ZPhdZddlmZddlmZddlmZddlmZddl m Z e dgdgee d gd gd gd d d d dddZ d S)z/Transform a dataset into an imbalanced dataset.)Counter)Mapping)RandomUnderSampler)check_sampling_strategy)validate_paramsz array-likeN random_stateboolean)Xysampling_strategyr verboseT)prefer_skip_nested_validationF)r r rc Nt|}t|tst|rt ||dfi|}|rt d|t |d|}|||\} } |rt dt| | | fS)aT Turn a dataset into an imbalanced dataset with a specific sampling strategy. A simple toy dataset to visualize clustering and classification algorithms. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, dataframe} of shape (n_samples, n_features) Matrix containing the data to be imbalanced. y : array-like of shape (n_samples,) Corresponding label for each sample in X. sampling_strategy : dict or callable, Ratio to use for resampling the data set. - 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 a ``dict``. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class. random_state : int, RandomState instance or None, default=None 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 the RandomState instance used by np.random. verbose : bool, default=False Show information regarding the sampling. **kwargs : dict Dictionary of additional keyword arguments to pass to ``sampling_strategy``. Returns ------- X_resampled : {ndarray, dataframe} of shape (n_samples_new, n_features) The array containing the imbalanced data. y_resampled : ndarray of shape (n_samples_new) The corresponding label of `X_resampled`. Notes ----- See :ref:`sphx_glr_auto_examples_applications_plot_multi_class_under_sampling.py`, :ref:`sphx_glr_auto_examples_datasets_plot_make_imbalance.py`, and :ref:`sphx_glr_auto_examples_api_plot_sampling_strategy_usage.py`. Examples -------- >>> from collections import Counter >>> from sklearn.datasets import load_iris >>> from imblearn.datasets import make_imbalance >>> data = load_iris() >>> X, y = data.data, data.target >>> print(f'Distribution before imbalancing: {Counter(y)}') Distribution before imbalancing: Counter({0: 50, 1: 50, 2: 50}) >>> X_res, y_res = make_imbalance(X, y, ... sampling_strategy={0: 10, 1: 20, 2: 30}, ... random_state=42) >>> print(f'Distribution after imbalancing: {Counter(y_res)}') Distribution after imbalancing: Counter({2: 30, 1: 20, 0: 10}) zunder-samplingz4The original target distribution in the dataset is: F)r replacementr zMake the dataset imbalanced: )r isinstancercallablerprintr fit_resample) r r r r rkwargs target_statssampling_strategy_rus X_resampled y_resampleds \/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/imblearn/datasets/_imbalance.pymake_imbalancersf1::L#W-- :K1L1L 4 q"2  6<  U S\SSTTT ,!   C #//155KF Dgk.B.BDDEEE  ##) __doc__ collectionsrcollections.abcrunder_samplingrutilsrutils._sklearn_compatrrrrrr&s55 ######//////++++++333333^^%x6'(; #'    $$[$[$[$[$  [$[$[$r