Fix to get Test and Train Fitness for final solution of Mult-objective Optimization. Fixes #166

grammars/supervised_learning/supervised_learning.bnf

@@ -2,7 +2,7 @@
 	 	  (<e>-<e>) |
 		  (<e>*<e>) |
 		  aq(<e>,<e>) |
-		  x[:, <varidx>] |
+		  x[<varidx>] |
 		  <c>
 <varidx> ::= GE_RANGE:dataset_n_vars
 <c> ::= <d>.<d> | -<d>.<d>

src/fitness/supervised_learning/regression_random_polynomial.py

@@ -59,7 +59,6 @@ def __init__(self):
         # if we want a separate test set, generate a set of fitness
         # cases for it
         if params['DATASET_TEST']:
-            self.training_test = True
             self.test_in = np.random.random((n_vars, n_samples))
             self.test_exp = p.eval(self.training_in)
 

src/fitness/supervised_learning/supervised_learning.py

@@ -37,9 +37,6 @@ def __init__(self):
         # Find number of variables.
         self.n_vars = np.shape(self.training_in)[1] # sklearn convention
 
-        # Regression/classification-style problems use training and test data.
-        if params['DATASET_TEST']:
-            self.training_test = True
 
     def evaluate(self, ind, **kwargs):
         """

src/stats/stats.py

@@ -115,7 +115,7 @@ def get_soo_stats(individuals, end):
         stdout.flush()
 
     # Generate test fitness on regression problems
-    if hasattr(params['FITNESS_FUNCTION'], "training_test") and end:
+    if params["DATASET_TEST"] and end:
         # Save training fitness.
         trackers.best_ever.training_fitness = copy(trackers.best_ever.fitness)
 
@@ -240,7 +240,7 @@ def get_moo_stats(individuals, end):
         stdout.flush()
 
     # Generate test fitness on regression problems
-    if hasattr(params['FITNESS_FUNCTION'], "training_test") and end:
+    if params["DATASET_TEST"] and end:
 
         for ind in trackers.best_ever:
             # Iterate over all individuals in the first front.
@@ -249,7 +249,10 @@ def get_moo_stats(individuals, end):
             ind.training_fitness = copy(ind.fitness)
 
             # Evaluate test fitness.
-            ind.test_fitness = params['FITNESS_FUNCTION'](ind, dist='test')
+            ind.test_fitness = [
+                func(ind, dist="test")
+                for func in params["FITNESS_FUNCTION"].fitness_functions
+            ]
 
             # Set main fitness as training fitness.
             ind.fitness = ind.training_fitness
@@ -364,9 +367,8 @@ def print_final_stats():
     :return: Nothing.
     """
 
-    if hasattr(params['FITNESS_FUNCTION'], "training_test"):
-        print("\n\nBest:\n  Training fitness:\t",
-              trackers.best_ever.training_fitness)
+    if params["DATASET_TEST"]:
+        print("\n\nBest:\n  Training fitness:\t", trackers.best_ever.training_fitness)
         print("  Test fitness:\t\t", trackers.best_ever.test_fitness)
     else:
         print("\n\nBest:\n  Fitness:\t", trackers.best_ever.fitness)

src/utilities/stats/file_io.py

@@ -68,7 +68,7 @@ def save_best_ind_to_file(stats, ind, end=False, name="best"):
     savefile.write("Phenotype:\n" + str(ind.phenotype) + "\n\n")
     savefile.write("Genotype:\n" + str(ind.genome) + "\n")
     savefile.write("Tree:\n" + str(ind.tree) + "\n")
-    if hasattr(params['FITNESS_FUNCTION'], "training_test"):
+    if params["DATASET_TEST"]:
         if end:
             savefile.write("\nTraining fitness:\n" + str(ind.training_fitness))
             savefile.write("\nTest fitness:\n" + str(ind.test_fitness))