mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-31 16:07:41 +03:00
Merge branch 'develop' of https://github.com/explosion/spaCy into develop
This commit is contained in:
Ines Montani
commit
e257e66ab9
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|
@ -67,10 +67,7 @@ def evaluate(
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corpus = Corpus(data_path, data_path)
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nlp = util.load_model(model)
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dev_dataset = list(corpus.dev_dataset(nlp, gold_preproc=gold_preproc))
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begin = timer()
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scores = nlp.evaluate(dev_dataset, verbose=False)
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end = timer()
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nwords = sum(len(ex.predicted) for ex in dev_dataset)
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metrics = {
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"TOK": "token_acc",
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"TAG": "tag_acc",
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@ -82,17 +79,21 @@ def evaluate(
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"NER P": "ents_p",
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"NER R": "ents_r",
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"NER F": "ents_f",
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"Textcat": "cats_score",
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"Sent P": "sents_p",
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"Sent R": "sents_r",
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"Sent F": "sents_f",
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"TEXTCAT": "cats_score",
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"SENT P": "sents_p",
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"SENT R": "sents_r",
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"SENT F": "sents_f",
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"SPEED": "speed",
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}
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results = {}
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for metric, key in metrics.items():
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if key in scores:
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if key == "cats_score":
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metric = metric + " (" + scores.get("cats_score_desc", "unk") + ")"
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results[metric] = f"{scores[key]*100:.2f}"
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if key == "speed":
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results[metric] = f"{scores[key]:.0f}"
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else:
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results[metric] = f"{scores[key]*100:.2f}"
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data = {re.sub(r"[\s/]", "_", k.lower()): v for k, v in results.items()}
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msg.table(results, title="Results")
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|
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@ -1,5 +1,4 @@
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from typing import Optional, Dict, Any, Tuple, Union, Callable, List
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from timeit import default_timer as timer
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import srsly
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import tqdm
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from pathlib import Path
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@ -248,14 +247,11 @@ def create_evaluation_callback(
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dev_examples = list(dev_examples)
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n_words = sum(len(ex.predicted) for ex in dev_examples)
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batch_size = cfg["eval_batch_size"]
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start_time = timer()
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if optimizer.averages:
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with nlp.use_params(optimizer.averages):
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scores = nlp.evaluate(dev_examples, batch_size=batch_size)
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else:
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scores = nlp.evaluate(dev_examples, batch_size=batch_size)
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end_time = timer()
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wps = n_words / (end_time - start_time)
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# Calculate a weighted sum based on score_weights for the main score
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weights = cfg["score_weights"]
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try:
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@ -264,7 +260,6 @@ def create_evaluation_callback(
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keys = list(scores.keys())
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err = Errors.E983.format(dict="score_weights", key=str(e), keys=keys)
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raise KeyError(err)
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scores["speed"] = wps
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return weighted_score, scores
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return evaluate
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@ -446,7 +441,7 @@ def update_meta(
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training: Union[Dict[str, Any], Config], nlp: Language, info: Dict[str, Any]
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) -> None:
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nlp.meta["performance"] = {}
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for metric in training["scores_weights"]:
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for metric in training["score_weights"]:
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nlp.meta["performance"][metric] = info["other_scores"][metric]
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for pipe_name in nlp.pipe_names:
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nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name]
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|
|
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@ -432,12 +432,12 @@ class Errors:
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"Current DocBin: {current}\nOther DocBin: {other}")
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E169 = ("Can't find module: {module}")
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E170 = ("Cannot apply transition {name}: invalid for the current state.")
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E171 = ("Matcher.add received invalid on_match callback argument: expected "
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E171 = ("Matcher.add received invalid 'on_match' callback argument: expected "
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"callable or None, but got: {arg_type}")
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E175 = ("Can't remove rule for unknown match pattern ID: {key}")
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E176 = ("Alias '{alias}' is not defined in the Knowledge Base.")
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E177 = ("Ill-formed IOB input detected: {tag}")
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E178 = ("Invalid pattern. Expected list of dicts but got: {pat}. Maybe you "
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E178 = ("Each pattern should be a list of dicts, but got: {pat}. Maybe you "
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"accidentally passed a single pattern to Matcher.add instead of a "
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"list of patterns? If you only want to add one pattern, make sure "
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"to wrap it in a list. For example: matcher.add('{key}', [pattern])")
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|
|
@ -483,6 +483,10 @@ class Errors:
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E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
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# TODO: fix numbering after merging develop into master
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E947 = ("Matcher.add received invalid 'greedy' argument: expected "
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"a string value from {expected} but got: '{arg}'")
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E948 = ("Matcher.add received invalid 'patterns' argument: expected "
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"a List, but got: {arg_type}")
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E952 = ("The section '{name}' is not a valid section in the provided config.")
