package sklearn

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sklearn
- Sklearn

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type tag = [

| `QuantileTransformer

]

type t =
  [ `BaseEstimator | `Object | `QuantileTransformer | `TransformerMixin ] Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_transformer : t -> [ `TransformerMixin ] Obj.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val create : 
  ?n_quantiles:int ->
  ?output_distribution:string ->
  ?ignore_implicit_zeros:bool ->
  ?subsample:int ->
  ?random_state:int ->
  ?copy:bool ->
  unit ->
  t

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.

Read more in the :ref:`User Guide <preprocessing_transformer>`.

.. versionadded:: 0.19

Parameters ---------- n_quantiles : int, 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_distribution : str, optional (default='uniform') Marginal distribution for the transformed data. The choices are 'uniform' (default) or 'normal'.

ignore_implicit_zeros : bool, 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.

subsample : int, 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_state : int, 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 :term:`Glossary <random_state>`

copy : boolean, 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.

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(...)

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.

For a comparison of the different scalers, transformers, and normalizers, see :ref:`examples/preprocessing/plot_all_scaling.py <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.

val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

Compute the quantiles used for transforming.

Parameters ---------- X : ndarray 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 ------- self : object

val fit_transform : 
  ?y:[> `ArrayLike ] Np.Obj.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

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)

y : ndarray of shape (n_samples,), default=None Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val inverse_transform : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Back-projection to the original space.

Returns ------- Xt : ndarray or sparse matrix, shape (n_samples, n_features) The projected data.

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

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.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val transform : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Feature-wise transformation of the data.

Returns ------- Xt : ndarray or sparse matrix, shape (n_samples, n_features) The projected data.

val n_quantiles_ : t -> int

Attribute n_quantiles_: get value or raise Not_found if None.

val n_quantiles_opt : t -> int option

Attribute n_quantiles_: get value as an option.

val quantiles_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute quantiles_: get value or raise Not_found if None.

val quantiles_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute quantiles_: get value as an option.

val references_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute references_: get value or raise Not_found if None.

val references_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute references_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.