mirror of
https://github.com/explosion/spaCy.git
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9e0322de1a
In the v3 scorer refactoring, `token_acc` was implemented incorrectly. It should use `precision` instead of `fscore` for the measure of correctly aligned tokens / number of predicted tokens. Fix the docs to reflect that the measure uses the number of predicted tokens rather than the number of gold tokens.
1014 lines
40 KiB
Python
1014 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.precision,
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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][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][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(
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pred_per_feat.get(field, set()), gold_per_feat.get(field, set())
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)
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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 and not has_annotation(gold_doc):
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continue
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# Find all labels in gold
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labels = set([k.label_ for k in getter(gold_doc, attr)])
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# If labeled, find all labels in pred
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if has_annotation is None or (
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has_annotation is not None and has_annotation(pred_doc)
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):
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labels |= set([k.label_ for k in getter(pred_doc, attr)])
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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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if has_annotation is None or (
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has_annotation is not None and has_annotation(pred_doc)
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):
|
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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)
|
||
# Assemble final result
|
||
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,
|
||
}
|
||
if labeled:
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||
final_scores[f"{attr}_per_type"] = None
|
||
if len(score) > 0:
|
||
final_scores[f"{attr}_p"] = score.precision
|
||
final_scores[f"{attr}_r"] = score.recall
|
||
final_scores[f"{attr}_f"] = score.fscore
|
||
if labeled:
|
||
final_scores[f"{attr}_per_type"] = {
|
||
k: v.to_dict() for k, v in score_per_type.items()
|
||
}
|
||
return final_scores
|
||
|
||
@staticmethod
|
||
def score_cats(
|
||
examples: Iterable[Example],
|
||
attr: str,
|
||
*,
|
||
getter: Callable[[Doc, str], Any] = getattr,
|
||
labels: Iterable[str] = SimpleFrozenList(),
|
||
multi_label: bool = True,
|
||
positive_label: Optional[str] = None,
|
||
threshold: Optional[float] = None,
|
||
**cfg,
|
||
) -> Dict[str, Any]:
|
||
"""Returns PRF and ROC AUC scores for a doc-level attribute with a
|
||
dict with scores for each label like Doc.cats. The reported overall
|
||
score depends on the scorer settings.
|
||
|
||
examples (Iterable[Example]): Examples to score
|
||
attr (str): The attribute to score.
|
||
getter (Callable[[Doc, str], Any]): Defaults to getattr. If provided,
|
||
getter(doc, attr) should return the values for the individual doc.
|
||
labels (Iterable[str]): The set of possible labels. Defaults to [].
|
||
multi_label (bool): Whether the attribute allows multiple labels.
|
||
Defaults to True. When set to False (exclusive labels), missing
|
||
gold labels are interpreted as 0.0 and the threshold is set to 0.0.
|
||
positive_label (str): The positive label for a binary task with
|
||
exclusive classes. Defaults to None.
|
||
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.
|
||
RETURNS (Dict[str, Any]): A dictionary containing the scores, with
|
||
inapplicable scores as None:
|
||
for all:
|
||
attr_score (one of attr_micro_f / attr_macro_f / attr_macro_auc),
|
||
attr_score_desc (text description of the overall score),
|
||
attr_micro_p,
|
||
attr_micro_r,
|
||
attr_micro_f,
|
||
attr_macro_p,
|
||
attr_macro_r,
|
||
attr_macro_f,
|
||
attr_macro_auc,
|
||
attr_f_per_type,
|
||
attr_auc_per_type
|
||
|
||
DOCS: https://spacy.io/api/scorer#score_cats
|
||
"""
|
||
if threshold is None:
|
||
threshold = 0.5 if multi_label else 0.0
|
||
if not multi_label:
|
||
threshold = 0.0
|
||
f_per_type = {label: PRFScore() for label in labels}
|
||
auc_per_type = {label: ROCAUCScore() for label in labels}
|
||
labels = set(labels)
|
||
for example in examples:
|
||
# Through this loop, None in the gold_cats indicates missing label.
|
||
pred_cats = getter(example.predicted, attr)
|
||
pred_cats = {k: v for k, v in pred_cats.items() if k in labels}
|
||
gold_cats = getter(example.reference, attr)
|
||
gold_cats = {k: v for k, v in gold_cats.items() if k in labels}
|
||
|
||
for label in labels:
|
||
pred_score = pred_cats.get(label, 0.0)
|
||
gold_score = gold_cats.get(label)
|
||
if not gold_score and not multi_label:
|
||
gold_score = 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)
|
||
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 pred_label == gold_label:
|
||
f_per_type[pred_label].tp += 1
|
||
else:
|
||
f_per_type[gold_label].fn += 1
|
||
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 > 0:
|
||
f_per_type[gold_label].fn += 1
|
||
elif pred_cats:
|
||
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
|
||
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][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][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]
|
||
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
|