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], )
def test_score_2(): """Ensure that the TPOT score function outputs a known score for a fixed pipeline""" tpot_obj = TPOT() tpot_obj._training_classes = training_classes tpot_obj._training_features = training_features tpot_obj.pbar = tqdm(total=1, disable=True) known_score = 0.981993770448 # Assumes use of the TPOT balanced_accuracy function # Reify pipeline with known score tpot_obj._optimized_pipeline = creator.Individual.\ from_string('_logistic_regression(input_df, 1.0, 0, True)', tpot_obj._pset) # Get score from TPOT score = tpot_obj.score(testing_features, testing_classes) # http://stackoverflow.com/questions/5595425/ def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) assert isclose(known_score, score)
def test_score_2(): """Assert that the TPOT score function outputs a known score for a fixed pipeline""" tpot_obj = TPOT() tpot_obj.pbar = tqdm(total=1, disable=True) known_score = 0.986318199045 # Assumes use of the TPOT balanced_accuracy function # Reify pipeline with known score tpot_obj._optimized_pipeline = creator.Individual.\ from_string('RandomForestClassifier(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) # Get score from TPOT score = tpot_obj.score(testing_features, testing_classes) # http://stackoverflow.com/questions/5595425/ def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) assert isclose(known_score, score)
def test_score_2(): """Ensure that the TPOT score function outputs a known score for a fixed pipeline""" tpot_obj = TPOT() tpot_obj._training_classes = training_classes tpot_obj._training_features = training_features tpot_obj.pbar = tqdm(total=1, disable=True) known_score = 0.981993770448 # Assumes use of the TPOT balanced_accuracy function # Reify pipeline with known score tpot_obj._optimized_pipeline = creator.Individual.\ from_string('_logistic_regression(input_df, 1.0, 0, True)', tpot_obj._pset) # Get score from TPOT score = tpot_obj.score(testing_features, testing_classes) # http://stackoverflow.com/questions/5595425/ def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) assert isclose(known_score, score)
def test_score_2(): """Assert that the TPOT score function outputs a known score for a fixed pipeline""" tpot_obj = TPOT() tpot_obj.pbar = tqdm(total=1, disable=True) known_score = 0.986318199045 # Assumes use of the TPOT balanced_accuracy function # Reify pipeline with known score tpot_obj._optimized_pipeline = creator.Individual.\ from_string('RandomForestClassifier(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) # Get score from TPOT score = tpot_obj.score(testing_features, testing_classes) # http://stackoverflow.com/questions/5595425/ def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) assert isclose(known_score, score)