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scikit-learn · Jun 22, 2023 · 9763ac4 · 9763ac4
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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: a79876df52c6cf37e3784905a35991c8
+config: 004f6708f4c01c6ec17cf1096cafe5cd
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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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  13.611 seconds)
+   **Total running time of the script:** ( 0 minutes  13.488 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  10.242 seconds)
+   **Total running time of the script:** ( 0 minutes  10.570 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
@@ -159,9 +159,9 @@ dataset): unsupervised feature extraction / dimensionality reduction
  .. code-block:: none
 
     Extracting the top 150 eigenfaces from 966 faces
-    done in 0.083s
+    done in 0.084s
     Projecting the input data on the eigenfaces orthonormal basis
-    done in 0.007s
+    done in 0.009s
 
 
 
@@ -199,7 +199,7 @@ Train a SVM classification model
  .. code-block:: none
 
     Fitting the classifier to the training set
-    done in 5.807s
+    done in 5.959s
     Best estimator found by grid search:
     SVC(C=76823.03433306453, class_weight='balanced', gamma=0.003418945823095797)
 
@@ -359,7 +359,7 @@ tensorflow to implement such models.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  6.776 seconds)
+   **Total running time of the script:** ( 0 minutes  6.990 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: 0.056501s
+    Complexity: 4948 | Hamming Loss (Misclassification Ratio): 0.2675 | Pred. Time: 0.061294s
 
     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: 0.042988s
+    Complexity: 1847 | Hamming Loss (Misclassification Ratio): 0.3264 | Pred. Time: 0.045601s
 
     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: 0.035415s
+    Complexity: 997 | Hamming Loss (Misclassification Ratio): 0.3383 | Pred. Time: 0.038330s
 
     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: 0.032616s
+    Complexity: 802 | Hamming Loss (Misclassification Ratio): 0.3582 | Pred. Time: 0.033368s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.05)
-    Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000182s
+    Complexity: 18 | MSE: 5558.7313 | Pred. Time: 0.000186s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.1)
-    Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000266s
+    Complexity: 36 | MSE: 5289.8022 | Pred. Time: 0.000261s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.2)
-    Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000415s
+    Complexity: 72 | MSE: 5193.8353 | Pred. Time: 0.000419s
 
     Benchmarking NuSVR(C=1000.0, gamma=3.0517578125e-05, nu=0.35)
-    Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000638s
+    Complexity: 124 | MSE: 5131.3279 | Pred. Time: 0.000640s
 
     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: 0.000877s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=10)
     Complexity: 10 | MSE: 4066.4812 | Pred. Time: 0.000164s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=25)
-    Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000186s
+    Complexity: 25 | MSE: 3551.1723 | Pred. Time: 0.000191s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=50)
-    Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000225s
+    Complexity: 50 | MSE: 3445.2171 | Pred. Time: 0.000226s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=75)
-    Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000268s
+    Complexity: 75 | MSE: 3433.0358 | Pred. Time: 0.000256s
 
     Benchmarking GradientBoostingRegressor(learning_rate=0.05, max_depth=2)
-    Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000302s
+    Complexity: 100 | MSE: 3456.0602 | Pred. Time: 0.000291s
 
 
 
@@ -453,7 +453,7 @@ under-fitting or over-fitting.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  5.167 seconds)
+   **Total running time of the script:** ( 0 minutes  5.242 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 0.64s ( 1504 docs/s)
-              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.65s ( 1490 docs/s)
-          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.67s ( 1438 docs/s)
-      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.67s ( 1431 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.915 in 0.69s ( 1395 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.855 in 0.69s ( 1389 docs/s)
+          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 0.70s ( 1372 docs/s)
+      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.933 in 0.70s ( 1367 docs/s)
 
 
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 2.08s ( 1879 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 2.08s ( 1876 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 2.09s ( 1868 docs/s)
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 2.10s ( 1865 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.938 in 1.89s ( 2073 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.936 in 1.89s ( 2070 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 1.90s ( 2062 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.941 in 1.90s ( 2059 docs/s)
 
 
-                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.18s ( 2144 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.18s ( 2142 docs/s)
-          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 3.19s ( 2136 docs/s)
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 3.19s ( 2134 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.07s ( 2218 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.952 in 3.08s ( 2216 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.900 in 3.09s ( 2210 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.953 in 3.09s ( 2208 docs/s)
 
 
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 4.28s ( 2279 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 4.28s ( 2277 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 4.29s ( 2273 docs/s)
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 4.30s ( 2271 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.949 in 4.25s ( 2295 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.953 in 4.25s ( 2294 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 4.26s ( 2290 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 4.26s ( 2288 docs/s)
 
 
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.944 in 5.24s ( 2230 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.956 in 5.24s ( 2229 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.915 in 5.25s ( 2225 docs/s)
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 5.25s ( 2224 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.944 in 5.21s ( 2240 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.956 in 5.21s ( 2239 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.915 in 5.22s ( 2236 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.950 in 5.23s ( 2235 docs/s)
 
 
-                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.965 in 6.37s ( 2297 docs/s)
-              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.903 in 6.37s ( 2296 docs/s)
-          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.924 in 6.38s ( 2292 docs/s)
-      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.957 in 6.38s ( 2291 docs/s)
+                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.965 in 6.39s ( 2290 docs/s)
+              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.903 in 6.39s ( 2289 docs/s)
+          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.924 in 6.40s ( 2286 docs/s)
+      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.957 in 6.40s ( 2285 docs/s)
 
 
-                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 7.41s ( 2342 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 7.41s ( 2341 docs/s)
-          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 7.42s ( 2339 docs/s)
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 7.42s ( 2338 docs/s)
+                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 7.40s ( 2344 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.933 in 7.41s ( 2343 docs/s)
+          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 7.42s ( 2341 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.952 in 7.42s ( 2340 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  8.373 seconds)
+   **Total running time of the script:** ( 0 minutes  8.403 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  1.295 seconds)
+   **Total running time of the script:** ( 0 minutes  1.361 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  16.286 seconds)
+   **Total running time of the script:** ( 0 minutes  16.256 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: 6.26s
+    time elapsed: 6.35s
 
 
 
@@ -308,7 +308,7 @@ References
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  6.447 seconds)
+   **Total running time of the script:** ( 0 minutes  6.537 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  7.806 seconds)
+   **Total running time of the script:** ( 0 minutes  6.857 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  9.726 seconds)
+   **Total running time of the script:** ( 0 minutes  9.515 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 1.116s.
+    done in 1.120s.
     Extracting tf-idf features for NMF...
-    done in 0.275s.
+    done in 0.277s.
     Extracting tf features for LDA...
-    done in 0.272s.
+    done in 0.268s.
 
     Fitting the NMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000...
     done in 0.076s.
 
 
      Fitting the NMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000...
-    done in 1.086s.
+    done in 1.070s.
 
 
      Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
     done in 0.086s.
 
 
      Fitting the MiniBatchNMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128...
-    done in 0.235s.
+    done in 0.246s.
 
 
      Fitting LDA models with tf features, n_samples=2000 and n_features=1000...
-    done in 2.796s.
+    done in 2.788s.
 
 
 
@@ -329,7 +329,7 @@ proportional to (n_samples * iterations).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  12.051 seconds)
+   **Total running time of the script:** ( 0 minutes  12.101 seconds)
 
 
 .. _sphx_glr_download_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py: