MissingIndicator#

class cuml.preprocessing.MissingIndicator(*args, **kwargs)[source]#

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.
`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[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:

selfobject: Returns self.

fit_transform(X, y=None) → SparseCumlArray[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) or (n_samples, n_features_with_missing): The missing indicator for input data. The data type of Xt will be boolean.

transform(X) → SparseCumlArray[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) or (n_samples, n_features_with_missing): The missing indicator for input data. The data type of Xt will be boolean.