imblearn.under_sampling
.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 undersampling 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 multiclass 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 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 fromsklearn.neighbors.base.KNeighborsMixin
that will be used to find the k_neighbors. n_neighbors_ver3int or object, default=3
If
int
, NearMiss3 algorithm start by a phase of resampling. 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 fromsklearn.neighbors.base.KNeighborsMixin
that will be used to find the k_neighbors. n_jobsint, default=None
Number of CPU cores used during the crossvalidation loop.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details.
See also
RandomUnderSampler
Random undersample the dataset.
InstanceHardnessThreshold
Use of classifier to undersample a dataset.
Notes
The methods are based on [R7968144cb2621].
Supports multiclass resampling.
References
 R7968144cb2621
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})
 Attributes
 sample_indices_ndarray of shape (n_new_samples)
Indices of the samples selected.
New in version 0.4.

__init__
(self, sampling_strategy='auto', version=1, n_neighbors=3, n_neighbors_ver3=3, n_jobs=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(self, X, y)[source]¶ Check inputs and statistics of the sampler.
You should use
fit_resample
in all cases. Parameters
 X{arraylike, dataframe, sparse matrix} of shape (n_samples, n_features)
Data array.
 yarraylike of shape (n_samples,)
Target array.
 Returns
 selfobject
Return the instance itself.

fit_resample
(self, X, y)[source]¶ Resample the dataset.
 Parameters
 X{arraylike, dataframe, sparse matrix} of shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
 yarraylike of shape (n_samples,)
Corresponding label for each sample in X.
 Returns
 X_resampled{arraylike, dataframe, sparse matrix} of shape (n_samples_new, n_features)
The array containing the resampled data.
 y_resampledarraylike of shape (n_samples_new,)
The corresponding label of
X_resampled
.

fit_sample
(self, X, y)[source]¶ Resample the dataset.
 Parameters
 X{arraylike, dataframe, sparse matrix} of shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
 yarraylike of shape (n_samples,)
Corresponding label for each sample in X.
 Returns
 X_resampled{arraylike, dataframe, sparse matrix} of shape (n_samples_new, n_features)
The array containing the resampled data.
 y_resampledarraylike of shape (n_samples_new,)
The corresponding label of
X_resampled
.

get_params
(self, 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
 paramsmapping of string to any
Parameter names mapped to their values.

set_params
(self, **params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
 selfobject
Estimator instance.