboost_gs/xgboost_sklearn.py_in
new file mode 100644
index 0000000..00cc866
--- /dev/null
+++ b/src/ports/postgres/modules/xgboost_gs/xgboost_sklearn.py_in
@@ -0,0 +1,488 @@
+# -*- coding:utf-8 -*-
+# Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+#
+# openGauss is licensed under Mulan PSL v2.
+# You can use this software according to the terms
+# and conditions of the Mulan PSL v2.
+# You may obtain a copy of Mulan PSL v2 at:
+#
+#          http://license.coscl.org.cn/MulanPSL2
+#
+# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS,
+# WITHOUT WARRANTIES OF ANY KIND,
+# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+# See the Mulan PSL v2 for more details.
+# ----------------------------------------------------------------------------
+
+# ----------------------------------------------------------------------
+# Xgboost regression and Classification use sklearn API.
+# ----------------------------------------------------------------------
+import ast
+import collections
+import itertools
+import plpy
+
+import cPickle as pickle
+import pandas as pd
+
+from utilities.validate_args import is_var_valid
+from utilities.validate_args import table_exists
+from utilities.validate_args import table_is_empty
+from utilities.validate_args import columns_exist_in_table
+from utilities.validate_args import get_cols
+from utilities.validate_args import unquote_ident
+from utilities.validate_args import quote_ident
+from utilities.utilities import _assert
+from utilities.control import MinWarning
+
+from xgboost_utilities import get_features_to_use
+
+
+def xgboost_sk_classifier(schema_madlib, input_table, model_table, id_column, dependent_varname,
+                            str_of_features, str_of_features_to_exclude, params_str, evaluation, verbose, **kwargs):
+    """
+        Args:
+        @param input_table,                 -- name of input table
+        @param model_table,                 -- name of output table
+        @param id_column
+        @param dependent_varname,           -- name of dependent variable  Y
+        @param str_of_features,             -- name of independent variables X, Comma separated column names to be
+                                                used as the predictors, can be '*'
+                                                to include all columns except the dependent_variable
+        @param str_of_features_to_exclude   -- Comma separated column names to be excluded if str_of_features is '*',
+                                               Ignore 'str_of_features_to_exclude' if 'str_of_features' is not '*'
+        @param params_str,                  -- names of paraters for xgboost
+        @param verbose                      -- verbose or not
+    """
+    with MinWarning('warning'):
+        _validate_args(schema_madlib, input_table, model_table, id_column,
+                       dependent_varname, str_of_features, str_of_features_to_exclude, params_str)
+
+    _, features_list = get_features_to_use(schema_madlib, input_table, id_column, dependent_varname,
+                                        str_of_features, str_of_features_to_exclude, verbose)
+
+    sql = '''SELECT {schema_madlib}.xgboost_grid_search('{input_table}', '{grid_search_results_tbl}', '{id_column}',
+        '{dependent_varname}', ARRAY{features_list}, '{xgb_type}', $${params_str}$$, {evaluation}, {verbose});
+        '''.format(
+        schema_madlib=schema_madlib,
+        input_table=input_table,
+        grid_search_results_tbl=model_table,
+        id_column=id_column,
+        dependent_varname=dependent_varname,
+        features_list=features_list,
+        xgb_type='C',
+        params_str=params_str,
+        evaluation=evaluation,
+        verbose=verbose
+    )
+    if verbose:
+        plpy.info(sql)
+
+    ret = plpy.execute(sql)
+    return ret[0]['xgboost_grid_search']
+
+
+def xgboost_sk_regressor(schema_madlib, input_table, model_table, id_column,
+                            dependent_varname, str_of_features, str_of_features_to_exclude, params_str, evaluation, verbose, **kwargs):
+    """
+        Args:
+        @param input_table,                 -- name of input table
+        @param model_table,                 -- name of output table
+        @param id_column                    -- id column of the table
+        @param dependent_varname,           -- name of dependent variable  Y
+        @param str_of_features,             -- name of independent variables  X
+        @param params_str,                  -- {key: [value1, valule2]}
+        @param verbose
+    """
+    with MinWarning('warning'):
+        _validate_args(schema_madlib, input_table, model_table, id_column,
+                       dependent_varname, str_of_features, str_of_features_to_exclude, params_str)
+
+    _, features_list = get_features_to_use(schema_madlib, input_table, id_column, dependent_varname,
+                                        str_of_features, str_of_features_to_exclude, verbose)
+
+    sql = '''SELECT {schema_madlib}.xgboost_grid_search('{input_table}', '{grid_search_results_tbl}', '{id_column}',
+        '{dependent_varname}', ARRAY{features_list}, '{xgb_type}', $${params_str}$$, {evaluation}, {verbose});
+        '''.format(
+        schema_madlib=schema_madlib,
+        input_table=input_table,
+        grid_search_results_tbl=model_table,
+        id_column=id_column,
+        dependent_varname=dependent_varname,
+        features_list=features_list,
+        xgb_type='R',
+        params_str=params_str,
+        evaluation=evaluation,
+        verbose=verbose
+    )
+    if verbose:
+        plpy.info(sql)
+
+    ret = plpy.execute(sql)
+    return ret[0]['xgboost_grid_search']
+
+
+def xgboost_sk_predict(schema_madlib, input_table, model_table, output_table, id_column, verbose, **kwargs):
+    """
+        Args:
+        @param input_table,                 -- Input: name of input table
+        @param model_table,                 -- Input: name of model table
+        @param output_table,                -- Output: save the results.
+        @param id_column
+        @param verbose
+    """
+    with MinWarning('warning'):
+        _validate_predict(schema_madlib, input_table, model_table, output_table, id_column)
+
+    sql = '''SELECT y_type, features, model FROM {model_table}'''.format(
+        model_table=model_table)
+    result = plpy.execute(sql)
+
+    # get the type of label.
