docs: Build documentation triggered by 381550c

Author: github-actions[bot]

docs/latest/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import subprocess\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nfrom sklearn import datasets, linear_model\nfrom sklearn import svm\nfrom sklearn.model_selection import cross_validate as sklearn_cross_validate\n\nfrom skore import load\nimport skore.cross_validate"
+        "import subprocess\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nfrom sklearn import datasets, linear_model\nfrom sklearn import svm\nfrom sklearn.model_selection import cross_validate as sklearn_cross_validate\n\nimport skore"
       ]
     },
     {
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "my_project_gs = load(\"my_project_cv.skore\")"
+        "my_project_gs = skore.load(\"my_project_cv.skore\")"
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "X, y = datasets.load_iris(return_X_y=True)\nclf = svm.SVC(kernel='linear', C=1, random_state=0)"
+        "X, y = datasets.load_iris(return_X_y=True)\nclf = svm.SVC(kernel=\"linear\", C=1, random_state=0)"
       ]
     },
     {
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "cv_results = sklearn_cross_validate(clf, X, y, cv=5)\ncv_results['test_score']"
+        "cv_results = sklearn_cross_validate(clf, X, y, cv=5)\ncv_results[\"test_score\"]"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "scores = sklearn_cross_validate(\n    clf, X, y, cv=5,\n    scoring=['accuracy', 'precision_macro'],\n)\nprint(scores['test_accuracy'])\nprint(scores['test_precision_macro'])"
+        "scores = sklearn_cross_validate(\n    clf,\n    X,\n    y,\n    cv=5,\n    scoring=[\"accuracy\", \"precision_macro\"],\n)\nprint(scores[\"test_accuracy\"])\nprint(scores[\"test_precision_macro\"])"
       ]
     },
     {

docs/latest/_downloads/6a98626692e1880e5e2663974edc45be/plot_02_overview_skore_ui.zip

@@ -10,7 +10,6 @@
 ML/DS projects.
 """
 
-
 # %%
 import subprocess
 
@@ -21,8 +20,7 @@
 from sklearn import svm
 from sklearn.model_selection import cross_validate as sklearn_cross_validate
 
-from skore import load
-import skore.cross_validate
+import skore
 
 
 # %%
@@ -39,7 +37,7 @@
 
 
 # %%
-my_project_gs = load("my_project_cv.skore")
+my_project_gs = skore.load("my_project_cv.skore")
 
 # %%
 # Cross-validation in scikit-learn
@@ -61,25 +59,28 @@
 
 # %%
 X, y = datasets.load_iris(return_X_y=True)
-clf = svm.SVC(kernel='linear', C=1, random_state=0)
+clf = svm.SVC(kernel="linear", C=1, random_state=0)
 
 # %%
 # Single metric evaluation using ``cross_validate``:
 
 # %%
 cv_results = sklearn_cross_validate(clf, X, y, cv=5)
-cv_results['test_score']
+cv_results["test_score"]
 
 # %%
 # Multiple metric evaluation using ``cross_validate``:
 
 # %%
 scores = sklearn_cross_validate(
-    clf, X, y, cv=5,
-    scoring=['accuracy', 'precision_macro'],
+    clf,
+    X,
+    y,
+    cv=5,
+    scoring=["accuracy", "precision_macro"],
 )
-print(scores['test_accuracy'])
-print(scores['test_precision_macro'])
+print(scores["test_accuracy"])
+print(scores["test_precision_macro"])
 
 # %%
 # In scikit-learn, why do we recommend using ``cross_validate`` over ``cross_val_score``?

docs/latest/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -792,7 +792,7 @@ project, such as grid search or cross-validation results.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 6.924 seconds)
+   **Total running time of the script:** (0 minutes 7.259 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_01_getting_started.py:

docs/latest/_downloads/97b4b84f5410e1134b3cbe5e4682d2a1/plot_01_getting_started.zip

@@ -459,9 +459,9 @@ Numpy array:
  .. code-block:: none
 
 
-    array([[-1.35373974,  0.23013285, -0.21272882],
-           [ 1.60946869, -0.12528379, -0.01554795],
-           [-1.07571799, -1.32837446,  1.01232954]])
+    array([[-0.46000552, -0.88204074, -2.56690302],
+           [ 0.02101194,  0.81484799, -0.4595374 ],
+           [ 0.38646844, -0.53958006, -0.97968473]])
 
 
 
@@ -513,43 +513,43 @@ Pandas data frame:
       <tbody>
         <tr>
           <th>0</th>
-          <td>-0.455804</td>
-          <td>-0.577020</td>
-          <td>-0.056895</td>
-          <td>1.694401</td>
-          <td>0.554412</td>
+          <td>0.706108</td>
+          <td>-0.444957</td>
+          <td>0.518134</td>
+          <td>0.102208</td>
+          <td>-0.172436</td>
         </tr>
         <tr>
           <th>1</th>
-          <td>0.215798</td>
-          <td>-1.919659</td>
-          <td>-0.180821</td>
-          <td>-1.383658</td>
-          <td>0.320623</td>
+          <td>-0.867566</td>
+          <td>0.369605</td>
+          <td>-0.396423</td>
+          <td>-0.005231</td>
+          <td>-0.560875</td>
         </tr>
         <tr>
           <th>2</th>
-          <td>-0.419148</td>
-          <td>-1.325382</td>
-          <td>0.953337</td>
-          <td>-0.526009</td>
-          <td>1.735644</td>
+          <td>0.374918</td>
+          <td>-0.304119</td>
+          <td>0.058169</td>
+          <td>0.828861</td>
+          <td>1.302512</td>
         </tr>
         <tr>
           <th>3</th>
-          <td>-2.192932</td>
-          <td>-0.964590</td>
-          <td>-0.265942</td>
-          <td>-0.825370</td>
-          <td>0.748187</td>
+          <td>-0.607682</td>
+          <td>-1.101300</td>
+          <td>0.493301</td>
+          <td>0.214144</td>
+          <td>0.596417</td>
         </tr>
         <tr>
           <th>4</th>
-          <td>-0.841158</td>
-          <td>2.310570</td>
-          <td>-0.224974</td>
-          <td>0.775593</td>
-          <td>-0.944139</td>
+          <td>-1.330183</td>
+          <td>0.580809</td>
+          <td>-0.416686</td>
+          <td>0.788476</td>
+          <td>0.320062</td>
         </tr>
       </tbody>
     </table>
@@ -2026,7 +2026,7 @@ Moreover, you can store fitted scikit-learn pipelines:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.525 seconds)
+   **Total running time of the script:** (0 minutes 1.462 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_02_overview_skore_ui.py: