Pushing the docs to dev/ for branch: main, commit c1cfc4d4f36f9c00413e20d0ef85bed208a502ca

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -68,6 +68,31 @@
 )
 hist_no_interact.fit(X, y)
 
+# %%
+# New and enhanced displays
+# -------------------------
+# :class:`~metrics.PredictionErrorDisplay` provides a way to analyze regression
+# models in a qualitative manner.
+import matplotlib.pyplot as plt
+from sklearn.metrics import PredictionErrorDisplay
+
+fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
+_ = PredictionErrorDisplay.from_estimator(
+    hist_no_interact, X, y, kind="actual_vs_predicted", ax=axs[0]
+)
+_ = PredictionErrorDisplay.from_estimator(
+    hist_no_interact, X, y, kind="residual_vs_predicted", ax=axs[1]
+)
+
+# %%
+# :class:`~model_selection.LearningCurveDisplay` is now available to plot
+# results from :func:`~model_selection.learning_curve`.
+from sklearn.model_selection import LearningCurveDisplay
+
+_ = LearningCurveDisplay.from_estimator(
+    hist_no_interact, X, y, cv=5, n_jobs=2, train_sizes=np.linspace(0.1, 1, 5)
+)
+
 # %%
 # Faster parser in :func:`~datasets.fetch_openml`
 # -----------------------------------------------
@@ -95,10 +120,13 @@
 # Improved efficiency of many estimators
 # --------------------------------------
 # In version 1.1 the efficiency of many estimators relying on the computation of
-# pairwise distances was greatly improved for float64 dense input. In version 1.2,
-# the efficiency of these estimators was further improved for all combinations of
-# float32/float64 and dense/sparse input (for all metrics except euclidean). It
-# concerns essentially clustering, manifold learning and neighbor search algorithms.
-# A detailed list of the impacted estimators can be found in the
-# :ref:`changelog <changes_1_2>`. The main benefits are a reduced memory footprint
+# pairwise distances (essentially estimators related to clustering, manifold
+# learning and neighbors search algorithms) was greatly improved for float64
+# dense input. Efficiency improvement especially were a reduced memory footprint
 # and a much better scalability on multi-core machines.
+# In version 1.2, the efficiency of these estimators was further improved for all
+# combinations of dense and sparse inputs on float32 and float64 datasets, except
+# the sparse-dense and dense-sparse combinations for the Euclidean and Squared
+# Euclidean Distance metrics.
+# A detailed list of the impacted estimators can be found in the
+# :ref:`changelog <changes_1_2>`.

dev/_downloads/138e7c706c17949c3098ff8074b03ce7/plot_release_highlights_1_2_0.py

@@ -148,7 +148,7 @@
       },
       "outputs": [],
       "source": [
-        "print(\"Training interaction constraint HistGradientBoostingRegressor...\")\ntic = time()\nest_no_interactions = HistGradientBoostingRegressor(\n    interaction_cst=[[i] for i in range(X_train.shape[1])]\n)\nest_no_interactions.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")"
+        "print(\"Training interaction constraint HistGradientBoostingRegressor...\")\ntic = time()\nest_no_interactions = HistGradientBoostingRegressor(interaction_cst=\"no_interactions\")\nest_no_interactions.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")"
       ]
     },
     {

dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb

@@ -270,9 +270,7 @@
 
 print("Training interaction constraint HistGradientBoostingRegressor...")
 tic = time()
-est_no_interactions = HistGradientBoostingRegressor(
-    interaction_cst=[[i] for i in range(X_train.shape[1])]
-)
+est_no_interactions = HistGradientBoostingRegressor(interaction_cst="no_interactions")
 est_no_interactions.fit(X_train, y_train)
 print(f"done in {time() - tic:.3f}s")
 

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -54,6 +54,42 @@
         "from sklearn.datasets import load_diabetes\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nX, y = load_diabetes(return_X_y=True, as_frame=True)\n\nhist_no_interact = HistGradientBoostingRegressor(\n    interaction_cst=[[i] for i in range(X.shape[1])], random_state=0\n)\nhist_no_interact.fit(X, y)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## New and enhanced displays\n:class:`~metrics.PredictionErrorDisplay` provides a way to analyze regression\nmodels in a qualitative manner.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nfrom sklearn.metrics import PredictionErrorDisplay\n\nfig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"actual_vs_predicted\", ax=axs[0]\n)\n_ = PredictionErrorDisplay.from_estimator(\n    hist_no_interact, X, y, kind=\"residual_vs_predicted\", ax=axs[1]\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ":class:`~model_selection.LearningCurveDisplay` is now available to plot\nresults from :func:`~model_selection.learning_curve`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "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": {},
@@ -83,7 +119,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Improved efficiency of many estimators\nIn version 1.1 the efficiency of many estimators relying on the computation of\npairwise distances was greatly improved for float64 dense input. In version 1.2,\nthe efficiency of these estimators was further improved for all combinations of\nfloat32/float64 and dense/sparse input (for all metrics except euclidean). It\nconcerns essentially clustering, manifold learning and neighbor search algorithms.\nA detailed list of the impacted estimators can be found in the\n`changelog <changes_1_2>`. The main benefits are a reduced memory footprint\nand a much better scalability on multi-core machines.\n\n"
+        "## Improved efficiency of many estimators\nIn version 1.1 the efficiency of many estimators relying on the computation of\npairwise distances (essentially estimators related to clustering, manifold\nlearning and neighbors search algorithms) was greatly improved for float64\ndense input. Efficiency improvement especially were a reduced memory footprint\nand a much better scalability on multi-core machines.\nIn version 1.2, the efficiency of these estimators was further improved for all\ncombinations of dense and sparse inputs on float32 and float64 datasets, except\nthe sparse-dense and dense-sparse combinations for the Euclidean and Squared\nEuclidean Distance metrics.\nA detailed list of the impacted estimators can be found in the\n`changelog <changes_1_2>`.\n\n"
       ]
     }
   ],