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Refactor Coval Scoring code #10875

Merged
merged 14 commits into from
Jun 22, 2022
33 changes: 33 additions & 0 deletions licenses/3rd_party_licenses.txt
Original file line number Diff line number Diff line change
Expand Up @@ -127,3 +127,36 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.


coval
-----

* Files: scorer.py

The implementations of ClusterEvaluator, lea, get_cluster_info, and
get_markable_assignments are adapted from coval, which is distributed
under the following license:

The MIT License (MIT)

Copyright 2018 Nafise Sadat Moosavi (ns.moosavi at gmail dot com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

124 changes: 0 additions & 124 deletions spacy/coref_scorer.py

This file was deleted.

4 changes: 3 additions & 1 deletion spacy/ml/models/coref.py
Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,9 @@ def convert_coref_clusterer_inputs(model: Model, X: List[Floats2d], is_train: bo
def backprop(args: ArgsKwargs) -> List[Floats2d]:
# convert to xp and wrap in list
gradients = torch2xp(args.args[0])
assert isinstance(gradients, Floats2d)
#TODO why did this change? This was fine before merging master.
# It's still 2d but this assert fails.
#assert isinstance(gradients, Floats2d)
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return [gradients]

return ArgsKwargs(args=(word_features,), kwargs={}), backprop
Expand Down
17 changes: 0 additions & 17 deletions spacy/ml/models/coref_util.py
Original file line number Diff line number Diff line change
Expand Up @@ -32,23 +32,6 @@ def get_sentence_ids(doc):
return out


def doc2clusters(doc: Doc, prefix=DEFAULT_CLUSTER_PREFIX) -> MentionClusters:
"""Given a doc, give the mention clusters.

This is useful for scoring.
"""
out = []
for name, val in doc.spans.items():
if not name.startswith(prefix):
continue

cluster = []
for mention in val:
cluster.append((mention.start, mention.end))
out.append(cluster)
return out


# from model.py, refactored to be non-member
def get_predicted_antecedents(xp, antecedent_idx, antecedent_scores):
"""Get the ID of the antecedent for each span. -1 if no antecedent."""
Expand Down
49 changes: 18 additions & 31 deletions spacy/pipeline/coref.py
Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
from ..errors import Errors
from ..tokens import Doc
from ..vocab import Vocab
from ..util import registry

from ..ml.models.coref_util import (
create_gold_scores,
Expand All @@ -21,10 +22,9 @@
get_clusters_from_doc,
get_predicted_clusters,
DEFAULT_CLUSTER_PREFIX,
doc2clusters,
)

from ..coref_scorer import Evaluator, get_cluster_info, lea
from ..scorer import Scorer


default_config = """
Expand Down Expand Up @@ -56,7 +56,14 @@
"""
DEFAULT_COREF_MODEL = Config().from_str(default_config)["model"]

DEFAULT_CLUSTERS_PREFIX = "coref_clusters"

def coref_scorer(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
return Scorer.score_coref_clusters(examples, **kwargs)


@registry.scorers("spacy.coref_scorer.v1")
def make_coref_scorer():
return coref_scorer


@Language.factory(
Expand All @@ -66,19 +73,21 @@
default_config={
"model": DEFAULT_COREF_MODEL,
"span_cluster_prefix": DEFAULT_CLUSTER_PREFIX,
"scorer": {"@scorers": "spacy.coref_scorer.v1"},
},
default_score_weights={"coref_f": 1.0, "coref_p": None, "coref_r": None},
)
def make_coref(
nlp: Language,
name: str,
model,
span_cluster_prefix: str = "coref",
scorer: Optional[Callable],
span_cluster_prefix: str,
) -> "CoreferenceResolver":
"""Create a CoreferenceResolver component."""

return CoreferenceResolver(
nlp.vocab, model, name, span_cluster_prefix=span_cluster_prefix
nlp.vocab, model, name, span_cluster_prefix=span_cluster_prefix, scorer=scorer
)


Expand All @@ -95,7 +104,8 @@ def __init__(
name: str = "coref",
*,
span_mentions: str = "coref_mentions",
span_cluster_prefix: str,
span_cluster_prefix: str = DEFAULT_CLUSTER_PREFIX,
scorer: Optional[Callable] = coref_scorer,
) -> None:
"""Initialize a coreference resolution component.

Expand All @@ -117,7 +127,8 @@ def __init__(
self.span_cluster_prefix = span_cluster_prefix
self._rehearsal_model = None

self.cfg: Dict[str, Any] = {}
self.cfg: Dict[str, Any] = {"span_cluster_prefix": span_cluster_prefix}
self.scorer = scorer

def predict(self, docs: Iterable[Doc]) -> List[MentionClusters]:
"""Apply the pipeline's model to a batch of docs, without modifying them.
Expand Down Expand Up @@ -275,7 +286,6 @@ def get_loss(
log_marg = ops.softmax(score_matrix + ops.xp.log(top_gscores), axis=1)
log_norm = ops.softmax(score_matrix, axis=1)
grad = log_norm - log_marg
# gradients.append((grad, cidx))
loss = float((grad**2).sum())

return loss, grad
Expand Down Expand Up @@ -305,26 +315,3 @@ def initialize(

assert len(X) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=X, Y=Y)

def score(self, examples, **kwargs):
"""Score a batch of examples using LEA.
For details on how LEA works and why to use it see the paper:
Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric
Moosavi and Strube, 2016
https://api.semanticscholar.org/CorpusID:17606580
"""

evaluator = Evaluator(lea)

for ex in examples:
p_clusters = doc2clusters(ex.predicted, self.span_cluster_prefix)
g_clusters = doc2clusters(ex.reference, self.span_cluster_prefix)
cluster_info = get_cluster_info(p_clusters, g_clusters)
evaluator.update(cluster_info)

score = {
"coref_f": evaluator.get_f1(),
"coref_p": evaluator.get_precision(),
"coref_r": evaluator.get_recall(),
}
return score
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