mirror of https://github.com/explosion/spaCy.git
356 lines
14 KiB
Cython
356 lines
14 KiB
Cython
# cython: infer_types
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from libc.string cimport memset
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import srsly
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from collections import Counter
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import numpy
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import warnings
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from .attrs cimport POS, IS_SPACE
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from .parts_of_speech cimport SPACE
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from .lexeme cimport Lexeme
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from .strings import get_string_id
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from .attrs import LEMMA, intify_attrs
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from .parts_of_speech import IDS as POS_IDS
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from .errors import Errors, Warnings
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from .util import ensure_path
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from . import symbols
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def _normalize_props(props):
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"""Convert attrs dict so that POS is always by ID, other features are left
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as is as long as they are strings or IDs.
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"""
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out = {}
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props = dict(props)
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for key, value in props.items():
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# convert POS value to ID
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if key == POS:
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if hasattr(value, 'upper'):
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value = value.upper()
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if value in POS_IDS:
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value = POS_IDS[value]
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out[key] = value
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elif isinstance(key, str) and key.lower() == 'pos':
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out[POS] = POS_IDS[value.upper()]
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# sort values
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elif isinstance(value, str) and Morphology.VALUE_SEP in value:
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out[key] = Morphology.VALUE_SEP.join(
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sorted(value.split(Morphology.VALUE_SEP)))
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# accept any string or ID fields and values
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elif isinstance(key, (int, str)) and isinstance(value, (int, str)):
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out[key] = value
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else:
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warnings.warn(Warnings.W100.format(feature={key: value}))
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return out
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cdef class Morphology:
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'''Store the possible morphological analyses for a language, and index them
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by hash.
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To save space on each token, tokens only know the hash of their morphological
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analysis, so queries of morphological attributes are delegated
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to this class.
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'''
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FEATURE_SEP = "|"
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FIELD_SEP = "="
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VALUE_SEP = ","
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EMPTY_MORPH = "_" # not an empty string so that the PreshMap key is not 0
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def __init__(self, StringStore strings, tag_map, lemmatizer, exc=None):
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self.mem = Pool()
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self.strings = strings
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self.tags = PreshMap()
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self.load_tag_map(tag_map)
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self.lemmatizer = lemmatizer
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self._cache = PreshMapArray(self.n_tags)
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self._exc = {}
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if exc is not None:
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self.load_morph_exceptions(exc)
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def load_tag_map(self, tag_map):
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self.tag_map = {}
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self.reverse_index = {}
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# Add special space symbol. We prefix with underscore, to make sure it
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# always sorts to the end.
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if '_SP' in tag_map:
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space_attrs = tag_map.get('_SP')
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else:
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space_attrs = tag_map.get('SP', {POS: SPACE})
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if '_SP' not in tag_map:
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self.strings.add('_SP')
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tag_map = dict(tag_map)
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tag_map['_SP'] = space_attrs
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for i, (tag_str, attrs) in enumerate(sorted(tag_map.items())):
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attrs = _normalize_props(attrs)
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self.add(attrs)
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self.tag_map[tag_str] = dict(attrs)
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self.reverse_index[self.strings.add(tag_str)] = i
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self.tag_names = tuple(sorted(self.tag_map.keys()))
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self.n_tags = len(self.tag_map)
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self._cache = PreshMapArray(self.n_tags)
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def __reduce__(self):
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return (Morphology, (self.strings, self.tag_map, self.lemmatizer,
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self._exc), None, None)
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def add(self, features):
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"""Insert a morphological analysis in the morphology table, if not
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already present. The morphological analysis may be provided in the UD
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FEATS format as a string or in the tag map dict format.
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Returns the hash of the new analysis.
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"""
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cdef MorphAnalysisC* tag_ptr
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if isinstance(features, str):
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if features == self.EMPTY_MORPH:
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features = ""
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tag_ptr = <MorphAnalysisC*>self.tags.get(<hash_t>self.strings[features])
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if tag_ptr != NULL:
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return tag_ptr.key
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features = self.feats_to_dict(features)
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if not isinstance(features, dict):
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warnings.warn(Warnings.W100.format(feature=features))
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features = {}
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string_features = {self.strings.as_string(field): self.strings.as_string(values) for field, values in features.items()}
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# intified ("Field", "Field=Value") pairs
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field_feature_pairs = []
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for field in sorted(string_features):
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values = string_features[field]
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for value in values.split(self.VALUE_SEP):
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field_feature_pairs.append((
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self.strings.add(field),
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self.strings.add(field + self.FIELD_SEP + value),
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))
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cdef MorphAnalysisC tag = self.create_morph_tag(field_feature_pairs)
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# the hash key for the tag is either the hash of the normalized UFEATS
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# string or the hash of an empty placeholder (using the empty string
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# would give a hash key of 0, which is not good for PreshMap)
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norm_feats_string = self.normalize_features(features)
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if norm_feats_string:
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tag.key = self.strings.add(norm_feats_string)
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else:
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tag.key = self.strings.add(self.EMPTY_MORPH)
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self.insert(tag)
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return tag.key
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def normalize_features(self, features):
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"""Create a normalized UFEATS string from a features string or dict.
