MissingIndicator

class cuml.preprocessing.MissingIndicator(*args, **kwargs)

Binary indicators for missing values.

Note that this component typically should not be used in a vanilla Pipeline consisting of transformers and a classifier, but rather could be added using a FeatureUnion or ColumnTransformer.

Parameters:

missing_valuesnumber, string, np.nan (default) or None

The placeholder for the missing values. All occurrences of missing_values will be imputed. For pandas’ dataframes with nullable integer dtypes with missing values, missing_values should be set to np.nan, since pd.NA will be converted to np.nan.

featuresstr, default=None

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.

sparseboolean or “auto”, default=None

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_newboolean, 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).

Methods

fit(X[, y])	Fit the transformer on X.
fit_transform(X[, y])	Generate missing values indicator for X.
get_feature_names_out([input_features])	Get output feature names for transformation.
transform(X)	Generate missing values indicator for X.

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]])

fit(

X,

y=None,

) → MissingIndicator

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:

selfobject

Returns self.

fit_transform(X, y=None)

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.

get_feature_names_out(input_features=None)

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input feature names.

Returns:

feature_names_outnumpy.ndarray of str objects.

Transformed feature names.

get_params(deep=True)

Returns a dict of all params owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is there in this method, then it doesn’t have to override this method

set_params(**params)

Accepts a dict of params and updates the corresponding ones owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is, there in this method, then it doesn’t have to override this method

transform(X)

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.