InstanceHardnessThreshold
- class imbens.sampler.InstanceHardnessThreshold(*, estimator=None, sampling_strategy='auto', random_state=None, cv=5, n_jobs=None)
Undersample based on the instance hardness threshold.
Read more in the User Guide.
- Parameters:
- estimatorestimator object, default=None
Classifier to be used to estimate instance hardness of the samples. By default a
RandomForestClassifier
will be used. Ifstr
, the choices using a string are the following:'knn'
,'decision-tree'
,'random-forest'
,'adaboost'
,'gradient-boosting'
and'linear-svm'
. If object, an estimator inherited fromClassifierMixin
and having an attributepredict_proba()
.- sampling_strategyfloat, str, dict, callable, default=’auto’
Sampling information to sample 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 \(\alpha_{us} = N_{m} / N_{rM}\) where \(N_{m}\) is the number of samples in the minority class and \(N_{rM}\) is the number of samples in the majority class after resampling.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:'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
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
.
- cvint, default=5
Number of folds to be used when estimating samples’ instance hardness.
- 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.
- Attributes:
- sample_indices_ndarray of shape (n_new_samples,)
Indices of the samples selected.
See also
NearMiss
Undersample based on near-miss search.
RandomUnderSampler
Random under-sampling.
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
Examples
>>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imbens.sampler._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 %s' % Counter(y)) Original dataset shape Counter({1: 900, 0: 100}) >>> iht = InstanceHardnessThreshold(random_state=42) >>> X_res, y_res = iht.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) Resampled dataset shape Counter({1: 5..., 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.
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 withX
andy
, 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.