Note

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Introducing the set_output API#

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.

fromsklearn.datasetsimport load_iris
fromsklearn.model_selectionimport train_test_split
X, y = load_iris (as_frame=True, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split (X, y, stratify=y, random_state=0)
X_train.head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
60 5.0 2.0 3.5 1.0
1 4.9 3.0 1.4 0.2
8 4.4 2.9 1.4 0.2
93 5.0 2.3 3.3 1.0
106 4.9 2.5 4.5 1.7


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

fromsklearn.preprocessingimport StandardScaler
scaler = StandardScaler ().set_output(transform="pandas")
scaler.fit(X_train)
X_test_scaled = scaler.transform(X_test)
X_test_scaled.head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
39 -0.894264 0.798301 -1.271411 -1.327605
12 -1.244466 -0.086944 -1.327407 -1.459074
48 -0.660797 1.462234 -1.271411 -1.327605
23 -0.894264 0.576989 -1.159419 -0.933197
81 -0.427329 -1.414810 -0.039497 -0.275851


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.

fromsklearn.feature_selectionimport SelectPercentile
fromsklearn.linear_modelimport LogisticRegression
fromsklearn.pipelineimport make_pipeline
clf = make_pipeline (
 StandardScaler (), SelectPercentile (percentile=75), LogisticRegression ()
)
clf.set_output(transform="pandas")
clf.fit(X_train, y_train)

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 with compose.ColumnTransformer and heterogeneous data.

fromsklearn.datasetsimport fetch_openml
X, y = fetch_openml ("titanic", version=1, as_frame=True, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split (X, y, stratify=y)

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

fromsklearnimport set_config
fromsklearn.composeimport ColumnTransformer
fromsklearn.imputeimport SimpleImputer
fromsklearn.preprocessingimport 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)

0.7621951219512195

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

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

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

set_config (transform_output="default")

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.

fromsklearnimport config_context
scaler = StandardScaler ()
scaler.fit(X_train[num_cols])



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()
age fare
391 -0.044009 -0.125325
701 -0.880239 -0.471468
591 -1.716470 -0.124794
1196 -0.044009 -0.456257
1049 -0.671182 -0.342893


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([[-0.04400864, -0.12532481],
 [-0.88023923, -0.47146783],
 [-1.71646982, -0.12479447],
 [-0.04400864, -0.45625688],
 [-0.67118158, -0.34289311]])

Total running time of the script: (0 minutes 0.143 seconds)

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