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E953 = ("Mismatched IDs received by the Tok2Vec listener: {id1} vs. {id2}")
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E954 = ("The Tok2Vec listener did not receive a valid input.")
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|
|
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@ -14,6 +14,7 @@ from thinc.api import get_current_ops, Config, require_gpu, Optimizer
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import srsly
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import multiprocessing as mp
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from itertools import chain, cycle
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from timeit import default_timer as timer
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from .tokens.underscore import Underscore
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from .vocab import Vocab, create_vocab
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@ -1130,7 +1131,14 @@ class Language:
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kwargs.setdefault("verbose", verbose)
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kwargs.setdefault("nlp", self)
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scorer = Scorer(**kwargs)
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docs = list(eg.predicted for eg in examples)
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texts = [eg.reference.text for eg in examples]
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docs = [eg.predicted for eg in examples]
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start_time = timer()
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# tokenize the texts only for timing purposes
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if not hasattr(self.tokenizer, "pipe"):
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_ = [self.tokenizer(text) for text in texts]
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else:
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_ = list(self.tokenizer.pipe(texts))
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for name, pipe in self.pipeline:
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kwargs = component_cfg.get(name, {})
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kwargs.setdefault("batch_size", batch_size)
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@ -1138,11 +1146,18 @@ class Language:
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docs = _pipe(docs, pipe, kwargs)
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else:
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docs = pipe.pipe(docs, **kwargs)
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# iterate over the final generator
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if len(self.pipeline):
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docs = list(docs)
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end_time = timer()
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for i, (doc, eg) in enumerate(zip(docs, examples)):
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if verbose:
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print(doc)
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eg.predicted = doc
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return scorer.score(examples)
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results = scorer.score(examples)
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n_words = sum(len(eg.predicted) for eg in examples)
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results["speed"] = n_words / (end_time - start_time)
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return results
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@contextmanager
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def use_params(self, params: dict):
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|
|
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@ -66,6 +66,7 @@ cdef class Matcher:
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cdef public object validate
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cdef public object _patterns
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cdef public object _callbacks
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cdef public object _filter
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cdef public object _extensions
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cdef public object _extra_predicates
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cdef public object _seen_attrs
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|
|
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|
|
@ -1,6 +1,9 @@
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# cython: infer_types=True, cython: profile=True
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from typing import List
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from libcpp.vector cimport vector
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from libc.stdint cimport int32_t
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from libc.string cimport memset, memcmp
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from cymem.cymem cimport Pool
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from murmurhash.mrmr cimport hash64
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|
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@ -41,6 +44,7 @@ cdef class Matcher:
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self._extra_predicates = []
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self._patterns = {}
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||||
self._callbacks = {}
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||||
self._filter = {}
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self._extensions = {}
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self._seen_attrs = set()
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self.vocab = vocab
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|
|
@ -68,7 +72,7 @@ cdef class Matcher:
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"""
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return self._normalize_key(key) in self._patterns
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def add(self, key, patterns, *_patterns, on_match=None):
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def add(self, key, patterns, *, on_match=None, greedy: str=None):
|
||||
"""Add a match-rule to the matcher. A match-rule consists of: an ID
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||||
key, an on_match callback, and one or more patterns.
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|
|
@ -86,11 +90,10 @@ cdef class Matcher:
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'+': Require the pattern to match 1 or more times.
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'*': Allow the pattern to zero or more times.
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|
||||
The + and * operators are usually interpretted "greedily", i.e. longer
|
||||
matches are returned where possible. However, if you specify two '+'
|
||||
and '*' patterns in a row and their matches overlap, the first
|
||||
operator will behave non-greedily. This quirk in the semantics makes
|
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the matcher more efficient, by avoiding the need for back-tracking.
|
||||
The + and * operators return all possible matches (not just the greedy
|
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ones). However, the "greedy" argument can filter the final matches
|
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by returning a non-overlapping set per key, either taking preference to
|
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the first greedy match ("FIRST"), or the longest ("LONGEST").
|
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|
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As of spaCy v2.2.2, Matcher.add supports the future API, which makes
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the patterns the second argument and a list (instead of a variable
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|
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@ -100,16 +103,15 @@ cdef class Matcher:
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key (str): The match ID.
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patterns (list): The patterns to add for the given key.
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on_match (callable): Optional callback executed on match.
|
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*_patterns (list): For backwards compatibility: list of patterns to add
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as variable arguments. Will be ignored if a list of patterns is
|
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provided as the second argument.
|
||||
greedy (str): Optional filter: "FIRST" or "LONGEST".
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"""
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errors = {}
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if on_match is not None and not hasattr(on_match, "__call__"):
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raise ValueError(Errors.E171.format(arg_type=type(on_match)))
|
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if patterns is None or hasattr(patterns, "__call__"): # old API
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on_match = patterns
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patterns = _patterns
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if patterns is None or not isinstance(patterns, List): # old API
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raise ValueError(Errors.E948.format(arg_type=type(patterns)))
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if greedy is not None and greedy not in ["FIRST", "LONGEST"]:
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raise ValueError(Errors.E947.format(expected=["FIRST", "LONGEST"], arg=greedy))
|
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for i, pattern in enumerate(patterns):
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if len(pattern) == 0:
|
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raise ValueError(Errors.E012.format(key=key))
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|
|
@ -132,6 +134,7 @@ cdef class Matcher:
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raise ValueError(Errors.E154.format())
|
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self._patterns.setdefault(key, [])
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self._callbacks[key] = on_match
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self._filter[key] = greedy
|
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self._patterns[key].extend(patterns)
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|
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def remove(self, key):
|
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|
|
@ -217,6 +220,7 @@ cdef class Matcher:
|
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length = doclike.end - doclike.start
|
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else:
|
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raise ValueError(Errors.E195.format(good="Doc or Span", got=type(doclike).__name__))
|
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cdef Pool tmp_pool = Pool()
|
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if len(set([LEMMA, POS, TAG]) & self._seen_attrs) > 0 \
|
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and not doc.is_tagged:
|
||||
raise ValueError(Errors.E155.format())
|
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|
|
@ -224,11 +228,42 @@ cdef class Matcher:
|
|||
raise ValueError(Errors.E156.format())
|
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matches = find_matches(&self.patterns[0], self.patterns.size(), doclike, length,
|
||||
extensions=self._extensions, predicates=self._extra_predicates)
|
||||
for i, (key, start, end) in enumerate(matches):
|
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final_matches = []
|
||||
pairs_by_id = {}
|
||||
# For each key, either add all matches, or only the filtered, non-overlapping ones
|
||||
for (key, start, end) in matches:
|
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span_filter = self._filter.get(key)
|
||||
if span_filter is not None:
|
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pairs = pairs_by_id.get(key, [])
|
||||
pairs.append((start,end))
|
||||
pairs_by_id[key] = pairs
|
||||
else:
|
||||
final_matches.append((key, start, end))
|
||||
matched = <char*>tmp_pool.alloc(length, sizeof(char))
|
||||
empty = <char*>tmp_pool.alloc(length, sizeof(char))
|
||||
for key, pairs in pairs_by_id.items():
|
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memset(matched, 0, length * sizeof(matched[0]))
|
||||
span_filter = self._filter.get(key)
