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())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


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.

from sklearn.datasets import 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".

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()
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.

from sklearn import config_context

scaler = StandardScaler()
scaler.fit(X_train[num_cols])
StandardScaler()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


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]])

Total running time of the script: (0 minutes 0.148 seconds)

Related examples

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2

Displaying Pipelines

Displaying Pipelines

Column Transformer with Mixed Types

Column Transformer with Mixed Types

Release Highlights for scikit-learn 1.4

Release Highlights for scikit-learn 1.4

Gallery generated by Sphinx-Gallery