explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-26 09:56:28 +03:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #12842 from svlandeg/sync_v4

Browse Source 
Sync v4 with latest from master and develop
This commit is contained in:
Sofie Van Landeghem 2023-07-24 12:13:04 +02:00 committed by GitHub
commit eaaac5a08c
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
86 changed files with 1311 additions and 806 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
.github/workflows/tests.yml vendored
View File

@ -45,6 +45,12 @@ jobs:
run: |
python -m pip install flake8==5.0.4
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
- name: cython-lint
run: |
python -m pip install cython-lint -c requirements.txt
# E501: line too log, W291: trailing whitespace, E266: too many leading '#' for block comment
cython-lint spacy --ignore E501,W291,E266
tests:
name: Test
needs: Validate

2
Makefile
View File

@ -1,7 +1,7 @@
SHELL := /bin/bash
ifndef SPACY_EXTRAS
override SPACY_EXTRAS = spacy-lookups-data==1.0.2 jieba spacy-pkuseg==0.0.28 sudachipy sudachidict_core pymorphy2
override SPACY_EXTRAS = spacy-lookups-data==1.0.3
endif
ifndef PYVER

1
requirements.txt
View File

@ -36,4 +36,5 @@ types-setuptools>=57.0.0
types-requests
types-setuptools>=57.0.0
black==22.3.0
cython-lint>=0.15.0; python_version >= "3.7"
isort>=5.0,<6.0

2
spacy/cli/init_pipeline.py
View File

@ -32,6 +32,7 @@ def init_vectors_cli(
mode: str = Opt("default", "--mode", "-m", help="Vectors mode: default or floret"),
verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
jsonl_loc: Optional[Path] = Opt(None, "--lexemes-jsonl", "-j", help="Location of JSONL-formatted attributes file", hidden=True),
attr: str = Opt("ORTH", "--attr", "-a", help="Optional token attribute to use for vectors, e.g. LOWER or NORM"),
# fmt: on
):
"""Convert word vectors for use with spaCy. Will export an nlp object that
@ -53,6 +54,7 @@ def init_vectors_cli(
truncate=truncate,
prune=prune,
mode=mode,
attr=attr,
)
msg.good(f"Successfully converted {len(nlp.vocab.vectors)} vectors")
nlp.to_disk(output_dir)

4
spacy/cli/templates/quickstart_training.jinja
View File

@ -128,7 +128,7 @@ grad_factor = 1.0
{% if "span_finder" in components -%}
[components.span_finder]
factory = "span_finder"
max_length = null
max_length = 25
min_length = null
scorer = {"@scorers":"spacy.span_finder_scorer.v1"}
spans_key = "sc"
@ -415,7 +415,7 @@ width = ${components.tok2vec.model.encode.width}
{% if "span_finder" in components %}
[components.span_finder]
factory = "span_finder"
max_length = null
max_length = 25
min_length = null
scorer = {"@scorers":"spacy.span_finder_scorer.v1"}
spans_key = "sc"

3
spacy/displacy/render.py
View File

@ -1,4 +1,3 @@
import itertools
import uuid
from typing import Any, Dict, List, Optional, Tuple, Union
@ -218,7 +217,7 @@ class SpanRenderer:
+ (self.offset_step * (len(entities) - 1))
)
markup += self.span_template.format(
text=token["text"],
text=escape_html(token["text"]),
span_slices=slices,
span_starts=starts,
total_height=total_height,

3
spacy/errors.py
View File

@ -208,6 +208,9 @@ class Warnings(metaclass=ErrorsWithCodes):
W123 = ("Argument `enable` with value {enable} does not contain all values specified in the config option "
"`enabled` ({enabled}). Be aware that this might affect other components in your pipeline.")
W124 = ("{host}:{port} is already in use, using the nearest available port {serve_port} as an alternative.")
W125 = ("The StaticVectors key_attr is no longer used. To set a custom "
"key attribute for vectors, configure it through Vectors(attr=) or "
"'spacy init vectors --attr'")
# v4 warning strings
W400 = ("`use_upper=False` is ignored, the upper layer is always enabled")

41
spacy/kb/kb.pyx
View File

@ -12,8 +12,9 @@ from .candidate import Candidate
cdef class KnowledgeBase:
"""A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
"""A `KnowledgeBase` instance stores unique identifiers for entities and
their textual aliases, to support entity linking of named entities to
real-world concepts.
This is an abstract class and requires its operations to be implemented.
DOCS: https://spacy.io/api/kb
@ -31,7 +32,9 @@ cdef class KnowledgeBase:
self.entity_vector_length = entity_vector_length
self.mem = Pool()
def get_candidates_batch(self, mentions: SpanGroup) -> Iterable[Iterable[Candidate]]:
def get_candidates_batch(
self, mentions: SpanGroup
) -> Iterable[Iterable[Candidate]]:
"""
Return candidate entities for a specified Span mention. Each candidate defines at least the entity and the
entity's embedding vector. Depending on the KB implementation, further properties - such as the prior
@ -52,7 +55,9 @@ cdef class KnowledgeBase:
RETURNS (Iterable[Candidate]): Identified candidates.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="get_candidates", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="get_candidates", name=self.__name__
)
)
def get_vectors(self, entities: Iterable[str]) -> Iterable[Iterable[float]]:
@ -70,7 +75,9 @@ cdef class KnowledgeBase:
RETURNS (Iterable[float]): Vector for specified entity.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="get_vector", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="get_vector", name=self.__name__
)
)
def to_bytes(self, **kwargs) -> bytes:
@ -78,7 +85,9 @@ cdef class KnowledgeBase:
RETURNS (bytes): Current state as binary string.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="to_bytes", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="to_bytes", name=self.__name__
)
)
def from_bytes(self, bytes_data: bytes, *, exclude: Tuple[str] = tuple()):
@ -87,27 +96,37 @@ cdef class KnowledgeBase:
exclude (Tuple[str]): Properties to exclude when restoring KB.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="from_bytes", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="from_bytes", name=self.__name__
)
)
def to_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
def to_disk(
self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()
) -> None:
"""
Write KnowledgeBase content to disk.
path (Union[str, Path]): Target file path.
exclude (Iterable[str]): List of components to exclude.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="to_disk", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="to_disk", name=self.__name__
)
)
def from_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
def from_disk(
self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()
) -> None:
"""
Load KnowledgeBase content from disk.
path (Union[str, Path]): Target file path.
exclude (Iterable[str]): List of components to exclude.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="from_disk", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="from_disk", name=self.__name__
)
)
@property

62
spacy/kb/kb_in_memory.pxd
View File

@ -55,23 +55,28 @@ cdef class InMemoryLookupKB(KnowledgeBase):
# optional data, we can let users configure a DB as the backend for this.
cdef object _features_table
cdef inline int64_t c_add_vector(self, vector[float] entity_vector) nogil:
"""Add an entity vector to the vectors table."""
cdef int64_t new_index = self._vectors_table.size()
self._vectors_table.push_back(entity_vector)
return new_index
cdef inline int64_t c_add_entity(self, hash_t entity_hash, float freq,
int32_t vector_index, int feats_row) nogil:
cdef inline int64_t c_add_entity(
self,
hash_t entity_hash,
float freq,
int32_t vector_index,
int feats_row
) nogil:
"""Add an entry to the vector of entries.
After calling this method, make sure to update also the _entry_index using the return value"""
After calling this method, make sure to update also the _entry_index
using the return value"""
# This is what we'll map the entity hash key to. It's where the entry will sit
# in the vector of entries, so we can get it later.
cdef int64_t new_index = self._entries.size()
# Avoid struct initializer to enable nogil, cf https://github.com/cython/cython/issues/1642
# Avoid struct initializer to enable nogil, cf.
# https://github.com/cython/cython/issues/1642
cdef KBEntryC entry
entry.entity_hash = entity_hash
entry.vector_index = vector_index
@ -81,11 +86,17 @@ cdef class InMemoryLookupKB(KnowledgeBase):
self._entries.push_back(entry)
return new_index
cdef inline int64_t c_add_aliases(self, hash_t alias_hash, vector[int64_t] entry_indices, vector[float] probs) nogil:
"""Connect a mention to a list of potential entities with their prior probabilities .
After calling this method, make sure to update also the _alias_index using the return value"""
# This is what we'll map the alias hash key to. It's where the alias will be defined
# in the vector of aliases.
cdef inline int64_t c_add_aliases(
self,
hash_t alias_hash,
vector[int64_t] entry_indices,
vector[float] probs
) nogil:
"""Connect a mention to a list of potential entities with their prior
probabilities. After calling this method, make sure to update also the
_alias_index using the return value"""
# This is what we'll map the alias hash key to. It's where the alias will be
# defined in the vector of aliases.
cdef int64_t new_index = self._aliases_table.size()
# Avoid struct initializer to enable nogil
@ -98,8 +109,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
cdef inline void _create_empty_vectors(self, hash_t dummy_hash) nogil:
"""
Initializing the vectors and making sure the first element of each vector is a dummy,
because the PreshMap maps pointing to indices in these vectors can not contain 0 as value
Initializing the vectors and making sure the first element of each vector is a
dummy, because the PreshMap maps pointing to indices in these vectors can not
contain 0 as value.
cf. https://github.com/explosion/preshed/issues/17
"""
cdef int32_t dummy_value = 0
@ -130,12 +142,18 @@ cdef class InMemoryLookupKB(KnowledgeBase):
cdef class Writer:
cdef FILE* _fp
cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1
cdef int write_header(
self, int64_t nr_entries, int64_t entity_vector_length
) except -1
cdef int write_vector_element(self, float element) except -1
cdef int write_entry(self, hash_t entry_hash, float entry_freq, int32_t vector_index) except -1
cdef int write_entry(
self, hash_t entry_hash, float entry_freq, int32_t vector_index
) except -1
cdef int write_alias_length(self, int64_t alias_length) except -1
cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1
cdef int write_alias_header(
self, hash_t alias_hash, int64_t candidate_length
) except -1
cdef int write_alias(self, int64_t entry_index, float prob) except -1
cdef int _write(self, void* value, size_t size) except -1
@ -143,12 +161,18 @@ cdef class Writer:
cdef class Reader:
cdef FILE* _fp
cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1
cdef int read_header(
self, int64_t* nr_entries, int64_t* entity_vector_length
) except -1
cdef int read_vector_element(self, float* element) except -1
cdef int read_entry(self, hash_t* entity_hash, float* freq, int32_t* vector_index) except -1
cdef int read_entry(
self, hash_t* entity_hash, float* freq, int32_t* vector_index
) except -1
cdef int read_alias_length(self, int64_t* alias_length) except -1
cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1
cdef int read_alias_header(
self, hash_t* alias_hash, int64_t* candidate_length
) except -1
cdef int read_alias(self, int64_t* entry_index, float* prob) except -1
cdef int _read(self, void* value, size_t size) except -1

146
spacy/kb/kb_in_memory.pyx
View File

@ -1,5 +1,5 @@
# cython: infer_types=True, profile=True
from typing import Any, Callable, Dict, Iterable, Union
from typing import Any, Callable, Dict, Iterable
import srsly
@ -27,8 +27,9 @@ from .candidate import InMemoryCandidate
cdef class InMemoryLookupKB(KnowledgeBase):
"""An `InMemoryLookupKB` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
"""An `InMemoryLookupKB` instance stores unique identifiers for entities
and their textual aliases, to support entity linking of named entities to
real-world concepts.
DOCS: https://spacy.io/api/inmemorylookupkb
"""
@ -71,7 +72,8 @@ cdef class InMemoryLookupKB(KnowledgeBase):
def add_entity(self, str entity, float freq, vector[float] entity_vector):
"""
Add an entity to the KB, optionally specifying its log probability based on corpus frequency
Add an entity to the KB, optionally specifying its log probability
based on corpus frequency.
Return the hash of the entity ID/name at the end.
"""
cdef hash_t entity_hash = self.vocab.strings.add(entity)
@ -83,14 +85,20 @@ cdef class InMemoryLookupKB(KnowledgeBase):
# Raise an error if the provided entity vector is not of the correct length
if len(entity_vector) != self.entity_vector_length:
raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length))
raise ValueError(
Errors.E141.format(
found=len(entity_vector), required=self.entity_vector_length
)
)
vector_index = self.c_add_vector(entity_vector=entity_vector)
new_index = self.c_add_entity(entity_hash=entity_hash,
freq=freq,
vector_index=vector_index,
feats_row=-1) # Features table currently not implemented
new_index = self.c_add_entity(
entity_hash=entity_hash,
freq=freq,
vector_index=vector_index,
feats_row=-1
) # Features table currently not implemented
self._entry_index[entity_hash] = new_index
return entity_hash
@ -115,7 +123,12 @@ cdef class InMemoryLookupKB(KnowledgeBase):
else:
entity_vector = vector_list[i]
if len(entity_vector) != self.entity_vector_length:
raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length))
raise ValueError(
Errors.E141.format(
found=len(entity_vector),
required=self.entity_vector_length
)
)
entry.entity_hash = entity_hash
entry.freq = freq_list[i]
@ -149,11 +162,15 @@ cdef class InMemoryLookupKB(KnowledgeBase):
previous_alias_nr = self.get_size_aliases()
# Throw an error if the length of entities and probabilities are not the same
if not len(entities) == len(probabilities):
raise ValueError(Errors.E132.format(alias=alias,
entities_length=len(entities),
probabilities_length=len(probabilities)))
raise ValueError(
Errors.E132.format(
alias=alias,
entities_length=len(entities),
probabilities_length=len(probabilities))
)
# Throw an error if the probabilities sum up to more than 1 (allow for some rounding errors)
# Throw an error if the probabilities sum up to more than 1 (allow for
# some rounding errors)
prob_sum = sum(probabilities)
if prob_sum > 1.00001:
raise ValueError(Errors.E133.format(alias=alias, sum=prob_sum))
@ -170,40 +187,47 @@ cdef class InMemoryLookupKB(KnowledgeBase):
for entity, prob in zip(entities, probabilities):
entity_hash = self.vocab.strings[entity]
if not entity_hash in self._entry_index:
if entity_hash not in self._entry_index:
raise ValueError(Errors.E134.format(entity=entity))
entry_index = <int64_t>self._entry_index.get(entity_hash)
entry_indices.push_back(int(entry_index))
probs.push_back(float(prob))
new_index = self.c_add_aliases(alias_hash=alias_hash, entry_indices=entry_indices, probs=probs)
new_index = self.c_add_aliases(
alias_hash=alias_hash, entry_indices=entry_indices, probs=probs
)
self._alias_index[alias_hash] = new_index
if previous_alias_nr + 1 != self.get_size_aliases():
raise RuntimeError(Errors.E891.format(alias=alias))
return alias_hash
def append_alias(self, str alias, str entity, float prior_prob, ignore_warnings=False):
def append_alias(
self, str alias, str entity, float prior_prob, ignore_warnings=False
):
"""
For an alias already existing in the KB, extend its potential entities with one more.
For an alias already existing in the KB, extend its potential entities
with one more.
Throw a warning if either the alias or the entity is unknown,
or when the combination is already previously recorded.
Throw an error if this entity+prior prob would exceed the sum of 1.
For efficiency, it's best to use the method `add_alias` as much as possible instead of this one.
For efficiency, it's best to use the method `add_alias` as much as
possible instead of this one.
"""
# Check if the alias exists in the KB
cdef hash_t alias_hash = self.vocab.strings[alias]
if not alias_hash in self._alias_index:
if alias_hash not in self._alias_index:
raise ValueError(Errors.E176.format(alias=alias))
# Check if the entity exists in the KB
cdef hash_t entity_hash = self.vocab.strings[entity]
if not entity_hash in self._entry_index:
if entity_hash not in self._entry_index:
raise ValueError(Errors.E134.format(entity=entity))
entry_index = <int64_t>self._entry_index.get(entity_hash)
# Throw an error if the prior probabilities (including the new one) sum up to more than 1
# Throw an error if the prior probabilities (including the new one)
# sum up to more than 1
alias_index = <int64_t>self._alias_index.get(alias_hash)
alias_entry = self._aliases_table[alias_index]
current_sum = sum([p for p in alias_entry.probs])
@ -236,12 +260,13 @@ cdef class InMemoryLookupKB(KnowledgeBase):
def _get_alias_candidates(self, str alias) -> Iterable[InMemoryCandidate]:
"""
Return candidate entities for an alias. Each candidate defines the entity, the original alias,
and the prior probability of that alias resolving to that entity.
Return candidate entities for an alias. Each candidate defines the
entity, the original alias, and the prior probability of that alias
resolving to that entity.
If the alias is not known in the KB, and empty list is returned.
"""
cdef hash_t alias_hash = self.vocab.strings[alias]
if not alias_hash in self._alias_index:
if alias_hash not in self._alias_index:
return []
alias_index = <int64_t>self._alias_index.get(alias_hash)
alias_entry = self._aliases_table[alias_index]
@ -270,8 +295,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
return self._vectors_table[self._entries[entry_index].vector_index]
def get_prior_prob(self, str entity, str alias):
""" Return the prior probability of a given alias being linked to a given entity,
or return 0.0 when this combination is not known in the knowledge base"""
""" Return the prior probability of a given alias being linked to a
given entity, or return 0.0 when this combination is not known in the
knowledge base."""
cdef hash_t alias_hash = self.vocab.strings[alias]
cdef hash_t entity_hash = self.vocab.strings[entity]
@ -282,7 +308,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
entry_index = self._entry_index[entity_hash]
alias_entry = self._aliases_table[alias_index]
for (entry_index, prior_prob) in zip(alias_entry.entry_indices, alias_entry.probs):
for (entry_index, prior_prob) in zip(
alias_entry.entry_indices, alias_entry.probs
):
if self._entries[entry_index].entity_hash == entity_hash:
return prior_prob
@ -295,13 +323,19 @@ cdef class InMemoryLookupKB(KnowledgeBase):
"""Serialize the current state to a binary string.
"""
def serialize_header():
header = (self.get_size_entities(), self.get_size_aliases(), self.entity_vector_length)
header = (
self.get_size_entities(),
self.get_size_aliases(),
self.entity_vector_length
)
return srsly.json_dumps(header)
def serialize_entries():
i = 1
tuples = []
for entry_hash, entry_index in sorted(self._entry_index.items(), key=lambda x: x[1]):
for entry_hash, entry_index in sorted(
self._entry_index.items(), key=lambda x: x[1]
):
entry = self._entries[entry_index]
assert entry.entity_hash == entry_hash
assert entry_index == i
@ -314,7 +348,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
headers = []
indices_lists = []
probs_lists = []
for alias_hash, alias_index in sorted(self._alias_index.items(), key=lambda x: x[1]):
for alias_hash, alias_index in sorted(
self._alias_index.items(), key=lambda x: x[1]
):
alias = self._aliases_table[alias_index]
assert alias_index == i
candidate_length = len(alias.entry_indices)
@ -372,7 +408,7 @@ cdef class InMemoryLookupKB(KnowledgeBase):
indices = srsly.json_loads(all_data[1])
probs = srsly.json_loads(all_data[2])
for header, indices, probs in zip(headers, indices, probs):
alias_hash, candidate_length = header
alias_hash, _candidate_length = header
alias.entry_indices = indices
alias.probs = probs
self._aliases_table[i] = alias
@ -421,10 +457,14 @@ cdef class InMemoryLookupKB(KnowledgeBase):
writer.write_vector_element(element)
i = i+1
# dumping the entry records in the order in which they are in the _entries vector.
# index 0 is a dummy object not stored in the _entry_index and can be ignored.
# dumping the entry records in the order in which they are in the
# _entries vector.
# index 0 is a dummy object not stored in the _entry_index and can
# be ignored.
i = 1
for entry_hash, entry_index in sorted(self._entry_index.items(), key=lambda x: x[1]):
for entry_hash, entry_index in sorted(
self._entry_index.items(), key=lambda x: x[1]
):
entry = self._entries[entry_index]
assert entry.entity_hash == entry_hash
assert entry_index == i
@ -436,7 +476,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
# dumping the aliases in the order in which they are in the _alias_index vector.
# index 0 is a dummy object not stored in the _aliases_table and can be ignored.
i = 1
for alias_hash, alias_index in sorted(self._alias_index.items(), key=lambda x: x[1]):
for alias_hash, alias_index in sorted(
self._alias_index.items(), key=lambda x: x[1]
):
alias = self._aliases_table[alias_index]
assert alias_index == i
@ -542,7 +584,8 @@ cdef class Writer:
def __init__(self, path):
assert isinstance(path, Path)
content = bytes(path)
cdef bytes bytes_loc = content.encode('utf8') if type(content) == str else content
cdef bytes bytes_loc = content.encode('utf8') \
if type(content) == str else content
self._fp = fopen(<char*>bytes_loc, 'wb')
if not self._fp:
raise IOError(Errors.E146.format(path=path))
@ -552,14 +595,18 @@ cdef class Writer:
cdef size_t status = fclose(self._fp)
assert status == 0
cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1:
cdef int write_header(
self, int64_t nr_entries, int64_t entity_vector_length
) except -1:
self._write(&nr_entries, sizeof(nr_entries))
self._write(&entity_vector_length, sizeof(entity_vector_length))
cdef int write_vector_element(self, float element) except -1:
self._write(&element, sizeof(element))
cdef int write_entry(self, hash_t entry_hash, float entry_freq, int32_t vector_index) except -1:
cdef int write_entry(
self, hash_t entry_hash, float entry_freq, int32_t vector_index
) except -1:
self._write(&entry_hash, sizeof(entry_hash))
self._write(&entry_freq, sizeof(entry_freq))
self._write(&vector_index, sizeof(vector_index))
@ -568,7 +615,9 @@ cdef class Writer:
cdef int write_alias_length(self, int64_t alias_length) except -1:
self._write(&alias_length, sizeof(alias_length))
cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1:
cdef int write_alias_header(
self, hash_t alias_hash, int64_t candidate_length
) except -1:
self._write(&alias_hash, sizeof(alias_hash))
self._write(&candidate_length, sizeof(candidate_length))
@ -584,16 +633,19 @@ cdef class Writer:
cdef class Reader:
def __init__(self, path):
content = bytes(path)
cdef bytes bytes_loc = content.encode('utf8') if type(content) == str else content
cdef bytes bytes_loc = content.encode('utf8') \
if type(content) == str else content
self._fp = fopen(<char*>bytes_loc, 'rb')
if not self._fp:
PyErr_SetFromErrno(IOError)
status = fseek(self._fp, 0, 0) # this can be 0 if there is no header
fseek(self._fp, 0, 0) # this can be 0 if there is no header
def __dealloc__(self):
fclose(self._fp)
cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1:
cdef int read_header(
self, int64_t* nr_entries, int64_t* entity_vector_length
) except -1:
status = self._read(nr_entries, sizeof(int64_t))
if status < 1:
if feof(self._fp):
@ -613,7 +665,9 @@ cdef class Reader:
return 0 # end of file
raise IOError(Errors.E145.format(param="vector element"))
cdef int read_entry(self, hash_t* entity_hash, float* freq, int32_t* vector_index) except -1:
cdef int read_entry(
self, hash_t* entity_hash, float* freq, int32_t* vector_index
) except -1:
status = self._read(entity_hash, sizeof(hash_t))
if status < 1:
if feof(self._fp):
@ -644,7 +698,9 @@ cdef class Reader:
return 0 # end of file
raise IOError(Errors.E145.format(param="alias length"))
cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1:
cdef int read_alias_header(
self, hash_t* alias_hash, int64_t* candidate_length
) except -1:
status = self._read(alias_hash, sizeof(hash_t))
if status < 1:
if feof(self._fp):

47
spacy/language.py
View File

@ -740,6 +740,11 @@ class Language:
)
)
pipe = source.get_pipe(source_name)
# There is no actual solution here. Either the component has the right
# name for the source pipeline or the component has the right name for
# the current pipeline. This prioritizes the current pipeline.
if hasattr(pipe, "name"):
pipe.name = name
# Make sure the source config is interpolated so we don't end up with
# orphaned variables in our final config
source_config = source.config.interpolate()
@ -817,6 +822,7 @@ class Language:
pipe_index = self._get_pipe_index(before, after, first, last)
self._pipe_meta[name] = self.get_factory_meta(factory_name)
self._components.insert(pipe_index, (name, pipe_component))
self._link_components()
return pipe_component
def _get_pipe_index(
@ -956,6 +962,7 @@ class Language:
if old_name in self._config["initialize"]["components"]:
init_cfg = self._config["initialize"]["components"].pop(old_name)
self._config["initialize"]["components"][new_name] = init_cfg
self._link_components()
def remove_pipe(self, name: str) -> Tuple[str, PipeCallable]:
"""Remove a component from the pipeline.
@ -979,6 +986,7 @@ class Language:
# Make sure the name is also removed from the set of disabled components
if name in self.disabled:
self._disabled.remove(name)
self._link_components()
return removed
def disable_pipe(self, name: str) -> None:
@ -1823,8 +1831,16 @@ class Language:
# The problem is we need to do it during deserialization...And the
# components don't receive the pipeline then. So this does have to be
# here :(
# First, fix up all the internal component names in case they have
# gotten out of sync due to sourcing components from different
# pipelines, since find_listeners uses proc2.name for the listener
# map.
for name, proc in self.pipeline:
if hasattr(proc, "name"):
proc.name = name
for i, (name1, proc1) in enumerate(self.pipeline):
if isinstance(proc1, ty.ListenedToComponent):
proc1.listener_map = {}
for name2, proc2 in self.pipeline[i + 1 :]:
proc1.find_listeners(proc2)
@ -1934,7 +1950,6 @@ class Language:
# Later we replace the component config with the raw config again.
interpolated = filled.interpolate() if not filled.is_interpolated else filled
pipeline = interpolated.get("components", {})
sourced = util.get_sourced_components(interpolated)
# If components are loaded from a source (existing models), we cache
# them here so they're only loaded once
source_nlps = {}
@ -1962,6 +1977,7 @@ class Language:
raw_config=raw_config,
)
else:
assert "source" in pipe_cfg
# We need the sourced components to reference the same
# vocab without modifying the current vocab state **AND**
# we still want to load the source model vectors to perform
@ -1981,6 +1997,10 @@ class Language:
source_name = pipe_cfg.get("component", pipe_name)
listeners_replaced = False
if "replace_listeners" in pipe_cfg:
# Make sure that the listened-to component has the
# state of the source pipeline listener map so that the
# replace_listeners method below works as intended.
source_nlps[model]._link_components()
for name, proc in source_nlps[model].pipeline:
if source_name in getattr(proc, "listening_components", []):
source_nlps[model].replace_listeners(
@ -1992,6 +2012,8 @@ class Language:
nlp.add_pipe(
source_name, source=source_nlps[model], name=pipe_name
)
# At this point after nlp.add_pipe, the listener map
# corresponds to the new pipeline.
if model not in source_nlp_vectors_hashes:
source_nlp_vectors_hashes[model] = hash(
source_nlps[model].vocab.vectors.to_bytes(
@ -2046,27 +2068,6 @@ class Language:
raise ValueError(
Errors.E942.format(name="pipeline_creation", value=type(nlp))
)
# Detect components with listeners that are not frozen consistently
for name, proc in nlp.pipeline:
if isinstance(proc, ty.ListenedToComponent):
# Remove listeners not in the pipeline
listener_names = proc.listening_components
unused_listener_names = [
ll for ll in listener_names if ll not in nlp.pipe_names
]
for listener_name in unused_listener_names:
for listener in proc.listener_map.get(listener_name, []):
proc.remove_listener(listener, listener_name)
for listener_name in proc.listening_components:
# e.g. tok2vec/transformer
# If it's a component sourced from another pipeline, we check if
# the tok2vec listeners should be replaced with standalone tok2vec
# models (e.g. so component can be frozen without its performance
# degrading when other components/tok2vec are updated)
paths = sourced.get(listener_name, {}).get("replace_listeners", [])
if paths:
nlp.replace_listeners(name, listener_name, paths)
return nlp
def replace_listeners(
@ -2081,7 +2082,7 @@ class Language:
useful when training a pipeline with components sourced from an existing
pipeline: if multiple components (e.g. tagger, parser, NER) listen to
the same tok2vec component, but some of them are frozen and not updated,
their performance may degrade significally as the tok2vec component is
their performance may degrade significantly as the tok2vec component is
updated with new data. To prevent this, listeners can be replaced with
a standalone tok2vec layer that is owned by the component and doesn't
change if the component isn't updated.

17
spacy/lexeme.pyx
View File

@ -1,7 +1,6 @@
# cython: embedsignature=True
# Compiler crashes on memory view coercion without this. Should report bug.
cimport numpy as np
from cython.view cimport array as cvarray
from libc.string cimport memset
np.import_array()
@ -35,7 +34,7 @@ from .typedefs cimport attr_t, flags_t
from .attrs import intify_attrs
from .errors import Errors, Warnings
OOV_RANK = 0xffffffffffffffff # UINT64_MAX
OOV_RANK = 0xffffffffffffffff # UINT64_MAX
memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
EMPTY_LEXEME.id = OOV_RANK
@ -105,7 +104,7 @@ cdef class Lexeme:
if isinstance(value, float):
continue
elif isinstance(value, (int, long)):
Lexeme.set_struct_attr(self.c, attr, value)
Lexeme.set_struct_attr(self.c, attr, value)
else:
Lexeme.set_struct_attr(self.c, attr, self.vocab.strings.add(value))
@ -137,10 +136,12 @@ cdef class Lexeme:
if hasattr(other, "orth"):
if self.c.orth == other.orth:
return 1.0
elif hasattr(other, "__len__") and len(other) == 1 \
and hasattr(other[0], "orth"):
if self.c.orth == other[0].orth:
return 1.0
elif (
hasattr(other, "__len__") and len(other) == 1
and hasattr(other[0], "orth")
and self.c.orth == other[0].orth
):
return 1.0
if self.vector_norm == 0 or other.vector_norm == 0:
warnings.warn(Warnings.W008.format(obj="Lexeme"))
return 0.0
@ -149,7 +150,7 @@ cdef class Lexeme:
result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
# ensure we get a scalar back (numpy does this automatically but cupy doesn't)
return result.item()
@property
def has_vector(self):
"""RETURNS (bool): Whether a word vector is associated with the object.

10
spacy/matcher/dependencymatcher.pyx
View File

@ -108,7 +108,7 @@ cdef class DependencyMatcher:
key (str): The match ID.
RETURNS (bool): Whether the matcher contains rules for this match ID.
"""
return self.has_key(key)
return self.has_key(key) # no-cython-lint: W601
def _validate_input(self, pattern, key):
idx = 0
@ -264,7 +264,7 @@ cdef class DependencyMatcher:
def remove(self, key):
key = self._normalize_key(key)
if not key in self._patterns:
if key not in self._patterns:
raise ValueError(Errors.E175.format(key=key))
self._patterns.pop(key)
self._raw_patterns.pop(key)
@ -382,7 +382,7 @@ cdef class DependencyMatcher:
return []
return [doc[node].head]
def _gov(self,doc,node):
def _gov(self, doc, node):
return list(doc[node].children)
def _dep_chain(self, doc, node):
@ -443,7 +443,7 @@ cdef class DependencyMatcher:
def _right_child(self, doc, node):
return [child for child in doc[node].rights]
def _left_child(self, doc, node):
return [child for child in doc[node].lefts]
@ -461,7 +461,7 @@ cdef class DependencyMatcher:
if doc[node].head.i > node:
return [doc[node].head]
return []
def _left_parent(self, doc, node):
if doc[node].head.i < node:
return [doc[node].head]

246
spacy/matcher/matcher.pyx
View File

@ -12,25 +12,13 @@ import warnings
import srsly
from ..attrs cimport (
DEP,
ENT_IOB,
ID,
LEMMA,
MORPH,
NULL_ATTR,
ORTH,
POS,
TAG,
attr_id_t,
)
from ..attrs cimport DEP, ENT_IOB, ID, LEMMA, MORPH, NULL_ATTR, POS, TAG
from ..structs cimport TokenC
from ..tokens.doc cimport Doc, get_token_attr_for_matcher
from ..tokens.morphanalysis cimport MorphAnalysis
from ..tokens.span cimport Span
from ..tokens.token cimport Token
from ..typedefs cimport attr_t
from ..vocab cimport Vocab
from ..errors import Errors, MatchPatternError, Warnings
from ..schemas import validate_token_pattern
@ -42,7 +30,6 @@ from ..attrs import IDS
from ..errors import Errors, MatchPatternError, Warnings
from ..schemas import validate_token_pattern
from ..strings import get_string_id
from ..util import registry
from .levenshtein import levenshtein_compare
DEF PADDING = 5
@ -93,9 +80,9 @@ cdef class Matcher:
key (str): The match ID.
RETURNS (bool): Whether the matcher contains rules for this match ID.
"""
return self.has_key(key)
return self.has_key(key) # no-cython-lint: W601
def add(self, key, patterns, *, on_match=None, greedy: str=None):
def add(self, key, patterns, *, on_match=None, greedy: str = None):
"""Add a match-rule to the matcher. A match-rule consists of: an ID
key, an on_match callback, and one or more patterns.
@ -149,8 +136,13 @@ cdef class Matcher:
key = self._normalize_key(key)
for pattern in patterns:
try:
specs = _preprocess_pattern(pattern, self.vocab,
self._extensions, self._extra_predicates, self._fuzzy_compare)
specs = _preprocess_pattern(
pattern,
self.vocab,
self._extensions,
self._extra_predicates,
self._fuzzy_compare
)
self.patterns.push_back(init_pattern(self.mem, key, specs))
for spec in specs:
for attr, _ in spec[1]:
@ -174,7 +166,7 @@ cdef class Matcher:
key (str): The ID of the match rule.
"""
norm_key = self._normalize_key(key)
if not norm_key in self._patterns:
if norm_key not in self._patterns:
raise ValueError(Errors.E175.format(key=key))
self._patterns.pop(norm_key)
self._callbacks.pop(norm_key)
@ -274,8 +266,15 @@ cdef class Matcher:
if self.patterns.empty():
matches = []
else:
matches = find_matches(&self.patterns[0], self.patterns.size(), doclike, length,
extensions=self._extensions, predicates=self._extra_predicates, with_alignments=with_alignments)
matches = find_matches(
&self.patterns[0],
self.patterns.size(),
doclike,
length,
extensions=self._extensions,
predicates=self._extra_predicates,
with_alignments=with_alignments
)
final_matches = []
pairs_by_id = {}
# For each key, either add all matches, or only the filtered,
@ -299,9 +298,9 @@ cdef class Matcher:
memset(matched, 0, length * sizeof(matched[0]))
span_filter = self._filter.get(key)
if span_filter == "FIRST":
sorted_pairs = sorted(pairs, key=lambda x: (x[0], -x[1]), reverse=False) # sort by start
sorted_pairs = sorted(pairs, key=lambda x: (x[0], -x[1]), reverse=False) # sort by start
elif span_filter == "LONGEST":
sorted_pairs = sorted(pairs, key=lambda x: (x[1]-x[0], -x[0]), reverse=True) # reverse sort by length
sorted_pairs = sorted(pairs, key=lambda x: (x[1]-x[0], -x[0]), reverse=True) # reverse sort by length
else:
raise ValueError(Errors.E947.format(expected=["FIRST", "LONGEST"], arg=span_filter))
for match in sorted_pairs:
@ -373,7 +372,6 @@ cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, e
cdef vector[MatchC] matches
cdef vector[vector[MatchAlignmentC]] align_states
cdef vector[vector[MatchAlignmentC]] align_matches
cdef PatternStateC state
cdef int i, j, nr_extra_attr
cdef Pool mem = Pool()
output = []
@ -395,14 +393,22 @@ cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, e
value = token.vocab.strings[value]
extra_attr_values[i * nr_extra_attr + index] = value
# Main loop
cdef int nr_predicate = len(predicates)
for i in range(length):
for j in range(n):
states.push_back(PatternStateC(patterns[j], i, 0))
if with_alignments != 0:
align_states.resize(states.size())
transition_states(states, matches, align_states, align_matches, predicate_cache,
doclike[i], extra_attr_values, predicates, with_alignments)
transition_states(
states,
matches,
align_states,
align_matches,
predicate_cache,
doclike[i],
extra_attr_values,
predicates,
with_alignments
)
extra_attr_values += nr_extra_attr
predicate_cache += len(predicates)
# Handle matches that end in 0-width patterns
@ -428,18 +434,28 @@ cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, e
return output
cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& matches,
vector[vector[MatchAlignmentC]]& align_states, vector[vector[MatchAlignmentC]]& align_matches,
int8_t* cached_py_predicates,
Token token, const attr_t* extra_attrs, py_predicates, bint with_alignments) except *:
cdef void transition_states(
vector[PatternStateC]& states,
vector[MatchC]& matches,
vector[vector[MatchAlignmentC]]& align_states,
vector[vector[MatchAlignmentC]]& align_matches,
int8_t* cached_py_predicates,
Token token,
const attr_t* extra_attrs,
py_predicates,
bint with_alignments
) except *:
cdef int q = 0
cdef vector[PatternStateC] new_states
cdef vector[vector[MatchAlignmentC]] align_new_states
cdef int nr_predicate = len(py_predicates)
for i in range(states.size()):
if states[i].pattern.nr_py >= 1:
update_predicate_cache(cached_py_predicates,
states[i].pattern, token, py_predicates)
update_predicate_cache(
cached_py_predicates,
states[i].pattern,
token,
py_predicates
)
action = get_action(states[i], token.c, extra_attrs,
cached_py_predicates)
if action == REJECT:
@ -475,8 +491,12 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match
align_new_states.push_back(align_states[q])
states[q].pattern += 1
if states[q].pattern.nr_py != 0:
update_predicate_cache(cached_py_predicates,
states[q].pattern, token, py_predicates)
update_predicate_cache(
cached_py_predicates,
states[q].pattern,
token,
py_predicates
)
action = get_action(states[q], token.c, extra_attrs,
cached_py_predicates)
# Update alignment before the transition of current state
@ -492,8 +512,12 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match
ent_id = get_ent_id(state.pattern)
if action == MATCH:
matches.push_back(
MatchC(pattern_id=ent_id, start=state.start,
length=state.length+1))
MatchC(
pattern_id=ent_id,
start=state.start,
length=state.length+1
)
)
# `align_matches` always corresponds to `matches` 1:1
if with_alignments != 0:
align_matches.push_back(align_states[q])
@ -501,23 +525,35 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match
# push match without last token if length > 0
if state.length > 0:
matches.push_back(
MatchC(pattern_id=ent_id, start=state.start,
length=state.length))
MatchC(
pattern_id=ent_id,
start=state.start,
length=state.length
)
)
# MATCH_DOUBLE emits matches twice,
# add one more to align_matches in order to keep 1:1 relationship
if with_alignments != 0:
align_matches.push_back(align_states[q])
# push match with last token
matches.push_back(
MatchC(pattern_id=ent_id, start=state.start,
length=state.length+1))
MatchC(
pattern_id=ent_id,
start=state.start,
length=state.length + 1
)
)
# `align_matches` always corresponds to `matches` 1:1
if with_alignments != 0:
align_matches.push_back(align_states[q])
elif action == MATCH_REJECT:
matches.push_back(
MatchC(pattern_id=ent_id, start=state.start,
length=state.length))
MatchC(
pattern_id=ent_id,
start=state.start,
length=state.length
)
)
# `align_matches` always corresponds to `matches` 1:1
if with_alignments != 0:
align_matches.push_back(align_states[q])
@ -540,8 +576,12 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match
align_states.push_back(align_new_states[i])
cdef int update_predicate_cache(int8_t* cache,
const TokenPatternC* pattern, Token token, predicates) except -1:
cdef int update_predicate_cache(
int8_t* cache,
const TokenPatternC* pattern,
Token token,
predicates
) except -1:
# If the state references any extra predicates, check whether they match.
# These are cached, so that we don't call these potentially expensive
# Python functions more than we need to.
@ -587,10 +627,12 @@ cdef void finish_states(vector[MatchC]& matches, vector[PatternStateC]& states,
else:
state.pattern += 1
cdef action_t get_action(PatternStateC state,
const TokenC* token, const attr_t* extra_attrs,
const int8_t* predicate_matches) nogil:
cdef action_t get_action(
PatternStateC state,
const TokenC * token,
const attr_t * extra_attrs,
const int8_t * predicate_matches
) nogil:
"""We need to consider:
a) Does the token match the specification? [Yes, No]
b) What's the quantifier? [1, 0+, ?]
@ -656,53 +698,56 @@ cdef action_t get_action(PatternStateC state,
is_match = not is_match
quantifier = ONE
if quantifier == ONE:
if is_match and is_final:
# Yes, final: 1000
return MATCH
elif is_match and not is_final:
# Yes, non-final: 0100
return ADVANCE
elif not is_match and is_final:
# No, final: 0000
return REJECT
else:
return REJECT
if is_match and is_final:
# Yes, final: 1000
return MATCH
elif is_match and not is_final:
# Yes, non-final: 0100
return ADVANCE
elif not is_match and is_final:
# No, final: 0000
return REJECT
else:
return REJECT
elif quantifier == ZERO_PLUS:
if is_match and is_final:
# Yes, final: 1001
return MATCH_EXTEND
elif is_match and not is_final:
# Yes, non-final: 0011
return RETRY_EXTEND
elif not is_match and is_final:
# No, final 2000 (note: Don't include last token!)
return MATCH_REJECT
else:
# No, non-final 0010
return RETRY
if is_match and is_final:
# Yes, final: 1001
return MATCH_EXTEND
elif is_match and not is_final:
# Yes, non-final: 0011
return RETRY_EXTEND
elif not is_match and is_final:
# No, final 2000 (note: Don't include last token!)
return MATCH_REJECT
else:
# No, non-final 0010
return RETRY
elif quantifier == ZERO_ONE:
if is_match and is_final:
# Yes, final: 3000
# To cater for a pattern ending in "?", we need to add
# a match both with and without the last token
return MATCH_DOUBLE
elif is_match and not is_final:
# Yes, non-final: 0110
# We need both branches here, consider a pair like:
# pattern: .?b string: b
# If we 'ADVANCE' on the .?, we miss the match.
return RETRY_ADVANCE
elif not is_match and is_final:
# No, final 2000 (note: Don't include last token!)
return MATCH_REJECT
else:
# No, non-final 0010
return RETRY
if is_match and is_final:
# Yes, final: 3000
# To cater for a pattern ending in "?", we need to add
# a match both with and without the last token
return MATCH_DOUBLE
elif is_match and not is_final:
# Yes, non-final: 0110
# We need both branches here, consider a pair like:
# pattern: .?b string: b
# If we 'ADVANCE' on the .?, we miss the match.
return RETRY_ADVANCE
elif not is_match and is_final:
# No, final 2000 (note: Don't include last token!)
return MATCH_REJECT
else:
# No, non-final 0010
return RETRY
cdef int8_t get_is_match(PatternStateC state,
const TokenC* token, const attr_t* extra_attrs,
const int8_t* predicate_matches) nogil:
cdef int8_t get_is_match(
PatternStateC state,
const TokenC* token,
const attr_t* extra_attrs,
const int8_t* predicate_matches
) nogil:
for i in range(state.pattern.nr_py):
if predicate_matches[state.pattern.py_predicates[i]] == -1:
return 0
@ -867,7 +912,7 @@ class _FuzzyPredicate:
self.is_extension = is_extension
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
fuzz = self.predicate[len("FUZZY"):] # number after prefix
fuzz = self.predicate[len("FUZZY"):] # number after prefix
self.fuzzy = int(fuzz) if fuzz else -1
self.fuzzy_compare = fuzzy_compare
self.key = _predicate_cache_key(self.attr, self.predicate, value, fuzzy=self.fuzzy)
@ -1089,7 +1134,7 @@ def _get_extra_predicates_dict(attr, value_dict, vocab, predicate_types,
elif cls == _FuzzyPredicate:
if isinstance(value, dict):
# add predicates inside fuzzy operator
fuzz = type_[len("FUZZY"):] # number after prefix
fuzz = type_[len("FUZZY"):] # number after prefix
fuzzy_val = int(fuzz) if fuzz else -1
output.extend(_get_extra_predicates_dict(attr, value, vocab, predicate_types,
extra_predicates, seen_predicates,
@ -1108,8 +1153,9 @@ def _get_extra_predicates_dict(attr, value_dict, vocab, predicate_types,
return output
def _get_extension_extra_predicates(spec, extra_predicates, predicate_types,
seen_predicates):
def _get_extension_extra_predicates(
spec, extra_predicates, predicate_types, seen_predicates
):
output = []
for attr, value in spec.items():
if isinstance(value, dict):
@ -1138,7 +1184,7 @@ def _get_operators(spec):
return (ONE,)
elif spec["OP"] in lookup:
return lookup[spec["OP"]]
#Min_max {n,m}
# Min_max {n,m}
elif spec["OP"].startswith("{") and spec["OP"].endswith("}"):
# {n} --> {n,n} exactly n ONE,(n)
# {n,m}--> {n,m} min of n, max of m ONE,(n),ZERO_ONE,(m)
@ -1149,8 +1195,8 @@ def _get_operators(spec):
min_max = min_max if "," in min_max else f"{min_max},{min_max}"
n, m = min_max.split(",")
#1. Either n or m is a blank string and the other is numeric -->isdigit
#2. Both are numeric and n <= m
# 1. Either n or m is a blank string and the other is numeric -->isdigit
# 2. Both are numeric and n <= m
if (not n.isdecimal() and not m.isdecimal()) or (n.isdecimal() and m.isdecimal() and int(n) > int(m)):
keys = ", ".join(lookup.keys()) + ", {n}, {n,m}, {n,}, {,m} where n and m are integers and n <= m "
raise ValueError(Errors.E011.format(op=spec["OP"], opts=keys))

