def test_predict_proba2(): """Assert that the TPOT predict_proba function returns a numpy matrix filled with probabilities (float)""" tpot_obj = TPOTClassifier() pipeline_string = ( 'DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini' ', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,' 'DecisionTreeClassifier__min_samples_split=5)') tpot_obj._optimized_pipeline = creator.Individual.from_string( pipeline_string, 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_proba(testing_features) rows = result.shape[0] columns = result.shape[1] try: for i in range(rows): for j in range(columns): float_range(result[i][j]) assert True except Exception: assert False
def test_predict_2(): """Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)""" tpot_obj = TPOTClassifier() 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(): """Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)""" tpot_obj = TPOTClassifier() 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(): """Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)""" tpot_obj = TPOTClassifier() pipeline_string= ('DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini' ', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,' 'DecisionTreeClassifier__min_samples_split=5)') tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, 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(): """Assert that the TPOTClassifier score function outputs a known score for a fix pipeline""" tpot_obj = TPOTClassifier() known_score = 0.977777777778 # Assumes use of the TPOT balanced_accuracy function # Reify pipeline with known score pipeline_string= ('KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=10, ' 'KNeighborsClassifier__p=1,KNeighborsClassifier__weights=uniform)') tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, 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(): """Assert that the TPOTClassifier score function outputs a known score for a fixed pipeline""" tpot_obj = TPOTClassifier() 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(): """Assert that the TPOTClassifier score function outputs a known score for a fixed pipeline""" tpot_obj = TPOTClassifier() 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_predict_proba2(): """Assert that the TPOT predict_proba function returns a numpy matrix filled with probabilities (float)""" tpot_obj = TPOTClassifier() pipeline_string= ('DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini' ', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,' 'DecisionTreeClassifier__min_samples_split=5)') tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, 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_proba(testing_features) rows = result.shape[0] columns = result.shape[1] try: for i in range(rows): for j in range(columns): float_range(result[i][j]) assert True except Exception: assert False