+    y_type = result[0]['y_type']
+    features_list = result[0]['features']
+    features_list = [quote_ident(x) for x in features_list]
+
+    # 1) Extract feature names from information_schema
+    all_columns = get_cols(input_table)
+
+    for f in features_list:
+        if f not in all_columns:
+            plpy.error('''xgboost_sk_predict, {f} donot find in training table.'''.format(str(f)))
+
+    # 2) get model
+    model = result[0]['model']
+    model = pickle.loads(model)
+
+    # 3) Fetch dataset for model training
+    sql = """select {col} from {input_table} order by {id_column}""".format(
+        id_column=id_column,
+        col=','.join(features_list),
+        input_table=input_table
+    )
+
+    if verbose:
+        plpy.info("xgboost_sk_predict() " + sql)
+
+    result = plpy.execute(sql)
+    test_df = pd.DataFrame.from_records(result)
+
+    features_list = [unquote_ident(x) for x in features_list]
+    features = filter(lambda x: x in test_df.columns, features_list)
+    if verbose:
+        plpy.info("xgboost_sk_predict() - features: " + str(features))
+    test_df = test_df[features]
+
+    # get id
+    sql = """select {id_column} from {input_table} order by {id_column}""".format(
+        id_column=id_column,
+        input_table=input_table
+    )
+    result = plpy.execute(sql)
+
+    id_df = pd.DataFrame.from_records(result)
+    id_df = id_df.reset_index(drop=True)
+    id_df = id_df[id_df.columns[0]]
+
+    # start to predict
+    predict_res = model.predict(test_df)
+
+    sql = '''CREATE TABLE {output_table} (id bigint, prediction {y_type})'''.format(
+        output_table=output_table, y_type=y_type)
+    if verbose:
+        plpy.info("xgboost_sk_predict() " + sql)
+
+    plpy.execute(sql)
+    _assert(len(predict_res) == len(id_df),
+            "Xgboost predict error: len(id) != len(predict)")
+
+    sql = """INSERT INTO {output_table} VALUES ({id}, {prediction});"""
+    for i in range(len(id_df)):
+        plpy.execute(
+            sql.format(id=id_df[i],
+                       prediction=predict_res[i],
+                       output_table=output_table
+                       )
+        )
+
+# ------------------------------------------------------------------------------
+# -- validate args  ------------------------------------------------------------
+# ------------------------------------------------------------------------------
+def _validate_args(schema_madlib, input_table, model_table, id_column,
+                   dependent_varname, str_of_features, str_of_features_to_exclude, params_str):
+    """
+    @brief validate the arguments
+    """
+    # 1, check input_table: name.
+    _assert(input_table and input_table.strip().lower() not in ('null', ''),
+            "Xgboost error: Invalid data table name!")
+    _assert(table_exists(input_table),
+            "Xgboost error: Data table does not exist!")
+    _assert(not table_is_empty(input_table),
+            "Xgboost error: Data table is empty!")
+
+    # 2, check input table: id, Dependent variable, features.
+    _assert(columns_exist_in_table(input_table, [id_column]),
+            "Xgboost error: id_column doesnot exist in the input_table")
+
+    _assert(dependent_varname is None or dependent_varname.strip().lower() not in ('null', ''),
+            "Xgboost error: Invalid dependent column name!")
+
+    _assert(is_var_valid(input_table, dependent_varname),
+            "Xgboost error: Invalid dependent variable ({0}).".format(dependent_varname))
+
+    _assert(str_of_features and str_of_features.strip(),
+            "Xgboost error: Features to include is empty.")
+
+    if str_of_features.strip() != '*':
+        _assert(is_var_valid(input_table, str_of_features),
+                "Xgboost error: Invalid feature list ({0})".format(str_of_features))
+
+    if str_of_features.strip() != '*':
+        _assert(dependent_varname not in str_of_features,
+            "Xgboost error: Dependent_varname in str_of_features")
+
+    # 3, check output table.
+    _assert(model_table is not None and model_table.strip().lower() not in ('null', ''),
+            "Xgboost error: Invalid output table name!")
+    _assert(not table_exists(model_table, only_first_schema=True),
+            "Output table name already exists. Drop the table before calling the function.")
+
+    # 4, check params_str
+    if params_str != '':
+        # let xgboost to check the parameter
+        pass
+
+
+def _validate_predict(schema_madlib, input_table, model_table, output_table, id_column):
+    # TODO, check all tables.
+    _assert(input_table is not None and input_table.strip().lower()
+            not in ('null', ''), "Xgboost error: Invalid data table name!")
+    _assert(table_exists(input_table),
+            "Xgboost error: Data table does not exist!")
+    _assert(not table_is_empty(input_table),
+            "Xgboost error: Data table is empty!")
+
+    _assert(model_table is not None and model_table.strip().lower()
+            not in ('null', ''), "Xgboost error: Invalid Model table name!")
+    _assert(table_exists(model_table),
+            "Xgboost error: Model table does not exist!")
+    _assert(not table_is_empty(model_table),
+            "Xgboost error: Model table is empty!")
+    _assert(output_table is not None and output_table.strip().lower()
+            not in ('null', ''), "Xgboost error: Invalid output table name!")
+    _assert(not table_exists(output_table, only_first_schema=True),
+            "Output table name already exists. Drop the table before calling the function.")
+
+# ------------------------------------------------------------------------------
+# -- Online help function ------------------------------------------------------
+# ------------------------------------------------------------------------------
+def xgboost_sk_classifier_help_message(schema_madlib, message, **kwargs):
+    """ Help message for Xgboost XGBClassifier sklearn API
+
+    @brief
+    Args:
+        @param schema_madlib string, Name of the schema madlib
+        @param message string, Help message indicator
+
+    Returns:
+        String. Contain the help message string
+    """
+    if not message:
+        help_string="""
+        -----------------------------------------------------------------------
+                                    SUMMARY
+        -----------------------------------------------------------------------
+        XGBoost sklearn API Training Library.
