.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/miscellaneous/plot_pipeline_display.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py: ================================================================= 显示流水线 ================================================================= 在 Jupyter Notebook 中显示流水线的默认配置是 `'diagram'` ，即 `set_config(display='diagram')` 。要停用 HTML 表示形式，请使用 `set_config(display='text')` 。 要在流水线的可视化中查看更详细的步骤，请点击流水线中的步骤。 .. GENERATED FROM PYTHON SOURCE LINES 12-13 显示包含预处理步骤和分类器的管道 .. GENERATED FROM PYTHON SOURCE LINES 15-16 本节构建了一个包含预处理步骤 :class:`~sklearn.preprocessing.StandardScaler` 和分类器 :class:`~sklearn.linear_model.LogisticRegression` 的 :class:`~sklearn.pipeline.Pipeline` ，并展示其可视化表示。 .. GENERATED FROM PYTHON SOURCE LINES 16-28 .. code-block:: Python from sklearn import set_config from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler steps = [ ("preprocessing", StandardScaler()), ("classifier", LogisticRegression()), ] pipe = Pipeline(steps) .. GENERATED FROM PYTHON SOURCE LINES 29-30 要可视化图表，默认设置为 `display='diagram'` 。 .. GENERATED FROM PYTHON SOURCE LINES 30-34 .. code-block:: Python set_config(display="diagram") pipe # click on the diagram below to see the details of each step .. raw:: html 
Pipeline(steps=[('preprocessing', StandardScaler()),
                    ('classifier', LogisticRegression())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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.. GENERATED FROM PYTHON SOURCE LINES 35-36 要查看文本管道，请更改为 `display='text'` 。 .. GENERATED FROM PYTHON SOURCE LINES 36-40 .. code-block:: Python set_config(display="text") pipe .. rst-class:: sphx-glr-script-out .. code-block:: none Pipeline(steps=[('preprocessing', StandardScaler()), ('classifier', LogisticRegression())]) .. GENERATED FROM PYTHON SOURCE LINES 41-42 恢复默认显示 .. GENERATED FROM PYTHON SOURCE LINES 42-44 .. code-block:: Python set_config(display="diagram") .. GENERATED FROM PYTHON SOURCE LINES 45-46 显示一个包含多个预处理步骤和分类器的管道 .. GENERATED FROM PYTHON SOURCE LINES 48-49 本节构建了一个包含多个预处理步骤的 :class:`~sklearn.pipeline.Pipeline` ，包括 :class:`~sklearn.preprocessing.PolynomialFeatures` 和 :class:`~sklearn.preprocessing.StandardScaler` ，以及一个分类器步骤 :class:`~sklearn.linear_model.LogisticRegression` ，并显示其可视化表示。 .. GENERATED FROM PYTHON SOURCE LINES 49-62 .. code-block:: Python from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline from sklearn.preprocessing import PolynomialFeatures, StandardScaler steps = [ ("standard_scaler", StandardScaler()), ("polynomial", PolynomialFeatures(degree=3)), ("classifier", LogisticRegression(C=2.0)), ] pipe = Pipeline(steps) pipe # click on the diagram below to see the details of each step .. raw:: html 
Pipeline(steps=[('standard_scaler', StandardScaler()),
                    ('polynomial', PolynomialFeatures(degree=3)),
                    ('classifier', LogisticRegression(C=2.0))])
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.. GENERATED FROM PYTHON SOURCE LINES 63-64 显示管道、降维和分类器 .. GENERATED FROM PYTHON SOURCE LINES 66-67 本节构建了一个包含降维步骤 :class:`~sklearn.decomposition.PCA` 和分类器 :class:`~sklearn.svm.SVC` 的 :class:`~sklearn.pipeline.Pipeline` ，并展示其可视化表示。 .. GENERATED FROM PYTHON SOURCE LINES 67-76 .. code-block:: Python from sklearn.decomposition import PCA from sklearn.pipeline import Pipeline from sklearn.svm import SVC steps = [("reduce_dim", PCA(n_components=4)), ("classifier", SVC(kernel="linear"))] pipe = Pipeline(steps) pipe # click on the diagram below to see the details of each step .. raw:: html 
Pipeline(steps=[('reduce_dim', PCA(n_components=4)),
                    ('classifier', SVC(kernel='linear'))])
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.. GENERATED FROM PYTHON SOURCE LINES 77-78 显示一个复杂的管道链列转换器 .. GENERATED FROM PYTHON SOURCE LINES 80-81 本节构建了一个复杂的 :class:`~sklearn.pipeline.Pipeline` ，其中包含一个 :class:`~sklearn.compose.ColumnTransformer` 和一个分类器 :class:`~sklearn.linear_model.LogisticRegression` ，并展示其可视化表示。 .. GENERATED FROM PYTHON SOURCE LINES 81-117 .. code-block:: Python import numpy as np from sklearn.compose import ColumnTransformer from sklearn.impute import SimpleImputer from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline, make_pipeline from sklearn.preprocessing import OneHotEncoder, StandardScaler numeric_preprocessor = Pipeline( steps=[ ("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")), ("scaler", StandardScaler()), ] ) categorical_preprocessor = Pipeline( steps=[ ( "imputation_constant", SimpleImputer(fill_value="missing", strategy="constant"), ), ("onehot", OneHotEncoder(handle_unknown="ignore")), ] ) preprocessor = ColumnTransformer( [ ("categorical", categorical_preprocessor, ["state", "gender"]), ("numerical", numeric_preprocessor, ["age", "weight"]), ] ) pipe = make_pipeline(preprocessor, LogisticRegression(max_iter=500)) pipe # click on the diagram below to see the details of each step .. raw:: html 
Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('categorical',
                                                      Pipeline(steps=[('imputation_constant',
                                                                       SimpleImputer(fill_value='missing',
                                                                                     strategy='constant')),
