package sklearn

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package sklearn

sklearn
- Sklearn

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Class type

type tag = [

| `PredefinedSplit

]

type t = [ `BaseCrossValidator | `Object | `PredefinedSplit ] Obj.t

val of_pyobject : Py.Object.t -> t

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

val as_cross_validator : t -> [ `BaseCrossValidator ] Obj.t

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

Predefined split cross-validator

Provides train/test indices to split data into train/test sets using a predefined scheme specified by the user with the ``test_fold`` parameter.

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

.. versionadded:: 0.16

Parameters ---------- test_fold : array-like, shape (n_samples,) The entry ``test_foldi`` represents the index of the test set that sample ``i`` belongs to. It is possible to exclude sample ``i`` from any test set (i.e. include sample ``i`` in every training set) by setting ``test_foldi`` equal to -1.

Examples -------- >>> import numpy as np >>> from sklearn.model_selection import PredefinedSplit >>> X = np.array([1, 2], [3, 4], [1, 2], [3, 4]) >>> y = np.array(0, 0, 1, 1) >>> test_fold = 0, 1, -1, 1 >>> ps = PredefinedSplit(test_fold) >>> ps.get_n_splits() 2 >>> print(ps) PredefinedSplit(test_fold=array( 0, 1, -1, 1)) >>> for train_index, test_index in ps.split(): ... print('TRAIN:', train_index, 'TEST:', test_index) ... X_train, X_test = Xtrain_index, Xtest_index ... y_train, y_test = ytrain_index, ytest_index TRAIN: 1 2 3 TEST: 0 TRAIN: 0 2 TEST: 1 3

val get_n_splits : 
  ?x:Py.Object.t ->
  ?y:Py.Object.t ->
  ?groups:Py.Object.t ->
  [> tag ] Obj.t ->
  int

Returns the number of splitting iterations in the cross-validator

Parameters ---------- X : object Always ignored, exists for compatibility.

y : object Always ignored, exists for compatibility.

groups : object Always ignored, exists for compatibility.

Returns ------- n_splits : int Returns the number of splitting iterations in the cross-validator.

val split : 
  ?x:Py.Object.t ->
  ?y:Py.Object.t ->
  ?groups:Py.Object.t ->
  [> tag ] Obj.t ->
  ([> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t) Seq.t

Generate indices to split data into training and test set.

Parameters ---------- X : object Always ignored, exists for compatibility.

y : object Always ignored, exists for compatibility.

groups : object Always ignored, exists for compatibility.

Yields ------ train : ndarray The training set indices for that split.

test : ndarray The testing set indices for that split.

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 : Format.formatter -> t -> unit

Pretty-print the object to a formatter.