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https://github.com/explosion/spaCy.git
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baf19fd652
* Provide top-level score as `attr_score` * Provide a description of the score as `attr_score_desc` * Provide all potential scores keys, setting unused keys to `None` * Update CLI evaluate accordingly
756 lines
28 KiB
Python
756 lines
28 KiB
Python
import numpy as np
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from .errors import Errors
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from .util import get_lang_class
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from .morphology import Morphology
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class PRFScore:
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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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def to_dict(self):
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return {"p": self.precision, "r": self.recall, "f": self.fscore}
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class ROCAUCScore:
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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:
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"""Compute evaluation scores."""
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def __init__(self, nlp=None, **cfg):
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"""Initialize the Scorer.
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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.nlp = nlp
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self.cfg = cfg
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if not nlp:
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# create a default pipeline
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nlp = get_lang_class("xx")()
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nlp.add_pipe("senter")
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nlp.add_pipe("tagger")
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nlp.add_pipe("morphologizer")
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nlp.add_pipe("parser")
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nlp.add_pipe("ner")
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nlp.add_pipe("textcat")
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self.nlp = nlp
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def score(self, examples):
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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))
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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, **cfg):
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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): A dictionary containing the scores token_acc/p/r/f.
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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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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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return {
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"token_acc": acc_score.fscore,
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"token_p": prf_score.precision,
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"token_r": prf_score.recall,
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"token_f": prf_score.fscore,
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}
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@staticmethod
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def score_token_attr(examples, attr, getter=getattr, **cfg):
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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): 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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RETURNS (dict): A dictionary containing the accuracy score under the
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key attr_acc.
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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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for gold_i, token in enumerate(gold_doc):
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gold_tags.add((gold_i, getter(token, attr)))
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pred_tags = set()
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for token in pred_doc:
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if token.orth_.isspace():
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continue
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if align.x2y.lengths[token.i] == 1:
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gold_i = align.x2y[token.i].dataXd[0, 0]
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pred_tags.add((gold_i, getter(token, attr)))
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tag_score.score_set(pred_tags, gold_tags)
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return {
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attr + "_acc": tag_score.fscore,
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}
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@staticmethod
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def score_token_attr_per_feat(examples, attr, getter=getattr, **cfg):
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"""Return PRF scores per feat for a token attribute in 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): 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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RETURNS (dict): A dictionary containing the per-feat PRF scores unders
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the key attr_per_feat.
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"""
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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 = {}
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for gold_i, token in enumerate(gold_doc):
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morph = str(getter(token, attr))
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if 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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pred_per_feat = {}
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for token in pred_doc:
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if token.orth_.isspace():
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continue
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if align.x2y.lengths[token.i] == 1:
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gold_i = align.x2y[token.i].dataXd[0, 0]
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morph = str(getter(token, attr))
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if morph:
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for feat in morph.split("|"):
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field, values = feat.split("=")
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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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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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return {
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attr + "_per_feat": per_feat,
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}
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@staticmethod
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def score_spans(examples, attr, getter=getattr, **cfg):
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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): Defaults to getattr. If provided,
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getter(doc, attr) should return the spans for the individual doc.
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RETURNS (dict): A dictionary containing the PRF scores under the
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keys attr_p/r/f and the per-type PRF scores under attr_per_type.
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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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# Find all labels in gold and doc
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labels = set(
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[k.label_ for k in getter(gold_doc, attr)]
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+ [k.label_ for k in getter(pred_doc, attr)]
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)
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# Set up all labels for per type scoring and prepare gold per type
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gold_per_type = {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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# Special case for ents:
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# If we have missing values in the gold, we can't easily tell
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# whether our NER predictions are true.
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# It seems bad but it's what we've always done.
