File size: 4,059 Bytes
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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import numpy as np
from ._group_metric_result import GroupMetricResult
from ._input_manipulations import _convert_to_ndarray_and_squeeze
_MESSAGE_SIZE_MISMATCH = "Array {0} is not the same size as {1}"
def metric_by_group(metric_function,
y_true, y_pred, group_membership,
sample_weight=None,
**kwargs):
r"""Apply a metric to each subgroup of a set of data.
:param metric_function: Function ``(y_true, y_pred, sample_weight=None, \*\*kwargs)``
:param y_true: Array of ground-truth values
:param y_pred: Array of predicted values
:param group_membership: Array Indicating the group to which each input value belongs
:param sample_weight: Optional weights to apply to each input value
:param \*\*kwargs: Optional arguments to be passed to the `metric_function`
:return: Object containing the result of applying ``metric_function`` to the entire dataset
and to each group identified in ``group_membership``.
If the ``metric_function`` returns a scalar, then additional fields are populated
:rtype: :class:`GroupMetricResult`
"""
_check_array_sizes(y_true, y_pred, 'y_true', 'y_pred')
_check_array_sizes(y_true, group_membership, 'y_true', 'group_membership')
if sample_weight is not None:
_check_array_sizes(y_true, sample_weight, 'y_true', 'sample_weight')
result = GroupMetricResult()
# Make everything a numpy array
# This allows for fast slicing of the groups
y_a = _convert_to_ndarray_and_squeeze(y_true)
y_p = _convert_to_ndarray_and_squeeze(y_pred)
g_d = _convert_to_ndarray_and_squeeze(group_membership)
s_w = None
if sample_weight is not None:
s_w = _convert_to_ndarray_and_squeeze(sample_weight)
# Evaluate the overall metric with the numpy arrays
# This ensures consistency in how metric_function is called
if s_w is not None:
result.overall = metric_function(y_a, y_p, sample_weight=s_w, **kwargs)
else:
result.overall = metric_function(y_a, y_p, **kwargs)
groups = np.unique(group_membership)
for group in groups:
group_indices = (group == g_d)
group_actual = y_a[group_indices]
group_predict = y_p[group_indices]
group_weight = None
if s_w is not None:
group_weight = s_w[group_indices]
result.by_group[group] = metric_function(group_actual,
group_predict,
sample_weight=group_weight,
**kwargs)
else:
result.by_group[group] = metric_function(group_actual,
group_predict,
**kwargs)
return result
def make_group_metric(metric_function):
"""Turn a regular metric into a grouped metric.
:param metric_function: The function to be wrapped. This must have signature
``(y_true, y_pred, sample_weight, **kwargs)``
:type metric_function: func
:return: A wrapped version of the supplied metric_function. It will have
signature ``(y_true, y_pred, group_membership, sample_weight, **kwargs)``
:rtype: func
"""
def wrapper(y_true, y_pred, group_membership, sample_weight=None, **kwargs):
return metric_by_group(metric_function,
y_true,
y_pred,
group_membership,
sample_weight,
**kwargs)
# Improve the name of the returned function
wrapper.__name__ = "group_{0}".format(metric_function.__name__)
return wrapper
def _check_array_sizes(a, b, a_name, b_name):
if len(a) != len(b):
raise ValueError(_MESSAGE_SIZE_MISMATCH.format(b_name, a_name))
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