class pyts.preprocessing.QuantileTransformer(n_quantiles=1000, output_distribution='uniform', subsample=100000, random_state=None)[source]

Transform samples using quantiles information.

This method transforms the samples to follow a uniform or a normal distribution. Therefore, for a given sample, 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 sample independently.

The cumulative distribution function of a feature is used to project the original values. Note that this transform is non-linear.

n_quantiles : int, optional (default = 1000)

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

output_distribution : ‘uniform’ or ‘normal’ (default = ‘uniform’)

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

subsample : int, optional (default = 1e5)

Maximum number of timestamps used to estimate the quantiles for computational efficiency.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Note that this is used by subsampling and smoothing noise.


>>> from pyts.datasets import load_gunpoint
>>> from pyts.preprocessing import QuantileTransformer
>>> X, _, _, _  = load_gunpoint(return_X_y=True)
>>> qt = QuantileTransformer(n_quantiles=10)
>>> qt.transform(X) # doctest: +ELLIPSIS


__init__(self[, n_quantiles, …]) Initialize self.
fit(self[, X, y]) Pass.
fit_transform(self, X[, y]) Fit to data, then transform it.
get_params(self[, deep]) Get parameters for this estimator.
set_params(self, \*\*params) Set the parameters of this estimator.
transform(self, X) Transform the data.
__init__(self, n_quantiles=1000, output_distribution='uniform', subsample=100000, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(self, X=None, y=None)[source]






self : object
fit_transform(self, 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.

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

**fit_params : dict

Additional fit parameters.

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)

Get parameters for this estimator.

deep : bool, default=True

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

params : mapping of string to any

Parameter names mapped to their values.

set_params(self, **params)

Set the parameters of this estimator.

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

**params : dict

Estimator parameters.

self : object

Estimator instance.

transform(self, X)[source]

Transform the data.

X : array-like, shape = (n_samples, n_timestamps)

Data to transform.

X_new : array-like, shape = (n_samples, n_timestamps)

Transformed data.

Examples using pyts.preprocessing.QuantileTransformer