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609 lines
22 KiB
Python
609 lines
22 KiB
Python
# coding: utf8
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from __future__ import division, print_function, unicode_literals
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import numpy as np
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from .gold import tags_to_entities, GoldParse
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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 ROCAUCScore(object):
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"""
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An AUC ROC score.
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"""
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def __init__(self):
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self.golds = []
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self.cands = []
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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):
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self.cands.append(cand)
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self.golds.append(gold)
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@property
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def score(self):
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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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try:
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self.saved_score = _roc_auc_score(self.golds, self.cands)
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# catch ValueError: Only one class present in y_true.
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# ROC AUC score is not defined in that case.
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except ValueError:
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self.saved_score = -float("inf")
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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(object):
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"""Compute evaluation scores."""
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def __init__(self, eval_punct=False, pipeline=None):
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"""Initialize the Scorer.
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eval_punct (bool): Evaluate the dependency attachments to and from
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punctuation.
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RETURNS (Scorer): The newly created object.
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DOCS: https://spacy.io/api/scorer#init
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"""
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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.labelled_per_dep = dict()
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self.tags = PRFScore()
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self.ner = PRFScore()
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self.ner_per_ents = dict()
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self.eval_punct = eval_punct
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self.textcat = None
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self.textcat_per_cat = dict()
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self.textcat_positive_label = None
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self.textcat_multilabel = False
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if pipeline:
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for name, model in pipeline:
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if name == "textcat":
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self.textcat_positive_label = model.cfg.get("positive_label", None)
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if self.textcat_positive_label:
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self.textcat = PRFScore()
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if not model.cfg.get("exclusive_classes", False):
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self.textcat_multilabel = True
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for label in model.cfg.get("labels", []):
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self.textcat_per_cat[label] = ROCAUCScore()
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else:
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for label in model.cfg.get("labels", []):
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self.textcat_per_cat[label] = PRFScore()
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@property
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def tags_acc(self):
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"""RETURNS (float): Part-of-speech tag accuracy (fine grained tags,
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i.e. `Token.tag`).
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"""
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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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"""RETURNS (float): Tokenization accuracy."""
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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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"""RETURNS (float): Unlabelled dependency score."""
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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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"""RETURNS (float): Labelled dependency score."""
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return self.labelled.fscore * 100
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@property
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def las_per_type(self):
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"""RETURNS (dict): Scores per dependency label.
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"""
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return {
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k: {"p": v.precision * 100, "r": v.recall * 100, "f": v.fscore * 100}
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for k, v in self.labelled_per_dep.items()
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}
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@property
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def ents_p(self):
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"""RETURNS (float): Named entity accuracy (precision)."""
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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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"""RETURNS (float): Named entity accuracy (recall)."""
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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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"""RETURNS (float): Named entity accuracy (F-score)."""
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return self.ner.fscore * 100
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@property
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def ents_per_type(self):
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"""RETURNS (dict): Scores per entity label.
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"""
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return {
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k: {"p": v.precision * 100, "r": v.recall * 100, "f": v.fscore * 100}
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for k, v in self.ner_per_ents.items()
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}
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@property
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def textcat_score(self):
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"""RETURNS (float): f-score on positive label for binary exclusive,
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macro-averaged f-score for 3+ exclusive,
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macro-averaged AUC ROC score for multilabel (-1 if undefined)
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"""
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if not self.textcat_multilabel:
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# binary multiclass
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if self.textcat_positive_label:
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return self.textcat.fscore * 100
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# other multiclass
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return (
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sum([score.fscore for label, score in self.textcat_per_cat.items()])
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/ (len(self.textcat_per_cat) + 1e-100)
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* 100
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)
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# multilabel
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return max(
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sum([score.score for label, score in self.textcat_per_cat.items()])
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/ (len(self.textcat_per_cat) + 1e-100),
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-1,
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)
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@property
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def textcats_per_cat(self):
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"""RETURNS (dict): Scores per textcat label.
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"""
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if not self.textcat_multilabel:
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return {
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k: {"p": v.precision * 100, "r": v.recall * 100, "f": v.fscore * 100}
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for k, v in self.textcat_per_cat.items()
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}
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return {
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k: {"roc_auc_score": max(v.score, -1)}
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for k, v in self.textcat_per_cat.items()
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}
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@property
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def scores(self):
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"""RETURNS (dict): All scores with keys `uas`, `las`, `ents_p`,
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`ents_r`, `ents_f`, `tags_acc`, `token_acc`, and `textcat_score`.
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"""
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return {
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"uas": self.uas,
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"las": self.las,
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"las_per_type": self.las_per_type,
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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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"ents_per_type": self.ents_per_type,
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"tags_acc": self.tags_acc,
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"token_acc": self.token_acc,
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"textcat_score": self.textcat_score,
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"textcats_per_cat": self.textcats_per_cat,
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}
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def score(self, doc, gold, verbose=False, punct_labels=("p", "punct")):
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"""Update the evaluation scores from a single Doc / GoldParse pair.
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doc (Doc): The predicted annotations.
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gold (GoldParse): The correct annotations.
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verbose (bool): Print debugging information.
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punct_labels (tuple): Dependency labels for punctuation. Used to
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evaluate dependency attachments to punctuation if `eval_punct` is
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`True`.
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DOCS: https://spacy.io/api/scorer#score
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"""
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if len(doc) != len(gold):
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gold = GoldParse.from_annot_tuples(
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doc, tuple(zip(*gold.orig_annot)) + (gold.cats,)
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)
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gold_deps = set()
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gold_deps_per_dep = {}
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gold_tags = set()
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gold_ents = set(tags_to_entities([annot[-1] 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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if dep.lower() not in self.labelled_per_dep:
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self.labelled_per_dep[dep.lower()] = PRFScore()
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if dep.lower() not in gold_deps_per_dep:
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gold_deps_per_dep[dep.lower()] = set()
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gold_deps_per_dep[dep.lower()].add((id_, head, dep.lower()))
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cand_deps = set()
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cand_deps_per_dep = {}
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cand_tags = set()
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for token in doc:
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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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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 token.dep_.lower() not in self.labelled_per_dep:
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self.labelled_per_dep[token.dep_.lower()] = PRFScore()
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if token.dep_.lower() not in cand_deps_per_dep:
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cand_deps_per_dep[token.dep_.lower()] = set()
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cand_deps_per_dep[token.dep_.lower()].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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# Find all NER labels in gold and doc
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ent_labels = set([x[0] for x in gold_ents] + [k.label_ for k in doc.ents])
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# Set up all labels for per type scoring and prepare gold per type
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gold_per_ents = {ent_label: set() for ent_label in ent_labels}
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for ent_label in ent_labels:
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if ent_label not in self.ner_per_ents:
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self.ner_per_ents[ent_label] = PRFScore()
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gold_per_ents[ent_label].update(
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[x for x in gold_ents if x[0] == ent_label]
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)
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# Find all candidate labels, for all and per type
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cand_ents = set()
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cand_per_ents = {ent_label: set() for ent_label in ent_labels}
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for ent in doc.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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self.ner_per_ents[ent.label_].fp += 1
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else:
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cand_ents.add((ent.label_, first, last))
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cand_per_ents[ent.label_].add((ent.label_, first, last))
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# Scores per ent
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for k, v in self.ner_per_ents.items():
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if k in cand_per_ents:
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v.score_set(cand_per_ents[k], gold_per_ents[k])
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# Score for all ents
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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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for dep in self.labelled_per_dep:
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self.labelled_per_dep[dep].score_set(cand_deps_per_dep.get(dep, set()), gold_deps_per_dep.get(dep, set()))
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self.unlabelled.score_set(
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set(item[:2] for item in cand_deps), set(item[:2] for item in gold_deps)
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)
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if (
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len(gold.cats) > 0
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and set(self.textcat_per_cat) == set(gold.cats)
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and set(gold.cats) == set(doc.cats)
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):
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goldcat = max(gold.cats, key=gold.cats.get)
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candcat = max(doc.cats, key=doc.cats.get)
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if self.textcat_positive_label:
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self.textcat.score_set(
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set([self.textcat_positive_label]) & set([candcat]),
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set([self.textcat_positive_label]) & set([goldcat]),
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)
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for label in self.textcat_per_cat:
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if self.textcat_multilabel:
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self.textcat_per_cat[label].score_set(
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doc.cats[label], gold.cats[label]
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)
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else:
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self.textcat_per_cat[label].score_set(
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set([label]) & set([candcat]), set([label]) & set([goldcat])
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)
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elif len(self.textcat_per_cat) > 0:
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model_labels = set(self.textcat_per_cat)
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eval_labels = set(gold.cats)
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raise ValueError(
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Errors.E162.format(model_labels=model_labels, eval_labels=eval_labels)
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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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#############################################################################
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#
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# The following implementation of roc_auc_score() is adapted from
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# scikit-learn, which is distributed under the following license:
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#
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# New BSD License
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#
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# Copyright (c) 2007–2019 The scikit-learn developers.
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# All rights reserved.
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#
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# a. Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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# b. Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# c. Neither the name of the Scikit-learn Developers nor the names of
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# its contributors may be used to endorse or promote products
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# derived from this software without specific prior written
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# permission.
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#
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
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# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
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# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
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# DAMAGE.
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def _roc_auc_score(y_true, y_score):
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"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
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from prediction scores.
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Note: this implementation is restricted to the binary classification task
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Parameters
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----------
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y_true : array, shape = [n_samples] or [n_samples, n_classes]
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True binary labels or binary label indicators.
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The multiclass case expects shape = [n_samples] and labels
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with values in ``range(n_classes)``.
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y_score : array, shape = [n_samples] or [n_samples, n_classes]
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Target scores, can either be probability estimates of the positive
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class, confidence values, or non-thresholded measure of decisions
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(as returned by "decision_function" on some classifiers). For binary
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y_true, y_score is supposed to be the score of the class with greater
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label. The multiclass case expects shape = [n_samples, n_classes]
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where the scores correspond to probability estimates.
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Returns
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-------
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auc : float
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References
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----------
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.. [1] `Wikipedia entry for the Receiver operating characteristic
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<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
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.. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
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Letters, 2006, 27(8):861-874.
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.. [3] `Analyzing a portion of the ROC curve. McClish, 1989
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<https://www.ncbi.nlm.nih.gov/pubmed/2668680>`_
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"""
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if len(np.unique(y_true)) != 2:
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raise ValueError(Errors.E165)
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fpr, tpr, _ = _roc_curve(y_true, y_score)
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return _auc(fpr, tpr)
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def _roc_curve(y_true, y_score):
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"""Compute Receiver operating characteristic (ROC)
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Note: this implementation is restricted to the binary classification task.
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Parameters
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----------
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y_true : array, shape = [n_samples]
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True binary labels. If labels are not either {-1, 1} or {0, 1}, then
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pos_label should be explicitly given.
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y_score : array, shape = [n_samples]
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Target scores, can either be probability estimates of the positive
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class, confidence values, or non-thresholded measure of decisions
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(as returned by "decision_function" on some classifiers).
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Returns
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-------
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fpr : array, shape = [>2]
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Increasing false positive rates such that element i is the false
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positive rate of predictions with score >= thresholds[i].
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tpr : array, shape = [>2]
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Increasing true positive rates such that element i is the true
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positive rate of predictions with score >= thresholds[i].
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thresholds : array, shape = [n_thresholds]
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Decreasing thresholds on the decision function used to compute
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fpr and tpr. `thresholds[0]` represents no instances being predicted
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and is arbitrarily set to `max(y_score) + 1`.
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Notes
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-----
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Since the thresholds are sorted from low to high values, they
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are reversed upon returning them to ensure they correspond to both ``fpr``
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and ``tpr``, which are sorted in reversed order during their calculation.
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References
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----------
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.. [1] `Wikipedia entry for the Receiver operating characteristic
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<https://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_
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.. [2] Fawcett T. An introduction to ROC analysis[J]. Pattern Recognition
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Letters, 2006, 27(8):861-874.
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"""
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fps, tps, thresholds = _binary_clf_curve(y_true, y_score)
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# Add an extra threshold position
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# to make sure that the curve starts at (0, 0)
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tps = np.r_[0, tps]
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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))
|
||
|
||
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
|