import collections
-import operator
import typing
import warnings
@@ -149,17 +148,15 @@ Source code for audmetric.core.api
import audeer
-from audmetric.core.utils import (
- assert_equal_length,
- infer_labels,
- scores_per_subgroup_and_class,
-)
+from audmetric.core.utils import assert_equal_length
+from audmetric.core.utils import infer_labels
+from audmetric.core.utils import scores_per_subgroup_and_class
[docs]def accuracy(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
- labels: typing.Sequence[typing.Union[str, int]] = None
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ labels: typing.Sequence[typing.Union[str, int]] = None,
) -> float:
r"""Classification accuracy.
@@ -199,10 +196,7 @@ Source code for audmetric.core.api
# keep where both prediction and truth contained in `labels`
label_mask = np.nonzero(
- np.logical_or(
- np.isin(truth, labels),
- np.isin(prediction, labels)
- )
+ np.logical_or(np.isin(truth, labels), np.isin(prediction, labels))
)
truth = truth[label_mask]
prediction = prediction[label_mask]
@@ -214,8 +208,10 @@ Source code for audmetric.core.api
[docs]def concordance_cc(
- truth: typing.Sequence[float],
- prediction: typing.Sequence[float],
+ truth: typing.Sequence[float],
+ prediction: typing.Sequence[float],
+ *,
+ ignore_nan: bool = False,
) -> float:
r"""Concordance correlation coefficient.
@@ -234,6 +230,10 @@ Source code for audmetric.core.api
Args:
truth: ground truth values
prediction: predicted values
+ ignore_nan: if ``True``
+ all samples that contain ``NaN``
+ in ``truth`` or ``prediction``
+ are ignored
Returns:
concordance correlation coefficient :math:`\in [-1, 1]`
@@ -243,7 +243,7 @@ Source code for audmetric.core.api
Examples:
>>> concordance_cc([0, 1, 2], [0, 1, 1])
- 0.6666666666666666
+ 0.6666666666666665
"""
assert_equal_length(truth, prediction)
@@ -253,33 +253,37 @@ Source code for audmetric.core.api
if not isinstance(prediction, np.ndarray):
prediction = np.array(list(prediction))
+ if ignore_nan:
+ mask = ~(np.isnan(truth) | np.isnan(prediction))
+ truth = truth[mask]
+ prediction = prediction[mask]
+
if len(prediction) < 2:
return np.NaN
- r = pearson_cc(prediction, truth)
- x_mean = prediction.mean()
- y_mean = truth.mean()
- x_std = prediction.std()
- y_std = truth.std()
- denominator = (
- x_std * x_std
- + y_std * y_std
- + (x_mean - y_mean) * (x_mean - y_mean)
- )
+ length = prediction.size
+ mean_y = np.mean(truth)
+ mean_x = np.mean(prediction)
+ a = prediction - mean_x
+ b = truth - mean_y
+
+ numerator = 2 * np.dot(a, b)
+ denominator = np.dot(a, a) + np.dot(b, b) + length * (mean_x - mean_y) ** 2
+
if denominator == 0:
ccc = np.nan
else:
- ccc = 2 * r * x_std * y_std / denominator
+ ccc = numerator / denominator
return float(ccc)
[docs]def confusion_matrix(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
- labels: typing.Sequence[typing.Any] = None,
- *,
- normalize: bool = False,
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ labels: typing.Sequence[typing.Any] = None,
+ *,
+ normalize: bool = False,
) -> typing.List[typing.List[typing.Union[int, float]]]:
r"""Confusion matrix.
@@ -332,14 +336,8 @@ Source code for audmetric.core.api
[docs]def detection_error_tradeoff(
- 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]]
- ],
+ truth: typing.Sequence[typing.Union[bool, int]],
+ prediction: typing.Sequence[typing.Union[bool, int, float]],
) -> typing.Tuple[np.ndarray, np.ndarray, np.ndarray]:
r"""Detection error tradeoff for verification experiments.
@@ -362,8 +360,8 @@ Source code for audmetric.core.api
whereas prediction values
can also contain similarity scores, e.g. ``[0.8, 0.1, ...]``.
- The implementation is identical with the one provided
- by the pyeer_ package.
+ The implementation was inspired by pyeer.eer_stats.calculate_roc but has
+ been accelerated by using numpy-arrays instead of lists.
.. _detection error tradeoff (DET): https://en.wikipedia.org/wiki/Detection_error_tradeoff
.. _pyeer: https://github.com/manuelaguadomtz/pyeer
@@ -398,11 +396,13 @@ Source code for audmetric.core.api
iscores_number = len(iscores)
# Labeling genuine scores as 1 and impostor scores as 0
- gscores = list(zip(gscores, [1] * gscores_number))
- iscores = list(zip(iscores, [0] * iscores_number))
+ gscores = np.column_stack((gscores, np.ones(gscores_number, dtype=int)))
+ iscores = np.column_stack((iscores, np.zeros(iscores_number, dtype=int)))
# Stacking scores
- scores = np.array(sorted(gscores + iscores, key=operator.itemgetter(0)))
+ all_scores = np.concatenate([gscores, iscores])
+ sorted_indices = np.argsort(all_scores[:, 0])
+ scores = all_scores[sorted_indices]
cumul = np.cumsum(scores[:, 1])
# Grouping scores
@@ -421,7 +421,7 @@ Source code for audmetric.core.api
[docs]def edit_distance(
truth: typing.Union[str, typing.Sequence[int]],
- prediction: typing.Union[str, typing.Sequence[int]]
+ prediction: typing.Union[str, typing.Sequence[int]],
) -> int:
r"""Edit distance between two sequences of chars or ints.
@@ -438,8 +438,8 @@ Source code for audmetric.core.api
edit distance
Examples:
- >>> truth = 'lorem'
- >>> prediction = 'lorm'
+ >>> truth = "lorem"
+ >>> prediction = "lorm"
>>> edit_distance(truth, prediction)
1
>>> truth = [0, 1, 2]
@@ -467,9 +467,11 @@ Source code for audmetric.core.api
m1[0] = i + 1
for j in range(len(truth)):
cost = 0 if prediction[i] == truth[j] else 1
- m1[j + 1] = min(m1[j] + 1, # deletion
- m0[j + 1] + 1, # insertion
- m0[j] + cost) # substitution
+ m1[j + 1] = min(
+ m1[j] + 1, # deletion
+ m0[j + 1] + 1, # insertion
+ m0[j] + cost,
+ ) # substitution
for j in range(len(m0)):
m0[j] = m1[j]
@@ -478,14 +480,8 @@ Source code for audmetric.core.api
[docs]def equal_error_rate(
- 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]]
- ],
+ truth: typing.Sequence[typing.Union[bool, int]],
+ prediction: typing.Sequence[typing.Union[bool, int, float]],
) -> typing.Tuple[float, collections.namedtuple]:
r"""Equal error rate for verification tasks.
@@ -552,12 +548,12 @@ Source code for audmetric.core.api
"""
Stats = collections.namedtuple(
- 'stats',
+ "stats",
[
- 'fmr', # False match rates (FMR)
- 'fnmr', # False non-match rates (FNMR)
- 'thresholds', # Thresholds
- 'threshold', # verification threshold for EER
+ "fmr", # False match rates (FMR)
+ "fnmr", # False non-match rates (FNMR)
+ "thresholds", # Thresholds
+ "threshold", # verification threshold for EER
],
)
fmr, fnmr, thresholds = detection_error_tradeoff(truth, prediction)
@@ -568,9 +564,9 @@ Source code for audmetric.core.api
t2 = t2[0]
else:
warnings.warn(
- 'The false match rate '
- 'and false non-match rate curves '
- 'do not intersect each other.',
+ "The false match rate "
+ "and false non-match rate curves "
+ "do not intersect each other.",
RuntimeWarning,
)
eer = 1.0
@@ -590,9 +586,7 @@ Source code for audmetric.core.api
[docs]def event_error_rate(
- truth: typing.Union[
- str, typing.Sequence[typing.Union[str, typing.Sequence[int]]]
- ],
+ truth: typing.Union[str, typing.Sequence[typing.Union[str, typing.Sequence[int]]]],
prediction: typing.Union[
str, typing.Sequence[typing.Union[str, typing.Sequence[int]]]
],
@@ -623,9 +617,9 @@ Source code for audmetric.core.api
0.5
>>> event_error_rate([[0, 1], [2]], [[0], [2]])
0.25
- >>> event_error_rate(['lorem'], ['lorm'])
+ >>> event_error_rate(["lorem"], ["lorm"])
0.2
- >>> event_error_rate(['lorem', 'ipsum'], ['lorm', 'ipsum'])
+ >>> event_error_rate(["lorem", "ipsum"], ["lorm", "ipsum"])
0.1
"""
@@ -634,7 +628,7 @@ Source code for audmetric.core.api
assert_equal_length(truth, prediction)
- eer = 0.
+ eer = 0.0
for t, p in zip(truth, prediction):
n = max(len(t), len(p))
@@ -646,11 +640,11 @@ Source code for audmetric.core.api
[docs]def fscore_per_class(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
- labels: typing.Sequence[typing.Any] = None,
- *,
- zero_division: float = 0,
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ labels: typing.Sequence[typing.Any] = None,
+ *,
+ zero_division: float = 0,
) -> typing.Dict[str, float]:
r"""F-score per class.
@@ -705,16 +699,14 @@ Source code for audmetric.core.api
[docs]def linkability(
- 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]]
- ],
- omega: float = 1.0,
- nbins: int = None,
+ 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]]
+ ],
+ omega: float = 1.0,
+ nbins: int = None,
) -> float:
r"""Linkability for verification tasks.
@@ -767,14 +759,12 @@ Source code for audmetric.core.api
>>> truth = [1, 0] * int(samples / 2)
>>> prediction = []
>>> for _ in range(int(samples / 2)):
- ... prediction.extend(
- ... [np.random.uniform(0, 0.2), np.random.uniform(0.8, 1.0)]
- ... )
+ ... prediction.extend([np.random.uniform(0, 0.2), np.random.uniform(0.8, 1.0)])
>>> linkability(truth, prediction)
0.9747999999999999
>>> truth = [1, 0, 0, 0] * int(samples / 4)
>>> prediction = [np.random.uniform(0, 1) for _ in range(samples)]
- >>> linkability(truth, prediction, omega=1/3)
+ >>> linkability(truth, prediction, omega=1 / 3)
0.0
""" # noqa: E501
@@ -798,25 +788,22 @@ Source code for audmetric.core.api
y1 = np.histogram(mated_scores, bins=bin_edges, density=True)[0]
y2 = np.histogram(non_mated_scores, bins=bin_edges, density=True)[0]
# LR = P[s|mated ]/P[s|non-mated]
- LR = np.divide(y1, y2, out=np.ones_like(y1), where=y2 != 0)
- D = 2 * (omega * LR / (1 + omega*LR)) - 1
+ lr = np.divide(y1, y2, out=np.ones_like(y1), where=y2 != 0)
+ d = 2 * (omega * lr / (1 + omega * lr)) - 1
# Def of D
- D[omega * LR <= 1] = 0
+ d[omega * lr <= 1] = 0
# Taking care of inf/NaN
- mask = [
- True if y2[i] == 0 and y1[i] != 0 else False
- for i in range(len(y1))
- ]
- D[mask] = 1
+ mask = [True if y2[i] == 0 and y1[i] != 0 else False for i in range(len(y1))]
+ d[mask] = 1
# Global measure using trapz numerical integration
- Dsys = np.trapz(x=bin_centers, y=D * y1)
+ d_sys = np.trapz(x=bin_centers, y=d * y1)
- return Dsys
+ return d_sys
[docs]def mean_absolute_error(
- truth: typing.Sequence[float],
- prediction: typing.Sequence[float],
+ truth: typing.Sequence[float],
+ prediction: typing.Sequence[float],
) -> float:
r"""Mean absolute error.
