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Update matcher usage examples [ci skip]

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Ines Montani 2020-07-02 15:39:45 +02:00
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58
website/docs/api/matcher.md
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@ -27,8 +27,7 @@ string where an integer is expected) or unexpected property names.
## Matcher.\_\_call\_\_ {#call tag="method"} ## Matcher.\_\_call\_\_ {#call tag="method"}
Find all token sequences matching the supplied patterns on the `Doc`. As of Find all token sequences matching the supplied patterns on the `Doc` or `Span`.
spaCy v2.3, the `Matcher` can also be called on `Span` objects.
> #### Example > #### Example
> >
@ -37,29 +36,16 @@ spaCy v2.3, the `Matcher` can also be called on `Span` objects.
> >
> matcher = Matcher(nlp.vocab) > matcher = Matcher(nlp.vocab)
> pattern = [{"LOWER": "hello"}, {"LOWER": "world"}] > pattern = [{"LOWER": "hello"}, {"LOWER": "world"}]
> matcher.add("HelloWorld", None, pattern) > matcher.add("HelloWorld", [pattern])
> doc = nlp("hello world!") > doc = nlp("hello world!")
> matches = matcher(doc) > matches = matcher(doc)
> ``` > ```
| Name | Type | Description | | Name | Type | Description |
| ----------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | ----------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `doclike` | `Doc`/`Span` | The document to match over or a `Span` (as of v2.3). | | `doclike` | `Doc`/`Span` | The `Doc` or `Span` to match over. |
| **RETURNS** | list | 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. | | **RETURNS** | list | 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. |
<Infobox title="Important note" variant="warning">
By default, the matcher **does not perform any action** on matches, like tagging
matched phrases with entity types. Instead, actions need to be specified when
**adding patterns or entities**, by passing in a callback function as the
`on_match` argument on [`add`](/api/matcher#add). This allows you to define
custom actions per pattern within the same matcher. For example, you might only
want to merge some entity types, and set custom flags for other matched
patterns. For more details and examples, see the usage guide on
[rule-based matching](/usage/rule-based-matching).
</Infobox>
## Matcher.pipe {#pipe tag="method"} ## Matcher.pipe {#pipe tag="method"}
Match a stream of documents, yielding them in turn. Match a stream of documents, yielding them in turn.
@ -92,7 +78,7 @@ patterns.
> ```python > ```python
> matcher = Matcher(nlp.vocab) > matcher = Matcher(nlp.vocab)
> assert len(matcher) == 0 > assert len(matcher) == 0
> matcher.add("Rule", None, [{"ORTH": "test"}]) > matcher.add("Rule", [[{"ORTH": "test"}]])
> assert len(matcher) == 1 > assert len(matcher) == 1
> ``` > ```
@ -108,9 +94,9 @@ Check whether the matcher contains rules for a match ID.
> >
> ```python > ```python
> matcher = Matcher(nlp.vocab) > matcher = Matcher(nlp.vocab)
> assert 'Rule' not in matcher > assert "Rule" not in matcher
> matcher.add('Rule', None, [{'ORTH': 'test'}]) > matcher.add("Rule", [[{'ORTH': 'test'}]])
> assert 'Rule' in matcher > assert "Rule" in matcher
> ``` > ```
| Name | Type | Description | | Name | Type | Description |
@ -133,35 +119,35 @@ overwritten.
> print('Matched!', matches) > print('Matched!', matches)
> >
> matcher = Matcher(nlp.vocab) > matcher = Matcher(nlp.vocab)
> matcher.add("HelloWorld", on_match, [{"LOWER": "hello"}, {"LOWER": "world"}]) > patterns = [
> matcher.add("GoogleMaps", on_match, [{"ORTH": "Google"}, {"ORTH": "Maps"}]) > [{"LOWER": "hello"}, {"LOWER": "world"}],
> [{"ORTH": "Google"}, {"ORTH": "Maps"}]
> ]
> matcher.add("TEST_PATTERNS", patterns)
> doc = nlp("HELLO WORLD on Google Maps.") > doc = nlp("HELLO WORLD on Google Maps.")
