spaCy/website/docs/api/attributeruler.md
2020-08-17 16:45:24 +02:00

13 KiB

title tag source new teaser api_string_name api_trainable
AttributeRuler class spacy/pipeline/attributeruler.py 3 Pipeline component for rule-based token attribute assignment attribute_ruler false

The attribute ruler lets you set token attributes for tokens identified by Matcher patterns. The attribute ruler is typically used to handle exceptions for token attributes and to map values between attributes such as mapping fine-grained POS tags to coarse-grained POS tags.

Config and implementation

The default config is defined by the pipeline component factory and describes how the component should be configured. You can override its settings via the config argument on nlp.add_pipe or in your config.cfg for training.

Example

config = {
   "pattern_dicts": None,
   "validate": True,
}
nlp.add_pipe("attribute_ruler", config=config)
Setting Description
pattern_dicts A list of pattern dicts with the keys as the arguments to AttributeRuler.add (patterns/attrs/index) to add as patterns. Defaults to None. Optional[Iterable[Dict[str, Union[List[dict], dict, int]]]]
validate Whether patterns should be validated (passed to the Matcher). Defaults to False. bool
https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/attributeruler.py

AttributeRuler.__init__

Initialize the attribute ruler. If pattern dicts are supplied here, they need to be a list of dictionaries with "patterns", "attrs", and optional "index" keys, e.g.:

pattern_dicts = [
    {"patterns": [[{"TAG": "VB"}]], "attrs": {"POS": "VERB"}},
    {"patterns": [[{"LOWER": "an"}]], "attrs": {"LEMMA": "a"}},
]

Example

# Construction via add_pipe
attribute_ruler = nlp.add_pipe("attribute_ruler")
Name Description
vocab The shared vocabulary to pass to the matcher. Vocab
name Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. str
keyword-only
pattern_dicts Optional patterns to load in on initialization. Defaults to None. Optional[Iterable[Dict[str, Union[List[dict], dict, int]]]]
validate Whether patterns should be validated (passed to the Matcher). Defaults to False. bool

AttributeRuler.__call__

Apply the attribute ruler to a Doc, setting token attributes for tokens matched by the provided patterns.

Name Description
doc The document to process. Doc
RETURNS The processed document. Doc

AttributeRuler.add

Add patterns to the attribute ruler. The patterns are a list of Matcher patterns and the attributes are a dict of attributes to set on the matched token. If the pattern matches a span of more than one token, the index can be used to set the attributes for the token at that index in the span. The index may be negative to index from the end of the span.

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
patterns = [[{"TAG": "VB"}]]
attrs = {"POS": "VERB"}
attribute_ruler.add(patterns=patterns, attrs=attrs)
Name Description
patterns The Matcher patterns to add. Iterable[List[Dict[Union[int, str], Any]]]
attrs The attributes to assign to the target token in the matched span. Dict[str, Any]
index The index of the token in the matched span to modify. May be negative to index from the end of the span. Defaults to 0. int

AttributeRuler.add_patterns

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
pattern_dicts = [
  {
    "patterns": [[{"TAG": "VB"}]],
    "attrs": {"POS": "VERB"}
  },
  {
    "patterns": [[{"LOWER": "two"}, {"LOWER": "apples"}]],
    "attrs": {"LEMMA": "apple"},
    "index": -1
  },
]
attribute_ruler.add_patterns(pattern_dicts)

Add patterns from a list of pattern dicts with the keys as the arguments to AttributeRuler.add.

Name Description
pattern_dicts The patterns to add. Iterable[Dict[str, Union[List[dict], dict, int]]]

AttributeRuler.patterns

Get all patterns that have been added to the attribute ruler in the patterns_dict format accepted by AttributeRuler.add_patterns.

Name Description
RETURNS The patterns added to the attribute ruler. List[Dict[str, Union[List[dict], dict, int]]]

AttributeRuler.load_from_tag_map

Load attribute ruler patterns from a tag map.

Name Description
tag_map The tag map that maps fine-grained tags to coarse-grained tags and morphological features. Dict[str, Dict[Union[int, str], Union[int, str]]]

AttributeRuler.load_from_morph_rules

Load attribute ruler patterns from morph rules.

Name Description
morph_rules The morph rules that map token text and fine-grained tags to coarse-grained tags, lemmas and morphological features. Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]

AttributeRuler.to_disk

Serialize the pipe to disk.

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
attribute_ruler.to_disk("/path/to/attribute_ruler")
Name Description
path A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
exclude String names of serialization fields to exclude. Iterable[str]

AttributeRuler.from_disk

Load the pipe from disk. Modifies the object in place and returns it.

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
attribute_ruler.from_disk("/path/to/attribute_ruler")
Name Description
path A path to a directory. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
exclude String names of serialization fields to exclude. Iterable[str]
RETURNS The modified AttributeRuler object. AttributeRuler

AttributeRuler.to_bytes

Example

attribute_ruler = nlp.add_pipe("attribute_ruler")
attribute_ruler_bytes = attribute_ruler.to_bytes()

Serialize the pipe to a bytestring.

Name Description
keyword-only
exclude String names of serialization fields to exclude. Iterable[str]
RETURNS The serialized form of the AttributeRuler object. bytes

AttributeRuler.from_bytes

Load the pipe from a bytestring. Modifies the object in place and returns it.

Example

attribute_ruler_bytes = attribute_ruler.to_bytes()
attribute_ruler = nlp.add_pipe("attribute_ruler")
attribute_ruler.from_bytes(attribute_ruler_bytes)
Name Description
bytes_data The data to load from. bytes
keyword-only
exclude String names of serialization fields to exclude. Iterable[str]
RETURNS The AttributeRuler object. AttributeRuler

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

data = attribute_ruler.to_disk("/path", exclude=["vocab"])
Name Description
vocab The shared Vocab.
patterns The Matcher patterns. You usually don't want to exclude this.
attrs The attributes to set. You usually don't want to exclude this.
indices The token indices. You usually don't want to exclude this.