mirror of
https://github.com/explosion/spaCy.git
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2bcceb80c4
* Refactor the Scorer to improve flexibility
Refactor the `Scorer` to improve flexibility for arbitrary pipeline
components.
* Individual pipeline components provide their own `evaluate` methods
that score a list of `Example`s and return a dictionary of scores
* `Scorer` is initialized either:
* with a provided pipeline containing components to be scored
* with a default pipeline containing the built-in statistical
components (senter, tagger, morphologizer, parser, ner)
* `Scorer.score` evaluates a list of `Example`s and returns a dictionary
of scores referring to the scores provided by the components in the
pipeline
Significant differences:
* `tags_acc` is renamed to `tag_acc` to be consistent with `token_acc`
and the new `morph_acc`, `pos_acc`, and `lemma_acc`
* Scoring is no longer cumulative: `Scorer.score` scores a list of
examples rather than a single example and does not retain any state
about previously scored examples
* PRF values in the returned scores are no longer multiplied by 100
* Add kwargs to Morphologizer.evaluate
* Create generalized scoring methods in Scorer
* Generalized static scoring methods are added to `Scorer`
* Methods require an attribute (either on Token or Doc) that is
used to key the returned scores
Naming differences:
* `uas`, `las`, and `las_per_type` in the scores dict are renamed to
`dep_uas`, `dep_las`, and `dep_las_per_type`
Scoring differences:
* `Doc.sents` is now scored as spans rather than on sentence-initial
token positions so that `Doc.sents` and `Doc.ents` can be scored with
the same method (this lowers scores since a single incorrect sentence
start results in two incorrect spans)
* Simplify / extend hasattr check for eval method
* Add hasattr check to tokenizer scoring
* Simplify to hasattr check for component scoring
* Reset Example alignment if docs are set
Reset the Example alignment if either doc is set in case the
tokenization has changed.
* Add PRF tokenization scoring for tokens as spans
Add PRF scores for tokens as character spans. The scores are:
* token_acc: # correct tokens / # gold tokens
* token_p/r/f: PRF for (token.idx, token.idx + len(token))
* Add docstring to Scorer.score_tokenization
* Rename component.evaluate() to component.score()
* Update Scorer API docs
* Update scoring for positive_label in textcat
* Fix TextCategorizer.score kwargs
* Update Language.evaluate docs
* Update score names in default config
430 lines
15 KiB
Cython
430 lines
15 KiB
Cython
import warnings
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import numpy
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from ..tokens.doc cimport Doc
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from ..tokens.span cimport Span
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from ..tokens.span import Span
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from ..attrs import IDS
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from .align import Alignment
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from .iob_utils import biluo_to_iob, biluo_tags_from_offsets, biluo_tags_from_doc
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from .iob_utils import spans_from_biluo_tags
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from ..errors import Errors, Warnings
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from ..syntax import nonproj
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cpdef Doc annotations2doc(vocab, tok_annot, doc_annot):
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""" Create a Doc from dictionaries with token and doc annotations. """
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attrs, array = _annot2array(vocab, tok_annot, doc_annot)
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output = Doc(vocab, words=tok_annot["ORTH"], spaces=tok_annot["SPACY"])
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if "entities" in doc_annot:
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_add_entities_to_doc(output, doc_annot["entities"])
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if array.size:
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output = output.from_array(attrs, array)
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# links are currently added with ENT_KB_ID on the token level
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output.cats.update(doc_annot.get("cats", {}))
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return output
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cdef class Example:
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def __init__(self, Doc predicted, Doc reference, *, alignment=None):
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if predicted is None:
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raise TypeError(Errors.E972.format(arg="predicted"))
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if reference is None:
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raise TypeError(Errors.E972.format(arg="reference"))
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self.x = predicted
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self.y = reference
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self._alignment = alignment
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def __len__(self):
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return len(self.predicted)
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property predicted:
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def __get__(self):
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return self.x
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def __set__(self, doc):
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self.x = doc
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self._alignment = None
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property reference:
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def __get__(self):
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return self.y
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def __set__(self, doc):
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self.y = doc
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self._alignment = None
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def copy(self):
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return Example(
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self.x.copy(),
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self.y.copy()
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)
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@classmethod
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def from_dict(cls, Doc predicted, dict example_dict):
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if predicted is None:
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raise ValueError(Errors.E976.format(n="first", type="Doc"))
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if example_dict is None:
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raise ValueError(Errors.E976.format(n="second", type="dict"))
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example_dict = _fix_legacy_dict_data(example_dict)
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tok_dict, doc_dict = _parse_example_dict_data(example_dict)
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if "ORTH" not in tok_dict:
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tok_dict["ORTH"] = [tok.text for tok in predicted]
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tok_dict["SPACY"] = [tok.whitespace_ for tok in predicted]
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return Example(
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predicted,
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annotations2doc(predicted.vocab, tok_dict, doc_dict)
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)
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@property
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def alignment(self):
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if self._alignment is None:
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spacy_words = [token.orth_ for token in self.predicted]
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gold_words = [token.orth_ for token in self.reference]
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if gold_words == []:
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gold_words = spacy_words
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self._alignment = Alignment.from_strings(spacy_words, gold_words)
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return self._alignment
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def get_aligned(self, field, as_string=False):
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"""Return an aligned array for a token attribute."""
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align = self.alignment.x2y
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vocab = self.reference.vocab
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gold_values = self.reference.to_array([field])
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output = [None] * len(self.predicted)
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for token in self.predicted:
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if token.is_space:
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output[token.i] = None
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else:
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values = gold_values[align[token.i].dataXd]
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values = values.ravel()
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if len(values) == 0:
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output[token.i] = None
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elif len(values) == 1:
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output[token.i] = values[0]
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elif len(set(list(values))) == 1:
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# If all aligned tokens have the same value, use it.
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output[token.i] = values[0]
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else:
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output[token.i] = None
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if as_string and field not in ["ENT_IOB", "SENT_START"]:
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output = [vocab.strings[o] if o is not None else o for o in output]
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return output
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def get_aligned_parse(self, projectivize=True):
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cand_to_gold = self.alignment.x2y
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gold_to_cand = self.alignment.y2x
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aligned_heads = [None] * self.x.length
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aligned_deps = [None] * self.x.length
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heads = [token.head.i for token in self.y]
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deps = [token.dep_ for token in self.y]
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if projectivize:
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heads, deps = nonproj.projectivize(heads, deps)
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for cand_i in range(self.x.length):
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if cand_to_gold.lengths[cand_i] == 1:
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gold_i = cand_to_gold[cand_i].dataXd[0, 0]
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if gold_to_cand.lengths[heads[gold_i]] == 1:
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aligned_heads[cand_i] = int(gold_to_cand[heads[gold_i]].dataXd[0, 0])
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aligned_deps[cand_i] = deps[gold_i]
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return aligned_heads, aligned_deps
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def get_aligned_spans_x2y(self, x_spans):
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return self._get_aligned_spans(self.y, x_spans, self.alignment.x2y)
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def get_aligned_spans_y2x(self, y_spans):
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return self._get_aligned_spans(self.x, y_spans, self.alignment.y2x)
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def _get_aligned_spans(self, doc, spans, align):
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seen = set()
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output = []
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for span in spans:
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indices = align[span.start : span.end].data.ravel()
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indices = [idx for idx in indices if idx not in seen]
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if len(indices) >= 1:
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aligned_span = Span(doc, indices[0], indices[-1] + 1, label=span.label)
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target_text = span.text.lower().strip().replace(" ", "")
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our_text = aligned_span.text.lower().strip().replace(" ", "")
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if our_text == target_text:
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output.append(aligned_span)
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seen.update(indices)
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return output
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def get_aligned_ner(self):
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if not self.y.is_nered:
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return [None] * len(self.x) # should this be 'missing' instead of 'None' ?
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x_ents = self.get_aligned_spans_y2x(self.y.ents)
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# Default to 'None' for missing values
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x_tags = biluo_tags_from_offsets(
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self.x,
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[(e.start_char, e.end_char, e.label_) for e in x_ents],
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missing=None
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)
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# Now fill the tokens we can align to O.
