`L i"dZddlZddlmZddlmZmZddlm Z ee edhdgejgdgddgd d dd d Z ee e edhdgddgddgdd dddZy)z5Utilities for handling weights based on class labels.N)sparse) StrOptionsvalidate_params)_check_sample_weightbalancedz array-like) class_weightclassesy sample_weightT)prefer_skip_nested_validation)r cddlm}t|t|z r td|t |dk(r5t j |jdt jd}|S|dk(r|}|j|}tt j||js tdt||}t j|| }|jt |j|zz } | |j!|}|St j |jdt jd}g} t#|D]#\} } | |vr || || <| j%| %t |t | z } | r@| t |k7r2t j&| j)}td |d |S) aEstimate class weights for unbalanced datasets. Parameters ---------- class_weight : dict, "balanced" or None If "balanced", class weights will be given by `n_samples / (n_classes * np.bincount(y))` or their weighted equivalent if `sample_weight` is provided. If a dictionary is given, keys are classes and values are corresponding class weights. If `None` is given, the class weights will be uniform. classes : ndarray Array of the classes occurring in the data, as given by `np.unique(y_org)` with `y_org` the original class labels. y : array-like of shape (n_samples,) Array of original class labels per sample. sample_weight : array-like of shape (n_samples,), default=None Array of weights that are assigned to individual samples. Only used when `class_weight='balanced'`. Returns ------- class_weight_vect : ndarray of shape (n_classes,) Array with `class_weight_vect[i]` the weight for i-th class. References ---------- The "balanced" heuristic is inspired by Logistic Regression in Rare Events Data, King, Zen, 2001. Examples -------- >>> import numpy as np >>> from sklearn.utils.class_weight import compute_class_weight >>> y = [1, 1, 1, 1, 0, 0] >>> compute_class_weight(class_weight="balanced", classes=np.unique(y), y=y) array([1.5 , 0.75]) ) LabelEncoderz8classes should include all valid labels that can be in yrC)dtypeorderrz.classes should have valid labels that are in y)weightsz The classes, z, are not in class_weight) preprocessingrset ValueErrorlennponesshapefloat64 fit_transformallisinclasses_rbincountsum transform enumerateappendarraytolist)r r r r rweightley_indweighted_class_counts recip_frequnweighted_classesicn_weighted_classes$unweighted_classes_user_friendly_strs `/mnt/ssd/data/python-lab/Trading/venv/lib/python3.12/site-packages/sklearn/utils/class_weight.pycompute_class_weightr3 sh- 1vG STTs<0A5q)3G@ M?  # ^  #2777BKK01MN N,]A> " E= I*..0  4 4 BLL12& M!q)3Gg& -DAqL (Oq "))!,  - !\C0B,CC "4L8I"I3588>> from sklearn.utils.class_weight import compute_sample_weight >>> y = [1, 1, 1, 1, 0, 0] >>> compute_sample_weight(class_weight="balanced", y=y) array([0.75, 0.75, 0.75, 0.75, 1.5 , 1.5 ]) r)rNrzAThe only valid class_weight for subsampling is 'balanced'. Given .zSFor multi-output, class_weight should be a list of dicts, or the string 'balanced'.zYFor multi-output, number of elements in class_weight should match number of outputs. Got z element(s) while having z outputs.)r r clip)modegr)axisr)rissparser atleast_1dndimreshaperr isinstancedictlistrrangetoarrayflattenuniquetaker3 searchsortedrrr%prodr) r r r5 n_outputsexpanded_class_weightky_full classes_fullclasses_missingclass_weight_k y_subsampleclasses_subsampleweight_ks r2compute_sample_weightrTjsCn ??1  MM!  66Q; 1g&A I|z9 !N! %   Q  :lD#A%  d +L0AY0N**-l*;)<r\s; :,z:,7>JJ<^&-  #'EIQQhtZ %=tDO , $' #' 7;u!u!r4