def test_matrix_filter_iris(self):
x_train, x_test, y_train, y_test, feature_names, _ = create_iris_data()
models = create_models(x_train, y_train)
for model in models:
categorical_features = []
run_error_analyzer(model, x_test, y_test, feature_names,
categorical_features)
def test_matrix_filter_binary_classification(self):
x_train, y_train, x_test, y_test, _ = \
create_binary_classification_dataset()
feature_names = list(x_train.columns)
models = create_models(x_train, y_train)
for model in models:
categorical_features = []
run_error_analyzer(model, x_test, y_test, feature_names,
categorical_features)
def test_importances_cancer(self):
x_train, x_test, y_train, y_test, feature_names, _ = \
create_cancer_data()
models = create_models(x_train, y_train)
for model in models:
categorical_features = []
run_error_analyzer(model, x_test, y_test, feature_names,
categorical_features)
def test_raianalyzer_binary(self):
x_train, y_train, x_test, y_test, classes = \
create_binary_classification_dataset()
x_train = pd.DataFrame(x_train)
x_test = pd.DataFrame(x_test)
models = create_models(x_train, y_train)
x_train[LABELS] = y_train
x_test[LABELS] = y_test
for model in models:
run_raianalyzer(model, x_train, x_test, LABELS, classes)
def test_raianalyzer_cancer(self):
x_train, x_test, y_train, y_test, feature_names, classes = \
create_cancer_data()
x_train = pd.DataFrame(x_train, columns=feature_names)
x_test = pd.DataFrame(x_test, columns=feature_names)
models = create_models(x_train, y_train)
x_train[LABELS] = y_train
x_test[LABELS] = y_test
for model in models:
run_raianalyzer(model, x_train, x_test, LABELS, classes)