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- import numpy as np
- from .extmath import stable_cumsum
- def _weighted_percentile(array, sample_weight, percentile=50):
- """Compute weighted percentile
- Computes lower weighted percentile. If `array` is a 2D array, the
- `percentile` is computed along the axis 0.
- .. versionchanged:: 0.24
- Accepts 2D `array`.
- 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: int or float, default=50
- Percentile to compute. Must be value between 0 and 100.
- Returns
- -------
- percentile : int if `array` 1D, ndarray if `array` 2D
- Weighted percentile.
- """
- n_dim = array.ndim
- if n_dim == 0:
- return array[()]
- if array.ndim == 1:
- array = array.reshape((-1, 1))
- # When sample_weight 1D, repeat for each array.shape[1]
- if array.shape != sample_weight.shape and array.shape[0] == sample_weight.shape[0]:
- sample_weight = np.tile(sample_weight, (array.shape[1], 1)).T
- sorted_idx = np.argsort(array, axis=0)
- sorted_weights = np.take_along_axis(sample_weight, sorted_idx, axis=0)
- # Find index of median prediction for each sample
- weight_cdf = stable_cumsum(sorted_weights, axis=0)
- adjusted_percentile = percentile / 100 * weight_cdf[-1]
- # For percentile=0, ignore leading observations with sample_weight=0. GH20528
- mask = adjusted_percentile == 0
- adjusted_percentile[mask] = np.nextafter(
- adjusted_percentile[mask], adjusted_percentile[mask] + 1
- )
- percentile_idx = np.array(
- [
- np.searchsorted(weight_cdf[:, i], adjusted_percentile[i])
- for i in range(weight_cdf.shape[1])
- ]
- )
- percentile_idx = np.array(percentile_idx)
- # In rare cases, percentile_idx equals to sorted_idx.shape[0]
- max_idx = sorted_idx.shape[0] - 1
- percentile_idx = np.apply_along_axis(
- lambda x: np.clip(x, 0, max_idx), axis=0, arr=percentile_idx
- )
- col_index = np.arange(array.shape[1])
- percentile_in_sorted = sorted_idx[percentile_idx, col_index]
- percentile = array[percentile_in_sorted, col_index]
- return percentile[0] if n_dim == 1 else percentile
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