TomekLinks

class imbalanced_ensemble.sampler.under_sampling.TomekLinks(**kwargs)

Under-sampling by removing Tomek’s links.

Read more in the User Guide.

Parameters

sampling_strategystr, list or callable

Sampling information to sample the data set.

• When str, specify the class targeted by the resampling. Note the the number of samples will not be equal in each. 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 list, the list contains the classes targeted by the resampling.

• When callable, function taking y and returns a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.

n_jobsint, default=None

Number of CPU cores used during the cross-validation loop. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

Attributes

sample_indices_ndarray of shape (n_new_samples,)

Indices of the samples selected.

See also

EditedNearestNeighbours

Undersample by samples edition.

CondensedNearestNeighbour

Undersample by samples condensation.

RandomUnderSampling

Randomly under-sample the dataset.

Notes

This method is based on [1].

Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [1].

References

1(1,2)

I. Tomek, “Two modifications of CNN,” In Systems, Man, and Cybernetics, IEEE Transactions on, vol. 6, pp 769-772, 1976.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imbalanced_ensemble.sampler.under_sampling import TomekLinks 
>>> 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})
>>> tl = TomekLinks()
>>> X_res, y_res = tl.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
Resampled dataset shape Counter({1: 897, 0: 100})

Methods

fit(X, y)	Check inputs and statistics of the sampler.
fit_resample(X, y, *[, sample_weight])	Resample the dataset.
get_params([deep])	Get parameters for this estimator.
is_tomek(y, nn_index, class_type)	Detect if samples are Tomek’s link.
set_params(**params)	Set the parameters of this estimator.

fit(X, y)

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, *, sample_weight=None, **kwargs)

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.

sample_weightarray-like of shape (n_samples,), default=None

Corresponding weight for each sample in X.

• If None, perform normal resampling and return (X_resampled, y_resampled).

• If array-like, the given sample_weight will be resampled along with X and y, and the resampled sample weights will be added to returns. The function will return (X_resampled, y_resampled, sample_weight_resampled).

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.

sample_weight_resampledarray-like of shape (n_samples_new,), default=None

The corresponding weight of X_resampled. Only will be returned if input sample_weight is not None.

get_params(deep=True)

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.

static is_tomek(y, nn_index, class_type)

Detect if samples are Tomek’s link.

More precisely, it uses the target vector and the first neighbour of every sample point and looks for Tomek pairs. Returning a boolean vector with True for majority Tomek links.

Parameters

yndarray of shape (n_samples,)

Target vector of the data set, necessary to keep track of whether a sample belongs to minority or not.

nn_indexndarray of shape (len(y),)

The index of the closes nearest neighbour to a sample point.

class_typeint or str

The label of the minority class.

Returns

is_tomekndarray of shape (len(y), )

Boolean vector on len( # samples ), with True for majority samples that are Tomek links.

set_params(**params)

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.