|
||||
if span_filter == "FIRST":
|
||||
sorted_pairs = sorted(pairs, key=lambda x: (x[0], -x[1]), reverse=False) # sort by start
|
||||
elif span_filter == "LONGEST":
|
||||
sorted_pairs = sorted(pairs, key=lambda x: (x[1]-x[0], -x[0]), reverse=True) # reverse sort by length
|
||||
else:
|
||||
raise ValueError(Errors.E947.format(expected=["FIRST", "LONGEST"], arg=span_filter))
|
||||
for (start, end) in sorted_pairs:
|
||||
assert 0 <= start < end # Defend against segfaults
|
||||
span_len = end-start
|
||||
# If no tokens in the span have matched
|
||||
if memcmp(&matched[start], &empty[start], span_len * sizeof(matched[0])) == 0:
|
||||
final_matches.append((key, start, end))
|
||||
# Mark tokens that have matched
|
||||
memset(&matched[start], 1, span_len * sizeof(matched[0]))
|
||||
# perform the callbacks on the filtered set of results
|
||||
for i, (key, start, end) in enumerate(final_matches):
|
||||
on_match = self._callbacks.get(key, None)
|
||||
if on_match is not None:
|
||||
on_match(self, doc, i, matches)
|
||||
return matches
|
||||
on_match(self, doc, i, final_matches)
|
||||
return final_matches
|
||||
|
||||
def _normalize_key(self, key):
|
||||
if isinstance(key, basestring):
|
||||
|
|
@ -239,9 +274,9 @@ cdef class Matcher:
|
|||
|
||||
def unpickle_matcher(vocab, patterns, callbacks):
|
||||
matcher = Matcher(vocab)
|
||||
for key, specs in patterns.items():
|
||||
for key, pattern in patterns.items():
|
||||
callback = callbacks.get(key, None)
|
||||
matcher.add(key, callback, *specs)
|
||||
matcher.add(key, pattern, on_match=callback)
|
||||
return matcher
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ def merge_subtokens(doc: Doc, label: str = "subtok") -> Doc:
|
|||
"""
|
||||
# TODO: make stateful component with "label" config
|
||||
merger = Matcher(doc.vocab)
|
||||
merger.add("SUBTOK", None, [{"DEP": label, "op": "+"}])
|
||||
merger.add("SUBTOK", [[{"DEP": label, "op": "+"}]])
|
||||
matches = merger(doc)
|
||||
spans = filter_spans([doc[start : end + 1] for _, start, end in matches])
|
||||
with doc.retokenize() as retokenizer:
|
||||
|
|
|
|||
|
|
@ -63,18 +63,11 @@ def test_matcher_len_contains(matcher):
|
|||
assert "TEST2" not in matcher
|
||||
|
||||
|
||||
def test_matcher_add_new_old_api(en_vocab):
|
||||
def test_matcher_add_new_api(en_vocab):
|
||||
doc = Doc(en_vocab, words=["a", "b"])
|
||||
patterns = [[{"TEXT": "a"}], [{"TEXT": "a"}, {"TEXT": "b"}]]
|
||||
matcher = Matcher(en_vocab)
|
||||
matcher.add("OLD_API", None, *patterns)
|
||||
assert len(matcher(doc)) == 2
|
||||
matcher = Matcher(en_vocab)
|
||||
on_match = Mock()
|
||||
matcher.add("OLD_API_CALLBACK", on_match, *patterns)
|
||||
assert len(matcher(doc)) == 2
|
||||
assert on_match.call_count == 2
|
||||
# New API: add(key: str, patterns: List[List[dict]], on_match: Callable)
|
||||
matcher = Matcher(en_vocab)
|
||||
matcher.add("NEW_API", patterns)
|
||||
assert len(matcher(doc)) == 2
|
||||
|
|
@ -176,7 +169,7 @@ def test_matcher_match_zero_plus(matcher):
|
|||
|
||||
def test_matcher_match_one_plus(matcher):
|
||||
control = Matcher(matcher.vocab)
|
||||
control.add("BasicPhilippe", None, [{"ORTH": "Philippe"}])
|
||||
control.add("BasicPhilippe", [[{"ORTH": "Philippe"}]])
|
||||
doc = Doc(control.vocab, words=["Philippe", "Philippe"])
|
||||
m = control(doc)
|
||||
assert len(m) == 2
|
||||
|
|
|
|||
|
|
@ -7,18 +7,10 @@ from spacy.tokens import Doc, Span
|
|||
|
||||
|
||||
pattern1 = [{"ORTH": "A"}, {"ORTH": "A", "OP": "*"}]
|
||||
pattern2 = [{"ORTH": "A"}, {"ORTH": "A"}]
|
||||
pattern2 = [{"ORTH": "A", "OP": "*"}, {"ORTH": "A"}]
|
||||
pattern3 = [{"ORTH": "A"}, {"ORTH": "A"}]
|
||||
pattern4 = [
|
||||
{"ORTH": "B"},
|
||||
{"ORTH": "A", "OP": "*"},
|
||||
{"ORTH": "B"},
|
||||
]
|
||||
pattern5 = [
|
||||
{"ORTH": "B", "OP": "*"},
|
||||
{"ORTH": "A", "OP": "*"},
|
||||
{"ORTH": "B"},
|
||||
]
|
||||
pattern4 = [{"ORTH": "B"}, {"ORTH": "A", "OP": "*"}, {"ORTH": "B"}]
|
||||
pattern5 = [{"ORTH": "B", "OP": "*"}, {"ORTH": "A", "OP": "*"}, {"ORTH": "B"}]
|
||||
|
||||
re_pattern1 = "AA*"
|
||||
re_pattern2 = "A*A"
|
||||
|
|
@ -26,10 +18,16 @@ re_pattern3 = "AA"
|
|||
re_pattern4 = "BA*B"
|
||||
re_pattern5 = "B*A*B"
|
||||
|
||||
longest1 = "A A A A A"
|
||||
longest2 = "A A A A A"
|
||||
longest3 = "A A"
|
||||
longest4 = "B A A A A A B" # "FIRST" would be "B B"
|
||||
longest5 = "B B A A A A A B"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def text():
|
||||
return "(ABBAAAAAB)."
|
||||
return "(BBAAAAAB)."
