QuantileTransformer

class sklearn.preprocessing.QuantileTransformer(*, n_quantiles=1000, output_distribution='uniform', ignore_implicit_zeros=False, subsample=10000, random_state=None, copy=True)

Transform features using quantiles information.

This method transforms the features to follow a uniform or a normal distribution. Therefore, for a given feature, this transformation tends to spread out the most frequent values. It also reduces the impact of (marginal) outliers: this is therefore a robust preprocessing scheme.

The transformation is applied on each feature independently. First an estimate of the cumulative distribution function of a feature is used to map the original values to a uniform distribution. The obtained values are then mapped to the desired output distribution using the associated quantile function. Features values of new/unseen data that fall below or above the fitted range will be mapped to the bounds of the output distribution. Note that this transform is non-linear. It may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable.

For example visualizations, refer to Compare QuantileTransformer with other scalers.

Read more in the User Guide.

Added in version 0.19.

Parameters:

n_quantilesint, default=1000

Number of quantiles to be computed. It corresponds to the number of landmarks used to discretize the cumulative distribution function.

Changed in version 1.10: n_quantiles is no longer capped according to the number of samples. The number of quantiles is now always equal to the value of n_quantiles.

output_distribution{‘uniform’, ‘normal’}, default=’uniform’

Marginal distribution for the transformed data. The choices are ‘uniform’ (default) or ‘normal’.

ignore_implicit_zerosbool, default=False

Only applies to sparse matrices. If True, the sparse entries of the matrix are discarded to compute the quantile statistics, including when subsampling. If False, these entries are treated as zeros.

subsampleint or None, default=10_000

Maximum number of samples used to estimate the quantiles for computational efficiency. Note that the subsampling procedure may differ for value-identical sparse and dense matrices. Disable subsampling by setting subsample=None.

Added in version 1.5: The option None to disable subsampling was added.

random_stateint, RandomState instance or None, default=None

Determines random number generation for subsampling and smoothing noise. Please see subsample for more details. Pass an int for reproducible results across multiple function calls. See Glossary.

copybool, default=True

Set to False to perform inplace transformation and avoid a copy (if the input is already a numpy array).

Attributes:

n_quantiles_int

The number of quantiles used to discretize the cumulative distribution function. Always equal to n_quantiles.

quantiles_ndarray of shape (n_quantiles, n_features)

The values corresponding the quantiles of reference.

references_ndarray of shape (n_quantiles, )

Quantiles of references.

n_features_in_int

Number of features seen during fit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 1.0.

See also

quantile_transform

Equivalent function without the estimator API.

PowerTransformer

Perform mapping to a normal distribution using a power transform.

StandardScaler

Perform standardization that is faster, but less robust to outliers.

RobustScaler

Perform robust standardization that removes the influence of outliers but does not put outliers and inliers on the same scale.

Notes

NaNs are treated as missing values: disregarded in fit, and maintained in transform.

Examples

>>> import numpy as np
>>> from sklearn.preprocessing import QuantileTransformer
>>> rng = np.random.RandomState(0)
>>> X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0)
>>> qt = QuantileTransformer(n_quantiles=10, random_state=0)
>>> qt.fit_transform(X)
array([...])

fit(X, y=None, sample_weight=None)

Compute the quantiles used for transforming.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse CSC matrix. Additionally, the sparse matrix needs to be nonnegative if ignore_implicit_zeros is False.

yNone

Ignored.

sample_weightarray-like of shape (n_samples,), default=None

Individual weights for each sample. Sample weights are not supported for sparse inputs.

Added in version 1.10.

Returns:

selfobject

Fitted transformer.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters. Pass only if the estimator accepts additional params in its fit method.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

• If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

• If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects

Same as input features.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

inverse_transform(X)

Back-projection to the original space.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse CSC_matrix. Additionally, the sparse matrix needs to be nonnegative if ignore_implicit_zeros is False.

Returns:

X_original{ndarray, sparse matrix} of (n_samples, n_features)

The projected data.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → QuantileTransformer

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:

selfobject

The updated object.

set_output(*, transform=None)

Set output container.

Refer to the user guide for more details and Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• "polars": Polars output

• None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(X)

Feature-wise transformation of the data.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse CSC matrix. Additionally, the sparse matrix needs to be nonnegative if ignore_implicit_zeros is False.

Returns:

Xt{ndarray, sparse matrix} of shape (n_samples, n_features)

The projected data.

Gallery examples

Effect of transforming the targets in regression model

Effect of transforming the targets in regression model

Partial Dependence and Individual Conditional Expectation Plots

Partial Dependence and Individual Conditional Expectation Plots

Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers

Map data to a normal distribution

Map data to a normal distribution