skore/skore-hub-project/src/skore_hub_project/metric/precision.py at 78e1510f513ccc34e7b17650b550061df683899b · probabl-ai/skore · GitHub

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
"""Class definition of the payload used to send a precision metric to ``hub``."""

from __future__ import annotations

from typing import ClassVar, Literal

from .metric import CrossValidationReportMetric, EstimatorReportMetric, cast_to_float


class Precision(EstimatorReportMetric):  # noqa: D101
    accessor: ClassVar[str] = "metrics.precision"
    name: str = "precision"
    verbose_name: str = "Precision (macro)"
    greater_is_better: bool = True
    position: None = None

    def compute(self) -> None:
        """Compute the value of the metric."""
        try:
            function = self.report.metrics.precision
        except AttributeError:
            self.value = None
        else:
            self.value = cast_to_float(
                function(data_source=self.data_source, average="macro")
            )


class PrecisionTrain(Precision):  # noqa: D101
    data_source: Literal["train"] = "train"


class PrecisionTest(Precision):  # noqa: D101
    data_source: Literal["test"] = "test"


class PrecisionMean(CrossValidationReportMetric):  # noqa: D101
    accessor: ClassVar[str] = "metrics.precision"
    name: str = "precision_mean"
    verbose_name: str = "Precision (macro) - MEAN"
    greater_is_better: bool = True
    position: None = None

    def compute(self) -> None:
        """Compute the value of the metric."""
        try:
            function = self.report.metrics.precision
        except AttributeError:
            self.value = None
        else:
            dataframe = function(
                data_source=self.data_source, aggregate="mean", average="macro"
            )
            self.value = cast_to_float(dataframe.iloc[0, 0])


class PrecisionTrainMean(PrecisionMean):  # noqa: D101
    data_source: Literal["train"] = "train"


class PrecisionTestMean(PrecisionMean):  # noqa: D101
    data_source: Literal["test"] = "test"


class PrecisionStd(CrossValidationReportMetric):  # noqa: D101
    accessor: ClassVar[str] = "metrics.precision"
    name: str = "precision_std"
    verbose_name: str = "Precision (macro) - STD"
    greater_is_better: bool = False
    position: None = None

    def compute(self) -> None:
        """Compute the value of the metric."""
        try:
            function = self.report.metrics.precision
        except AttributeError:
            self.value = None
        else:
            dataframe = function(
                data_source=self.data_source, aggregate="std", average="macro"
            )
            self.value = cast_to_float(dataframe.iloc[0, 0])


class PrecisionTrainStd(PrecisionStd):  # noqa: D101
    data_source: Literal["train"] = "train"


class PrecisionTestStd(PrecisionStd):  # noqa: D101
    data_source: Literal["test"] = "test"