> matches = matcher(doc) > matches = matcher(doc)
> ``` > ```
| Name | Type | Description | <Infobox title="Changed in v3.0" variant="warning">
| ----------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
| `*patterns` | list | Match pattern. A pattern consists of a list of dicts, where each dict describes a token. |
<Infobox title="Changed in v2.2.2" variant="warning"> As of spaCy v3.0, `Matcher.add` takes a list of patterns as the second argument
As of spaCy 2.2.2, `Matcher.add` also supports the new API, which will become
the default in the future. The patterns are now the second argument and a list
(instead of a variable number of arguments). The `on_match` callback becomes an (instead of a variable number of arguments). The `on_match` callback becomes an
optional keyword argument. optional keyword argument.
```diff ```diff
patterns = [[{"TEXT": "Google"}, {"TEXT": "Now"}], [{"TEXT": "GoogleNow"}]] patterns = [[{"TEXT": "Google"}, {"TEXT": "Now"}], [{"TEXT": "GoogleNow"}]]
- matcher.add("GoogleNow", None, *patterns)
+ matcher.add("GoogleNow", patterns)
- matcher.add("GoogleNow", on_match, *patterns) - matcher.add("GoogleNow", on_match, *patterns)
+ matcher.add("GoogleNow", patterns, on_match=on_match) + matcher.add("GoogleNow", patterns, on_match=on_match)
``` ```
</Infobox> </Infobox>
| Name | Type | Description |
| ---------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `patterns` | list | Match pattern. A pattern consists of a list of dicts, where each dict describes a token. |
| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
## Matcher.remove {#remove tag="method" new="2"} ## Matcher.remove {#remove tag="method" new="2"}
Remove a rule from the matcher. A `KeyError` is raised if the match ID does not Remove a rule from the matcher. A `KeyError` is raised if the match ID does not
@ -170,7 +156,7 @@ exist.
> #### Example > #### Example
> >
> ```python > ```python
> matcher.add("Rule", None, [{"ORTH": "test"}]) > matcher.add("Rule", [[{"ORTH": "test"}]])
> assert "Rule" in matcher > assert "Rule" in matcher
> matcher.remove("Rule") > matcher.remove("Rule")
> assert "Rule" not in matcher > assert "Rule" not in matcher
@ -188,7 +174,7 @@ Retrieve the pattern stored for a key. Returns the rule as an
> #### Example > #### Example
> >
> ```python > ```python
> matcher.add("Rule", None, [{"ORTH": "test"}]) > matcher.add("Rule", [[{"ORTH": "test"}]])
> on_match, patterns = matcher.get("Rule") > on_match, patterns = matcher.get("Rule")
> ``` > ```

34
website/docs/api/phrasematcher.md
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@ -52,7 +52,7 @@ Find all token sequences matching the supplied patterns on the `Doc`.
> from spacy.matcher import PhraseMatcher > from spacy.matcher import PhraseMatcher
> >
> matcher = PhraseMatcher(nlp.vocab) > matcher = PhraseMatcher(nlp.vocab)
> matcher.add("OBAMA", None, nlp("Barack Obama")) > matcher.add("OBAMA", [nlp("Barack Obama")])
> doc = nlp("Barack Obama lifts America one last time in emotional farewell") > doc = nlp("Barack Obama lifts America one last time in emotional farewell")
> matches = matcher(doc) > matches = matcher(doc)
> ``` > ```
@ -104,7 +104,7 @@ patterns.
> ```python > ```python
> matcher = PhraseMatcher(nlp.vocab) > matcher = PhraseMatcher(nlp.vocab)
> assert len(matcher) == 0 > assert len(matcher) == 0
> matcher.add("OBAMA", None, nlp("Barack Obama")) > matcher.add("OBAMA", [nlp("Barack Obama")])
> assert len(matcher) == 1 > assert len(matcher) == 1
> ``` > ```
@ -121,7 +121,7 @@ Check whether the matcher contains rules for a match ID.
> ```python > ```python
> matcher = PhraseMatcher(nlp.vocab) > matcher = PhraseMatcher(nlp.vocab)
> assert "OBAMA" not in matcher > assert "OBAMA" not in matcher
> matcher.add("OBAMA", None, nlp("Barack Obama")) > matcher.add("OBAMA", [nlp("Barack Obama")])
> assert "OBAMA" in matcher > assert "OBAMA" in matcher
> ``` > ```
@ -145,36 +145,32 @@ overwritten.
> print('Matched!', matches) > print('Matched!', matches)
> >
> matcher = PhraseMatcher(nlp.vocab) > matcher = PhraseMatcher(nlp.vocab)
> matcher.add("OBAMA", on_match, nlp("Barack Obama")) > matcher.add("OBAMA", [nlp("Barack Obama")], on_match=on_match)
> matcher.add("HEALTH", on_match, nlp("health care reform"), > matcher.add("HEALTH", [nlp("health care reform"), nlp("healthcare reform")], on_match=on_match)
> nlp("healthcare reform"))
> doc = nlp("Barack Obama urges Congress to find courage to defend his healthcare reforms") > doc = nlp("Barack Obama urges Congress to find courage to defend his healthcare reforms")
> matches = matcher(doc) > matches = matcher(doc)
> ``` > ```
| Name | Type | Description | <Infobox title="Changed in v3.0" variant="warning">
| ---------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
| `*docs` | `Doc` | `Doc` objects of the phrases to match. |
<Infobox title="Changed in v2.2.2" variant="warning"> As of spaCy v3.0, `PhraseMatcher.add` takes a list of patterns as the second
argument (instead of a variable number of arguments). The `on_match` callback
As of spaCy 2.2.2, `PhraseMatcher.add` also supports the new API, which will
become the default in the future. The `Doc` patterns are now the second argument
and a list (instead of a variable number of arguments). The `on_match` callback
becomes an optional keyword argument. becomes an optional keyword argument.
```diff ```diff
patterns = [nlp("health care reform"), nlp("healthcare reform")] patterns = [nlp("health care reform"), nlp("healthcare reform")]
- matcher.add("HEALTH", None, *patterns)
+ matcher.add("HEALTH", patterns)
- matcher.add("HEALTH", on_match, *patterns) - matcher.add("HEALTH", on_match, *patterns)
+ matcher.add("HEALTH", patterns, on_match=on_match) + matcher.add("HEALTH", patterns, on_match=on_match)
``` ```
</Infobox> </Infobox>
| Name | Type | Description |
| ---------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `docs` | list | `Doc` objects of the phrases to match. |
| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
## PhraseMatcher.remove {#remove tag="method" new="2.2"} ## PhraseMatcher.remove {#remove tag="method" new="2.2"}
Remove a rule from the matcher by match ID. A `KeyError` is raised if the key Remove a rule from the matcher by match ID. A `KeyError` is raised if the key
@ -184,7 +180,7 @@ does not exist.