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features (Union[dict, str]): Features as dict or UFEATS string.
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RETURNS (str): Features as normalized UFEATS string.
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"""
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if isinstance(features, str):
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features = self.feats_to_dict(features)
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if not isinstance(features, dict):
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warnings.warn(Warnings.W100.format(feature=features))
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features = {}
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features = _normalize_props(features)
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string_features = {self.strings.as_string(field): self.strings.as_string(values) for field, values in features.items()}
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# normalized UFEATS string with sorted fields and values
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norm_feats_string = self.FEATURE_SEP.join(sorted([
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self.FIELD_SEP.join([field, values])
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for field, values in string_features.items()
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]))
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return norm_feats_string or self.EMPTY_MORPH
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cdef MorphAnalysisC create_morph_tag(self, field_feature_pairs) except *:
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"""Creates a MorphAnalysisC from a list of intified
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("Field", "Field=Value") tuples where fields with multiple values have
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been split into individual tuples, e.g.:
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[("Field1", "Field1=Value1"), ("Field1", "Field1=Value2"),
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("Field2", "Field2=Value3")]
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"""
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cdef MorphAnalysisC tag
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tag.length = len(field_feature_pairs)
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tag.fields = <attr_t*>self.mem.alloc(tag.length, sizeof(attr_t))
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tag.features = <attr_t*>self.mem.alloc(tag.length, sizeof(attr_t))
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for i, (field, feature) in enumerate(field_feature_pairs):
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tag.fields[i] = field
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tag.features[i] = feature
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return tag
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cdef int insert(self, MorphAnalysisC tag) except -1:
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cdef hash_t key = tag.key
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if self.tags.get(key) == NULL:
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tag_ptr = <MorphAnalysisC*>self.mem.alloc(1, sizeof(MorphAnalysisC))
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tag_ptr[0] = tag
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self.tags.set(key, <void*>tag_ptr)
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def get(self, hash_t morph):
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tag = <MorphAnalysisC*>self.tags.get(morph)
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if tag == NULL:
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return []
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else:
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return self.strings[tag.key]
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def lemmatize(self, const univ_pos_t univ_pos, attr_t orth, morphology):
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if orth not in self.strings:
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return orth
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cdef unicode py_string = self.strings[orth]
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if self.lemmatizer is None:
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return self.strings.add(py_string.lower())
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cdef list lemma_strings
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cdef unicode lemma_string
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# Normalize features into a dict keyed by the field, to make life easier
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# for the lemmatizer. Handles string-to-int conversion too.
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string_feats = {}
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for key, value in morphology.items():
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if value is True:
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name, value = self.strings.as_string(key).split('_', 1)
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string_feats[name] = value
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else:
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string_feats[self.strings.as_string(key)] = self.strings.as_string(value)
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lemma_strings = self.lemmatizer(py_string, univ_pos, string_feats)
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lemma_string = lemma_strings[0]
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lemma = self.strings.add(lemma_string)
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return lemma
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def add_special_case(self, unicode tag_str, unicode orth_str, attrs,
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force=False):
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"""Add a special-case rule to the morphological analyser. Tokens whose
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tag and orth match the rule will receive the specified properties.
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tag (str): The part-of-speech tag to key the exception.
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orth (str): The word-form to key the exception.
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"""
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attrs = dict(attrs)
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attrs = _normalize_props(attrs)
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self.add(attrs)
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attrs = intify_attrs(attrs, self.strings, _do_deprecated=True)
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self._exc[(tag_str, self.strings.add(orth_str))] = attrs
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cdef int assign_untagged(self, TokenC* token) except -1:
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"""Set morphological attributes on a token without a POS tag. Uses
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the lemmatizer's lookup() method, which looks up the string in the
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table provided by the language data as lemma_lookup (if available).
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"""
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if token.lemma == 0:
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orth_str = self.strings[token.lex.orth]
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lemma = self.lemmatizer.lookup(orth_str, orth=token.lex.orth)
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token.lemma = self.strings.add(lemma)
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cdef int assign_tag(self, TokenC* token, tag_str) except -1:
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cdef attr_t tag = self.strings.as_int(tag_str)
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if tag in self.reverse_index:
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tag_id = self.reverse_index[tag]
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self.assign_tag_id(token, tag_id)
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else:
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token.tag = tag
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cdef int assign_tag_id(self, TokenC* token, int tag_id) except -1:
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if tag_id > self.n_tags:
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raise ValueError(Errors.E014.format(tag=tag_id))
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# Ensure spaces get tagged as space.
