imblearn.under_sampling
.NearMiss¶
-
class
imblearn.under_sampling.
NearMiss
(sampling_strategy='auto', return_indices=False, random_state=None, version=1, n_neighbors=3, n_neighbors_ver3=3, n_jobs=1, ratio=None)[source][source]¶ Class to perform under-sampling based on NearMiss methods.
Read more in the User Guide.
Parameters: - sampling_strategy : float, 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 majority class over the number of samples in the minority class after resampling. Therefore, the ratio is expressed aswhere
and
are the number of samples in the majority class after resampling and the number of samples in the minority class, respectively.
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.
- return_indices : bool, optional (default=False)
Whether or not to return the indices of the samples randomly selected from the majority class.
Deprecated since version 0.4:
return_indices
is deprecated. Use the attributesample_indices_
instead.- random_state : int, RandomState instance or None, optional (default=None)
Control the randomization of the algorithm.
- If int,
random_state
is the seed used by the random number generator; - If
RandomState
instance, random_state is the random number generator; - If
None
, the random number generator is theRandomState
instance used bynp.random
.
Deprecated since version 0.4:
random_state
is deprecated in 0.4 and will be removed in 0.6.- If int,
- version : int, optional (default=1)
Version of the NearMiss to use. Possible values are 1, 2 or 3.
- n_neighbors : int or object, optional (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_ver3 : int or object, optional (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 fromsklearn.neighbors.base.KNeighborsMixin
that will be used to find the k_neighbors.- n_jobs : int, optional (default=1)
The number of threads to open if possible.
- ratio : str, dict, or callable
Deprecated since version 0.4: Use the parameter
sampling_strategy
instead. It will be removed in 0.6.
Notes
The methods are based on [1].
Supports multi-class resampling.
References
[1] (1, 2) 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 # doctest: +NORMALIZE_WHITESPACE >>> 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, shape (n_new_samples)
Indices of the samples selected.
New in version 0.4:
sample_indices_
used instead ofreturn_indices=True
.
-
__init__
(sampling_strategy='auto', return_indices=False, random_state=None, version=1, n_neighbors=3, n_neighbors_ver3=3, n_jobs=1, ratio=None)[source][source]¶ Initialize self. See help(type(self)) for accurate signature.
-
fit
(X, y)[source]¶ Check inputs and statistics of the sampler.
You should use
fit_resample
in all cases.Parameters: - X : {array-like, sparse matrix}, shape (n_samples, n_features)
Data array.
- y : array-like, shape (n_samples,)
Target array.
Returns: - self : object
Return the instance itself.
-
fit_resample
(X, y)[source]¶ Resample the dataset.
Parameters: - X : {array-like, sparse matrix}, shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
- y : array-like, shape (n_samples,)
Corresponding label for each sample in X.
Returns: - X_resampled : {array-like, sparse matrix}, shape (n_samples_new, n_features)
The array containing the resampled data.
- y_resampled : array-like, shape (n_samples_new,)
The corresponding label of X_resampled.
-
fit_sample
(X, y)[source]¶ Resample the dataset.
Parameters: - X : {array-like, sparse matrix}, shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
- y : array-like, shape (n_samples,)
Corresponding label for each sample in X.
Returns: - X_resampled : {array-like, sparse matrix}, shape (n_samples_new, n_features)
The array containing the resampled data.
- y_resampled : array-like, shape (n_samples_new,)
The corresponding label of X_resampled.
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: - deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: - params : mapping of string to any
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 pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: - self