mirror of
https://github.com/explosion/spaCy.git
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12974bf4d9
* Add micro PRF for morph scoring For pipelines where morph features are added by more than one component and a reference training corpus may not contain all features, a micro PRF score is more flexible than a simple accuracy score. An example is the reading and inflection features added by the Japanese tokenizer. * Use `morph_micro_f` as the default morph score for Japanese morphologizers. * Update docstring * Fix typo in docstring * Update Scorer API docs * Fix results type * Organize score list by attribute prefix
1008 lines
40 KiB
Python
1008 lines
40 KiB
Python
from typing import Optional, Iterable, Dict, Set, List, Any, Callable, Tuple
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from typing import TYPE_CHECKING
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import numpy as np
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from collections import defaultdict
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from .training import Example
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from .tokens import Token, Doc, Span
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from .errors import Errors
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from .util import get_lang_class, SimpleFrozenList
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from .morphology import Morphology
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if TYPE_CHECKING:
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# This lets us add type hints for mypy etc. without causing circular imports
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from .language import Language # noqa: F401
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DEFAULT_PIPELINE = ("senter", "tagger", "morphologizer", "parser", "ner", "textcat")
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MISSING_VALUES = frozenset([None, 0, ""])
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class PRFScore:
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"""A precision / recall / F score."""
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def __init__(
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self,
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*,
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tp: int = 0,
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fp: int = 0,
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fn: int = 0,
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) -> None:
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self.tp = tp
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self.fp = fp
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self.fn = fn
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def __len__(self) -> int:
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return self.tp + self.fp + self.fn
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def __iadd__(self, other):
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self.tp += other.tp
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self.fp += other.fp
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self.fn += other.fn
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return self
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def __add__(self, other):
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return PRFScore(
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tp=self.tp + other.tp, fp=self.fp + other.fp, fn=self.fn + other.fn
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)
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def score_set(self, cand: set, gold: set) -> None:
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self.tp += len(cand.intersection(gold))
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self.fp += len(cand - gold)
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self.fn += len(gold - cand)
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@property
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def precision(self) -> float:
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return self.tp / (self.tp + self.fp + 1e-100)
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@property
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def recall(self) -> float:
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return self.tp / (self.tp + self.fn + 1e-100)
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@property
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def fscore(self) -> float:
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p = self.precision
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r = self.recall
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return 2 * ((p * r) / (p + r + 1e-100))
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def to_dict(self) -> Dict[str, float]:
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return {"p": self.precision, "r": self.recall, "f": self.fscore}
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class ROCAUCScore:
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"""An AUC ROC score. This is only defined for binary classification.
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Use the method is_binary before calculating the score, otherwise it
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may throw an error."""
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def __init__(self) -> None:
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self.golds: List[Any] = []
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self.cands: List[Any] = []
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self.saved_score = 0.0
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self.saved_score_at_len = 0
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def score_set(self, cand, gold) -> None:
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self.cands.append(cand)
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self.golds.append(gold)
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def is_binary(self):
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return len(np.unique(self.golds)) == 2
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@property
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def score(self):
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if not self.is_binary():
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raise ValueError(Errors.E165.format(label=set(self.golds)))
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if len(self.golds) == self.saved_score_at_len:
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return self.saved_score
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self.saved_score = _roc_auc_score(self.golds, self.cands)
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self.saved_score_at_len = len(self.golds)
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return self.saved_score
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class Scorer:
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"""Compute evaluation scores."""
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def __init__(
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self,
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nlp: Optional["Language"] = None,
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default_lang: str = "xx",
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default_pipeline: Iterable[str] = DEFAULT_PIPELINE,
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**cfg,
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) -> None:
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"""Initialize the Scorer.
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DOCS: https://spacy.io/api/scorer#init
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"""
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self.cfg = cfg
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if nlp:
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self.nlp = nlp
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else:
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nlp = get_lang_class(default_lang)()
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for pipe in default_pipeline:
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nlp.add_pipe(pipe)
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self.nlp = nlp
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def score(self, examples: Iterable[Example]) -> Dict[str, Any]:
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"""Evaluate a list of Examples.
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examples (Iterable[Example]): The predicted annotations + correct annotations.
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RETURNS (Dict): A dictionary of scores.
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DOCS: https://spacy.io/api/scorer#score
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"""
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scores = {}
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if hasattr(self.nlp.tokenizer, "score"):
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scores.update(self.nlp.tokenizer.score(examples, **self.cfg)) # type: ignore
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for name, component in self.nlp.pipeline:
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if hasattr(component, "score"):
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scores.update(component.score(examples, **self.cfg))
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return scores
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@staticmethod
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def score_tokenization(examples: Iterable[Example], **cfg) -> Dict[str, Any]:
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"""Returns accuracy and PRF scores for tokenization.
