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Remove support for Python 3.8 #60

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merged 3 commits into from
Oct 23, 2024
Merged

Remove support for Python 3.8 #60

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389ac0f
Purge 3.8 from project files.
ChristianGeng Oct 23, 2024
adbe522
Update api.py.
ChristianGeng Oct 23, 2024
9c3453a
Fix typing in utils.
ChristianGeng Oct 23, 2024
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2 changes: 0 additions & 2 deletions .github/workflows/test.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -15,8 +15,6 @@ jobs:
os: [ ubuntu-latest, macOS-latest, windows-latest ]
python-version: [ '3.10' ]
include:
- os: ubuntu-latest
python-version: '3.8'
- os: ubuntu-latest
python-version: '3.9'
- os: ubuntu-latest
Expand Down
155 changes: 74 additions & 81 deletions audmetric/core/api.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,8 @@
from __future__ import annotations

import collections
import typing
from collections.abc import Callable
from collections.abc import Sequence
import warnings

import numpy as np
Expand All @@ -12,9 +15,9 @@


def accuracy(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
labels: typing.Sequence[typing.Union[str, int]] = None,
truth: Sequence[object],
prediction: Sequence[object],
labels: Sequence[str | int] = None,
) -> float:
r"""Classification accuracy.

Expand Down Expand Up @@ -66,8 +69,8 @@ def accuracy(


def concordance_cc(
truth: typing.Sequence[float],
prediction: typing.Sequence[float],
truth: Sequence[float],
prediction: Sequence[float],
*,
ignore_nan: bool = False,
) -> float:
Expand Down Expand Up @@ -137,12 +140,12 @@ def concordance_cc(


def confusion_matrix(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
labels: typing.Sequence[typing.Any] = None,
truth: Sequence[object],
prediction: Sequence[object],
labels: Sequence[object] = None,
*,
normalize: bool = False,
) -> typing.List[typing.List[typing.Union[int, float]]]:
) -> list[list[int | float]]:
r"""Confusion matrix.

Args:
Expand Down Expand Up @@ -194,9 +197,9 @@ def confusion_matrix(


def detection_error_tradeoff(
truth: typing.Sequence[typing.Union[bool, int]],
prediction: typing.Sequence[typing.Union[bool, int, float]],
) -> typing.Tuple[np.ndarray, np.ndarray, np.ndarray]:
truth: Sequence[bool | int],
prediction: Sequence[bool | int | float],
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
r"""Detection error tradeoff for verification experiments.

The `detection error tradeoff (DET)`_
Expand Down Expand Up @@ -278,8 +281,8 @@ def detection_error_tradeoff(


def edit_distance(
truth: typing.Union[str, typing.Sequence[int]],
prediction: typing.Union[str, typing.Sequence[int]],
truth: str | Sequence[int],
prediction: str | Sequence[int],
) -> int:
r"""Edit distance between two sequences of chars or ints.

Expand Down Expand Up @@ -338,9 +341,9 @@ def edit_distance(


def equal_error_rate(
truth: typing.Sequence[typing.Union[bool, int]],
prediction: typing.Sequence[typing.Union[bool, int, float]],
) -> typing.Tuple[float, collections.namedtuple]:
truth: Sequence[bool | int],
prediction: Sequence[bool | int | float],
) -> tuple[float, collections.namedtuple]:
r"""Equal error rate for verification tasks.

The equal error rate (EER) is the point
Expand Down Expand Up @@ -444,10 +447,8 @@ def equal_error_rate(


def event_error_rate(
truth: typing.Union[str, typing.Sequence[typing.Union[str, typing.Sequence[int]]]],
prediction: typing.Union[
str, typing.Sequence[typing.Union[str, typing.Sequence[int]]]
],
truth: str | Sequence[str | Sequence[int]],
prediction: (str | Sequence[str | Sequence[int]]),
) -> float:
r"""Event error rate based on edit distance.

Expand Down Expand Up @@ -498,12 +499,12 @@ def event_error_rate(


def fscore_per_class(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
labels: typing.Sequence[typing.Any] = None,
truth: Sequence[object],
prediction: Sequence[object],
labels: Sequence[object] = None,
*,
zero_division: float = 0,
) -> typing.Dict[str, float]:
) -> dict[str, float]:
r"""F-score per class.

.. math::
Expand Down Expand Up @@ -557,12 +558,8 @@ def fscore_per_class(


def linkability(
truth: typing.Union[
typing.Union[bool, int], typing.Sequence[typing.Union[bool, int]]
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],
prediction: typing.Union[
typing.Union[bool, int, float], typing.Sequence[typing.Union[bool, int, float]]
],
truth: (bool | int | Sequence[bool | int]),
prediction: (bool | int | float | Sequence[bool | int | float]),
omega: float = 1.0,
nbins: int = None,
) -> float:
Expand Down Expand Up @@ -660,8 +657,8 @@ def linkability(


def mean_absolute_error(
truth: typing.Sequence[float],
prediction: typing.Sequence[float],
truth: Sequence[float],
prediction: Sequence[float],
) -> float:
r"""Mean absolute error.

