Pushing the docs to dev/ for branch: main, commit e4a7edc4aec597cba8b2bbce704772c7872e55f8

Author: sklearn-ci

dev/.buildinfo

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 # 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: b9f175218537e76fd189e9201b1a1414
+config: c09677a24a3c9c3d4e9b637554da2b91
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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+"""
+===========================================
+Displaying estimators and complex pipelines
+===========================================
+
+This example illustrates different ways estimators and pipelines can be
+displayed.
+"""
+
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.impute import SimpleImputer
+from sklearn.compose import make_column_transformer
+from sklearn.linear_model import LogisticRegression
+
+
+# %%
+# Compact text representation
+# ---------------------------
+#
+# Estimators will only show the parameters that have been set to non-default
+# values when displayed as a string. This reduces the visual noise and makes it
+# easier to spot what the differences are when comparing instances.
+
+lr = LogisticRegression(penalty="l1")
+print(lr)
+
+# %%
+# Rich HTML representation
+# ------------------------
+# In notebooks estimators and pipelines will use a rich HTML representation.
+# This is particularly useful to summarise the
+# structure of pipelines and other composite estimators, with interactivity to
+# provide detail.  Click on the example image below to expand Pipeline
+# elements.  See :ref:`visualizing_composite_estimators` for how you can use
+# this feature.
+
+num_proc = make_pipeline(SimpleImputer(strategy="median"), StandardScaler())
+
+cat_proc = make_pipeline(
+    SimpleImputer(strategy="constant", fill_value="missing"),
+    OneHotEncoder(handle_unknown="ignore"),
+)
+
+preprocessor = make_column_transformer(
+    (num_proc, ("feat1", "feat3")), (cat_proc, ("feat0", "feat2"))
+)
+
+clf = make_pipeline(preprocessor, LogisticRegression())
+clf

dev/_downloads/3d3a6f50107423c5ea56fcd4a7153bf7/plot_estimator_representation.py

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Displaying estimators and complex pipelines\n\nThis example illustrates different ways estimators and pipelines can be\ndisplayed.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.linear_model import LogisticRegression"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compact text representation\n\nEstimators will only show the parameters that have been set to non-default\nvalues when displayed as a string. This reduces the visual noise and makes it\neasier to spot what the differences are when comparing instances.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "lr = LogisticRegression(penalty=\"l1\")\nprint(lr)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Rich HTML representation\nIn notebooks estimators and pipelines will use a rich HTML representation.\nThis is particularly useful to summarise the\nstructure of pipelines and other composite estimators, with interactivity to\nprovide detail.  Click on the example image below to expand Pipeline\nelements.  See `visualizing_composite_estimators` for how you can use\nthis feature.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "num_proc = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\n\ncat_proc = make_pipeline(\n    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n    OneHotEncoder(handle_unknown=\"ignore\"),\n)\n\npreprocessor = make_column_transformer(\n    (num_proc, (\"feat1\", \"feat3\")), (cat_proc, (\"feat0\", \"feat2\"))\n)\n\nclf = make_pipeline(preprocessor, LogisticRegression())\nclf"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}