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:NAMEimblearn - Toolbox for imbalanced dataset in machine learning.DESCRIPTION``imbalanced-learn`` is a set of python methods to deal with imbalanceddatset in machine learning and pattern recognition.Subpackages-----------combineModule which provides methods based on over-sampling and under-sampling.ensembleModule which provides methods generating an ensemble ofunder-sampled subsets.exceptionsModule including custom warnings and error clases used acrossimbalanced-learn.kerasModule which provides custom generator, layers for deep learning usingkeras.metricsModule which provides metrics to quantified the classification performancewith imbalanced dataset.over_samplingModule which provides methods to under-sample a dataset.tensorflowModule which provides custom generator, layers for deep learning usingtensorflow.under-samplingModule which provides methods to over-sample a dataset.utilsModule including various utilities.pipelineModule which allowing to create pipeline with scikit-learn estimators.PACKAGE CONTENTS_versionbasecombine (package)datasets (package)ensemble (package)exceptionskeras (package)metrics (package)over_sampling (package)pipelinetensorflow (package)tests (package)under_sampling (package)utils (package)CLASSESimblearn.base.BaseSampler(imblearn.base.SamplerMixin)imblearn.base.FunctionSamplerclass 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__']VERSION0.4.0.dev0FILE/opt/conda/lib/python3.6/site-packages/imbalanced_learn-0.4.0.dev0-py3.6.egg/imblearn/__init__.py