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Pushing the docs to dev/ for branch: main, commit 7601f87b7a8faaf0d3bd14add9a050b127cbb865
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‎dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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‎dev/_downloads/26f110ad6cff1a8a7c58b1a00d8b8b5a/plot_column_transformer_mixed_types.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## HTML representation of ``Pipeline``\n When the ``Pipeline`` is printed out in a jupyter notebook an HTML\n representation of the estimator is displayed as follows:\n\n"
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"## HTML representation of ``Pipeline`` (display diagram)\n When the ``Pipeline`` is printed out in a jupyter notebook an HTML\n representation of the estimator is displayed as follows:\n\n"
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]
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},
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{

‎dev/_downloads/6ebaebc92484478ceb119d24fe9df21c/plot_pipeline_display.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Displaying Pipelines\n\nThe default configuration for displaying a pipeline is `'text'` where\n`set_config(display='text')`. To visualize the diagram in Jupyter Notebook,\nuse `set_config(display='diagram')` and then output the pipeline object.\n\nTo see more detailed steps in the visualization of the pipeline, click on the\nsteps in the pipeline.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Displaying a Pipeline with a Preprocessing Step and Classifier\n This section constructs a :class:`~sklearn.pipeline.Pipeline` with a preprocessing\n step, :class:`~sklearn.preprocessing.StandardScaler`, and classifier,\n :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual\n representation.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import set_config\n\nsteps = [\n (\"preprocessing\", StandardScaler()),\n (\"classifier\", LogisticRegression()),\n]\npipe = Pipeline(steps)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To view the text pipeline, the default is `display='text'`.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"set_config(display=\"text\")\npipe"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To visualize the diagram, change `display='diagram'`.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"set_config(display=\"diagram\")\npipe # click on the diagram below to see the details of each step"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Displaying a Pipeline Chaining Multiple Preprocessing Steps & Classifier\n This section constructs a :class:`~sklearn.pipeline.Pipeline` with multiple\n preprocessing steps, :class:`~sklearn.preprocessing.PolynomialFeatures` and\n :class:`~sklearn.preprocessing.StandardScaler`, and a classifer step,\n :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual\n representation.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler, PolynomialFeatures\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import set_config\n\nsteps = [\n (\"standard_scaler\", StandardScaler()),\n (\"polynomial\", PolynomialFeatures(degree=3)),\n (\"classifier\", LogisticRegression(C=2.0)),\n]\npipe = Pipeline(steps)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To visualize the diagram, change to display='diagram'\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"set_config(display=\"diagram\")\npipe # click on the diagram below to see the details of each step"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Displaying a Pipeline and Dimensionality Reduction and Classifier\n This section constructs a :class:`~sklearn.pipeline.Pipeline` with a\n dimensionality reduction step, :class:`~sklearn.decomposition.PCA`,\n a classifier, :class:`~sklearn.svm.SVC`, and displays its visual\n representation.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from sklearn.pipeline import Pipeline\nfrom sklearn.svm import SVC\nfrom sklearn.decomposition import PCA\nfrom sklearn import set_config\n\nsteps = [(\"reduce_dim\", PCA(n_components=4)), (\"classifier\", SVC(kernel=\"linear\"))]\npipe = Pipeline(steps)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To visualize the diagram, change to `display='diagram'`.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"set_config(display=\"diagram\")\npipe # click on the diagram below to see the details of each step"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Displaying a Complex Pipeline Chaining a Column Transformer\n This section constructs a complex :class:`~sklearn.pipeline.Pipeline` with a\n :class:`~sklearn.compose.ColumnTransformer` and a classifier,\n :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual\n representation.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import numpy as np\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import set_config\n\nnumeric_preprocessor = Pipeline(\n steps=[\n (\"imputation_mean\", SimpleImputer(missing_values=np.nan, strategy=\"mean\")),\n (\"scaler\", StandardScaler()),\n ]\n)\n\ncategorical_preprocessor = Pipeline(\n steps=[\n (\n \"imputation_constant\",\n SimpleImputer(fill_value=\"missing\", strategy=\"constant\"),\n ),\n (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\")),\n ]\n)\n\npreprocessor = ColumnTransformer(\n [\n (\"categorical\", categorical_preprocessor, [\"state\", \"gender\"]),\n (\"numerical\", numeric_preprocessor, [\"age\", \"weight\"]),\n ]\n)\n\npipe = make_pipeline(preprocessor, LogisticRegression(max_iter=500))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To visualize the diagram, change to `display='diagram'`\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"set_config(display=\"diagram\")\npipe # click on the diagram below to see the details of each step"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Displaying a Grid Search over a Pipeline with a Classifier\n This section constructs a :class:`~sklearn.model_selection.GridSearchCV`\n over a :class:`~sklearn.pipeline.Pipeline` with\n :class:`~sklearn.ensemble.RandomForestClassifier` and displays its visual\n representation.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import numpy as np\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn import set_config\n\nnumeric_preprocessor = Pipeline(\n steps=[\n (\"imputation_mean\", SimpleImputer(missing_values=np.nan, strategy=\"mean\")),\n (\"scaler\", StandardScaler()),\n ]\n)\n\ncategorical_preprocessor = Pipeline(\n steps=[\n (\n \"imputation_constant\",\n SimpleImputer(fill_value=\"missing\", strategy=\"constant\"),\n ),\n (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\")),\n ]\n)\n\npreprocessor = ColumnTransformer(\n [\n (\"categorical\", categorical_preprocessor, [\"state\", \"gender\"]),\n (\"numerical\", numeric_preprocessor, [\"age\", \"weight\"]),\n ]\n)\n\npipe = Pipeline(\n steps=[(\"preprocessor\", preprocessor), (\"classifier\", RandomForestClassifier())]\n)\n\nparam_grid = {\n \"classifier__n_estimators\": [200, 500],\n \"classifier__max_features\": [\"auto\", \"sqrt\", \"log2\"],\n \"classifier__max_depth\": [4, 5, 6, 7, 8],\n \"classifier__criterion\": [\"gini\", \"entropy\"],\n}\n\ngrid_search = GridSearchCV(pipe, param_grid=param_grid, n_jobs=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To visualize the diagram, change to `display='diagram'`.\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"set_config(display=\"diagram\")\ngrid_search # click on the diagram below to see the details of each step"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

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‎dev/_downloads/79c38d2f2cb1f2ef7d68e0cc7ea7b4e4/plot_column_transformer_mixed_types.py

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print("model score: %.3f" % clf.score(X_test, y_test))
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# %%
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# HTML representation of ``Pipeline``
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# HTML representation of ``Pipeline`` (display diagram)
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###############################################################################
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# When the ``Pipeline`` is printed out in a jupyter notebook an HTML
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# representation of the estimator is displayed as follows:

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