SVMSMOTE¶
- class imblearn.over_sampling.SVMSMOTE(*, sampling_strategy='auto', random_state=None, k_neighbors=5, n_jobs=None, m_neighbors=10, svm_estimator=None, out_step=0.5)[source]¶
Over-sampling using SVM-SMOTE.
Variant of SMOTE algorithm which use an SVM algorithm to detect sample to use for generating new synthetic samples as proposed in [2].
Read more in the User Guide.
New in version 0.4.
- Parameters
- sampling_strategyfloat, 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 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 after resampling and is the number of samples in the majority class.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:'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_stateint, RandomState instance, 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
.
- k_neighborsint or object, default=5
If
int
, number of nearest neighbours to used to construct synthetic samples. If object, an estimator that inherits fromKNeighborsMixin
that will be used to find the k_neighbors.- 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.- m_neighborsint or object, default=10
If int, number of nearest neighbours to use to determine if a minority sample is in danger. If object, an estimator that inherits from
KNeighborsMixin
that will be used to find the m_neighbors.- svm_estimatorestimator object, default=SVC()
A parametrized
SVC
classifier can be passed.- out_stepfloat, default=0.5
Step size when extrapolating.
See also
SMOTE
Over-sample using SMOTE.
SMOTENC
Over-sample using SMOTE for continuous and categorical features.
SMOTEN
Over-sample using the SMOTE variant specifically for categorical features only.
BorderlineSMOTE
Over-sample using Borderline-SMOTE.
ADASYN
Over-sample using ADASYN.
KMeansSMOTE
Over-sample applying a clustering before to oversample using SMOTE.
Notes
See the original papers: [2] for more details.
Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [1].
References
- 1
N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of artificial intelligence research, 321-357, 2002.
- 2(1,2)
H. M. Nguyen, E. W. Cooper, K. Kamei, “Borderline over-sampling for imbalanced data classification,” International Journal of Knowledge Engineering and Soft Data Paradigms, 3(1), pp.4-21, 2009.
Examples
>>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.over_sampling import SVMSMOTE >>> 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}) >>> sm = SVMSMOTE(random_state=42) >>> X_res, y_res = sm.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) Resampled dataset shape Counter({0: 900, 1: 900})
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.