+
+        For more details on function usage:
+            SELECT {schema_madlib}.xgboost_sk_classifier('usage')
+        """
+    elif message in ['usage', 'help', '?']:
+        help_string="""
+        -----------------------------------------------------------------------
+                                        USAGE
+        -----------------------------------------------------------------------
+        SELECT {schema_madlib}.xgboost_sk_classifier(
+            input_table TEXT,   -- in, Data table name
+            model_table TEXT,   -- out, save model and features
+            id_column TEXT,     -- id column of the dataset table
+            dependent_varname TEXT,          -- y, class label
+            str_of_features  TEXT,           -- X, Comma separated column names to be
+                                                    used as the predictors, can be '*'
+                                                    to include all columns except the
+                                                    dependent_variable
+            str_of_features_to_exclude TEXT, -- List of column names (comma-separated string) to
+                                                    exlude from the predictors list,
+                                                    if str_of_features is '*'
+            params_str TEXT,                 -- xgboost parameters
+            evaluation BOOL,                 -- evaluation the model? default FALSE
+            verbose bool                     -- Boolean, whether to print more info, default is False
+         );
+
+        -----------------------------------------------------------------------
+                                   parameters
+        -----------------------------------------------------------------------
+        below is optional parameters:
+
+        evalution:
+            default is FALSE, if you set evaluation=TRUE, it will split the training dataset(0.8 for training, 0.2 for testing),
+            and calculate metrics.
+            For binary classification: ['precision', 'recall', 'fscore', 'support']
+            OR for multi-calssification: ['kappa', 'acc']
+
+        params_str:
+            For example, if the input is:
+                $${{'booster': ['gbtree'], 'eta': 0.1, 'max_depth': [5,6,7], 'objective': ['binary:logistic']}}$$
+            OR
+                $${{'booster': ('gbtree',), 'eta': 0.1, 'max_depth': (5,6,7), 'objective': ('binary:logistic',)}}$$
+            Then, cartesian_product is:
+                [('booster = gbtree', 'eta = 0.1', 'max_depth = 5', 'objective = binary:logistic'),
+                ('booster = gbtree', 'eta = 0.1', 'max_depth = 6', 'objective = binary:logistic'),
+                ('booster = gbtree', 'eta = 0.1', 'max_depth = 7', 'objective = binary:logistic')]
+
+        select {schema_madlib}.xgboost_sk_Classifier('inputTable', 'output_modelTable', 'id', 'Y', 'x1, x2, x3', NULL, 
+            $${{'booster': ['gbtree'], 'eta': 0.1, 'max_depth': 5, 'objective': ('binary:logistic',)}}$$, '', TRUE, TRUE);
+
+        -----------------------------------------------------------------------
+                                        OUTPUT
+        -----------------------------------------------------------------------
+        The output table ('model_table' above) has the following columns:
+            id                -- index
+            train_timestamp   -- training time
+            model_name        -- model name
+            y_type            -- the type of independent variable
+            metrics           -- default is OFF, evaluation, for binary classification: ['precision', 'recall', 'fscore', 'support']
+                                 OR for multi-calssification: ['kappa', 'acc']
+            features          -- feature in binary model
+            model             -- binary model
+            params            -- xgboost prarms, {{key:value}}
+        """
+    else:
+        help_string="No such option. Use {schema_madlib}.xgboost_sk_classifier()"
+
+    return help_string.format(schema_madlib=schema_madlib)
+
+
+def xgboost_sk_regressor_help_message(schema_madlib, message, **kwargs):
+    """ Help message for Xgboost XGBRegressor sklearn API
+
+    @brief
+    Args:
+        @param schema_madlib string, Name of the schema madlib
+        @param message string, Help message indicator
+
+    Returns:
+        String. Contain the help message string
+    """
+    if not message:
+        help_string="""
+        -----------------------------------------------------------------------
+                                    SUMMARY
+        -----------------------------------------------------------------------
+        XGBoost sklearn API Training Library.
+
+        For more details on function usage:
+            SELECT {schema_madlib}.xgboost_sk_regressor('usage')
+        """
+    elif message in ['usage', 'help', '?']:
+        help_string="""
+        -----------------------------------------------------------------------
+                                        USAGE
+        -----------------------------------------------------------------------
+        SELECT {schema_madlib}.xgboost_sk_regressor(
+            input_table TEXT,                 -- in, dataset table
+            model_table TEXT,                 -- out, save model and features
+            id_column TEXT,                   -- id column of the dataset table
+            dependent_varname TEXT,           -- y, class label
+            str_of_features TEXT,             -- X, Comma separated column names to be
+                                                    used as the predictors, can be '*'
+                                                    to include all columns except the
+                                                    dependent_variable
+            str_of_features_to_exclude TEXT,  -- List of column names (comma-separated string) to
+                                                    exlude from the predictors list, if str_of_features is '*'
+            params_str TEXT,                  -- xgboost parameters
+            evaluation BOOL,                  -- evaluation the model? default FALSE
+            verbose BOOL                      -- Boolean, whether to print more info, default is False
+         );
+
+        -----------------------------------------------------------------------
+                                   parameters
+        -----------------------------------------------------------------------
+        below is optional parameters:
+
+        evalution:
+            default is FALSE, if you set evaluation=TRUE, it will split the training dataset(0.8 for training, 0.2 for testing),
+            and calculate metrics: ['mae', 'mse', 'R2squared', 'rmse']
+
+        params_str:
+            For example, if the input is:
+                $${{'booster': ['gbtree'], 'eta': 0.1, 'max_depth': [5,6,7], 'objective':['reg:linear']}}$$
+            OR
+                $${{'booster': ('gbtree',), 'eta': 0.1, 'max_depth': (5,6,7), 'objective':('reg:linear')}}$$
+            Then, cartesian_product is:
+                [('booster = gbtree', 'eta = 0.1', 'max_depth = 5', 'objective = reg:linear'),
+                ('booster = gbtree', 'eta = 0.1', 'max_depth = 6', 'objective = reg:linear'),
+                ('booster = gbtree', 'eta = 0.1', 'max_depth = 7', 'objective = reg:linear')]
+
+        select {schema_madlib}.xgboost_sk_Regressor('input_TABLE', 'output_model_table', 'id', 'Y', 'x1, x2, x3, x4', 'x5, x6',
+            $${{'booster': ['gbtree'], 'eta': (0.1), 'max_depth': (5,6,7), 'objective':['reg:linear']}}$$, TRUE, TRUE);
+
+        -----------------------------------------------------------------------
+                                        OUTPUT
+        -----------------------------------------------------------------------
+        The output table ('model_table' above) has the following columns:
+            train_timestamp   -- training time
+            model_name        -- model name
+            y_type            -- the type of independent variable
+            metrics           -- if evaluation is TRUE, you will get ['mae', 'mse', 'R2squared', 'rmse']
+            features          -- feature in binary model
+            model             -- binary model
+            params            -- xgboost prarms, {{key:value}}
+            params_indx       -- index
+        """
+    else:
+        help_string="No such option. Use {schema_madlib}.xgboost_sk_regressor()"
+
+    return help_string.format(schema_madlib=schema_madlib)
+
+
+def xgboost_sk_predict_help_message(schema_madlib, message, **kwargs):
+    """ Help message for Xgboost predict sklearn API
+
+    @brief
+    Args:
+        @param schema_madlib string, Name of the schema madlib
+        @param message string, Help message indicator
+
+    Returns:
+        String. Contain the help message string
+    """
+    if not message:
+        help_string="""
+        -----------------------------------------------------------------------
+                                    SUMMARY
+        -----------------------------------------------------------------------
+        XGBoost sklearn API Training Library.
+
+        For more details on function usage:
+            SELECT {schema_madlib}.xgboost_sk_predict('usage')
+        """
+    elif message in ['usage', 'help', '?']:
+        help_string="""
+        -----------------------------------------------------------------------
+                                        USAGE
+        -----------------------------------------------------------------------
+        SELECT {schema_madlib}.xgboost_sk_predict(
+            input_table          TEXT,    -- in, test dataset
+            model_table          TEXT,    -- in, model table from training
+            output_table         TEXT,    -- out, prediction of the test
+            id_column            TEXT,    -- id column of the test dataset
+            verbose              BOOL     -- Boolean, whether to print more info, default is False
+        );
+        """
+    else:
+        help_string="No such option. Use {schema_madlib}.xgboost_sk_predict()"
+
+    return help_string.format(schema_madlib=schema_madlib)
diff --git a/src/ports/postgres/modules/xgboost_gs/xgboost_sklearn.sql_in b/src/ports/postgres/modules/xgboost_gs/xgboost_sklearn.sql_in
new file mode 100644
index 0000000..19df7d4
--- /dev/null
+++ b/src/ports/postgres/modules/xgboost_gs/xgboost_sklearn.sql_in
@@ -0,0 +1,197 @@
+/*
+ * Portions Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+ *
+ * openGauss is licensed under Mulan PSL v2.
+ * You can use this software according to the terms and conditions of the Mulan PSL v2.
+ * You may obtain a copy of Mulan PSL v2 at:
+ *
+ *          http://license.coscl.org.cn/MulanPSL2
+ *
+ * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
+ * EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+ * MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+ * See the Mulan PSL v2 for more details.
+ * -------------------------------------------------------------------------
+ */
+
+m4_include(`SQLCommon.m4')
+
+----------------------------------------------------------------------------------------------
+------ Help messages
+----------------------------------------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_classifier(
+    message    TEXT
+) RETURNS TEXT AS $XX$
+    PythonFunction(xgboost_gs, xgboost_sklearn, xgboost_sk_classifier_help_message)
+$XX$ LANGUAGE plpythonu IMMUTABLE;
+-------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_classifier()
+RETURNS TEXT AS $XX$
+    SELECT MADLIB_SCHEMA.xgboost_sk_classifier(''::TEXT);
+$XX$
+LANGUAGE sql IMMUTABLE;
+
+-----------------------------------------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_regressor(
+    message    TEXT
+) RETURNS TEXT AS $XX$
+    PythonFunction(xgboost_gs, xgboost_sklearn, xgboost_sk_regressor_help_message)
+$XX$ LANGUAGE plpythonu IMMUTABLE;
+-------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_regressor()
+RETURNS TEXT
+AS $XX$
+    SELECT MADLIB_SCHEMA.xgboost_sk_regressor(''::TEXT);
+$XX$
+LANGUAGE sql IMMUTABLE;
+----------------------------------------------------------------------------------------------
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_predict(
+    message    TEXT
+) RETURNS TEXT AS $$
+    PythonFunction(xgboost_gs, xgboost_sklearn, xgboost_sk_predict_help_message)
+$$ LANGUAGE plpythonu IMMUTABLE;
+-------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_predict()
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.xgboost_sk_predict(''::TEXT);
+$$
+LANGUAGE sql IMMUTABLE;
+-------------------------------------------------------------
+
+
+----------------------------------------------------------------------------------------------
+------ xgboost Classifier Algo
+----------------------------------------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_classifier(
+    input_table TEXT,                -- in, dataset table
+    model_table TEXT,                -- out, save model and features
+    id_column TEXT,                  -- id column of the dataset table
+    dependent_varname TEXT,          -- y, class label
+    str_of_features  TEXT,           -- X,
+    str_of_features_to_exclude TEXT, -- List of column names (comma-separated string) to exlude from the predictors list.
+    params_str TEXT,                 -- xgboost parameters
+    evaluation BOOL,                 -- evaluation the training model, default FALSE.
+    verbose BOOL
+)
+RETURNS TEXT AS $$
+PythonFunction(xgboost_gs, xgboost_sklearn, xgboost_sk_classifier)
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_classifier(
+    input_table TEXT,
+    model_table TEXT,
+    id_column TEXT,
+    dependent_varname TEXT,          -- y, class label
+    str_of_features TEXT,            -- X,
+    str_of_features_to_exclude TEXT, -- List of column names (comma-separated string) to exlude from the predictors list.
+    params_str TEXT,
+    evaluation BOOL                  -- evaluation the training model, default FALSE.
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.xgboost_sk_classifier($1, $2, $3, $4, $5, $6, $7, $8, FALSE);
+$$ LANGUAGE sql;
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_classifier(
+    input_table TEXT,
+    model_table TEXT,
+    id_column TEXT,
+    dependent_varname TEXT,          -- y, class label
+    str_of_features TEXT,            -- X,
+    str_of_features_to_exclude TEXT, -- List of column names (comma-separated string) to exlude from the predictors list.
+    params_str TEXT
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.xgboost_sk_classifier($1, $2, $3, $4, $5, $6, $7, FALSE);
+$$ LANGUAGE sql;
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_classifier(
+    input_table TEXT,
+    model_table TEXT,
+    id_column TEXT,
+    dependent_varname TEXT,          -- y, class label
+    str_of_features TEXT,            -- X,
+    str_of_features_to_exclude TEXT  -- List of column names (comma-separated string) to exlude from the predictors list.
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.xgboost_sk_classifier($1, $2, $3, $4, $5, $6, '');
+$$ LANGUAGE sql;
+
+----------------------------------------------------------------------------------------------
+------ xgboost XGBRegressor
+----------------------------------------------------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_regressor(
+    input_table TEXT,                -- in, dataset table
+    model_table TEXT,                -- out, save model and features
+    id_column TEXT,                  -- id column of the dataset table
+    dependent_varname TEXT,          -- y, class label
+    str_of_features TEXT,            -- X,
+    str_of_features_to_exclude TEXT, -- List of column names (comma-separated string) to exlude from the predictors list.
+    params_str TEXT,                 -- xgboost parameters
+    evaluation BOOL,                 -- evaluation the training model, default FALSE.
+    verbose BOOL
+)
+RETURNS TEXT AS $$
+PythonFunction(xgboost_gs, xgboost_sklearn, xgboost_sk_regressor)
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_regressor(
+    input_table TEXT,
+    model_table TEXT,
+    id_column TEXT,
+    dependent_varname TEXT,          -- y, class label
+    str_of_features TEXT,            -- X,
+    str_of_features_to_exclude TEXT, -- List of column names (comma-separated string) to exlude from the predictors list.
+    params_str TEXT,
+    evaluation BOOL                  -- evaluation the training model, default FALSE.
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.xgboost_sk_regressor($1, $2, $3, $4, $5, $6, $7, $8, FALSE);
+$$ LANGUAGE sql;
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_regressor(
+    input_table TEXT,
+    model_table TEXT,
+    id_column TEXT,
+    dependent_varname TEXT,          -- y, class label
+    str_of_features TEXT,            -- X,
+    str_of_features_to_exclude TEXT, -- List of column names (comma-separated string) to exlude from the predictors list.
+    params_str TEXT
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.xgboost_sk_regressor($1, $2, $3, $4, $5, $6, $7, FALSE);
+$$ LANGUAGE sql;
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_regressor(
+    input_table TEXT,
+    model_table TEXT,
+    id_column TEXT,
+    dependent_varname TEXT,          -- y, class label
+    str_of_features TEXT,            -- X,
+    str_of_features_to_exclude TEXT  -- List of column names (comma-separated string) to exlude from the predictors list.
+)
+RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.xgboost_sk_regressor($1, $2, $3, $4, $5, $6, '');
+$$ LANGUAGE sql;
+
+------------------------------------------------
+-- XGBoost predict
+------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_predict(
+    input_table          TEXT,    -- in, test dataset
+    model_table          TEXT,    -- in, model table from training
+    output_table         TEXT,    -- out, prediction of the test
+    id_column            TEXT,    -- id column of the test dataset
+    verbose              BOOL
+) RETURNS TEXT AS $$
+PythonFunction(xgboost_gs, xgboost_sklearn, xgboost_sk_predict)
+$$ LANGUAGE plpythonu;
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_sk_predict(
+    input_table          TEXT,
+    model_table          TEXT,
+    output_table         TEXT,
+    id_column            TEXT
+) RETURNS TEXT AS $$
+    SELECT MADLIB_SCHEMA.xgboost_sk_predict($1, $2, $3, $4, FALSE);
+$$ LANGUAGE sql;
diff --git a/src/ports/postgres/modules/xgboost_gs/xgboost_utilities.py_in b/src/ports/postgres/modules/xgboost_gs/xgboost_utilities.py_in
new file mode 100644
index 0000000..2355ba7
--- /dev/null
+++ b/src/ports/postgres/modules/xgboost_gs/xgboost_utilities.py_in
@@ -0,0 +1,420 @@
+# -*- coding:utf-8 -*-
+# Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+#
+# openGauss is licensed under Mulan PSL v2.
+# You can use this software according to the terms
+# and conditions of the Mulan PSL v2.
+# You may obtain a copy of Mulan PSL v2 at:
+#
+#          http://license.coscl.org.cn/MulanPSL2
+#
+# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS,
+# WITHOUT WARRANTIES OF ANY KIND,
+# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+# See the Mulan PSL v2 for more details.
+# ----------------------------------------------------------------------------
+
+# ----------------------------------------------------------------------
+# Xgboost regression and Classification use sklearn API.
+# ----------------------------------------------------------------------
+import ast
+import collections
+import itertools
+import plpy
+import re
+
+import xgboost as xgb
+import numpy as np
+import cPickle as pickle
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import precision_recall_fscore_support
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error
+from sklearn.metrics import r2_score
+from sklearn.metrics import cohen_kappa_score
+from sklearn.metrics import accuracy_score
+
+from utilities.utilities import unique_string
+from utilities.utilities import split_quoted_delimited_str
+from utilities.utilities import _assert
+from utilities.control import MinWarning
+from utilities.validate_args import get_cols
+from utilities.validate_args import get_expr_type
+from utilities.validate_args import quote_ident
+from utilities.validate_args import unquote_ident
+from utilities.validate_args import _get_table_schema_names
+
+# xgboost donot support category, xgboost only deals with numeric columns
+support_type_list = ('TINYINT', 'SMALLINT', 'INTEGER', 'BINARY_INTEGER', 'BIGINT', 'DECIMAL',
+                    'SMALLSERIAL', 'SERIAL', 'BIGSERIAL',
+                    'REAL', 'FLOAT4', 'DOUBLE PRECISION', 'FLOAT8', 'FLOAT', 'BINARY_DOUBLE', 'DEC',
+                    'BOOLEAN')
+
+
+def get_features_to_use(schema_madlib, input_table, id_column, dependent_varname, str_of_features, str_of_features_to_exclude, verbose=False):
+    """
+        get all columns and used columns.
