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.

from sklearn.datasets import load_iris
from sklearn.model_selection import 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.

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()

	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.

from sklearn.feature_selection import SelectPercentile
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import 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)

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

from sklearn.datasets import fetch_openml

X, y = fetch_openml(
    "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
)
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".

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)

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.

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.

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.

from sklearn import config_context

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

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()

	age	fare
1088	0.151101	-0.479229
1001	NaN	-0.188153
660	-0.393297	-0.263234
657	-1.975455	-0.263234
285	2.532843	3.546068

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]])