FunctionSampler#

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

Construct a sampler from calling an arbitrary callable.

See also

sklearn.preprocessing.FunctionTransfomer: Stateless transformer.

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(f'Resampled dataset shape {sorted(Counter(y_res).items())}')
Resampled dataset shape [(0, 100), (1, 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.
`set_params`(**params)	Set the parameters of this estimator.

fit(X, y)[source]#

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)[source]#

Resample the dataset.

Parameters:

X{array-like, 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, 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)[source]#

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()[source]#

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)[source]#

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)[source]#

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: