.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/compose/plot_column_transformer_mixed_types.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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_compose_plot_column_transformer_mixed_types.py: =================================== Column Transformer with Mixed Types =================================== .. currentmodule:: sklearn This example illustrates how to apply different preprocessing and feature extraction pipelines to different subsets of features, using :class:`~compose.ColumnTransformer`. This is particularly handy for the case of datasets that contain heterogeneous data types, since we may want to scale the numeric features and one-hot encode the categorical ones. In this example, the numeric data is standard-scaled after mean-imputation, while the categorical data is one-hot encoded after imputing missing values with a new category (``'missing'``). In addition, we show two different ways to dispatch the columns to the particular pre-processor: by column names and by column data types. Finally, the preprocessing pipeline is integrated in a full prediction pipeline using :class:`~pipeline.Pipeline`, together with a simple classification model. .. GENERATED FROM PYTHON SOURCE LINES 26-50 .. code-block:: default # Author: Pedro Morales # # License: BSD 3 clause import numpy as np from sklearn.compose import ColumnTransformer from sklearn.datasets import fetch_openml from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split, GridSearchCV np.random.seed(0) # Load data from https://www.openml.org/d/40945 X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True) # Alternatively X and y can be obtained directly from the frame attribute: # X = titanic.frame.drop('survived', axis=1) # y = titanic.frame['survived'] .. GENERATED FROM PYTHON SOURCE LINES 51-69 Use ``ColumnTransformer`` by selecting column by names ############################################################################## We will train our classifier with the following features: Numeric Features: * ``age``: float; * ``fare``: float. Categorical Features: * ``embarked``: categories encoded as strings ``{'C', 'S', 'Q'}``; * ``sex``: categories encoded as strings ``{'female', 'male'}``; * ``pclass``: ordinal integers ``{1, 2, 3}``. We create the preprocessing pipelines for both numeric and categorical data. Note that ``pclass`` could either be treated as a categorical or numeric feature. .. GENERATED FROM PYTHON SOURCE LINES 69-96 .. code-block:: default numeric_features = ["age", "fare"] numeric_transformer = Pipeline( steps=[("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())] ) categorical_features = ["embarked", "sex", "pclass"] categorical_transformer = OneHotEncoder(handle_unknown="ignore") preprocessor = ColumnTransformer( transformers=[ ("num", numeric_transformer, numeric_features), ("cat", categorical_transformer, categorical_features), ] ) # Append classifier to preprocessing pipeline. # Now we have a full prediction pipeline. clf = Pipeline( steps=[("preprocessor", preprocessor), ("classifier", LogisticRegression())] ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) clf.fit(X_train, y_train) print("model score: %.3f" % clf.score(X_test, y_test)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none model score: 0.790 .. GENERATED FROM PYTHON SOURCE LINES 97-101 HTML representation of ``Pipeline`` (display diagram) ############################################################################## When the ``Pipeline`` is printed out in a jupyter notebook an HTML representation of the estimator is displayed as follows: .. GENERATED FROM PYTHON SOURCE LINES 101-106 .. code-block:: default from sklearn import set_config set_config(display="diagram") clf .. raw:: html 
Pipeline(steps=[('preprocessor',
                     ColumnTransformer(transformers=[('num',
                                                      Pipeline(steps=[('imputer',
                                                                       SimpleImputer(strategy='median')),
                                                                      ('scaler',
                                                                       StandardScaler())]),
                                                      ['age', 'fare']),
                                                     ('cat',
                                                      OneHotEncoder(handle_unknown='ignore'),
                                                      ['embarked', 'sex',
                                                       'pclass'])])),
                    ('classifier', LogisticRegression())])
Please rerun this cell to show the HTML repr or trust the notebook.