Expand Down Expand Up @@ -694,8 +691,8 @@ def mean_absolute_error(


def mean_squared_error(
truth: typing.Sequence[float],
prediction: typing.Sequence[float],
truth: Sequence[float],
prediction: Sequence[float],
) -> float:
r"""Mean squared error.

Expand Down Expand Up @@ -728,8 +725,8 @@ def mean_squared_error(


def pearson_cc(
truth: typing.Sequence[float],
prediction: typing.Sequence[float],
truth: Sequence[float],
prediction: Sequence[float],
) -> float:
r"""Pearson correlation coefficient.

Expand Down Expand Up @@ -770,12 +767,12 @@ def pearson_cc(


def precision_per_class(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
labels: typing.Sequence[typing.Any] = None,
truth: Sequence[object],
prediction: Sequence[object],
labels: Sequence[object] = None,
*,
zero_division: float = 0,
) -> typing.Dict[str, float]:
) -> dict[str, float]:
r"""Precision per class.

.. math::
Expand Down Expand Up @@ -815,12 +812,12 @@ def precision_per_class(


def recall_per_class(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
labels: typing.Sequence[typing.Any] = None,
truth: Sequence[object],
prediction: Sequence[object],
labels: Sequence[object] = None,
*,
zero_division: float = 0,
) -> typing.Dict[str, float]:
) -> dict[str, float]:
r"""Recall per class.

.. math::
Expand Down Expand Up @@ -860,23 +857,23 @@ def recall_per_class(


def unweighted_average_bias(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
protected_variable: typing.Sequence[typing.Any],
labels: typing.Sequence[typing.Any] = None,
truth: Sequence[object],
prediction: Sequence[object],
protected_variable: Sequence[object],
labels: Sequence[object] = None,
*,
subgroups: typing.Sequence[typing.Any] = None,
metric: typing.Callable[
subgroups: Sequence[object] = None,
metric: Callable[
[
typing.Sequence[typing.Any],
typing.Sequence[typing.Any],
typing.Optional[typing.Sequence[str]],
Sequence[object],
Sequence[object],
Sequence[str] | None,
],
typing.Dict[str, float],
dict[str, float],
] = fscore_per_class,
reduction: typing.Callable[
reduction: Callable[
[
typing.Sequence[float],
Sequence[float],
],
float,
] = np.std,
Expand Down Expand Up @@ -1001,9 +998,9 @@ def unweighted_average_bias(


def unweighted_average_fscore(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
labels: typing.Sequence[typing.Any] = None,
truth: Sequence[object],
prediction: Sequence[object],
labels: Sequence[object] = None,
*,
zero_division: float = 0,
) -> float:
Expand Down Expand Up @@ -1045,9 +1042,9 @@ def unweighted_average_fscore(


def unweighted_average_precision(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
labels: typing.Sequence[typing.Any] = None,
truth: Sequence[object],
prediction: Sequence[object],
labels: Sequence[object] = None,
*,
zero_division: float = 0,
) -> float:
Expand Down Expand Up @@ -1088,9 +1085,9 @@ def unweighted_average_precision(


def unweighted_average_recall(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
labels: typing.Sequence[typing.Any] = None,
truth: Sequence[object],
prediction: Sequence[object],
labels: Sequence[object] = None,
*,
zero_division: float = 0,
) -> float:
Expand Down Expand Up @@ -1131,10 +1128,10 @@ def unweighted_average_recall(


def weighted_confusion_error(
truth: typing.Sequence[typing.Any],
prediction: typing.Sequence[typing.Any],
weights: typing.Sequence[typing.Sequence[typing.Union[int, float]]],
labels: typing.Sequence[typing.Any] = None,
truth: Sequence[object],
prediction: Sequence[object],
weights: Sequence[Sequence[int | float]],
labels: Sequence[object] = None,
) -> float:
r"""Weighted confusion error.

Expand Down Expand Up @@ -1187,8 +1184,8 @@ def weighted_confusion_error(


def word_error_rate(
truth: typing.Sequence[typing.Sequence[str]],
prediction: typing.Sequence[typing.Sequence[str]],
truth: Sequence[Sequence[str]],
prediction: Sequence[Sequence[str]],
) -> float:
r"""Word error rate based on edit distance.

Expand Down Expand Up @@ -1229,13 +1226,9 @@ def word_error_rate(


def _matching_scores(
truth: typing.Union[
typing.Union[bool, int], typing.Sequence[typing.Union[bool, int]]
],
prediction: typing.Union[
typing.Union[bool, int, float], typing.Sequence[typing.Union[bool, int, float]]
],
) -> typing.Tuple[np.ndarray, np.ndarray]:
truth: (bool | int | Sequence[bool | int]),
prediction: (bool | int | float | Sequence[bool | int | float]),
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) -> tuple[np.ndarray, np.ndarray]:
r"""Mated and non-mated scores for verification tasks.

For verification task,
Expand Down Expand Up @@ -1274,7 +1267,7 @@ def _matching_scores(
"""
truth = np.array(truth)

allowed_truth_values = set([1, 0, True, False])
allowed_truth_values = {1, 0, True, False}
if not set(truth).issubset(allowed_truth_values):
raise ValueError(
"'truth' is only allowed to contain "
Expand Down
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