val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?missing_values:[ `S of string | `None | `PyObject of Py.Object.t ] ->
?features:string ->
?sparse:[ `Bool of bool | `Auto ] ->
?error_on_new:bool ->
unit ->
tBinary indicators for missing values.
Note that this component typically should not be used in a vanilla :class:`Pipeline` consisting of transformers and a classifier, but rather could be added using a :class:`FeatureUnion` or :class:`ColumnTransformer`.
Read more in the :ref:`User Guide <impute>`.
Parameters ---------- missing_values : number, string, np.nan (default) or None The placeholder for the missing values. All occurrences of `missing_values` will be indicated (True in the output array), the other values will be marked as False.
features : str, default=None Whether the imputer mask should represent all or a subset of features.
sparse : boolean or "auto", default=None Whether the imputer mask format should be sparse or dense.
error_on_new : boolean, default=None If True (default), transform will raise an error when there are features with missing values in transform that have no missing values in fit. This is applicable only when ``features="missing-only"``.
Attributes ---------- features_ : ndarray, shape (n_missing_features,) or (n_features,) The features indices which will be returned when calling ``transform``. They are computed during ``fit``. For ``features='all'``, it is to ``range(n_features)``.
Examples -------- >>> import numpy as np >>> from sklearn.impute import MissingIndicator >>> X1 = np.array([np.nan, 1, 3],
... [4, 0, np.nan],
... [8, 1, 0]) >>> X2 = np.array([5, 1, np.nan],
... [np.nan, 2, 3],
... [2, 4, 0]) >>> indicator = MissingIndicator() >>> indicator.fit(X1) MissingIndicator() >>> X2_tr = indicator.transform(X2) >>> X2_tr array([False, True],
[ True, False],
[False, False])
val fit : ?y:Py.Object.t -> x:Arr.t -> t -> tFit the transformer on X.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) Input data, where ``n_samples`` is the number of samples and ``n_features`` is the number of features.
Returns ------- self : object Returns self.
val fit_transform : ?y:Py.Object.t -> x:Arr.t -> t -> Arr.tGenerate missing values indicator for X.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input data to complete.
Returns ------- Xt : ndarray or sparse matrix, shape (n_samples, n_features) or (n_samples, n_features_with_missing) The missing indicator for input data. The data type of ``Xt`` will be boolean.
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 set_params : ?params:(string * Py.Object.t) list -> 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.
Generate missing values indicator for X.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input data to complete.
Returns ------- Xt : ndarray or sparse matrix, shape (n_samples, n_features) or (n_samples, n_features_with_missing) The missing indicator for input data. The data type of ``Xt`` will be boolean.
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.