6
spacy/matcher/phrasematcher.pyx
View File

@ -2,16 +2,14 @@
from collections import defaultdict
from typing import List
from libc.stdint cimport uintptr_t
from preshed.maps cimport map_clear, map_get, map_init, map_iter, map_set
import warnings
from ..attrs cimport DEP, LEMMA, MORPH, ORTH, POS, TAG
from ..attrs cimport DEP, LEMMA, MORPH, POS, TAG
from ..attrs import IDS
from ..structs cimport TokenC
from ..tokens.span cimport Span
from ..tokens.token cimport Token
from ..typedefs cimport attr_t
@ -160,7 +158,6 @@ cdef class PhraseMatcher:
del self._callbacks[key]
del self._docs[key]
def _add_from_arrays(self, key, specs, *, on_match=None):
"""Add a preprocessed list of specs, with an optional callback.
@ -196,7 +193,6 @@ cdef class PhraseMatcher:
result = internal_node
map_set(self.mem, <MapStruct*>result, self.vocab.strings[key], NULL)
def add(self, key, docs, *, on_match=None):
"""Add a match-rule to the phrase-matcher. A match-rule consists of: an ID
key, a list of one or more patterns, and (optionally) an on_match callback.

10
spacy/ml/staticvectors.py
View File

@ -1,3 +1,4 @@
import warnings
from typing import Callable, List, Optional, Sequence, Tuple, cast
from thinc.api import Model, Ops, registry
@ -5,7 +6,8 @@ from thinc.initializers import glorot_uniform_init
from thinc.types import Floats1d, Floats2d, Ints1d, Ragged
from thinc.util import partial
from ..errors import Errors
from ..attrs import ORTH
from ..errors import Errors, Warnings
from ..tokens import Doc
from ..vectors import Mode
from ..vocab import Vocab
@ -24,6 +26,8 @@ def StaticVectors(
linear projection to control the dimensionality. If a dropout rate is
specified, the dropout is applied per dimension over the whole batch.
"""
if key_attr != "ORTH":
warnings.warn(Warnings.W125, DeprecationWarning)
return Model(
"static_vectors",
forward,
@ -40,9 +44,9 @@ def forward(
token_count = sum(len(doc) for doc in docs)
if not token_count:
return _handle_empty(model.ops, model.get_dim("nO"))
key_attr: int = model.attrs["key_attr"]
keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
vocab: Vocab = docs[0].vocab
key_attr: int = getattr(vocab.vectors, "attr", ORTH)
keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
if vocab.vectors.mode == Mode.default:
V = model.ops.asarray(vocab.vectors.data)

55
spacy/ml/tb_framework.pyx
View File

@ -1,5 +1,5 @@
# cython: infer_types=True, cdivision=True, boundscheck=False
from typing import Any, List, Optional, Tuple, TypeVar, cast
from typing import Any, List, Optional, Tuple, cast
from libc.stdlib cimport calloc, free, realloc
from libc.string cimport memcpy, memset
@ -23,7 +23,7 @@ from thinc.api import (
from thinc.backends.cblas cimport CBlas, saxpy, sgemm
from thinc.types import Floats1d, Floats2d, Floats3d, Floats4d, Ints1d, Ints2d
from thinc.types import Floats2d, Floats3d, Floats4d, Ints1d, Ints2d
from ..errors import Errors
from ..pipeline._parser_internals import _beam_utils
@ -136,7 +136,7 @@ def init(
Y: Optional[Tuple[List[State], List[Floats2d]]] = None,
):
if X is not None:
docs, moves = X
docs, _ = X
model.get_ref("tok2vec").initialize(X=docs)
else:
model.get_ref("tok2vec").initialize()
@ -145,7 +145,6 @@ def init(
current_nO = model.maybe_get_dim("nO")
if current_nO is None or current_nO != inferred_nO:
model.attrs["resize_output"](model, inferred_nO)
nO = model.get_dim("nO")
nP = model.get_dim("nP")
nH = model.get_dim("nH")
nI = model.get_dim("nI")
@ -192,9 +191,10 @@ class TransitionModelInputs:
self,
docs: List[Doc],
moves: TransitionSystem,
actions: Optional[List[Ints1d]]=None,
max_moves: int=0,
states: Optional[List[State]]=None):
actions: Optional[List[Ints1d]] = None,
max_moves: int = 0,
states: Optional[List[State]] = None,
):
"""
actions (Optional[List[Ints1d]]): actions to apply for each Doc.
docs (List[Doc]): Docs to predict transition sequences for.
@ -234,12 +234,12 @@ def forward(model, inputs: TransitionModelInputs, is_train: bool):
return _forward_greedy_cpu(model, moves, states, feats, seen_mask, actions=actions)
else:
return _forward_fallback(model, moves, states, tokvecs, backprop_tok2vec,
feats, backprop_feats, seen_mask, is_train, actions=actions,
max_moves=inputs.max_moves)
feats, backprop_feats, seen_mask, is_train, actions=actions,
max_moves=inputs.max_moves)
def _forward_greedy_cpu(model: Model, TransitionSystem moves, states: List[StateClass], np.ndarray feats,
np.ndarray[np.npy_bool, ndim=1] seen_mask, actions: Optional[List[Ints1d]]=None):
np.ndarray[np.npy_bool, ndim = 1] seen_mask, actions: Optional[List[Ints1d]] = None):
cdef vector[StateC*] c_states
cdef StateClass state
for state in states:
@ -257,9 +257,10 @@ def _forward_greedy_cpu(model: Model, TransitionSystem moves, states: List[State
return (states, scores), backprop
cdef list _parse_batch(CBlas cblas, TransitionSystem moves, StateC** states,
WeightsC weights, SizesC sizes, actions: Optional[List[Ints1d]]=None):
cdef int i, j
cdef int i
cdef vector[StateC *] unfinished
cdef ActivationsC activations = _alloc_activations(sizes)
cdef np.ndarray step_scores
@ -276,7 +277,7 @@ cdef list _parse_batch(CBlas cblas, TransitionSystem moves, StateC** states,
if actions is None:
# Validate actions, argmax, take action.
c_transition_batch(moves, states, <const float*>step_scores.data, sizes.classes,
sizes.states)
sizes.states)
else:
c_apply_actions(moves, states, <const int*>step_actions.data, sizes.states)
for i in range(sizes.states):
@ -302,8 +303,9 @@ def _forward_fallback(
backprop_feats,
seen_mask,
is_train: bool,
actions: Optional[List[Ints1d]]=None,
max_moves: int=0):
actions: Optional[List[Ints1d]] = None,
max_moves: int = 0,
):
nF = model.get_dim("nF")
output = model.get_ref("output")
hidden_b = model.get_param("hidden_b")
@ -371,7 +373,7 @@ def _forward_fallback(
for clas in set(model.attrs["unseen_classes"]):
if (d_scores[:, clas] < 0).any():
model.attrs["unseen_classes"].remove(clas)
d_scores *= seen_mask == False
d_scores *= seen_mask == False # no-cython-lint
# Calculate the gradients for the parameters of the output layer.
# The weight gemm is (nS, nO) @ (nS, nH).T
output.inc_grad("b", d_scores.sum(axis=0))
@ -571,13 +573,13 @@ cdef void _resize_activations(ActivationsC* A, SizesC n) nogil:
A._max_size = n.states
else:
A.token_ids = <int*>realloc(A.token_ids,
n.states * n.feats * sizeof(A.token_ids[0]))
n.states * n.feats * sizeof(A.token_ids[0]))
A.unmaxed = <float*>realloc(A.unmaxed,
n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0]))
n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0]))
A.hiddens = <float*>realloc(A.hiddens,
n.states * n.hiddens * sizeof(A.hiddens[0]))
n.states * n.hiddens * sizeof(A.hiddens[0]))
A.is_valid = <int*>realloc(A.is_valid,
n.states * n.classes * sizeof(A.is_valid[0]))
n.states * n.classes * sizeof(A.is_valid[0]))
A._max_size = n.states
A._curr_size = n.states
@ -599,9 +601,9 @@ cdef void _predict_states(CBlas cblas, ActivationsC* A, float* scores, StateC**
else:
# Compute hidden-to-output
sgemm(cblas)(False, True, n.states, n.classes, n.hiddens,
1.0, <const float *>A.hiddens, n.hiddens,
<const float *>W.hidden_weights, n.hiddens,
0.0, scores, n.classes)
1.0, <const float *>A.hiddens, n.hiddens,
<const float *>W.hidden_weights, n.hiddens,
0.0, scores, n.classes)
# Add bias
for i in range(n.states):
saxpy(cblas)(n.classes, 1., W.hidden_bias, 1, &scores[i*n.classes], 1)
@ -617,12 +619,12 @@ cdef void _predict_states(CBlas cblas, ActivationsC* A, float* scores, StateC**
scores[i*n.classes+j] = min_
cdef void _sum_state_features(CBlas cblas, float* output,
const float* cached, const int* token_ids, SizesC n) nogil:
cdef int idx, b, f, i
cdef void _sum_state_features(CBlas cblas, float* output, const float* cached,
const int* token_ids, SizesC n) nogil:
cdef int idx, b, f
cdef const float* feature
cdef int B = n.states
cdef int O = n.hiddens * n.pieces
cdef int O = n.hiddens * n.pieces # no-cython-lint
cdef int F = n.feats
cdef int T = n.tokens
padding = cached + (T * F * O)
@ -637,4 +639,3 @@ cdef void _sum_state_features(CBlas cblas, float* output,
feature = &cached[idx]
saxpy(cblas)(O, one, <const float*>feature, 1, &output[b*O], 1)
token_ids += F

9
spacy/morphology.pyx
View File

@ -80,15 +80,13 @@ cdef class Morphology:
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])
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
@ -140,7 +138,7 @@ cdef class Morphology:
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
# 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)
@ -246,6 +244,7 @@ cdef int get_n_by_field(attr_t* results, const shared_ptr[MorphAnalysisC] morph,
n_results += 1
return n_results
def unpickle_morphology(strings, tags):
cdef Morphology morphology = Morphology(strings)
for tag in tags:

2
spacy/parts_of_speech.pxd
View File

@ -8,7 +8,7 @@ cpdef enum univ_pos_t:
ADV = symbols.ADV
AUX = symbols.AUX
CONJ = symbols.CONJ
CCONJ = symbols.CCONJ # U20
CCONJ = symbols.CCONJ # U20
DET = symbols.DET
INTJ = symbols.INTJ
NOUN = symbols.NOUN

17
spacy/pipeline/_edit_tree_internals/edit_trees.pxd
View File

@ -46,11 +46,18 @@ cdef struct EditTreeC:
bint is_match_node
NodeC inner
cdef inline EditTreeC edittree_new_match(len_t prefix_len, len_t suffix_len,
uint32_t prefix_tree, uint32_t suffix_tree):
cdef MatchNodeC match_node = MatchNodeC(prefix_len=prefix_len,
suffix_len=suffix_len, prefix_tree=prefix_tree,
suffix_tree=suffix_tree)
cdef inline EditTreeC edittree_new_match(
len_t prefix_len,
len_t suffix_len,
uint32_t prefix_tree,
uint32_t suffix_tree
):
cdef MatchNodeC match_node = MatchNodeC(
prefix_len=prefix_len,
suffix_len=suffix_len,
prefix_tree=prefix_tree,
suffix_tree=suffix_tree
)
cdef NodeC inner = NodeC(match_node=match_node)
return EditTreeC(is_match_node=True, inner=inner)

14
spacy/pipeline/_edit_tree_internals/edit_trees.pyx
View File

@ -5,8 +5,6 @@ from libc.string cimport memset
from libcpp.pair cimport pair
from libcpp.vector cimport vector
from pathlib import Path
from ...typedefs cimport hash_t
from ... import util
@ -25,17 +23,16 @@ cdef LCS find_lcs(str source, str target):
target (str): The second string.
RETURNS (LCS): The spans of the longest common subsequences.
"""
cdef Py_ssize_t source_len = len(source)
cdef Py_ssize_t target_len = len(target)
cdef size_t longest_align = 0;
cdef size_t longest_align = 0
cdef int source_idx, target_idx
cdef LCS lcs
cdef Py_UCS4 source_cp, target_cp
memset(&lcs, 0, sizeof(lcs))
cdef vector[size_t] prev_aligns = vector[size_t](target_len);
cdef vector[size_t] cur_aligns = vector[size_t](target_len);
cdef vector[size_t] prev_aligns = vector[size_t](target_len)
cdef vector[size_t] cur_aligns = vector[size_t](target_len)
for (source_idx, source_cp) in enumerate(source):
for (target_idx, target_cp) in enumerate(target):
@ -89,7 +86,7 @@ cdef class EditTrees:
cdef LCS lcs = find_lcs(form, lemma)
cdef EditTreeC tree
cdef uint32_t tree_id, prefix_tree, suffix_tree
cdef uint32_t prefix_tree, suffix_tree
if lcs_is_empty(lcs):
tree = edittree_new_subst(self.strings.add(form), self.strings.add(lemma))
else:
@ -108,7 +105,7 @@ cdef class EditTrees:
return self._tree_id(tree)
cdef uint32_t _tree_id(self, EditTreeC tree):
# If this tree has been constructed before, return its identifier.
# If this tree has been constructed before, return its identifier.
cdef hash_t hash = edittree_hash(tree)
cdef unordered_map[hash_t, uint32_t].iterator iter = self.map.find(hash)
if iter != self.map.end():
@ -289,6 +286,7 @@ def _tree2dict(tree):
tree = tree["inner"]["subst_node"]
return(dict(tree))
def _dict2tree(tree):
errors = validate_edit_tree(tree)
if errors:

7
spacy/pipeline/_parser_internals/_beam_utils.pyx
View File

@ -1,12 +1,8 @@
# cython: infer_types=True
# cython: profile=True
cimport numpy as np
import numpy
from cpython.ref cimport Py_XDECREF, PyObject
from ...typedefs cimport class_t, hash_t
from ...typedefs cimport class_t
from .transition_system cimport Transition, TransitionSystem
from ...errors import Errors
@ -146,7 +142,6 @@ def update_beam(TransitionSystem moves, states, golds, model, int width, beam_de
cdef MaxViolation violn
pbeam = BeamBatch(moves, states, golds, width=width, density=beam_density)
gbeam = BeamBatch(moves, states, golds, width=width, density=0.0)
cdef StateClass state
beam_maps = []
backprops = []
violns = [MaxViolation() for _ in range(len(states))]

1
spacy/pipeline/_parser_internals/_state.pxd
View File

@ -280,7 +280,6 @@ cdef cppclass StateC:
return n
int n_L(int head) nogil const:
return n_arcs(this._left_arcs, head)

18
spacy/pipeline/_parser_internals/arc_eager.pyx
View File

@ -9,7 +9,7 @@ from ...strings cimport hash_string
from ...structs cimport TokenC
from ...tokens.doc cimport Doc, set_children_from_heads
from ...tokens.token cimport MISSING_DEP
from ...typedefs cimport attr_t, hash_t
from ...typedefs cimport attr_t
from ...training import split_bilu_label
@ -68,8 +68,9 @@ cdef struct GoldParseStateC:
weight_t pop_cost
cdef GoldParseStateC create_gold_state(Pool mem, const StateC* state,
heads, labels, sent_starts) except *:
cdef GoldParseStateC create_gold_state(
Pool mem, const StateC* state, heads, labels, sent_starts
) except *:
cdef GoldParseStateC gs
gs.length = len(heads)
gs.stride = 1
@ -82,7 +83,7 @@ cdef GoldParseStateC create_gold_state(Pool mem, const StateC* state,
gs.n_kids_in_stack = <int32_t*>mem.alloc(gs.length, sizeof(gs.n_kids_in_stack[0]))
for i, is_sent_start in enumerate(sent_starts):
if is_sent_start == True:
if is_sent_start is True:
gs.state_bits[i] = set_state_flag(
gs.state_bits[i],
IS_SENT_START,
@ -210,6 +211,7 @@ cdef class ArcEagerGold:
def update(self, StateClass stcls):
update_gold_state(&self.c, stcls.c)
def _get_aligned_sent_starts(example):
"""Get list of SENT_START attributes aligned to the predicted tokenization.
If the reference has not sentence starts, return a list of None values.
@ -524,7 +526,6 @@ cdef class Break:
"""
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef int i
if st.buffer_length() < 2:
return False
elif st.B(1) != st.B(0) + 1:
@ -556,8 +557,8 @@ cdef class Break:
cost -= 1
if gold.heads[si] == b0:
cost -= 1
if not is_sent_start(gold, state.B(1)) \
and not is_sent_start_unknown(gold, state.B(1)):
if not is_sent_start(gold, state.B(1)) and\
not is_sent_start_unknown(gold, state.B(1)):
cost += 1
return cost
@ -805,7 +806,6 @@ cdef class ArcEager(TransitionSystem):
raise TypeError(Errors.E909.format(name="ArcEagerGold"))
cdef ArcEagerGold gold_ = gold
gold_state = gold_.c
n_gold = 0
if self.c[i].is_valid(stcls.c, self.c[i].label):
cost = self.c[i].get_cost(stcls.c, &gold_state, self.c[i].label)
else:
@ -878,7 +878,7 @@ cdef class ArcEager(TransitionSystem):
print("Gold")
for token in example.y:
print(token.i, token.text, token.dep_, token.head.text)
aligned_heads, aligned_labels = example.get_aligned_parse()
aligned_heads, _aligned_labels = example.get_aligned_parse()
print("Aligned heads")
for i, head in enumerate(aligned_heads):
print(example.x[i], example.x[head] if head is not None else "__")

