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spaCy/spacy/scorer.py
Adriane Boyd a4b32b9552
Handle missing reference values in scorer (#6286) 
* Handle missing reference values in scorer

Handle missing values in reference doc during scoring where it is
possible to detect an unset state for the attribute. If no reference
docs contain annotation, `None` is returned instead of a score. `spacy
evaluate` displays `-` for missing scores and the missing scores are
saved as `None`/`null` in the metrics.

Attributes without unset states:

* `token.head`: relies on `token.dep` to recognize unset values
* `doc.cats`: unable to handle missing annotation

Additional changes:

* add optional `has_annotation` check to `score_scans` to replace
`doc.sents` hack
* update `score_token_attr_per_feat` to handle missing and empty morph
representations
* fix bug in `Doc.has_annotation` for normalization of `IS_SENT_START`
vs. `SENT_START`

* Fix import

* Update return types
2020-11-03 15:47:18 +01:00

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from typing import Optional, Iterable, Dict, Set, Any, Callable, TYPE_CHECKING
import numpy as np
from collections import defaultdict
from .training import Example
from .tokens import Token, Doc, Span, MorphAnalysis
from .errors import Errors
from .util import get_lang_class, SimpleFrozenList
from .morphology import Morphology
if TYPE_CHECKING:
# This lets us add type hints for mypy etc. without causing circular imports
from .language import Language # noqa: F401
DEFAULT_PIPELINE = ("senter", "tagger", "morphologizer", "parser", "ner", "textcat")
MISSING_VALUES = frozenset([None, 0, ""])
class PRFScore:
"""A precision / recall / F score."""
def __init__(self) -> None:
self.tp = 0
self.fp = 0
self.fn = 0
def __len__(self) -> int:
return self.tp + self.fp + self.fn
def __iadd__(self, other):
self.tp += other.tp
self.fp += other.fp
self.fn += other.fn
return self
def __add__(self, other):
return PRFScore(
tp=self.tp + other.tp, fp=self.fp + other.fp, fn=self.fn + other.fn
)
def score_set(self, cand: set, gold: set) -> None:
self.tp += len(cand.intersection(gold))
self.fp += len(cand - gold)
self.fn += len(gold - cand)
@property
def precision(self) -> float:
return self.tp / (self.tp + self.fp + 1e-100)
@property
def recall(self) -> float:
return self.tp / (self.tp + self.fn + 1e-100)
@property
def fscore(self) -> float:
p = self.precision
r = self.recall
return 2 * ((p * r) / (p + r + 1e-100))
def to_dict(self) -> Dict[str, float]:
return {"p": self.precision, "r": self.recall, "f": self.fscore}
class ROCAUCScore:
"""An AUC ROC score. This is only defined for binary classification.
Use the method is_binary before calculating the score, otherwise it
may throw an error."""
def __init__(self) -> None:
self.golds = []
self.cands = []
self.saved_score = 0.0
self.saved_score_at_len = 0
def score_set(self, cand, gold) -> None:
self.cands.append(cand)
self.golds.append(gold)
def is_binary(self):
return len(np.unique(self.golds)) == 2
@property
def score(self):
if not self.is_binary():
raise ValueError(Errors.E165.format(label=set(self.golds)))
if len(self.golds) == self.saved_score_at_len:
return self.saved_score
self.saved_score = _roc_auc_score(self.golds, self.cands)
self.saved_score_at_len = len(self.golds)
return self.saved_score
class Scorer:
"""Compute evaluation scores."""
def __init__(
self,
nlp: Optional["Language"] = None,
default_lang: str = "xx",
default_pipeline: Iterable[str] = DEFAULT_PIPELINE,
**cfg,
) -> None:
"""Initialize the Scorer.
DOCS: https://nightly.spacy.io/api/scorer#init
"""
self.nlp = nlp
self.cfg = cfg
if not nlp:
nlp = get_lang_class(default_lang)()
for pipe in default_pipeline:
nlp.add_pipe(pipe)
self.nlp = nlp
def score(self, examples: Iterable[Example]) -> Dict[str, Any]:
"""Evaluate a list of Examples.
examples (Iterable[Example]): The predicted annotations + correct annotations.
RETURNS (Dict): A dictionary of scores.
DOCS: https://nightly.spacy.io/api/scorer#score
"""
scores = {}
if hasattr(self.nlp.tokenizer, "score"):
scores.update(self.nlp.tokenizer.score(examples, **self.cfg))
for name, component in self.nlp.pipeline:
if hasattr(component, "score"):
scores.update(component.score(examples, **self.cfg))
return scores
@staticmethod
def score_tokenization(examples: Iterable[Example], **cfg) -> Dict[str, Any]:
"""Returns accuracy and PRF scores for tokenization.
* token_acc: # correct tokens / # gold tokens
* token_p/r/f: PRF for token character spans
examples (Iterable[Example]): Examples to score
RETURNS (Dict[str, Any]): A dictionary containing the scores
token_acc/p/r/f.
DOCS: https://nightly.spacy.io/api/scorer#score_tokenization
"""
acc_score = PRFScore()
prf_score = PRFScore()
for example in examples:
gold_doc = example.reference
pred_doc = example.predicted
if gold_doc.has_unknown_spaces:
continue
align = example.alignment
gold_spans = set()
pred_spans = set()
for token in gold_doc:
if token.orth_.isspace():
continue
gold_spans.add((token.idx, token.idx + len(token)))
for token in pred_doc:
if token.orth_.isspace():
continue
pred_spans.add((token.idx, token.idx + len(token)))
if align.x2y.lengths[token.i] != 1:
acc_score.fp += 1
else:
acc_score.tp += 1
prf_score.score_set(pred_spans, gold_spans)
if len(acc_score) > 0:
return {
"token_acc": acc_score.fscore,
"token_p": prf_score.precision,
"token_r": prf_score.recall,
"token_f": prf_score.fscore,
}
else:
return {
"token_acc": None,
"token_p": None,
"token_r": None,
"token_f": None
}
@staticmethod
def score_token_attr(
examples: Iterable[Example],
attr: str,
*,
getter: Callable[[Token, str], Any] = getattr,
missing_values: Set[Any] = MISSING_VALUES,
**cfg,
) -> Dict[str, Any]:
"""Returns an accuracy score for a token-level attribute.
examples (Iterable[Example]): Examples to score
attr (str): The attribute to score.
getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
getter(token, attr) should return the value of the attribute for an
individual token.
RETURNS (Dict[str, Any]): A dictionary containing the accuracy score
under the key attr_acc.
DOCS: https://nightly.spacy.io/api/scorer#score_token_attr
"""
tag_score = PRFScore()
for example in examples:
gold_doc = example.reference
pred_doc = example.predicted
align = example.alignment
gold_tags = set()
missing_indices = set()
for gold_i, token in enumerate(gold_doc):
value = getter(token, attr)
if value not in missing_values:
gold_tags.add((gold_i, getter(token, attr)))
else:
missing_indices.add(gold_i)
pred_tags = set()
for token in pred_doc:
if token.orth_.isspace():
continue
if align.x2y.lengths[token.i] == 1:
gold_i = align.x2y[token.i].dataXd[0, 0]
if gold_i not in missing_indices:
pred_tags.add((gold_i, getter(token, attr)))
tag_score.score_set(pred_tags, gold_tags)
score_key = f"{attr}_acc"
if len(tag_score) == 0:
return {score_key: None}
else:
return {score_key: tag_score.fscore}
@staticmethod
def score_token_attr_per_feat(
examples: Iterable[Example],
attr: str,
*,
getter: Callable[[Token, str], Any] = getattr,
missing_values: Set[Any] = MISSING_VALUES,
**cfg,
) -> Dict[str, Any]:
"""Return PRF scores per feat for a token attribute in UFEATS format.
examples (Iterable[Example]): Examples to score
attr (str): The attribute to score.
getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
getter(token, attr) should return the value of the attribute for an
individual token.
RETURNS (dict): A dictionary containing the per-feat PRF scores under
the key attr_per_feat.
"""
per_feat = {}
for example in examples:
pred_doc = example.predicted
gold_doc = example.reference
align = example.alignment
gold_per_feat = {}
missing_indices = set()
for gold_i, token in enumerate(gold_doc):
value = getter(token, attr)
morph = gold_doc.vocab.strings[value]
if value not in missing_values and morph != Morphology.EMPTY_MORPH:
for feat in morph.split(Morphology.FEATURE_SEP):
field, values = feat.split(Morphology.FIELD_SEP)
if field not in per_feat:
per_feat[field] = PRFScore()
if field not in gold_per_feat:
gold_per_feat[field] = set()
gold_per_feat[field].add((gold_i, feat))
else:
missing_indices.add(gold_i)
pred_per_feat = {}
for token in pred_doc:
if token.orth_.isspace():
continue
if align.x2y.lengths[token.i] == 1:
gold_i = align.x2y[token.i].dataXd[0, 0]
if gold_i not in missing_indices:
value = getter(token, attr)
morph = gold_doc.vocab.strings[value]
if value not in missing_values and morph != Morphology.EMPTY_MORPH:
for feat in morph.split(Morphology.FEATURE_SEP):
field, values = feat.split(Morphology.FIELD_SEP)
if field not in per_feat:
per_feat[field] = PRFScore()
if field not in pred_per_feat:
pred_per_feat[field] = set()
pred_per_feat[field].add((gold_i, feat))
for field in per_feat:
per_feat[field].score_set(
pred_per_feat.get(field, set()), gold_per_feat.get(field, set())
)
score_key = f"{attr}_per_feat"
if any([len(v) for v in per_feat.values()]):
result = {k: v.to_dict() for k, v in per_feat.items()}
return {score_key: result}
else:
return {score_key: None}
@staticmethod
def score_spans(
examples: Iterable[Example],
attr: str,
*,
getter: Callable[[Doc, str], Iterable[Span]] = getattr,
has_annotation: Optional[Callable[[Doc], bool]] = None,
**cfg,
) -> Dict[str, Any]:
"""Returns PRF scores for labeled spans.
examples (Iterable[Example]): Examples to score
attr (str): The attribute to score.
getter (Callable[[Doc, str], Iterable[Span]]): Defaults to getattr. If
provided, getter(doc, attr) should return the spans for the
individual doc.
RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under
the keys attr_p/r/f and the per-type PRF scores under attr_per_type.
DOCS: https://nightly.spacy.io/api/scorer#score_spans
"""
score = PRFScore()
score_per_type = dict()
for example in examples:
pred_doc = example.predicted
gold_doc = example.reference
# Option to handle docs without sents
if has_annotation is not None:
if not has_annotation(gold_doc):
continue
# Find all labels in gold and doc
labels = set(
[k.label_ for k in getter(gold_doc, attr)]
+ [k.label_ for k in getter(pred_doc, attr)]
)
# Set up all labels for per type scoring and prepare gold per type
gold_per_type = {label: set() for label in labels}
for label in labels:
if label not in score_per_type:
score_per_type[label] = PRFScore()
# Find all predidate labels, for all and per type
gold_spans = set()
pred_spans = set()
for span in getter(gold_doc, attr):
gold_span = (span.label_, span.start, span.end - 1)
gold_spans.add(gold_span)
gold_per_type[span.label_].add((span.label_, span.start, span.end - 1))
pred_per_type = {label: set() for label in labels}
for span in example.get_aligned_spans_x2y(getter(pred_doc, attr)):
pred_spans.add((span.label_, span.start, span.end - 1))
pred_per_type[span.label_].add((span.label_, span.start, span.end - 1))
# Scores per label
for k, v in score_per_type.items():
if k in pred_per_type:
v.score_set(pred_per_type[k], gold_per_type[k])
# Score for all labels
score.score_set(pred_spans, gold_spans)
if len(score) > 0:
return {
f"{attr}_p": score.precision,
f"{attr}_r": score.recall,
f"{attr}_f": score.fscore,
f"{attr}_per_type": {k: v.to_dict() for k, v in score_per_type.items()},
}
else:
return {
f"{attr}_p": None,
f"{attr}_r": None,
f"{attr}_f": None,
f"{attr}_per_type": None,
}
@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.
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://nightly.spacy.io/api/scorer#score_cats
"""
if threshold is None:
threshold = 0.5 if multi_label else 0.0
f_per_type = {label: PRFScore() for label in labels}
auc_per_type = {label: ROCAUCScore() for label in labels}
labels = set(labels)
if labels:
for eg in examples:
labels.update(eg.predicted.cats.keys())
labels.update(eg.reference.cats.keys())
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
else:
pred_label, pred_score = max(pred_cats, 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 = {
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]
) -> 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 (TODO): https://nightly.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
)
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,
**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).
RETURNS (Dict[str, Any]): A dictionary containing the scores:
attr_uas, attr_las, and attr_las_per_type.
DOCS: https://nightly.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 = {}
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 = {}
for token in pred_doc:
if token.orth_.isspace():
continue
if align.x2y.lengths[token.i] != 1:
gold_i = None
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]) -> 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 following license:
#
# New BSD License
#
# Copyright (c) 2007–2019 The scikit-learn developers.
# All rights reserved.
#
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# a. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# b. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# c. Neither the name of the Scikit-learn Developers nor the names of
# its contributors may be used to endorse or promote products
# derived from this software without specific prior written
# permission.
#
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
# DAMAGE.
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
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