TomekLinks

class imblearn.under_sampling.TomekLinks(*, sampling_strategy='auto', n_jobs=None)

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:

sampling_strategy_dict

Dictionary containing the information to sample the dataset. The keys corresponds to the class labels from which to sample and the values are the number of samples to sample.

sample_indices_ndarray of shape (n_new_samples,)

Indices of the samples selected.

New in version 0.4.

n_features_in_int

Number of features in the input dataset.

New in version 0.9.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 0.10.

See also

EditedNearestNeighbours

Undersample by samples edition.

CondensedNearestNeighbour

Undersample by samples condensation.

RandomUnderSampler

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 imblearn.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)	Resample the dataset.
get_feature_names_out([input_features])	Get output feature names for transformation.
get_metadata_routing()	Get metadata routing of this object.
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)

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_feature_names_out(input_features=None)

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

• If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

• If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects

Same as input features.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

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.

Examples using imblearn.under_sampling.TomekLinks

How to use sampling_strategy in imbalanced-learn

How to use sampling_strategy in imbalanced-learn

Illustration of the definition of a Tomek link

Illustration of the definition of a Tomek link

Compare under-sampling samplers

Compare under-sampling samplers