                                                                      ('onehot',
                                                                       OneHotEncoder(handle_unknown='ignore'))]),
                                                      ['state', 'gender']),
                                                     ('numerical',
                                                      Pipeline(steps=[('imputation_mean',
                                                                       SimpleImputer()),
                                                                      ('scaler',
                                                                       StandardScaler())]),
                                                      ['age', 'weight'])])),
                    ('logisticregression', LogisticRegression(max_iter=500))])
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.. GENERATED FROM PYTHON SOURCE LINES 118-121 展示一个包含分类器的管道的网格搜索 ############################################################################### 本节构建了一个包含 :class:`~sklearn.ensemble.RandomForestClassifier` 的 :class:`~sklearn.pipeline.Pipeline` 上的 :class:`~sklearn.model_selection.GridSearchCV` ，并显示其可视化表示。 .. GENERATED FROM PYTHON SOURCE LINES 121-168 .. code-block:: Python import numpy as np from sklearn.compose import ColumnTransformer from sklearn.ensemble import RandomForestClassifier from sklearn.impute import SimpleImputer from sklearn.model_selection import GridSearchCV from sklearn.pipeline import Pipeline, make_pipeline from sklearn.preprocessing import OneHotEncoder, StandardScaler numeric_preprocessor = Pipeline( steps=[ ("imputation_mean", SimpleImputer(missing_values=np.nan, strategy="mean")), ("scaler", StandardScaler()), ] ) categorical_preprocessor = Pipeline( steps=[ ( "imputation_constant", SimpleImputer(fill_value="missing", strategy="constant"), ), ("onehot", OneHotEncoder(handle_unknown="ignore")), ] ) preprocessor = ColumnTransformer( [ ("categorical", categorical_preprocessor, ["state", "gender"]), ("numerical", numeric_preprocessor, ["age", "weight"]), ] ) pipe = Pipeline( steps=[("preprocessor", preprocessor), ("classifier", RandomForestClassifier())] ) param_grid = { "classifier__n_estimators": [200, 500], "classifier__max_features": ["auto", "sqrt", "log2"], "classifier__max_depth": [4, 5, 6, 7, 8], "classifier__criterion": ["gini", "entropy"], } grid_search = GridSearchCV(pipe, param_grid=param_grid, n_jobs=1) grid_search # click on the diagram below to see the details of each step .. raw:: html 
GridSearchCV(estimator=Pipeline(steps=[('preprocessor',
                                            ColumnTransformer(transformers=[('categorical',
                                                                             Pipeline(steps=[('imputation_constant',
                                                                                              SimpleImputer(fill_value='missing',
                                                                                                            strategy='constant')),
                                                                                             ('onehot',
                                                                                              OneHotEncoder(handle_unknown='ignore'))]),
                                                                             ['state',
                                                                              'gender']),
                                                                            ('numerical',
                                                                             Pipeline(steps=[('imputation_mean',
                                                                                              SimpleImputer()),
                                                                                             ('scaler',
                                                                                              StandardScaler())]),
                                                                             ['age',
                                                                              'weight'])])),
                                           ('classifier',
                                            RandomForestClassifier())]),
                 n_jobs=1,
                 param_grid={'classifier__criterion': ['gini', 'entropy'],
                             'classifier__max_depth': [4, 5, 6, 7, 8],
                             'classifier__max_features': ['auto', 'sqrt', 'log2'],
                             'classifier__n_estimators': [200, 500]})
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.. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.045 seconds) .. _sphx_glr_download_auto_examples_miscellaneous_plot_pipeline_display.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_pipeline_display.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_pipeline_display.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_pipeline_display.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_pipeline_display.zip ` .. include:: plot_pipeline_display.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_