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O = 2 # I=1, O=2, B=3
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for i, ent_iob in enumerate(self.get_aligned("ENT_IOB")):
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if x_tags[i] is None:
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if ent_iob == O:
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x_tags[i] = "O"
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elif self.x[i].is_space:
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x_tags[i] = "O"
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return x_tags
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def to_dict(self):
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return {
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"doc_annotation": {
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"cats": dict(self.reference.cats),
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"entities": biluo_tags_from_doc(self.reference),
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"links": self._links_to_dict()
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},
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"token_annotation": {
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"ids": [t.i+1 for t in self.reference],
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"words": [t.text for t in self.reference],
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"tags": [t.tag_ for t in self.reference],
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"lemmas": [t.lemma_ for t in self.reference],
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"pos": [t.pos_ for t in self.reference],
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"morphs": [t.morph_ for t in self.reference],
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"heads": [t.head.i for t in self.reference],
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"deps": [t.dep_ for t in self.reference],
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"sent_starts": [int(bool(t.is_sent_start)) for t in self.reference]
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}
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}
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def _links_to_dict(self):
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links = {}
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for ent in self.reference.ents:
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if ent.kb_id_:
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links[(ent.start_char, ent.end_char)] = {ent.kb_id_: 1.0}
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return links
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def split_sents(self):
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""" Split the token annotations into multiple Examples based on
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sent_starts and return a list of the new Examples"""
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if not self.reference.is_sentenced:
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return [self]
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align = self.alignment.y2x
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seen_indices = set()
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output = []
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for y_sent in self.reference.sents:
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indices = align[y_sent.start : y_sent.end].data.ravel()
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indices = [idx for idx in indices if idx not in seen_indices]
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if indices:
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x_sent = self.predicted[indices[0] : indices[-1] + 1]
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output.append(Example(x_sent.as_doc(), y_sent.as_doc()))
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seen_indices.update(indices)
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return output
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property text:
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def __get__(self):
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return self.x.text
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def __str__(self):
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return str(self.to_dict())
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def __repr__(self):
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return str(self.to_dict())
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def _annot2array(vocab, tok_annot, doc_annot):
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attrs = []
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values = []
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for key, value in doc_annot.items():
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if value:
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if key == "entities":
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pass
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elif key == "links":
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ent_kb_ids = _parse_links(vocab, tok_annot["ORTH"], tok_annot["SPACY"], value)
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tok_annot["ENT_KB_ID"] = ent_kb_ids
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elif key == "cats":
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pass
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else:
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raise ValueError(Errors.E974.format(obj="doc", key=key))
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for key, value in tok_annot.items():
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if key not in IDS:
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raise ValueError(Errors.E974.format(obj="token", key=key))
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elif key in ["ORTH", "SPACY"]:
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pass
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elif key == "HEAD":
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attrs.append(key)
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values.append([h-i for i, h in enumerate(value)])
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elif key == "SENT_START":
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attrs.append(key)
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values.append(value)
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elif key == "MORPH":
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attrs.append(key)
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values.append([vocab.morphology.add(v) for v in value])
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else:
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attrs.append(key)
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try:
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values.append([vocab.strings.add(v) for v in value])
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except TypeError:
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types= set([type(v) for v in value])
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raise TypeError(Errors.E969.format(field=key, types=types))
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array = numpy.asarray(values, dtype="uint64")
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return attrs, array.T
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def _add_entities_to_doc(doc, ner_data):
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if ner_data is None:
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return
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elif ner_data == []:
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doc.ents = []
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elif isinstance(ner_data[0], tuple):
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return _add_entities_to_doc(
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doc,
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biluo_tags_from_offsets(doc, ner_data)
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)
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elif isinstance(ner_data[0], str) or ner_data[0] is None:
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return _add_entities_to_doc(
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doc,
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spans_from_biluo_tags(doc, ner_data)
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)
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elif isinstance(ner_data[0], Span):
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# Ugh, this is super messy. Really hard to set O entities
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doc.ents = ner_data
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doc.ents = [span for span in ner_data if span.label_]
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else:
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raise ValueError(Errors.E973)
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def _parse_example_dict_data(example_dict):
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return (
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example_dict["token_annotation"],
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example_dict["doc_annotation"]
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)
