70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
|
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
|