visuals.py

import numpy as np
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, scoring=None, obj_line=None,
                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
    from sklearn.model_selection import learning_curve
    from matplotlib import pyplot as plt
    
    plt.figure()
    plt.title(title)
    if ylim is not None:
        plt.ylim(*ylim)
    plt.xlabel("Training examples")
    plt.ylabel("Score")
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, scoring=scoring, 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(True)

    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="Testing score")
    plt.grid(True)
    if obj_line:
        plt.axhline(y=obj_line, color='blue')

    plt.legend(loc="best")