sklearn.impute
.KNNImputer¶
- class sklearn.impute.KNNImputer(*, missing_values=nan, n_neighbors=5, weights='uniform', metric='nan_euclidean', copy=True, add_indicator=False)[source]¶
Imputation for completing missing values using k-Nearest Neighbors.
Each sample’s missing values are imputed using the mean value from
n_neighbors
nearest neighbors found in the training set. Two samples are close if the features that neither is missing are close.Read more in the User Guide.
New in version 0.22.
- Parameters:
- missing_valuesint, float, str, np.nan or None, default=np.nan
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, sincepd.NA
will be converted to np.nan.- n_neighborsint, default=5
Number of neighboring samples to use for imputation.
- weights{‘uniform’, ‘distance’} or callable, default=’uniform’
Weight function used in prediction. Possible values:
‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.
‘distance’ : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.
callable : a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights.
- metric{‘nan_euclidean’} or callable, default=’nan_euclidean’
Distance metric for searching neighbors. Possible values:
‘nan_euclidean’
callable : a user-defined function which conforms to the definition of
_pairwise_callable(X, Y, metric, **kwds)
. The function accepts two arrays, X and Y, and amissing_values
keyword inkwds
and returns a scalar distance value.
- copybool, default=True
If True, a copy of X will be created. If False, imputation will be done in-place whenever possible.
- add_indicatorbool, default=False
If True, a
MissingIndicator
transform will stack onto the 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:
- indicator_
MissingIndicator
Indicator used to add binary indicators for missing values.
None
if add_indicator is False.- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
- indicator_
See also
SimpleImputer
Univariate imputer for completing missing values with simple strategies.
IterativeImputer
Multivariate imputer that estimates values to impute for each feature with missing values from all the others.
References
Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing value estimation methods for DNA microarrays, BIOINFORMATICS Vol. 17 no. 6, 2001 Pages 520-525.
Examples
>>> import numpy as np >>> from sklearn.impute import KNNImputer >>> X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]] >>> imputer = KNNImputer(n_neighbors=2) >>> imputer.fit_transform(X) array([[1. , 2. , 4. ], [3. , 4. , 3. ], [5.5, 6. , 5. ], [8. , 8. , 7. ]])
Methods
fit
(X[, y])Fit the imputer on X.
fit_transform
(X[, y])Fit to data, then transform it.
get_feature_names_out
([input_features])Get output feature names for transformation.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Impute all missing values in X.
- fit(X, y=None)[source]¶
Fit the imputer on X.
- Parameters:
- Xarray-like shape of (n_samples, n_features)
Input data, where
n_samples
is the number of samples andn_features
is the number of features.- yIgnored
Not used, present here for API consistency by convention.
- Returns:
- selfobject
The fitted
KNNImputer
class instance.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- Parameters:
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns:
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_feature_names_out(input_features=None)[source]¶
Get output feature names for transformation.
- Parameters:
- input_featuresarray-like of str or None, default=None
Input features.
If
input_features
isNone
, thenfeature_names_in_
is used as feature names in. Iffeature_names_in_
is not defined, then the following input feature names are generated:["x0", "x1", ..., "x(n_features_in_ - 1)"]
.If
input_features
is an array-like, theninput_features
must matchfeature_names_in_
iffeature_names_in_
is defined.
- Returns:
- feature_names_outndarray of str objects
Transformed feature names.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
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
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- transform(X)[source]¶
Impute all missing values in X.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
The input data to complete.
- Returns:
- Xarray-like of shape (n_samples, n_output_features)
The imputed dataset.
n_output_features
is the number of features that is not always missing duringfit
.
Examples using sklearn.impute.KNNImputer
¶
Release Highlights for scikit-learn 0.22
Imputing missing values before building an estimator