reporter.py 文件源码

def plot_learning_curve(estimator, X, y, train_sizes=np.linspace(.1, 1.0, 5),
                        cv=None, n_jobs=1, ax=None):
    '''
    Plot the learning curve for `estimator`.

    Parameters
    ----------
    estimator : sklearn.Estimator
    X : array-like
    y : array-like
    train_sizes : array-like
        list of floats between 0 and 1
    cv : int
    n_jobs : int
    ax : matplotlib.axes
    '''
    # http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
    if ax is None:
        fig, ax = plt.subplots()
    ax.set_xlabel("Training examples")
    ax.set_ylabel("Score")
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes
    )
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    plt.grid()

    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                     train_scores_mean + train_scores_std, alpha=0.1,
                     color="r")
    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                     test_scores_mean + test_scores_std, alpha=0.1, color="g")
    plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
             label="Training score")
    plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
             label="Cross-validation score")

    plt.legend(loc="best")
    return ax