Pushing the docs to dev/ for branch: master, commit dc4255f383b9add94b0afb24dbcc39108fa2c09f

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -226,69 +226,69 @@ main code
            learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1,
            penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True,
            verbose=0, warm_start=False)
-    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.027996s
+    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.025243s
 
     Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
            eta0=0.0, fit_intercept=True, l1_ratio=0.5, learning_rate='optimal',
            loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet',
            power_t=0.5, random_state=None, shuffle=True, verbose=0,
            warm_start=False)
-    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.020397s
+    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.018530s
 
     Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
            eta0=0.0, fit_intercept=True, l1_ratio=0.75,
            learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1,
            penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True,
            verbose=0, warm_start=False)
-    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.017002s
+    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.016046s
 
     Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
            eta0=0.0, fit_intercept=True, l1_ratio=0.9, learning_rate='optimal',
            loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet',
            power_t=0.5, random_state=None, shuffle=True, verbose=0,
            warm_start=False)
-    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.015065s
+    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.013686s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000387s
+    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000360s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.25, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000697s
+    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000643s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.5, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001188s
+    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001113s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.75, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001677s
+    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001572s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.9, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001931s
+    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001799s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
                  max_leaf_nodes=None, min_impurity_split=1e-07,
                  min_samples_leaf=1, min_samples_split=2,
                  min_weight_fraction_leaf=0.0, n_estimators=10, presort='auto',
                  random_state=None, subsample=1.0, verbose=0, warm_start=False)
-    Complexity: 10 | MSE: 28.9793 | Pred. Time: 0.000119s
+    Complexity: 10 | MSE: 28.9793 | Pred. Time: 0.000116s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
                  max_leaf_nodes=None, min_impurity_split=1e-07,
                  min_samples_leaf=1, min_samples_split=2,
                  min_weight_fraction_leaf=0.0, n_estimators=50, presort='auto',
                  random_state=None, subsample=1.0, verbose=0, warm_start=False)
-    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000207s
+    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000196s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -297,7 +297,7 @@ main code
                  min_weight_fraction_leaf=0.0, n_estimators=100,
                  presort='auto', random_state=None, subsample=1.0, verbose=0,
                  warm_start=False)
-    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000301s
+    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000291s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -306,7 +306,7 @@ main code
                  min_weight_fraction_leaf=0.0, n_estimators=200,
                  presort='auto', random_state=None, subsample=1.0, verbose=0,
                  warm_start=False)
-    Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000478s
+    Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000450s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -315,10 +315,10 @@ main code
                  min_weight_fraction_leaf=0.0, n_estimators=500,
                  presort='auto', random_state=None, subsample=1.0, verbose=0,
                  warm_start=False)
-    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.001047s
+    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.000974s
 
 
-**Total running time of the script:** ( 0 minutes  25.398 seconds)
+**Total running time of the script:** ( 0 minutes  22.821 seconds)
 
 
 

dev/_downloads/auto_examples_python.zip

@@ -356,46 +356,46 @@ maximum
  Out::
 
     Test set is 878 documents (108 positive)
-      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.913 in 1.74s (  554 docs/s)
-              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.74s (  552 docs/s)
-                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.75s (  551 docs/s)
-          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.78s (  540 docs/s)
+      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.913 in 1.63s (  591 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.63s (  590 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.63s (  588 docs/s)
+          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.67s (  575 docs/s)
 
 
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 5.05s (  774 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.06s (  773 docs/s)
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 5.06s (  772 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.10s (  767 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 4.73s (  826 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 4.74s (  825 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 4.74s (  825 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 4.77s (  819 docs/s)
 
 
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 8.38s (  814 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 8.38s (  813 docs/s)
-                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 8.38s (  813 docs/s)
-          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 8.42s (  810 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 7.90s (  863 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 7.90s (  863 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 7.91s (  862 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 7.94s (  859 docs/s)
 
 
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 11.73s (  831 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 11.74s (  831 docs/s)
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 11.74s (  831 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 11.78s (  828 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 11.03s (  885 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 11.03s (  884 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 11.03s (  884 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 11.07s (  881 docs/s)
 
 
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.76s (  791 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.76s (  791 docs/s)
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.76s (  791 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 14.80s (  789 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.86s (  842 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.86s (  842 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.86s (  842 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 13.90s (  840 docs/s)
 
