learning_curves.png

learning_curves.py

"""Plot error curves for various dataset sizes

This plot emphasizes the fact that adding more data might help: by
following the trend, one can expect test score to further increase and
help reduce the overfit a bit as the slope of the test score curve is
still not null on the right hand side of the plot, especially when the prior /
regularizer parameter alpha is low.

Furthermore as the train error seems to remain pretty close to the perfect
score when the dataset size grows one can estimate that the model does not
underfit too much (is not too simplistic / biased).

See the following blog post for more details on how to interpret them:

  http://digitheadslabnotebook.blogspot.com/2011/12/practical-advice-for-applying-machine.html

"""
import numpy as np
from time import time

from sklearn.cross_validation import ShuffleSplit
from sklearn.datasets import fetch_20newsgroups_vectorized
from sklearn.naive_bayes import MultinomialNB
from sklearn.base import clone


def score_curves(clf_orig, X, y, n_runs=5, test_fraction=0.1,
                 train_fraction_range=np.linspace(0.1, 0.9, 10)):

    n_datasets = train_fraction_range.shape[0]
    training_score = np.zeros((n_datasets, n_runs))
    test_score = np.zeros((n_datasets, n_runs))
    training_time = np.zeros((n_datasets, n_runs))

    for i, train_fraction in enumerate(train_fraction_range):
        print "Train fraction: %0.2f" % train_fraction

        cv = ShuffleSplit(n_samples, n_iterations=n_runs,
                          test_fraction=test_fraction,
                          train_fraction=train_fraction)
        for j, (train, test) in enumerate(cv):
            clf = clone(clf_orig)
            t0 = time()
            clf.fit(X[train], y[train])
            training_time[i, j] = time() - t0
            training_score[i, j] = clf.score(X[train], y[train])
            test_score[i, j] = clf.score(X[test], y[test])

    return training_score, test_score, training_time


if __name__ == "__main__":
    data = fetch_20newsgroups_vectorized(subset='all')
    categories = data.target_names
    X = data.data
    y = data.target
    n_samples = y.shape[0]
    n_features = X.shape[1]
    print "n_samples: %d, n_features: %d" % (n_samples, n_features)

    clf = MultinomialNB(alpha=.01)
    train_fraction_range = np.linspace(0.1, 0.9, 5)
    test_fraction = 0.1

    train_score, test_score, training_time = score_curves(
        clf, X, y, train_fraction_range=train_fraction_range,
        test_fraction=test_fraction)

    print clf
    mean_test_score = test_score.mean(axis=1)
    mean_train_score = train_score.mean(axis=1)
    gap = np.abs(mean_test_score - mean_train_score)
    print "Best test score: %0.2f" % mean_test_score.max()
    print "Gap at train_fraction=%0.2f: %0.2f" % (
        train_fraction_range[-1], gap[-1])

    import pylab as pl
    plots = []
    plots.append(pl.errorbar(train_fraction_range,
                             mean_train_score,
                             train_score.std(axis=1)))
    plots.append(pl.errorbar(train_fraction_range,
                             mean_test_score,
                             test_score.std(axis=1)))

    pl.legend(plots, ('train', 'test'), loc='lower right')

    pl.title("Learning curves for %r\n"
             "Best test score: %0.2f - Gap: %0.2f" %
             (clf, mean_test_score.max(), gap[-1]))
    pl.ylim(0.0, 1.0)
    pl.ylabel('Classification score')
    pl.xlabel('Fraction of the dataset used for training\n'
              'The test fraction is fixed to %0.2f' % test_fraction)
    pl.show()

learning_curves_biased.png

learning_curves_overfit.png