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* token_acc: # correct tokens / # gold tokens
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* token_p/r/f: PRF for token character spans
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examples (Iterable[Example]): Examples to score
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RETURNS (Dict[str, Any]): A dictionary containing the scores
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token_acc/p/r/f.
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DOCS: https://spacy.io/api/scorer#score_tokenization
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"""
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acc_score = PRFScore()
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prf_score = PRFScore()
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for example in examples:
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gold_doc = example.reference
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pred_doc = example.predicted
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if gold_doc.has_unknown_spaces:
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continue
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align = example.alignment
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gold_spans = set()
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pred_spans = set()
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for token in gold_doc:
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if token.orth_.isspace():
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continue
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gold_spans.add((token.idx, token.idx + len(token)))
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for token in pred_doc:
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if token.orth_.isspace():
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continue
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pred_spans.add((token.idx, token.idx + len(token)))
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if align.x2y.lengths[token.i] != 1:
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acc_score.fp += 1
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else:
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acc_score.tp += 1
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prf_score.score_set(pred_spans, gold_spans)
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if len(acc_score) > 0:
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return {
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"token_acc": acc_score.fscore,
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"token_p": prf_score.precision,
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"token_r": prf_score.recall,
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"token_f": prf_score.fscore,
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}
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else:
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return {
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"token_acc": None,
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"token_p": None,
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"token_r": None,
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"token_f": None,
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}
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@staticmethod
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def score_token_attr(
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examples: Iterable[Example],
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attr: str,
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*,
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getter: Callable[[Token, str], Any] = getattr,
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missing_values: Set[Any] = MISSING_VALUES, # type: ignore[assignment]
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**cfg,
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) -> Dict[str, Any]:
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"""Returns an accuracy score for a token-level attribute.
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examples (Iterable[Example]): Examples to score
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attr (str): The attribute to score.
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getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
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getter(token, attr) should return the value of the attribute for an
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individual token.
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missing_values (Set[Any]): Attribute values to treat as missing annotation
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in the reference annotation.
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RETURNS (Dict[str, Any]): A dictionary containing the accuracy score
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under the key attr_acc.
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DOCS: https://spacy.io/api/scorer#score_token_attr
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"""
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tag_score = PRFScore()
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for example in examples:
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gold_doc = example.reference
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pred_doc = example.predicted
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align = example.alignment
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gold_tags = set()
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missing_indices = set()
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for gold_i, token in enumerate(gold_doc):
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value = getter(token, attr)
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if value not in missing_values:
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gold_tags.add((gold_i, getter(token, attr)))
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else:
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missing_indices.add(gold_i)
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pred_tags = set()
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for token in pred_doc:
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if token.orth_.isspace():
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continue
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if align.x2y.lengths[token.i] == 1:
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gold_i = align.x2y[token.i].dataXd[0, 0]
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if gold_i not in missing_indices:
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pred_tags.add((gold_i, getter(token, attr)))
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tag_score.score_set(pred_tags, gold_tags)
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score_key = f"{attr}_acc"
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if len(tag_score) == 0:
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return {score_key: None}
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else:
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return {score_key: tag_score.fscore}
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@staticmethod
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def score_token_attr_per_feat(
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examples: Iterable[Example],
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attr: str,
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*,
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getter: Callable[[Token, str], Any] = getattr,
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missing_values: Set[Any] = MISSING_VALUES, # type: ignore[assignment]
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**cfg,
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) -> Dict[str, Any]:
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"""Return micro PRF and PRF scores per feat for a token attribute in
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UFEATS format.
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examples (Iterable[Example]): Examples to score
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attr (str): The attribute to score.
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getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
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getter(token, attr) should return the value of the attribute for an
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individual token.
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missing_values (Set[Any]): Attribute values to treat as missing
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annotation in the reference annotation.
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RETURNS (dict): A dictionary containing the micro PRF scores under the
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key attr_micro_p/r/f and the per-feat PRF scores under
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attr_per_feat.
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"""
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micro_score = PRFScore()
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per_feat = {}
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for example in examples:
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pred_doc = example.predicted
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gold_doc = example.reference
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align = example.alignment
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gold_per_feat: Dict[str, Set] = {}
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missing_indices = set()
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for gold_i, token in enumerate(gold_doc):
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value = getter(token, attr)
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morph = gold_doc.vocab.strings[value]
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if value not in missing_values and morph != Morphology.EMPTY_MORPH:
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for feat in morph.split(Morphology.FEATURE_SEP):
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field, values = feat.split(Morphology.FIELD_SEP)
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if field not in per_feat:
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per_feat[field] = PRFScore()
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if field not in gold_per_feat:
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gold_per_feat[field] = set()
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gold_per_feat[field].add((gold_i, feat))
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else:
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missing_indices.add(gold_i)
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pred_per_feat: Dict[str, Set] = {}
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for token in pred_doc:
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if token.orth_.isspace():
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continue
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if align.x2y.lengths[token.i] == 1:
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gold_i = align.x2y[token.i].dataXd[0, 0]
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if gold_i not in missing_indices:
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value = getter(token, attr)
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morph = gold_doc.vocab.strings[value]
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if (
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value not in missing_values
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and morph != Morphology.EMPTY_MORPH
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):
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for feat in morph.split(Morphology.FEATURE_SEP):
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field, values = feat.split(Morphology.FIELD_SEP)
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if field not in per_feat:
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per_feat[field] = PRFScore()
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if field not in pred_per_feat:
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pred_per_feat[field] = set()
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pred_per_feat[field].add((gold_i, feat))
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for field in per_feat:
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micro_score.score_set(pred_per_feat.get(field, set()), gold_per_feat.get(field, set()))
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per_feat[field].score_set(
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pred_per_feat.get(field, set()), gold_per_feat.get(field, set())
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)
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result: Dict[str, Any] = {}
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if len(micro_score) > 0:
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result[f"{attr}_micro_p"] = micro_score.precision
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result[f"{attr}_micro_r"] = micro_score.recall
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result[f"{attr}_micro_f"] = micro_score.fscore
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result[f"{attr}_per_feat"] = {k: v.to_dict() for k, v in per_feat.items()}
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else:
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result[f"{attr}_micro_p"] = None
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result[f"{attr}_micro_r"] = None
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result[f"{attr}_micro_f"] = None
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result[f"{attr}_per_feat"] = None
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return result
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@staticmethod
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def score_spans(
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examples: Iterable[Example],
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attr: str,
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*,
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getter: Callable[[Doc, str], Iterable[Span]] = getattr,
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has_annotation: Optional[Callable[[Doc], bool]] = None,
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labeled: bool = True,
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allow_overlap: bool = False,
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**cfg,
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) -> Dict[str, Any]:
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"""Returns PRF scores for labeled spans.
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examples (Iterable[Example]): Examples to score
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attr (str): The attribute to score.
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getter (Callable[[Doc, str], Iterable[Span]]): Defaults to getattr. If
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provided, getter(doc, attr) should return the spans for the
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individual doc.
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has_annotation (Optional[Callable[[Doc], bool]]) should return whether a `Doc`
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has annotation for this `attr`. Docs without annotation are skipped for
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scoring purposes.
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labeled (bool): Whether or not to include label information in
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the evaluation. If set to 'False', two spans will be considered
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equal if their start and end match, irrespective of their label.
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allow_overlap (bool): Whether or not to allow overlapping spans.
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If set to 'False', the alignment will automatically resolve conflicts.
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RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under
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the keys attr_p/r/f and the per-type PRF scores under attr_per_type.
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DOCS: https://spacy.io/api/scorer#score_spans
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"""
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score = PRFScore()
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score_per_type = dict()
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for example in examples:
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pred_doc = example.predicted
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gold_doc = example.reference
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# Option to handle docs without annotation for this attribute
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if has_annotation is not None:
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if not has_annotation(gold_doc):
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continue
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# Find all labels in gold and doc
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labels = set(
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[k.label_ for k in getter(gold_doc, attr)]
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+ [k.label_ for k in getter(pred_doc, attr)]
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)
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# Set up all labels for per type scoring and prepare gold per type
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gold_per_type: Dict[str, Set] = {label: set() for label in labels}
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for label in labels:
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if label not in score_per_type:
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score_per_type[label] = PRFScore()
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# Find all predidate labels, for all and per type
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gold_spans = set()
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pred_spans = set()
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for span in getter(gold_doc, attr):
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gold_span: Tuple
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if labeled:
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gold_span = (span.label_, span.start, span.end - 1)
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else:
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gold_span = (span.start, span.end - 1)
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gold_spans.add(gold_span)
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gold_per_type[span.label_].add(gold_span)
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pred_per_type: Dict[str, Set] = {label: set() for label in labels}
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for span in example.get_aligned_spans_x2y(
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getter(pred_doc, attr), allow_overlap
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):
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pred_span: Tuple
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if labeled:
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pred_span = (span.label_, span.start, span.end - 1)
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else:
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pred_span = (span.start, span.end - 1)
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pred_spans.add(pred_span)
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pred_per_type[span.label_].add(pred_span)
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# Scores per label
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if labeled:
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for k, v in score_per_type.items():
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if k in pred_per_type:
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v.score_set(pred_per_type[k], gold_per_type[k])
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# Score for all labels
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score.score_set(pred_spans, gold_spans)
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# Assemble final result
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final_scores: Dict[str, Any] = {
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f"{attr}_p": None,
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f"{attr}_r": None,
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f"{attr}_f": None,
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}
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if labeled:
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final_scores[f"{attr}_per_type"] = None
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if len(score) > 0:
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final_scores[f"{attr}_p"] = score.precision
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final_scores[f"{attr}_r"] = score.recall
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final_scores[f"{attr}_f"] = score.fscore
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if labeled:
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final_scores[f"{attr}_per_type"] = {
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k: v.to_dict() for k, v in score_per_type.items()
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}
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return final_scores
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@staticmethod
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def score_cats(
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examples: Iterable[Example],
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attr: str,
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*,
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getter: Callable[[Doc, str], Any] = getattr,
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labels: Iterable[str] = SimpleFrozenList(),
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multi_label: bool = True,
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positive_label: Optional[str] = None,
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threshold: Optional[float] = None,
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**cfg,
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) -> Dict[str, Any]:
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"""Returns PRF and ROC AUC scores for a doc-level attribute with a
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dict with scores for each label like Doc.cats. The reported overall
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score depends on the scorer settings.
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examples (Iterable[Example]): Examples to score
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attr (str): The attribute to score.
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getter (Callable[[Doc, str], Any]): Defaults to getattr. If provided,
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getter(doc, attr) should return the values for the individual doc.
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labels (Iterable[str]): The set of possible labels. Defaults to [].
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multi_label (bool): Whether the attribute allows multiple labels.
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Defaults to True.
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positive_label (str): The positive label for a binary task with
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exclusive classes. Defaults to None.
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threshold (float): Cutoff to consider a prediction "positive". Defaults
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to 0.5 for multi-label, and 0.0 (i.e. whatever's highest scoring)
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otherwise.
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RETURNS (Dict[str, Any]): A dictionary containing the scores, with
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inapplicable scores as None:
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for all:
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attr_score (one of attr_micro_f / attr_macro_f / attr_macro_auc),
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attr_score_desc (text description of the overall score),
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attr_micro_p,
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attr_micro_r,
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attr_micro_f,
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attr_macro_p,
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attr_macro_r,
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attr_macro_f,
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attr_macro_auc,
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attr_f_per_type,
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attr_auc_per_type
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DOCS: https://spacy.io/api/scorer#score_cats
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"""
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if threshold is None:
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threshold = 0.5 if multi_label else 0.0
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f_per_type = {label: PRFScore() for label in labels}
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auc_per_type = {label: ROCAUCScore() for label in labels}
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labels = set(labels)
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if labels:
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for eg in examples:
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labels.update(eg.predicted.cats.keys())
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labels.update(eg.reference.cats.keys())
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for example in examples:
|
||
# Through this loop, None in the gold_cats indicates missing label.
|
||
pred_cats = getter(example.predicted, attr)
|
||
gold_cats = getter(example.reference, attr)
|
||
|
||
for label in labels:
|
||
pred_score = pred_cats.get(label, 0.0)
|
||
gold_score = gold_cats.get(label, 0.0)
|
||
if gold_score is not None:
|
||
auc_per_type[label].score_set(pred_score, gold_score)
|
||
if multi_label:
|
||
for label in labels:
|
||
pred_score = pred_cats.get(label, 0.0)
|
||
gold_score = gold_cats.get(label, 0.0)
|
||
if gold_score is not None:
|
||
if pred_score >= threshold and gold_score > 0:
|
||
f_per_type[label].tp += 1
|
||
elif pred_score >= threshold and gold_score == 0:
|
||
f_per_type[label].fp += 1
|
||
elif pred_score < threshold and gold_score > 0:
|
||
f_per_type[label].fn += 1
|
||
elif pred_cats and gold_cats:
|
||
# Get the highest-scoring for each.
|
||
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
|
||
gold_label, gold_score = max(gold_cats.items(), key=lambda it: it[1])
|
||
if gold_score is not None:
|
||
if pred_label == gold_label and pred_score >= threshold:
|
||
f_per_type[pred_label].tp += 1
|
||
else:
|
||
f_per_type[gold_label].fn += 1
|
||
if pred_score >= threshold:
|
||
f_per_type[pred_label].fp += 1
|
||
elif gold_cats:
|
||
gold_label, gold_score = max(gold_cats, key=lambda it: it[1])
|
||
if gold_score is not None and gold_score > 0:
|
||
f_per_type[gold_label].fn += 1
|
||
elif pred_cats:
|
||
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()
|
||
for label_prf in f_per_type.values():
|
||
micro_prf.tp += label_prf.tp
|
||
micro_prf.fn += label_prf.fn
|
||
micro_prf.fp += label_prf.fp
|
||
n_cats = len(f_per_type) + 1e-100
|
||
macro_p = sum(prf.precision for prf in f_per_type.values()) / n_cats
|
||
macro_r = sum(prf.recall for prf in f_per_type.values()) / n_cats
|
||
macro_f = sum(prf.fscore for prf in f_per_type.values()) / n_cats
|
||
# Limit macro_auc to those labels with gold annotations,
|
||
# but still divide by all cats to avoid artificial boosting of datasets with missing labels
|
||
macro_auc = (
|
||
sum(auc.score if auc.is_binary() else 0.0 for auc in auc_per_type.values())
|
||
/ n_cats
|
||
)
|
||
results: Dict[str, Any] = {
|
||
f"{attr}_score": None,
|
||
f"{attr}_score_desc": None,
|
||
f"{attr}_micro_p": micro_prf.precision,
|
||
f"{attr}_micro_r": micro_prf.recall,
|
||
f"{attr}_micro_f": micro_prf.fscore,
|
||
f"{attr}_macro_p": macro_p,
|
||
f"{attr}_macro_r": macro_r,
|
||
f"{attr}_macro_f": macro_f,
|
||
f"{attr}_macro_auc": macro_auc,
|
||
f"{attr}_f_per_type": {k: v.to_dict() for k, v in f_per_type.items()},
|
||
f"{attr}_auc_per_type": {
|
||
k: v.score if v.is_binary() else None for k, v in auc_per_type.items()
|
||
},
|
||
}
|
||
if len(labels) == 2 and not multi_label and positive_label:
|
||
positive_label_f = results[f"{attr}_f_per_type"][positive_label]["f"]
|
||
results[f"{attr}_score"] = positive_label_f
|
||
results[f"{attr}_score_desc"] = f"F ({positive_label})"
|
||
elif not multi_label:
|
||
results[f"{attr}_score"] = results[f"{attr}_macro_f"]
|
||
results[f"{attr}_score_desc"] = "macro F"
|
||
else:
|
||
results[f"{attr}_score"] = results[f"{attr}_macro_auc"]
|
||
results[f"{attr}_score_desc"] = "macro AUC"
|
||
return results
|
||
|
||
@staticmethod
|
||
def score_links(
|
||
examples: Iterable[Example], *, negative_labels: Iterable[str], **cfg
|
||
) -> Dict[str, Any]:
|
||
"""Returns PRF for predicted links on the entity level.
|
||
To disentangle the performance of the NEL from the NER,
|
||
this method only evaluates NEL links for entities that overlap
|
||
between the gold reference and the predictions.
|
||
|
||
examples (Iterable[Example]): Examples to score
|
||
negative_labels (Iterable[str]): The string values that refer to no annotation (e.g. "NIL")
|
||
RETURNS (Dict[str, Any]): A dictionary containing the scores.
|
||
|
||
DOCS: https://spacy.io/api/scorer#score_links
|
||
"""
|
||
f_per_type = {}
|
||
for example in examples:
|
||
gold_ent_by_offset = {}
|
||
for gold_ent in example.reference.ents:
|
||
gold_ent_by_offset[(gold_ent.start_char, gold_ent.end_char)] = gold_ent
|
||
|
||
for pred_ent in example.predicted.ents:
|
||
gold_span = gold_ent_by_offset.get(
|
||
(pred_ent.start_char, pred_ent.end_char), None
|
||
)
|
||
if gold_span is not None:
|
||
label = gold_span.label_
|
||
if label not in f_per_type:
|
||
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
|
||
if gold is not None:
|
||
pred = pred_ent.kb_id_
|
||
if gold in negative_labels and pred in negative_labels:
|
||
# ignore true negatives
|
||
pass
|
||
elif gold == pred:
|
||
f_per_type[label].tp += 1
|
||
elif gold in negative_labels:
|
||
f_per_type[label].fp += 1
|
||
elif pred in negative_labels:
|
||
f_per_type[label].fn += 1
|
||
else:
|
||
# 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()
|
||
for label_prf in f_per_type.values():
|
||
micro_prf.tp += label_prf.tp
|
||
micro_prf.fn += label_prf.fn
|
||
micro_prf.fp += label_prf.fp
|
||
n_labels = len(f_per_type) + 1e-100
|
||
macro_p = sum(prf.precision for prf in f_per_type.values()) / n_labels
|
||
macro_r = sum(prf.recall for prf in f_per_type.values()) / n_labels
|
||
macro_f = sum(prf.fscore for prf in f_per_type.values()) / n_labels
|
||
results = {
|
||
f"nel_score": micro_prf.fscore,
|
||
f"nel_score_desc": "micro F",
|
||
f"nel_micro_p": micro_prf.precision,
|
||
f"nel_micro_r": micro_prf.recall,
|
||
f"nel_micro_f": micro_prf.fscore,
|
||
f"nel_macro_p": macro_p,
|
||
f"nel_macro_r": macro_r,
|
||
f"nel_macro_f": macro_f,
|
||
f"nel_f_per_type": {k: v.to_dict() for k, v in f_per_type.items()},
|
||
}
|
||
return results
|
||
|
||
@staticmethod
|
||
def score_deps(
|
||
examples: Iterable[Example],
|
||
attr: str,
|
||
*,
|
||
getter: Callable[[Token, str], Any] = getattr,
|
||
head_attr: str = "head",
|
||
head_getter: Callable[[Token, str], Token] = getattr,
|
||
ignore_labels: Iterable[str] = SimpleFrozenList(),
|
||
missing_values: Set[Any] = MISSING_VALUES, # type: ignore[assignment]
|
||
**cfg,
|
||
) -> Dict[str, Any]:
|
||
"""Returns the UAS, LAS, and LAS per type scores for dependency
|
||
parses.
|
||
|
||
examples (Iterable[Example]): Examples to score
|
||
attr (str): The attribute containing the dependency label.
|
||
getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
|
||
getter(token, attr) should return the value of the attribute for an
|
||
individual token.
|
||
head_attr (str): The attribute containing the head token. Defaults to
|
||
'head'.
|
||
head_getter (Callable[[Token, str], Token]): Defaults to getattr. If provided,
|
||
head_getter(token, attr) should return the value of the head for an
|
||
individual token.
|
||
ignore_labels (Tuple): Labels to ignore while scoring (e.g., punct).
|
||
missing_values (Set[Any]): Attribute values to treat as missing annotation
|
||
in the reference annotation.
|
||
RETURNS (Dict[str, Any]): A dictionary containing the scores:
|
||
attr_uas, attr_las, and attr_las_per_type.
|
||
|
||
DOCS: https://spacy.io/api/scorer#score_deps
|
||
"""
|
||
unlabelled = PRFScore()
|
||
labelled = PRFScore()
|
||
labelled_per_dep = dict()
|
||
missing_indices = set()
|
||
for example in examples:
|
||
gold_doc = example.reference
|
||
pred_doc = example.predicted
|
||
align = example.alignment
|
||
gold_deps = set()
|
||
gold_deps_per_dep: Dict[str, Set] = {}
|
||
for gold_i, token in enumerate(gold_doc):
|
||
dep = getter(token, attr)
|
||
head = head_getter(token, head_attr)
|
||
if dep not in missing_values:
|
||
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()
|
||
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))
|
||
else:
|
||
missing_indices.add(gold_i)
|
||
pred_deps = set()
|
||
pred_deps_per_dep: Dict[str, Set] = {}
|
||
for token in pred_doc:
|
||
if token.orth_.isspace():
|
||
continue
|
||
if align.x2y.lengths[token.i] != 1:
|
||
gold_i = None # type: ignore
|
||
else:
|
||
gold_i = align.x2y[token.i].dataXd[0, 0]
|
||
if gold_i not in missing_indices:
|
||
dep = getter(token, attr)
|
||
head = head_getter(token, head_attr)
|
||
if dep not in ignore_labels and token.orth_.strip():
|
||
if align.x2y.lengths[head.i] == 1:
|
||
gold_head = align.x2y[head.i].dataXd[0, 0]
|
||
else:
|
||
gold_head = None
|
||
# None is indistinct, so we can't just add it to the set
|
||
# Multiple (None, None) deps are possible
|
||
if gold_i is None or gold_head is None:
|
||
unlabelled.fp += 1
|
||
labelled.fp += 1
|
||
else:
|
||
pred_deps.add((gold_i, gold_head, dep))
|
||
if dep not in labelled_per_dep:
|
||
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))
|
||
labelled.score_set(pred_deps, gold_deps)
|
||
for dep in labelled_per_dep:
|
||
labelled_per_dep[dep].score_set(
|
||
pred_deps_per_dep.get(dep, set()), gold_deps_per_dep.get(dep, set())
|
||
)
|
||
unlabelled.score_set(
|
||
set(item[:2] for item in pred_deps), set(item[:2] for item in gold_deps)
|
||
)
|
||
if len(unlabelled) > 0:
|
||
return {
|
||
f"{attr}_uas": unlabelled.fscore,
|
||
f"{attr}_las": labelled.fscore,
|
||
f"{attr}_las_per_type": {
|
||
k: v.to_dict() for k, v in labelled_per_dep.items()
|
||
},
|
||
}
|
||
else:
|
||
return {
|
||
f"{attr}_uas": None,
|
||
f"{attr}_las": None,
|
||
f"{attr}_las_per_type": None,
|
||
}
|
||
|
||
|
||
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)
|
||
for eg in examples:
|
||
if not eg.y.has_annotation("ENT_IOB"):
|
||
continue
|
||
golds = {(e.label_, e.start, e.end) for e in eg.y.ents}
|
||
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()
|
||
indices = align_x2y[pred_ent.start : pred_ent.end].dataXd.ravel()
|
||
if len(indices):
|
||
g_span = eg.y[indices[0] : indices[-1] + 1]
|
||
# Check we aren't missing annotation on this span. If so,
|
||
# our prediction is neither right nor wrong, we just
|
||
# ignore it.
|
||
if all(token.ent_iob != 0 for token in g_span):
|
||
key = (pred_ent.label_, indices[0], indices[-1] + 1)
|
||
if key in golds:
|
||
score_per_type[pred_ent.label_].tp += 1
|
||
golds.remove(key)
|
||
else:
|
||
score_per_type[pred_ent.label_].fp += 1
|
||
for label, start, end in golds:
|
||
score_per_type[label].fn += 1
|
||
totals = PRFScore()
|
||
for prf in score_per_type.values():
|
||
totals += prf
|
||
if len(totals) > 0:
|
||
return {
|
||
"ents_p": totals.precision,
|
||
"ents_r": totals.recall,
|
||
"ents_f": totals.fscore,
|
||
"ents_per_type": {k: v.to_dict() for k, v in score_per_type.items()},
|
||
}
|
||
else:
|
||
return {
|
||
"ents_p": None,
|
||
"ents_r": None,
|
||
"ents_f": None,
|
||
"ents_per_type": None,
|
||
}
|
||
|
||
|
||
# The following implementation of roc_auc_score() is adapted from
|
||
# scikit-learn, which is distributed under the New BSD License.
|
||
# Copyright (c) 2007–2019 The scikit-learn developers.
|
||
# See licenses/3rd_party_licenses.txt
|
||
def _roc_auc_score(y_true, y_score):
|
||
"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
|
||
from prediction scores.
|
||
|
||
Note: this implementation is restricted to the binary classification task
|
||
|
||
Parameters
|
||
----------
|
||
y_true : array, shape = [n_samples] or [n_samples, n_classes]
|
||
True binary labels or binary label indicators.
|
||
The multiclass case expects shape = [n_samples] and labels
|
||
with values in ``range(n_classes)``.
|
||
|
||
y_score : array, shape = [n_samples] or [n_samples, n_classes]
|
||
Target scores, can either be probability estimates of the positive
|
||
class, confidence values, or non-thresholded measure of decisions
|
||
(as returned by "decision_function" on some classifiers). For binary
|
||
y_true, y_score is supposed to be the score of the class with greater
|
||
label. The multiclass case expects shape = [n_samples, n_classes]
|
||
where the scores correspond to probability estimates.
|
||
|
||
Returns
|
||
-------
|
||
auc : float
|
||
|
||
References
|
||
----------
|
||
.. [1] `Wikipedia entry for the Receiver operating characteristic
|
||
<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
|
||
|
||
.. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
|
||
Letters, 2006, 27(8):861-874.
|
||
|
||
.. [3] `Analyzing a portion of the ROC curve. McClish, 1989
|
||
<https://www.ncbi.nlm.nih.gov/pubmed/2668680>`_
|
||
"""
|
||
if len(np.unique(y_true)) != 2:
|
||
raise ValueError(Errors.E165.format(label=np.unique(y_true)))
|
||
fpr, tpr, _ = _roc_curve(y_true, y_score)
|
||
return _auc(fpr, tpr)
|
||
|
||
|
||
def _roc_curve(y_true, y_score):
|
||
"""Compute Receiver operating characteristic (ROC)
|
||
|
||
Note: this implementation is restricted to the binary classification task.
|
||
|
||
Parameters
|
||
----------
|
||
|
||
y_true : array, shape = [n_samples]
|
||
True binary labels. If labels are not either {-1, 1} or {0, 1}, then
|
||
pos_label should be explicitly given.
|
||
|
||
y_score : array, shape = [n_samples]
|
||
Target scores, can either be probability estimates of the positive
|
||
class, confidence values, or non-thresholded measure of decisions
|
||
(as returned by "decision_function" on some classifiers).
|
||
|
||
Returns
|
||
-------
|
||
fpr : array, shape = [>2]
|
||
Increasing false positive rates such that element i is the false
|
||
positive rate of predictions with score >= thresholds[i].
|
||
|
||
tpr : array, shape = [>2]
|
||
Increasing true positive rates such that element i is the true
|
||
positive rate of predictions with score >= thresholds[i].
|
||
|
||
thresholds : array, shape = [n_thresholds]
|
||
Decreasing thresholds on the decision function used to compute
|
||
fpr and tpr. `thresholds[0]` represents no instances being predicted
|
||
and is arbitrarily set to `max(y_score) + 1`.
|
||
|
||
Notes
|
||
-----
|
||
Since the thresholds are sorted from low to high values, they
|
||
are reversed upon returning them to ensure they correspond to both ``fpr``
|
||
and ``tpr``, which are sorted in reversed order during their calculation.
|
||
|
||
References
|
||
----------
|
||
.. [1] `Wikipedia entry for the Receiver operating characteristic
|
||
<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
|
||
|
||
.. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
|
||
Letters, 2006, 27(8):861-874.
|
||
"""
|
||
fps, tps, thresholds = _binary_clf_curve(y_true, y_score)
|
||
|
||
# Add an extra threshold position
|
||
# to make sure that the curve starts at (0, 0)
|
||
tps = np.r_[0, tps]
|
||
fps = np.r_[0, fps]
|
||
thresholds = np.r_[thresholds[0] + 1, thresholds]
|
||
|
||
if fps[-1] <= 0:
|
||
fpr = np.repeat(np.nan, fps.shape)
|
||
else:
|
||
fpr = fps / fps[-1]
|
||
|
||
if tps[-1] <= 0:
|
||
tpr = np.repeat(np.nan, tps.shape)
|
||
else:
|
||
tpr = tps / tps[-1]
|
||
|
||
return fpr, tpr, thresholds
|
||
|
||
|
||
def _binary_clf_curve(y_true, y_score):
|
||
"""Calculate true and false positives per binary classification threshold.
|
||
|
||
Parameters
|
||
----------
|
||
y_true : array, shape = [n_samples]
|
||
True targets of binary classification
|
||
|
||
y_score : array, shape = [n_samples]
|
||
Estimated probabilities or decision function
|
||
|
||
Returns
|
||
-------
|
||
fps : array, shape = [n_thresholds]
|
||
A count of false positives, at index i being the number of negative
|
||
samples assigned a score >= thresholds[i]. The total number of
|
||
negative samples is equal to fps[-1] (thus true negatives are given by
|
||
fps[-1] - fps).
|
||
|
||
tps : array, shape = [n_thresholds <= len(np.unique(y_score))]
|
||
An increasing count of true positives, at index i being the number
|
||
of positive samples assigned a score >= thresholds[i]. The total
|
||
number of positive samples is equal to tps[-1] (thus false negatives
|
||
are given by tps[-1] - tps).
|
||
|
||
thresholds : array, shape = [n_thresholds]
|
||
Decreasing score values.
|
||
"""
|
||
pos_label = 1.0
|
||
|
||
y_true = np.ravel(y_true)
|
||
y_score = np.ravel(y_score)
|
||
|
||
# make y_true a boolean vector
|
||
y_true = y_true == pos_label
|
||
|
||
# sort scores and corresponding truth values
|
||
desc_score_indices = np.argsort(y_score, kind="mergesort")[::-1]
|
||
y_score = y_score[desc_score_indices]
|
||
y_true = y_true[desc_score_indices]
|
||
weight = 1.0
|
||
|
||
# y_score typically has many tied values. Here we extract
|
||
# the indices associated with the distinct values. We also
|
||
# concatenate a value for the end of the curve.
|
||
distinct_value_indices = np.where(np.diff(y_score))[0]
|
||
threshold_idxs = np.r_[distinct_value_indices, y_true.size - 1]
|
||
|
||
# accumulate the true positives with decreasing threshold
|
||
tps = _stable_cumsum(y_true * weight)[threshold_idxs]
|
||
fps = 1 + threshold_idxs - tps
|
||
return fps, tps, y_score[threshold_idxs]
|
||
|
||
|
||
def _stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08):
|
||
"""Use high precision for cumsum and check that final value matches sum
|
||
|
||
Parameters
|
||
----------
|
||
arr : array-like
|
||
To be cumulatively summed as flat
|
||
axis : int, optional
|
||
Axis along which the cumulative sum is computed.
|
||
The default (None) is to compute the cumsum over the flattened array.
|
||
rtol : float
|
||
Relative tolerance, see ``np.allclose``
|
||
atol : float
|
||
Absolute tolerance, see ``np.allclose``
|
||
"""
|
||
out = np.cumsum(arr, axis=axis, dtype=np.float64)
|
||
expected = np.sum(arr, axis=axis, dtype=np.float64)
|
||
if not np.all(
|
||
np.isclose(
|
||
out.take(-1, axis=axis), expected, rtol=rtol, atol=atol, equal_nan=True
|
||
)
|
||
):
|
||
raise ValueError(Errors.E163)
|
||
return out
|
||
|
||
|
||
def _auc(x, y):
|
||
"""Compute Area Under the Curve (AUC) using the trapezoidal rule
|
||
|
||
This is a general function, given points on a curve. For computing the
|
||
area under the ROC-curve, see :func:`roc_auc_score`.
|
||
|
||
Parameters
|
||
----------
|
||
x : array, shape = [n]
|
||
x coordinates. These must be either monotonic increasing or monotonic
|
||
decreasing.
|
||
y : array, shape = [n]
|
||
y coordinates.
|
||
|
||
Returns
|
||
-------
|
||
auc : float
|
||
"""
|
||
x = np.ravel(x)
|
||
y = np.ravel(y)
|
||
|
||
direction = 1
|
||
dx = np.diff(x)
|
||
if np.any(dx < 0):
|
||
if np.all(dx <= 0):
|
||
direction = -1
|
||
else:
|
||
raise ValueError(Errors.E164.format(x=x))
|
||
|
||
area = direction * np.trapz(y, x)
|
||
if isinstance(area, np.memmap):
|
||
# Reductions such as .sum used internally in np.trapz do not return a
|
||
# scalar by default for numpy.memmap instances contrary to
|
||
# regular numpy.ndarray instances.
|
||
area = area.dtype.type(area)
|
||
return area
|