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import logging
from collections import defaultdict
from typing import Callable, Dict, List, Optional, Sequence, Union
from pandas import MultiIndex
from pytorch_ie import Annotation, AnnotationLayer, Document, DocumentMetric
from pytorch_ie.annotations import BinaryRelation
from pytorch_ie.core.metric import T
from pytorch_ie.utils.hydra import resolve_target
from src.hydra_callbacks.save_job_return_value import to_py_obj
logger = logging.getLogger(__name__)
class RankingMetricsSKLearn(DocumentMetric):
"""Ranking metrics for documents with binary relations.
This metric computes the ranking metrics for retrieval tasks, where
relation heads are the queries and the relation tails are the candidates.
The metric is computed for each head and the results are averaged. It is meant to
be used with Scikit-learn metrics such as `sklearn.metrics.ndcg_score` (Normalized
Discounted Cumulative Gain), `sklearn.metrics.label_ranking_average_precision_score`
(LRAP), etc., see
https://scikit-learn.org/stable/modules/model_evaluation.html#multilabel-ranking-metrics.
Args:
metrics (Dict[str, Union[str, Callable]]): A dictionary of metric names and their
corresponding functions. The function can be a string (name of the function, e.g.,
sklearn.metrics.ndcg_score) or a callable.
layer (str): The name of the annotation layer containing the binary relations, e.g.,
"binary_relations" when applied to TextDocumentsWithLabeledSpansAndBinaryRelations.
use_manual_average (Optional[List[str]]): A list of metric names to use for manual
averaging. If provided, the metric scores will be calculated for each
head and then averaged. Otherwise, all true and predicted scores will be
passed to the metric function at once.
exclude_singletons (Optional[List[str]]): A list of metric names to exclude singletons
from the computation, i.e., entries (heads) where the number of candidates is 1.
label (Optional[str]): If provided, only the relations with this label will be used
to compute the metrics. This is useful for filtering out relations that are not
relevant for the task at hand (e.g., when having multiple relation types in the
same layer).
score_threshold (float): If provided, only the relations with a score greater than or
equal to this threshold will be used to compute the metrics.
default_score (float): The default score to use for missing relations, either in the
target or prediction. Default is 0.0.
use_all_spans (bool): Whether to consider all spans in the document as queries and
candidates or only the spans that are present in the target and prediction.
span_label_blacklist (Optional[List[str]]): If provided, ignore the relations with
heads/tails that are in this list. When using use_all_spans=True, this also
restricts the candidates to those that are not in the blacklist.
show_as_markdown (bool): Whether to show the results as markdown. Default is False.
markdown_precision (int): The precision for displaying the results in markdown.
Default is 4.
"""
def __init__(
self,
metrics: Dict[str, Union[str, Callable]],
layer: str,
use_manual_average: Optional[List[str]] = None,
exclude_singletons: Optional[List[str]] = None,
label: Optional[str] = None,
score_threshold: float = 0.0,
default_score: float = 0.0,
use_all_spans: bool = False,
span_label_blacklist: Optional[List[str]] = None,
show_as_markdown: bool = False,
markdown_precision: int = 4,
plot: bool = False,
):
self.metrics = {
name: resolve_target(metric) if isinstance(metric, str) else metric
for name, metric in metrics.items()
}
self.use_manual_average = set(use_manual_average or [])
self.exclude_singletons = set(exclude_singletons or [])
self.annotation_layer_name = layer
self.annotation_label = label
self.score_threshold = score_threshold
self.default_score = default_score
self.use_all_spans = use_all_spans
self.span_label_blacklist = span_label_blacklist
self.show_as_markdown = show_as_markdown
self.markdown_precision = markdown_precision
self.plot = plot
super().__init__()
def reset(self) -> None:
self._preds: List[List[float]] = []
self._targets: List[List[float]] = []
def get_head2tail2score(
self, relations: Sequence[BinaryRelation]
) -> Dict[Annotation, Dict[Annotation, float]]:
result: Dict[Annotation, Dict[Annotation, float]] = defaultdict(dict)
for rel in relations:
if (
(self.annotation_label is None or rel.label == self.annotation_label)
and (rel.score >= self.score_threshold)
and (
self.span_label_blacklist is None
or (
rel.head.label not in self.span_label_blacklist
and rel.tail.label not in self.span_label_blacklist
)
)
):
result[rel.head][rel.tail] = rel.score
return result
def _update(self, document: Document) -> None:
annotation_layer: AnnotationLayer[BinaryRelation] = document[self.annotation_layer_name]
target_head2tail2score = self.get_head2tail2score(annotation_layer)
prediction_head2tail2score = self.get_head2tail2score(annotation_layer.predictions)
all_spans = set()
# get spans from all layers targeted by the annotation (relation) layer
for span_layer in annotation_layer.target_layers.values():
all_spans.update(span_layer)
if self.span_label_blacklist is not None:
all_spans = {span for span in all_spans if span.label not in self.span_label_blacklist}
if self.use_all_spans:
all_heads = all_spans
else:
all_heads = set(target_head2tail2score) | set(prediction_head2tail2score)
all_targets: List[List[float]] = []
all_predictions: List[List[float]] = []
for head in all_heads:
target_tail2score = target_head2tail2score.get(head, {})
prediction_tail2score = prediction_head2tail2score.get(head, {})
if self.use_all_spans:
# use all spans as tails
tails = set(span for span in all_spans if span != head)
else:
# use only the tails that are in the target or prediction
tails = set(target_tail2score) | set(prediction_tail2score)
target_scores = [target_tail2score.get(t, self.default_score) for t in tails]
prediction_scores = [prediction_tail2score.get(t, self.default_score) for t in tails]
all_targets.append(target_scores)
all_predictions.append(prediction_scores)
self._targets.extend(all_targets)
self._preds.extend(all_predictions)
def do_plot(self):
raise NotImplementedError()
def _compute(self) -> T:
if self.plot:
self.do_plot()
result = {}
for name, metric in self.metrics.items():
targets, preds = self._targets, self._preds
if name in self.exclude_singletons:
targets = [t for t in targets if len(t) > 1]
preds = [p for p in preds if len(p) > 1]
num_singletons = len(self._targets) - len(targets)
logger.warning(
f"Excluding {num_singletons} singletons (out of {len(self._targets)} "
f"entries) from {name} metric calculation."
)
if name in self.use_manual_average:
scores = [
metric(y_true=[tgts], y_score=[prds]) for tgts, prds in zip(targets, preds)
]
result[name] = sum(scores) / len(scores) if len(scores) > 0 else 0.0
else:
result[name] = metric(y_true=targets, y_score=preds)
result = to_py_obj(result)
if self.show_as_markdown:
import pandas as pd
series = pd.Series(result)
if isinstance(series.index, MultiIndex):
if len(series.index.levels) > 1:
# in fact, this is not a series anymore
series = series.unstack(-1)
else:
series.index = series.index.get_level_values(0)
logger.info(
f"{self.current_split}\n{series.round(self.markdown_precision).to_markdown()}"
)
return result
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