+        return: quote_idented column.
+    """
+    other_col_set = set([id_column, dependent_varname])
+    other_col_set |= set(unquote_ident(i) for i in [id_column, dependent_varname])
+
+    if str_of_features.strip() == '*':
+        all_col = get_cols(input_table)        # get all columns in order with quote_ident!
+        all_col_set = set(all_col)
+        exclude_set = set(split_quoted_delimited_str(str_of_features_to_exclude))
+        feature_set = all_col_set - exclude_set
+        filtered_feature_list = list(feature_set - other_col_set)
+        filtered_feature_list = [t for t in all_col if t in filtered_feature_list] # columns order is important
+    else:
+        all_col_set = set(get_cols(input_table))
+        feature_list = split_quoted_delimited_str(str_of_features)
+        feature_exclude = split_quoted_delimited_str(str_of_features_to_exclude)
+        return_set = set(feature_list) - set(feature_exclude) - other_col_set
+        # instead of returning list(return_set) we create a list that has
+        # elements in same order as original feature_list
+        filtered_feature_list = [feat for feat in feature_list if feat in return_set]
+
+    if verbose:
+        plpy.info("get_features_to_use() " + str(all_col_set) + " -- " + str(filtered_feature_list))
+    return all_col_set, filtered_feature_list
+
+def get_column_and_ret_type(schema_madlib, input_table, id_column, dependent_varname, features_list, verbose=False):
+    '''
+        1. check the data type of the input table.
+        2. get the type of dependent_varname.
+    '''
+    # Extract feature names from information_schema, and check type.
+    col_list = features_list + [dependent_varname]   # X + [y] with unquote_idented
+
+    # input_table = .
+    s_name, t_name = _get_table_schema_names(input_table, True)
+    sql = """SELECT 
+                column_name, data_type
+            FROM
+                information_schema.columns
+            WHERE
+                table_name = '{t_name}' AND table_schema IN {s_name} AND column_name IN {col_list};
+    """.format(
+        t_name=t_name,
+        s_name=s_name,
+        col_list=str(col_list).replace('[', '(').replace(']', ')').replace('"', '')
+    )
+    if verbose:
+        plpy.info("get_column_and_ret_type() " + sql)
+
+    result = plpy.execute(sql)
+
+    # xgboost do not support category, xgboost only deals with numeric columns
+    for r in result:
+        if verbose:
+            plpy.info("get_column_and_ret_type() check type: " + r['column_name'] + " --> " + r['data_type'])
+        if r['data_type'].upper() not in support_type_list:
+            plpy.error("Wrong Xgboost type, " + r['column_name'] + " is " + r['data_type'])
+
+    # save the type of dependent_variable.
+    y_type = get_expr_type(dependent_varname, input_table)
+
+    if verbose:
+        plpy.info("get_column_and_ret_type() lable type is: " + str(y_type))
+
+    return y_type
+
+
+def save_table_as_bytea(schema_madlib, input_table, id_column,
+                                     dependent_varname, features_list, verbose, **kwargs):
+    """
+        transfrom table => dataframe => dumpfile
+    """
+    # 1) Extract feature names from information_schema
+    col_list = features_list + [dependent_varname]   # X + [y], quote_idented
+
+    # 2) Fetch dataset for model training
+    sql = """select {id_column}, {col} from {input_table}""".format(
+        id_column=id_column,
+        col=','.join(col_list),
+        input_table=input_table
+    )
+
+    if verbose:
+        plpy.info("save_table_as_bytea() " + str(sql))
+
+    result = plpy.execute(sql)
+    # => df
+    df = pd.DataFrame.from_records(result)
+
+    if verbose:
+        plpy.info("save_table_as_bytea() " + str(df.columns))
+
+    # dump dataframe.
+    return pickle.dumps(df)
+
+
+def xgboost_train_parallel(schema_madlib, dframe, dependent_varname, features_list,
+                            xgb_type, params, evaluation, verbose, **kwargs):
+    """
+        xgboost grid search Algorithm
+        Args:
+        @param dframe,                    -- name of input table
+        @param dependent_varname,         -- name of dependent variable  Y
+        @param features_list,             -- name of independent variables  X
+        @param xgb_type,                  -- 'C' or 'R', classificaiton or Regression
+        @param params,                    -- dictionary. params = "{'eta':0.1, 'max_depth':2, 'min_child_weight':3}"
+    """
+    # 1) Load the dataset for model training
+    df = pickle.loads(dframe)
+
+    # 2) Train XGBoost model & return a serialized representation to store in table, keep the order.
+    features_list = [unquote_ident(x) for x in features_list]
+    features = filter(lambda x: x in df.columns, features_list)
+    if verbose:
+        plpy.info("xgboost_train_parallel(): " + str(df.columns) + " <>" + str(features))
+    X = df[features]
+    y = df[unquote_ident(dependent_varname)]
+
+    if evaluation:
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    else:
+        X_train, y_train = X, y
+
+    if verbose:
+        plpy.info("xgboost_train_parallel() X_train.columns: " + str(X_train.columns))
+        plpy.info("xgboost_train_parallel() X_train.dtypes: " + str(X_train.dtypes))
+
+    _assert(xgb_type in ('c', 'C', 'R', 'r'),
+            "Xgboost error: xgb_type must be 'C' or 'R'")
+
+    # Train gradient boosted trees
+    # tuple string => dictionary
+    if type(params) == str:
+        params = ast.literal_eval(params)
+    if type(params) != tuple:
+        plpy.error("Wrong XGboost parameters " + str(params))
+
+    params_dict = dict()
+    for p in params:
+        k, v = p.split("=")
+        params_dict[k.strip()] = v.strip()
+
+    if verbose:
+        plpy.info("xgboost_train_parallel() params list: " + str(params_dict))
+        plpy.info("xgboost_train_parallel() train type: " + xgb_type)
+
+    model_metrics = "evalution is OFF, no metrics"
+
+    if xgb_type == 'C' or xgb_type == 'c':
+        categories = len(set(y_train))
+        plpy.info("xgboost_train_parallel() Classification categories: " + str(categories))
+
+        if categories < 2:
+            plpy.error("Wrong Xgboost Categories: " + str(categories))
+
+        gbm = xgb.XGBClassifier(**params_dict)
+
+        # Fit model
+        gbm.fit(X_train, y_train)
+
+        # 3) Compute and return model metrics score
+        if evaluation:
+            y_pred_test = gbm.predict(X_test)
+
+            if categories == 2:
+                scores = np.array(precision_recall_fscore_support(y_test, y_pred_test)).transpose()
+
+                # precision: TP / (TP + FP) where tp is the number of true positives and fp the number of false positives.