@@ -849,8 +836,8 @@ Source code for audmetric.core.api
[docs]def mean_squared_error(
- truth: typing.Sequence[float],
- prediction: typing.Sequence[float],
+ truth: typing.Sequence[float],
+ prediction: typing.Sequence[float],
) -> float:
r"""Mean squared error.
@@ -883,8 +870,8 @@ Source code for audmetric.core.api
[docs]def pearson_cc(
- truth: typing.Sequence[float],
- prediction: typing.Sequence[float],
+ truth: typing.Sequence[float],
+ prediction: typing.Sequence[float],
) -> float:
r"""Pearson correlation coefficient.
@@ -925,11 +912,11 @@ Source code for audmetric.core.api
[docs]def precision_per_class(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
- labels: typing.Sequence[typing.Any] = None,
- *,
- zero_division: float = 0,
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ labels: typing.Sequence[typing.Any] = None,
+ *,
+ zero_division: float = 0,
) -> typing.Dict[str, float]:
r"""Precision per class.
@@ -961,7 +948,7 @@ Source code for audmetric.core.api
matrix = np.array(confusion_matrix(truth, prediction, labels))
total = matrix.sum(axis=0)
- old_settings = np.seterr(invalid='ignore')
+ old_settings = np.seterr(invalid="ignore")
recall = matrix.diagonal() / total
np.seterr(**old_settings)
recall[np.isnan(recall)] = zero_division
@@ -970,11 +957,11 @@ Source code for audmetric.core.api
[docs]def recall_per_class(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
- labels: typing.Sequence[typing.Any] = None,
- *,
- zero_division: float = 0,
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ labels: typing.Sequence[typing.Any] = None,
+ *,
+ zero_division: float = 0,
) -> typing.Dict[str, float]:
r"""Recall per class.
@@ -1006,7 +993,7 @@ Source code for audmetric.core.api
matrix = np.array(confusion_matrix(truth, prediction, labels))
total = matrix.sum(axis=1)
- old_settings = np.seterr(invalid='ignore')
+ old_settings = np.seterr(invalid="ignore")
recall = matrix.diagonal() / total
np.seterr(**old_settings)
recall[np.isnan(recall)] = zero_division
@@ -1015,26 +1002,26 @@ Source code for audmetric.core.api
[docs]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,
- *,
- subgroups: typing.Sequence[typing.Any] = None,
- metric: typing.Callable[
- [
- typing.Sequence[typing.Any],
- typing.Sequence[typing.Any],
- typing.Optional[typing.Sequence[str]],
- ],
- typing.Dict[str, float]
- ] = fscore_per_class,
- reduction: typing.Callable[
- [
- typing.Sequence[float],
- ],
- float,
- ] = np.std,
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ protected_variable: typing.Sequence[typing.Any],
+ labels: typing.Sequence[typing.Any] = None,
+ *,
+ subgroups: typing.Sequence[typing.Any] = None,
+ metric: typing.Callable[
+ [
+ typing.Sequence[typing.Any],
+ typing.Sequence[typing.Any],
+ typing.Optional[typing.Sequence[str]],
+ ],
+ typing.Dict[str, float],
+ ] = fscore_per_class,
+ reduction: typing.Callable[
+ [
+ typing.Sequence[float],
+ ],
+ float,
+ ] = np.std,
) -> float:
r"""Unweighted average bias of protected variable.
@@ -1091,26 +1078,27 @@ Source code for audmetric.core.api
``protected_variable``
Examples:
- >>> unweighted_average_bias([1, 1], [1, 0], ['male', 'female'])
+ >>> unweighted_average_bias([1, 1], [1, 0], ["male", "female"])
0.5
>>> unweighted_average_bias(
- ... [1, 1], [1, 0], ['male', 'female'],
- ... subgroups=['female', 'male'],
+ ... [1, 1],
+ ... [1, 0],
+ ... ["male", "female"],
+ ... subgroups=["female", "male"],
... reduction=lambda x: x[0] - x[1],
... )
-1.0
- >>> unweighted_average_bias(
- ... [0, 1], [1, 0], ['male', 'female'],
- ... metric=recall_per_class
- ... )
+ >>> unweighted_average_bias([0, 1], [1, 0], ["male", "female"], metric=recall_per_class)
nan
>>> unweighted_average_bias(
- ... [0, 0, 0, 0], [1, 1, 0, 0], ['a', 'b', 'c', 'd'],
+ ... [0, 0, 0, 0],
+ ... [1, 1, 0, 0],
+ ... ["a", "b", "c", "d"],
... metric=recall_per_class,
... )
0.5
- """
+ """ # noqa: E501
if labels is None:
labels = infer_labels(truth, prediction)
@@ -1134,14 +1122,14 @@ Source code for audmetric.core.api
zero_division=np.nan,
)
- bias = 0.
+ bias = 0.0
denominator = 0
for label in labels:
scores_subgroup = [
- scores[subgroup][label] for subgroup in subgroups
- if label in scores[subgroup]
- and not np.isnan(scores[subgroup][label])
+ scores[subgroup][label]
+ for subgroup in subgroups
+ if label in scores[subgroup] and not np.isnan(scores[subgroup][label])
]
# compute score divergence only where more than 1 score per class
if len(scores_subgroup) > 1:
@@ -1155,11 +1143,11 @@ Source code for audmetric.core.api
[docs]def unweighted_average_fscore(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
- labels: typing.Sequence[typing.Any] = None,
- *,
- zero_division: float = 0,
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ labels: typing.Sequence[typing.Any] = None,
+ *,
+ zero_division: float = 0,
) -> float:
r"""Unweighted average F-score.
@@ -1199,11 +1187,11 @@ Source code for audmetric.core.api
[docs]def unweighted_average_precision(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
- labels: typing.Sequence[typing.Any] = None,
- *,
- zero_division: float = 0,
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ labels: typing.Sequence[typing.Any] = None,
+ *,
+ zero_division: float = 0,
) -> float:
r"""Unweighted average precision.
@@ -1242,11 +1230,11 @@ Source code for audmetric.core.api
[docs]def unweighted_average_recall(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
- labels: typing.Sequence[typing.Any] = None,
- *,
- zero_division: float = 0,
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ labels: typing.Sequence[typing.Any] = None,
+ *,
+ zero_division: float = 0,
) -> float:
r"""Unweighted average recall.
@@ -1316,11 +1304,10 @@ Source code for audmetric.core.api
weighted confusion error
Examples:
-
>>> truth = [0, 1, 2]
>>> prediction = [0, 2, 0]
>>> # penalize only errors > 1
- >>> weights = [[0, 0 , 1], [0, 0, 0], [1, 0, 0]]
+ >>> weights = [[0, 0, 1], [0, 0, 0], [1, 0, 0]]
>>> weighted_confusion_error(truth, prediction, weights)
0.5
@@ -1331,10 +1318,10 @@ Source code for audmetric.core.api
if not cm.shape == weights.shape:
raise ValueError(
- 'Shape of weights '
- f'{weights.shape} '
- 'does not match shape of confusion matrix '
- f'{cm.shape}.'
+ "Shape of weights "
+ f"{weights.shape} "
+ "does not match shape of confusion matrix "
+ f"{cm.shape}."
)
weighted_cm = cm * weights
@@ -1343,7 +1330,7 @@ Source code for audmetric.core.api
[docs]def word_error_rate(
truth: typing.Sequence[typing.Sequence[str]],
- prediction: typing.Sequence[typing.Sequence[str]]
+ prediction: typing.Sequence[typing.Sequence[str]],
) -> float:
r"""Word error rate based on edit distance.
@@ -1358,15 +1345,15 @@ Source code for audmetric.core.api
ValueError: if ``truth`` and ``prediction`` differ in length
Examples:
- >>> truth = [['lorem', 'ipsum'], ['north', 'wind', 'and', 'sun']]
- >>> prediction = [['lorm', 'ipsum'], ['north', 'wind']]
+ >>> truth = [["lorem", "ipsum"], ["north", "wind", "and", "sun"]]
+ >>> prediction = [["lorm", "ipsum"], ["north", "wind"]]
>>> word_error_rate(truth, prediction)
0.5
"""
assert_equal_length(truth, prediction)
- wer = 0.
+ wer = 0.0
for t, p in zip(truth, prediction):
# map words to ints
@@ -1385,18 +1372,16 @@ Source code for audmetric.core.api
def _matching_scores(
truth: typing.Union[
- typing.Union[bool, int],
- typing.Sequence[typing.Union[bool, int]]
+ 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.Union[bool, int, float], typing.Sequence[typing.Union[bool, int, float]]
],
) -> typing.Tuple[np.ndarray, np.ndarray]:
r"""Mated and non-mated scores for verification tasks.
For verification task,
- predictions are usually seperated
+ predictions are usually separated
in all predictions belonging
to the matching examples,
and all other predictions.
@@ -1472,7 +1457,7 @@ Source code for audmetric.core.api
- Built with Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -1480,7 +1465,7 @@ Source code for audmetric.core.api
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/_modules/audmetric/core/utils.html b/_modules/audmetric/core/utils.html
index b6212ac..44136b7 100644
--- a/_modules/audmetric/core/utils.html
+++ b/_modules/audmetric/core/utils.html
@@ -29,7 +29,7 @@
-
+
@@ -65,7 +65,7 @@
- v1.2.0
+ v1.2.1
@@ -146,20 +146,20 @@ Source code for audmetric.core.utils
def assert_equal_length(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
):
r"""Assert truth and prediction have equal length."""
if len(truth) != len(prediction):
raise ValueError(
- f'Truth and prediction differ in length: '
- f'{len(truth)} != {len(prediction)}.'
+ f"Truth and prediction differ in length: "
+ f"{len(truth)} != {len(prediction)}."
)
[docs]def infer_labels(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
) -> typing.List[typing.Any]:
r"""Infer labels from truth and prediction.
@@ -178,21 +178,21 @@ Source code for audmetric.core.utils
def scores_per_subgroup_and_class(
- truth: typing.Sequence[typing.Any],
- prediction: typing.Sequence[typing.Any],
- protected_variable: typing.Sequence[typing.Any],
- metric: typing.Callable[
- [
- typing.Sequence[typing.Any],
- typing.Sequence[typing.Any],
- typing.Optional[typing.Sequence[str]],
- float
- ],
- typing.Dict[str, float],
+ truth: typing.Sequence[typing.Any],
+ prediction: typing.Sequence[typing.Any],
+ protected_variable: typing.Sequence[typing.Any],
+ metric: typing.Callable[
+ [
+ typing.Sequence[typing.Any],
+ typing.Sequence[typing.Any],
+ typing.Optional[typing.Sequence[str]],
+ float,
],
- labels: typing.Sequence[typing.Any],
- subgroups: typing.Sequence[typing.Any],
- zero_division: float,
+ typing.Dict[str, float],
+ ],
+ labels: typing.Sequence[typing.Any],
+ subgroups: typing.Sequence[typing.Any],
+ zero_division: float,
) -> typing.Dict[typing.Hashable, typing.Dict]:
r"""Compute scores per class for each subgroup based on metric.
@@ -216,20 +216,32 @@ Source code for audmetric.core.utils
Examples:
>>> import audmetric
>>> scores_per_subgroup_and_class(
- ... [1, 1], [0, 1], ['male', 'female'], audmetric.recall_per_class,
- ... [0, 1], ['male', 'female'], 0.)
+ ... [1, 1],
+ ... [0, 1],
+ ... ["male", "female"],
+ ... audmetric.recall_per_class,
+ ... [0, 1],
+ ... ["male", "female"],
+ ... 0.0,
+ ... )
{'male': {0: 0.0, 1: 0.0}, 'female': {0: 0.0, 1: 1.0}}
>>> scores_per_subgroup_and_class(
- ... [1, 1], [0, 1], ['male', 'female'], audmetric.precision_per_class,
- ... [0, 1], ['male', 'female'], zero_division=np.nan)
+ ... [1, 1],
+ ... [0, 1],
+ ... ["male", "female"],
+ ... audmetric.precision_per_class,
+ ... [0, 1],
+ ... ["male", "female"],
+ ... zero_division=np.nan,
+ ... )
{'male': {0: 0.0, 1: nan}, 'female': {0: nan, 1: 1.0}}
""" # noqa: E501
if set(subgroups) - set(protected_variable):
raise ValueError(
- f'`subgroups` contains manifestations of the protected '
- f'variable which are not contained in `protected_variable`: '
- f'{set(subgroups) - set(protected_variable)}'
+ f"`subgroups` contains manifestations of the protected "
+ f"variable which are not contained in `protected_variable`: "
+ f"{set(subgroups) - set(protected_variable)}"
)
truth = np.array(truth)
@@ -261,7 +273,7 @@ Source code for audmetric.core.utils
- Built with Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -269,7 +281,7 @@ Source code for audmetric.core.utils
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/_modules/index.html b/_modules/index.html
index dde6d07..35174ac 100644
--- a/_modules/index.html
+++ b/_modules/index.html
@@ -29,7 +29,7 @@
-
+
@@ -65,7 +65,7 @@
- v1.2.0
+ v1.2.1
@@ -156,7 +156,7 @@ All modules for which code is available
- Built with Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -164,7 +164,7 @@ All modules for which code is available
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
index ce0d0d3..dbe4802 100644
--- a/_sources/index.rst.txt
+++ b/_sources/index.rst.txt
@@ -1,5 +1,3 @@
-.. documentation master file
-
.. include:: ../README.rst
.. toctree::
diff --git a/_static/pygments.css b/_static/pygments.css
index 7a18115..8054382 100644
--- a/_static/pygments.css
+++ b/_static/pygments.css
@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
.highlight .cs { color: #60a0b0; background-color: #fff0f0 } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
diff --git a/api/audmetric.accuracy.html b/api/audmetric.accuracy.html
index 82a1efa..97ae6be 100644
--- a/api/audmetric.accuracy.html
+++ b/api/audmetric.accuracy.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -228,7 +228,7 @@ accuracy()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -236,7 +236,7 @@ accuracy()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.concordance_cc.html b/api/audmetric.concordance_cc.html
index 7a60724..f77ad30 100644
--- a/api/audmetric.concordance_cc.html
+++ b/api/audmetric.concordance_cc.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -168,7 +168,7 @@
concordance_cc()¶
-
-audmetric.concordance_cc(truth, prediction)[source]¶
+audmetric.concordance_cc(truth, prediction, *, ignore_nan=False)[source]¶
Concordance correlation coefficient.
@@ -240,7 +244,7 @@ concordance_cc()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.confusion_matrix.html b/api/audmetric.confusion_matrix.html
index fc55b66..6d67495 100644
--- a/api/audmetric.confusion_matrix.html
+++ b/api/audmetric.confusion_matrix.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -227,7 +227,7 @@ confusion_matrix()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -235,7 +235,7 @@ confusion_matrix()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.detection_error_tradeoff.html b/api/audmetric.detection_error_tradeoff.html
index e3675d1..2073fdb 100644
--- a/api/audmetric.detection_error_tradeoff.html
+++ b/api/audmetric.detection_error_tradeoff.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -186,13 +186,13 @@ detection_error_tradeoff()truth may only contain entries like [1, 0, True, False...],
whereas prediction values
can also contain similarity scores, e.g. [0.8, 0.1, ...].
-The implementation is identical with the one provided
-by the pyeer package.
+The implementation was inspired by pyeer.eer_stats.calculate_roc but has
+been accelerated by using numpy-arrays instead of lists.
- Parameters
-truth (Union[bool, int, Sequence[Union[bool, int]]]) – ground truth classes
prediction (Union[bool, int, float, Sequence[Union[bool, int, float]]]) – predicted classes or similarity scores
truth (Sequence[Union[bool, int]]) – ground truth classes
prediction (Sequence[Union[bool, int, float]]) – predicted classes or similarity scores
- Return type
@@ -245,7 +245,7 @@ detection_error_tradeoff()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -253,7 +253,7 @@ detection_error_tradeoff()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.edit_distance.html b/api/audmetric.edit_distance.html
index 1fc382b..44606e9 100644
--- a/api/audmetric.edit_distance.html
+++ b/api/audmetric.edit_distance.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -186,8 +186,8 @@ edit_distance()Examples
->>> truth = 'lorem'
->>> prediction = 'lorm'
+>>> truth = "lorem"
+>>> prediction = "lorm"
>>> edit_distance(truth, prediction)
1
>>> truth = [0, 1, 2]
@@ -223,7 +223,7 @@ edit_distance()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -231,7 +231,7 @@ edit_distance()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.equal_error_rate.html b/api/audmetric.equal_error_rate.html
index 9b6f5b2..ff73b27 100644
--- a/api/audmetric.equal_error_rate.html
+++ b/api/audmetric.equal_error_rate.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -202,8 +202,8 @@ equal_error_rate()
Parameters
-truth (Union[bool, int, Sequence[Union[bool, int]]]) – ground truth classes
prediction (Union[bool, int, float, Sequence[Union[bool, int, float]]]) – predicted classes or similarity scores
truth (Sequence[Union[bool, int]]) – ground truth classes
prediction (Sequence[Union[bool, int, float]]) – predicted classes or similarity scores
Return type
@@ -264,7 +264,7 @@ equal_error_rate()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -272,7 +272,7 @@ equal_error_rate()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.event_error_rate.html b/api/audmetric.event_error_rate.html
index 869629a..268e7ad 100644
--- a/api/audmetric.event_error_rate.html
+++ b/api/audmetric.event_error_rate.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -199,9 +199,9 @@ event_error_rate()0.5
>>> event_error_rate([[0, 1], [2]], [[0], [2]])
0.25
->>> event_error_rate(['lorem'], ['lorm'])
+>>> event_error_rate(["lorem"], ["lorm"])
0.2
->>> event_error_rate(['lorem', 'ipsum'], ['lorm', 'ipsum'])
+>>> event_error_rate(["lorem", "ipsum"], ["lorm", "ipsum"])
0.1
@@ -232,7 +232,7 @@ event_error_rate()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -240,7 +240,7 @@ event_error_rate()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.fscore_per_class.html b/api/audmetric.fscore_per_class.html
index 3fc3c42..d8417ef 100644
--- a/api/audmetric.fscore_per_class.html
+++ b/api/audmetric.fscore_per_class.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -225,7 +225,7 @@ fscore_per_class()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -233,7 +233,7 @@ fscore_per_class()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.html b/api/audmetric.html
index 7b3dcc1..4a63765 100644
--- a/api/audmetric.html
+++ b/api/audmetric.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -258,7 +258,7 @@
- Built with Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -266,7 +266,7 @@
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.linkability.html b/api/audmetric.linkability.html
index a75b527..4815fc5 100644
--- a/api/audmetric.linkability.html
+++ b/api/audmetric.linkability.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -223,14 +223,12 @@ linkability()>>> truth = [1, 0] * int(samples / 2)
>>> prediction = []
>>> for _ in range(int(samples / 2)):
-... prediction.extend(
-... [np.random.uniform(0, 0.2), np.random.uniform(0.8, 1.0)]
-... )
+... prediction.extend([np.random.uniform(0, 0.2), np.random.uniform(0.8, 1.0)])
>>> linkability(truth, prediction)
0.9747999999999999
>>> truth = [1, 0, 0, 0] * int(samples / 4)
>>> prediction = [np.random.uniform(0, 1) for _ in range(samples)]
->>> linkability(truth, prediction, omega=1/3)
+>>> linkability(truth, prediction, omega=1 / 3)
0.0
@@ -261,7 +259,7 @@ linkability()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -269,7 +267,7 @@ linkability()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.mean_absolute_error.html b/api/audmetric.mean_absolute_error.html
index c395652..7dcf6db 100644
--- a/api/audmetric.mean_absolute_error.html
+++ b/api/audmetric.mean_absolute_error.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -221,7 +221,7 @@ mean_absolute_error()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -229,7 +229,7 @@ mean_absolute_error()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.mean_squared_error.html b/api/audmetric.mean_squared_error.html
index c88611f..c56d315 100644
--- a/api/audmetric.mean_squared_error.html
+++ b/api/audmetric.mean_squared_error.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -221,7 +221,7 @@ mean_squared_error()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -229,7 +229,7 @@ mean_squared_error()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.pearson_cc.html b/api/audmetric.pearson_cc.html
index ccd4c08..42e2739 100644
--- a/api/audmetric.pearson_cc.html
+++ b/api/audmetric.pearson_cc.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -223,7 +223,7 @@ pearson_cc()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -231,7 +231,7 @@ pearson_cc()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.precision_per_class.html b/api/audmetric.precision_per_class.html
index 13a3287..bff3041 100644
--- a/api/audmetric.precision_per_class.html
+++ b/api/audmetric.precision_per_class.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -224,7 +224,7 @@ precision_per_class()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -232,7 +232,7 @@ precision_per_class()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.recall_per_class.html b/api/audmetric.recall_per_class.html
index a0f3a6a..f99870e 100644
--- a/api/audmetric.recall_per_class.html
+++ b/api/audmetric.recall_per_class.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -224,7 +224,7 @@ recall_per_class()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -232,7 +232,7 @@ recall_per_class()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.unweighted_average_bias.html b/api/audmetric.unweighted_average_bias.html
index ceb4651..8c0016c 100644
--- a/api/audmetric.unweighted_average_bias.html
+++ b/api/audmetric.unweighted_average_bias.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -232,21 +232,22 @@ unweighted_average_bias()Examples
->>> unweighted_average_bias([1, 1], [1, 0], ['male', 'female'])
+>>> unweighted_average_bias([1, 1], [1, 0], ["male", "female"])
0.5
>>> unweighted_average_bias(
-... [1, 1], [1, 0], ['male', 'female'],
-... subgroups=['female', 'male'],
+... [1, 1],
+... [1, 0],
+... ["male", "female"],
+... subgroups=["female", "male"],
... reduction=lambda x: x[0] - x[1],
... )
-1.0
->>> unweighted_average_bias(
-... [0, 1], [1, 0], ['male', 'female'],
-... metric=recall_per_class
-... )
+>>> unweighted_average_bias([0, 1], [1, 0], ["male", "female"], metric=recall_per_class)
nan
>>> unweighted_average_bias(
-... [0, 0, 0, 0], [1, 1, 0, 0], ['a', 'b', 'c', 'd'],
+... [0, 0, 0, 0],
+... [1, 1, 0, 0],
+... ["a", "b", "c", "d"],
... metric=recall_per_class,
... )
0.5
@@ -279,7 +280,7 @@ unweighted_average_bias()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -287,7 +288,7 @@ unweighted_average_bias()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.unweighted_average_fscore.html b/api/audmetric.unweighted_average_fscore.html
index 53da372..56103c3 100644
--- a/api/audmetric.unweighted_average_fscore.html
+++ b/api/audmetric.unweighted_average_fscore.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -226,7 +226,7 @@ unweighted_average_fscore()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -234,7 +234,7 @@ unweighted_average_fscore()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.unweighted_average_precision.html b/api/audmetric.unweighted_average_precision.html
index f1273db..c49c87a 100644
--- a/api/audmetric.unweighted_average_precision.html
+++ b/api/audmetric.unweighted_average_precision.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -225,7 +225,7 @@ unweighted_average_precision()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -233,7 +233,7 @@ unweighted_average_precision()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.unweighted_average_recall.html b/api/audmetric.unweighted_average_recall.html
index fe8dcc1..78c304f 100644
--- a/api/audmetric.unweighted_average_recall.html
+++ b/api/audmetric.unweighted_average_recall.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -225,7 +225,7 @@ unweighted_average_recall()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -233,7 +233,7 @@ unweighted_average_recall()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.utils.html b/api/audmetric.utils.html
index a98a961..40c50d4 100644
--- a/api/audmetric.utils.html
+++ b/api/audmetric.utils.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -182,7 +182,7 @@
- Built with Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -190,7 +190,7 @@
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.utils.infer_labels.html b/api/audmetric.utils.infer_labels.html
index f870e06..0ff292f 100644
--- a/api/audmetric.utils.infer_labels.html
+++ b/api/audmetric.utils.infer_labels.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -194,7 +194,7 @@ infer_labels()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -202,7 +202,7 @@ infer_labels()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.weighted_confusion_error.html b/api/audmetric.weighted_confusion_error.html
index 8692169..ee84ec5 100644
--- a/api/audmetric.weighted_confusion_error.html
+++ b/api/audmetric.weighted_confusion_error.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -203,7 +203,7 @@ weighted_confusion_error()>>> truth = [0, 1, 2]
>>> prediction = [0, 2, 0]
>>> # penalize only errors > 1
->>> weights = [[0, 0 , 1], [0, 0, 0], [1, 0, 0]]
+>>> weights = [[0, 0, 1], [0, 0, 0], [1, 0, 0]]
>>> weighted_confusion_error(truth, prediction, weights)
0.5
@@ -235,7 +235,7 @@ weighted_confusion_error()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -243,7 +243,7 @@ weighted_confusion_error()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/api/audmetric.word_error_rate.html b/api/audmetric.word_error_rate.html
index 1ba1f24..e03435b 100644
--- a/api/audmetric.word_error_rate.html
+++ b/api/audmetric.word_error_rate.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -188,8 +188,8 @@ word_error_rate()Examples
->>> truth = [['lorem', 'ipsum'], ['north', 'wind', 'and', 'sun']]
->>> prediction = [['lorm', 'ipsum'], ['north', 'wind']]
+>>> truth = [["lorem", "ipsum"], ["north", "wind", "and", "sun"]]
+>>> prediction = [["lorm", "ipsum"], ["north", "wind"]]
>>> word_error_rate(truth, prediction)
0.5
@@ -221,7 +221,7 @@ word_error_rate()Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -229,7 +229,7 @@ word_error_rate()
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/changelog.html b/changelog.html
index 525b4ce..cd69934 100644
--- a/changelog.html
+++ b/changelog.html
@@ -29,7 +29,7 @@
-
+
@@ -66,7 +66,7 @@
- v1.2.0
+ v1.2.1
@@ -104,6 +104,7 @@
- Contributing
- Changelog
+- Version 1.2.1 (2024/02/28)
- Version 1.2.0 (2023/05/08)
- Version 1.1.6 (2023/01/03)
- Version 1.1.5 (2023/01/03)
@@ -157,6 +158,17 @@ ChangelogKeep a Changelog,
and this project adheres to Semantic Versioning.
+
+Version 1.2.1 (2024/02/28)¶
+
+Added: ignore_nan argument
+to audmetric.concordance_cc()
Added: support for Python 3.12
Changed: speedup audmetric.detection_error_tradeoff()
Fixed: avoid deprecation warning for pkg_resources
Removed: support for Python 3.7
+
Version 1.2.0 (2023/05/08)¶
@@ -254,7 +266,7 @@ Version 1.0.0 (2021/06/09)Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -262,7 +274,7 @@ Version 1.0.0 (2021/06/09)
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/contributing.html b/contributing.html
index 7d2f336..513c2c5 100644
--- a/contributing.html
+++ b/contributing.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -152,30 +152,37 @@
Contributing¶
Everyone is invited to contribute to this project.
Feel free to create a pull request .
-If you find errors, omissions, inconsistencies or other things
-that need improvement, please create an issue.
+If you find errors,
+omissions,
+inconsistencies,
+or other things
+that need improvement,
+please create an issue.
Development Installation¶
-Instead of pip-installing the latest release from PyPI,
-you should get the newest development version from Github:
+Instead of pip-installing the latest release from PyPI,
+you should get the newest development version from Github:
git clone https://github.com/audeering/audmetric/
cd audmetric
-# Create virutal environment for this project
+# Create virtual environment for this project
# e.g.
# virtualenv --python="python3" $HOME/.envs/audmetric
# source $HOME/.envs/audmetric/bin/activate
pip install -r requirements.txt
-This way, your installation always stays up-to-date,
+
This way,
+your installation always stays up-to-date,
even if you pull new changes from the Github repository.
Coding Convention¶
We follow the PEP8 convention for Python code
-and check for correct syntax with flake8.
-Exceptions are defined under the [flake8] section
-in setup.cfg.
+and use ruff as a linter and code formatter.
+In addition,
+we check for common spelling errors with codespell.
+Both tools and possible exceptions
+are defined in pyproject.toml.
The checks are executed in the CI using pre-commit.
You can enable those checks locally by executing:
pip install pre-commit # consider system wide installation
@@ -183,26 +190,28 @@ Coding Conventionpre-commit run --all-files
-Afterwards flake8 is executed
+
Afterwards ruff and codespell are executed
every time you create a commit.
-You can also install flake8
+
You can also install ruff and codespell
and call it directly:
-pip install flake8 # consider system wide installation
-flake8
+pip install ruff codespell # consider system wide installation
+ruff check --fix . # lint all Python files, and fix any fixable errors
+ruff format . # format code of all Python files
+codespell
It can be restricted to specific folders:
-flake8 audfoo/ tests/
+ruff check audmetric/ tests/
+codespell audmetric/ tests/
Building the Documentation¶
If you make changes to the documentation,
-you can re-create the HTML pages using Sphinx.
+you can re-create the HTML pages using Sphinx.
You can install it and a few other necessary packages with:
-pip install -r requirements.txt
-pip install -r docs/requirements.txt
+pip install -r docs/requirements.txt
To create the HTML pages, use:
@@ -212,13 +221,13 @@ Building the Documentationbuild/sphinx/html/.
It is also possible to automatically check if all links are still valid:
-python -m sphinx docs/ build/sphinx/linkcheck -b linkcheck
+python -m sphinx docs/ build/sphinx/html -b linkcheck
Running the Tests¶
-You’ll need pytest for that.
+
You’ll need pytest for that.
It can be installed with:
pip install -r tests/requirements.txt
@@ -263,7 +272,7 @@ Creating a New ReleaseSphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -271,7 +280,7 @@ Creating a New Release
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/genindex.html b/genindex.html
index 5c44138..351298a 100644
--- a/genindex.html
+++ b/genindex.html
@@ -29,7 +29,7 @@
-
+
@@ -65,7 +65,7 @@
- v1.2.0
+ v1.2.1
@@ -323,7 +323,7 @@ W
- Built with Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -331,7 +331,7 @@ W
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/index.html b/index.html
index bb78a5f..1a952c2 100644
--- a/index.html
+++ b/index.html
@@ -29,7 +29,7 @@
-
+
@@ -66,7 +66,7 @@
- v1.2.0
+ v1.2.1
@@ -142,7 +142,7 @@
audmetric¶
-
+ 
audmetric includes several equations
to estimate the performance of a machine learning prediction algorithm.
Some of the metrics are also available in sklearn,
@@ -179,7 +179,7 @@
audmetricSphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -187,7 +187,7 @@ audmetric
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/install.html b/install.html
index d69208a..af5ea2e 100644
--- a/install.html
+++ b/install.html
@@ -29,7 +29,7 @@
-
+
@@ -67,7 +67,7 @@
- v1.2.0
+ v1.2.1
@@ -175,7 +175,7 @@ InstallationSphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -183,7 +183,7 @@ Installation
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/objects.inv b/objects.inv
index f82e91c..53dec38 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/py-modindex.html b/py-modindex.html
index 1a3258e..e0660a2 100644
--- a/py-modindex.html
+++ b/py-modindex.html
@@ -29,7 +29,7 @@
-
+
@@ -68,7 +68,7 @@
- v1.2.0
+ v1.2.1
@@ -179,7 +179,7 @@ Python Module Index
- Built with Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -187,7 +187,7 @@ Python Module Index
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/search.html b/search.html
index c9f0b0c..78cc083 100644
--- a/search.html
+++ b/search.html
@@ -29,7 +29,7 @@
-
+
@@ -65,7 +65,7 @@
- v1.2.0
+ v1.2.1
@@ -166,7 +166,7 @@
- Built with Sphinx on 2023/05/08 using the audEERING theme
+ Built with Sphinx on 2024/02/28 using the audEERING theme
@@ -174,7 +174,7 @@
- © 2019-2023 audEERING GmbH
+ © 2019-2024 audEERING GmbH
diff --git a/searchindex.js b/searchindex.js
index 5be8b99..5db6e11 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["api/audmetric", "api/audmetric.accuracy", "api/audmetric.concordance_cc", "api/audmetric.confusion_matrix", "api/audmetric.detection_error_tradeoff", "api/audmetric.edit_distance", "api/audmetric.equal_error_rate", "api/audmetric.event_error_rate", "api/audmetric.fscore_per_class", "api/audmetric.linkability", "api/audmetric.mean_absolute_error", "api/audmetric.mean_squared_error", "api/audmetric.pearson_cc", "api/audmetric.precision_per_class", "api/audmetric.recall_per_class", "api/audmetric.unweighted_average_bias", "api/audmetric.unweighted_average_fscore", "api/audmetric.unweighted_average_precision", "api/audmetric.unweighted_average_recall", "api/audmetric.utils", "api/audmetric.utils.infer_labels", "api/audmetric.weighted_confusion_error", "api/audmetric.word_error_rate", "changelog", "contributing", "genindex", "index", "install"], "filenames": ["api/audmetric.rst", "api/audmetric.accuracy.rst", "api/audmetric.concordance_cc.rst", "api/audmetric.confusion_matrix.rst", "api/audmetric.detection_error_tradeoff.rst", "api/audmetric.edit_distance.rst", "api/audmetric.equal_error_rate.rst", "api/audmetric.event_error_rate.rst", "api/audmetric.fscore_per_class.rst", "api/audmetric.linkability.rst", "api/audmetric.mean_absolute_error.rst", "api/audmetric.mean_squared_error.rst", "api/audmetric.pearson_cc.rst", "api/audmetric.precision_per_class.rst", "api/audmetric.recall_per_class.rst", "api/audmetric.unweighted_average_bias.rst", "api/audmetric.unweighted_average_fscore.rst", "api/audmetric.unweighted_average_precision.rst", "api/audmetric.unweighted_average_recall.rst", "api/audmetric.utils.rst", "api/audmetric.utils.infer_labels.rst", "api/audmetric.weighted_confusion_error.rst", "api/audmetric.word_error_rate.rst", "changelog.rst", "contributing.rst", "genindex.rst", "index.rst", "install.rst"], "titles": ["audmetric", "accuracy()", "concordance_cc()", "confusion_matrix()", "detection_error_tradeoff()", "edit_distance()", "equal_error_rate()", "event_error_rate()", "fscore_per_class()", "linkability()", "mean_absolute_error()", "mean_squared_error()", "pearson_cc()", "precision_per_class()", "recall_per_class()", "unweighted_average_bias()", "unweighted_average_fscore()", "unweighted_average_precision()", "unweighted_average_recall()", "audmetric.utils", "infer_labels()", "weighted_confusion_error()", "word_error_rate()", "Changelog", "Contributing", "Index", "audmetric", "Installation"], "terms": {"audmetr": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 27], "truth": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23], "predict": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 26], "label": [1, 3, 8, 13, 14, 15, 16, 17, 18, 20, 21], "none": [1, 3, 8, 9, 13, 14, 15, 16, 17, 18, 21], "sourc": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 24, 27], "classif": 1, 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