【Python】样本不均衡处理模块imblearn文档（待更新）
安装地址：https://github.com/scikit-learn-contrib/imbalanced-learn
参考：https://blog.csdn.net/kizgel/article/details/78553009#214-数学公式
Help on package imblearn:
NAME
imblearn - Toolbox for imbalanced dataset in machine learning.
DESCRIPTION
``imbalanced-learn`` is a set of python methods to deal with imbalanced
datset in machine learning and pattern recognition.
Subpackages
-----------
combine
Module which provides methods based on over-sampling and under-sampling.
ensemble
Module which provides methods generating an ensemble of
under-sampled subsets.
exceptions
Module including custom warnings and error clases used across
imbalanced-learn.
keras
Module which provides custom generator, layers for deep learning using
keras.
metrics
Module which provides metrics to quantified the classification performance
with imbalanced dataset.
over_sampling
Module which provides methods to under-sample a dataset.
tensorflow
Module which provides custom generator, layers for deep learning using
tensorflow.
under-sampling
Module which provides methods to over-sample a dataset.
utils
Module including various utilities.
pipeline
Module which allowing to create pipeline with scikit-learn estimators.
PACKAGE CONTENTS
_version
base
combine (package)
datasets (package)
ensemble (package)
exceptions
keras (package)
metrics (package)
over_sampling (package)
pipeline
tensorflow (package)
tests (package)
under_sampling (package)
utils (package)
CLASSES
imblearn.base.BaseSampler(imblearn.base.SamplerMixin)
imblearn.base.FunctionSampler
class FunctionSampler(BaseSampler)
| Construct a sampler from calling an arbitrary callable.
|
| Read more in the :ref:`User Guide <function_sampler>`.
|
| 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
| :ref:`sphx_glr_auto_examples_plot_outlier_rejections.py`
|
| 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)]
|
| Method resolution order:
| FunctionSampler
| BaseSampler
| SamplerMixin
| abc.NewBase
| sklearn.base.BaseEstimator
| builtins.object
|
| Methods defined here:
|
| __init__(self, func=None, accept_sparse=True, kw_args=None)
| Initialize self. See help(type(self)) for accurate signature.
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __abstractmethods__ = frozenset()
|
| ----------------------------------------------------------------------
| Data descriptors inherited from BaseSampler:
|
| ratio_
|
| ----------------------------------------------------------------------
| Methods inherited from SamplerMixin:
|
| fit(self, X, y)
| 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(self, X, y)
| 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 = fit_resample(self, X, y)
| 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`.
|
| ----------------------------------------------------------------------
| Methods inherited from sklearn.base.BaseEstimator:
|
| __getstate__(self)
|
| __repr__(self)
| Return repr(self).
|
| __setstate__(self, state)
|
| get_params(self, deep=True)
| 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(self, **params)
| 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
|
| ----------------------------------------------------------------------
| Data descriptors inherited from sklearn.base.BaseEstimator:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
DATA
__all__ = ['FunctionSampler', '__version__']
VERSION
0.4.0.dev0
FILE
/opt/conda/lib/python3.6/site-packages/imbalanced_learn-0.4.0.dev0-py3.6.egg/imblearn/__init__.py