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if attr == "ents" and not all(token.ent_iob != 0 for token in gold_doc):
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continue
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for span in getter(gold_doc, attr):
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gold_span = (span.label_, span.start, span.end - 1)
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gold_spans.add(gold_span)
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gold_per_type[span.label_].add((span.label_, span.start, span.end - 1))
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pred_per_type = {label: set() for label in labels}
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for span in example.get_aligned_spans_x2y(getter(pred_doc, attr)):
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pred_spans.add((span.label_, span.start, span.end - 1))
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pred_per_type[span.label_].add((span.label_, span.start, span.end - 1))
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# Scores per label
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for k, v in score_per_type.items():
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if k in pred_per_type:
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v.score_set(pred_per_type[k], gold_per_type[k])
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# Score for all labels
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score.score_set(pred_spans, gold_spans)
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results = {
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attr + "_p": score.precision,
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attr + "_r": score.recall,
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attr + "_f": score.fscore,
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attr + "_per_type": {k: v.to_dict() for k, v in score_per_type.items()},
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}
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return results
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@staticmethod
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def score_cats(
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examples,
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attr,
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getter=getattr,
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labels=[],
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multi_label=True,
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positive_label=None,
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**cfg
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):
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"""Returns PRF and ROC AUC scores for a doc-level attribute with a
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dict with scores for each label like Doc.cats. The reported overall
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score depends on the scorer settings.
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examples (Iterable[Example]): Examples to score
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attr (str): The attribute to score.
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getter (callable): Defaults to getattr. If provided,
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getter(doc, attr) should return the values for the individual doc.
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labels (Iterable[str]): The set of possible labels. Defaults to [].
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multi_label (bool): Whether the attribute allows multiple labels.
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Defaults to True.
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positive_label (str): The positive label for a binary task with
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exclusive classes. Defaults to None.
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RETURNS (dict): A dictionary containing the scores, with inapplicable
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scores as None:
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for all:
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attr_score (one of attr_f / attr_macro_f / attr_macro_auc),
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attr_score_desc (text description of the overall score),
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attr_f_per_type,
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attr_auc_per_type
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for binary exclusive with positive label: attr_p/r/f
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for 3+ exclusive classes, macro-averaged fscore: attr_macro_f
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for multilabel, macro-averaged AUC: attr_macro_auc
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"""
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score = PRFScore()
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f_per_type = dict()
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auc_per_type = dict()
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for label in labels:
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f_per_type[label] = PRFScore()
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auc_per_type[label] = ROCAUCScore()
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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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gold_values = getter(gold_doc, attr)
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pred_values = getter(pred_doc, attr)
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if (
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len(gold_values) > 0
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and set(f_per_type) == set(auc_per_type) == set(gold_values)
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and set(gold_values) == set(pred_values)
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):
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gold_val = max(gold_values, key=gold_values.get)
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pred_val = max(pred_values, key=pred_values.get)
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if positive_label:
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score.score_set(
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set([positive_label]) & set([pred_val]),
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set([positive_label]) & set([gold_val]),
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)
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for label in set(gold_values):
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auc_per_type[label].score_set(
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pred_values[label], gold_values[label]
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)
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f_per_type[label].score_set(
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set([label]) & set([pred_val]), set([label]) & set([gold_val])
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)
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elif len(f_per_type) > 0:
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model_labels = set(f_per_type)
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eval_labels = set(gold_values)
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raise ValueError(
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Errors.E162.format(
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model_labels=model_labels, eval_labels=eval_labels
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)
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)
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elif len(auc_per_type) > 0:
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model_labels = set(auc_per_type)
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eval_labels = set(gold_values)
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raise ValueError(
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Errors.E162.format(
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model_labels=model_labels, eval_labels=eval_labels
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)
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)
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results = {
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attr + "_score": None,
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attr + "_score_desc": None,
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attr + "_p": None,
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attr + "_r": None,
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attr + "_f": None,
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attr + "_macro_f": None,
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attr + "_macro_auc": None,
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attr + "_f_per_type": {k: v.to_dict() for k, v in f_per_type.items()},
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attr + "_auc_per_type": {k: v.score for k, v in auc_per_type.items()},
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}
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if len(labels) == 2 and not multi_label and positive_label:
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results[attr + "_p"] = score.precision
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results[attr + "_r"] = score.recall
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results[attr + "_f"] = score.fscore
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results[attr + "_score"] = results[attr + "_f"]
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results[attr + "_score_desc"] = "F (" + positive_label + ")"
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elif not multi_label:
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results[attr + "_macro_f"] = sum(
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[score.fscore for label, score in f_per_type.items()]
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) / (len(f_per_type) + 1e-100)
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results[attr + "_score"] = results[attr + "_macro_f"]
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results[attr + "_score_desc"] = "macro F"
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else:
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results[attr + "_macro_auc"] = max(
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sum([score.score for label, score in auc_per_type.items()])
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/ (len(auc_per_type) + 1e-100),
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-1,
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)
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results[attr + "_score"] = results[attr + "_macro_auc"]
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results[attr + "_score_desc"] = "macro AUC"
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return results
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@staticmethod
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def score_deps(
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examples,
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attr,
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getter=getattr,
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head_attr="head",
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head_getter=getattr,
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ignore_labels=tuple(),
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**cfg
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):
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"""Returns the UAS, LAS, and LAS per type scores for dependency
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parses.
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examples (Iterable[Example]): Examples to score
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attr (str): The attribute containing the dependency label.
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getter (callable): 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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head_attr (str): The attribute containing the head token. Defaults to
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'head'.
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head_getter (callable): Defaults to getattr. If provided,
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head_getter(token, attr) should return the value of the head for an
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individual token.
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ignore_labels (Tuple): Labels to ignore while scoring (e.g., punct).
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RETURNS (dict): A dictionary containing the scores:
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attr_uas, attr_las, and attr_las_per_type.
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"""
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unlabelled = PRFScore()
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labelled = PRFScore()
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labelled_per_dep = dict()
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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_deps = set()
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gold_deps_per_dep = {}
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for gold_i, token in enumerate(gold_doc):
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dep = getter(token, attr)
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head = head_getter(token, head_attr)
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if dep not in ignore_labels:
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gold_deps.add((gold_i, head.i, dep))
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if dep not in labelled_per_dep:
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labelled_per_dep[dep] = PRFScore()
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if dep not in gold_deps_per_dep:
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gold_deps_per_dep[dep] = set()
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gold_deps_per_dep[dep].add((gold_i, head.i, dep))
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pred_deps = set()
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pred_deps_per_dep = {}
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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 = None
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else:
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gold_i = align.x2y[token.i].dataXd[0, 0]
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dep = getter(token, attr)
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head = head_getter(token, head_attr)
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if dep not in ignore_labels and token.orth_.strip():
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if align.x2y.lengths[head.i] == 1:
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gold_head = align.x2y[head.i].dataXd[0, 0]
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else:
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gold_head = None
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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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unlabelled.fp += 1
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labelled.fp += 1
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else:
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pred_deps.add((gold_i, gold_head, dep))
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if dep not in labelled_per_dep:
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labelled_per_dep[dep] = PRFScore()
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if dep not in pred_deps_per_dep:
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pred_deps_per_dep[dep] = set()
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pred_deps_per_dep[dep].add((gold_i, gold_head, dep))
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labelled.score_set(pred_deps, gold_deps)
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for dep in labelled_per_dep:
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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)
|
||
)
|
||
return {
|
||
attr + "_uas": unlabelled.fscore,
|
||
attr + "_las": labelled.fscore,
|
||
attr
|
||
+ "_las_per_type": {k: v.to_dict() for k, v in labelled_per_dep.items()},
|
||
}
|
||
|
||
|
||
#############################################################################
|
||
#
|
||
# 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)
|
||
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))
|
||
|
||
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
|