imblearn.under_sampling.InstanceHardnessThreshold

class imblearn.under_sampling.InstanceHardnessThreshold(estimator=None, ratio=’auto’, return_indices=False, random_state=None, cv=5, n_jobs=1, **kwargs)[source][source]

Class to perform under-sampling based on the instance hardness threshold.

Read more in the User Guide.

Parameters:

estimator : object, optional (default=RandomForestClassifier())

Classifier to be used to estimate instance hardness of the samples. By default a sklearn.ensemble.RandomForestClassifer will be used. If str, the choices using a string are the following: 'knn', 'decision-tree', 'random-forest', 'adaboost', 'gradient-boosting' and 'linear-svm'. If object, an estimator inherited from sklearn.base.ClassifierMixin and having an attribute predict_proba.

Deprecated since version 0.2: estimator as a string object is deprecated from 0.2 and will be replaced in 0.4. Use sklearn.base.ClassifierMixin object instead.

ratio : str, dict, or callable, optional (default=’auto’)

Ratio to use for resampling the data set.

  • If str, has to be one of: (i) 'minority': resample the minority class; (ii) 'majority': resample the majority class, (iii) 'not minority': resample all classes apart of the minority class, (iv) 'all': resample all classes, and (v) 'auto': correspond to 'all' with for over-sampling methods and 'not minority' for under-sampling methods. The classes targeted will be over-sampled or under-sampled to achieve an equal number of sample with the majority or minority class.
  • If dict, the keys correspond to the targeted classes. The values correspond to the desired number of samples.
  • If callable, function taking y and returns a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples.

Warning

This algorithm is a cleaning under-sampling method. When providing a dict, only the targeted classes will be used; the number of samples will be discarded.

return_indices : bool, optional (default=False)

Whether or not to return the indices of the samples randomly selected from the majority class.

random_state : int, RandomState instance or None, optional (default=None)

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.

cv : int, optional (default=5)

Number of folds to be used when estimating samples’ instance hardness.

n_jobs : int, optional (default=1)

The number of threads to open if possible.

**kwargs:

Option for the different classifier.

Deprecated since version 0.2: **kwargs has been deprecated from 0.2 and will be replaced in 0.4. Use sklearn.base.ClassifierMixin object instead to pass parameter associated to an estimator.

Notes

The method is based on [R45].

Supports mutli-class resampling. A one-vs.-rest scheme is used when sampling a class as proposed in [R45].

See Instance Hardness Threshold.

References

[R45](1, 2, 3) D. Smith, Michael R., Tony Martinez, and Christophe Giraud-Carrier. “An instance level analysis of data complexity.” Machine learning 95.2 (2014): 225-256.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.under_sampling import InstanceHardnessThreshold
>>> 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 {}'.format(Counter(y)))
Original dataset shape Counter({1: 900, 0: 100})
>>> iht = InstanceHardnessThreshold(random_state=42)
>>> X_res, y_res = iht.fit_sample(X, y)
>>> print('Resampled dataset shape {}'.format(Counter(y_res)))
Resampled dataset shape Counter({1: 840, 0: 100})
__init__(estimator=None, ratio=’auto’, return_indices=False, random_state=None, cv=5, n_jobs=1, **kwargs)[source][source]
fit(X, y)[source]

Find the classes statistics before to perform sampling.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : array-like, shape (n_samples,)

Corresponding label for each sample in X.
Returns:

self : object,

Return self.
fit_sample(X, y)[source]

Fit the statistics and resample the data directly.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : array-like, shape (n_samples,)

Corresponding label for each sample in X.
Returns:

X_resampled : {array-like, sparse matrix}, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampled : array-like, 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.
sample(X, y)[source]

Resample the dataset.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : array-like, shape (n_samples,)

Corresponding label for each sample in X.
Returns:

X_resampled : {ndarray, sparse matrix}, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampled : ndarray, shape (n_samples_new)

The corresponding label of X_resampled
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

Examples using imblearn.under_sampling.InstanceHardnessThreshold