Pushing the docs to dev/ for branch: master, commit 6e664528a7d12998c8681a065cac0f6e5ba5171a

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: 0.028643s
+    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.025108s
 
     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: 0.020735s
+    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.018948s
 
     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: 0.017476s
+    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.015756s
 
     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: 0.015822s
+    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.014044s
 
     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: 0.000390s
+    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000361s
 
     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: 0.000693s
+    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000642s
 
     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: 0.001189s
+    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001103s
 
     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: 0.001670s
+    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001561s
 
     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: 0.001919s
+    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001784s
 
     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: 0.000111s
 
     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: 0.000212s
+    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000194s
 
     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: 0.000302s
+    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000279s
 
     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: 0.000445s
 
     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: 0.001051s
+    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.000965s
 
 
-**Total running time of the script:** ( 0 minutes  25.022 seconds)
+**Total running time of the script:** ( 0 minutes  22.421 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 (  553 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 (  550 docs/s)
-          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.78s (  539 docs/s)
+      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.913 in 1.61s (  598 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.61s (  596 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.62s (  595 docs/s)
+          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.65s (  582 docs/s)
 
 
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 5.07s (  771 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.07s (  770 docs/s)
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 5.08s (  770 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.11s (  764 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 4.62s (  845 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 4.63s (  845 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 4.63s (  844 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 4.67s (  838 docs/s)
 
 
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 8.41s (  811 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 8.41s (  811 docs/s)
-                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 8.41s (  810 docs/s)
-          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 8.45s (  807 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 7.77s (  877 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 7.78s (  877 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 7.78s (  876 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 7.81s (  872 docs/s)
 
 
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 11.79s (  827 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 11.80s (  827 docs/s)
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 11.80s (  827 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 11.84s (  824 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 10.81s (  902 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 10.82s (  902 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 10.82s (  902 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 10.85s (  899 docs/s)
 
 
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.79s (  789 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.79s (  789 docs/s)
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.80s (  789 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 14.83s (  787 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.56s (  861 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.56s (  861 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.56s (  861 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 13.60s (  859 docs/s)
 
 
-      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 18.15s (  805 docs/s)
-              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 18.15s (  805 docs/s)
-                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 18.16s (  805 docs/s)
-          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 18.19s (  803 docs/s)
+      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 16.70s (  875 docs/s)
+              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 16.71s (  875 docs/s)
+                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 16.71s (  875 docs/s)
+          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 16.74s (  873 docs/s)
 
 
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 21.31s (  814 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 21.32s (  814 docs/s)
-                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 21.32s (  814 docs/s)
-          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 21.36s (  812 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 19.66s (  882 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 19.67s (  882 docs/s)
+                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 19.67s (  882 docs/s)
+          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 19.70s (  881 docs/s)
 
 
 Plot results
@@ -526,7 +526,7 @@ Plot results
 
 
 
-**Total running time of the script:** ( 0 minutes  23.295 seconds)
+**Total running time of the script:** ( 0 minutes  21.558 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  4.691 seconds)
+**Total running time of the script:** ( 0 minutes  4.366 seconds)
 
 
 

dev/_images/math/4348c558bdcaf61d6295789dc1931545beaf726f.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 3.54s
 
 
-**Total running time of the script:** ( 0 minutes  3.848 seconds)
+**Total running time of the script:** ( 0 minutes  3.540 seconds)
 
 
 

dev/_images/math/cbe6a0921e1d81c24f5493414498fdc7c5cca2c2.png

@@ -65,7 +65,7 @@ References
 
      Area under the ROC curve : 0.993919
 
-    time elapsed: 6.82s
+    time elapsed: 6.35s
 
 
 
@@ -247,7 +247,7 @@ References
     plot_species_distribution()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  6.818 seconds)
+**Total running time of the script:** ( 0 minutes  6.355 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_001.png

@@ -340,7 +340,7 @@ Visualization
 
 
 
-**Total running time of the script:** ( 0 minutes  11.560 seconds)
+**Total running time of the script:** ( 0 minutes  52.040 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0011.png

@@ -164,7 +164,7 @@ contributed to fewer projections than the central disk.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  9.914 seconds)
+**Total running time of the script:** ( 0 minutes  9.281 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_002.png

@@ -106,7 +106,7 @@ representation of the checkerboard structure.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.953 seconds)
+**Total running time of the script:** ( 0 minutes  0.893 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0021.png

@@ -93,7 +93,7 @@ the biclusters.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.239 seconds)
+**Total running time of the script:** ( 0 minutes  0.232 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_003.png

@@ -159,7 +159,7 @@ Plot the data and the predicted probabilities
 
 
 
-**Total running time of the script:** ( 0 minutes  0.279 seconds)
+**Total running time of the script:** ( 0 minutes  0.260 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0031.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  2.255 seconds)
+**Total running time of the script:** ( 0 minutes  2.117 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_004.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  0.707 seconds)
+**Total running time of the script:** ( 0 minutes  0.655 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0041.png

@@ -147,7 +147,7 @@ Plot calibration plots
 
 
 
-**Total running time of the script:** ( 0 minutes  2.636 seconds)
+**Total running time of the script:** ( 0 minutes  2.502 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png

@@ -113,7 +113,7 @@ a result it can identify only the first class.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  2.539 seconds)
+**Total running time of the script:** ( 0 minutes  2.467 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_001.png

@@ -153,7 +153,7 @@ set.
     plt.tight_layout()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  8.016 seconds)
+**Total running time of the script:** ( 0 minutes  7.582 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0011.png

@@ -120,7 +120,7 @@ This example is commented in the
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.773 seconds)
+**Total running time of the script:** ( 0 minutes  0.723 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_002.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  6.122 seconds)
+**Total running time of the script:** ( 0 minutes  5.650 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0021.png

@@ -204,7 +204,7 @@ plot functions
 
 
 
-**Total running time of the script:** ( 0 minutes  0.419 seconds)
+**Total running time of the script:** ( 0 minutes  0.384 seconds)