from data.data_processing import process_train_test
from sklearn.model_selection import cross_validate, ParameterGrid


def crossvalidate_params(classifier, params, experiments_train, metadata_train, y_train, cv=5):
    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}
    classifier_params = {key: params[key] for key in params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}
    X_train, _ = process_train_test(process_params, experiments_train, metadata_train, scale=True)
    clf = classifier(**classifier_params)
    return cross_validate(clf, X_train, y_train.to_numpy().ravel(), cv=cv, return_estimator=True)


def param_grid_search(classifier, param_grid, experiments_train, metadata_train, y_train, cv=5):
    results = []
    for params in ParameterGrid(param_grid):
        try:
            results.append([params, crossvalidate_params(classifier, params, experiments_train, metadata_train, y_train, cv=cv)])
            print(results[-1])
        except Exception as e:
            pass  # print(params, e)
    return results


def evaluate_classifier_params(classifier, params, X_train, y_train, X_test, y_test, iters=10):
    train_score_mean = 0
    test_score_mean = 0
    for i in range(iters):
        clf = classifier(**params)
        clf.fit(X_train, y_train.to_numpy().ravel())
        train_score_mean += clf.score(X_train, y_train.to_numpy().ravel())
        test_score_mean += clf.score(X_test, y_test.to_numpy().ravel())
    return train_score_mean / iters, test_score_mean / iters