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* Clean up old Matcher call style related stuff In v2 Matcher.add was called with (key, on_match, *patterns). In v3 this was changed to (key, patterns, *, on_match=None), but there were various points where the old call syntax was documented or handled specially. This removes all those. The Matcher itself didn't need any code changes, as it just gives a generic type error. However the PhraseMatcher required some changes because it would automatically "fix" the old call style. Surprisingly, the tokenizer was still using the old call style in one place. After these changes tests failed in two places: 1. one test for the "new" call style, including the "old" call style. I removed this test. 2. deserializing the PhraseMatcher fails because the input docs are a set. I am not sure why 2 is happening - I guess it's a quirk of the serialization format? - so for now I just convert the set to a list when deserializing. The check that the input Docs are a List in the PhraseMatcher is a new check, but makes it parallel with the other Matchers, which seemed like the right thing to do. * Add notes related to input docs / deserialization type * Remove Typing import * Remove old note about call style change * Apply suggestions from code review Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Use separate method for setting internal doc representations In addition to the title change, this changes the internal dict to be a defaultdict, instead of a dict with frequent use of setdefault. * Add _add_from_arrays for unpickling * Cleanup around adding from arrays This moves adding to internal structures into the private batch method, and removes the single-add method. This has one behavioral change for `add`, in that if something is wrong with the list of input Docs (such as one of the items not being a Doc), valid items before the invalid one will not be added. Also the callback will not be updated if anything is invalid. This change should not be significant. This also adds a test to check failure when given a non-Doc. * Update spacy/matcher/phrasematcher.pyx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
162 lines
8.5 KiB
Markdown
162 lines
8.5 KiB
Markdown
---
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title: PhraseMatcher
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teaser: Match sequences of tokens, based on documents
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tag: class
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source: spacy/matcher/phrasematcher.pyx
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new: 2
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---
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The `PhraseMatcher` lets you efficiently match large terminology lists. While
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the [`Matcher`](/api/matcher) lets you match sequences based on lists of token
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descriptions, the `PhraseMatcher` accepts match patterns in the form of `Doc`
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objects. See the [usage guide](/usage/rule-based-matching#phrasematcher) for
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examples.
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## PhraseMatcher.\_\_init\_\_ {#init tag="method"}
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Create the rule-based `PhraseMatcher`. Setting a different `attr` to match on
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will change the token attributes that will be compared to determine a match. By
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default, the incoming `Doc` is checked for sequences of tokens with the same
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`ORTH` value, i.e. the verbatim token text. Matching on the attribute `LOWER`
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will result in case-insensitive matching, since only the lowercase token texts
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are compared. In theory, it's also possible to match on sequences of the same
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part-of-speech tags or dependency labels.
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If `validate=True` is set, additional validation is performed when pattern are
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added. At the moment, it will check whether a `Doc` has attributes assigned that
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aren't necessary to produce the matches (for example, part-of-speech tags if the
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`PhraseMatcher` matches on the token text). Since this can often lead to
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significantly worse performance when creating the pattern, a `UserWarning` will
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be shown.
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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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> ```
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| Name | Description |
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| --------------------------------------- | ------------------------------------------------------------------------------------------------------ |
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| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ |
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| `attr` <Tag variant="new">2.1</Tag> | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. ~~Union[int, str]~~ |
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| `validate` <Tag variant="new">2.1</Tag> | Validate patterns added to the matcher. ~~bool~~ |
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## PhraseMatcher.\_\_call\_\_ {#call tag="method"}
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Find all token sequences matching the supplied patterns on the `Doc` or `Span`.
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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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>
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> matcher = PhraseMatcher(nlp.vocab)
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> matcher.add("OBAMA", [nlp("Barack Obama")])
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> doc = nlp("Barack Obama lifts America one last time in emotional farewell")
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> matches = matcher(doc)
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> ```
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| Name | Description |
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| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `doclike` | The `Doc` or `Span` to match over. ~~Union[Doc, Span]~~ |
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| _keyword-only_ | |
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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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<Infobox title="Note on retrieving the string representation of the match_id" variant="warning">
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Because spaCy stores all strings as integers, the `match_id` you get back will
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be an integer, too – but you can always get the string representation by looking
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it up in the vocabulary's `StringStore`, i.e. `nlp.vocab.strings`:
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```python
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match_id_string = nlp.vocab.strings[match_id]
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```
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</Infobox>
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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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number of rules (identical with the number of IDs), not the number of individual
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patterns.
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> #### Example
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>
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> ```python
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> matcher = PhraseMatcher(nlp.vocab)
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> assert len(matcher) == 0
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> matcher.add("OBAMA", [nlp("Barack Obama")])
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> assert len(matcher) == 1
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> ```
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| Name | Description |
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| ----------- | ---------------------------- |
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| **RETURNS** | The number of rules. ~~int~~ |
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## PhraseMatcher.\_\_contains\_\_ {#contains tag="method"}
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Check whether the matcher contains rules for a match ID.
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> #### Example
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>
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> ```python
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> matcher = PhraseMatcher(nlp.vocab)
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> assert "OBAMA" not in matcher
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> matcher.add("OBAMA", [nlp("Barack Obama")])
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> assert "OBAMA" in matcher
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> ```
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| Name | Description |
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| ----------- | -------------------------------------------------------------- |
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| `key` | The match ID. ~~str~~ |
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| **RETURNS** | Whether the matcher contains rules for this match ID. ~~bool~~ |
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## PhraseMatcher.add {#add tag="method"}
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Add a rule to the matcher, consisting of an ID key, one or more patterns, and a
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optional callback function to act on the matches. The callback function will
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receive the arguments `matcher`, `doc`, `i` and `matches`. If a pattern already
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exists for the given ID, the patterns will be extended. An `on_match` callback
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will be overwritten.
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> #### Example
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>
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> ```python
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> def on_match(matcher, doc, id, matches):
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> print('Matched!', matches)
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>
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> matcher = PhraseMatcher(nlp.vocab)
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> matcher.add("OBAMA", [nlp("Barack Obama")], on_match=on_match)
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> matcher.add("HEALTH", [nlp("health care reform"), nlp("healthcare reform")], on_match=on_match)
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> doc = nlp("Barack Obama urges Congress to find courage to defend his healthcare reforms")
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> matches = matcher(doc)
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> ```
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `key` | An ID for the thing you're matching. ~~str~~ |
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| `docs` | `Doc` objects of the phrases to match. ~~List[Doc]~~ |
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| _keyword-only_ | |
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| `on_match` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. ~~Optional[Callable[[Matcher, Doc, int, List[tuple], Any]]~~ |
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## PhraseMatcher.remove {#remove tag="method" new="2.2"}
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Remove a rule from the matcher by match ID. A `KeyError` is raised if the key
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does not exist.
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> #### Example
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>
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> ```python
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> matcher = PhraseMatcher(nlp.vocab)
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> matcher.add("OBAMA", [nlp("Barack Obama")])
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> assert "OBAMA" in matcher
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> matcher.remove("OBAMA")
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> assert "OBAMA" not in matcher
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> ```
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| Name | Description |
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| ----- | --------------------------------- |
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| `key` | The ID of the match rule. ~~str~~ |
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