validate.py 文件源码

def validate_formula(formula, training_data, column_being_predicted, cross_val_n=3, validation_size=.10):
    '''
        Accept a formula in the StatsModels.formula.api style, some training data and
        some test values that must match the value being predicted by the formula.

        returns: trained_model, cross_scores
    '''
    cross_val_scores = []
    for _ in xrange(cross_val_n):
        X_train, X_test, _, _ = train_test_split(
            training_data,
            training_data[column_being_predicted],
            test_size=validation_size
        )

        model = smf.ols(formula=formula, data=X_train).fit()
        test_values = X_test[column_being_predicted]
        score = root_mean_log_squared_error(model, X_test, test_values)
        cross_val_scores.append(score)

    return (model, cross_val_scores)