{
  "cells": [
    {
      "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": [
        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler"
      ]
    },
    {
      "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"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.21"
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  "nbformat": 4,
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