patch 4.0

unittest/sql/optimizer/cost_model_utils/fit_nl.py

@@ -0,0 +1,147 @@
+#!/bin/env python
+__author__ = 'dongyun.zdy'
+
+
+import math
+import numpy as np
+from scipy.optimize import leastsq
+from scipy.optimize import curve_fit
+import sys
+from lmfit import Model
+import getopt
+
+
+
+def nl_model_form(args,
+                  Tstartup,
+                  #Tqual,
+                  Tres,
+                  Tfail,
+                  Tleft_row,
+                  Tright_row
+                  ):
+    (
+        Nrow_res,
+        Nrow_left,
+        Nrow_right,
+        Nright_cache_in,
+        Nright_cache_out,
+        Nright_cache_clear,
+        Nequal_cond
+    ) = args
+
+    total_cost = Tstartup
+    total_cost += Nrow_res * Tres
+    #total_cost += Nequal_cond * Tqual
+    total_cost += (Nequal_cond - Nrow_res) * Tfail
+    total_cost += Nrow_left * Tleft_row
+    total_cost += Nrow_right * Tright_row
+
+    return total_cost
+
+eval_count = 0
+
+def nl_model_arr(arg_sets,
+                 Tstartup,
+                 #Tqual,
+                 Tres,
+                 Tfail,
+                 Tleft_row,
+                 Tright_row
+                 ):
+    res = [nl_model_form(single_arg_set,
+                         Tstartup,
+                         #Tqual,
+                         Tres,
+                         Tfail,
+                         Tleft_row,
+                         Tright_row
+                         ) for single_arg_set in arg_sets]
+    global eval_count
+    eval_count += 1
+    return np.array(res)
+
+
+nl_model = Model(nl_model_arr)
+nl_model.set_param_hint("Tstartup", min=0.0, max = 50)
+#nl_model.set_param_hint("Tqual", min=0.0)
+nl_model.set_param_hint("Tres", min=0.0)
+nl_model.set_param_hint("Tfail", min=0.0)
+nl_model.set_param_hint("Tleft_row", min=0.0)
+nl_model.set_param_hint("Tright_row", min=0.0)
+
+
+def extract_info_from_line(line):
+    splited = line.split(",")
+    line_info = []
+    for item in splited:
+        line_info.append(float(item))
+    return line_info
+
+
+if __name__ == '__main__':
+    file_name = "scan_model.res.formal.prep"
+    out_file_name = "scan_model.fit"
+
+    sys.argv.extend("-i nl.prep -o nl.model".split(" "))
+
+    output_fit_res = False
+    wrong_arg = False
+    opts,args = getopt.getopt(sys.argv[1:],"i:o:")
+    for op, value in opts:
+        if "-i" == op:
+            file_name = value
+        elif "-o" == op:
+            output_fit_res = True
+            out_file_name = value
+        else:
+            wrong_arg = True
+
+    if wrong_arg:
+        print "wrong arg"
+        sys.exit(1)
+
+    file = open(file_name, "r")
+    arg_sets = []
+    times = []
+    case_params = []
+    for line in file:
+        case_param = extract_info_from_line(line)
+        case_params.append(case_param)
+        arg_sets.append((case_param[6],     #Nrow_res
+                         case_param[0],     #Nrow_left
+                         case_param[1],     #Nrow_right
+                         case_param[-3],    #Nright_cache_in
+                         case_param[-2],    #Nright_cache_out
+                         case_param[-1],    #Nright_cache_clear
+                         case_param[8]      #Nequal_cond
+                         ))
+        times.append(case_param[7])
+    file.close()
+    arg_sets_np = np.array(arg_sets)
+    times_np = np.array(times)
+    #10, 0.20406430879623488, 0.016618100054245379, 14.0, 4.5, 37.0, -0.005, 0.5, -7.0
+    result = nl_model.fit(times_np, arg_sets=arg_sets_np,
+                             Tstartup=50.0,
+                             #Tqual=0.1,
+                             Tres=0.3,
+                             Tfail=0.3,
+                             Tleft_row=0.3,
+                             Tright_row=0.3
+                            )
+
+
+    res_line = str(result.best_values["Tstartup"]) + ","
+    #res_line += str(result.best_values["Tqual"]) + ","
+    res_line += str(result.best_values["Tres"]) + ","
+    res_line += str(result.best_values["Tfail"]) + ","
+    res_line += str(result.best_values["Tleft_row"]) + ","
+    res_line += str(result.best_values["Tright_row"])
+
+
+    print result.fit_report()
+
+    if output_fit_res:
+        out_file = open(out_file_name, "w")
+        out_file.write(res_line)
+        out_file.close()