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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 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 | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Functionality for computing metrics, with a particular focus on group metrics.
For our purpose, a metric is a function with signature
``f(y_true, y_pred, ....)``
where ``y_true`` are the set of true values and ``y_pred`` are
values predicted by a machine learning algorithm. Other
arguments may be present (most often sample weights), which will
affect how the metric is calculated.
The group metrics in this module have signatures
``g(y_true, y_pred, group_membership, ...)``
where ``group_membership`` is an array of values indicating
a group to which each pair of true and predicted values belong.
The metric is evaluated for the entire set of data, and also
for each subgroup identified in ``group_membership``.
"""
from ._extra_metrics import balanced_root_mean_squared_error, fallout_rate # noqa: F401
from ._extra_metrics import mean_overprediction, mean_prediction # noqa: F401
from ._extra_metrics import mean_underprediction, miss_rate # noqa: F401
from ._extra_metrics import selection_rate, specificity_score # noqa: F401
from ._extra_metrics import group_fallout_rate # noqa: F401
from ._extra_metrics import group_miss_rate # noqa: F401
from ._extra_metrics import group_specificity_score # noqa: F401
from ._extra_metrics import group_balanced_root_mean_squared_error # noqa: F401
from ._extra_metrics import group_mean_overprediction # noqa: F401
from ._extra_metrics import group_mean_prediction # noqa: F401
from ._extra_metrics import group_mean_underprediction # noqa: F401
from ._selection_rate import group_selection_rate # noqa: F401
from ._skm_wrappers import group_accuracy_score, group_confusion_matrix # noqa: F401
from ._skm_wrappers import group_precision_score, group_recall_score # noqa: F401
from ._skm_wrappers import group_roc_auc_score, group_zero_one_loss # noqa: F401
from ._skm_wrappers import group_max_error # noqa: F401
from ._skm_wrappers import group_mean_absolute_error # noqa: F401
from ._skm_wrappers import group_mean_squared_error # noqa: F401
from ._skm_wrappers import group_mean_squared_log_error # noqa: F401
from ._skm_wrappers import group_median_absolute_error # noqa: F401
from ._skm_wrappers import group_root_mean_squared_error # noqa: F401
from ._skm_wrappers import group_r2_score # noqa: F401
from ._group_metric_result import GroupMetricResult # noqa: F401
from ._group_metric_set import GroupMetricSet # noqa: F401
from ._metrics_engine import make_group_metric, metric_by_group # noqa: F401
# -------------------------------------------
_extra_metrics = [
"balanced_root_mean_squared_error",
"fallout_rate",
"mean_prediction",
"mean_overprediction",
"mean_underprediction",
"miss_rate",
"selection_rate",
"specificity_score"
]
_group_metrics = [
"group_accuracy_score",
"group_balanced_root_mean_squared_error",
"group_confusion_matrix",
"group_fallout_rate",
"group_max_error",
"group_mean_absolute_error",
"group_mean_prediction",
"group_mean_overprediction",
"group_mean_squared_error",
"group_mean_squared_log_error",
"group_mean_underprediction",
"group_median_absolute_error",
"group_miss_rate",
"group_precision_score",
"group_r2_score",
"group_recall_score",
"group_roc_auc_score",
"group_root_mean_squared_error",
"group_selection_rate",
"group_specificity_score",
"group_zero_one_loss"
]
_engine = [
"GroupMetricResult",
"make_group_metric",
"metric_by_group"
]
__all__ = _engine + _extra_metrics + _group_metrics
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