CondensedNearestNeighbour

class imbens.sampler.CondensedNearestNeighbour(*, sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1, n_jobs=None)

Undersample based on the condensed nearest neighbour method.

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.

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 the RandomState instance used by np.random.

n_neighborsint or estimator object, default=None

If int, size of the neighbourhood to consider to compute the nearest neighbors. If object, an estimator that inherits from KNeighborsMixin that will be used to find the nearest-neighbors. If None, a KNeighborsClassifier with a 1-NN rules will be used.

n_seeds_Sint, default=1

Number of samples to extract in order to build the set S.

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 editing samples.

RepeatedEditedNearestNeighbours

Undersample by repeating ENN algorithm.

AllKNN

Undersample using ENN and various number of neighbours.

Notes

The method is based on [1].

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

References

[1] (1,2)

P. Hart, “The condensed nearest neighbor rule,” In Information Theory, IEEE Transactions on, vol. 14(3), pp. 515-516, 1968.

Examples

>>> from collections import Counter 
>>> from sklearn.datasets import fetch_mldata 
>>> from imbens.sampler._under_sampling import CondensedNearestNeighbour 
>>> pima = fetch_mldata('diabetes_scale') 
>>> X, y = pima['data'], pima['target'] 
>>> print('Original dataset shape %s' % Counter(y)) 
Original dataset shape Counter({1: 500, -1: 268}) 
>>> cnn = CondensedNearestNeighbour(random_state=42) 
>>> X_res, y_res = cnn.fit_resample(X, y) 
>>> print('Resampled dataset shape %s' % Counter(y_res)) 
Resampled dataset shape Counter({-1: 268, 1: 227}) 

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.
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.

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.