def test_matrix_filter_boston(self):
X_train, X_test, y_train, y_test, feature_names = create_boston_data()
model_task = ModelTask.REGRESSION
run_error_analyzer_on_models(X_train, y_train, X_test, y_test,
feature_names, model_task)
# Test with single feature instead of two features
run_error_analyzer_on_models(X_train,
y_train,
X_test,
y_test,
feature_names,
model_task,
matrix_features=[feature_names[0]])
# Note: Third feature has few unique values, tests code path
# without binning data
run_error_analyzer_on_models(X_train,
y_train,
X_test,
y_test,
feature_names,
model_task,
matrix_features=[feature_names[3]])
def test_importances_boston(self):
X_train, X_test, y_train, y_test, feature_names = \
create_boston_data()
models = create_models_regression(X_train, y_train)
for model in models:
categorical_features = []
run_error_analyzer(model, X_test, y_test, feature_names,
categorical_features)
def boston():
x_train, x_test, y_train, y_test, features = create_boston_data()
yield {
DatasetConstants.X_TRAIN: x_train,
DatasetConstants.X_TEST: x_test,
DatasetConstants.Y_TRAIN: y_train,
DatasetConstants.Y_TEST: y_test,
DatasetConstants.FEATURES: features
}
def test_modelanalysis_boston(self, manager_type):
x_train, x_test, y_train, y_test, feature_names = \
create_boston_data()
x_train = pd.DataFrame(x_train, columns=feature_names)
x_test = pd.DataFrame(x_test, columns=feature_names)
models = create_models_regression(x_train, y_train)
x_train[LABELS] = y_train
x_test[LABELS] = y_test
manager_args = {DESIRED_RANGE: [10, 20]}
for model in models:
run_model_analysis(model, x_train, x_test, LABELS, ['RM'],
manager_type, manager_args)
def test_matrix_filter_boston_filters(self):
X_train, X_test, y_train, y_test, feature_names = create_boston_data()
filters = [{
'arg': [0.675],
'column': 'NOX',
'method': 'less and equal'
}, {
'arg': [7.141000000000001],
'column': 'RM',
'method': 'greater'
}]
model_task = ModelTask.REGRESSION
run_error_analyzer_on_models(X_train,
y_train,
X_test,
y_test,
feature_names,
model_task,
filters=filters)