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# It seems pretty arbitrary to put this logic here, but there's really
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# nowhere better. I guess the justification is that this is where the
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# specific word and the tag interact. Still, we should have a better
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# way to enforce this rule, or figure out why the statistical model fails.
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# Related to Issue #220
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if Lexeme.c_check_flag(token.lex, IS_SPACE):
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tag_id = self.reverse_index[self.strings.add('_SP')]
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tag_str = self.tag_names[tag_id]
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features = dict(self.tag_map.get(tag_str, {}))
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if features:
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pos = self.strings.as_int(features.pop(POS))
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else:
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pos = 0
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cdef attr_t lemma = <attr_t>self._cache.get(tag_id, token.lex.orth)
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if lemma == 0:
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# Ugh, self.lemmatize has opposite arg order from self.lemmatizer :(
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lemma = self.lemmatize(pos, token.lex.orth, features)
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self._cache.set(tag_id, token.lex.orth, <void*>lemma)
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token.lemma = lemma
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token.pos = <univ_pos_t>pos
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token.tag = self.strings[tag_str]
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token.morph = self.add(features)
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if (self.tag_names[tag_id], token.lex.orth) in self._exc:
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self._assign_tag_from_exceptions(token, tag_id)
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cdef int _assign_tag_from_exceptions(self, TokenC* token, int tag_id) except -1:
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key = (self.tag_names[tag_id], token.lex.orth)
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cdef dict attrs
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attrs = self._exc[key]
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token.pos = attrs.get(POS, token.pos)
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token.lemma = attrs.get(LEMMA, token.lemma)
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def load_morph_exceptions(self, dict morph_rules):
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self._exc = {}
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# Map (form, pos) to attributes
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for tag, exc in morph_rules.items():
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for orth, attrs in exc.items():
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attrs = _normalize_props(attrs)
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self.add_special_case(self.strings.as_string(tag), self.strings.as_string(orth), attrs)
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@property
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def exc(self):
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# generate the serializable exc in the MORPH_RULES format from the
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# internal tuple-key format
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morph_rules = {}
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for (tag, orth) in sorted(self._exc):
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if not tag in morph_rules:
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morph_rules[tag] = {}
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morph_rules[tag][self.strings[orth]] = self._exc[(tag, orth)]
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return morph_rules
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@staticmethod
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def feats_to_dict(feats):
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if not feats or feats == Morphology.EMPTY_MORPH:
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return {}
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return {field: Morphology.VALUE_SEP.join(sorted(values.split(Morphology.VALUE_SEP))) for field, values in
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[feat.split(Morphology.FIELD_SEP) for feat in feats.split(Morphology.FEATURE_SEP)]}
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@staticmethod
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def dict_to_feats(feats_dict):
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if len(feats_dict) == 0:
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return ""
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return Morphology.FEATURE_SEP.join(sorted([Morphology.FIELD_SEP.join([field, Morphology.VALUE_SEP.join(sorted(values.split(Morphology.VALUE_SEP)))]) for field, values in feats_dict.items()]))
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@staticmethod
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def list_to_feats(feats_list):
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if len(feats_list) == 0:
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return ""
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feats_dict = {}
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for feat in feats_list:
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field, value = feat.split(Morphology.FIELD_SEP)
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if field not in feats_dict:
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feats_dict[field] = set()
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feats_dict[field].add(value)
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feats_dict = {field: Morphology.VALUE_SEP.join(sorted(values)) for field, values in feats_dict.items()}
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return Morphology.dict_to_feats(feats_dict)
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cdef int check_feature(const MorphAnalysisC* morph, attr_t feature) nogil:
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cdef int i
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for i in range(morph.length):
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if morph.features[i] == feature:
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return True
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return False
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cdef list list_features(const MorphAnalysisC* morph):
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cdef int i
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features = []
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for i in range(morph.length):
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features.append(morph.features[i])
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return features
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cdef np.ndarray get_by_field(const MorphAnalysisC* morph, attr_t field):
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cdef np.ndarray results = numpy.zeros((morph.length,), dtype="uint64")
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n = get_n_by_field(<uint64_t*>results.data, morph, field)
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return results[:n]
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cdef int get_n_by_field(attr_t* results, const MorphAnalysisC* morph, attr_t field) nogil:
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cdef int n_results = 0
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cdef int i
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for i in range(morph.length):
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if morph.fields[i] == field:
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results[n_results] = morph.features[i]
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n_results += 1
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return n_results
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