fix all unit tests and fix a issue for warm start

tests/export_tests.py

@@ -54,10 +54,16 @@
 training_features, testing_features, training_target, testing_target = \
     train_test_split(mnist_data.data.astype(np.float64), mnist_data.target.astype(np.float64), random_state=42)
 
+tpot_obj = TPOTClassifier()
+tpot_obj._fit_init()
+
+tpot_obj_reg = TPOTRegressor()
+tpot_obj_reg._fit_init()
 
 def test_export_random_ind():
     """Assert that the TPOTClassifier can generate the same pipeline export with random seed of 39."""
     tpot_obj = TPOTClassifier(random_state=39)
+    tpot_obj._fit_init()
     tpot_obj._pbar = tqdm(total=1, disable=True)
     pipeline = tpot_obj._toolbox.individual()
     expected_code = """import numpy as np
@@ -86,7 +92,6 @@ def test_export_random_ind():
 
 def test_export():
     """Assert that TPOT's export function throws a RuntimeError when no optimized pipeline exists."""
-
     assert_raises(RuntimeError, tpot_obj.export, "test_export.py")
     pipeline_string = (
         'KNeighborsClassifier(CombineDFs('
@@ -106,7 +111,8 @@ def test_export():
 
 def test_generate_pipeline_code():
     """Assert that generate_pipeline_code() returns the correct code given a specific pipeline."""
-    tpot_obj = TPOTClassifier()
+
+    tpot_obj._fit_init()
     pipeline = [
         'KNeighborsClassifier',
         [
@@ -148,7 +154,7 @@ def test_generate_pipeline_code():
 
 def test_generate_pipeline_code_2():
     """Assert that generate_pipeline_code() returns the correct code given a specific pipeline with two CombineDFs."""
-    tpot_obj = TPOTClassifier()
+
     pipeline = [
         'KNeighborsClassifier',
         [
@@ -200,7 +206,7 @@ def test_generate_pipeline_code_2():
 
 def test_generate_import_code():
     """Assert that generate_import_code() returns the correct set of dependancies for a given pipeline."""
-    tpot_obj = TPOTClassifier()
+
     pipeline = creator.Individual.from_string('GaussianNB(RobustScaler(input_matrix))', tpot_obj._pset)
 
     expected_code = """import numpy as np
@@ -215,7 +221,7 @@ def test_generate_import_code():
 
 def test_generate_import_code_2():
     """Assert that generate_import_code() returns the correct set of dependancies and dependancies are importable."""
-    tpot_obj = TPOTClassifier()
+
     pipeline_string = (
         'KNeighborsClassifier(CombineDFs('
         'DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini, '
@@ -241,7 +247,6 @@ def test_generate_import_code_2():
 
 def test_operators():
     """Assert that the TPOT operators match the output of their sklearn counterparts."""
-    tpot_obj = TPOTClassifier(random_state=42)
     for op in tpot_obj.operators:
         check_export.description = ("Assert that the TPOT {} operator exports "
                                     "as expected".format(op.__name__))
@@ -263,7 +268,7 @@ def check_export(op, tpot_obj):
 
 def test_export_pipeline():
     """Assert that exported_pipeline() generated a compile source file as expected given a fixed pipeline."""
-    tpot_obj = TPOTClassifier()
+
     pipeline_string = (
         'KNeighborsClassifier(CombineDFs('
         'DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini, '
@@ -305,7 +310,7 @@ def test_export_pipeline():
 
 def test_export_pipeline_2():
     """Assert that exported_pipeline() generated a compile source file as expected given a fixed simple pipeline (only one classifier)."""
-    tpot_obj = TPOTClassifier()
+
     pipeline_string = (
         'KNeighborsClassifier('
         'input_matrix, '
@@ -336,7 +341,7 @@ def test_export_pipeline_2():
 
 def test_export_pipeline_3():
     """Assert that exported_pipeline() generated a compile source file as expected given a fixed simple pipeline with a preprocessor."""
-    tpot_obj = TPOTClassifier()
+
     pipeline_string = (
         'DecisionTreeClassifier(SelectPercentile(input_matrix, SelectPercentile__percentile=20),'
         'DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8,'
@@ -370,7 +375,7 @@ def test_export_pipeline_3():
 
 def test_export_pipeline_4():
     """Assert that exported_pipeline() generated a compile source file as expected given a fixed simple pipeline with input_matrix in CombineDFs."""
-    tpot_obj = TPOTClassifier()
+
     pipeline_string = (
         'KNeighborsClassifier(CombineDFs('
         'DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini, '
@@ -413,14 +418,13 @@ def test_export_pipeline_4():
 
