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scikit-learn · Jul 7, 2023 · 46ba6e4 · 46ba6e4
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diff --git a/dev/.buildinfo b/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: 5c879347b182b3b40f0311234ff7c36a
+config: 2a8960333c83034c100d25731c9a27de
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
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diff --git a/dev/_images/sphx_glr_plot_successive_halving_heatmap_001.png b/dev/_images/sphx_glr_plot_successive_halving_heatmap_001.png
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diff --git a/dev/_images/sphx_glr_plot_theilsen_001.png b/dev/_images/sphx_glr_plot_theilsen_001.png
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diff --git a/dev/_sources/auto_examples/applications/plot_cyclical_feature_engineering.rst.txt b/dev/_sources/auto_examples/applications/plot_cyclical_feature_engineering.rst.txt
@@ -2555,7 +2555,7 @@ instead of `RidgeCV`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  12.607 seconds)
+   **Total running time of the script:** ( 0 minutes  11.777 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_cyclical_feature_engineering.py:

diff --git a/dev/_sources/auto_examples/applications/plot_digits_denoising.rst.txt b/dev/_sources/auto_examples/applications/plot_digits_denoising.rst.txt
@@ -312,7 +312,7 @@ will depend of the parameters `n_components`, `gamma`, and `alpha`.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  9.947 seconds)
+   **Total running time of the script:** ( 0 minutes  9.784 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_digits_denoising.py:

diff --git a/dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt b/dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
@@ -159,7 +159,7 @@ dataset): unsupervised feature extraction / dimensionality reduction
  .. code-block:: none
 
     Extracting the top 150 eigenfaces from 966 faces
-    done in 0.075s
+    done in 0.088s
     Projecting the input data on the eigenfaces orthonormal basis
     done in 0.008s
 
@@ -199,7 +199,7 @@ Train a SVM classification model
  .. code-block:: none
 
     Fitting the classifier to the training set
-    done in 5.973s
+    done in 5.699s
     Best estimator found by grid search:
     SVC(C=76823.03433306453, class_weight='balanced', gamma=0.003418945823095797)
 
@@ -242,7 +242,7 @@ Quantitative evaluation of the model quality on the test set
  .. code-block:: none
 
     Predicting people's names on the test set
-    done in 0.045s
+    done in 0.046s
                        precision    recall  f1-score   support
 
          Ariel Sharon       0.75      0.69      0.72        13
@@ -359,7 +359,7 @@ tensorflow to implement such models.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  6.912 seconds)
+   **Total running time of the script:** ( 0 minutes  6.677 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_face_recognition.py:

diff --git a/dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt b/dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
@@ -388,49 +388,49 @@ ensemble is not as detrimental.
 
     Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.25, loss='modified_huber',
                   n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-    Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.056985s
+    Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.055810s
 
     Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.5, loss='modified_huber',
                   n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-    Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.046402s
+    Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.041923s
 
     Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.75, loss='modified_huber',
                   n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-    Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.038430s
+    Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.034407s
 
     Benchmarking SGDClassifier(alpha=0.001, l1_ratio=0.9, loss='modified_huber',
                   n_iter_no_change=2, penalty='elasticnet', tol=0.1)
-    Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.034691s
+    Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.031689s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.05)
-    Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000179s
+    Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000184s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.1)
-    Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000260s
+    Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000257s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.2)
-    Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000452s
+    Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000416s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.35)
-    Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000684s
+    Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000635s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05)
-    Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.000956s
+    Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.000868s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=10)
-    Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000304s
+    Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000157s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=25)
-    Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000184s
+    Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000182s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=50)
-    Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000245s
+    Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000216s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=75)
-    Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000282s
+    Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000253s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2)
-    Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000282s
+    Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000280s
 
 
 
@@ -453,7 +453,7 @@ under-fitting or over-fitting.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  5.269 seconds)
+   **Total running time of the script:** ( 0 minutes  4.571 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_model_complexity_influence.py:

diff --git a/dev/_sources/auto_examples/applications/plot_out_of_core_classification.rst.txt b/dev/_sources/auto_examples/applications/plot_out_of_core_classification.rst.txt
@@ -383,34 +383,34 @@ maximum
  .. code-block:: none
 
     Test set is 878 documents (108 positive)
-                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.915 in 0.61s ( 1568 docs/s)
-              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.62s ( 1553 docs/s)
-          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.64s ( 1500 docs/s)
-      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.64s ( 1493 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.915 in 0.63s ( 1526 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.64s ( 1514 docs/s)
+          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.66s ( 1466 docs/s)
+      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.66s ( 1460 docs/s)
 
 
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 1.78s ( 2195 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 1.78s ( 2191 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 1.79s ( 2181 docs/s)
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 1.80s ( 2177 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 1.80s ( 2175 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 1.80s ( 2171 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 1.81s ( 2159 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 1.81s ( 2156 docs/s)
 
