QuantileTransformer#

class cuml.preprocessing.QuantileTransformer(*args, **kwargs)[source]#

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.

Parameters:

n_quantilesint, optional (default=1000 or n_samples): Number of quantiles to be computed. It corresponds to the number of landmarks used to discretize the cumulative distribution function. If n_quantiles is larger than the number of samples, n_quantiles is set to the number of samples as a larger number of quantiles does not give a better approximation of the cumulative distribution function estimator.
output_distributionstr, optional (default=’uniform’): Marginal distribution for the transformed data. The choices are ‘uniform’ (default) or ‘normal’.
ignore_implicit_zerosbool, optional (default=False): Only applies to sparse matrices. If True, the sparse entries of the matrix are discarded to compute the quantile statistics. If False, these entries are treated as zeros.
subsampleint, optional (default=1e5): 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.
random_stateint, RandomState instance or None, optional (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
copyboolean, optional, (default=True): Set to False to perform inplace transformation and avoid a copy (if the input is already a numpy array).

Attributes:

n_quantiles_integer: The actual number of quantiles used to discretize the cumulative distribution function.
quantiles_ndarray, shape (n_quantiles, n_features): The values corresponding the quantiles of reference.
references_ndarray, shape(n_quantiles, ): Quantiles of references.

Methods

`fit`(X[, y])	Compute the quantiles used for transforming.
`inverse_transform`(X)	Back-projection to the original space.
`transform`(X)	Feature-wise transformation of the data.

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 cupy as cp
>>> from cuml.preprocessing import QuantileTransformer
>>> rng = cp.random.RandomState(0)
>>> X = cp.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, ) → QuantileTransformer[source]#

Compute the quantiles used for transforming.

Parameters:

Xndarray or sparse matrix, 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:

selfobject

fit_transform(X, y=None, **fit_params)[source]#

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:

X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
yndarray of shape (n_samples,), default=None: Target values.
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_feature_names_out(input_features=None)[source]#

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_params(deep=True)[source]#: Returns a dict of all params owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is there in this method, then it doesn’t have to override this method

inverse_transform(X)[source]#

Back-projection to the original space.

Parameters:

Xndarray or sparse matrix, 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:

Xtndarray or sparse matrix, shape (n_samples, n_features): The projected data.

set_params(**params)[source]#: Accepts a dict of params and updates the corresponding ones owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is, there in this method, then it doesn’t have to override this method

transform(X)[source]#

Feature-wise transformation of the data.

Parameters:

Xndarray or sparse matrix, 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:

Xtndarray or sparse matrix, shape (n_samples, n_features): The projected data.