type t = [ `BaseEstimator | `MinMaxScaler | `Object | `TransformerMixin ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create : ?feature_range:Py.Object.t -> ?copy:bool -> unit -> tTransform features by scaling each feature to a given range.
This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.
The transformation is given by::
X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min
where min, max = feature_range.
The transformation is calculated as::
X_scaled = scale * X + min - X.min(axis=0) * scale where scale = (max - min) / (X.max(axis=0) - X.min(axis=0))
This transformation is often used as an alternative to zero mean, unit variance scaling.
Read more in the :ref:`User Guide <preprocessing_scaler>`.
Parameters ---------- feature_range : tuple (min, max), default=(0, 1) Desired range of transformed data.
copy : bool, default=True Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).
Attributes ---------- min_ : ndarray of shape (n_features,) Per feature adjustment for minimum. Equivalent to ``min - X.min(axis=0) * self.scale_``
scale_ : ndarray of shape (n_features,) Per feature relative scaling of the data. Equivalent to ``(max - min) / (X.max(axis=0) - X.min(axis=0))``
.. versionadded:: 0.17 *scale_* attribute.
data_min_ : ndarray of shape (n_features,) Per feature minimum seen in the data
.. versionadded:: 0.17 *data_min_*
data_max_ : ndarray of shape (n_features,) Per feature maximum seen in the data
.. versionadded:: 0.17 *data_max_*
data_range_ : ndarray of shape (n_features,) Per feature range ``(data_max_ - data_min_)`` seen in the data
.. versionadded:: 0.17 *data_range_*
n_samples_seen_ : int The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across ``partial_fit`` calls.
Examples -------- >>> from sklearn.preprocessing import MinMaxScaler >>> data = [-1, 2], [-0.5, 6], [0, 10], [1, 18] >>> scaler = MinMaxScaler() >>> print(scaler.fit(data)) MinMaxScaler() >>> print(scaler.data_max_) 1. 18. >>> print(scaler.transform(data)) [0. 0. ]
[0.25 0.25]
[0.5 0.5 ]
[1. 1. ] >>> print(scaler.transform([2, 2])) [1.5 0. ]
See also -------- minmax_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 -> tCompute the minimum and maximum to be used for later scaling.
Parameters ---------- X : array-like of shape (n_samples, n_features) The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.
y : None Ignored.
Returns ------- self : object Fitted scaler.
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.tFit 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 : 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.
Returns ------- X_new : numpy array of shape n_samples, n_features_new Transformed array.
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.
Undo the scaling of X according to feature_range.
Parameters ---------- X : array-like of shape (n_samples, n_features) Input data that will be transformed. It cannot be sparse.
Returns ------- Xt : array-like of shape (n_samples, n_features) Transformed data.
val partial_fit :
?y:Py.Object.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tOnline computation of min and max on 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 of 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 -> tSet 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.
Scale features of X according to feature_range.
Parameters ---------- X : array-like of shape (n_samples, n_features) Input data that will be transformed.
Returns ------- Xt : array-like of shape (n_samples, n_features) Transformed data.
Attribute data_min_: get value or raise Not_found if None.
Attribute data_max_: get value or raise Not_found if None.
Attribute data_range_: get value or raise Not_found if None.
Attribute data_range_: get value as an option.
val n_samples_seen_ : t -> intAttribute n_samples_seen_: get value or raise Not_found if None.
val n_samples_seen_opt : t -> int optionAttribute n_samples_seen_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.