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Deprecate (Phrase)Matcher.pipe
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Ines Montani
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@ -112,6 +112,9 @@ class Warnings:
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"word segmenters: {supported}. Defaulting to {default}.")
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W104 = ("Skipping modifications for '{target}' segmenter. The current "
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"segmenter is '{current}'.")
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W105 = ("As of spaCy v3.0, the {matcher}.pipe method is deprecated. If you "
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"need to match on a stream of documents, you can use nlp.pipe and "
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"call the {matcher} on each Doc object.")
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@add_codes
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@ -176,18 +176,10 @@ cdef class Matcher:
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return (self._callbacks[key], self._patterns[key])
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def pipe(self, docs, batch_size=1000, return_matches=False, as_tuples=False):
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"""Match a stream of documents, yielding them in turn.
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docs (Iterable[Union[Doc, Span]]): A stream of documents or spans.
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batch_size (int): Number of documents to accumulate into a working set.
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return_matches (bool): Yield the match lists along with the docs, making
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results (doc, matches) tuples.
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as_tuples (bool): Interpret the input stream as (doc, context) tuples,
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and yield (result, context) tuples out.
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If both return_matches and as_tuples are True, the output will
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be a sequence of ((doc, matches), context) tuples.
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YIELDS (Doc): Documents, in order.
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"""Match a stream of documents, yielding them in turn. Deprecated as of
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spaCy v3.0.
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"""
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warnings.warn(Warnings.W105.format(matcher="Matcher"), DeprecationWarning)
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if as_tuples:
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for doc, context in docs:
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matches = self(doc)
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@ -292,20 +292,10 @@ cdef class PhraseMatcher:
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idx += 1
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def pipe(self, stream, batch_size=1000, return_matches=False, as_tuples=False):
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"""Match a stream of documents, yielding them in turn.
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docs (iterable): A stream of documents.
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batch_size (int): Number of documents to accumulate into a working set.
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return_matches (bool): Yield the match lists along with the docs, making
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results (doc, matches) tuples.
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as_tuples (bool): Interpret the input stream as (doc, context) tuples,
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and yield (result, context) tuples out.
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If both return_matches and as_tuples are True, the output will
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be a sequence of ((doc, matches), context) tuples.
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YIELDS (Doc): Documents, in order.
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DOCS: https://spacy.io/api/phrasematcher#pipe
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"""Match a stream of documents, yielding them in turn. Deprecated as of
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spaCy v3.0.
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"""
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warnings.warn(Warnings.W105.format(matcher="PhraseMatcher"), DeprecationWarning)
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if as_tuples:
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for doc, context in stream:
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matches = self(doc)
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@ -484,3 +484,12 @@ def test_matcher_as_spans(matcher):
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assert isinstance(matches[1], Span)
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assert matches[1].text == "Java"
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assert matches[1].label_ == "Java"
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def test_matcher_deprecated(matcher):
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doc = Doc(matcher.vocab, words=["hello", "world"])
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with pytest.warns(DeprecationWarning) as record:
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for _ in matcher.pipe([doc]):
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pass
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assert record.list
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assert "spaCy v3.0" in str(record.list[0].message)
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@ -303,3 +303,14 @@ def test_phrase_matcher_as_spans(en_vocab):
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assert isinstance(matches[1], Span)
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assert matches[1].text == "test"
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assert matches[1].label_ == "B"
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def test_phrase_matcher_deprecated(en_vocab):
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matcher = PhraseMatcher(en_vocab)
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matcher.add("TEST", [Doc(en_vocab, words=["helllo"])])
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doc = Doc(en_vocab, words=["hello", "world"])
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with pytest.warns(DeprecationWarning) as record:
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for _ in matcher.pipe([doc]):
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pass
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assert record.list
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assert "spaCy v3.0" in str(record.list[0].message)
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@ -123,27 +123,6 @@ Find all token sequences matching the supplied patterns on the `Doc` or `Span`.
