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Remove beta.

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This commit is contained in:
Raphael Mitsch 2022-09-02 14:21:06 +02:00
parent 110850f095
commit 24b69a1be8
3 changed files with 24 additions and 37 deletions
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9
spacy/pipeline/spancat.py
View File

@ -1,8 +1,7 @@
from functools import partial
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any
from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
from thinc.api import Optimizer
from thinc.types import Ragged, Ints2d, Floats2d, Ints1d
from thinc.types import Ragged, Ints2d, Floats2d
import numpy
@ -166,8 +165,8 @@ def spancat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
@registry.scorers("spacy.spancat_scorer.v1")
def make_spancat_scorer(beta: float = 1.0):
return partial(spancat_score, beta=beta)
def make_spancat_scorer():
return spancat_score
class SpanCategorizer(TrainablePipe):

5
spacy/pipeline/textcat_multilabel.py
View File

@ -1,4 +1,3 @@
from functools import partial
from typing import Iterable, Optional, Dict, List, Callable, Any
from thinc.types import Floats2d
from thinc.api import Model, Config
@ -122,8 +121,8 @@ def textcat_multilabel_score(examples: Iterable[Example], **kwargs) -> Dict[str,
@registry.scorers("spacy.textcat_multilabel_scorer.v1")
def make_textcat_multilabel_scorer(beta: float = 1.0):
return partial(textcat_multilabel_score, beta=beta)
def make_textcat_multilabel_scorer():
return textcat_multilabel_score
class MultiLabel_TextCategorizer(TextCategorizer):

47
spacy/scorer.py
View File

@ -102,8 +102,6 @@ class ROCAUCScore:
class Scorer:
"""Compute evaluation scores."""
BETA = 1.0
def __init__(
self,
nlp: Optional["Language"] = None,
@ -152,9 +150,8 @@ class Scorer:
DOCS: https://spacy.io/api/scorer#score_tokenization
"""
beta = cfg.get("beta", Scorer.BETA)
acc_score = PRFScore(beta=beta)
prf_score = PRFScore(beta=beta)
acc_score = PRFScore()
prf_score = PRFScore()
for example in examples:
gold_doc = example.reference
pred_doc = example.predicted
@ -214,7 +211,7 @@ class Scorer:
DOCS: https://spacy.io/api/scorer#score_token_attr
"""
tag_score = PRFScore(beta=cfg.get("beta", Scorer.BETA))
tag_score = PRFScore()
for example in examples:
gold_doc = example.reference
pred_doc = example.predicted
@ -265,8 +262,7 @@ class Scorer:
key attr_micro_p/r/f and the per-feat PRF scores under
attr_per_feat.
"""
beta = cfg.get("beta", Scorer.BETA)
micro_score = PRFScore(beta=beta)
micro_score = PRFScore()
per_feat = {}
for example in examples:
pred_doc = example.predicted
@ -281,7 +277,7 @@ class Scorer:
for feat in morph.split(Morphology.FEATURE_SEP):
field, values = feat.split(Morphology.FIELD_SEP)
if field not in per_feat:
per_feat[field] = PRFScore(beta=beta)
per_feat[field] = PRFScore()
if field not in gold_per_feat:
gold_per_feat[field] = set()
gold_per_feat[field].add((gold_i, feat))
@ -303,7 +299,7 @@ class Scorer:
for feat in morph.split(Morphology.FEATURE_SEP):
field, values = feat.split(Morphology.FIELD_SEP)
if field not in per_feat:
per_feat[field] = PRFScore(beta=beta)
per_feat[field] = PRFScore()
if field not in pred_per_feat:
pred_per_feat[field] = set()
pred_per_feat[field].add((gold_i, feat))
@ -336,7 +332,6 @@ class Scorer:
has_annotation: Optional[Callable[[Doc], bool]] = None,
labeled: bool = True,
allow_overlap: bool = False,
beta: float = 1.0,
**cfg,
) -> Dict[str, Any]:
"""Returns PRF scores for labeled spans.
@ -354,13 +349,12 @@ class Scorer:
equal if their start and end match, irrespective of their label.
allow_overlap (bool): Whether or not to allow overlapping spans.
If set to 'False', the alignment will automatically resolve conflicts.
beta (float): Beta coefficient for F-score calculation. Defaults to 1.0.
RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under
the keys attr_p/r/f and the per-type PRF scores under attr_per_type.
DOCS: https://spacy.io/api/scorer#score_spans
"""
score = PRFScore(beta=beta)
score = PRFScore()
score_per_type = dict()
for example in examples:
pred_doc = example.predicted
@ -379,7 +373,7 @@ class Scorer:
gold_per_type: Dict[str, Set] = {label: set() for label in labels}
for label in labels:
if label not in score_per_type:
score_per_type[label] = PRFScore(beta=beta)
score_per_type[label] = PRFScore()
# Find all predidate labels, for all and per type
gold_spans = set()
pred_spans = set()
@ -440,7 +434,6 @@ class Scorer:
multi_label: bool = True,
positive_label: Optional[str] = None,
threshold: Optional[float] = None,
beta: float = 1.0,
**cfg,
) -> Dict[str, Any]:
"""Returns PRF and ROC AUC scores for a doc-level attribute with a
@ -460,7 +453,6 @@ class Scorer:
threshold (float): Cutoff to consider a prediction "positive". Defaults
to 0.5 for multi-label, and 0.0 (i.e. whatever's highest scoring)
otherwise.
beta (float): Beta coefficient for F-score calculation.
RETURNS (Dict[str, Any]): A dictionary containing the scores, with
inapplicable scores as None:
for all:
@ -480,7 +472,7 @@ class Scorer:
"""
if threshold is None:
threshold = 0.5 if multi_label else 0.0
f_per_type = {label: PRFScore(beta=beta) for label in labels}
f_per_type = {label: PRFScore() for label in labels}
auc_per_type = {label: ROCAUCScore() for label in labels}
labels = set(labels)
if labels:
@ -528,7 +520,7 @@ class Scorer:
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
if pred_score >= threshold:
f_per_type[pred_label].fp += 1
micro_prf = PRFScore(beta=beta)
micro_prf = PRFScore()
for label_prf in f_per_type.values():
micro_prf.tp += label_prf.tp
micro_prf.fn += label_prf.fn
@ -585,7 +577,6 @@ class Scorer:
DOCS: https://spacy.io/api/scorer#score_links
"""
beta = cfg.get("beta", Scorer.BETA)
f_per_type = {}
for example in examples:
gold_ent_by_offset = {}
@ -599,7 +590,7 @@ class Scorer:
if gold_span is not None:
label = gold_span.label_
if label not in f_per_type:
f_per_type[label] = PRFScore(beta=beta)
f_per_type[label] = PRFScore()
gold = gold_span.kb_id_
# only evaluating entities that overlap between gold and pred,
# to disentangle the performance of the NEL from the NER
@ -618,7 +609,7 @@ class Scorer:
# a wrong prediction (e.g. Q42 != Q3) counts as both a FP as well as a FN
f_per_type[label].fp += 1
f_per_type[label].fn += 1
micro_prf = PRFScore(beta=beta)
micro_prf = PRFScore()
for label_prf in f_per_type.values():
micro_prf.tp += label_prf.tp
micro_prf.fn += label_prf.fn
@ -673,9 +664,8 @@ class Scorer:
DOCS: https://spacy.io/api/scorer#score_deps
"""
beta = cfg.get("beta", Scorer.BETA)
unlabelled = PRFScore(beta=beta)
labelled = PRFScore(beta=beta)
unlabelled = PRFScore()
labelled = PRFScore()
labelled_per_dep = dict()
missing_indices = set()
for example in examples:
@ -691,7 +681,7 @@ class Scorer:
if dep not in ignore_labels:
gold_deps.add((gold_i, head.i, dep))
if dep not in labelled_per_dep:
labelled_per_dep[dep] = PRFScore(beta=beta)
labelled_per_dep[dep] = PRFScore()
if dep not in gold_deps_per_dep:
gold_deps_per_dep[dep] = set()
gold_deps_per_dep[dep].add((gold_i, head.i, dep))
@ -722,7 +712,7 @@ class Scorer:
else:
pred_deps.add((gold_i, gold_head, dep))
if dep not in labelled_per_dep:
labelled_per_dep[dep] = PRFScore(beta=beta)
labelled_per_dep[dep] = PRFScore()
if dep not in pred_deps_per_dep:
pred_deps_per_dep[dep] = set()
pred_deps_per_dep[dep].add((gold_i, gold_head, dep))
@ -753,7 +743,6 @@ class Scorer:
def get_ner_prf(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
"""Compute micro-PRF and per-entity PRF scores for a sequence of examples."""
score_per_type = defaultdict(PRFScore)
beta = kwargs.get("beta", Scorer.BETA)
for eg in examples:
if not eg.y.has_annotation("ENT_IOB"):
continue
@ -761,7 +750,7 @@ def get_ner_prf(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
align_x2y = eg.alignment.x2y
for pred_ent in eg.x.ents:
if pred_ent.label_ not in score_per_type:
score_per_type[pred_ent.label_] = PRFScore(beta=beta)
score_per_type[pred_ent.label_] = PRFScore()
indices = align_x2y[pred_ent.start : pred_ent.end]
if len(indices):
g_span = eg.y[indices[0] : indices[-1] + 1]
@ -777,7 +766,7 @@ def get_ner_prf(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
score_per_type[pred_ent.label_].fp += 1
for label, start, end in golds:
score_per_type[label].fn += 1
totals = PRFScore(beta=beta)
totals = PRFScore()
for prf in score_per_type.values():
totals += prf
if len(totals) > 0: