package sklearn

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package sklearn
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type tag = [
  1. | `MaxAbsScaler
]
type t = [ `BaseEstimator | `MaxAbsScaler | `Object | `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 : ?copy:bool -> unit -> t

Scale each feature by its maximum absolute value.

This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.

This scaler can also be applied to sparse CSR or CSC matrices.

.. versionadded:: 0.17

Parameters ---------- copy : boolean, optional, default is True Set to False to perform inplace scaling and avoid a copy (if the input is already a numpy array).

Attributes ---------- scale_ : ndarray, shape (n_features,) Per feature relative scaling of the data.

.. versionadded:: 0.17 *scale_* attribute.

max_abs_ : ndarray, shape (n_features,) Per feature maximum absolute value.

n_samples_seen_ : int The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across ``partial_fit`` calls.

Examples -------- >>> from sklearn.preprocessing import MaxAbsScaler >>> X = [ 1., -1., 2.], ... [ 2., 0., 0.], ... [ 0., 1., -1.] >>> transformer = MaxAbsScaler().fit(X) >>> transformer MaxAbsScaler() >>> transformer.transform(X) array([ 0.5, -1. , 1. ], [ 1. , 0. , 0. ], [ 0. , 1. , -0.5])

See also -------- maxabs_scale: Equivalent function without the estimator API.

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 maximum absolute value to be used for later scaling.

Parameters ---------- X : array-like, sparse matrix, shape n_samples, n_features The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.

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 -> Py.Object.t

Scale back the data to the original representation

Parameters ---------- X : array-like, sparse matrix The data that should be transformed back.

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

Online computation of max absolute value of X for later scaling.

All of X is processed as a single batch. This is intended for cases when :meth:`fit` is not feasible due to very large number of `n_samples` or because X is read from a continuous stream.

Parameters ---------- X : array-like, sparse matrix, shape n_samples, n_features The data used to compute the mean and standard deviation used for later scaling along the features axis.

y : None Ignored.

Returns ------- self : object Transformer instance.

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

Scale the data

Parameters ---------- X : array-like, sparse matrix The data that should be scaled.

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

Attribute scale_: get value or raise Not_found if None.

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

Attribute scale_: get value as an option.

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

Attribute max_abs_: get value or raise Not_found if None.

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

Attribute max_abs_: get value as an option.

val n_samples_seen_ : t -> int

Attribute n_samples_seen_: get value or raise Not_found if None.

val n_samples_seen_opt : t -> int option

Attribute n_samples_seen_: 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.