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Introducing the set_output API — scikit-learn 1.8.dev0 documentation

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This example will demonstrate the set_output API to configure transformers to output pandas DataFrames. set_output can be configured per estimator by calling the set_output method or globally by setting set_config(transform_output="pandas"). For details, see SLEP018.

First, we load the iris dataset as a DataFrame to demonstrate the set_output API.

To configure an estimator such as preprocessing.StandardScaler to return DataFrames, call set_output. This feature requires pandas to be installed.

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler().set_output(transform="pandas")

scaler.fit(X_train)
X_test_scaled = scaler.transform(X_test)
X_test_scaled.head()

set_output can be called after fit to configure transform after the fact.

scaler2 = StandardScaler()

scaler2.fit(X_train)
X_test_np = scaler2.transform(X_test)
print(f"Default output type: {type(X_test_np).__name__}")

scaler2.set_output(transform="pandas")
X_test_df = scaler2.transform(X_test)
print(f"Configured pandas output type: {type(X_test_df).__name__}")

Default output type: ndarray
Configured pandas output type: DataFrame

In a pipeline.Pipeline, set_output configures all steps to output DataFrames.

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('selectpercentile', SelectPercentile(percentile=75)),
                ('logisticregression', LogisticRegression())])

Each transformer in the pipeline is configured to return DataFrames. This means that the final logistic regression step contains the feature names of the input.

clf[-1].feature_names_in_

array(['sepal length (cm)', 'petal length (cm)', 'petal width (cm)'],
      dtype=object)

Note

If one uses the method set_params, the transformer will be replaced by a new one with the default output format.

clf.set_params(standardscaler=StandardScaler())
clf.fit(X_train, y_train)
clf[-1].feature_names_in_

array(['x0', 'x2', 'x3'], dtype=object)

To keep the intended behavior, use set_output on the new transformer beforehand

scaler = StandardScaler().set_output(transform="pandas")
clf.set_params(standardscaler=scaler)
clf.fit(X_train, y_train)
clf[-1].feature_names_in_

array(['sepal length (cm)', 'petal length (cm)', 'petal width (cm)'],
      dtype=object)

Next we load the titanic dataset to demonstrate set_output with compose.ColumnTransformer and heterogeneous data.

The set_output API can be configured globally by using set_config and setting transform_output to "pandas".

from sklearn import set_config
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler

set_config(transform_output="pandas")

num_pipe = make_pipeline(SimpleImputer(), StandardScaler())
num_cols = ["age", "fare"]
ct = ColumnTransformer(
    (
        ("numerical", num_pipe, num_cols),
        (
            "categorical",
            OneHotEncoder(
                sparse_output=False, drop="if_binary", handle_unknown="ignore"
            ),
            ["embarked", "sex", "pclass"],
        ),
    ),
    verbose_feature_names_out=False,
)
clf = make_pipeline(ct, SelectPercentile(percentile=50), LogisticRegression())
clf.fit(X_train, y_train)
clf.score(X_test, y_test)

With the global configuration, all transformers output DataFrames. This allows us to easily plot the logistic regression coefficients with the corresponding feature names.

import pandas as pd

log_reg = clf[-1]
coef = pd.Series(log_reg.coef_.ravel(), index=log_reg.feature_names_in_)
_ = coef.sort_values().plot.barh()

In order to demonstrate the config_context functionality below, let us first reset transform_output to its default value.

When configuring the output type with config_context the configuration at the time when transform or fit_transform are called is what counts. Setting these only when you construct or fit the transformer has no effect.

StandardScaler()

with config_context(transform_output="pandas"):
    # the output of transform will be a Pandas DataFrame
    X_test_scaled = scaler.transform(X_test[num_cols])
X_test_scaled.head()

outside of the context manager, the output will be a NumPy array

X_test_scaled = scaler.transform(X_test[num_cols])
X_test_scaled[:5]

array([[ 0.62830616, -0.06320955],
       [-0.05798371, -0.51570367],
       [ 1.31459603,  0.56662405],
       [-0.6756446 , -0.51227857],
       [-0.74427358, -0.49694966]])

Total running time of the script: (0 minutes 0.145 seconds)

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