 
-      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 18.13s (  806 docs/s)
-              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 18.13s (  806 docs/s)
-                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 18.14s (  806 docs/s)
-          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 18.17s (  804 docs/s)
+      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 17.02s (  859 docs/s)
+              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 17.02s (  859 docs/s)
+                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 17.03s (  858 docs/s)
+          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 17.06s (  857 docs/s)
 
 
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 21.29s (  815 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 21.29s (  815 docs/s)
-                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 21.30s (  815 docs/s)
-          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 21.33s (  813 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 19.99s (  868 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 19.99s (  868 docs/s)
+                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 19.99s (  868 docs/s)
+          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 20.03s (  866 docs/s)
 
 
 Plot results
@@ -526,7 +526,7 @@ Plot results
 
 
 
-**Total running time of the script:** ( 0 minutes  23.267 seconds)
+**Total running time of the script:** ( 0 minutes  21.888 seconds)
 
 
 

dev/_downloads/scikit-learn-docs.pdf

@@ -158,7 +158,7 @@ risk of over-fitting the data.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  4.703 seconds)
+**Total running time of the script:** ( 0 minutes  4.372 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_001.png

@@ -372,10 +372,10 @@ main code
     benchmarking with 100 features
     benchmarking with 250 features
     benchmarking with 500 features
-    example run in 3.85s
+    example run in 3.53s
 
 
-**Total running time of the script:** ( 0 minutes  3.857 seconds)
+**Total running time of the script:** ( 0 minutes  3.533 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0011.png

@@ -65,7 +65,7 @@ References
 
      Area under the ROC curve : 0.993919
 
-    time elapsed: 6.83s
+    time elapsed: 6.42s
 
 
 
@@ -247,7 +247,7 @@ References
     plot_species_distribution()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  6.833 seconds)
+**Total running time of the script:** ( 0 minutes  6.421 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_002.png

@@ -340,7 +340,7 @@ Visualization
 
 
 
-**Total running time of the script:** ( 0 minutes  11.658 seconds)
+**Total running time of the script:** ( 0 minutes  10.774 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0021.png

@@ -164,7 +164,7 @@ contributed to fewer projections than the central disk.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  9.971 seconds)
+**Total running time of the script:** ( 0 minutes  9.270 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_003.png

@@ -106,7 +106,7 @@ representation of the checkerboard structure.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.937 seconds)
+**Total running time of the script:** ( 0 minutes  0.866 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0031.png

@@ -93,7 +93,7 @@ the biclusters.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.244 seconds)
+**Total running time of the script:** ( 0 minutes  0.224 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_004.png

@@ -159,7 +159,7 @@ Plot the data and the predicted probabilities
 
 
 
-**Total running time of the script:** ( 0 minutes  0.285 seconds)
+**Total running time of the script:** ( 0 minutes  0.260 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0041.png

@@ -214,7 +214,7 @@ but not where it is transposed-sigmoid (e.g., Gaussian naive Bayes).
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  2.258 seconds)
+**Total running time of the script:** ( 0 minutes  2.098 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png

@@ -204,7 +204,7 @@ would have resulted in a similar decrease in log-loss.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.718 seconds)
+**Total running time of the script:** ( 0 minutes  0.658 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_001.png

@@ -147,7 +147,7 @@ Plot calibration plots
 
 
 
-**Total running time of the script:** ( 0 minutes  2.660 seconds)
+**Total running time of the script:** ( 0 minutes  2.475 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0011.png

@@ -113,7 +113,7 @@ a result it can identify only the first class.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  2.577 seconds)
+**Total running time of the script:** ( 0 minutes  2.354 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_002.png

@@ -153,7 +153,7 @@ set.
     plt.tight_layout()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  8.073 seconds)
+**Total running time of the script:** ( 0 minutes  7.558 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0021.png

@@ -120,7 +120,7 @@ This example is commented in the
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.808 seconds)
+**Total running time of the script:** ( 0 minutes  0.725 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_thumb.png

@@ -86,7 +86,7 @@ Shows how shrinkage improves classification.
     shrinkage Linear Discriminant Analysis (1 discriminative feature)')
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  6.235 seconds)
+**Total running time of the script:** ( 0 minutes  5.683 seconds)
 
 
 

dev/_images/sphx_glr_plot_cluster_comparison_001.png

@@ -204,7 +204,7 @@ plot functions
 
 
 
-**Total running time of the script:** ( 0 minutes  0.435 seconds)
+**Total running time of the script:** ( 0 minutes  0.391 seconds)