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def _fix_legacy_dict_data(example_dict):
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token_dict = example_dict.get("token_annotation", {})
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doc_dict = example_dict.get("doc_annotation", {})
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for key, value in example_dict.items():
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if value:
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if key in ("token_annotation", "doc_annotation"):
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pass
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elif key == "ids":
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pass
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elif key in ("cats", "links"):
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doc_dict[key] = value
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elif key in ("ner", "entities"):
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doc_dict["entities"] = value
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else:
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token_dict[key] = value
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# Remap keys
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remapping = {
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"words": "ORTH",
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"tags": "TAG",
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"pos": "POS",
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"lemmas": "LEMMA",
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"deps": "DEP",
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"heads": "HEAD",
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"sent_starts": "SENT_START",
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"morphs": "MORPH",
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"spaces": "SPACY",
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}
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old_token_dict = token_dict
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token_dict = {}
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for key, value in old_token_dict.items():
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if key in ("text", "ids", "brackets"):
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pass
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elif key.lower() in remapping:
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token_dict[remapping[key.lower()]] = value
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else:
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raise KeyError(Errors.E983.format(key=key, dict="token_annotation", keys=remapping.keys()))
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text = example_dict.get("text", example_dict.get("raw"))
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if _has_field(token_dict, "ORTH") and not _has_field(token_dict, "SPACY"):
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token_dict["SPACY"] = _guess_spaces(text, token_dict["ORTH"])
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if "HEAD" in token_dict and "SENT_START" in token_dict:
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# If heads are set, we don't also redundantly specify SENT_START.
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token_dict.pop("SENT_START")
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warnings.warn(Warnings.W092)
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return {
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"token_annotation": token_dict,
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"doc_annotation": doc_dict
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}
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def _has_field(annot, field):
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if field not in annot:
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return False
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elif annot[field] is None:
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return False
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elif len(annot[field]) == 0:
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return False
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elif all([value is None for value in annot[field]]):
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return False
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else:
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return True
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def _parse_ner_tags(biluo_or_offsets, vocab, words, spaces):
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if isinstance(biluo_or_offsets[0], (list, tuple)):
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# Convert to biluo if necessary
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# This is annoying but to convert the offsets we need a Doc
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# that has the target tokenization.
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reference = Doc(vocab, words=words, spaces=spaces)
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biluo = biluo_tags_from_offsets(reference, biluo_or_offsets)
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else:
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biluo = biluo_or_offsets
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ent_iobs = []
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ent_types = []
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for iob_tag in biluo_to_iob(biluo):
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if iob_tag in (None, "-"):
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ent_iobs.append("")
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ent_types.append("")
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else:
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ent_iobs.append(iob_tag.split("-")[0])
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if iob_tag.startswith("I") or iob_tag.startswith("B"):
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ent_types.append(iob_tag.split("-", 1)[1])
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else:
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ent_types.append("")
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return ent_iobs, ent_types
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def _parse_links(vocab, words, spaces, links):
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reference = Doc(vocab, words=words, spaces=spaces)
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starts = {token.idx: token.i for token in reference}
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ends = {token.idx + len(token): token.i for token in reference}
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ent_kb_ids = ["" for _ in reference]
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for index, annot_dict in links.items():
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true_kb_ids = []
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for key, value in annot_dict.items():
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if value == 1.0:
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true_kb_ids.append(key)
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if len(true_kb_ids) > 1:
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raise ValueError(Errors.E980)
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if len(true_kb_ids) == 1:
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start_char, end_char = index
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start_token = starts.get(start_char)
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end_token = ends.get(end_char)
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if start_token is None or end_token is None:
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raise ValueError(Errors.E981)
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for i in range(start_token, end_token+1):
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ent_kb_ids[i] = true_kb_ids[0]
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return ent_kb_ids
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def _guess_spaces(text, words):
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if text is None:
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return None
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spaces = []
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text_pos = 0
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# align words with text
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for word in words:
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try:
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word_start = text[text_pos:].index(word)
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except ValueError:
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spaces.append(True)
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continue
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text_pos += word_start + len(word)
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if text_pos < len(text) and text[text_pos] == " ":
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spaces.append(True)
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else:
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spaces.append(False)
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return spaces
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