sklearn.impute.MissingIndicator

class sklearn.impute.MissingIndicator(missing_values=nan, features=’missing-only’, sparse=’auto’, error_on_new=True) [source]

Binary indicators for missing values.

Parameters:

missing_values : number, string, np.nan (default) or None The placeholder for the missing values. All occurrences of missing_values will be imputed. features : str, optional Whether the imputer mask should represent all or a subset of features. If “missing-only” (default), the imputer mask will only represent features containing missing values during fit time. If “all”, the imputer mask will represent all features. sparse : boolean or “auto”, optional Whether the imputer mask format should be sparse or dense. If “auto” (default), the imputer mask will be of same type as input. If True, the imputer mask will be a sparse matrix. If False, the imputer mask will be a numpy array. error_on_new : boolean, optional 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(error_on_new=True, features='missing-only',
         missing_values=nan, sparse='auto')
>>> X2_tr = indicator.transform(X2)
>>> X2_tr
array([[False,  True],
       [ True, False],
       [False, False]])

Methods

fit(X[, y])	Fit the transformer on X.
fit_transform(X[, y])	Generate missing values indicator for X.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Generate missing values indicator for X.

__init__(missing_values=nan, features=’missing-only’, sparse=’auto’, error_on_new=True) [source]

fit(X, y=None) [source]

Fit 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.

fit_transform(X, y=None) [source]

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) The missing indicator for input data. The data type of Xt will be boolean.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional 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.

set_params(**params) [source]

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.

Returns:	self

transform(X) [source]

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) The missing indicator for input data. The data type of Xt will be boolean.

Examples using sklearn.impute.MissingIndicator

Imputing missing values before building an estimator