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fc0f7bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import numpy as np
from ._metrics_engine import metric_by_group
def selection_rate(y_true, y_pred, *, pos_label=1, sample_weight=None):
"""Calculate the fraction of predicted labels matching the 'good' outcome.
The argument `pos_label` specifies the 'good' outcome.
"""
selected = (np.squeeze(np.asarray(y_pred)) == pos_label)
s_w = np.ones(len(selected))
if sample_weight is not None:
s_w = np.squeeze(np.asarray(sample_weight))
return np.dot(selected, s_w) / s_w.sum()
def group_selection_rate(y_true, y_pred, group_membership,
*, pos_label=1, sample_weight=None):
"""Wrap :func:`selection_rate` as a group metric.
The arguments are the same, with the addition of the
`group_membership` array.
"""
def internal_sel_wrapper(y_true, y_pred, sample_weight=None):
return selection_rate(y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight)
return metric_by_group(internal_sel_wrapper,
y_true, y_pred, group_membership,
sample_weight=sample_weight)
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