Pushing the docs for revision for branch: master, commit 593dbcc073549e21e8a8d371d85e7f4e8f33dcf1

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 0d839c7aff39e2bc97dda02409424dd9
+config: 7428c9dd0e2fe5a32dd39c1f52ee5c08
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_build/html/stable/_static/ML_MAPS_README.html

@@ -110,7 +110,7 @@
     plt.errorbar(n_candidates_values, accuracies_c[i, :],
                  stds_accuracies[i, :], c=colors[i])
 
-plt.legend(loc='upper left', fontsize='small')
+plt.legend(loc='upper left', prop=dict(size='small'))
 plt.ylim([0, 1.2])
 plt.xlim(min(n_candidates_values), max(n_candidates_values))
 plt.ylabel("Accuracy")

dev/_downloads/gp_diabetes_dataset.py

@@ -126,7 +126,7 @@
              fmt='o-', c='r', label='LSHForest')
 plt.plot(n_samples_values, average_times_exact, c='b',
          label="NearestNeighbors(algorithm='brute', metric='cosine')")
-plt.legend(loc='upper left', fontsize='small')
+plt.legend(loc='upper left', prop=dict(size='small'))
 plt.ylim(0, None)
 plt.ylabel("Average query time in seconds")
 plt.xlabel("n_samples")

dev/_downloads/plot_approximate_nearest_neighbors_hyperparameters.py

@@ -58,25 +58,27 @@
 # weights
 plt.figure(figsize=(6, 5))
 plt.title("Weights of the model")
-plt.plot(clf.coef_, 'b-', label="ARD estimate")
-plt.plot(ols.coef_, 'r--', label="OLS estimate")
-plt.plot(w, 'g-', label="Ground truth")
+plt.plot(clf.coef_, color='darkblue', linestyle='-', linewidth=2,
+         label="ARD estimate")
+plt.plot(ols.coef_, color='yellowgreen', linestyle=':', linewidth=2,
+         label="OLS estimate")
+plt.plot(w, color='orange', linestyle='-', linewidth=2, label="Ground truth")
 plt.xlabel("Features")
 plt.ylabel("Values of the weights")
 plt.legend(loc=1)
 
 plt.figure(figsize=(6, 5))
 plt.title("Histogram of the weights")
-plt.hist(clf.coef_, bins=n_features, log=True)
-plt.plot(clf.coef_[relevant_features], 5 * np.ones(len(relevant_features)),
-         'ro', label="Relevant features")
+plt.hist(clf.coef_, bins=n_features, color='navy', log=True)
+plt.scatter(clf.coef_[relevant_features], 5 * np.ones(len(relevant_features)),
+            color='gold', marker='o', label="Relevant features")
 plt.ylabel("Features")
 plt.xlabel("Values of the weights")
 plt.legend(loc=1)
 
 plt.figure(figsize=(6, 5))
 plt.title("Marginal log-likelihood")
-plt.plot(clf.scores_)
+plt.plot(clf.scores_, color='navy', linewidth=2)
 plt.ylabel("Score")
 plt.xlabel("Iterations")
 plt.show()