Pushing the docs to dev/ for branch: main, commit c4c546355667b070edd5c892b206aa4a97af9a0b

Author: sklearn-ci

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: dfa35fafdd8dd7a4a77d17a12c7daaa4
+config: 29c9b69abd7e3ef515aa5285b888f1e3
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/00ae629d652473137a3905a5e08ea815/plot_iris_dtc.py

@@ -14,6 +14,7 @@
 
 We also show the tree structure of a model built on all of the features.
 """
+
 # %%
 # First load the copy of the Iris dataset shipped with scikit-learn:
 from sklearn.datasets import load_iris

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -22,6 +22,7 @@
 Brier score.
 
 """
+
 # Authors:
 # Mathieu Blondel <[email protected]>
 # Alexandre Gramfort <[email protected]>

dev/_downloads/0b39f715b5e32f01df3d212b6d822b82/plot_calibration.py

@@ -163,7 +163,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\n\nlinear_regression = LinearRegression()\nquantile_regression = QuantileRegressor(quantile=0.5, alpha=0, solver=solver)\n\ny_pred_lr = linear_regression.fit(X, y_pareto).predict(X)\ny_pred_qr = quantile_regression.fit(X, y_pareto).predict(X)\n\nprint(f\"\"\"Training error (in-sample performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_lr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_lr):.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_qr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_qr):.3f}\n    \"\"\")"
+        "from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\n\nlinear_regression = LinearRegression()\nquantile_regression = QuantileRegressor(quantile=0.5, alpha=0, solver=solver)\n\ny_pred_lr = linear_regression.fit(X, y_pareto).predict(X)\ny_pred_qr = quantile_regression.fit(X, y_pareto).predict(X)\n\nprint(\n    f\"\"\"Training error (in-sample performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_lr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_lr):.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {mean_absolute_error(y_pareto, y_pred_qr):.3f}\n    MSE = {mean_squared_error(y_pareto, y_pred_qr):.3f}\n    \"\"\"\n)"
       ]
     },
     {
@@ -181,7 +181,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.model_selection import cross_validate\n\ncv_results_lr = cross_validate(\n    linear_regression,\n    X,\n    y_pareto,\n    cv=3,\n    scoring=[\"neg_mean_absolute_error\", \"neg_mean_squared_error\"],\n)\ncv_results_qr = cross_validate(\n    quantile_regression,\n    X,\n    y_pareto,\n    cv=3,\n    scoring=[\"neg_mean_absolute_error\", \"neg_mean_squared_error\"],\n)\nprint(f\"\"\"Test error (cross-validated performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {-cv_results_lr[\"test_neg_mean_absolute_error\"].mean():.3f}\n    MSE = {-cv_results_lr[\"test_neg_mean_squared_error\"].mean():.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {-cv_results_qr[\"test_neg_mean_absolute_error\"].mean():.3f}\n    MSE = {-cv_results_qr[\"test_neg_mean_squared_error\"].mean():.3f}\n    \"\"\")"
+        "from sklearn.model_selection import cross_validate\n\ncv_results_lr = cross_validate(\n    linear_regression,\n    X,\n    y_pareto,\n    cv=3,\n    scoring=[\"neg_mean_absolute_error\", \"neg_mean_squared_error\"],\n)\ncv_results_qr = cross_validate(\n    quantile_regression,\n    X,\n    y_pareto,\n    cv=3,\n    scoring=[\"neg_mean_absolute_error\", \"neg_mean_squared_error\"],\n)\nprint(\n    f\"\"\"Test error (cross-validated performance)\n    {linear_regression.__class__.__name__}:\n    MAE = {-cv_results_lr[\"test_neg_mean_absolute_error\"].mean():.3f}\n    MSE = {-cv_results_lr[\"test_neg_mean_squared_error\"].mean():.3f}\n    {quantile_regression.__class__.__name__}:\n    MAE = {-cv_results_qr[\"test_neg_mean_absolute_error\"].mean():.3f}\n    MSE = {-cv_results_qr[\"test_neg_mean_squared_error\"].mean():.3f}\n    \"\"\"\n)"
       ]
     },
     {

dev/_downloads/10754505339af88c10b8a48127535a4a/plot_quantile_regression.ipynb

@@ -17,6 +17,7 @@
 datapoint.
 
