RepeatedEditedNearestNeighbours#
- class imblearn.under_sampling.RepeatedEditedNearestNeighbours(*, sampling_strategy='auto', n_neighbors=3, max_iter=100, kind_sel='all', n_jobs=None)[source]#
Undersample based on the repeated edited nearest neighbour method.
This method will repeat several time the ENN algorithm.
Read more in the User Guide.
- Parameters:
- sampling_strategystr, list or callable
Sampling information to sample the data set.
When
str
, specify the class targeted by the resampling. Note the the number of samples will not be equal in each. Possible choices are:'majority'
: resample only the majority class;'not minority'
: resample all classes but the minority class;'not majority'
: resample all classes but the majority class;'all'
: resample all classes;'auto'
: equivalent to'not minority'
.When
list
, the list contains the classes targeted by the resampling.When callable, function taking
y
and returns adict
. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.
- n_neighborsint or object, default=3
If
int
, size of the neighbourhood to consider to compute the nearest neighbors. If object, an estimator that inherits fromKNeighborsMixin
that will be used to find the nearest-neighbors.- max_iterint, default=100
Maximum number of iterations of the edited nearest neighbours algorithm for a single run.
- kind_sel{‘all’, ‘mode’}, default=’all’
Strategy to use in order to exclude samples.
If
'all'
, all neighbours will have to agree with the samples of interest to not be excluded.If
'mode'
, the majority vote of the neighbours will be used in order to exclude a sample.
The strategy
"all"
will be less conservative than'mode'
. Thus, more samples will be removed whenkind_sel="all"
generally.- n_jobsint, default=None
Number of CPU cores used during the cross-validation loop.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.
- Attributes:
- sampling_strategy_dict
Dictionary containing the information to sample the dataset. The keys corresponds to the class labels from which to sample and the values are the number of samples to sample.
- nn_estimator object
Validated K-nearest Neighbours estimator linked to the parameter
n_neighbors
.- enn_sampler object
The validated
EditedNearestNeighbours
instance.- sample_indices_ndarray of shape (n_new_samples,)
Indices of the samples selected.
New in version 0.4.
- n_iter_int
Number of iterations run.
New in version 0.6.
- n_features_in_int
Number of features in the input dataset.
New in version 0.9.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during
fit
. Defined only whenX
has feature names that are all strings.New in version 0.10.
See also
CondensedNearestNeighbour
Undersample by condensing samples.
EditedNearestNeighbours
Undersample by editing samples.
AllKNN
Undersample using ENN and various number of neighbours.
Notes
The method is based on [1]. A one-vs.-rest scheme is used when sampling a class as proposed in [1].
Supports multi-class resampling.
References
Examples
>>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.under_sampling import RepeatedEditedNearestNeighbours >>> X, y = make_classification(n_classes=2, class_sep=2, ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) >>> print('Original dataset shape %s' % Counter(y)) Original dataset shape Counter({1: 900, 0: 100}) >>> renn = RepeatedEditedNearestNeighbours() >>> X_res, y_res = renn.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) Resampled dataset shape Counter({1: 887, 0: 100})
Methods
fit
(X, y)Check inputs and statistics of the sampler.
fit_resample
(X, y)Resample the dataset.
get_feature_names_out
([input_features])Get output feature names for transformation.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
- fit(X, y)[source]#
Check inputs and statistics of the sampler.
You should use
fit_resample
in all cases.- Parameters:
- X{array-like, dataframe, sparse matrix} of shape (n_samples, n_features)
Data array.
- yarray-like of shape (n_samples,)
Target array.
- Returns:
- selfobject
Return the instance itself.
- fit_resample(X, y)[source]#
Resample the dataset.
- Parameters:
- X{array-like, dataframe, sparse matrix} of shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
- yarray-like of shape (n_samples,)
Corresponding label for each sample in X.
- Returns:
- X_resampled{array-like, dataframe, sparse matrix} of shape (n_samples_new, n_features)
The array containing the resampled data.
- y_resampledarray-like of shape (n_samples_new,)
The corresponding label of
X_resampled
.
- 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
Same as input features.
- get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- 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.