.. GENERATED FROM PYTHON SOURCE LINES 107-115 Use ``ColumnTransformer`` by selecting column by data types ############################################################################## When dealing with a cleaned dataset, the preprocessing can be automatic by using the data types of the column to decide whether to treat a column as a numerical or categorical feature. :func:`sklearn.compose.make_column_selector` gives this possibility. First, let's only select a subset of columns to simplify our example. .. GENERATED FROM PYTHON SOURCE LINES 115-119 .. code-block:: default subset_feature = ["embarked", "sex", "pclass", "age", "fare"] X_train, X_test = X_train[subset_feature], X_test[subset_feature] .. GENERATED FROM PYTHON SOURCE LINES 120-121 Then, we introspect the information regarding each column data type. .. GENERATED FROM PYTHON SOURCE LINES 121-124 .. code-block:: default X_train.info() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Int64Index: 1047 entries, 1118 to 684 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 embarked 1045 non-null category 1 sex 1047 non-null category 2 pclass 1047 non-null float64 3 age 841 non-null float64 4 fare 1046 non-null float64 dtypes: category(2), float64(3) memory usage: 35.0 KB .. GENERATED FROM PYTHON SOURCE LINES 125-130 We can observe that the `embarked` and `sex` columns were tagged as `category` columns when loading the data with ``fetch_openml``. Therefore, we can use this information to dispatch the categorical columns to the ``categorical_transformer`` and the remaining columns to the ``numerical_transformer``. .. GENERATED FROM PYTHON SOURCE LINES 132-137 .. note:: In practice, you will have to handle yourself the column data type. If you want some columns to be considered as `category`, you will have to convert them into categorical columns. If you are using pandas, you can refer to their documentation regarding `Categorical data `_. .. GENERATED FROM PYTHON SOURCE LINES 137-154 .. code-block:: default from sklearn.compose import make_column_selector as selector preprocessor = ColumnTransformer( transformers=[ ("num", numeric_transformer, selector(dtype_exclude="category")), ("cat", categorical_transformer, selector(dtype_include="category")), ] ) clf = Pipeline( steps=[("preprocessor", preprocessor), ("classifier", LogisticRegression())] ) clf.fit(X_train, y_train) print("model score: %.3f" % clf.score(X_test, y_test)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none model score: 0.794 .. GENERATED FROM PYTHON SOURCE LINES 155-158 The resulting score is not exactly the same as the one from the previous pipeline because the dtype-based selector treats the ``pclass`` column as a numeric feature instead of a categorical feature as previously: .. GENERATED FROM PYTHON SOURCE LINES 158-161 .. code-block:: default selector(dtype_exclude="category")(X_train) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ['pclass', 'age', 'fare'] .. GENERATED FROM PYTHON SOURCE LINES 162-165 .. code-block:: default selector(dtype_include="category")(X_train) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ['embarked', 'sex'] .. GENERATED FROM PYTHON SOURCE LINES 166-174 Using the prediction pipeline in a grid search ############################################################################# Grid search can also be performed on the different preprocessing steps defined in the ``ColumnTransformer`` object, together with the classifier's hyperparameters as part of the ``Pipeline``. We will search for both the imputer strategy of the numeric preprocessing and the regularization parameter of the logistic regression using :class:`~sklearn.model_selection.GridSearchCV`. .. GENERATED FROM PYTHON SOURCE LINES 174-183 .. code-block:: default param_grid = { "preprocessor__num__imputer__strategy": ["mean", "median"], "classifier__C": [0.1, 1.0, 10, 100], } grid_search = GridSearchCV(clf, param_grid, cv=10) grid_search .. raw:: html 
GridSearchCV(cv=10,
                 estimator=Pipeline(steps=[('preprocessor',
                                            ColumnTransformer(transformers=[('num',
                                                                             Pipeline(steps=[('imputer',
                                                                                              SimpleImputer(strategy='median')),
                                                                                             ('scaler',
                                                                                              StandardScaler())]),
                                                                             <sklearn.compose._column_transformer.make_column_selector object at 0x7effdfa8f1c0>),
                                                                            ('cat',
                                                                             OneHotEncoder(handle_unknown='ignore'),
                                                                             <sklearn.compose._column_transformer.make_column_selector object at 0x7effdfa8f9a0>)])),
                                           ('classifier', LogisticRegression())]),
                 param_grid={'classifier__C': [0.1, 1.0, 10, 100],
                             'preprocessor__num__imputer__strategy': ['mean',
                                                                      'median']})
Please rerun this cell to show the HTML repr or trust the notebook.


.. GENERATED FROM PYTHON SOURCE LINES 184-187 Calling 'fit' triggers the cross-validated search for the best hyper-parameters combination: .. GENERATED FROM PYTHON SOURCE LINES 187-192 .. code-block:: default grid_search.fit(X_train, y_train) print("Best params:") print(grid_search.best_params_) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Best params: {'classifier__C': 0.1, 'preprocessor__num__imputer__strategy': 'mean'} .. GENERATED FROM PYTHON SOURCE LINES 193-194 The internal cross-validation scores obtained by those parameters is: .. GENERATED FROM PYTHON SOURCE LINES 194-196 .. code-block:: default print(f"Internal CV score: {grid_search.best_score_:.3f}") .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Internal CV score: 0.784 .. GENERATED FROM PYTHON SOURCE LINES 197-198 We can also introspect the top grid search results as a pandas dataframe: .. GENERATED FROM PYTHON SOURCE LINES 198-211 .. code-block:: default import pandas as pd cv_results = pd.DataFrame(grid_search.cv_results_) cv_results = cv_results.sort_values("mean_test_score", ascending=False) cv_results[ [ "mean_test_score", "std_test_score", "param_preprocessor__num__imputer__strategy", "param_classifier__C", ] ].head(5) .. raw:: html
mean_test_score std_test_score param_preprocessor__num__imputer__strategy param_classifier__C
0 0.784167 0.035824 mean 0.1
2 0.780366 0.032722 mean 1.0
1 0.780348 0.037245 median 0.1
4 0.779414 0.033105 mean 10
6 0.779414 0.033105 mean 100


.. GENERATED FROM PYTHON SOURCE LINES 212-216 The best hyper-parameters have be used to re-fit a final model on the full training set. We can evaluate that final model on held out test data that was not used for hyperparameter tuning. .. GENERATED FROM PYTHON SOURCE LINES 216-222 .. code-block:: default print( ( "best logistic regression from grid search: %.3f" % grid_search.score(X_test, y_test) ) ) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none best logistic regression from grid search: 0.794 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.622 seconds) .. _sphx_glr_download_auto_examples_compose_plot_column_transformer_mixed_types.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.0.X?urlpath=lab/tree/notebooks/auto_examples/compose/plot_column_transformer_mixed_types.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_column_transformer_mixed_types.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_column_transformer_mixed_types.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_