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garchangel Aug 25th, 2019 57 Never
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  1. def generate_data(n_samples, n_features):
  2.     X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])
  3.     if n_features > 1:
  4.         X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])
  5.     return X, y
  6. acc_clf1, acc_clf2 = [], []
  7. n_features_range = range(1, n_features_max + 1, step)
  8. for n_features in n_features_range:
  9.     score_clf1, score_clf2 = 0, 0
  10.     for _ in range(n_averages):
  11.         X, y = generate_data(n_train, n_features)
  12.         clf1 = LinearDiscriminantAnalysis(solver='lsqr', shrinkage='auto').fit(X, y)
  13.         clf2 = LinearDiscriminantAnalysis(solver='lsqr', shrinkage=None).fit(X, y)
  14.         X, y = generate_data(n_test, n_features)
  15.         score_clf1 += clf1.score(X, y)
  16.         score_clf2 += clf2.score(X, y)
  17.     acc_clf1.append(score_clf1 / n_averages)
  18.     acc_clf2.append(score_clf2 / n_averages)
  19. features_samples_ratio = np.array(n_features_range) / n_train
  20. plt.plot(features_samples_ratio, acc_clf1, linewidth=2,
  21.          label="Linear Discriminant Analysis with shrinkage", color='navy')
  22. plt.plot(features_samples_ratio, acc_clf2, linewidth=2,
  23.          label="Linear Discriminant Analysis", color='gold')
  24. plt.xlabel('n_features / n_samples')
  25. plt.ylabel('Classification accuracy')
  26. plt.legend(loc=1, prop={'size': 12})
  27. plt.suptitle('Linear Discriminant Analysis vs. \
  28. shrinkage Linear Discriminant Analysis (1 discriminative feature)')
  29. plt.show()
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