SimpleImputer#

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

Imputation transformer for completing missing values.

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.

strategystring, default=’mean’

The imputation strategy.

  • If “mean”, then replace missing values using the mean along each column. Can only be used with numeric data.

  • If “median”, then replace missing values using the median along each column. Can only be used with numeric data.

  • If “most_frequent”, then replace missing using the most frequent value along each column. Can be used with strings or numeric data.

  • If “constant”, then replace missing values with fill_value. Can be used with strings or numeric data.

strategy=”constant” for fixed value imputation.

fill_valuestring or numerical value, default=None

When strategy == “constant”, fill_value is used to replace all occurrences of missing_values. If left to the default, fill_value will be 0 when imputing numerical data and “missing_value” for strings or object data types.

verboseinteger, default=0

Controls the verbosity of the imputer.

copyboolean, default=True

If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if copy=False:

  • If X is not an array of floating values;

  • If X is encoded as a CSR matrix;

  • If add_indicator=True.

add_indicatorboolean, default=False

If True, a MissingIndicator transform will stack onto output of the imputer’s transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won’t appear on the missing indicator even if there are missing values at transform/test time.

Attributes:
statistics_array of shape (n_features,)

The imputation fill value for each feature. Computing statistics can result in np.nan values. During transform(), features corresponding to np.nan statistics will be discarded.

Methods

fit(X[, y])

Fit the imputer on X.

transform(X)

Impute all missing values in X.

See also

IterativeImputer

Multivariate imputation of missing values.

Notes

Columns which only contained missing values at fit() are discarded upon transform() if strategy is not “constant”.

Examples

>>> import cupy as cp
>>> from cuml.preprocessing import SimpleImputer
>>> imp_mean = SimpleImputer(missing_values=cp.nan, strategy='mean')
>>> imp_mean.fit(cp.asarray([[7, 2, 3], [4, cp.nan, 6], [10, 5, 9]]))
SimpleImputer()
>>> X = [[cp.nan, 2, 3], [4, cp.nan, 6], [10, cp.nan, 9]]
>>> print(imp_mean.transform(cp.asarray(X)))
[[ 7.   2.   3. ]
 [ 4.   3.5  6. ]
 [10.   3.5  9. ]]
fit(X, y=None) → SimpleImputer[source]#

Fit the imputer 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:
selfSimpleImputer
transform(X) → SparseCumlArray[source]#

Impute all missing values in X.

Parameters:
X{array-like, sparse matrix}, shape (n_samples, n_features)

The input data to complete.