> >
> ```python > ```python
> matcher = PhraseMatcher(nlp.vocab) > matcher = PhraseMatcher(nlp.vocab)
> matcher.add("OBAMA", None, nlp("Barack Obama")) > matcher.add("OBAMA", [nlp("Barack Obama")])
> assert "OBAMA" in matcher > assert "OBAMA" in matcher
> matcher.remove("OBAMA") > matcher.remove("OBAMA")
> assert "OBAMA" not in matcher > assert "OBAMA" not in matcher

2
website/docs/usage/processing-pipelines.md
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@ -407,7 +407,7 @@ class EntityMatcher(object):
def __init__(self, nlp, terms, label): def __init__(self, nlp, terms, label):
patterns = [nlp.make_doc(text) for text in terms] patterns = [nlp.make_doc(text) for text in terms]
self.matcher = PhraseMatcher(nlp.vocab) self.matcher = PhraseMatcher(nlp.vocab)
self.matcher.add(label, None, *patterns) self.matcher.add(label, patterns)
def __call__(self, doc): def __call__(self, doc):
matches = self.matcher(doc) matches = self.matcher(doc)

45
website/docs/usage/rule-based-matching.md
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@ -98,9 +98,7 @@ print([token.text for token in doc])
First, we initialize the `Matcher` with a vocab. The matcher must always share First, we initialize the `Matcher` with a vocab. The matcher must always share
the same vocab with the documents it will operate on. We can now call the same vocab with the documents it will operate on. We can now call
[`matcher.add()`](/api/matcher#add) with an ID and our custom pattern. The [`matcher.add()`](/api/matcher#add) with an ID and a list of patterns.
second argument lets you pass in an optional callback function to invoke on a
successful match. For now, we set it to `None`.
```python ```python
### {executable="true"} ### {executable="true"}
@ -111,7 +109,7 @@ nlp = spacy.load("en_core_web_sm")
matcher = Matcher(nlp.vocab) matcher = Matcher(nlp.vocab)
# Add match ID "HelloWorld" with no callback and one pattern # Add match ID "HelloWorld" with no callback and one pattern
pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}] pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}]
matcher.add("HelloWorld", None, pattern) matcher.add("HelloWorld", [pattern])
doc = nlp("Hello, world! Hello world!") doc = nlp("Hello, world! Hello world!")
matches = matcher(doc) matches = matcher(doc)
@ -137,9 +135,11 @@ Optionally, we could also choose to add more than one pattern, for example to
also match sequences without punctuation between "hello" and "world": also match sequences without punctuation between "hello" and "world":
```python ```python
matcher.add("HelloWorld", None, patterns = [
[{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}], [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"LOWER": "world"}],
[{"LOWER": "hello"}, {"LOWER": "world"}]) [{"LOWER": "hello"}, {"LOWER": "world"}]
]
matcher.add("HelloWorld", patterns)
``` ```
By default, the matcher will only return the matches and **not do anything By default, the matcher will only return the matches and **not do anything
@ -413,7 +413,7 @@ nlp = spacy.load("en_core_web_sm")
matcher = Matcher(nlp.vocab, validate=True) matcher = Matcher(nlp.vocab, validate=True)
# Add match ID "HelloWorld" with unsupported attribute CASEINSENSITIVE # Add match ID "HelloWorld" with unsupported attribute CASEINSENSITIVE
pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"CASEINSENSITIVE": "world"}] pattern = [{"LOWER": "hello"}, {"IS_PUNCT": True}, {"CASEINSENSITIVE": "world"}]
matcher.add("HelloWorld", None, pattern) matcher.add("HelloWorld", [pattern])
# 🚨 Raises an error: # 🚨 Raises an error:
# MatchPatternError: Invalid token patterns for matcher rule 'HelloWorld' # MatchPatternError: Invalid token patterns for matcher rule 'HelloWorld'
# Pattern 0: # Pattern 0:
@ -446,7 +446,7 @@ def add_event_ent(matcher, doc, i, matches):
print(entity.text) print(entity.text)
