Pushing the docs to 1.2/ for branch: 1.2.X, commit dc580a8ef5ee2a8aea80498388690e2213118efd

Author: sklearn-ci

1.2/.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: ee66a46362debe0a1889a563899a7be7
+config: e03f48e4a7a8954c4c5c39af4d86b428
 tags: 645f666f9bcd5a90fca523b33c5a78b7

1.2/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -93,15 +93,49 @@
     hist_no_interact, X, y, cv=5, n_jobs=2, train_sizes=np.linspace(0.1, 1, 5)
 )
 
+# %%
+# :class:`~inspection.PartialDependenceDisplay` exposes a new parameter
+# `categorical_features` to display partial dependence for categorical features
+# using bar plots and heatmaps.
+from sklearn.datasets import fetch_openml
+
+X, y = fetch_openml(
+    "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
+)
+X = X.select_dtypes(["number", "category"]).drop(columns=["body"])
+
+# %%
+from sklearn.preprocessing import OrdinalEncoder
+from sklearn.pipeline import make_pipeline
+
+categorical_features = ["pclass", "sex", "embarked"]
+model = make_pipeline(
+    ColumnTransformer(
+        transformers=[("cat", OrdinalEncoder(), categorical_features)],
+        remainder="passthrough",
+    ),
+    HistGradientBoostingRegressor(random_state=0),
+).fit(X, y)
+
+# %%
+from sklearn.inspection import PartialDependenceDisplay
+
+fig, ax = plt.subplots(figsize=(14, 4), constrained_layout=True)
+_ = PartialDependenceDisplay.from_estimator(
+    model,
+    X,
+    features=["age", "sex", ("pclass", "sex")],
+    categorical_features=categorical_features,
+    ax=ax,
+)
+
 # %%
 # Faster parser in :func:`~datasets.fetch_openml`
 # -----------------------------------------------
 # :func:`~datasets.fetch_openml` now supports a new `"pandas"` parser that is
 # more memory and CPU efficient. In v1.4, the default will change to
 # `parser="auto"` which will automatically use the `"pandas"` parser for dense
 # data and `"liac-arff"` for sparse data.
-from sklearn.datasets import fetch_openml
-
 X, y = fetch_openml(
     "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
 )

1.2/_downloads/138e7c706c17949c3098ff8074b03ce7/plot_release_highlights_1_2_0.py

@@ -90,6 +90,46 @@
         "from sklearn.model_selection import LearningCurveDisplay\n\n_ = LearningCurveDisplay.from_estimator(\n    hist_no_interact, X, y, cv=5, n_jobs=2, train_sizes=np.linspace(0.1, 1, 5)\n)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ":class:`~inspection.PartialDependenceDisplay` exposes a new parameter\n`categorical_features` to display partial dependence for categorical features\nusing bar plots and heatmaps.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.datasets import fetch_openml\n\nX, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nX = X.select_dtypes([\"number\", \"category\"]).drop(columns=[\"body\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.pipeline import make_pipeline\n\ncategorical_features = [\"pclass\", \"sex\", \"embarked\"]\nmodel = make_pipeline(\n    ColumnTransformer(\n        transformers=[(\"cat\", OrdinalEncoder(), categorical_features)],\n        remainder=\"passthrough\",\n    ),\n    HistGradientBoostingRegressor(random_state=0),\n).fit(X, y)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.inspection import PartialDependenceDisplay\n\nfig, ax = plt.subplots(figsize=(14, 4), constrained_layout=True)\n_ = PartialDependenceDisplay.from_estimator(\n    model,\n    X,\n    features=[\"age\", \"sex\", (\"pclass\", \"sex\")],\n    categorical_features=categorical_features,\n    ax=ax,\n)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -105,7 +145,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import fetch_openml\n\nX, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nX.head()"
+        "X, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nX.head()"
       ]
     },
     {