Ki% SSKJrJr SSjrg))_find_matching_floating_dtypeget_namespace_and_deviceNc >[U5upEn[XS9nURXUS9nURXUS9nURX'US9nURnUS:XaU$URS:XaUR US5nUR UR :waJUR SUR S:Xa*UR XR SS45RnURn U S:XaUR US5nURUSSS9n URXSS 9n UR Sn UU S URXS 94n URURU 55(a$URURU5U SS 9nSX'URU RSS 9nUR SnURU U4XvS9n[U5GHunnUS - US -nUS:HnUR!UUUUS-5UU'UR#[%UR S5Vs/sHnUR'UUS4UU5PM sn5nU R SS- nUR)USU5nURUR SUS 9nU UU4nU(aUUU4U- nUUR+U5R,:nUR)US-SU5nU UU4nUUUU4S:HnUH8n UU UU 4n!UR'UU S4U!SS9n"U"U:aUU n"U U"U 4UU 'M: UR/UUUU4UUU4UUU4-S- 5USU4'GMUUU4USU4'GM U S:XaUSnUS:XaUS$U$s snf)a Compute the weighted percentile. Implement an array API compatible (weighted version) of NumPy's 'inverted_cdf' method when `average=False` (default) and 'averaged_inverted_cdf' when `average=True`. For an array ordered by increasing values, when the percentile lies exactly on a data point: * 'inverted_cdf' takes the exact data point. * 'averaged_inverted_cdf' takes the average of the exact data point and the one above it (this means it gives the same result as `median` for unit weights). E.g., for the array [1, 2, 3, 4] the percentile rank at each data point would be [25, 50, 75, 100]. Percentile rank 50 lies on '2'. 'average_inverted_cdf' computes the average of '2' and '3', making it 'symmetrical' because if you reverse the array, rank 50 would fall on '3'. It also matches 'median'. On the other hand, 'inverted_cdf', which does not satisfy the symmetry property, would give '2'. When the requested percentile lies between two data points, both methods return the higher data point. E.g., for the array [1, 2, 3, 4, 5] the percentile rank at each data point would be [20, 40, 60, 80, 100]. Percentile rank 50, lies between '2' and '3'. Taking the higher data point is symmetrical because if you reverse the array, 50 would lie between '4' and '3'. Both methods match median in this case. If `array` is a 2D array, the `values` are selected along axis 0. `NaN` values are ignored by setting their weights to 0. If `array` is 2D, this is done in a column-isolated manner: a `NaN` in the second column, does not impact the percentile computed for the first column even if `sample_weight` is 1D. .. versionchanged:: 0.24 Accepts 2D `array`. .. versionchanged:: 1.7 Supports handling of `NaN` values. .. versionchanged:: 1.8 Supports `average`, which calculates percentile using the "averaged_inverted_cdf" method. Parameters ---------- array : 1D or 2D array Values to take the weighted percentile of. sample_weight: 1D or 2D array Weights for each value in `array`. Must be same shape as `array` or of shape `(array.shape[0],)`. percentile_rank: scalar or 1D array, default=50 The probability level(s) of the percentile(s) to compute, in percent. Must be between 0 and 100. If a 1D array, computes all percentiles (along each axis 0 if `array` is 2D). average : bool, default=False If `True`, uses the "averaged_inverted_cdf" quantile method, otherwise defaults to "inverted_cdf". "averaged_inverted_cdf" is symmetrical with unit `sample_weight`, such that the total of `sample_weight` below or equal to `_weighted_percentile(percentile_rank)` is the same as the total of `sample_weight` above or equal to `_weighted_percentile(100-percentile_rank)`. This symmetry is not guaranteed with non-unit weights. xp : array_namespace, default=None The standard-compatible namespace for `array`. Default: infer. Returns ------- percentile : scalar, 1D array, or 2D array Weighted percentile at the requested probability level(s). If `array` is 1D and `percentile_rank` is scalar, returns a scalar. If `array` is 2D and `percentile_rank` is scalar, returns a 1D array of shape `(array.shape[1],)` If `array` is 1D and `percentile_rank` is 1D, returns a 1D array of shape `(percentile_rank.shape[0],)` If `array` is 2D and `percentile_rank` is 1D, returns a 2D array of shape `(array.shape[1], percentile_rank.shape[0])` )xp)dtypedevicer)r )r F)axisstable)r )r .)rd).r .right)side).r)r.)rrasarrayndimreshapeshapetileTargsorttake_along_axisarangeanyisnancumulative_sumempty enumerate nextafterstackrange searchsortedclipfinfoepswhere)#array sample_weightpercentile_rankaverager_rfloating_dtypen_dimn_dim_percentile sorted_idxsorted_weights n_featureslargest_value_per_columnsorted_nan_mask weight_cdf n_percentilesresultp_idxp_rankadjusted_percentile_rankmask feature_idxpercentile_indicesmax_idx col_indicespercentile_in_sortedfraction_aboveis_fraction_abovepercentile_plus_one_indicespercentile_plus_one_in_sortedzero_weight_colscol_idxcdf_val next_indexs# U/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/sklearn/utils/stats.py_weighted_percentilerI sf-U3MB625@N JJu6J BEJJ}6JRMjjvjVO JJE z  zzQ 5'* {{m)))ekk!n @S@STU@V.V A/BCEE &++1**_d;E%8J'' 'JNQJ$7RYYzYAA  vvbhh/011,,RXXe_jq,Q*+'"">#3#3!"K{C/02J;2W$>   ""1%)WW%7GDii Avi> )*VV !/.1I1M1M M *,''2Dq2H!W*U ',6+[8- ) +;[HIQN  ,$W.@.I%IJ__w|,gG- (!3G!3V3u s6$N )2FN)sklearn.utils._array_apirrrIrHrNsAEK4rM