pattern = [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}] pattern = [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}]
matcher.add("GoogleIO", add_event_ent, pattern) matcher.add("GoogleIO", [pattern], on_match=add_event_ent)
doc = nlp("This is a text about Google I/O") doc = nlp("This is a text about Google I/O")
matches = matcher(doc) matches = matcher(doc)
``` ```
@ -509,19 +509,18 @@ import spacy
from spacy.matcher import Matcher from spacy.matcher import Matcher
from spacy.tokens import Token from spacy.tokens import Token
# We're using a class because the component needs to be initialised with # We're using a class because the component needs to be initialized with
# the shared vocab via the nlp object # the shared vocab via the nlp object
class BadHTMLMerger(object): class BadHTMLMerger(object):
def __init__(self, nlp): def __init__(self, nlp):
patterns = [
[{"ORTH": "<"}, {"LOWER": "br"}, {"ORTH": ">"}],
[{"ORTH": "<"}, {"LOWER": "br/"}, {"ORTH": ">"}],
]
# Register a new token extension to flag bad HTML # Register a new token extension to flag bad HTML
Token.set_extension("bad_html", default=False) Token.set_extension("bad_html", default=False)
self.matcher = Matcher(nlp.vocab) self.matcher = Matcher(nlp.vocab)
self.matcher.add( self.matcher.add("BAD_HTML", patterns)
"BAD_HTML",
None,
[{"ORTH": "<"}, {"LOWER": "br"}, {"ORTH": ">"}],
[{"ORTH": "<"}, {"LOWER": "br/"}, {"ORTH": ">"}],
)
def __call__(self, doc): def __call__(self, doc):
# This method is invoked when the component is called on a Doc # This method is invoked when the component is called on a Doc
@ -616,7 +615,7 @@ def collect_sents(matcher, doc, i, matches):
pattern = [{"LOWER": "facebook"}, {"LEMMA": "be"}, {"POS": "ADV", "OP": "*"}, pattern = [{"LOWER": "facebook"}, {"LEMMA": "be"}, {"POS": "ADV", "OP": "*"},
{"POS": "ADJ"}] {"POS": "ADJ"}]
matcher.add("FacebookIs", collect_sents, pattern) # add pattern matcher.add("FacebookIs", [pattern], on_match=collect_sents) # add pattern
doc = nlp("I'd say that Facebook is evil. – Facebook is pretty cool, right?") doc = nlp("I'd say that Facebook is evil. – Facebook is pretty cool, right?")
matches = matcher(doc) matches = matcher(doc)
@ -671,7 +670,7 @@ nlp = spacy.load("en_core_web_sm")
matcher = Matcher(nlp.vocab) matcher = Matcher(nlp.vocab)
pattern = [{"ORTH": "("}, {"SHAPE": "ddd"}, {"ORTH": ")"}, {"SHAPE": "ddd"}, pattern = [{"ORTH": "("}, {"SHAPE": "ddd"}, {"ORTH": ")"}, {"SHAPE": "ddd"},
{"ORTH": "-", "OP": "?"}, {"SHAPE": "ddd"}] {"ORTH": "-", "OP": "?"}, {"SHAPE": "ddd"}]
matcher.add("PHONE_NUMBER", None, pattern) matcher.add("PHONE_NUMBER", [pattern])
doc = nlp("Call me at (123) 456 789 or (123) 456 789!") doc = nlp("Call me at (123) 456 789 or (123) 456 789!")
print([t.text for t in doc]) print([t.text for t in doc])
@ -734,11 +733,11 @@ def label_sentiment(matcher, doc, i, matches):
elif doc.vocab.strings[match_id] == "SAD": elif doc.vocab.strings[match_id] == "SAD":
doc.sentiment -= 0.1 # Subtract 0.1 for negative sentiment doc.sentiment -= 0.1 # Subtract 0.1 for negative sentiment
matcher.add("HAPPY", label_sentiment, *pos_patterns) # Add positive pattern matcher.add("HAPPY", pos_patterns, on_match=label_sentiment) # Add positive pattern
matcher.add("SAD", label_sentiment, *neg_patterns) # Add negative pattern matcher.add("SAD", neg_patterns, on_match=label_sentiment) # Add negative pattern
# Add pattern for valid hashtag, i.e. '#' plus any ASCII token # Add pattern for valid hashtag, i.e. '#' plus any ASCII token
matcher.add("HASHTAG", None, [{"ORTH": "#"}, {"IS_ASCII": True}]) matcher.add("HASHTAG", [[{"ORTH": "#"}, {"IS_ASCII": True}]])
doc = nlp("Hello world 😀 #MondayMotivation") doc = nlp("Hello world 😀 #MondayMotivation")
matches = matcher(doc) matches = matcher(doc)
@ -841,7 +840,7 @@ matcher = PhraseMatcher(nlp.vocab)
terms = ["Barack Obama", "Angela Merkel", "Washington, D.C."] terms = ["Barack Obama", "Angela Merkel", "Washington, D.C."]
