forked from 170010011/fr
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
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import numpy as np
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from .extmath import stable_cumsum
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from .fixes import _take_along_axis
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def _weighted_percentile(array, sample_weight, percentile=50):
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"""Compute weighted percentile
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Computes lower weighted percentile. If `array` is a 2D array, the
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`percentile` is computed along the axis 0.
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.. versionchanged:: 0.24
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Accepts 2D `array`.
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Parameters
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----------
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array : 1D or 2D array
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Values to take the weighted percentile of.
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sample_weight: 1D or 2D array
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Weights for each value in `array`. Must be same shape as `array` or
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of shape `(array.shape[0],)`.
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percentile: int, default=50
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Percentile to compute. Must be value between 0 and 100.
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Returns
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-------
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percentile : int if `array` 1D, ndarray if `array` 2D
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Weighted percentile.
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"""
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n_dim = array.ndim
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if n_dim == 0:
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return array[()]
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if array.ndim == 1:
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array = array.reshape((-1, 1))
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# When sample_weight 1D, repeat for each array.shape[1]
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if (array.shape != sample_weight.shape and
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array.shape[0] == sample_weight.shape[0]):
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sample_weight = np.tile(sample_weight, (array.shape[1], 1)).T
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sorted_idx = np.argsort(array, axis=0)
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sorted_weights = _take_along_axis(sample_weight, sorted_idx, axis=0)
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# Find index of median prediction for each sample
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weight_cdf = stable_cumsum(sorted_weights, axis=0)
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adjusted_percentile = percentile / 100 * weight_cdf[-1]
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percentile_idx = np.array([
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np.searchsorted(weight_cdf[:, i], adjusted_percentile[i])
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for i in range(weight_cdf.shape[1])
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])
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percentile_idx = np.array(percentile_idx)
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# In rare cases, percentile_idx equals to sorted_idx.shape[0]
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max_idx = sorted_idx.shape[0] - 1
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percentile_idx = np.apply_along_axis(lambda x: np.clip(x, 0, max_idx),
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axis=0, arr=percentile_idx)
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col_index = np.arange(array.shape[1])
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percentile_in_sorted = sorted_idx[percentile_idx, col_index]
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percentile = array[percentile_in_sorted, col_index]
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return percentile[0] if n_dim == 1 else percentile
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