def test_extra_trees_3():
"""Ensure that the TPOT ExtraTreesClassifier outputs the same as the sklearn version when min_weight > 0.5"""
tpot_obj = TPOT()
result = tpot_obj._extra_trees(training_testing_data, 0, 1., 0.6)
result = result[result['group'] == 'testing']
etc = ExtraTreesClassifier(n_estimators=500, random_state=42, max_features=1., min_weight_fraction_leaf=0.5, criterion='gini')
etc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, etc.predict(testing_features))
def test_extra_trees_3():
"""Ensure that the TPOT ExtraTreesClassifier outputs the same as the sklearn version when min_weight > 0.5"""
tpot_obj = TPOT()
result = tpot_obj._extra_trees(training_testing_data, 0, 1., 0.6)
result = result[result['group'] == 'testing']
etc = ExtraTreesClassifier(n_estimators=500, random_state=42, max_features=1., min_weight_fraction_leaf=0.5, criterion='gini')
etc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, etc.predict(testing_features))
def test_extra_trees_3():
"""Ensure that the TPOT ExtraTreesClassifier outputs the same as the sklearn version when max_features > the number of features"""
tpot_obj = TPOT()
training_features = training_testing_data.loc[training_testing_data['group'] == 'training'].drop(tpot_obj.non_feature_columns, axis=1)
num_features = len(training_features.columns)
result = tpot_obj._extra_trees(training_testing_data, 0, num_features + 1)
result = result[result['group'] == 'testing']
etc = ExtraTreesClassifier(n_estimators=500, random_state=42, max_features=num_features, criterion='gini')
etc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, etc.predict(testing_features))
def test_extra_trees_3():
"""Ensure that the TPOT ExtraTreesClassifier outputs the same as the sklearn version when max_features > the number of features"""
tpot_obj = TPOT()
training_features = training_testing_data.loc[training_testing_data['group'] == 'training'].drop(tpot_obj.non_feature_columns, axis=1)
num_features = len(training_features.columns)
result = tpot_obj._extra_trees(training_testing_data, 0, num_features + 1)
result = result[result['group'] == 'testing']
etc = ExtraTreesClassifier(n_estimators=500, random_state=42, max_features=num_features, criterion='gini')
etc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, etc.predict(testing_features))
