spaCy/spacy/morphology.pyx
2023-07-19 17:41:29 +02:00

253 lines
9.3 KiB
Cython

# cython: infer_types
import warnings
from typing import Dict, List, Optional, Tuple, Union
import numpy
from cython.operator cimport dereference as deref
from libcpp.memory cimport shared_ptr
from . import symbols
from .errors import Warnings
cdef class Morphology:
"""Store the possible morphological analyses for a language, and index them
by hash.
To save space on each token, tokens only know the hash of their
morphological analysis, so queries of morphological attributes are delegated
to this class.
"""
FEATURE_SEP = "|"
FIELD_SEP = "="
VALUE_SEP = ","
# not an empty string so we can distinguish unset morph from empty morph
EMPTY_MORPH = symbols.NAMES[symbols._]
def __init__(self, StringStore strings):
self.strings = strings
def __reduce__(self):
tags = set([self.get(self.strings[s]) for s in self.strings])
tags -= set([""])
return (unpickle_morphology, (self.strings, sorted(tags)), None, None)
cdef shared_ptr[MorphAnalysisC] _lookup_tag(self, hash_t tag_hash):
match = self.tags.find(tag_hash)
if match != self.tags.const_end():
return deref(match).second
else:
return shared_ptr[MorphAnalysisC]()
def _normalize_attr(self, attr_key : Union[int, str], attr_value : Union[int, str]) -> Optional[Tuple[str, Union[str, List[str]]]]:
if isinstance(attr_key, (int, str)) and isinstance(attr_value, (int, str)):
attr_key = self.strings.as_string(attr_key)
attr_value = self.strings.as_string(attr_value)
# Preserve multiple values as a list
if self.VALUE_SEP in attr_value:
values = attr_value.split(self.VALUE_SEP)
values.sort()
attr_value = values
else:
warnings.warn(Warnings.W100.format(feature={attr_key: attr_value}))
return None
return attr_key, attr_value
def _str_to_normalized_feat_dict(self, feats: str) -> Dict[str, str]:
if not feats or feats == self.EMPTY_MORPH:
return {}
out = []
for feat in feats.split(self.FEATURE_SEP):
field, values = feat.split(self.FIELD_SEP, 1)
normalized_attr = self._normalize_attr(field, values)
if normalized_attr is None:
continue
out.append((normalized_attr[0], normalized_attr[1]))
out.sort(key=lambda x: x[0])
return dict(out)
def _dict_to_normalized_feat_dict(self, feats: Dict[Union[int, str], Union[int, str]]) -> Dict[str, str]:
out = []
for field, values in feats.items():
normalized_attr = self._normalize_attr(field, values)
if normalized_attr is None:
continue
out.append((normalized_attr[0], normalized_attr[1]))
out.sort(key=lambda x: x[0])
return dict(out)
def _normalized_feat_dict_to_str(self, feats: Dict[str, str]) -> str:
norm_feats_string = self.FEATURE_SEP.join([
self.FIELD_SEP.join([field, self.VALUE_SEP.join(values) if isinstance(values, list) else values])
for field, values in feats.items()
])
return norm_feats_string or self.EMPTY_MORPH
cdef hash_t _add(self, features):
"""Insert a morphological analysis in the morphology table, if not
already present. The morphological analysis may be provided in the UD
FEATS format as a string or in the tag map dict format.
Returns the hash of the new analysis.
"""
cdef hash_t tag_hash = 0
cdef shared_ptr[MorphAnalysisC] tag
if isinstance(features, str):
if features == "":
features = self.EMPTY_MORPH
tag_hash = self.strings[features]
tag = self._lookup_tag(tag_hash)
if tag:
return deref(tag).key
features = self._str_to_normalized_feat_dict(features)
elif isinstance(features, dict):
features = self._dict_to_normalized_feat_dict(features)
else:
warnings.warn(Warnings.W100.format(feature=features))
features = {}
# the hash key for the tag is either the hash of the normalized UFEATS
# string or the hash of an empty placeholder
norm_feats_string = self._normalized_feat_dict_to_str(features)
tag_hash = self.strings.add(norm_feats_string)
tag = self._lookup_tag(tag_hash)
if tag:
return deref(tag).key
self._intern_morph_tag(tag_hash, features)
return tag_hash
cdef void _intern_morph_tag(self, hash_t tag_key, feats):
# intified ("Field", "Field=Value") pairs where fields with multiple values have
# been split into individual tuples, e.g.:
# [("Field1", "Field1=Value1"), ("Field1", "Field1=Value2"),
# ("Field2", "Field2=Value3")]
field_feature_pairs = []
