imblearn.over_sampling
.ADASYN¶

class
imblearn.over_sampling.
ADASYN
(sampling_strategy='auto', random_state=None, n_neighbors=5, n_jobs=1, ratio=None)[source][source]¶ Perform oversampling using Adaptive Synthetic (ADASYN) sampling approach for imbalanced datasets.
Read more in the User Guide.
Parameters:  sampling_strategy : float, str, dict or callable, (default=’auto’)
Sampling information to resample 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 as where and are the number of samples in the minority class after resampling and the number of samples in the majority class, respectively.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:'minority'
: resample only the minority 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 majority'
.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.
 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
.
 If int,
 n_neighbors : int int or object, optional (default=5)
If
int
, number of nearest neighbours to used to construct synthetic samples. 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)
Number of threads to run the algorithm when it is possible.
 ratio : str, dict, or callable
Deprecated since version 0.4: Use the parameter
sampling_strategy
instead. It will be removed in 0.6.
See also
SMOTE
 Oversample using SMOTE.
Notes
The implementation is based on [1].
Supports multiclass resampling. A onevs.rest scheme is used.
References
[1] (1, 2) He, Haibo, Yang Bai, Edwardo A. Garcia, and Shutao Li. “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” In IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 13221328, 2008. Examples
>>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.over_sampling import ADASYN # 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}) >>> ada = ADASYN(random_state=42) >>> X_res, y_res = ada.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) Resampled dataset shape Counter({0: 904, 1: 900})

__init__
(sampling_strategy='auto', random_state=None, n_neighbors=5, 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 : {arraylike, sparse matrix}, shape (n_samples, n_features)
Data array.
 y : arraylike, shape (n_samples,)
Target array.
Returns:  self : object
Return the instance itself.

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

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