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| `as_spans` <Tag variant="new">3</Tag> | Instead of tuples, return a list of [`Span`](/api/span) objects of the matches, with the `match_id` assigned as the span label. Defaults to `False`. ~~bool~~ |
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| **RETURNS** | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. If `as_spans` is set to `True`, a list of `Span` objects is returned instead. ~~Union[List[Tuple[int, int, int]], List[Span]]~~ |
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## Matcher.pipe {#pipe tag="method"}
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Match a stream of documents, yielding them in turn.
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> #### Example
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>
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> ```python
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> from spacy.matcher import Matcher
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> matcher = Matcher(nlp.vocab)
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> for doc in matcher.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `docs` | A stream of documents or spans. ~~Iterable[Union[Doc, Span]]~~ |
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| `batch_size` | The number of documents to accumulate into a working set. ~~int~~ |
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| `return_matches` <Tag variant="new">2.1</Tag> | Yield the match lists along with the docs, making results `(doc, matches)` tuples. ~~bool~~ |
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| `as_tuples` | Interpret the input stream as `(doc, context)` tuples, and yield `(result, context)` tuples out. If both `return_matches` and `as_tuples` are `True`, the output will be a sequence of `((doc, matches), context)` tuples. ~~bool~~ |
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| **YIELDS** | Documents, in order. ~~Union[Doc, Tuple[Doc, Any], Tuple[Tuple[Doc, Any], Any]]~~ |
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## Matcher.\_\_len\_\_ {#len tag="method" new="2"}
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Get the number of rules added to the matcher. Note that this only returns the
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@ -76,27 +76,6 @@ match_id_string = nlp.vocab.strings[match_id]
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</Infobox>
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## PhraseMatcher.pipe {#pipe tag="method"}
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Match a stream of documents, yielding them in turn.
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> #### Example
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>
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> ```python
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> from spacy.matcher import PhraseMatcher
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> matcher = PhraseMatcher(nlp.vocab)
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> for doc in matcher.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `docs` | A stream of documents. ~~Iterable[Doc]~~ |
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| `batch_size` | The number of documents to accumulate into a working set. ~~int~~ |
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| `return_matches` <Tag variant="new">2.1</Tag> | Yield the match lists along with the docs, making results `(doc, matches)` tuples. ~~bool~~ |
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| `as_tuples` | Interpret the input stream as `(doc, context)` tuples, and yield `(result, context)` tuples out. If both `return_matches` and `as_tuples` are `True`, the output will be a sequence of `((doc, matches), context)` tuples. ~~bool~~ |
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| **YIELDS** | Documents and optional matches or context in order. ~~Union[Doc, Tuple[Doc, Any], Tuple[Tuple[Doc, Any], Any]]~~ |
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## PhraseMatcher.\_\_len\_\_ {#len tag="method"}
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Get the number of rules added to the matcher. Note that this only returns the
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@ -856,15 +856,6 @@ for token in doc:
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print(token.text, token._.is_hashtag)
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```
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To process a stream of social media posts, we can use
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[`Language.pipe`](/api/language#pipe), which will return a stream of `Doc`
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objects that we can pass to [`Matcher.pipe`](/api/matcher#pipe).
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```python
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docs = nlp.pipe(LOTS_OF_TWEETS)
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matches = matcher.pipe(docs)
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```
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## Efficient phrase matching {#phrasematcher}
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If you need to match large terminology lists, you can also use the
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@ -389,6 +389,7 @@ Note that spaCy v3.0 now requires **Python 3.6+**.
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| `GoldParse` | [`Example`](/api/example) |
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| `GoldCorpus` | [`Corpus`](/api/corpus) |
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| `KnowledgeBase.load_bulk`, `KnowledgeBase.dump` | [`KnowledgeBase.from_disk`](/api/kb#from_disk), [`KnowledgeBase.to_disk`](/api/kb#to_disk) |
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| `Matcher.pipe`, `PhraseMatcher.pipe` | not needed |
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| `spacy init-model` | [`spacy init model`](/api/cli#init-model) |
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| `spacy debug-data` | [`spacy debug data`](/api/cli#debug-data) |
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| `spacy profile` | [`spacy debug profile`](/api/cli#debug-profile) |
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