imblearn.FunctionSampler

class imblearn.FunctionSampler(func=None, accept_sparse=True, kw_args=None)[source][source]

Construct a sampler from calling an arbitrary callable.

Read more in the User Guide.

Parameters:
func : callable or None,

The callable to use for the transformation. This will be passed the same arguments as transform, with args and kwargs forwarded. If func is None, then func will be the identity function.

accept_sparse : bool, optional (default=True)

Whether sparse input are supported. By default, sparse inputs are supported.

kw_args : dict, optional (default=None)

The keyword argument expected by func.

Notes

See Customized sampler to implement an outlier rejections estimator

Examples

>>> import numpy as np
>>> from sklearn.datasets import make_classification
>>> from imblearn import FunctionSampler
>>> 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)

We can create to select only the first ten samples for instance.

>>> def func(X, y):
...   return X[:10], y[:10]
>>> sampler = FunctionSampler(func=func)
>>> X_res, y_res = sampler.fit_resample(X, y)
>>> np.all(X_res == X[:10])
True
>>> np.all(y_res == y[:10])
True

We can also create a specific function which take some arguments.

>>> from collections import Counter
>>> from imblearn.under_sampling import RandomUnderSampler
>>> def func(X, y, sampling_strategy, random_state):
...   return RandomUnderSampler(
...       sampling_strategy=sampling_strategy,
...       random_state=random_state).fit_resample(X, y)
>>> sampler = FunctionSampler(func=func,
...                           kw_args={'sampling_strategy': 'auto',
...                                    'random_state': 0})
>>> X_res, y_res = sampler.fit_resample(X, y)
>>> print('Resampled dataset shape {}'.format(
...     sorted(Counter(y_res).items())))
Resampled dataset shape [(0, 100), (1, 100)]
__init__(func=None, accept_sparse=True, kw_args=None)[source][source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, y)[source]

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

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

Data array.

y : array-like, shape (n_samples,)

Target array.

Returns:
self : object

Return the instance itself.

fit_resample(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 : {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.

fit_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 : {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.

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