Introducing the set_output API (original) (raw)

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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, seeSLEP018.

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())])

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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 withcompose.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()

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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([[ 0.1511007 , -0.47922861], [ nan, -0.18815268], [-0.39329747, -0.26323428], [-1.97545464, -0.26323428], [ 2.53284267, 3.54606834]])

Total running time of the script: (0 minutes 0.168 seconds)

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