16
spacy/pipeline/_parser_internals/ner.pyx
View File

@ -1,8 +1,4 @@
import os
import random
from cymem.cymem cimport Pool
from libc.stdint cimport int32_t
from libcpp.memory cimport shared_ptr
from libcpp.vector cimport vector
@ -14,7 +10,7 @@ from ...tokens.span import Span
from ...attrs cimport IS_SPACE
from ...lexeme cimport Lexeme
from ...structs cimport SpanC, TokenC
from ...structs cimport SpanC
from ...tokens.span cimport Span
from ...typedefs cimport attr_t, weight_t
@ -138,11 +134,10 @@ cdef class BiluoPushDown(TransitionSystem):
OUT: Counter()
}
actions[OUT][''] = 1 # Represents a token predicted to be outside of any entity
actions[UNIT][''] = 1 # Represents a token prohibited to be in an entity
actions[UNIT][''] = 1 # Represents a token prohibited to be in an entity
for entity_type in kwargs.get('entity_types', []):
for action in (BEGIN, IN, LAST, UNIT):
actions[action][entity_type] = 1
moves = ('M', 'B', 'I', 'L', 'U')
for example in kwargs.get('examples', []):
for token in example.y:
ent_type = token.ent_type_
@ -324,7 +319,6 @@ cdef class BiluoPushDown(TransitionSystem):
raise TypeError(Errors.E909.format(name="BiluoGold"))
cdef BiluoGold gold_ = gold
gold_state = gold_.c
n_gold = 0
if self.c[i].is_valid(stcls.c, self.c[i].label):
cost = self.c[i].get_cost(stcls.c, &gold_state, self.c[i].label)
else:
@ -487,10 +481,8 @@ cdef class In:
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
gold = <GoldNERStateC*>_gold
move = IN
cdef int next_act = gold.ner[s.B(1)].move if s.B(1) >= 0 else OUT
cdef int g_act = gold.ner[s.B(0)].move
cdef attr_t g_tag = gold.ner[s.B(0)].label
cdef bint is_sunk = _entity_is_sunk(s, gold.ner)
if g_act == MISSING:
@ -550,12 +542,10 @@ cdef class Last:
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
gold = <GoldNERStateC*>_gold
move = LAST
b0 = s.B(0)
ent_start = s.E(0)
cdef int g_act = gold.ner[b0].move
cdef attr_t g_tag = gold.ner[b0].label
cdef int cost = 0
@ -655,7 +645,6 @@ cdef class Unit:
return cost
cdef class Out:
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
@ -678,7 +667,6 @@ cdef class Out:
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
gold = <GoldNERStateC*>_gold
cdef int g_act = gold.ner[s.B(0)].move
cdef attr_t g_tag = gold.ner[s.B(0)].label
cdef weight_t cost = 0
if g_act == MISSING:
pass

12
spacy/pipeline/_parser_internals/nonproj.pyx
View File

@ -125,14 +125,17 @@ def decompose(label):
def is_decorated(label):
return DELIMITER in label
def count_decorated_labels(gold_data):
freqs = {}
for example in gold_data:
proj_heads, deco_deps = projectivize(example.get_aligned("HEAD"),
example.get_aligned("DEP"))
# set the label to ROOT for each root dependent
deco_deps = ['ROOT' if head == i else deco_deps[i]
for i, head in enumerate(proj_heads)]
deco_deps = [
'ROOT' if head == i else deco_deps[i]
for i, head in enumerate(proj_heads)
]
# count label frequencies
for label in deco_deps:
if is_decorated(label):
@ -160,9 +163,9 @@ def projectivize(heads, labels):
cdef vector[int] _heads_to_c(heads):
cdef vector[int] c_heads;
cdef vector[int] c_heads
for head in heads:
if head == None:
if head is None:
c_heads.push_back(-1)
else:
assert head < len(heads)
@ -199,6 +202,7 @@ def _decorate(heads, proj_heads, labels):
deco_labels.append(labels[tokenid])
return deco_labels
def get_smallest_nonproj_arc_slow(heads):
cdef vector[int] c_heads = _heads_to_c(heads)
return _get_smallest_nonproj_arc(c_heads)

1
spacy/pipeline/_parser_internals/search.pxd
View File

@ -57,7 +57,6 @@ cdef class Beam:
cdef int advance(self, trans_func_t transition_func, hash_func_t hash_func,
void* extra_args) except -1
cdef int check_done(self, finish_func_t finish_func, void* extra_args) except -1
cdef inline void set_cell(self, int i, int j, weight_t score, int is_valid, weight_t cost) nogil:
self.scores[i][j] = score

12
spacy/pipeline/_parser_internals/search.pyx
View File

@ -1,11 +1,8 @@
# cython: profile=True, experimental_cpp_class_def=True, cdivision=True, infer_types=True
cimport cython
from libc.math cimport exp, log
from libc.string cimport memcpy, memset
import math
from cymem.cymem cimport Pool
from libc.math cimport exp
from libc.string cimport memcpy, memset
from preshed.maps cimport PreshMap
@ -70,7 +67,7 @@ cdef class Beam:
self.costs[i][j] = costs[j]
cdef int set_table(self, weight_t** scores, int** is_valid, weight_t** costs) except -1:
cdef int i, j
cdef int i
for i in range(self.width):
memcpy(self.scores[i], scores[i], sizeof(weight_t) * self.nr_class)
memcpy(self.is_valid[i], is_valid[i], sizeof(bint) * self.nr_class)
@ -176,7 +173,6 @@ cdef class Beam:
beam-width, and n is the number of classes.
"""
cdef Entry entry
cdef weight_t score
cdef _State* s
cdef int i, j, move_id
assert self.size >= 1
@ -269,7 +265,7 @@ cdef class MaxViolation:
# This can happen from non-monotonic actions
# If we find a better gold analysis this way, be sure to keep it.
elif pred._states[i].loss <= 0 \
and tuple(pred.histories[i]) not in seen_golds:
and tuple(pred.histories[i]) not in seen_golds:
g_scores.append(pred._states[i].score)
g_hist.append(list(pred.histories[i]))
for i in range(gold.size):

24
spacy/pipeline/_parser_internals/stateclass.pyx
View File

@ -1,6 +1,4 @@
# cython: infer_types=True
import numpy
from libcpp.vector cimport vector
from ...tokens.doc cimport Doc
@ -42,11 +40,11 @@ cdef class StateClass:
cdef vector[ArcC] arcs
self.c.get_arcs(&arcs)
return list(arcs)
#py_arcs = []
#for arc in arcs:
# if arc.head != -1 and arc.child != -1:
# py_arcs.append((arc.head, arc.child, arc.label))
#return arcs
# py_arcs = []
# for arc in arcs:
# if arc.head != -1 and arc.child != -1:
# py_arcs.append((arc.head, arc.child, arc.label))
# return arcs
def add_arc(self, int head, int child, int label):
self.c.add_arc(head, child, label)
@ -56,10 +54,10 @@ cdef class StateClass:
def H(self, int child):
return self.c.H(child)
def L(self, int head, int idx):
return self.c.L(head, idx)
def R(self, int head, int idx):
return self.c.R(head, idx)
@ -102,7 +100,7 @@ cdef class StateClass:
def H(self, int i):
return self.c.H(i)
def E(self, int i):
return self.c.E(i)
@ -120,7 +118,7 @@ cdef class StateClass:
def H_(self, int i):
return self.doc[self.c.H(i)]
def E_(self, int i):
return self.doc[self.c.E(i)]
@ -129,7 +127,7 @@ cdef class StateClass:
def R_(self, int i, int idx):
return self.doc[self.c.R(i, idx)]
def empty(self):
return self.c.empty()
@ -138,7 +136,7 @@ cdef class StateClass:
def at_break(self):
return False
#return self.c.at_break()
# return self.c.at_break()
def has_head(self, int i):
return self.c.has_head(i)

18
spacy/pipeline/_parser_internals/transition_system.pxd
View File

@ -20,11 +20,15 @@ cdef struct Transition:
int (*do)(StateC* state, attr_t label) nogil
ctypedef weight_t (*get_cost_func_t)(const StateC* state, const void* gold,
attr_tlabel) nogil
ctypedef weight_t (*move_cost_func_t)(const StateC* state, const void* gold) nogil
ctypedef weight_t (*label_cost_func_t)(const StateC* state, const void*
gold, attr_t label) nogil
ctypedef weight_t (*get_cost_func_t)(
const StateC* state, const void* gold, attr_tlabel
) nogil
ctypedef weight_t (*move_cost_func_t)(
const StateC* state, const void* gold
) nogil
ctypedef weight_t (*label_cost_func_t)(
const StateC* state, const void* gold, attr_t label
) nogil
ctypedef int (*do_func_t)(StateC* state, attr_t label) nogil
@ -56,7 +60,7 @@ cdef class TransitionSystem:
cdef void c_apply_actions(TransitionSystem moves, StateC** states, const int* actions,
int batch_size) nogil
int batch_size) nogil
cdef void c_transition_batch(TransitionSystem moves, StateC** states, const float* scores,
int nr_class, int batch_size) nogil
int nr_class, int batch_size) nogil

24
spacy/pipeline/_parser_internals/transition_system.pyx
View File

@ -10,9 +10,7 @@ from collections import Counter
import srsly
from ...structs cimport TokenC
from ...tokens.doc cimport Doc
from ...typedefs cimport attr_t, weight_t
from . cimport _beam_utils
from ._parser_utils cimport arg_max_if_valid
from .stateclass cimport StateClass
@ -270,7 +268,6 @@ cdef class TransitionSystem:
return self
def to_bytes(self, exclude=tuple()):
transitions = []
serializers = {
'moves': lambda: srsly.json_dumps(self.labels),
'strings': lambda: self.strings.to_bytes(),
@ -294,19 +291,19 @@ cdef class TransitionSystem:
cdef void c_apply_actions(TransitionSystem moves, StateC** states, const int* actions,
int batch_size) nogil:
cdef int i
cdef Transition action
cdef StateC* state
for i in range(batch_size):
state = states[i]
action = moves.c[actions[i]]
action.do(state, action.label)
state.history.push_back(action.clas)
int batch_size) nogil:
cdef int i
cdef Transition action
cdef StateC* state
for i in range(batch_size):
state = states[i]
action = moves.c[actions[i]]
action.do(state, action.label)
state.history.push_back(action.clas)
cdef void c_transition_batch(TransitionSystem moves, StateC** states, const float* scores,
int nr_class, int batch_size) nogil:
int nr_class, int batch_size) nogil:
is_valid = <int*>calloc(moves.n_moves, sizeof(int))
cdef int i, guess
cdef Transition action
@ -322,4 +319,3 @@ cdef void c_transition_batch(TransitionSystem moves, StateC** states, const floa
action.do(states[i], action.label)
states[i].history.push_back(guess)
free(is_valid)

2
spacy/pipeline/dep_parser.py
View File

@ -1,6 +1,6 @@
# cython: infer_types=True, profile=True, binding=True
from collections import defaultdict
from typing import Callable, Iterable, Optional
from typing import Callable, Optional
from thinc.api import Config, Model

14
spacy/pipeline/morphologizer.pyx
View File

@ -1,11 +1,9 @@
# cython: infer_types=True, profile=True, binding=True
from itertools import islice
from typing import Callable, Dict, Iterable, List, Optional, Union
from typing import Callable, Dict, Iterable, Optional, Union
import srsly
from thinc.api import Config, Model
from thinc.legacy import LegacySequenceCategoricalCrossentropy
from thinc.types import Floats2d, Ints1d
from ..morphology cimport Morphology
from ..tokens.doc cimport Doc
@ -16,10 +14,8 @@ from ..errors import Errors
from ..language import Language
from ..parts_of_speech import IDS as POS_IDS
from ..scorer import Scorer
from ..symbols import POS
from ..training import validate_examples, validate_get_examples
from ..util import registry
from .pipe import deserialize_config
from .tagger import ActivationsT, Tagger
# See #9050
@ -86,8 +82,11 @@ def morphologizer_score(examples, **kwargs):
results = {}
results.update(Scorer.score_token_attr(examples, "pos", **kwargs))
results.update(Scorer.score_token_attr(examples, "morph", getter=morph_key_getter, **kwargs))
results.update(Scorer.score_token_attr_per_feat(examples,
"morph", getter=morph_key_getter, **kwargs))
results.update(
Scorer.score_token_attr_per_feat(
examples, "morph", getter=morph_key_getter, **kwargs
)
)
return results
@ -249,7 +248,6 @@ class Morphologizer(Tagger):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef Vocab vocab = self.vocab
cdef bint overwrite = self.cfg["overwrite"]
cdef bint extend = self.cfg["extend"]

6
spacy/pipeline/ner.py
View File

@ -1,12 +1,12 @@
# cython: infer_types=True, profile=True, binding=True
from collections import defaultdict
from typing import Callable, Iterable, Optional
from typing import Callable, Optional
from thinc.api import Config, Model
from ..language import Language
from ..scorer import PRFScore, get_ner_prf
from ..training import remove_bilu_prefix, validate_examples
from ..scorer import get_ner_prf
from ..training import remove_bilu_prefix
from ..util import registry
from ._parser_internals.ner import BiluoPushDown
from ._parser_internals.transition_system import TransitionSystem

9
spacy/pipeline/pipe.pyx
View File

@ -1,12 +1,11 @@
# cython: infer_types=True, profile=True, binding=True
import warnings
from typing import Callable, Dict, Iterable, Iterator, Optional, Tuple, Union
from typing import Callable, Dict, Iterable, Iterator, Tuple, Union
import srsly
from ..tokens.doc cimport Doc
from ..errors import Errors, Warnings
from ..errors import Errors
from ..language import Language
from ..training import Example
from ..util import raise_error
@ -33,7 +32,7 @@ cdef class Pipe:
"""
raise NotImplementedError(Errors.E931.format(parent="Pipe", method="__call__", name=self.name))
def pipe(self, stream: Iterable[Doc], *, batch_size: int=128) -> Iterator[Doc]:
def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
"""Apply the pipe to a stream of documents. This usually happens under
the hood when the nlp object is called on a text and all components are
applied to the Doc.
@ -52,7 +51,7 @@ cdef class Pipe:
except Exception as e:
error_handler(self.name, self, [doc], e)
def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language=None):
def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language = None):
"""Initialize the pipe. For non-trainable components, this method
is optional. For trainable components, which should inherit
from the subclass TrainablePipe, the provided data examples

27
spacy/pipeline/sentencizer.pyx
View File

@ -7,7 +7,6 @@ from ..tokens.doc cimport Doc
from .. import util
from ..language import Language
from ..scorer import Scorer
from .pipe import Pipe
from .senter import senter_score
@ -34,17 +33,19 @@ class Sentencizer(Pipe):
DOCS: https://spacy.io/api/sentencizer
"""
default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
'।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄',
'᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿',
'‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶',
'꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒',
'﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀',
'𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼',
'𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐',
'𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂',
'𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈',
'｡', '。']
default_punct_chars = [
'!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
'।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄',
'᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿',
'‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶',
'꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒',
'﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀',
'𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼',
'𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐',
'𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂',
'𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈',
'｡', '。'
]
def __init__(
self,
@ -127,7 +128,6 @@ class Sentencizer(Pipe):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef int idx = 0
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
for j, tag_id in enumerate(doc_tag_ids):
@ -168,7 +168,6 @@ class Sentencizer(Pipe):
path = path.with_suffix(".json")
srsly.write_json(path, {"punct_chars": list(self.punct_chars), "overwrite": self.overwrite})
def from_disk(self, path, *, exclude=tuple()):
"""Load the sentencizer from disk.

4
spacy/pipeline/senter.pyx
View File

@ -1,11 +1,9 @@
# cython: infer_types=True, profile=True, binding=True
from itertools import islice
from typing import Callable, Dict, Iterable, List, Optional, Union
from typing import Callable, Iterable, Optional
import srsly
from thinc.api import Config, Model
from thinc.legacy import LegacySequenceCategoricalCrossentropy
from thinc.types import Floats2d, Ints1d
from ..tokens.doc cimport Doc

10
spacy/pipeline/span_finder.py
View File

@ -48,14 +48,14 @@ DEFAULT_SPAN_FINDER_MODEL = Config().from_str(span_finder_default_config)["model
"threshold": 0.5,
"model": DEFAULT_SPAN_FINDER_MODEL,
"spans_key": DEFAULT_SPANS_KEY,
"max_length": None,
"max_length": 25,
"min_length": None,
"scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
},
default_score_weights={
f"span_finder_{DEFAULT_SPANS_KEY}_f": 1.0,
f"span_finder_{DEFAULT_SPANS_KEY}_p": 0.0,
f"span_finder_{DEFAULT_SPANS_KEY}_r": 0.0,
f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
},
)
def make_span_finder(
@ -104,7 +104,7 @@ def make_span_finder_scorer():
def span_finder_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
kwargs = dict(kwargs)
attr_prefix = "span_finder_"
attr_prefix = "spans_"
key = kwargs["spans_key"]
kwargs.setdefault("attr", f"{attr_prefix}{key}")
kwargs.setdefault(

12
spacy/pipeline/tagger.pyx
View File

@ -1,27 +1,20 @@
# cython: infer_types=True, profile=True, binding=True
import warnings
from itertools import islice
from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy
import srsly
from thinc.api import Config, Model, set_dropout_rate
from thinc.legacy import LegacySequenceCategoricalCrossentropy
from thinc.types import Floats2d, Ints1d
from ..morphology cimport Morphology
from ..tokens.doc cimport Doc
from ..vocab cimport Vocab
from .. import util
from ..attrs import ID, POS
from ..errors import Errors, Warnings
from ..errors import Errors
from ..language import Language
from ..parts_of_speech import X
from ..scorer import Scorer
from ..training import validate_examples, validate_get_examples
from ..util import registry
from .pipe import deserialize_config
from .trainable_pipe import TrainablePipe
ActivationsT = Dict[str, Union[List[Floats2d], List[Ints1d]]]
@ -188,7 +181,6 @@ class Tagger(TrainablePipe):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef Vocab vocab = self.vocab
cdef bint overwrite = self.cfg["overwrite"]
labels = self.labels
for i, doc in enumerate(docs):
@ -281,7 +273,7 @@ class Tagger(TrainablePipe):
student_scores: Scores representing the student model's predictions.
RETURNS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/tagger#get_teacher_student_loss
"""
loss_func = LegacySequenceCategoricalCrossentropy(normalize=False)

