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%matplotlib inline

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.

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# Author: Pedro Morales <[email protected]>
#
# 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. Note that pclass could either be treated as a categorical or numeric feature.

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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))

HTML representation of Pipeline

When the Pipeline is printed out in a jupyter notebook an HTML representation of the estimator is displayed as follows:

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from sklearn import set_config

set_config(display='diagram')
clf

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.

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subset_feature = ['embarked', 'sex', 'pclass', 'age', 'fare']
X_train, X_test = X_train[subset_feature], X_test[subset_feature]

Then, we introspect the information regarding each column data type.

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X_train.info()

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 `_.

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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))

The resulting score is not exactly the same as the one from the previous pipeline becase the dtype-based selector treats the pclass columns as a numeric features instead of a categorical feature as previously:

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selector(dtype_exclude="category")(X_train)
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selector(dtype_include="category")(X_train)

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.

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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

Calling 'fit' triggers the cross-validated search for the best hyper-parameters combination:

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grid_search.fit(X_train, y_train)

print(f"Best params:")
print(grid_search.best_params_)

The internal cross-validation scores obtained by those parameters is:

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print(f"Internal CV score: {grid_search.best_score_:.3f}")

We can also introspect the top grid search results as a pandas dataframe:

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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)

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 hyparameter tuning.

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print(("best logistic regression from grid search: %.3f"
       % grid_search.score(X_test, y_test)))