26 lines
869 B
Python
26 lines
869 B
Python
|
import numpy as np
|
||
|
|
from scipy.stats import rankdata as scipy_rankdata
|
||
|
|
|
||
|
|
from sklearn.utils.extmath import stable_cumsum
|
||
|
|
from sklearn.utils.deprecation import deprecated
|
||
|
|
|
||
|
|
|
||
|
|
# Remove in sklearn 0.21
|
||
|
|
@deprecated("sklearn.utils.stats.rankdata was deprecated in version 0.19 and "
|
||
|
|
"will be removed in 0.21. Use scipy.stats.rankdata instead.")
|
||
|
|
def rankdata(*args, **kwargs):
|
||
|
|
return scipy_rankdata(*args, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def _weighted_percentile(array, sample_weight, percentile=50):
|
||
|
|
"""
|
||
|
|
Compute the weighted ``percentile`` of ``array`` with ``sample_weight``.
|
||
|
|
"""
|
||
|
|
sorted_idx = np.argsort(array)
|
||
|
|
|
||
|
|
# Find index of median prediction for each sample
|
||
|
|
weight_cdf = stable_cumsum(sample_weight[sorted_idx])
|
||
|
|
percentile_idx = np.searchsorted(
|
||
|
|
weight_cdf, (percentile / 100.) * weight_cdf[-1])
|
||
|
|
return array[sorted_idx[percentile_idx]]
|