def test_predict():
"""Assert that the TPOT predict function raises a ValueError when no optimized pipeline exists"""
tpot_obj = TPOT()
try:
tpot_obj.predict(testing_features)
assert False # Should be unreachable
except ValueError:
pass
def test_predict():
"""Ensure that the TPOT predict function raises a ValueError when no optimized pipeline exists"""
tpot_obj = TPOT()
try:
tpot_obj.predict(testing_features)
assert False # Should be unreachable
except ValueError:
pass
def test_predict_2():
"""Ensure that the TPOT predict function returns a DataFrame of shape (num_testing_rows,)"""
tpot_obj = TPOT()
tpot_obj._training_classes = training_classes
tpot_obj._training_features = training_features
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('_logistic_regression(input_df, 1.0, 0, True)', tpot_obj._pset)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0],)
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
tpot_obj = TPOT()
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('DecisionTreeClassifier(input_matrix)', tpot_obj._pset)
tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(expr=tpot_obj._optimized_pipeline)
tpot_obj._fitted_pipeline.fit(training_features, training_classes)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0],)
def test_predict_2():
"""Ensure that the TPOT predict function returns a DataFrame of shape (num_testing_rows,)"""
tpot_obj = TPOT()
tpot_obj._training_classes = training_classes
tpot_obj._training_features = training_features
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('_logistic_regression(input_df, 1.0, 0, True)', tpot_obj._pset)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0], )
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
tpot_obj = TPOT()
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('DecisionTreeClassifier(input_matrix)', tpot_obj._pset)
tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(
expr=tpot_obj._optimized_pipeline)
tpot_obj._fitted_pipeline.fit(training_features, training_classes)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0], )