35
spacy/pipeline/trainable_pipe.pyx
View File

@ -1,5 +1,4 @@
# cython: infer_types=True, profile=True, binding=True
import warnings
from typing import Callable, Dict, Iterable, Iterator, Optional, Tuple
import srsly
@ -8,7 +7,7 @@ from thinc.api import Model, Optimizer, set_dropout_rate
from ..tokens.doc cimport Doc
from .. import util
from ..errors import Errors, Warnings
from ..errors import Errors
from ..language import Language
from ..training import Example, validate_distillation_examples, validate_examples
from ..vocab import Vocab
@ -56,14 +55,14 @@ cdef class TrainablePipe(Pipe):
except Exception as e:
error_handler(self.name, self, [doc], e)
def distill(self,
teacher_pipe: Optional["TrainablePipe"],
examples: Iterable["Example"],
*,
drop: float=0.0,
sgd: Optional[Optimizer]=None,
losses: Optional[Dict[str, float]]=None) -> Dict[str, float]:
teacher_pipe: Optional["TrainablePipe"],
examples: Iterable["Example"],
*,
drop: float = 0.0,
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None
) -> Dict[str, float]:
"""Train a pipe (the student) on the predictions of another pipe
(the teacher). The student is typically trained on the probability
distribution of the teacher, but details may differ per pipe.
@ -79,7 +78,7 @@ cdef class TrainablePipe(Pipe):
losses (Optional[Dict[str, float]]): Optional record of loss during
distillation.
RETURNS: The updated losses dictionary.
DOCS: https://spacy.io/api/pipe#distill
"""
# By default we require a teacher pipe, but there are downstream
@ -103,7 +102,7 @@ cdef class TrainablePipe(Pipe):
losses[self.name] += loss
return losses
def pipe(self, stream: Iterable[Doc], *, batch_size: int=128) -> Iterator[Doc]:
def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
"""Apply the pipe to a stream of documents. This usually happens under
the hood when the nlp object is called on a text and all components are
applied to the Doc.
@ -150,9 +149,9 @@ cdef class TrainablePipe(Pipe):
def update(self,
examples: Iterable["Example"],
*,
drop: float=0.0,
sgd: Optimizer=None,
losses: Optional[Dict[str, float]]=None) -> Dict[str, float]:
drop: float = 0.0,
sgd: Optimizer = None,
losses: Optional[Dict[str, float]] = None) -> Dict[str, float]:
"""Learn from a batch of documents and gold-standard information,
updating the pipe's model. Delegates to predict and get_loss.
@ -186,8 +185,8 @@ cdef class TrainablePipe(Pipe):
def rehearse(self,
examples: Iterable[Example],
*,
sgd: Optimizer=None,
losses: Dict[str, float]=None,
sgd: Optimizer = None,
losses: Dict[str, float] = None,
**config) -> Dict[str, float]:
"""Perform a "rehearsal" update from a batch of data. Rehearsal updates
teach the current model to make predictions similar to an initial model,
@ -224,7 +223,7 @@ cdef class TrainablePipe(Pipe):
student_scores: Scores representing the student model's predictions.
RETURNS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/pipe#get_teacher_student_loss
"""
raise NotImplementedError(Errors.E931.format(parent="TrainablePipe", method="get_teacher_student_loss", name=self.name))
@ -238,7 +237,7 @@ cdef class TrainablePipe(Pipe):
"""
return util.create_default_optimizer()
def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language=None):
def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language = None):
"""Initialize the pipe for training, using data examples if available.
This method needs to be implemented by each TrainablePipe component,
ensuring the internal model (if available) is initialized properly

88
spacy/pipeline/transition_parser.pyx
View File

@ -6,15 +6,9 @@ from typing import Dict, Iterable, List, Optional, Tuple
cimport numpy as np
from cymem.cymem cimport Pool
from itertools import islice
from libc.stdlib cimport calloc, free
from libc.string cimport memcpy, memset
from libcpp.vector cimport vector
import contextlib
import random
import warnings
from itertools import islice
import numpy
import numpy.random
@ -23,44 +17,36 @@ from thinc.api import (
CupyOps,
NumpyOps,
Optimizer,
chain,
get_array_module,
get_ops,
set_dropout_rate,
softmax_activation,
use_ops,
)
from thinc.legacy import LegacySequenceCategoricalCrossentropy
from thinc.types import Floats2d, Ints1d
from ..ml.tb_framework import TransitionModelInputs
from ..tokens.doc cimport Doc
from ._parser_internals cimport _beam_utils
from ._parser_internals.search cimport Beam
from ._parser_internals.stateclass cimport StateC, StateClass
from .trainable_pipe cimport TrainablePipe
from ._parser_internals import _beam_utils
from ..typedefs cimport weight_t
from ..vocab cimport Vocab
from ._parser_internals cimport _beam_utils
from ._parser_internals.stateclass cimport StateC, StateClass
from ._parser_internals.transition_system cimport Transition, TransitionSystem
from .trainable_pipe cimport TrainablePipe
from .. import util
from ..errors import Errors, Warnings
from ..errors import Errors
from ..training import (
validate_distillation_examples,
validate_examples,
validate_get_examples,
)
from ._parser_internals import _beam_utils
# TODO: Remove when we switch to Cython 3.
cdef extern from "<algorithm>" namespace "std" nogil:
bint equal[InputIt1, InputIt2](InputIt1 first1, InputIt1 last1, InputIt2 first2) except +
NUMPY_OPS = NumpyOps()
@ -236,12 +222,13 @@ class Parser(TrainablePipe):
raise NotImplementedError
def distill(self,
teacher_pipe: Optional[TrainablePipe],
examples: Iterable["Example"],
*,
drop: float=0.0,
sgd: Optional[Optimizer]=None,
losses: Optional[Dict[str, float]]=None):
teacher_pipe: Optional[TrainablePipe],
examples: Iterable["Example"],
*,
drop: float = 0.0,
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None
):
"""Train a pipe (the student) on the predictions of another pipe
(the teacher). The student is trained on the transition probabilities
of the teacher.
@ -257,7 +244,7 @@ class Parser(TrainablePipe):
losses (Optional[Dict[str, float]]): Optional record of loss during
distillation.
RETURNS: The updated losses dictionary.
DOCS: https://spacy.io/api/dependencyparser#distill
"""
if teacher_pipe is None:
@ -291,11 +278,13 @@ class Parser(TrainablePipe):
# teacher's distributions.
student_inputs = TransitionModelInputs(docs=student_docs,
states=[state.copy() for state in states], moves=self.moves, max_moves=max_moves)
states=[state.copy() for state in states],
moves=self.moves,
max_moves=max_moves)
(student_states, student_scores), backprop_scores = self.model.begin_update(student_inputs)
actions = _states_diff_to_actions(states, student_states)
teacher_inputs = TransitionModelInputs(docs=[eg.reference for eg in examples],
states=states, moves=teacher_pipe.moves, actions=actions)
states=states, moves=teacher_pipe.moves, actions=actions)
(_, teacher_scores) = teacher_pipe.model.predict(teacher_inputs)
loss, d_scores = self.get_teacher_student_loss(teacher_scores, student_scores)
@ -308,10 +297,9 @@ class Parser(TrainablePipe):
return losses
def get_teacher_student_loss(
self, teacher_scores: List[Floats2d], student_scores: List[Floats2d],
normalize: bool=False,
self, teacher_scores: List[Floats2d], student_scores: List[Floats2d],
normalize: bool = False,
) -> Tuple[float, List[Floats2d]]:
"""Calculate the loss and its gradient for a batch of student
scores, relative to teacher scores.
@ -320,7 +308,7 @@ class Parser(TrainablePipe):
student_scores: Scores representing the student model's predictions.
RETURNS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/dependencyparser#get_teacher_student_loss
"""
@ -334,9 +322,9 @@ class Parser(TrainablePipe):
# ourselves.
teacher_scores = self.model.ops.softmax(self.model.ops.xp.vstack(teacher_scores),
axis=-1, inplace=True)
axis=-1, inplace=True)
student_scores = self.model.ops.softmax(self.model.ops.xp.vstack(student_scores),
axis=-1, inplace=True)
axis=-1, inplace=True)
assert teacher_scores.shape == student_scores.shape
@ -384,7 +372,6 @@ class Parser(TrainablePipe):
except Exception as e:
error_handler(self.name, self, batch_in_order, e)
def predict(self, docs):
if isinstance(docs, Doc):
docs = [docs]
@ -414,7 +401,6 @@ class Parser(TrainablePipe):
def set_annotations(self, docs, states_or_beams):
cdef StateClass state
cdef Beam beam
cdef Doc doc
states = _beam_utils.collect_states(states_or_beams, docs)
for i, (state, doc) in enumerate(zip(states, docs)):
@ -423,7 +409,6 @@ class Parser(TrainablePipe):
hook(doc)
def update(self, examples, *, drop=0., sgd=None, losses=None):
cdef StateClass state
if losses is None:
losses = {}
losses.setdefault(self.name, 0.)
@ -453,13 +438,15 @@ class Parser(TrainablePipe):
else:
init_states, gold_states, _ = self.moves.init_gold_batch(examples)
inputs = TransitionModelInputs(docs=docs, moves=self.moves,
max_moves=max_moves, states=[state.copy() for state in init_states])
inputs = TransitionModelInputs(docs=docs,
moves=self.moves,
max_moves=max_moves,
states=[state.copy() for state in init_states])
(pred_states, scores), backprop_scores = self.model.begin_update(inputs)
if sum(s.shape[0] for s in scores) == 0:
return losses
d_scores = self.get_loss((gold_states, init_states, pred_states, scores),
examples, max_moves)
examples, max_moves)
backprop_scores((pred_states, d_scores))
if sgd not in (None, False):
self.finish_update(sgd)
@ -500,9 +487,7 @@ class Parser(TrainablePipe):
cdef TransitionSystem moves = self.moves
cdef StateClass state
cdef int clas
cdef int nF = self.model.get_dim("nF")
cdef int nO = moves.n_moves
cdef int nS = sum([len(history) for history in histories])
cdef Pool mem = Pool()
cdef np.ndarray costs_i
is_valid = <int*>mem.alloc(nO, sizeof(int))
@ -569,8 +554,8 @@ class Parser(TrainablePipe):
return losses
def update_beam(self, examples, *, beam_width,
drop=0., sgd=None, losses=None, beam_density=0.0):
def update_beam(self, examples, *, beam_width, drop=0.,
sgd=None, losses=None, beam_density=0.0):
raise NotImplementedError
def set_output(self, nO):
@ -695,9 +680,10 @@ class Parser(TrainablePipe):
return states
# Parse the states that are too long with the teacher's parsing model.
teacher_inputs = TransitionModelInputs(docs=docs, moves=moves,
states=[state.copy() for state in to_cut])
(teacher_states, _ ) = teacher_pipe.model.predict(teacher_inputs)
teacher_inputs = TransitionModelInputs(docs=docs,
moves=moves,
states=[state.copy() for state in to_cut])
(teacher_states, _) = teacher_pipe.model.predict(teacher_inputs)
# Step through the teacher's actions and store every state after
# each multiple of max_length.
@ -795,6 +781,7 @@ def _states_to_actions(states: List[StateClass]) -> List[Ints1d]:
return actions
def _states_diff_to_actions(
before_states: List[StateClass],
after_states: List[StateClass]
@ -815,8 +802,9 @@ def _states_diff_to_actions(
c_state_before = before_state.c
c_state_after = after_state.c
assert equal(c_state_before.history.begin(), c_state_before.history.end(),
c_state_after.history.begin())
assert equal(c_state_before.history.begin(),
c_state_before.history.end(),
c_state_after.history.begin())
actions = []
while True:

10
spacy/strings.pyx
View File

@ -1,10 +1,8 @@
# cython: infer_types=True
from typing import Any, Callable, Iterable, Iterator, List, Optional, Tuple, Union
from typing import Iterable, Iterator, List, Optional, Tuple, Union
cimport cython
from libc.stdint cimport uint32_t
from libc.string cimport memcpy
from libcpp.set cimport set
from murmurhash.mrmr cimport hash64
import srsly
@ -244,7 +242,6 @@ cdef class StringStore:
cdef int n_length_bytes
cdef int i
cdef Utf8Str* string = <Utf8Str*>self.mem.alloc(1, sizeof(Utf8Str))
cdef uint32_t ulength = length
if length < sizeof(string.s):
string.s[0] = <unsigned char>length
memcpy(&string.s[1], chars, length)
@ -302,7 +299,7 @@ cpdef hash_t get_string_id(object string_or_hash) except -1:
try:
return hash_string(string_or_hash)
except:
except: # no-cython-lint
if _try_coerce_to_hash(string_or_hash, &str_hash):
# Coerce the integral key to the expected primitive hash type.
# This ensures that custom/overloaded "primitive" data types
@ -319,6 +316,5 @@ cdef inline bint _try_coerce_to_hash(object key, hash_t* out_hash):
try:
out_hash[0] = key
return True
except:
except: # no-cython-lint
return False

2
spacy/structs.pxd
View File

@ -52,7 +52,7 @@ cdef struct TokenC:
int sent_start
int ent_iob
attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth..
attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth..
attr_t ent_kb_id
hash_t ent_id

8
spacy/symbols.pxd
View File

@ -93,7 +93,7 @@ cdef enum symbol_t:
ADV
AUX
CONJ
CCONJ # U20
CCONJ # U20
DET
INTJ
NOUN
@ -419,7 +419,7 @@ cdef enum symbol_t:
ccomp
complm
conj
cop # U20
cop # U20
csubj
csubjpass
dep
@ -442,8 +442,8 @@ cdef enum symbol_t:
num
number
oprd
obj # U20
obl # U20
obj # U20
obl # U20
parataxis
partmod
pcomp

8
spacy/symbols.pyx
View File

@ -96,7 +96,7 @@ IDS = {
"ADV": ADV,
"AUX": AUX,
"CONJ": CONJ,
"CCONJ": CCONJ, # U20
"CCONJ": CCONJ, # U20
"DET": DET,
"INTJ": INTJ,
"NOUN": NOUN,
@ -421,7 +421,7 @@ IDS = {
"ccomp": ccomp,
"complm": complm,
"conj": conj,
"cop": cop, # U20
"cop": cop, # U20
"csubj": csubj,
"csubjpass": csubjpass,
"dep": dep,
@ -444,8 +444,8 @@ IDS = {
"num": num,
"number": number,
"oprd": oprd,
"obj": obj, # U20
"obl": obl, # U20
"obj": obj, # U20
"obl": obl, # U20
"parataxis": parataxis,
"partmod": partmod,
"pcomp": pcomp,

3
spacy/tests/matcher/test_pattern_validation.py
View File

@ -52,7 +52,8 @@ TEST_PATTERNS = [
@pytest.mark.parametrize(
"pattern", [[{"XX": "y"}, {"LENGTH": "2"}, {"TEXT": {"IN": 5}}]]
"pattern",
[[{"XX": "y"}], [{"LENGTH": "2"}], [{"TEXT": {"IN": 5}}], [{"text": {"in": 6}}]],
)
def test_matcher_pattern_validation(en_vocab, pattern):
matcher = Matcher(en_vocab, validate=True)

1
spacy/tests/package/test_requirements.py
View File

@ -11,6 +11,7 @@ def test_build_dependencies():
"flake8",
"hypothesis",
"pre-commit",
"cython-lint",
"black",
"isort",
"mypy",

49
spacy/tests/parser/_search.pyx
View File

@ -2,7 +2,7 @@
from cymem.cymem cimport Pool
from spacy.pipeline._parser_internals.search cimport Beam, MaxViolation
from spacy.typedefs cimport class_t, weight_t
from spacy.typedefs cimport class_t
import pytest
@ -42,32 +42,35 @@ cdef int destroy(Pool mem, void* state, void* extra_args) except -1:
state = <TestState*>state
mem.free(state)
@cytest
@pytest.mark.parametrize("nr_class,beam_width",
[
(2, 3),
(3, 6),
(4, 20),
]
)
[
(2, 3),
(3, 6),
(4, 20),
]
)
def test_init(nr_class, beam_width):
b = Beam(nr_class, beam_width)
assert b.size == 1
assert b.width == beam_width
assert b.nr_class == nr_class
@cytest
def test_init_violn():
MaxViolation()
@cytest
@pytest.mark.parametrize("nr_class,beam_width,length",
[
(2, 3, 3),
(3, 6, 15),
(4, 20, 32),
]
)
[
(2, 3, 3),
(3, 6, 15),
(4, 20, 32),
]
)
def test_initialize(nr_class, beam_width, length):
b = Beam(nr_class, beam_width)
b.initialize(initialize, destroy, length, NULL)
@ -79,11 +82,11 @@ def test_initialize(nr_class, beam_width, length):
@cytest
@pytest.mark.parametrize("nr_class,beam_width,length,extra",
[
(2, 3, 4, None),
(3, 6, 15, u"test beam 1"),
]
)
[
(2, 3, 4, None),
(3, 6, 15, u"test beam 1"),
]
)
def test_initialize_extra(nr_class, beam_width, length, extra):
b = Beam(nr_class, beam_width)
if extra is None:
@ -97,11 +100,11 @@ def test_initialize_extra(nr_class, beam_width, length, extra):
@cytest
@pytest.mark.parametrize("nr_class,beam_width,length",
[
(3, 6, 15),
(4, 20, 32),
]
)
[
(3, 6, 15),
(4, 20, 32),
]
)
def test_transition(nr_class, beam_width, length):
b = Beam(nr_class, beam_width)
b.initialize(initialize, destroy, length, NULL)

6
spacy/tests/pipeline/test_span_finder.py
View File

@ -230,10 +230,10 @@ def test_overfitting_IO():
# Test scoring
scores = nlp.evaluate(train_examples)
assert f"span_finder_{SPANS_KEY}_f" in scores
assert f"spans_{SPANS_KEY}_f" in scores
# It's not perfect 1.0 F1 because it's designed to overgenerate for now.
assert scores[f"span_finder_{SPANS_KEY}_p"] == 0.75
assert scores[f"span_finder_{SPANS_KEY}_r"] == 1.0
assert scores[f"spans_{SPANS_KEY}_p"] == 0.75
assert scores[f"spans_{SPANS_KEY}_r"] == 1.0
# also test that the spancat works for just a single entity in a sentence
doc = nlp("London")

91
spacy/tests/pipeline/test_tok2vec.py
View File

@ -192,8 +192,7 @@ def test_tok2vec_listener(with_vectors):
for tag in t[1]["tags"]:
tagger.add_label(tag)
# Check that the Tok2Vec component finds it listeners
assert tok2vec.listeners == []
# Check that the Tok2Vec component finds its listeners
optimizer = nlp.initialize(lambda: train_examples)
assert tok2vec.listeners == [tagger_tok2vec]
@ -221,7 +220,6 @@ def test_tok2vec_listener_callback():
assert nlp.pipe_names == ["tok2vec", "tagger"]
tagger = nlp.get_pipe("tagger")
tok2vec = nlp.get_pipe("tok2vec")
nlp._link_components()
docs = [nlp.make_doc("A random sentence")]
tok2vec.model.initialize(X=docs)
gold_array = [[1.0 for tag in ["V", "Z"]] for word in docs]
@ -430,29 +428,46 @@ def test_replace_listeners_from_config():
nlp.to_disk(dir_path)
base_model = str(dir_path)
new_config = {
"nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger", "ner"]},
"nlp": {
"lang": "en",
"pipeline": ["tok2vec", "tagger2", "ner3", "tagger4"],
},
"components": {
"tok2vec": {"source": base_model},
"tagger": {
"tagger2": {
"source": base_model,
"component": "tagger",
"replace_listeners": ["model.tok2vec"],
},
"ner": {"source": base_model},
"ner3": {
"source": base_model,
"component": "ner",
},
"tagger4": {
"source": base_model,
"component": "tagger",
},
},
}
new_nlp = util.load_model_from_config(new_config, auto_fill=True)
new_nlp.initialize(lambda: examples)
tok2vec = new_nlp.get_pipe("tok2vec")
tagger = new_nlp.get_pipe("tagger")
ner = new_nlp.get_pipe("ner")
assert tok2vec.listening_components == ["ner"]
tagger = new_nlp.get_pipe("tagger2")
ner = new_nlp.get_pipe("ner3")
assert "ner" not in new_nlp.pipe_names
assert "tagger" not in new_nlp.pipe_names
assert tok2vec.listening_components == ["ner3", "tagger4"]
assert any(isinstance(node, Tok2VecListener) for node in ner.model.walk())
assert not any(isinstance(node, Tok2VecListener) for node in tagger.model.walk())
t2v_cfg = new_nlp.config["components"]["tok2vec"]["model"]
assert t2v_cfg["@architectures"] == "spacy.Tok2Vec.v2"
assert new_nlp.config["components"]["tagger"]["model"]["tok2vec"] == t2v_cfg
assert new_nlp.config["components"]["tagger2"]["model"]["tok2vec"] == t2v_cfg
assert (
new_nlp.config["components"]["ner"]["model"]["tok2vec"]["@architectures"]
new_nlp.config["components"]["ner3"]["model"]["tok2vec"]["@architectures"]
== "spacy.Tok2VecListener.v1"
)
assert (
new_nlp.config["components"]["tagger4"]["model"]["tok2vec"]["@architectures"]
== "spacy.Tok2VecListener.v1"
)
@ -627,3 +642,57 @@ def test_tok2vec_distillation_teacher_annotations():
student_tok2vec.distill = tok2vec_distill_wrapper.__get__(student_tok2vec, Tok2Vec)
student_nlp.distill(teacher_nlp, train_examples_student, sgd=optimizer, losses={})
def test_tok2vec_listener_source_link_name():
"""The component's internal name and the tok2vec listener map correspond
to the most recently modified pipeline.
"""
orig_config = Config().from_str(cfg_string_multi)
nlp1 = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
assert nlp1.get_pipe("tok2vec").listening_components == ["tagger", "ner"]
nlp2 = English()
nlp2.add_pipe("tok2vec", source=nlp1)
nlp2.add_pipe("tagger", name="tagger2", source=nlp1)
# there is no way to have the component have the right name for both
# pipelines, right now the most recently modified pipeline is prioritized
assert nlp1.get_pipe("tagger").name == nlp2.get_pipe("tagger2").name == "tagger2"
# there is no way to have the tok2vec have the right listener map for both
# pipelines, right now the most recently modified pipeline is prioritized
assert nlp2.get_pipe("tok2vec").listening_components == ["tagger2"]
nlp2.add_pipe("ner", name="ner3", source=nlp1)
assert nlp2.get_pipe("tok2vec").listening_components == ["tagger2", "ner3"]
nlp2.remove_pipe("ner3")
assert nlp2.get_pipe("tok2vec").listening_components == ["tagger2"]
nlp2.remove_pipe("tagger2")
assert nlp2.get_pipe("tok2vec").listening_components == []
# at this point the tok2vec component corresponds to nlp2
assert nlp1.get_pipe("tok2vec").listening_components == []
# modifying the nlp1 pipeline syncs the tok2vec listener map back to nlp1
nlp1.add_pipe("sentencizer")
assert nlp1.get_pipe("tok2vec").listening_components == ["tagger", "ner"]
# modifying nlp2 syncs it back to nlp2
nlp2.add_pipe("sentencizer")
assert nlp1.get_pipe("tok2vec").listening_components == []
def test_tok2vec_listener_source_replace_listeners():
orig_config = Config().from_str(cfg_string_multi)
nlp1 = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
assert nlp1.get_pipe("tok2vec").listening_components == ["tagger", "ner"]
nlp1.replace_listeners("tok2vec", "tagger", ["model.tok2vec"])
assert nlp1.get_pipe("tok2vec").listening_components == ["ner"]
nlp2 = English()
nlp2.add_pipe("tok2vec", source=nlp1)
assert nlp2.get_pipe("tok2vec").listening_components == []
nlp2.add_pipe("tagger", source=nlp1)
assert nlp2.get_pipe("tok2vec").listening_components == []
nlp2.add_pipe("ner", name="ner2", source=nlp1)
assert nlp2.get_pipe("tok2vec").listening_components == ["ner2"]

50
spacy/tests/serialize/test_serialize_config.py
View File

@ -18,6 +18,7 @@ from spacy.ml.models import (
build_Tok2Vec_model,
)
from spacy.schemas import ConfigSchema, ConfigSchemaDistill, ConfigSchemaPretrain
from spacy.training import Example
from spacy.util import (
load_config,
load_config_from_str,
@ -469,6 +470,55 @@ def test_config_overrides():
assert nlp.pipe_names == ["tok2vec", "tagger"]
@pytest.mark.filterwarnings("ignore:\\[W036")
def test_config_overrides_registered_functions():
nlp = spacy.blank("en")
nlp.add_pipe("attribute_ruler")
with make_tempdir() as d:
nlp.to_disk(d)
nlp_re1 = spacy.load(
d,
config={
"components": {
"attribute_ruler": {
"scorer": {"@scorers": "spacy.tagger_scorer.v1"}
}
}
},
)
assert (
nlp_re1.config["components"]["attribute_ruler"]["scorer"]["@scorers"]
== "spacy.tagger_scorer.v1"
)
@registry.misc("test_some_other_key")
def misc_some_other_key():
return "some_other_key"
nlp_re2 = spacy.load(
d,
config={
"components": {
"attribute_ruler": {
"scorer": {
"@scorers": "spacy.overlapping_labeled_spans_scorer.v1",
"spans_key": {"@misc": "test_some_other_key"},
}
}
}
},
)
assert nlp_re2.config["components"]["attribute_ruler"]["scorer"][
"spans_key"
] == {"@misc": "test_some_other_key"}
# run dummy evaluation (will return None scores) in order to test that
# the spans_key value in the nested override is working as intended in
# the config
example = Example.from_dict(nlp_re2.make_doc("a b c"), {})
scores = nlp_re2.evaluate([example])
assert "spans_some_other_key_f" in scores
def test_config_interpolation():
config = Config().from_str(nlp_config_string, interpolate=False)
assert config["corpora"]["train"]["path"] == "${paths.train}"

2
spacy/tests/test_cli.py
View File

@ -697,7 +697,6 @@ def test_string_to_list_intify(value):
assert string_to_list(value, intify=True) == [1, 2, 3]
@pytest.mark.skip(reason="Temporarily skip before models are published")
def test_download_compatibility():
spec = SpecifierSet("==" + about.__version__)
spec.prereleases = False
@ -708,7 +707,6 @@ def test_download_compatibility():
assert get_minor_version(about.__version__) == get_minor_version(version)
@pytest.mark.skip(reason="Temporarily skip before models are published")
def test_validate_compatibility_table():
spec = SpecifierSet("==" + about.__version__)
spec.prereleases = False

19
spacy/tests/test_displacy.py
View File

@ -377,3 +377,22 @@ def test_displacy_manual_sorted_entities():
html = displacy.render(doc, style="ent", manual=True)
assert html.find("FIRST") < html.find("SECOND")
@pytest.mark.issue(12816)
def test_issue12816(en_vocab) -> None:
"""Test that displaCy's span visualizer escapes annotated HTML tags correctly."""
# Create a doc containing an annotated word and an unannotated HTML tag
doc = Doc(en_vocab, words=["test", "<TEST>"])
doc.spans["sc"] = [Span(doc, 0, 1, label="test")]
# Verify that the HTML tag is escaped when unannotated
html = displacy.render(doc, style="span")
assert "&lt;TEST&gt;" in html
# Annotate the HTML tag
doc.spans["sc"].append(Span(doc, 1, 2, label="test"))
# Verify that the HTML tag is still escaped
html = displacy.render(doc, style="span")
assert "&lt;TEST&gt;" in html

27
spacy/tests/test_misc.py
View File

@ -220,6 +220,10 @@ def test_minor_version(a1, a2, b1, b2, is_match):
{"training.batch_size": 128, "training.optimizer.learn_rate": 0.01},
{"training": {"batch_size": 128, "optimizer": {"learn_rate": 0.01}}},
),
(
{"attribute_ruler.scorer.@scorers": "spacy.tagger_scorer.v1"},
{"attribute_ruler": {"scorer": {"@scorers": "spacy.tagger_scorer.v1"}}},
),
],
)
def test_dot_to_dict(dot_notation, expected):
@ -228,6 +232,29 @@ def test_dot_to_dict(dot_notation, expected):
assert util.dict_to_dot(result) == dot_notation
@pytest.mark.parametrize(
"dot_notation,expected",
[
(
{"token.pos": True, "token._.xyz": True},
{"token": {"pos": True, "_": {"xyz": True}}},
),
(
{"training.batch_size": 128, "training.optimizer.learn_rate": 0.01},
{"training": {"batch_size": 128, "optimizer": {"learn_rate": 0.01}}},
),
(
{"attribute_ruler.scorer": {"@scorers": "spacy.tagger_scorer.v1"}},
{"attribute_ruler": {"scorer": {"@scorers": "spacy.tagger_scorer.v1"}}},
),
],
)
def test_dot_to_dict_overrides(dot_notation, expected):
result = util.dot_to_dict(dot_notation)
assert result == expected
assert util.dict_to_dot(result, for_overrides=True) == dot_notation
def test_set_dot_to_object():
config = {"foo": {"bar": 1, "baz": {"x": "y"}}, "test": {"a": {"b": "c"}}}
with pytest.raises(KeyError):

30
spacy/tests/vocab_vectors/test_vectors.py
View File

@ -401,6 +401,7 @@ def test_vectors_serialize():
row_r = v_r.add("D", vector=OPS.asarray([10, 20, 30, 40], dtype="f"))
assert row == row_r
assert_equal(OPS.to_numpy(v.data), OPS.to_numpy(v_r.data))
assert v.attr == v_r.attr
def test_vector_is_oov():
@ -645,3 +646,32 @@ def test_equality():
vectors1.resize((5, 9))
vectors2.resize((5, 9))
assert vectors1 == vectors2
def test_vectors_attr():
data = numpy.asarray([[0, 0, 0], [1, 2, 3], [9, 8, 7]], dtype="f")
# default ORTH
nlp = English()
nlp.vocab.vectors = Vectors(data=data, keys=["A", "B", "C"])
assert nlp.vocab.strings["A"] in nlp.vocab.vectors.key2row
assert nlp.vocab.strings["a"] not in nlp.vocab.vectors.key2row
assert nlp.vocab["A"].has_vector is True
assert nlp.vocab["a"].has_vector is False
assert nlp("A")[0].has_vector is True
assert nlp("a")[0].has_vector is False
# custom LOWER
nlp = English()
nlp.vocab.vectors = Vectors(data=data, keys=["a", "b", "c"], attr="LOWER")
assert nlp.vocab.strings["A"] not in nlp.vocab.vectors.key2row
assert nlp.vocab.strings["a"] in nlp.vocab.vectors.key2row
assert nlp.vocab["A"].has_vector is True
assert nlp.vocab["a"].has_vector is True
assert nlp("A")[0].has_vector is True
assert nlp("a")[0].has_vector is True
# add a new vectors entry
assert nlp.vocab["D"].has_vector is False
assert nlp.vocab["d"].has_vector is False
nlp.vocab.set_vector("D", numpy.asarray([4, 5, 6]))
assert nlp.vocab["D"].has_vector is True
assert nlp.vocab["d"].has_vector is True

75
spacy/tokenizer.pxd
View File

@ -26,24 +26,57 @@ cdef class Tokenizer:
cdef Doc _tokenize_affixes(self, str string, bint with_special_cases)
cdef int _apply_special_cases(self, Doc doc) except -1
cdef void _filter_special_spans(self, vector[SpanC] &original,
vector[SpanC] &filtered, int doc_len) nogil
cdef object _prepare_special_spans(self, Doc doc,
vector[SpanC] &filtered)
cdef int _retokenize_special_spans(self, Doc doc, TokenC* tokens,
object span_data)
cdef int _try_specials_and_cache(self, hash_t key, Doc tokens,
int* has_special,
bint with_special_cases) except -1
cdef int _tokenize(self, Doc tokens, str span, hash_t key,
int* has_special, bint with_special_cases) except -1
cdef str _split_affixes(self, str string,
vector[LexemeC*] *prefixes,
vector[LexemeC*] *suffixes, int* has_special,
bint with_special_cases)
cdef int _attach_tokens(self, Doc tokens, str string,
vector[LexemeC*] *prefixes,
vector[LexemeC*] *suffixes, int* has_special,
bint with_special_cases) except -1
cdef int _save_cached(self, const TokenC* tokens, hash_t key,
int* has_special, int n) except -1
cdef void _filter_special_spans(
self,
vector[SpanC] &original,
vector[SpanC] &filtered,
int doc_len,
) nogil
cdef object _prepare_special_spans(
self,
Doc doc,
vector[SpanC] &filtered,
)
cdef int _retokenize_special_spans(
self,
Doc doc,
TokenC* tokens,
object span_data,
)
cdef int _try_specials_and_cache(
self,
hash_t key,
Doc tokens,
int* has_special,
bint with_special_cases,
) except -1
cdef int _tokenize(
self,
Doc tokens,
str span,
hash_t key,
int* has_special,
bint with_special_cases,
) except -1
cdef str _split_affixes(
self,
str string,
vector[LexemeC*] *prefixes,
vector[LexemeC*] *suffixes, int* has_special,
bint with_special_cases,
)
cdef int _attach_tokens(
self,
Doc tokens,
str string,
vector[LexemeC*] *prefixes,
vector[LexemeC*] *suffixes, int* has_special,
bint with_special_cases,
) except -1
cdef int _save_cached(
self,
const TokenC* tokens,
hash_t key,
int* has_special,
int n,
) except -1

30
spacy/tokenizer.pyx
View File

@ -323,7 +323,7 @@ cdef class Tokenizer:
cdef int span_start
cdef int span_end
while i < doc.length:
if not i in span_data:
if i not in span_data:
tokens[i + offset] = doc.c[i]
i += 1
else:
@ -394,12 +394,14 @@ cdef class Tokenizer:
self._save_cached(&tokens.c[orig_size], orig_key, has_special,
tokens.length - orig_size)
cdef str _split_affixes(self, str string,
vector[const LexemeC*] *prefixes,
vector[const LexemeC*] *suffixes,
int* has_special,
bint with_special_cases):
cdef size_t i
cdef str _split_affixes(
self,
str string,
vector[const LexemeC*] *prefixes,
vector[const LexemeC*] *suffixes,
int* has_special,
bint with_special_cases
):
cdef str prefix
cdef str suffix
cdef str minus_pre
@ -444,10 +446,6 @@ cdef class Tokenizer:
vector[const LexemeC*] *suffixes,
int* has_special,
bint with_special_cases) except -1:
cdef bint specials_hit = 0
cdef bint cache_hit = 0
cdef int split, end
cdef const LexemeC* const* lexemes
cdef const LexemeC* lexeme
cdef str span
cdef int i
@ -457,9 +455,11 @@ cdef class Tokenizer:
if string:
if self._try_specials_and_cache(hash_string(string), tokens, has_special, with_special_cases):
pass
elif (self.token_match and self.token_match(string)) or \
(self.url_match and \
self.url_match(string)):
elif (
(self.token_match and self.token_match(string)) or
(self.url_match and self.url_match(string))
):
# We're always saying 'no' to spaces here -- the caller will
# fix up the outermost one, with reference to the original.
# See Issue #859
@ -820,7 +820,7 @@ cdef class Tokenizer:
self.infix_finditer = None
self.token_match = None
self.url_match = None
msg = util.from_bytes(bytes_data, deserializers, exclude)
util.from_bytes(bytes_data, deserializers, exclude)
if "prefix_search" in data and isinstance(data["prefix_search"], str):
self.prefix_search = re.compile(data["prefix_search"]).search
if "suffix_search" in data and isinstance(data["suffix_search"], str):

3
spacy/tokens/doc.pxd
View File

@ -31,7 +31,7 @@ cdef int token_by_start(const TokenC* tokens, int length, int start_char) except
cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2
cdef int [:,:] _get_lca_matrix(Doc, int start, int end)
cdef int [:, :] _get_lca_matrix(Doc, int start, int end)
cdef class Doc:
@ -61,7 +61,6 @@ cdef class Doc:
cdef int length
cdef int max_length
cdef public object noun_chunks_iterator
cdef object __weakref__

58
spacy/tokens/doc.pyx
View File

@ -35,6 +35,7 @@ from ..attrs cimport (
LENGTH,
MORPH,
NORM,
ORTH,
POS,
SENT_START,
SPACY,
@ -42,14 +43,13 @@ from ..attrs cimport (
attr_id_t,
)
from ..lexeme cimport EMPTY_LEXEME, Lexeme
from ..typedefs cimport attr_t, flags_t
from ..typedefs cimport attr_t
from .token cimport Token
from .. import parts_of_speech, schemas, util
from ..attrs import IDS, intify_attr
from ..compat import copy_reg, pickle
from ..compat import copy_reg
from ..errors import Errors, Warnings
from ..morphology import Morphology
from ..util import get_words_and_spaces
from .retokenizer import Retokenizer
from .underscore import Underscore, get_ext_args
@ -613,13 +613,26 @@ cdef class Doc:
"""
if "similarity" in self.user_hooks:
return self.user_hooks["similarity"](self, other)
if isinstance(other, (Lexeme, Token)) and self.length == 1:
if self.c[0].lex.orth == other.orth:
attr = getattr(self.vocab.vectors, "attr", ORTH)
cdef Token this_token
cdef Token other_token
cdef Lexeme other_lex
if len(self) == 1 and isinstance(other, Token):
this_token = self[0]
other_token = other
if Token.get_struct_attr(this_token.c, attr) == Token.get_struct_attr(other_token.c, attr):
return 1.0
elif isinstance(other, (Span, Doc)) and len(self) == len(other):
elif len(self) == 1 and isinstance(other, Lexeme):
this_token = self[0]
other_lex = other
if Token.get_struct_attr(this_token.c, attr) == Lexeme.get_struct_attr(other_lex.c, attr):
return 1.0
elif isinstance(other, (Doc, Span)) and len(self) == len(other):
similar = True
for i in range(self.length):
if self[i].orth != other[i].orth:
for i in range(len(self)):
this_token = self[i]
other_token = other[i]
if Token.get_struct_attr(this_token.c, attr) != Token.get_struct_attr(other_token.c, attr):
similar = False
break
if similar:
@ -767,7 +780,7 @@ cdef class Doc:
# TODO:
# 1. Test basic data-driven ORTH gazetteer
# 2. Test more nuanced date and currency regex
cdef attr_t entity_type, kb_id, ent_id
cdef attr_t kb_id, ent_id
cdef int ent_start, ent_end
ent_spans = []
for ent_info in ents:
@ -975,7 +988,6 @@ cdef class Doc:
>>> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
"""
cdef int i, j
cdef attr_id_t feature
cdef np.ndarray[attr_t, ndim=2] output
# Handle scalar/list inputs of strings/ints for py_attr_ids
# See also #3064
@ -987,8 +999,10 @@ cdef class Doc:
py_attr_ids = [py_attr_ids]
# Allow strings, e.g. 'lemma' or 'LEMMA'
try:
py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_)
for id_ in py_attr_ids]
py_attr_ids = [
(IDS[id_.upper()] if hasattr(id_, "upper") else id_)
for id_ in py_attr_ids
]
except KeyError as msg:
keys = list(IDS.keys())
raise KeyError(Errors.E983.format(dict="IDS", key=msg, keys=keys)) from None
@ -1022,8 +1036,6 @@ cdef class Doc:
DOCS: https://spacy.io/api/doc#count_by
"""
cdef int i
cdef attr_t attr
cdef size_t count
if counts is None:
counts = Counter()
@ -1085,7 +1097,6 @@ cdef class Doc:
cdef int i, col
cdef int32_t abs_head_index
cdef attr_id_t attr_id
cdef TokenC* tokens = self.c
cdef int length = len(array)
if length != len(self):
raise ValueError(Errors.E971.format(array_length=length, doc_length=len(self)))
@ -1226,7 +1237,7 @@ cdef class Doc:
span.label,
span.kb_id,
span.id,
span.text, # included as a check
span.text, # included as a check
))
char_offset += len(doc.text)
if len(doc) > 0 and ensure_whitespace and not doc[-1].is_space and not bool(doc[-1].whitespace_):
@ -1505,7 +1516,6 @@ cdef class Doc:
attributes are inherited from the syntactic root of the span.
RETURNS (Token): The first newly merged token.
"""
cdef str tag, lemma, ent_type
attr_len = len(attributes)
span_len = len(spans)
if not attr_len == span_len:
@ -1621,7 +1631,6 @@ cdef class Doc:
for token in char_span[1:]:
token.is_sent_start = False
for span_group in doc_json.get("spans", {}):
spans = []
for span in doc_json["spans"][span_group]:
@ -1653,7 +1662,7 @@ cdef class Doc:
start = token_by_char(self.c, self.length, token_data["start"])
value = token_data["value"]
self[start]._.set(token_attr, value)
for span_attr in doc_json.get("underscore_span", {}):
if not Span.has_extension(span_attr):
Span.set_extension(span_attr)
@ -1699,7 +1708,7 @@ cdef class Doc:
token_data["dep"] = token.dep_
token_data["head"] = token.head.i
data["tokens"].append(token_data)
if self.spans:
data["spans"] = {}
for span_group in self.spans:
@ -1750,7 +1759,7 @@ cdef class Doc:
data["underscore_span"] = {}
if attr not in data["underscore_span"]:
data["underscore_span"][attr] = []
data["underscore_span"][attr].append({"start": start, "end": end, "value": value, "label": _label, "kb_id": _kb_id, "id":_span_id})
data["underscore_span"][attr].append({"start": start, "end": end, "value": value, "label": _label, "kb_id": _kb_id, "id": _span_id})
for attr in underscore:
if attr not in user_keys:
@ -1773,7 +1782,6 @@ cdef class Doc:
output.fill(255)
cdef int i, j, start_idx, end_idx
cdef bytes byte_string
cdef unsigned char utf8_char
for i, byte_string in enumerate(byte_strings):
j = 0
start_idx = 0
@ -1826,8 +1834,6 @@ cdef int token_by_char(const TokenC* tokens, int length, int char_idx) except -2
cdef int set_children_from_heads(TokenC* tokens, int start, int end) except -1:
# note: end is exclusive
cdef TokenC* head
cdef TokenC* child
cdef int i
# Set number of left/right children to 0. We'll increment it in the loops.
for i in range(start, end):
@ -1927,7 +1933,7 @@ cdef int _get_tokens_lca(Token token_j, Token token_k):
return -1
cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end):
cdef int [:, :] _get_lca_matrix(Doc doc, int start, int end):
"""Given a doc and a start and end position defining a set of contiguous
tokens within it, returns a matrix of Lowest Common Ancestors (LCA), where
LCA[i, j] is the index of the lowest common ancestor among token i and j.
@ -1940,7 +1946,7 @@ cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end):
RETURNS (int [:, :]): memoryview of numpy.array[ndim=2, dtype=numpy.int32],
with shape (n, n), where n = len(doc).
"""
cdef int [:,:] lca_matrix
cdef int [:, :] lca_matrix
cdef int j, k
n_tokens= end - start
lca_mat = numpy.empty((n_tokens, n_tokens), dtype=numpy.int32)

