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* 🚨 Ignore all existing Mypy errors * 🏗 Add Mypy check to CI * Add types-mock and types-requests as dev requirements * Add additional type ignore directives * Add types packages to dev-only list in reqs test * Add types-dataclasses for python 3.6 * Add ignore to pretrain * 🏷 Improve type annotation on `run_command` helper The `run_command` helper previously declared that it returned an `Optional[subprocess.CompletedProcess]`, but it isn't actually possible for the function to return `None`. These changes modify the type annotation of the `run_command` helper and remove all now-unnecessary `# type: ignore` directives. * 🔧 Allow variable type redefinition in limited contexts These changes modify how Mypy is configured to allow variables to have their type automatically redefined under certain conditions. The Mypy documentation contains the following example: ```python def process(items: List[str]) -> None: # 'items' has type List[str] items = [item.split() for item in items] # 'items' now has type List[List[str]] ... ``` This configuration change is especially helpful in reducing the number of `# type: ignore` directives needed to handle the common pattern of: * Accepting a filepath as a string * Overwriting the variable using `filepath = ensure_path(filepath)` These changes enable redefinition and remove all `# type: ignore` directives rendered redundant by this change. * 🏷 Add type annotation to converters mapping * 🚨 Fix Mypy error in convert CLI argument verification * 🏷 Improve type annotation on `resolve_dot_names` helper * 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors` * 🏷 Add type annotations for more `Vocab` attributes * 🏷 Add loose type annotation for gold data compilation * 🏷 Improve `_format_labels` type annotation * 🏷 Fix `get_lang_class` type annotation * 🏷 Loosen return type of `Language.evaluate` * 🏷 Don't accept `Scorer` in `handle_scores_per_type` * 🏷 Add `string_to_list` overloads * 🏷 Fix non-Optional command-line options * 🙈 Ignore redefinition of `wandb_logger` in `loggers.py` * ➕ Install `typing_extensions` in Python 3.8+ The `typing_extensions` package states that it should be used when "writing code that must be compatible with multiple Python versions". Since SpaCy needs to support multiple Python versions, it should be used when newer `typing` module members are required. One example of this is `Literal`, which is available starting with Python 3.8. Previously SpaCy tried to import `Literal` from `typing`, falling back to `typing_extensions` if the import failed. However, Mypy doesn't seem to be able to understand what `Literal` means when the initial import means. Therefore, these changes modify how `compat` imports `Literal` by always importing it from `typing_extensions`. These changes also modify how `typing_extensions` is installed, so that it is a requirement for all Python versions, including those greater than or equal to 3.8. * 🏷 Improve type annotation for `Language.pipe` These changes add a missing overload variant to the type signature of `Language.pipe`. Additionally, the type signature is enhanced to allow type checkers to differentiate between the two overload variants based on the `as_tuple` parameter. Fixes #8772 * ➖ Don't install `typing-extensions` in Python 3.8+ After more detailed analysis of how to implement Python version-specific type annotations using SpaCy, it has been determined that by branching on a comparison against `sys.version_info` can be statically analyzed by Mypy well enough to enable us to conditionally use `typing_extensions.Literal`. This means that we no longer need to install `typing_extensions` for Python versions greater than or equal to 3.8! 🎉 These changes revert previous changes installing `typing-extensions` regardless of Python version and modify how we import the `Literal` type to ensure that Mypy treats it properly. * resolve mypy errors for Strict pydantic types * refactor code to avoid missing return statement * fix types of convert CLI command * avoid list-set confustion in debug_data * fix typo and formatting * small fixes to avoid type ignores * fix types in profile CLI command and make it more efficient * type fixes in projects CLI * put one ignore back * type fixes for render * fix render types - the sequel * fix BaseDefault in language definitions * fix type of noun_chunks iterator - yields tuple instead of span * fix types in language-specific modules * 🏷 Expand accepted inputs of `get_string_id` `get_string_id` accepts either a string (in which case it returns its ID) or an ID (in which case it immediately returns the ID). These changes extend the type annotation of `get_string_id` to indicate that it can accept either strings or IDs. * 🏷 Handle override types in `combine_score_weights` The `combine_score_weights` function allows users to pass an `overrides` mapping to override data extracted from the `weights` argument. Since it allows `Optional` dictionary values, the return value may also include `Optional` dictionary values. These changes update the type annotations for `combine_score_weights` to reflect this fact. * 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer` * 🏷 Fix redefinition of `wandb_logger` These changes fix the redefinition of `wandb_logger` by giving a separate name to each `WandbLogger` version. For backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` as `wandb_logger` for now. * more fixes for typing in language * type fixes in model definitions * 🏷 Annotate `_RandomWords.probs` as `NDArray` * 🏷 Annotate `tok2vec` layers to help Mypy * 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6 Also remove an import that I forgot to move to the top of the module 😅 * more fixes for matchers and other pipeline components * quick fix for entity linker * fixing types for spancat, textcat, etc * bugfix for tok2vec * type annotations for scorer * add runtime_checkable for Protocol * type and import fixes in tests * mypy fixes for training utilities * few fixes in util * fix import * 🐵 Remove unused `# type: ignore` directives * 🏷 Annotate `Language._components` * 🏷 Annotate `spacy.pipeline.Pipe` * add doc as property to span.pyi * small fixes and cleanup * explicit type annotations instead of via comment Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
176 lines
8.9 KiB
Markdown
176 lines
8.9 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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callback function to act on the matches. The callback function will receive the
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arguments `matcher`, `doc`, `i` and `matches`. If a pattern already exists for
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the given ID, the patterns will be extended. An `on_match` callback will be
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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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<Infobox title="Changed in v3.0" variant="warning">
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As of spaCy v3.0, `PhraseMatcher.add` takes a list of patterns as the second
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argument (instead of a variable number of arguments). The `on_match` callback
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becomes an optional keyword argument.
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```diff
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patterns = [nlp("health care reform"), nlp("healthcare reform")]
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- matcher.add("HEALTH", on_match, *patterns)
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+ matcher.add("HEALTH", patterns, on_match=on_match)
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```
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</Infobox>
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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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