def test_decision_tree_3():
"""Ensure that the TPOT decision tree method outputs the same as the sklearn decision tree when min_weight>0.5"""
tpot_obj = TPOT()
result = tpot_obj._decision_tree(training_testing_data, 0.6)
result = result[result['group'] == 'testing']
dtc = DecisionTreeClassifier(min_weight_fraction_leaf=0.5, random_state=42)
dtc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, dtc.predict(testing_features))
def test_decision_tree_3():
"""Ensure that the TPOT decision tree method outputs the same as the sklearn decision tree when min_weight>0.5"""
tpot_obj = TPOT()
result = tpot_obj._decision_tree(training_testing_data, 0.6)
result = result[result['group'] == 'testing']
dtc = DecisionTreeClassifier(min_weight_fraction_leaf=0.5, random_state=42)
dtc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, dtc.predict(testing_features))
def test_decision_tree():
"""Ensure that the TPOT decision tree method outputs the same as the sklearn decision tree"""
tpot_obj = TPOT()
result = tpot_obj._decision_tree(training_testing_data, 0, 0)
result = result[result['group'] == 'testing']
dtc = DecisionTreeClassifier(max_features='auto', max_depth=None, random_state=42)
dtc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, dtc.predict(testing_features))
def test_decision_tree_3():
"""Ensure that the TPOT decision tree method outputs the same as the sklearn decision tree when max_features>no. of features"""
tpot_obj = TPOT()
result = tpot_obj._decision_tree(training_testing_data, 100, 0)
result = result[result['group'] == 'testing']
dtc = DecisionTreeClassifier(max_features=64, max_depth=None, random_state=42)
dtc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, dtc.predict(testing_features))