 
-                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 2.90s ( 2349 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 2.91s ( 2346 docs/s)
-          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 2.92s ( 2338 docs/s)
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 2.92s ( 2336 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 2.92s ( 2338 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 2.92s ( 2336 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 2.93s ( 2329 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 2.93s ( 2327 docs/s)
 
 
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 4.04s ( 2413 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 4.05s ( 2411 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 4.05s ( 2406 docs/s)
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 4.06s ( 2405 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 4.03s ( 2419 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 4.04s ( 2417 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 4.04s ( 2413 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 4.05s ( 2411 docs/s)
 
 
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.944 in 5.01s ( 2330 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.956 in 5.01s ( 2329 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.915 in 5.02s ( 2325 docs/s)
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 5.03s ( 2324 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.944 in 5.01s ( 2331 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.956 in 5.01s ( 2330 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.915 in 5.02s ( 2327 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 5.02s ( 2326 docs/s)
 
 
                      SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.965 in 6.16s ( 2375 docs/s)
@@ -420,9 +420,9 @@ maximum
 
 
                      SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 7.19s ( 2413 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 7.20s ( 2412 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 7.19s ( 2413 docs/s)
           NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 7.20s ( 2410 docs/s)
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 7.21s ( 2409 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 7.20s ( 2409 docs/s)
 
 
 
@@ -583,7 +583,7 @@ before feeding them to the learner.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  8.128 seconds)
+   **Total running time of the script:** ( 0 minutes  8.126 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_out_of_core_classification.py:

diff --git a/dev/_sources/auto_examples/applications/plot_outlier_detection_wine.rst.txt b/dev/_sources/auto_examples/applications/plot_outlier_detection_wine.rst.txt
@@ -210,7 +210,7 @@ the data scatter matrix and the risk of over-fitting the data.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.277 seconds)
+   **Total running time of the script:** ( 0 minutes  1.251 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_outlier_detection_wine.py:

diff --git a/dev/_sources/auto_examples/applications/plot_prediction_latency.rst.txt b/dev/_sources/auto_examples/applications/plot_prediction_latency.rst.txt
@@ -473,7 +473,7 @@ Benchmark throughput
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  15.880 seconds)
+   **Total running time of the script:** ( 0 minutes  15.030 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_prediction_latency.py:

diff --git a/dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt b/dev/_sources/auto_examples/applications/plot_species_distribution_modeling.rst.txt
@@ -82,7 +82,7 @@ References
 
      Area under the ROC curve : 0.993919
 
-    time elapsed: 6.46s
+    time elapsed: 6.11s
 
 
 
@@ -308,7 +308,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  6.652 seconds)
+   **Total running time of the script:** ( 0 minutes  6.286 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_species_distribution_modeling.py:

diff --git a/dev/_sources/auto_examples/applications/plot_stock_market.rst.txt b/dev/_sources/auto_examples/applications/plot_stock_market.rst.txt
@@ -459,7 +459,7 @@ axis.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  7.145 seconds)
+   **Total running time of the script:** ( 0 minutes  7.781 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_stock_market.py:

diff --git a/dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.rst.txt b/dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.rst.txt
@@ -180,7 +180,7 @@ contributed to fewer projections than the central disk.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  9.655 seconds)
+   **Total running time of the script:** ( 0 minutes  9.539 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_tomography_l1_reconstruction.py:

diff --git a/..._sources/auto_examples/applications/plot_topics_extraction_with_nmf_lda.rst.txt b/..._sources/auto_examples/applications/plot_topics_extraction_with_nmf_lda.rst.txt
@@ -86,30 +86,30 @@ proportional to (n_samples * iterations).
  .. code-block:: none
 
     Loading dataset...
-    done in 1.110s.
+    done in 1.166s.
     Extracting tf-idf features for NMF...
-    done in 0.238s.
+    done in 0.237s.
     Extracting tf features for LDA...
-    done in 0.230s.
+    done in 0.233s.
 
     Fitting the NMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000...
-    done in 0.072s.
+    done in 0.071s.
 
 
      Fitting the NMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000...
-    done in 1.212s.
+    done in 1.249s.
 
 
      Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
-    done in 0.077s.
+    done in 0.081s.
 
 
      Fitting the MiniBatchNMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
-    done in 0.209s.
+    done in 0.224s.
 
 
      Fitting LDA models with tf features, n_samples=2000 and n_features=1000...
-    done in 1.979s.
+    done in 2.088s.
 
 
 
@@ -328,7 +328,7 @@ proportional to (n_samples * iterations).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  10.769 seconds)
+   **Total running time of the script:** ( 0 minutes  11.134 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py: