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make beta an optional parameter for f1_score function #93

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make beta an optional parameter for f1_score function #93

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10 changes: 7 additions & 3 deletions seqeval/metrics/sequence_labeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -281,7 +281,8 @@ def f1_score(y_true: List[List[str]], y_pred: List[List[str]],
mode: Optional[str] = None,
sample_weight: Optional[List[int]] = None,
zero_division: str = 'warn',
scheme: Optional[Type[Token]] = None):
scheme: Optional[Type[Token]] = None,
beta: float = 1.0):
"""Compute the F1 score.

The F1 score can be interpreted as a weighted average of the precision and
Expand Down Expand Up @@ -329,6 +330,9 @@ def f1_score(y_true: List[List[str]], y_pred: List[List[str]],
scheme : Token, [IOB2, IOE2, IOBES]

suffix : bool, False by default.

beta : float, 1.0 by default
The strength of recall versus precision in the F-score.

Returns:
score : float or array of float, shape = [n_unique_labels].
Expand All @@ -350,7 +354,7 @@ def f1_score(y_true: List[List[str]], y_pred: List[List[str]],
_, _, f, _ = precision_recall_fscore_support_v1(y_true, y_pred,
average=average,
warn_for=('f-score',),
beta=1,
beta=beta,
sample_weight=sample_weight,
zero_division=zero_division,
scheme=scheme,
Expand All @@ -359,7 +363,7 @@ def f1_score(y_true: List[List[str]], y_pred: List[List[str]],
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
average=average,
warn_for=('f-score',),
beta=1,
beta=beta,
sample_weight=sample_weight,
zero_division=zero_division,
suffix=suffix)
Expand Down