def test_random_forest_2():
"""Ensure that the TPOT random forest method outputs the same as the sklearn random forest when min_weight>0.5"""
tpot_obj = TPOT()
result = tpot_obj._random_forest(training_testing_data, 0.6)
result = result[result['group'] == 'testing']
rfc = RandomForestClassifier(n_estimators=500, min_weight_fraction_leaf=0.5, random_state=42, n_jobs=-1)
rfc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, rfc.predict(testing_features))
def test_random_forest_2():
"""Ensure that the TPOT random forest method outputs the same as the sklearn random forest when min_weight>0.5"""
tpot_obj = TPOT()
result = tpot_obj._random_forest(training_testing_data, 0.6)
result = result[result['group'] == 'testing']
rfc = RandomForestClassifier(n_estimators=500, min_weight_fraction_leaf=0.5, random_state=42, n_jobs=-1)
rfc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, rfc.predict(testing_features))
def test_random_forest():
"""Ensure that the TPOT random forest method outputs the same as the sklearn random forest"""
tpot_obj = TPOT()
result = tpot_obj._random_forest(training_testing_data, 100, 0)
result = result[result['group'] == 'testing']
rfc = RandomForestClassifier(n_estimators=100, max_features='auto', random_state=42, n_jobs=-1)
rfc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, rfc.predict(testing_features))
def test_random_forest_3():
"""Ensure that the TPOT random forest method outputs the same as the sklearn random forest when max_features>no. of features"""
tpot_obj = TPOT()
result = tpot_obj._random_forest(training_testing_data, 100)
result = result[result['group'] == 'testing']
rfc = RandomForestClassifier(n_estimators=500, max_features=64, random_state=42, n_jobs=-1)
rfc.fit(training_features, training_classes)
assert np.array_equal(result['guess'].values, rfc.predict(testing_features))
