def train(name): data_pipeline = DataPipeline X_train, X_test, y_train, y_test = load_and_split(name) X_after_processing = data_pipeline.fit_transform(X_train) print("Data processed!") LearningPipeline.fit(X_after_processing, y_train) print("Models trained!") predicted = calculate_performance(name, name, X_test, y_test, data_pipeline=data_pipeline) store_results(name, y_test, predicted, data_pipeline=data_pipeline) calculate_other_performance(name)
def calculate_other_performance(name): other_dataset = other_name(name) X_train, X_test, y_train, y_test = load_and_split(other_dataset) calculate_performance(name, other_dataset, X_test, y_test)
def calculate_stats(trained, testing): X_train, X_test, y_train, y_test = load_and_split(testing) return calculate_performance(trained, testing, X_test, y_test)