+                # recall: TP / (TP + FN) where tp is the number of true positives and fn the number of false negatives.
+                # fscore: F1=2PR/(P+R)
+                # support: is the number of occurrences of each class in y_true
+                model_metrics = pd.DataFrame(scores, columns=['precision', 'recall', 'f1score', 'support'])
+                model_metrics['class'] = gbm.classes_
+                model_metrics = model_metrics[['class'] + ['precision', 'recall', 'f1score', 'support']]
+
+                model_metrics = model_metrics.to_string(index=False)
+
+            else:
+                model_metrics = dict()
+                model_metrics['kappa'] = cohen_kappa_score(y_test, y_pred_test)
+                model_metrics['acc'] = accuracy_score(y_test, y_pred_test)
+                model_metrics = str(model_metrics)
+
+        # 1) save model_metric, 2) [x], 3) dump model, 4) current parameters
+        return model_metrics, features, pickle.dumps(gbm), str(params)
+
+    else:
+        gbm = xgb.XGBRegressor(**params_dict)
+        gbm.fit(X_train, y_train)
+
+        if evaluation:
+            # R2squared: coefficient of determination
+            metric_labels = ['mae', 'mse', 'R2squared', 'rmse']
+
+            y_test_pred = gbm.predict(X_test)
+            mae2 = mean_absolute_error(y_test_pred, y_test)
+            mse2 = mean_squared_error(y_test_pred, y_test)
+            r2 = r2_score(y_test_pred, y_test)
+            rmse2 = np.sqrt(mse2)
+
+            model_metrics = {'mae': mae2, 'mse': mse2, 'r2squared': r2, 'rmse': rmse2}
+            model_metrics = pd.DataFrame(model_metrics, index=list('1'))
+            model_metrics = model_metrics.to_string()
+
+        # 1) save model_metric, 2) [x], 3) dump model, 4) current parameters
+        return (model_metrics, features, pickle.dumps(gbm), params)
+
+
+def cartesian_product(params):
+    """
+        Expand a dict of parameters into a Cartesian product
+        For example, if the input is:
+            "{'booster': ['gbtree'], 'eta': 0.1, 'max_depth': (5,6,7), 'objective': ('binary:logistic',)}"
+        Then, cartesian_product is:
+            [('booster = gbtree', 'eta = 0.1', 'max_depth = 5', 'objective = binary:logistic'),
+            ('booster = gbtree', 'eta = 0.1', 'max_depth = 6', 'objective = binary:logistic'),
+            ('booster = gbtree', 'eta = 0.1', 'max_depth = 7', 'objective = binary:logistic')]
+    """
+    # transfer params => Cartesian product
+    cp_list = []
+
+    for key, val in params.items():
+        if val and isinstance(val, collections.Iterable):
+            r = []
+            for v in val:
+                r.append("""{k} = {v}""".format(k=key, v=v))
+        else:
+            r = ["""{k} = {v}""".format(k=key, v=val)]
+        cp_list.append(r)
+    cartesian_product = [i for i in itertools.product(*cp_list)]
+    return cartesian_product
+
+
+def xgboost_grid_search(schema_madlib, input_table, grid_search_results_tbl, id_column,
+                        dependent_varname, features_list, xgb_type, params_str, evaluation, verbose, **kwargs):
+    """
+        xgboost grid search main entry.
+    """
+
+    y_type = get_column_and_ret_type(schema_madlib, input_table, id_column, dependent_varname, features_list, verbose)
+ 
+    # Expand the grid-search parameters, Expand the params to run-grid search
+    if params_str is None or params_str is "" or ":" not in params_str:
+        params_str = "{}"
+        plpy.info("xgboost_grid_search() params_str type is: " + params_str)
+
+    # str => dict
+    params = ast.literal_eval(re.sub("[\\t]", "", params_str).strip())
+
+    # trans params => Cartesian product
+    params_grid = cartesian_product(params)
+
+    if verbose:
+        plpy.info("xgboost_grid_search() Cartesian product DONE: " + str(params_grid))
+
+    # Save each parameter list in the grid as a row in a distributed table
+    grid_search_params_temp_tbl = unique_string(desp='gridSearchXGB')
+    sql = """
+        DROP TABLE IF EXISTS {grid_search_params_temp_tbl};
+        CREATE TEMP TABLE {grid_search_params_temp_tbl} (id INT, params TEXT)
+        m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (id)')
+        m4_ifdef(`__GSDBMPP__', `DISTRIBUTE BY HASH(id)', `');
+    """.format(grid_search_params_temp_tbl=grid_search_params_temp_tbl)
+
+    if verbose:
+        plpy.info(sql)
+
+    plpy.execute(sql)
+
+    # insert grid search parameters into the g_s_p table
+    sql = """INSERT INTO {grid_search_params_temp_tbl} VALUES ({id}, $${val}$$);"""
+
+    for indx, val in enumerate(params_grid):
+        plpy.execute(
+            sql.format(
+                grid_search_params_temp_tbl=grid_search_params_temp_tbl,
+                id=indx + 1,
+                val=val
+            )
+        )
+
+    # Extract features from table and persist as serialized dataframe
+    col_list = features_list + [dependent_varname]   # X + [y], quote_idented
+    sql = """
+        DROP TABLE IF EXISTS {grid_search_params_temp_tbl}_df;
+        CREATE TEMP TABLE {grid_search_params_temp_tbl}_df m4_ifdef(`__GSDBMPP__', `DISTRIBUTE BY HASH(id)', `')
+        AS
+        (
+            SELECT
+                1 as id,
+                {schema_madlib}.__save_table_as_bytea__(
+                    '{input_table}',
+                    '{id_column}',
+                    '{class_label}',
+                    ARRAY[{features_list}],
+                    {verbose}
+                ) as df
+        ) m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (id)');
+    """.format(
+        schema_madlib=schema_madlib,
+        grid_search_params_temp_tbl=grid_search_params_temp_tbl,
+        input_table=input_table,
+        id_column=id_column,
+        class_label=dependent_varname,
+        features_list=str(features_list).replace('[', '').replace(']', ''),
+        verbose=verbose
+    )
+
+    if verbose:
+        plpy.info(sql)
+
+    plpy.execute(sql)
+
+    # 5) Invoke XGBoost's train by passing each row from parameter list table. This will run in parallel.
+    sql = """
+        CREATE TABLE {grid_search_results_tbl} m4_ifdef(`__GSDBMPP__', `DISTRIBUTE BY HASH(id)', `')
+        AS (
+            SELECT
+                id,
+                now() as train_timestamp,
+                '{input_table}' as source_table,
+                '{y_type}'::TEXT as y_type,
+                (mdl_results).metrics,
+                (mdl_results).features,
+                (mdl_results).model,
+                (mdl_results).params
+            FROM (
+                SELECT
+                    t1.id,
+                    {schema_madlib}.__xgboost_train_parallel__(
+                        df,
+                        '{class_label}',
+                        ARRAY[{features}],
+                        '{xgb_type}',
+                        params,
+                        {evaluation},
+                        {verbose}
+                    ) AS mdl_results
+                FROM
+                    {grid_search_params_temp_tbl} t1,
+                    {grid_search_params_temp_tbl}_df
+            )q
+        );
+    """.format(
+        schema_madlib=schema_madlib,
+        y_type=y_type,
+        grid_search_results_tbl=grid_search_results_tbl,
+        grid_search_params_temp_tbl=grid_search_params_temp_tbl,
+        xgb_type=xgb_type,
+        features=str(features_list).replace('[', '').replace(']', '').replace(',', ','),
+        input_table=input_table,
+        class_label=dependent_varname,
+        evaluation=evaluation,
+        verbose=verbose
+    )
+
+    if verbose:
+        plpy.info("xgboost_grid_search() " + sql)
+
+    plpy.execute(sql)
+    return """Grid search results saved in {tbl}""".format(tbl=grid_search_results_tbl)
diff --git a/src/ports/postgres/modules/xgboost_gs/xgboost_utilities.sql_in b/src/ports/postgres/modules/xgboost_gs/xgboost_utilities.sql_in
new file mode 100644
index 0000000..ea71952
--- /dev/null
+++ b/src/ports/postgres/modules/xgboost_gs/xgboost_utilities.sql_in
@@ -0,0 +1,81 @@
+/*
+ * Portions Copyright (c) 2020 Huawei Technologies Co.,Ltd.
+ *
+ * openGauss is licensed under Mulan PSL v2.
+ * You can use this software according to the terms and conditions of the Mulan PSL v2.
+ * You may obtain a copy of Mulan PSL v2 at:
+ *
+ *          http://license.coscl.org.cn/MulanPSL2
+ *
+ * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
+ * EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
+ * MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
+ * See the Mulan PSL v2 for more details.
+ * -------------------------------------------------------------------------
+ */
+ 
+m4_include(`SQLCommon.m4')
+
+----------------------------------------------------------------------------------------------
+------ xgboost grid search Algorithm
+----------------------------------------------------------------------------------------------
+
+------------------------------------------------
+-- Prepare Dataset BY pandas
+------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__save_table_as_bytea__(
+    input_table TEXT,           /* dataset table  */
+    id_column TEXT,             /* id column      */
+    dependent_varname TEXT,     /* class label    */
+    features_list text[],       /* independent_v  */
+    verbose BOOL
+)
+RETURNS bytea AS $$
+PythonFunction(xgboost_gs, xgboost_utilities, save_table_as_bytea)
+$$ LANGUAGE plpythonu;
+
+
+------------------------------------------------
+-- Type for internal train results
+------------------------------------------------
+DROP TYPE IF EXISTS MADLIB_SCHEMA.xgb_train_results_type cascade;
+CREATE TYPE MADLIB_SCHEMA.xgb_train_results_type AS(
+    metrics TEXT,         /* save metrices info, e.g. mae, mse */
+    features TEXT[],      /* save X */
+    model bytea,          /* dump model */
+    params TEXT           /* parameters used */
+);
+
+------------------------------------------------
+-- UDF to train XGBoost
+------------------------------------------------
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__xgboost_train_parallel__(
+    dframe bytea,                 /* df from pandas */
+    dependent_varname TEXT,       /* class label    */
+    features_list text[],         /* independent_v  */
+    xgb_type CHAR,                /* Classifier or Regression */
+    params TEXT,                  /* special param  */
+    evaluation BOOL,              /* evaluation the model? default FALSE */
+    verbose BOOL
+)
+RETURNS MADLIB_SCHEMA.xgb_train_results_type AS $$
+PythonFunction(xgboost_gs, xgboost_utilities, xgboost_train_parallel)
+$$ LANGUAGE plpythonu;
+
+------------------------------------------------
+-- XGBoost grid search
+------------------------------------------------
+
+CREATE OR REPLACE function MADLIB_SCHEMA.xgboost_grid_search(
+    input_table TEXT,
+    grid_search_results_tbl TEXT,
+    id_column TEXT,
+    dependent_varname TEXT,
+    features_list TEXT[],
+    xgb_type CHAR,                /* Classifier or Regressor */
+    params_str TEXT,
+    evaluation BOOL,
+    verbose BOOL
+)RETURNS TEXT AS $$
+PythonFunction(xgboost_gs, xgboost_utilities, xgboost_grid_search)
+$$ LANGUAGE plpythonu;