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Fix memory issues in Language.evaluate (#6386)
* Fix memory issues in Language.evaluate Reset annotation in predicted docs before evaluating and store all data in `examples`. * Minor refactor to docs generator init * Fix generator expression * Fix final generator check * Refactor pipeline loop * Handle examples generator in Language.evaluate * Add test with generator * Use make_doc
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@ -1290,6 +1290,7 @@ class Language:
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DOCS: https://nightly.spacy.io/api/language#evaluate
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DOCS: https://nightly.spacy.io/api/language#evaluate
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"""
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"""
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examples = list(examples)
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validate_examples(examples, "Language.evaluate")
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validate_examples(examples, "Language.evaluate")
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if batch_size is None:
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if batch_size is None:
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batch_size = self.batch_size
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batch_size = self.batch_size
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@ -1301,27 +1302,21 @@ class Language:
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kwargs = dict(scorer_cfg)
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kwargs = dict(scorer_cfg)
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kwargs.setdefault("nlp", self)
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kwargs.setdefault("nlp", self)
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scorer = Scorer(**kwargs)
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scorer = Scorer(**kwargs)
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texts = [eg.reference.text for eg in examples]
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# reset annotation in predicted docs and time tokenization
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docs = [eg.predicted for eg in examples]
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start_time = timer()
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start_time = timer()
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# tokenize the texts only for timing purposes
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for eg in examples:
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if not hasattr(self.tokenizer, "pipe"):
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eg.predicted = self.make_doc(eg.reference.text)
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_ = [self.tokenizer(text) for text in texts] # noqa: F841
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# apply all pipeline components
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else:
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_ = list(self.tokenizer.pipe(texts)) # noqa: F841
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for name, pipe in self.pipeline:
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for name, pipe in self.pipeline:
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kwargs = component_cfg.get(name, {})
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kwargs = component_cfg.get(name, {})
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kwargs.setdefault("batch_size", batch_size)
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kwargs.setdefault("batch_size", batch_size)
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docs = _pipe(docs, pipe, kwargs)
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for doc, eg in zip(
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# iterate over the final generator
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_pipe((eg.predicted for eg in examples), pipe, kwargs), examples
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if len(self.pipeline):
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):
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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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util.logger.debug(doc)
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eg.predicted = doc
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eg.predicted = doc
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end_time = timer()
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results = scorer.score(examples)
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results = scorer.score(examples)
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n_words = sum(len(doc) for doc in docs)
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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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results["speed"] = n_words / (end_time - start_time)
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return results
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return results
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@ -53,7 +53,12 @@ def test_language_evaluate(nlp):
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annots = {"doc_annotation": {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}}
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annots = {"doc_annotation": {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}}
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doc = Doc(nlp.vocab, words=text.split(" "))
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doc = Doc(nlp.vocab, words=text.split(" "))
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example = Example.from_dict(doc, annots)
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example = Example.from_dict(doc, annots)
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nlp.evaluate([example])
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scores = nlp.evaluate([example])
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assert scores["speed"] > 0
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# test with generator
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scores = nlp.evaluate(eg for eg in [example])
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assert scores["speed"] > 0
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# Not allowed to call with just one Example
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# Not allowed to call with just one Example
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with pytest.raises(TypeError):
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with pytest.raises(TypeError):
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@ -249,9 +249,8 @@ def create_evaluation_callback(
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weights = {key: value for key, value in weights.items() if value is not None}
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weights = {key: value for key, value in weights.items() if value is not None}
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def evaluate() -> Tuple[float, Dict[str, float]]:
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def evaluate() -> Tuple[float, Dict[str, float]]:
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dev_examples = list(dev_corpus(nlp))
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try:
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try:
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scores = nlp.evaluate(dev_examples)
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scores = nlp.evaluate(dev_corpus(nlp))
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except KeyError as e:
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except KeyError as e:
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raise KeyError(Errors.E900.format(pipeline=nlp.pipe_names)) from e
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raise KeyError(Errors.E900.format(pipeline=nlp.pipe_names)) from e
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# Calculate a weighted sum based on score_weights for the main score.
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# Calculate a weighted sum based on score_weights for the main score.
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