04_model_preparation.py 文件源码

def evaluate(model, name):
    """
    Evaluates model by cross validation.
    """
    # Get scores through cross validation
    score_f1 = cross_val_score(model, X, y, scoring='f1', cv=splitter_)
    score_pr = cross_val_score(model, X, y, scoring='precision', cv=splitter_)
    score_re = cross_val_score(model, X, y, scoring='recall', cv=splitter_)
    # Save image of score distributions
    save_dist(name, score_f1, score_pr, score_re)
    # Compute mean and std of each score
    result = DataFrame(index=['f1', 'precision', 'recall'],
                       columns=['mean', 'std'])
    result.loc['f1', 'mean'] = np.mean(score_f1)
    result.loc['precision', 'mean'] = np.mean(score_pr)
    result.loc['recall', 'mean'] = np.mean(score_re)
    result.loc['f1', 'std'] = np.std(score_f1)
    result.loc['precision', 'std'] = np.std(score_pr)
    result.loc['recall', 'std'] = np.std(score_re)
    print model
    print result