Adjust hyperparameters based on 1000 trial search

eureqa.py

@@ -5,24 +5,51 @@ import pathlib
 import numpy as np
 import pandas as pd
 
-def eureqa(X=None, y=None, threads=4, parsimony=1e-3, alpha=2.4,
-            maxsize=20, migration=True,
-            hofMigration=True, fractionReplacedHof=0.15,
-            shouldOptimizeConstants=True,
+# Dumped from hyperparam optimization
+default_alpha =                      2.288229
+default_annealing =                  1.000000
+default_fractionReplaced =           0.121271
+default_fractionReplacedHof =        0.065129
+default_ncyclesperiteration =    15831.000000
+default_niterations =               11.000000
+default_npop =                     105.000000
+default_parsimony =                  0.000465
+default_topn =                       6.000000
+default_weightAddNode =              0.454050
+default_weightDeleteNode =           0.603670
+default_weightDoNothing =            0.141223
+default_weightMutateConstant =       3.680211
+default_weightMutateOperator =       0.660488
+default_weightRandomize =            6.759691
+default_weightSimplify =             0.010442
+default_result =                     0.687007
+
+def eureqa(X=None, y=None, threads=4,
+            niterations=20,
+            ncyclesperiteration=int(default_ncyclesperiteration),
             binary_operators=["plus", "mult"],
             unary_operators=["cos", "exp", "sin"],
-            niterations=20, npop=120, annealing=True,
-            ncyclesperiteration=12000, fractionReplaced=0.1,
-            topn=2, equation_file='hall_of_fame.csv',
-            test='simple1',
-            weightMutateConstant=8.0,
-            weightMutateOperator=0.7,
-            weightAddNode=1.2,
-            weightDeleteNode=0.17,
-            weightSimplify=0.07,
-            weightRandomize=0.18,
-            weightDoNothing=1.7,
+            alpha=default_alpha,
+            annealing=True,
+            fractionReplaced=default_fractionReplaced,
+            fractionReplacedHof=default_fractionReplacedHof,
+            npop=int(default_npop),
+            parsimony=default_parsimony,
+            migration=True,
+            hofMigration=True,
+            shouldOptimizeConstants=True,
+            topn=int(default_topn),
+            weightAddNode=default_weightAddNode,
+            weightDeleteNode=default_weightDeleteNode,
+            weightDoNothing=default_weightDoNothing,
+            weightMutateConstant=default_weightMutateConstant,
+            weightMutateOperator=default_weightMutateOperator,
+            weightRandomize=default_weightRandomize,
+            weightSimplify=default_weightSimplify,
             timeout=None,
+            equation_file='hall_of_fame.csv',
+            test='simple1',
+            maxsize=20,
         ):
     """ Runs symbolic regression in Julia, to fit y given X.
     Either provide a 2D numpy array for X, 1D array for y, or declare a test to run.
@@ -163,15 +190,15 @@ if __name__ == "__main__":
     parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
 
     parser.add_argument("--threads", type=int, default=4, help="Number of threads")
-    parser.add_argument("--parsimony", type=float, default=0.001, help="How much to punish complexity")
-    parser.add_argument("--alpha", type=int, default=2.4, help="Scaling of temperature")
+    parser.add_argument("--parsimony", type=float, default=default_parsimony, help="How much to punish complexity")
+    parser.add_argument("--alpha", type=float, default=default_alpha, help="Scaling of temperature")
     parser.add_argument("--maxsize", type=int, default=20, help="Max size of equation")
     parser.add_argument("--niterations", type=int, default=20, help="Number of total migration periods")
-    parser.add_argument("--npop", type=int, default=120, help="Number of members per population")
-    parser.add_argument("--ncyclesperiteration", type=int, default=12000, help="Number of evolutionary cycles per migration")
-    parser.add_argument("--topn", type=int, default=2, help="How many best species to distribute from each population")
-    parser.add_argument("--fractionReplacedHof", type=float, default=0.15, help="Fraction of population to replace with hall of fame")
-    parser.add_argument("--fractionReplaced", type=float, default=0.1, help="Fraction of population to replace with best from other populations")
+    parser.add_argument("--npop", type=int, default=int(default_npop), help="Number of members per population")
+    parser.add_argument("--ncyclesperiteration", type=int, default=int(default_ncyclesperiteration), help="Number of evolutionary cycles per migration")
+    parser.add_argument("--topn", type=int, default=int(default_topn), help="How many best species to distribute from each population")
+    parser.add_argument("--fractionReplacedHof", type=float, default=default_fractionReplacedHof, help="Fraction of population to replace with hall of fame")
+    parser.add_argument("--fractionReplaced", type=float, default=default_fractionReplaced, help="Fraction of population to replace with best from other populations")
     parser.add_argument("--migration", type=bool, default=True, help="Whether to migrate")
     parser.add_argument("--hofMigration", type=bool, default=True, help="Whether to have hall of fame migration")
     parser.add_argument("--shouldOptimizeConstants", type=bool, default=True, help="Whether to use classical optimization on constants before every migration (doesn't impact performance that much)")