 def test_export_pipeline_5():
     """Assert that exported_pipeline() generated a compile source file as expected given a fixed simple pipeline with SelectFromModel."""
-    tpot_obj = TPOTRegressor()
     pipeline_string = (
         'DecisionTreeRegressor(SelectFromModel(input_matrix, '
         'SelectFromModel__ExtraTreesRegressor__max_features=0.05, SelectFromModel__ExtraTreesRegressor__n_estimators=100, '
         'SelectFromModel__threshold=0.05), DecisionTreeRegressor__max_depth=8,'
         'DecisionTreeRegressor__min_samples_leaf=5, DecisionTreeRegressor__min_samples_split=5)'
     )
-    pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
+    pipeline = creator.Individual.from_string(pipeline_string, tpot_obj_reg._pset)
     expected_code = """import numpy as np
 import pandas as pd
 from sklearn.ensemble import ExtraTreesRegressor
@@ -443,7 +447,7 @@ def test_export_pipeline_5():
 exported_pipeline.fit(training_features, training_target)
 results = exported_pipeline.predict(testing_features)
 """
-    assert expected_code == export_pipeline(pipeline, tpot_obj.operators, tpot_obj._pset)
+    assert expected_code == export_pipeline(pipeline, tpot_obj_reg.operators, tpot_obj_reg._pset)
 
 
 def test_operator_export():
@@ -465,22 +469,22 @@ def test_operator_export_2():
 
 def test_get_by_name():
     """Assert that the Operator class returns operators by name appropriately."""
-    tpot_obj = TPOTClassifier()
+
     assert get_by_name("SelectPercentile", tpot_obj.operators).__class__ == TPOTSelectPercentile.__class__
     assert get_by_name("SelectFromModel", tpot_obj.operators).__class__ == TPOTSelectFromModel.__class__
 
 
 def test_get_by_name_2():
     """Assert that get_by_name raises TypeError with a incorrect operator name."""
-    tpot_obj = TPOTClassifier()
+
     assert_raises(TypeError, get_by_name, "RandomForestRegressor", tpot_obj.operators)
     # use correct name
     ret_op_class = get_by_name("RandomForestClassifier", tpot_obj.operators)
 
 
 def test_get_by_name_3():
     """Assert that get_by_name raises ValueError with duplicate operators in operator dictionary."""
-    tpot_obj = TPOTClassifier()
+
     # no duplicate
     ret_op_class = get_by_name("SelectPercentile", tpot_obj.operators)
     # add a copy of TPOTSelectPercentile into operator list
@@ -506,6 +510,7 @@ def test_indent():
 def test_pipeline_score_save():
     """Assert that the TPOTClassifier can generate a scored pipeline export correctly."""
     tpot_obj = TPOTClassifier()
+    tpot_obj._fit_init()
     tpot_obj._pbar = tqdm(total=1, disable=True)
     pipeline_string = (
         'DecisionTreeClassifier(SelectPercentile(input_matrix, SelectPercentile__percentile=20),'

tests/stats_test.py

@@ -33,8 +33,9 @@
 
 def test_dict_initialization():
     """Asserts that gp_deap.initialize_stats_dict initializes individual statistics correctly"""
-    tpot = TPOTClassifier()
-    tb = tpot._toolbox
+    tpot_obj = TPOTClassifier()
+    tpot_obj._fit_init()
+    tb = tpot_obj._toolbox
 
     test_ind = tb.individual()
     initialize_stats_dict(test_ind)
@@ -48,6 +49,7 @@ def test_dict_initialization():
 def test_mate_operator_stats_update():
     """Assert that self._mate_operator updates stats as expected."""
     tpot_obj = TPOTClassifier()
+    tpot_obj._fit_init()
     ind1 = creator.Individual.from_string(
         'KNeighborsClassifier('
         'BernoulliNB(input_matrix, BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=False),'
@@ -99,6 +101,7 @@ def test_mate_operator_stats_update():
 def test_mut_operator_stats_update():
     """Asserts that self._random_mutation_operator updates stats as expected."""
     tpot_obj = TPOTClassifier()
+    tpot_obj._fit_init()
     ind = creator.Individual.from_string(
         'KNeighborsClassifier('
         'BernoulliNB(input_matrix, BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=False),'