 """
+
 # %%
 # Authors: Jan Hendrik Metzen <[email protected]>
 # License: BSD 3 clause

dev/_downloads/1dcd684ce26b8c407ec2c2d2101c5c73/plot_kernel_ridge_regression.py

@@ -15,7 +15,6 @@
 trade-off.
 """
 
-
 # %%
 # Generate sample data
 # --------------------

dev/_downloads/29c38fef6831de20867ac61e068f2461/plot_covariance_estimation.py

@@ -8,6 +8,7 @@
 Between Data Points", Science Feb. 2007
 
 """
+
 import numpy as np
 
 from sklearn import metrics

dev/_downloads/2f930acda654766f8ba0fee08887bb41/plot_affinity_propagation.py

@@ -20,6 +20,7 @@
 
 First a few imports and some random data for the rest of the script.
 """
+
 # %%
 
 import warnings

dev/_downloads/35686e86ab04c9899b73acd07be52bc7/plot_metadata_routing.py

@@ -20,7 +20,6 @@
 
 """
 
-
 # Author: Tim Head <[email protected]>
 #
 # License: BSD 3 clause

dev/_downloads/3a10dcfbc1a4bf1349c7101a429aa47b/plot_feature_transformation.py

@@ -13,6 +13,7 @@
 present for regular K-Means.
 
 """
+
 import matplotlib.pyplot as plt
 
 from sklearn.cluster import BisectingKMeans, KMeans

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -8,6 +8,7 @@
 Then, we will explore how they both deal with the addition of a hole
 in the data.
 """
+
 # %%
 # Swiss Roll
 # ---------------------------------------------------

dev/_downloads/73962cec5f14b10630f1a505fe761ab7/plot_bisect_kmeans.py

@@ -11,7 +11,6 @@
 
 """
 
-
 import numpy as np
 
 from sklearn.datasets import fetch_20newsgroups

dev/_downloads/7f0a2318ad82288d649c688011f52618/plot_swissroll.py

@@ -261,14 +261,16 @@
 y_pred_lr = linear_regression.fit(X, y_pareto).predict(X)
 y_pred_qr = quantile_regression.fit(X, y_pareto).predict(X)
 
-print(f"""Training error (in-sample performance)
+print(
+    f"""Training error (in-sample performance)
     {linear_regression.__class__.__name__}:
     MAE = {mean_absolute_error(y_pareto, y_pred_lr):.3f}
     MSE = {mean_squared_error(y_pareto, y_pred_lr):.3f}
     {quantile_regression.__class__.__name__}:
     MAE = {mean_absolute_error(y_pareto, y_pred_qr):.3f}
     MSE = {mean_squared_error(y_pareto, y_pred_qr):.3f}
-    """)
+    """
+)
 
 # %%
 # On the training set, we see that MAE is lower for
@@ -298,14 +300,16 @@
     cv=3,
     scoring=["neg_mean_absolute_error", "neg_mean_squared_error"],
 )
-print(f"""Test error (cross-validated performance)
+print(
+    f"""Test error (cross-validated performance)
     {linear_regression.__class__.__name__}:
     MAE = {-cv_results_lr["test_neg_mean_absolute_error"].mean():.3f}
     MSE = {-cv_results_lr["test_neg_mean_squared_error"].mean():.3f}
     {quantile_regression.__class__.__name__}:
     MAE = {-cv_results_qr["test_neg_mean_absolute_error"].mean():.3f}
     MSE = {-cv_results_qr["test_neg_mean_squared_error"].mean():.3f}
-    """)
+    """
+)
 
 # %%
 # We reach similar conclusions on the out-of-sample evaluation.

dev/_downloads/7f9c06d88a8d544a3815452dacaa0548/plot_semi_supervised_newsgroups.py

@@ -19,6 +19,7 @@
 <https://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html>`_.
 
 """
+
 # %%
 import matplotlib.pyplot as plt
 import numpy as np

dev/_downloads/8a712694e4d011e8f35bfcb1b1b5fc82/plot_quantile_regression.py

@@ -11,6 +11,7 @@
 .. _LFW: http://vis-www.cs.umass.edu/lfw/
 
 """
+
 # %%
 from time import time
 

dev/_downloads/9e22207e9bd6485b95f32783b59d9a80/plot_monotonic_constraints.py

@@ -31,6 +31,7 @@
 License: BSD 3 clause
 
 """
+
 # %%
 # Data Preparation
 # ----------------

dev/_downloads/b3a994b2ad66fe78bcedaf151ab78b07/plot_face_recognition.py

@@ -33,7 +33,6 @@
 time to initialize and low number of GaussianMixture iterations to converge.
 """
 
-
 # Author: Gordon Walsh <[email protected]>
 # Data generation code from Jake Vanderplas <[email protected]>