# Only run nlp.make_doc to speed things up # Only run nlp.make_doc to speed things up
patterns = [nlp.make_doc(text) for text in terms] patterns = [nlp.make_doc(text) for text in terms]
matcher.add("TerminologyList", None, *patterns) matcher.add("TerminologyList", patterns)
doc = nlp("German Chancellor Angela Merkel and US President Barack Obama " doc = nlp("German Chancellor Angela Merkel and US President Barack Obama "
"converse in the Oval Office inside the White House in Washington, D.C.") "converse in the Oval Office inside the White House in Washington, D.C.")
@ -890,7 +889,7 @@ from spacy.matcher import PhraseMatcher
nlp = English() nlp = English()
matcher = PhraseMatcher(nlp.vocab, attr="LOWER") matcher = PhraseMatcher(nlp.vocab, attr="LOWER")
patterns = [nlp.make_doc(name) for name in ["Angela Merkel", "Barack Obama"]] patterns = [nlp.make_doc(name) for name in ["Angela Merkel", "Barack Obama"]]
matcher.add("Names", None, *patterns) matcher.add("Names", patterns)
doc = nlp("angela merkel and us president barack Obama") doc = nlp("angela merkel and us president barack Obama")
for match_id, start, end in matcher(doc): for match_id, start, end in matcher(doc):
@ -924,7 +923,7 @@ from spacy.matcher import PhraseMatcher
nlp = English() nlp = English()
matcher = PhraseMatcher(nlp.vocab, attr="SHAPE") matcher = PhraseMatcher(nlp.vocab, attr="SHAPE")
matcher.add("IP", None, nlp("127.0.0.1"), nlp("127.127.0.0")) matcher.add("IP", [nlp("127.0.0.1"), nlp("127.127.0.0")])
doc = nlp("Often the router will have an IP address such as 192.168.1.1 or 192.168.2.1.") doc = nlp("Often the router will have an IP address such as 192.168.1.1 or 192.168.2.1.")
for match_id, start, end in matcher(doc): for match_id, start, end in matcher(doc):

8
website/docs/usage/spacy-101.md
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@ -751,10 +751,10 @@ matcher = Matcher(nlp.vocab)
def set_sentiment(matcher, doc, i, matches): def set_sentiment(matcher, doc, i, matches):
doc.sentiment += 0.1 doc.sentiment += 0.1
pattern1 = [{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}] pattern1 = [[{"ORTH": "Google"}, {"ORTH": "I"}, {"ORTH": "/"}, {"ORTH": "O"}]]
pattern2 = [[{"ORTH": emoji, "OP": "+"}] for emoji in ["😀", "😂", "🤣", "😍"]] patterns = [[{"ORTH": emoji, "OP": "+"}] for emoji in ["😀", "😂", "🤣", "😍"]]
matcher.add("GoogleIO", None, pattern1) # Match "Google I/O" or "Google i/o" matcher.add("GoogleIO", patterns1) # Match "Google I/O" or "Google i/o"
matcher.add("HAPPY", set_sentiment, *pattern2) # Match one or more happy emoji matcher.add("HAPPY", patterns2, on_match=set_sentiment) # Match one or more happy emoji
doc = nlp("A text about Google I/O 😀😀") doc = nlp("A text about Google I/O 😀😀")
matches = matcher(doc) matches = matcher(doc)