Pushing the docs to dev/ for branch: main, commit 3d16a21b6c9c6940509d98e5e0c030658f7c348c

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.utils import shuffle\n\n\ndef load_ames_housing():\n    df = fetch_openml(name=\"house_prices\", as_frame=True, parser=\"pandas\")\n    X = df.data\n    y = df.target\n\n    features = [\n        \"YrSold\",\n        \"HeatingQC\",\n        \"Street\",\n        \"YearRemodAdd\",\n        \"Heating\",\n        \"MasVnrType\",\n        \"BsmtUnfSF\",\n        \"Foundation\",\n        \"MasVnrArea\",\n        \"MSSubClass\",\n        \"ExterQual\",\n        \"Condition2\",\n        \"GarageCars\",\n        \"GarageType\",\n        \"OverallQual\",\n        \"TotalBsmtSF\",\n        \"BsmtFinSF1\",\n        \"HouseStyle\",\n        \"MiscFeature\",\n        \"MoSold\",\n    ]\n\n    X = X.loc[:,features]\n    X, y = shuffle(X, y, random_state=0)\n\n    X = X.iloc[:600]\n    y = y.iloc[:600]\n    return X, np.log(y)\n\n\nX, y = load_ames_housing()"
+        "import numpy as np\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.utils import shuffle\n\n\ndef load_ames_housing():\n    df = fetch_openml(name=\"house_prices\", as_frame=True, parser=\"pandas\")\n    X = df.data\n    y = df.target\n\n    features = [\n        \"YrSold\",\n        \"HeatingQC\",\n        \"Street\",\n        \"YearRemodAdd\",\n        \"Heating\",\n        \"MasVnrType\",\n        \"BsmtUnfSF\",\n        \"Foundation\",\n        \"MasVnrArea\",\n        \"MSSubClass\",\n        \"ExterQual\",\n        \"Condition2\",\n        \"GarageCars\",\n        \"GarageType\",\n        \"OverallQual\",\n        \"TotalBsmtSF\",\n        \"BsmtFinSF1\",\n        \"HouseStyle\",\n        \"MiscFeature\",\n        \"MoSold\",\n    ]\n\n    X = X[features]\n    X, y = shuffle(X, y, random_state=0)\n\n    X = X[:600]\n    y = y[:600]\n    return X, np.log(y)\n\n\nX, y = load_ames_housing()"
       ]
     },
     {

dev/_downloads/3c9b7bcd0b16f172ac12ffad61f3b5f0/plot_stack_predictors.ipynb

@@ -191,7 +191,6 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):
     batch_size=20,
     max_iter=50,
     random_state=rng,
-    n_init="auto",
 )
 kmeans_estimator.fit(faces_centered)
 plot_gallery(

dev/_downloads/4825fc8223d1af0f3b61080c3dea3a62/plot_faces_decomposition.py

@@ -72,11 +72,11 @@ def load_ames_housing():
         "MoSold",
     ]
 
-    X = X.loc[:,features]
+    X = X[features]
     X, y = shuffle(X, y, random_state=0)
 
-    X = X.iloc[:600]
-    y = y.iloc[:600]
+    X = X[:600]
+    y = y[:600]
     return X, np.log(y)
 
 

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -184,7 +184,7 @@
       },
       "outputs": [],
       "source": [
-        "kmeans_estimator = cluster.MiniBatchKMeans(\n    n_clusters=n_components,\n    tol=1e-3,\n    batch_size=20,\n    max_iter=50,\n    random_state=rng,\n    n_init=\"auto\",\n)\nkmeans_estimator.fit(faces_centered)\nplot_gallery(\n    \"Cluster centers - MiniBatchKMeans\",\n    kmeans_estimator.cluster_centers_[:n_components],\n)"
+        "kmeans_estimator = cluster.MiniBatchKMeans(\n    n_clusters=n_components,\n    tol=1e-3,\n    batch_size=20,\n    max_iter=50,\n    random_state=rng,\n)\nkmeans_estimator.fit(faces_centered)\nplot_gallery(\n    \"Cluster centers - MiniBatchKMeans\",\n    kmeans_estimator.cluster_centers_[:n_components],\n)"
       ]
     },
     {