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -41,25 +39,63 @@ def doc(en_tokenizer, text):
|
|||
@pytest.mark.parametrize(
|
||||
"pattern,re_pattern",
|
||||
[
|
||||
pytest.param(pattern1, re_pattern1, marks=pytest.mark.xfail()),
|
||||
pytest.param(pattern2, re_pattern2, marks=pytest.mark.xfail()),
|
||||
pytest.param(pattern3, re_pattern3, marks=pytest.mark.xfail()),
|
||||
(pattern1, re_pattern1),
|
||||
(pattern2, re_pattern2),
|
||||
(pattern3, re_pattern3),
|
||||
(pattern4, re_pattern4),
|
||||
pytest.param(pattern5, re_pattern5, marks=pytest.mark.xfail()),
|
||||
(pattern5, re_pattern5),
|
||||
],
|
||||
)
|
||||
def test_greedy_matching(doc, text, pattern, re_pattern):
|
||||
"""Test that the greedy matching behavior of the * op is consistant with
|
||||
def test_greedy_matching_first(doc, text, pattern, re_pattern):
|
||||
"""Test that the greedy matching behavior "FIRST" is consistent with
|
||||
other re implementations."""
|
||||
matcher = Matcher(doc.vocab)
|
||||
matcher.add(re_pattern, [pattern])
|
||||
matcher.add(re_pattern, [pattern], greedy="FIRST")
|
||||
matches = matcher(doc)
|
||||
re_matches = [m.span() for m in re.finditer(re_pattern, text)]
|
||||
for match, re_match in zip(matches, re_matches):
|
||||
assert match[1:] == re_match
|
||||
for (key, m_s, m_e), (re_s, re_e) in zip(matches, re_matches):
|
||||
# matching the string, not the exact position
|
||||
assert doc[m_s:m_e].text == doc[re_s:re_e].text
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"pattern,longest",
|
||||
[
|
||||
(pattern1, longest1),
|
||||
(pattern2, longest2),
|
||||
(pattern3, longest3),
|
||||
(pattern4, longest4),
|
||||
(pattern5, longest5),
|
||||
],
|
||||
)
|
||||
def test_greedy_matching_longest(doc, text, pattern, longest):
|
||||
"""Test the "LONGEST" greedy matching behavior"""
|
||||
matcher = Matcher(doc.vocab)
|
||||
matcher.add("RULE", [pattern], greedy="LONGEST")
|
||||
matches = matcher(doc)
|
||||
for (key, s, e) in matches:
|
||||
assert doc[s:e].text == longest
|
||||
|
||||
|
||||
def test_greedy_matching_longest_first(en_tokenizer):
|
||||
"""Test that "LONGEST" matching prefers the first of two equally long matches"""
|
||||
doc = en_tokenizer(" ".join("CCC"))
|
||||
matcher = Matcher(doc.vocab)
|
||||
pattern = [{"ORTH": "C"}, {"ORTH": "C"}]
|
||||
matcher.add("RULE", [pattern], greedy="LONGEST")
|
||||
matches = matcher(doc)
|
||||
# out of 0-2 and 1-3, the first should be picked
|
||||
assert len(matches) == 1
|
||||
assert matches[0][1] == 0
|
||||
assert matches[0][2] == 2
|
||||
|
||||
|
||||
def test_invalid_greediness(doc, text):
|
||||
matcher = Matcher(doc.vocab)
|
||||
with pytest.raises(ValueError):
|
||||
matcher.add("RULE", [pattern1], greedy="GREEDY")
|
||||
|
||||
|
||||
@pytest.mark.xfail
|
||||
@pytest.mark.parametrize(
|
||||
"pattern,re_pattern",
|
||||
[
|
||||
|
|
@ -74,7 +110,7 @@ def test_match_consuming(doc, text, pattern, re_pattern):
|
|||
"""Test that matcher.__call__ consumes tokens on a match similar to
|
||||
re.findall."""
|
||||
matcher = Matcher(doc.vocab)
|
||||
matcher.add(re_pattern, [pattern])
|
||||
matcher.add(re_pattern, [pattern], greedy="FIRST")
|
||||
matches = matcher(doc)
|
||||
re_matches = [m.span() for m in re.finditer(re_pattern, text)]
|
||||
assert len(matches) == len(re_matches)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user