NearMiss#
- class imblearn.under_sampling.NearMiss(*, sampling_strategy='auto', version=1, n_neighbors=3, n_neighbors_ver3=3, n_jobs=None)[source]#
Class to perform under-sampling based on NearMiss methods.
Read more in the User Guide.
- Parameters
- sampling_strategyfloat, str, dict, callable, default=’auto’
Sampling information to sample the data set.
When
float
, it corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class after resampling. Therefore, the ratio is expressed as where is the number of samples in the minority class and is the number of samples in the majority class after resampling.Warning
float
is only available for binary classification. An error is raised for multi-class classification.When
str
, specify the class targeted by the resampling. The number of samples in the different classes will be equalized. 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
dict
, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.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.
- versionint, default=1
Version of the NearMiss to use. Possible values are 1, 2 or 3.
- n_neighborsint or estimator object, default=3
If
int
, size of the neighbourhood to consider to compute the average distance to the minority point samples. If object, an estimator that inherits fromKNeighborsMixin
that will be used to find the k_neighbors. By default, it will be a 3-NN.- n_neighbors_ver3int or estimator object, default=3
If
int
, NearMiss-3 algorithm start by a phase of re-sampling. This parameter correspond to the number of neighbours selected create the subset in which the selection will be performed. If object, an estimator that inherits fromKNeighborsMixin
that will be used to find the k_neighbors. By default, it will be a 3-NN.- 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 object created from
n_neighbors
parameter.- sample_indices_ndarray of shape (n_new_samples,)
Indices of the samples selected.
New in version 0.4.
- n_features_in_int
Number of features in the input dataset.
New in version 0.9.
See also
RandomUnderSampler
Random undersample the dataset.
InstanceHardnessThreshold
Use of classifier to undersample a dataset.
Notes
The methods are based on [1].
Supports multi-class resampling.
References
- 1
I. Mani, I. Zhang. “kNN approach to unbalanced data distributions: a case study involving information extraction,” In Proceedings of workshop on learning from imbalanced datasets, 2003.
Examples
>>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.under_sampling import NearMiss >>> 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}) >>> nm = NearMiss() >>> X_res, y_res = nm.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) Resampled dataset shape Counter({0: 100, 1: 100})
Methods
fit
(X, y)Check inputs and statistics of the sampler.
fit_resample
(X, y)Resample the dataset.
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_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.