.. 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 =================================== This example illustrates how to apply different preprocessing and feature extraction pipelines to different subsets of features, using :class:`sklearn.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:`sklearn.pipeline.Pipeline`, together with a simple classification model. .. 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'] 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. .. code-block:: default numeric_features = ['age', 'fare'] numeric_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) categorical_features = ['embarked', 'sex', 'pclass'] categorical_transformer = Pipeline(steps=[ ('imputer', SimpleImputer(strategy='constant', fill_value='missing')), ('onehot', 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) 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 HTML representation of ``Pipeline`` ############################################################################## When the ``Pipeline`` is printed out in a jupyter notebook an HTML representation of the estimator is displayed as follows: .. code-block:: default from sklearn import set_config set_config(display='diagram') clf .. only:: builder_html .. raw:: html 
Pipeline(steps=[('preprocessor',
                         ColumnTransformer(transformers=[('num',
                                                          Pipeline(steps=[('imputer',
                                                                           SimpleImputer(strategy='median')),
                                                                          ('scaler',
                                                                           StandardScaler())]),
                                                          ['age', 'fare']),
                                                         ('cat',
                                                          Pipeline(steps=[('imputer',
                                                                           SimpleImputer(fill_value='missing',
                                                                                         strategy='constant')),
                                                                          ('onehot',
                                                                           OneHotEncoder(handle_unknown='ignore'))]),
                                                          ['embarked', 'sex',
                                                           'pclass'])])),
                        ('classifier', LogisticRegression())])
ColumnTransformer(transformers=[('num',
                                         Pipeline(steps=[('imputer',
                                                          SimpleImputer(strategy='median')),
                                                         ('scaler', StandardScaler())]),
                                         ['age', 'fare']),
                                        ('cat',
                                         Pipeline(steps=[('imputer',
                                                          SimpleImputer(fill_value='missing',
                                                                        strategy='constant')),
                                                         ('onehot',
                                                          OneHotEncoder(handle_unknown='ignore'))]),
                                         ['embarked', 'sex', 'pclass'])])
['age', 'fare']
SimpleImputer(strategy='median')
StandardScaler()
['embarked', 'sex', 'pclass']
SimpleImputer(fill_value='missing', strategy='constant')
OneHotEncoder(handle_unknown='ignore')
LogisticRegression()


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. .. code-block:: default subset_feature = ['embarked', 'sex', 'pclass', 'age', 'fare'] X = X[subset_feature] Then, we introspect the information regarding each column data type. .. code-block:: default X.info() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none RangeIndex: 1309 entries, 0 to 1308 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 embarked 1307 non-null category 1 sex 1309 non-null category 2 pclass 1309 non-null float64 3 age 1046 non-null float64 4 fare 1308 non-null float64 dtypes: category(2), float64(3) memory usage: 33.6 KB 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``. .. 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 `_. .. 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")) ]) # Reproduce the identical fit/score process X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) 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 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`. .. 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.fit(X_train, y_train) 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 2.749 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:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/compose/plot_column_transformer_mixed_types.ipynb :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 `_