# Feat dict is normalized at this point.
for field, values in feats.items():
field_key = self.strings.add(field)
if isinstance(values, list):
for value in values:
value_key = self.strings.add(field + self.FIELD_SEP + value)
field_feature_pairs.append((field_key, value_key))
else:
# We could box scalar values into a list and use a common
# code path to generate features but that incurs a small
# but measurable allocation/iteration overhead (as this
# branch is taken often enough).
value_key = self.strings.add(field + self.FIELD_SEP + values)
field_feature_pairs.append((field_key, value_key))
num_features = len(field_feature_pairs)
cdef shared_ptr[MorphAnalysisC] tag = shared_ptr[MorphAnalysisC](new MorphAnalysisC())
deref(tag).key = tag_key
deref(tag).features.resize(num_features)
for i in range(num_features):
deref(tag).features[i].field = field_feature_pairs[i][0]
deref(tag).features[i].value = field_feature_pairs[i][1]
self.tags[tag_key] = tag
cdef str get_morph_str(self, hash_t morph_key):
cdef shared_ptr[MorphAnalysisC] tag = self._lookup_tag(morph_key)
if not tag:
return ""
else:
return self.strings[deref(tag).key]
cdef shared_ptr[MorphAnalysisC] get_morph_c(self, hash_t morph_key):
return self._lookup_tag(morph_key)
cdef str _normalize_features(self, features):
"""Create a normalized FEATS string from a features string or dict.
features (Union[dict, str]): Features as dict or UFEATS string.
RETURNS (str): Features as normalized UFEATS string.
"""
if isinstance(features, str):
features = self._str_to_normalized_feat_dict(features)
elif isinstance(features, dict):
features = self._dict_to_normalized_feat_dict(features)
else:
warnings.warn(Warnings.W100.format(feature=features))
features = {}
return self._normalized_feat_dict_to_str(features)
def add(self, features):
return self._add(features)
def get(self, morph_key):
return self.get_morph_str(morph_key)
def normalize_features(self, features):
return self._normalize_features(features)
@staticmethod
def feats_to_dict(feats, *, sort_values=True):
if not feats or feats == Morphology.EMPTY_MORPH:
return {}
out = {}
for feat in feats.split(Morphology.FEATURE_SEP):
field, values = feat.split(Morphology.FIELD_SEP, 1)
if sort_values:
values = values.split(Morphology.VALUE_SEP)
values.sort()
values = Morphology.VALUE_SEP.join(values)
out[field] = values
return out
@staticmethod
def dict_to_feats(feats_dict):
if len(feats_dict) == 0:
return ""
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()]))
cdef int check_feature(const shared_ptr[MorphAnalysisC] morph, attr_t feature) nogil:
cdef int i
for i in range(deref(morph).features.size()):
if deref(morph).features[i].value == feature:
return True
return False
cdef list list_features(const shared_ptr[MorphAnalysisC] morph):
cdef int i
features = []
for i in range(deref(morph).features.size()):
features.append(deref(morph).features[i].value)
return features
cdef np.ndarray get_by_field(const shared_ptr[MorphAnalysisC] morph, attr_t field):
cdef np.ndarray results = numpy.zeros((deref(morph).features.size(),), dtype="uint64")
n = get_n_by_field(<uint64_t*>results.data, morph, field)
return results[:n]
cdef int get_n_by_field(attr_t* results, const shared_ptr[MorphAnalysisC] morph, attr_t field) nogil:
cdef int n_results = 0
cdef int i
for i in range(deref(morph).features.size()):
if deref(morph).features[i].field == field:
results[n_results] = deref(morph).features[i].value
n_results += 1
return n_results
def unpickle_morphology(strings, tags):
cdef Morphology morphology = Morphology(strings)
for tag in tags:
morphology.add(tag)
return morphology