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scikit-learn · Jun 29, 2023 · 62605e2 · 62605e2
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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: 9198e15989c84c8ec200232af5f4e7af
+config: fcbfdd0682e92a4f7a841774d65b3fa5
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_001.png b/dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_001.png
diff --git a/dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_002.png b/dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_002.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  13.448 seconds)
+   **Total running time of the script:** ( 0 minutes  13.926 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  10.188 seconds)
+   **Total running time of the script:** ( 0 minutes  10.104 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
@@ -161,7 +161,7 @@ dataset): unsupervised feature extraction / dimensionality reduction
     Extracting the top 150 eigenfaces from 966 faces
     done in 0.074s
     Projecting the input data on the eigenfaces orthonormal basis
-    done in 0.008s
+    done in 0.011s
 
 
 
@@ -199,7 +199,7 @@ Train a SVM classification model
  .. code-block:: none
 
     Fitting the classifier to the training set
-    done in 5.801s
+    done in 6.013s
     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 0.054s
+    done in 0.045s
                        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  6.799 seconds)
+   **Total running time of the script:** ( 0 minutes  6.935 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
@@ -388,25 +388,25 @@ 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: 0.057290s
+    Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.059666s
 
     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: 0.043787s
+    Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.044021s
 
     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: 0.037228s
+    Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.037463s
 
     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: 0.035850s
+    Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.033406s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.05)
-    Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000184s
+    Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000183s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.1)
-    Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000261s
+    Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000270s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.2)
     Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000418s
@@ -415,22 +415,22 @@ ensemble is not as detrimental.
     Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000643s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05)
-    Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.000874s
+    Complexity: 178 | MSE: 5149.0779 | Pred. Time: 0.001452s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=10)
-    Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000156s
+    Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000293s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=25)
-    Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000178s
+    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: 0.000234s
+    Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000223s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=75)
-    Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000252s
+    Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000388s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2)
-    Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000285s
+    Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000345s
 
 
 
@@ -453,7 +453,7 @@ under-fitting or over-fitting.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  5.282 seconds)
+   **Total running time of the script:** ( 0 minutes  5.309 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
@@ -383,46 +383,46 @@ 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 0.66s ( 1464 docs/s)
-              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.66s ( 1457 docs/s)
-          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.67s ( 1434 docs/s)
-      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.67s ( 1427 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.915 in 0.64s ( 1505 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.64s ( 1492 docs/s)
+          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.67s ( 1443 docs/s)
+      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.67s ( 1436 docs/s)
 
 
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 1.88s ( 2081 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 1.88s ( 2078 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 1.89s ( 2066 docs/s)
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 1.90s ( 2062 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 1.82s ( 2146 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 1.83s ( 2141 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 1.84s ( 2125 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 1.84s ( 2120 docs/s)
 
 
-                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.09s ( 2207 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.09s ( 2205 docs/s)
-          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 3.10s ( 2199 docs/s)
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 3.10s ( 2197 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.04s ( 2244 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.04s ( 2242 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 3.05s ( 2236 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 3.05s ( 2233 docs/s)
 
 
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 4.27s ( 2284 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 4.27s ( 2283 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 4.28s ( 2278 docs/s)
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 4.29s ( 2276 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 4.25s ( 2298 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 4.25s ( 2297 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 4.26s ( 2292 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 4.26s ( 2290 docs/s)
 
 
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.944 in 5.38s ( 2170 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.956 in 5.39s ( 2168 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.915 in 5.39s ( 2165 docs/s)
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 5.40s ( 2163 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.944 in 5.35s ( 2182 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.956 in 5.35s ( 2181 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.915 in 5.36s ( 2178 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 5.36s ( 2177 docs/s)
 
 
-                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.965 in 6.69s ( 2185 docs/s)
-              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.903 in 6.69s ( 2184 docs/s)
-          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.924 in 6.70s ( 2181 docs/s)
-      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.957 in 6.71s ( 2180 docs/s)
+                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.965 in 6.57s ( 2227 docs/s)
+              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.903 in 6.57s ( 2226 docs/s)
+          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.924 in 6.58s ( 2223 docs/s)
+      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.957 in 6.58s ( 2222 docs/s)
 
 
-                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 7.85s ( 2210 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 7.86s ( 2209 docs/s)
-          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 7.87s ( 2207 docs/s)
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 7.87s ( 2206 docs/s)
+                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 7.65s ( 2269 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 7.65s ( 2269 docs/s)
+          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 7.66s ( 2266 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 7.66s ( 2265 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  8.921 seconds)
+   **Total running time of the script:** ( 0 minutes  8.660 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  1.452 seconds)
+   **Total running time of the script:** ( 0 minutes  1.320 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  16.777 seconds)
+   **Total running time of the script:** ( 0 minutes  16.611 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: 7.27s
+    time elapsed: 6.98s
 
 
 
@@ -308,7 +308,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  7.497 seconds)
+   **Total running time of the script:** ( 0 minutes  7.170 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  7.264 seconds)
+   **Total running time of the script:** ( 0 minutes  3.639 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  9.672 seconds)
+   **Total running time of the script:** ( 0 minutes  9.788 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 1.171s.
+    done in 1.151s.
     Extracting tf-idf features for NMF...
-    done in 0.273s.
+    done in 0.280s.
     Extracting tf features for LDA...
-    done in 0.251s.
+    done in 0.281s.
 
     Fitting the NMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000...
-    done in 0.078s.
+    done in 0.079s.
 
 
      Fitting the NMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000...
-    done in 1.245s.
+    done in 1.441s.
 
 
      Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
-    done in 0.081s.
+    done in 0.090s.
 
 
      Fitting the MiniBatchNMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
-    done in 0.220s.
+    done in 0.250s.
 
 
      Fitting LDA models with tf features, n_samples=2000 and n_features=1000...
-    done in 2.308s.
+    done in 2.205s.
 
 
 
@@ -329,7 +329,7 @@ proportional to (n_samples * iterations).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  11.405 seconds)
+   **Total running time of the script:** ( 0 minutes  11.995 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py: