mirror of
https://github.com/explosion/spaCy.git
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3141e04822
* Add spacy.errors module * Update deprecation and user warnings * Replace errors and asserts with new error message system * Remove redundant asserts * Fix whitespace * Add messages for print/util.prints statements * Fix typo * Fix typos * Move CLI messages to spacy.cli._messages * Add decorator to display error code with message An implementation like this is nice because it only modifies the string when it's retrieved from the containing class – so we don't have to worry about manipulating tracebacks etc. * Remove unused link in spacy.about * Update errors for invalid pipeline components * Improve error for unknown factories * Add displaCy warnings * Update formatting consistency * Move error message to spacy.errors * Update errors and check if doc returned by component is None
141 lines
4.4 KiB
Python
141 lines
4.4 KiB
Python
# coding: utf8
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from __future__ import division, print_function, unicode_literals
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from .gold import tags_to_entities
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from .errors import Errors
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class PRFScore(object):
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"""
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A precision / recall / F score
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"""
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def __init__(self):
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self.tp = 0
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self.fp = 0
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self.fn = 0
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def score_set(self, cand, gold):
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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):
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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):
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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):
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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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class Scorer(object):
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def __init__(self, eval_punct=False):
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self.tokens = PRFScore()
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self.sbd = PRFScore()
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self.unlabelled = PRFScore()
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self.labelled = PRFScore()
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self.tags = PRFScore()
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self.ner = PRFScore()
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self.eval_punct = eval_punct
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@property
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def tags_acc(self):
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return self.tags.fscore * 100
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@property
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def token_acc(self):
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return self.tokens.precision * 100
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@property
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def uas(self):
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return self.unlabelled.fscore * 100
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@property
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def las(self):
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return self.labelled.fscore * 100
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@property
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def ents_p(self):
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return self.ner.precision * 100
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@property
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def ents_r(self):
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return self.ner.recall * 100
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@property
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def ents_f(self):
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return self.ner.fscore * 100
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@property
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def scores(self):
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return {
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'uas': self.uas,
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'las': self.las,
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'ents_p': self.ents_p,
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'ents_r': self.ents_r,
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'ents_f': self.ents_f,
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'tags_acc': self.tags_acc,
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'token_acc': self.token_acc
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}
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def score(self, tokens, gold, verbose=False, punct_labels=('p', 'punct')):
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if len(tokens) != len(gold):
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raise ValueError(Errors.E078.format(words_doc=len(tokens), words_gold=len(gold)))
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gold_deps = set()
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gold_tags = set()
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gold_ents = set(tags_to_entities([annot[-1]
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for annot in gold.orig_annot]))
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for id_, word, tag, head, dep, ner in gold.orig_annot:
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gold_tags.add((id_, tag))
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if dep not in (None, "") and dep.lower() not in punct_labels:
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gold_deps.add((id_, head, dep.lower()))
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cand_deps = set()
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cand_tags = set()
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for token in tokens:
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if token.orth_.isspace():
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continue
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gold_i = gold.cand_to_gold[token.i]
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if gold_i is None:
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if token.dep_.lower() not in punct_labels:
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self.tokens.fp += 1
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else:
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self.tokens.tp += 1
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cand_tags.add((gold_i, token.tag_))
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if token.dep_.lower() not in punct_labels and token.orth_.strip():
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gold_head = gold.cand_to_gold[token.head.i]
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# None is indistinct, so we can't just add it to the set
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# Multiple (None, None) deps are possible
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if gold_i is None or gold_head is None:
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self.unlabelled.fp += 1
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self.labelled.fp += 1
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else:
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cand_deps.add((gold_i, gold_head, token.dep_.lower()))
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if '-' not in [token[-1] for token in gold.orig_annot]:
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cand_ents = set()
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for ent in tokens.ents:
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first = gold.cand_to_gold[ent.start]
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last = gold.cand_to_gold[ent.end-1]
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if first is None or last is None:
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self.ner.fp += 1
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else:
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cand_ents.add((ent.label_, first, last))
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self.ner.score_set(cand_ents, gold_ents)
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self.tags.score_set(cand_tags, gold_tags)
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self.labelled.score_set(cand_deps, gold_deps)
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self.unlabelled.score_set(
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set(item[:2] for item in cand_deps),
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set(item[:2] for item in gold_deps),
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)
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if verbose:
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gold_words = [item[1] for item in gold.orig_annot]
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for w_id, h_id, dep in (cand_deps - gold_deps):
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print('F', gold_words[w_id], dep, gold_words[h_id])
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for w_id, h_id, dep in (gold_deps - cand_deps):
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print('M', gold_words[w_id], dep, gold_words[h_id])
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