53
spacy/tokens/graph.pyx
View File

@ -3,7 +3,7 @@ from typing import Generator, List, Tuple
cimport cython
from cython.operator cimport dereference
from libc.stdint cimport int32_t, int64_t
from libc.stdint cimport int32_t
from libcpp.pair cimport pair
from libcpp.unordered_map cimport unordered_map
from libcpp.unordered_set cimport unordered_set
@ -11,7 +11,6 @@ from libcpp.unordered_set cimport unordered_set
import weakref
from murmurhash.mrmr cimport hash64
from preshed.maps cimport map_get_unless_missing
from .. import Errors
@ -26,7 +25,7 @@ from .token import Token
cdef class Edge:
cdef readonly Graph graph
cdef readonly int i
def __init__(self, Graph graph, int i):
self.graph = graph
self.i = i
@ -42,7 +41,7 @@ cdef class Edge:
@property
def head(self) -> "Node":
return Node(self.graph, self.graph.c.edges[self.i].head)
@property
def tail(self) -> "Tail":
return Node(self.graph, self.graph.c.edges[self.i].tail)
@ -68,7 +67,7 @@ cdef class Node:
def __init__(self, Graph graph, int i):
"""A reference to a node of an annotation graph. Each node is made up of
an ordered set of zero or more token indices.
Node references are usually created by the Graph object itself, or from
the Node or Edge objects. You usually won't need to instantiate this
class yourself.
@ -107,13 +106,13 @@ cdef class Node:
@property
def is_none(self) -> bool:
"""Whether the node is a special value, indicating 'none'.
The NoneNode type is returned by the Graph, Edge and Node objects when
there is no match to a query. It has the same API as Node, but it always
returns NoneNode, NoneEdge or empty lists for its queries.
"""
return False
@property
def doc(self) -> "Doc":
"""The Doc object that the graph refers to."""
@ -128,19 +127,19 @@ cdef class Node:
def head(self, i=None, label=None) -> "Node":
"""Get the head of the first matching edge, searching by index, label,
both or neither.
For instance, `node.head(i=1)` will get the head of the second edge that
this node is a tail of. `node.head(i=1, label="ARG0")` will further
check that the second edge has the label `"ARG0"`.
If no matching node can be found, the graph's NoneNode is returned.
"""
return self.headed(i=i, label=label)
def tail(self, i=None, label=None) -> "Node":
"""Get the tail of the first matching edge, searching by index, label,
both or neither.
If no matching node can be found, the graph's NoneNode is returned.
"""
return self.tailed(i=i, label=label).tail
@ -169,7 +168,7 @@ cdef class Node:
cdef vector[int] edge_indices
self._find_edges(edge_indices, "head", label)
return [Node(self.graph, self.graph.c.edges[i].head) for i in edge_indices]
def tails(self, label=None) -> List["Node"]:
"""Find all matching tails of this node."""
cdef vector[int] edge_indices
@ -198,7 +197,7 @@ cdef class Node:
return NoneEdge(self.graph)
else:
return Edge(self.graph, idx)
def tailed(self, i=None, label=None) -> Edge:
"""Find the first matching edge tailed by this node.
If no matching edge can be found, the graph's NoneEdge is returned.
@ -281,7 +280,7 @@ cdef class NoneEdge(Edge):
def __init__(self, graph):
self.graph = graph
self.i = -1
@property
def doc(self) -> "Doc":
return self.graph.doc
@ -289,7 +288,7 @@ cdef class NoneEdge(Edge):
@property
def head(self) -> "NoneNode":
return NoneNode(self.graph)
@property
def tail(self) -> "NoneNode":
return NoneNode(self.graph)
@ -317,7 +316,7 @@ cdef class NoneNode(Node):
def __len__(self):
return 0
@property
def is_none(self):
return -1
@ -338,14 +337,14 @@ cdef class NoneNode(Node):
def walk_heads(self):
yield from []
def walk_tails(self):
yield from []
cdef class Graph:
"""A set of directed labelled relationships between sets of tokens.
EXAMPLE:
Construction 1
>>> graph = Graph(doc, name="srl")
@ -370,7 +369,9 @@ cdef class Graph:
>>> assert graph.has_node((0,))
>>> assert graph.has_edge((0,), (1,3), label="agent")
"""
def __init__(self, doc, *, name="", nodes=[], edges=[], labels=None, weights=None):
def __init__(
self, doc, *, name="", nodes=[], edges=[], labels=None, weights=None # no-cython-lint
):
"""Create a Graph object.
doc (Doc): The Doc object the graph will refer to.
@ -436,13 +437,11 @@ cdef class Graph:
def add_edge(self, head, tail, *, label="", weight=None) -> Edge:
"""Add an edge to the graph, connecting two groups of tokens.
If there is already an edge for the (head, tail, label) triple, it will
be returned, and no new edge will be created. The weight of the edge
will be updated if a weight is specified.
"""
label_hash = self.doc.vocab.strings.as_int(label)
weight_float = weight if weight is not None else 0.0
edge_index = add_edge(
&self.c,
EdgeC(
@ -476,11 +475,11 @@ cdef class Graph:
def has_edge(self, head, tail, label) -> bool:
"""Check whether a (head, tail, label) triple is an edge in the graph."""
return not self.get_edge(head, tail, label=label).is_none
def add_node(self, indices) -> Node:
"""Add a node to the graph and return it. Nodes refer to ordered sets
of token indices.
This method is idempotent: if there is already a node for the given
indices, it is returned without a new node being created.
"""
@ -508,7 +507,7 @@ cdef class Graph:
return NoneNode(self)
else:
return Node(self, node_index)
def has_node(self, tuple indices) -> bool:
"""Check whether the graph has a node for the given indices."""
return not self.get_node(indices).is_none
@ -568,7 +567,7 @@ cdef int add_node(GraphC* graph, vector[int32_t]& node) nogil:
graph.roots.insert(index)
graph.node_map.insert(pair[hash_t, int](key, index))
return index
cdef int get_node(const GraphC* graph, vector[int32_t] node) nogil:
key = hash64(&node[0], node.size() * sizeof(node[0]), 0)

2
spacy/tokens/morphanalysis.pyx
View File

@ -1,5 +1,4 @@
cimport numpy as np
from libc.string cimport memset
from ..errors import Errors
from ..morphology import Morphology
@ -94,4 +93,3 @@ cdef class MorphAnalysis:
def __repr__(self):
return self.to_json()

27
spacy/tokens/retokenizer.pyx
View File

@ -1,7 +1,6 @@
# cython: infer_types=True, bounds_check=False, profile=True
from cymem.cymem cimport Pool
from libc.stdlib cimport free, malloc
from libc.string cimport memcpy, memset
from libc.string cimport memset
import numpy
from thinc.api import get_array_module
@ -10,7 +9,7 @@ from ..attrs cimport MORPH, NORM
from ..lexeme cimport EMPTY_LEXEME, Lexeme
from ..structs cimport LexemeC, TokenC
from ..vocab cimport Vocab
from .doc cimport Doc, set_children_from_heads, token_by_end, token_by_start
from .doc cimport Doc, set_children_from_heads, token_by_start
from .span cimport Span
from .token cimport Token
@ -148,7 +147,7 @@ def _merge(Doc doc, merges):
syntactic root of the span.
RETURNS (Token): The first newly merged token.
"""
cdef int i, merge_index, start, end, token_index, current_span_index, current_offset, offset, span_index
cdef int i, merge_index, start, token_index, current_span_index, current_offset, offset, span_index
cdef Span span
cdef const LexemeC* lex
cdef TokenC* token
@ -166,7 +165,6 @@ def _merge(Doc doc, merges):
merges.sort(key=_get_start)
for merge_index, (span, attributes) in enumerate(merges):
start = span.start
end = span.end
spans.append(span)
# House the new merged token where it starts
token = &doc.c[start]
@ -204,8 +202,9 @@ def _merge(Doc doc, merges):
# for the merged region. To do this, we create a boolean array indicating
# whether the row is to be deleted, then use numpy.delete
if doc.tensor is not None and doc.tensor.size != 0:
doc.tensor = _resize_tensor(doc.tensor,
[(m[0].start, m[0].end) for m in merges])
doc.tensor = _resize_tensor(
doc.tensor, [(m[0].start, m[0].end) for m in merges]
)
# Memorize span roots and sets dependencies of the newly merged
# tokens to the dependencies of their roots.
span_roots = []
@ -268,11 +267,11 @@ def _merge(Doc doc, merges):
span_index += 1
if span_index < len(spans) and i == spans[span_index].start:
# First token in a span
doc.c[i - offset] = doc.c[i] # move token to its place
doc.c[i - offset] = doc.c[i] # move token to its place
offset += (spans[span_index].end - spans[span_index].start) - 1
in_span = True
if not in_span:
doc.c[i - offset] = doc.c[i] # move token to its place
doc.c[i - offset] = doc.c[i] # move token to its place
for i in range(doc.length - offset, doc.length):
memset(&doc.c[i], 0, sizeof(TokenC))
@ -346,7 +345,11 @@ def _split(Doc doc, int token_index, orths, heads, attrs):
if to_process_tensor:
xp = get_array_module(doc.tensor)
if xp is numpy:
doc.tensor = xp.append(doc.tensor, xp.zeros((nb_subtokens,doc.tensor.shape[1]), dtype="float32"), axis=0)
doc.tensor = xp.append(
doc.tensor,
xp.zeros((nb_subtokens, doc.tensor.shape[1]), dtype="float32"),
axis=0
)
else:
shape = (doc.tensor.shape[0] + nb_subtokens, doc.tensor.shape[1])
resized_array = xp.zeros(shape, dtype="float32")
@ -368,7 +371,8 @@ def _split(Doc doc, int token_index, orths, heads, attrs):
token.norm = 0 # reset norm
if to_process_tensor:
# setting the tensors of the split tokens to array of zeros
doc.tensor[token_index + i:token_index + i + 1] = xp.zeros((1,doc.tensor.shape[1]), dtype="float32")
doc.tensor[token_index + i:token_index + i + 1] = \
xp.zeros((1, doc.tensor.shape[1]), dtype="float32")
# Update the character offset of the subtokens
if i != 0:
token.idx = orig_token.idx + idx_offset
@ -456,7 +460,6 @@ def normalize_token_attrs(Vocab vocab, attrs):
def set_token_attrs(Token py_token, attrs):
cdef TokenC* token = py_token.c
cdef const LexemeC* lex = token.lex
cdef Doc doc = py_token.doc
# Assign attributes
for attr_name, attr_value in attrs.items():
if attr_name == "_": # Set extension attributes

32
spacy/tokens/span.pyx
View File

@ -1,5 +1,4 @@
cimport numpy as np
from libc.math cimport sqrt
from libcpp.memory cimport make_shared
import copy
@ -9,13 +8,13 @@ import numpy
from thinc.api import get_array_module
from ..attrs cimport *
from ..attrs cimport attr_id_t
from ..attrs cimport ORTH, attr_id_t
from ..lexeme cimport Lexeme
from ..parts_of_speech cimport univ_pos_t
from ..structs cimport LexemeC, TokenC
from ..structs cimport TokenC
from ..symbols cimport dep
from ..typedefs cimport attr_t, flags_t, hash_t
from ..typedefs cimport attr_t
from .doc cimport _get_lca_matrix, get_token_attr, token_by_end, token_by_start
from .token cimport Token
from ..errors import Errors, Warnings
from ..util import normalize_slice
@ -226,8 +225,8 @@ cdef class Span:
@property
def _(self):
cdef SpanC* span_c = self.span_c()
"""Custom extension attributes registered via `set_extension`."""
cdef SpanC* span_c = self.span_c()
return Underscore(Underscore.span_extensions, self,
start=span_c.start_char, end=span_c.end_char, label=self.label, kb_id=self.kb_id, span_id=self.id)
@ -371,13 +370,26 @@ cdef class Span:
"""
if "similarity" in self.doc.user_span_hooks:
return self.doc.user_span_hooks["similarity"](self, other)
if len(self) == 1 and hasattr(other, "orth"):
if self[0].orth == other.orth:
attr = getattr(self.doc.vocab.vectors, "attr", ORTH)
cdef Token this_token
cdef Token other_token
cdef Lexeme other_lex
if len(self) == 1 and isinstance(other, Token):
this_token = self[0]
other_token = other
if Token.get_struct_attr(this_token.c, attr) == Token.get_struct_attr(other_token.c, attr):
return 1.0
elif len(self) == 1 and isinstance(other, Lexeme):
this_token = self[0]
other_lex = other
if Token.get_struct_attr(this_token.c, attr) == Lexeme.get_struct_attr(other_lex.c, attr):
return 1.0
elif isinstance(other, (Doc, Span)) and len(self) == len(other):
similar = True
for i in range(len(self)):
if self[i].orth != getattr(other[i], "orth", None):
this_token = self[i]
other_token = other[i]
if Token.get_struct_attr(this_token.c, attr) != Token.get_struct_attr(other_token.c, attr):
similar = False
break
if similar:
@ -607,7 +619,6 @@ cdef class Span:
"""
return "".join([t.text_with_ws for t in self])
@property
def noun_chunks(self):
"""Iterate over the base noun phrases in the span. Yields base
@ -922,7 +933,6 @@ cdef class Span:
self.id_ = ent_id_
cdef int _count_words_to_root(const TokenC* token, int sent_length) except -1:
# Don't allow spaces to be the root, if there are
# better candidates

6
spacy/tokens/span_group.pyx
View File

@ -1,7 +1,7 @@
import struct
import weakref
from copy import deepcopy
from typing import TYPE_CHECKING, Iterable, Optional, Tuple, Union
from typing import Iterable, Optional, Union
import srsly
@ -36,7 +36,7 @@ cdef class SpanGroup:
DOCS: https://spacy.io/api/spangroup
"""
def __init__(self, doc, *, name="", attrs={}, spans=[]):
def __init__(self, doc, *, name="", attrs={}, spans=[]): # no-cython-lint
"""Create a SpanGroup.
doc (Doc): The reference Doc object.
@ -315,7 +315,7 @@ cdef class SpanGroup:
other_attrs = deepcopy(other_group.attrs)
span_group.attrs.update({
key: value for key, value in other_attrs.items() \
key: value for key, value in other_attrs.items()
if key not in span_group.attrs
})
if len(other_group):

4
spacy/tokens/token.pxd
View File

@ -26,7 +26,7 @@ cdef class Token:
cdef Token self = Token.__new__(Token, vocab, doc, offset)
return self
#cdef inline TokenC struct_from_attrs(Vocab vocab, attrs):
# cdef inline TokenC struct_from_attrs(Vocab vocab, attrs):
# cdef TokenC token
# attrs = normalize_attrs(attrs)
@ -98,12 +98,10 @@ cdef class Token:
elif feat_name == SENT_START:
token.sent_start = value
@staticmethod
cdef inline int missing_dep(const TokenC* token) nogil:
return token.dep == MISSING_DEP
@staticmethod
cdef inline int missing_head(const TokenC* token) nogil:
return Token.missing_dep(token)

22
spacy/tokens/token.pyx
View File

@ -1,13 +1,11 @@
# cython: infer_types=True
# Compiler crashes on memory view coercion without this. Should report bug.
cimport numpy as np
from cython.view cimport array as cvarray
np.import_array()
import warnings
import numpy
from thinc.api import get_array_module
from ..attrs cimport (
@ -216,11 +214,17 @@ cdef class Token:
"""
if "similarity" in self.doc.user_token_hooks:
return self.doc.user_token_hooks["similarity"](self, other)
if hasattr(other, "__len__") and len(other) == 1 and hasattr(other, "__getitem__"):
if self.c.lex.orth == getattr(other[0], "orth", None):
attr = getattr(self.doc.vocab.vectors, "attr", ORTH)
cdef Token this_token = self
cdef Token other_token
cdef Lexeme other_lex
if isinstance(other, Token):
other_token = other
if Token.get_struct_attr(this_token.c, attr) == Token.get_struct_attr(other_token.c, attr):
return 1.0
elif hasattr(other, "orth"):
if self.c.lex.orth == other.orth:
elif isinstance(other, Lexeme):
other_lex = other
if Token.get_struct_attr(this_token.c, attr) == Lexeme.get_struct_attr(other_lex.c, attr):
return 1.0
if self.vocab.vectors.n_keys == 0:
warnings.warn(Warnings.W007.format(obj="Token"))
@ -233,7 +237,7 @@ cdef class Token:
result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
# ensure we get a scalar back (numpy does this automatically but cupy doesn't)
return result.item()
def has_morph(self):
"""Check whether the token has annotated morph information.
Return False when the morph annotation is unset/missing.
@ -528,9 +532,9 @@ cdef class Token:
def __get__(self):
if self.i + 1 == len(self.doc):
return True
elif self.doc[self.i+1].is_sent_start == None:
elif self.doc[self.i+1].is_sent_start is None:
return None
elif self.doc[self.i+1].is_sent_start == True:
elif self.doc[self.i+1].is_sent_start is True:
return True
else:
return False

12
spacy/training/align.pyx
View File

@ -37,10 +37,14 @@ def get_alignments(A: List[str], B: List[str]) -> Tuple[List[List[int]], List[Li
b2a.append(set())
# Process the alignment at the current position
if A[token_idx_a] == B[token_idx_b] and \
(char_idx_a == 0 or \
char_to_token_a[char_idx_a - 1] < token_idx_a) and \
(char_idx_b == 0 or \
char_to_token_b[char_idx_b - 1] < token_idx_b):
(
char_idx_a == 0 or
char_to_token_a[char_idx_a - 1] < token_idx_a
) and \
(
char_idx_b == 0 or
char_to_token_b[char_idx_b - 1] < token_idx_b
):
# Current tokens are identical and both character offsets are the
# start of a token (either at the beginning of the document or the
# previous character belongs to a different token)

15
spacy/training/example.pyx
View File

@ -1,4 +1,3 @@
import warnings
from collections.abc import Iterable as IterableInstance
import numpy
@ -31,9 +30,9 @@ cpdef Doc annotations_to_doc(vocab, tok_annot, doc_annot):
attrs, array = _annot2array(vocab, tok_annot, doc_annot)
output = Doc(vocab, words=tok_annot["ORTH"], spaces=tok_annot["SPACY"])
if "entities" in doc_annot:
_add_entities_to_doc(output, doc_annot["entities"])
_add_entities_to_doc(output, doc_annot["entities"])
if "spans" in doc_annot:
_add_spans_to_doc(output, doc_annot["spans"])
_add_spans_to_doc(output, doc_annot["spans"])
if array.size:
output = output.from_array(attrs, array)
# links are currently added with ENT_KB_ID on the token level
@ -168,7 +167,6 @@ cdef class Example:
self._y_sig = y_sig
return self._cached_alignment
def _get_aligned_vectorized(self, align, gold_values):
# Fast path for Doc attributes/fields that are predominantly a single value,
# i.e., TAG, POS, MORPH.
@ -211,7 +209,6 @@ cdef class Example:
return output.tolist()
def _get_aligned_non_vectorized(self, align, gold_values):
# Slower path for fields that return multiple values (resulting
# in ragged arrays that cannot be vectorized trivially).
@ -228,7 +225,6 @@ cdef class Example:
return output
def get_aligned(self, field, as_string=False):
"""Return an aligned array for a token attribute."""
align = self.alignment.x2y
@ -337,7 +333,7 @@ cdef class Example:
missing=None
)
# Now fill the tokens we can align to O.
O = 2 # I=1, O=2, B=3
O = 2 # I=1, O=2, B=3 # no-cython-lint: E741
for i, ent_iob in enumerate(self.get_aligned("ENT_IOB")):
if x_tags[i] is None:
if ent_iob == O:
@ -347,7 +343,7 @@ cdef class Example:
return x_ents, x_tags
def get_aligned_ner(self):
x_ents, x_tags = self.get_aligned_ents_and_ner()
_x_ents, x_tags = self.get_aligned_ents_and_ner()
return x_tags
def get_matching_ents(self, check_label=True):
@ -405,7 +401,6 @@ cdef class Example:
return span_dict
def _links_to_dict(self):
links = {}
for ent in self.reference.ents:
@ -596,6 +591,7 @@ def _fix_legacy_dict_data(example_dict):
"doc_annotation": doc_dict
}
def _has_field(annot, field):
if field not in annot:
return False
@ -632,6 +628,7 @@ def _parse_ner_tags(biluo_or_offsets, vocab, words, spaces):
ent_types.append("")
return ent_iobs, ent_types
def _parse_links(vocab, words, spaces, links):
reference = Doc(vocab, words=words, spaces=spaces)
starts = {token.idx: token.i for token in reference}

31
spacy/training/gold_io.pyx
View File

@ -1,4 +1,3 @@
import json
import warnings
import srsly
@ -6,7 +5,7 @@ import srsly
from .. import util
from ..errors import Warnings
from ..tokens import Doc
from .iob_utils import offsets_to_biluo_tags, tags_to_entities
from .iob_utils import offsets_to_biluo_tags
def docs_to_json(docs, doc_id=0, ner_missing_tag="O"):
@ -23,7 +22,13 @@ def docs_to_json(docs, doc_id=0, ner_missing_tag="O"):
json_doc = {"id": doc_id, "paragraphs": []}
for i, doc in enumerate(docs):
raw = None if doc.has_unknown_spaces else doc.text
json_para = {'raw': raw, "sentences": [], "cats": [], "entities": [], "links": []}
json_para = {
'raw': raw,
"sentences": [],
"cats": [],
"entities": [],
"links": []
}
for cat, val in doc.cats.items():
json_cat = {"label": cat, "value": val}
json_para["cats"].append(json_cat)
@ -35,13 +40,17 @@ def docs_to_json(docs, doc_id=0, ner_missing_tag="O"):
if ent.kb_id_:
link_dict = {(ent.start_char, ent.end_char): {ent.kb_id_: 1.0}}
json_para["links"].append(link_dict)
biluo_tags = offsets_to_biluo_tags(doc, json_para["entities"], missing=ner_missing_tag)
biluo_tags = offsets_to_biluo_tags(
doc, json_para["entities"], missing=ner_missing_tag
)
attrs = ("TAG", "POS", "MORPH", "LEMMA", "DEP", "ENT_IOB")
include_annotation = {attr: doc.has_annotation(attr) for attr in attrs}
for j, sent in enumerate(doc.sents):
json_sent = {"tokens": [], "brackets": []}
for token in sent:
json_token = {"id": token.i, "orth": token.text, "space": token.whitespace_}
json_token = {
"id": token.i, "orth": token.text, "space": token.whitespace_
}
if include_annotation["TAG"]:
json_token["tag"] = token.tag_
if include_annotation["POS"]:
@ -125,9 +134,14 @@ def json_to_annotations(doc):
else:
sent_starts.append(-1)
if "brackets" in sent:
brackets.extend((b["first"] + sent_start_i,
b["last"] + sent_start_i, b["label"])
for b in sent["brackets"])
brackets.extend(
(
b["first"] + sent_start_i,
b["last"] + sent_start_i,
b["label"]
)
for b in sent["brackets"]
)
example["token_annotation"] = dict(
ids=ids,
@ -160,6 +174,7 @@ def json_to_annotations(doc):
)
yield example
def json_iterate(bytes utf8_str):
# We should've made these files jsonl...But since we didn't, parse out
# the docs one-by-one to reduce memory usage.

14
spacy/training/initialize.py
View File

@ -71,7 +71,8 @@ def init_nlp(config: Config, *, use_gpu: int = -1) -> "Language":
with nlp.select_pipes(enable=resume_components):
logger.info("Resuming training for: %s", resume_components)
nlp.resume_training(sgd=optimizer)
# Make sure that listeners are defined before initializing further
# Make sure that internal component names are synced and listeners are
# defined before initializing further
nlp._link_components()
with nlp.select_pipes(disable=[*frozen_components, *resume_components]):
if T["max_epochs"] == -1:
@ -305,9 +306,14 @@ def convert_vectors(
truncate: int,
prune: int,
mode: str = VectorsMode.default,
attr: str = "ORTH",
) -> None:
vectors_loc = ensure_path(vectors_loc)
if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
if attr != "ORTH":
raise ValueError(
"ORTH is the only attribute supported for vectors in .npz format."
)
nlp.vocab.vectors = Vectors(
strings=nlp.vocab.strings, data=numpy.load(vectors_loc.open("rb"))
)
@ -335,11 +341,15 @@ def convert_vectors(
nlp.vocab.vectors = Vectors(
strings=nlp.vocab.strings,
data=vectors_data,
attr=attr,
**floret_settings,
)
else:
nlp.vocab.vectors = Vectors(
strings=nlp.vocab.strings, data=vectors_data, keys=vector_keys
strings=nlp.vocab.strings,
data=vectors_data,
keys=vector_keys,
attr=attr,
)
nlp.vocab.deduplicate_vectors()
if prune >= 1 and mode != VectorsMode.floret:

28
spacy/util.py
View File

@ -518,7 +518,7 @@ def load_model_from_path(
if not meta:
meta = get_model_meta(model_path)
config_path = model_path / "config.cfg"
overrides = dict_to_dot(config)
overrides = dict_to_dot(config, for_overrides=True)
config = load_config(config_path, overrides=overrides)
nlp = load_model_from_config(
config,
@ -1486,14 +1486,19 @@ def dot_to_dict(values: Dict[str, Any]) -> Dict[str, dict]:
return result
def dict_to_dot(obj: Dict[str, dict]) -> Dict[str, Any]:
def dict_to_dot(obj: Dict[str, dict], *, for_overrides: bool = False) -> Dict[str, Any]:
"""Convert dot notation to a dict. For example: {"token": {"pos": True,
"_": {"xyz": True }}} becomes {"token.pos": True, "token._.xyz": True}.
values (Dict[str, dict]): The dict to convert.
obj (Dict[str, dict]): The dict to convert.
for_overrides (bool): Whether to enable special handling for registered
functions in overrides.
RETURNS (Dict[str, Any]): The key/value pairs.
"""
return {".".join(key): value for key, value in walk_dict(obj)}
return {
".".join(key): value
for key, value in walk_dict(obj, for_overrides=for_overrides)
}
def dot_to_object(config: Config, section: str):
@ -1535,13 +1540,20 @@ def set_dot_to_object(config: Config, section: str, value: Any) -> None:
def walk_dict(
node: Dict[str, Any], parent: List[str] = []
node: Dict[str, Any], parent: List[str] = [], *, for_overrides: bool = False
) -> Iterator[Tuple[List[str], Any]]:
"""Walk a dict and yield the path and values of the leaves."""
"""Walk a dict and yield the path and values of the leaves.
for_overrides (bool): Whether to treat registered functions that start with
@ as final values rather than dicts to traverse.
"""
for key, value in node.items():
key_parent = [*parent, key]
if isinstance(value, dict):
yield from walk_dict(value, key_parent)
if isinstance(value, dict) and (
not for_overrides
or not any(value_key.startswith("@") for value_key in value)
):
yield from walk_dict(value, key_parent, for_overrides=for_overrides)
else:
yield (key_parent, value)

49
spacy/vectors.pyx
View File

@ -1,10 +1,8 @@
cimport numpy as np
from cython.operator cimport dereference as deref
from libc.stdint cimport uint32_t, uint64_t
from libcpp.set cimport set as cppset
from murmurhash.mrmr cimport hash128_x64
import functools
import warnings
from enum import Enum
from typing import cast
@ -15,9 +13,11 @@ from thinc.api import Ops, get_array_module, get_current_ops
from thinc.backends import get_array_ops
from thinc.types import Floats2d
from .attrs cimport ORTH, attr_id_t
from .strings cimport StringStore
from . import util
from .attrs import IDS
from .errors import Errors, Warnings
from .strings import get_string_id
@ -63,8 +63,9 @@ cdef class Vectors:
cdef readonly uint32_t hash_seed
cdef readonly unicode bow
cdef readonly unicode eow
cdef readonly attr_id_t attr
def __init__(self, *, strings=None, shape=None, data=None, keys=None, mode=Mode.default, minn=0, maxn=0, hash_count=1, hash_seed=0, bow="<", eow=">"):
def __init__(self, *, strings=None, shape=None, data=None, keys=None, mode=Mode.default, minn=0, maxn=0, hash_count=1, hash_seed=0, bow="<", eow=">", attr="ORTH"):
"""Create a new vector store.
strings (StringStore): The string store.
@ -78,6 +79,8 @@ cdef class Vectors:
hash_seed (int): The floret hash seed (default: 0).
bow (str): The floret BOW string (default: "<").
eow (str): The floret EOW string (default: ">").
attr (Union[int, str]): The token attribute for the vector keys
(default: "ORTH").
DOCS: https://spacy.io/api/vectors#init
"""
@ -100,10 +103,18 @@ cdef class Vectors:
self.hash_seed = hash_seed
self.bow = bow
self.eow = eow
if isinstance(attr, (int, long)):
self.attr = attr
else:
attr = attr.upper()
if attr == "TEXT":
attr = "ORTH"
self.attr = IDS.get(attr, ORTH)
if self.mode == Mode.default:
if data is None:
if shape is None:
shape = (0,0)
shape = (0, 0)
ops = get_current_ops()
data = ops.xp.zeros(shape, dtype="f")
self._unset = cppset[int]({i for i in range(data.shape[0])})
@ -244,11 +255,10 @@ cdef class Vectors:
def __eq__(self, other):
# Check for equality, with faster checks first
return (
self.shape == other.shape
and self.key2row == other.key2row
and self.to_bytes(exclude=["strings"])
== other.to_bytes(exclude=["strings"])
)
self.shape == other.shape
and self.key2row == other.key2row
and self.to_bytes(exclude=["strings"]) == other.to_bytes(exclude=["strings"])
)
def resize(self, shape, inplace=False):
"""Resize the underlying vectors array. If inplace=True, the memory
@ -504,11 +514,12 @@ cdef class Vectors:
# vectors e.g. (10000, 300)
# sims e.g. (1024, 10000)
sims = xp.dot(batch, vectors.T)
best_rows[i:i+batch_size] = xp.argpartition(sims, -n, axis=1)[:,-n:]
scores[i:i+batch_size] = xp.partition(sims, -n, axis=1)[:,-n:]
best_rows[i:i+batch_size] = xp.argpartition(sims, -n, axis=1)[:, -n:]
scores[i:i+batch_size] = xp.partition(sims, -n, axis=1)[:, -n:]
if sort and n >= 2:
sorted_index = xp.arange(scores.shape[0])[:,None][i:i+batch_size],xp.argsort(scores[i:i+batch_size], axis=1)[:,::-1]
sorted_index = xp.arange(scores.shape[0])[:, None][i:i+batch_size], \
xp.argsort(scores[i:i+batch_size], axis=1)[:, ::-1]
scores[i:i+batch_size] = scores[sorted_index]
best_rows[i:i+batch_size] = best_rows[sorted_index]
@ -522,8 +533,12 @@ cdef class Vectors:
numpy_rows = get_current_ops().to_numpy(best_rows)
keys = xp.asarray(
[[row2key[row] for row in numpy_rows[i] if row in row2key]
for i in range(len(queries)) ], dtype="uint64")
[
[row2key[row] for row in numpy_rows[i] if row in row2key]
for i in range(len(queries))
],
dtype="uint64"
)
return (keys, best_rows, scores)
def to_ops(self, ops: Ops):
@ -543,6 +558,7 @@ cdef class Vectors:
"hash_seed": self.hash_seed,
"bow": self.bow,
"eow": self.eow,
"attr": self.attr,
}
def _set_cfg(self, cfg):
@ -553,6 +569,7 @@ cdef class Vectors:
self.hash_seed = cfg.get("hash_seed", 0)
self.bow = cfg.get("bow", "<")
self.eow = cfg.get("eow", ">")
self.attr = cfg.get("attr", ORTH)
def to_disk(self, path, *, exclude=tuple()):
"""Save the current state to a directory.
@ -564,9 +581,9 @@ cdef class Vectors:
"""
xp = get_array_module(self.data)
if xp is numpy:
save_array = lambda arr, file_: xp.save(file_, arr, allow_pickle=False)
save_array = lambda arr, file_: xp.save(file_, arr, allow_pickle=False) # no-cython-lint
else:
save_array = lambda arr, file_: xp.save(file_, arr)
save_array = lambda arr, file_: xp.save(file_, arr) # no-cython-lint
def save_vectors(path):
# the source of numpy.save indicates that the file object is closed after use.

44
spacy/vocab.pyx
View File

@ -1,6 +1,4 @@
# cython: profile=True
from libc.string cimport memcpy
import functools
import numpy
@ -19,7 +17,6 @@ from .errors import Errors
from .lang.lex_attrs import LEX_ATTRS, get_lang, is_stop
from .lang.norm_exceptions import BASE_NORMS
from .lookups import Lookups
from .util import registry
from .vectors import Mode as VectorsMode
from .vectors import Vectors
@ -50,8 +47,15 @@ cdef class Vocab:
DOCS: https://spacy.io/api/vocab
"""
def __init__(self, lex_attr_getters=None, strings=None, lookups=None,
oov_prob=-20., writing_system=None, get_noun_chunks=None):
def __init__(
self,
lex_attr_getters=None,
strings=None,
lookups=None,
oov_prob=-20.,
writing_system=None,
get_noun_chunks=None
):
"""Create the vocabulary.
lex_attr_getters (dict): A dictionary mapping attribute IDs to
@ -150,7 +154,6 @@ cdef class Vocab:
cdef LexemeC* lex
cdef hash_t key = self.strings[string]
lex = <LexemeC*>self._by_orth.get(key)
cdef size_t addr
if lex != NULL:
assert lex.orth in self.strings
if lex.orth != key:
@ -352,8 +355,13 @@ cdef class Vocab:
self[orth]
# Make prob negative so it sorts by rank ascending
# (key2row contains the rank)
priority = [(-lex.prob, self.vectors.key2row[lex.orth], lex.orth)
for lex in self if lex.orth in self.vectors.key2row]
priority = []
cdef Lexeme lex
cdef attr_t value
for lex in self:
value = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
if value in self.vectors.key2row:
priority.append((-lex.prob, self.vectors.key2row[value], value))
priority.sort()
indices = xp.asarray([i for (prob, i, key) in priority], dtype="uint64")
keys = xp.asarray([key for (prob, i, key) in priority], dtype="uint64")
@ -386,8 +394,10 @@ cdef class Vocab:
"""
if isinstance(orth, str):
orth = self.strings.add(orth)
if self.has_vector(orth):
return self.vectors[orth]
cdef Lexeme lex = self[orth]
key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
if self.has_vector(key):
return self.vectors[key]
xp = get_array_module(self.vectors.data)
vectors = xp.zeros((self.vectors_length,), dtype="f")
return vectors
@ -403,15 +413,16 @@ cdef class Vocab:
"""
if isinstance(orth, str):
orth = self.strings.add(orth)
if self.vectors.is_full and orth not in self.vectors:
cdef Lexeme lex = self[orth]
key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
if self.vectors.is_full and key not in self.vectors:
new_rows = max(100, int(self.vectors.shape[0]*1.3))
if self.vectors.shape[1] == 0:
width = vector.size
else:
width = self.vectors.shape[1]
self.vectors.resize((new_rows, width))
lex = self[orth] # Add word to vocab if necessary
row = self.vectors.add(orth, vector=vector)
row = self.vectors.add(key, vector=vector)
if row >= 0:
lex.rank = row
@ -426,7 +437,9 @@ cdef class Vocab:
"""
if isinstance(orth, str):
orth = self.strings.add(orth)
return orth in self.vectors
cdef Lexeme lex = self[orth]
key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
return key in self.vectors
property lookups:
def __get__(self):
@ -440,7 +453,6 @@ cdef class Vocab:
self.lookups.get_table("lexeme_norm"),
)
def to_disk(self, path, *, exclude=tuple()):
"""Save the current state to a directory.
@ -453,7 +465,6 @@ cdef class Vocab:
path = util.ensure_path(path)
if not path.exists():
path.mkdir()
setters = ["strings", "vectors"]
if "strings" not in exclude:
self.strings.to_disk(path / "strings.json")
if "vectors" not in exclude:
@ -472,7 +483,6 @@ cdef class Vocab:
DOCS: https://spacy.io/api/vocab#to_disk
"""
path = util.ensure_path(path)
getters = ["strings", "vectors"]
if "strings" not in exclude:
self.strings.from_disk(path / "strings.json") # TODO: add exclude?
if "vectors" not in exclude:

2
website/docs/api/architectures.mdx
View File

@ -303,7 +303,7 @@ mapped to a zero vector. See the documentation on
| `nM` | The width of the static vectors. ~~Optional[int]~~ |
| `dropout` | Optional dropout rate. If set, it's applied per dimension over the whole batch. Defaults to `None`. ~~Optional[float]~~ |
| `init_W` | The [initialization function](https://thinc.ai/docs/api-initializers). Defaults to [`glorot_uniform_init`](https://thinc.ai/docs/api-initializers#glorot_uniform_init). ~~Callable[[Ops, Tuple[int, ...]]], FloatsXd]~~ |
| `key_attr` | Defaults to `"ORTH"`. ~~str~~ |
| `key_attr` | This setting is ignored in spaCy v3.6+. To set a custom key attribute for vectors, configure it through [`Vectors`](/api/vectors) or [`spacy init vectors`](/api/cli#init-vectors). Defaults to `"ORTH"`. ~~str~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Ragged]~~ |
### spacy.FeatureExtractor.v1 {id="FeatureExtractor"}

2
website/docs/api/language.mdx
View File

@ -876,7 +876,7 @@ token-to-vector embedding component like [`Tok2Vec`](/api/tok2vec) or
training a pipeline with components sourced from an existing pipeline: if
multiple components (e.g. tagger, parser, NER) listen to the same
token-to-vector component, but some of them are frozen and not updated, their
performance may degrade significally as the token-to-vector component is updated
performance may degrade significantly as the token-to-vector component is updated
with new data. To prevent this, listeners can be replaced with a standalone
token-to-vector layer that is owned by the component and doesn't change if the
component isn't updated.

2
website/docs/api/spanfinder.mdx
View File

@ -60,7 +60,7 @@ architectures and their arguments and hyperparameters.
| `model` | A model instance that is given a list of documents and predicts a probability for each token. ~~Model[List[Doc], Floats2d]~~ |
| `spans_key` | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ |
| `threshold` | Minimum probability to consider a prediction positive. Defaults to `0.5`. ~~float~~ |
| `max_length` | Maximum length of the produced spans, defaults to `None` meaning unlimited length. ~~Optional[int]~~ |
| `max_length` | Maximum length of the produced spans, defaults to `25`. ~~Optional[int]~~ |
| `min_length` | Minimum length of the produced spans, defaults to `None` meaning shortest span length is 1. ~~Optional[int]~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |

12
website/docs/api/vectors.mdx
View File

@ -59,6 +59,7 @@ modified later.
| `hash_seed` <Tag variant="new">3.2</Tag> | The floret hash seed (default: `0`). ~~int~~ |
| `bow` <Tag variant="new">3.2</Tag> | The floret BOW string (default: `"<"`). ~~str~~ |
| `eow` <Tag variant="new">3.2</Tag> | The floret EOW string (default: `">"`). ~~str~~ |
| `attr` <Tag variant="new">3.6</Tag> | The token attribute for the vector keys (default: `"ORTH"`). ~~Union[int, str]~~ |
## Vectors.\_\_getitem\_\_ {id="getitem",tag="method"}
@ -452,8 +453,9 @@ Load state from a binary string.
## Attributes {id="attributes"}
| Name | Description |
| --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `data` | Stored vectors data. `numpy` is used for CPU vectors, `cupy` for GPU vectors. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
| `key2row` | Dictionary mapping word hashes to rows in the `Vectors.data` table. ~~Dict[int, int]~~ |
| `keys` | Array keeping the keys in order, such that `keys[vectors.key2row[key]] == key`. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
| Name | Description |
| ----------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `data` | Stored vectors data. `numpy` is used for CPU vectors, `cupy` for GPU vectors. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
| `key2row` | Dictionary mapping word hashes to rows in the `Vectors.data` table. ~~Dict[int, int]~~ |
| `keys` | Array keeping the keys in order, such that `keys[vectors.key2row[key]] == key`. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
| `attr` <Tag variant="new">3.6</Tag> | The token attribute for the vector keys. ~~int~~ |

6
website/docs/usage/linguistic-features.mdx
View File

@ -113,7 +113,7 @@ print(doc[2].morph) # 'Case=Nom|Person=2|PronType=Prs'
print(doc[2].pos_) # 'PRON'
```
## Lemmatization {id="lemmatization",model="lemmatizer",version="3"}
## Lemmatization {id="lemmatization",version="3"}
spaCy provides two pipeline components for lemmatization:
@ -170,7 +170,7 @@ nlp = spacy.blank("sv")
nlp.add_pipe("lemmatizer", config={"mode": "lookup"})
```
### Rule-based lemmatizer {id="lemmatizer-rule"}
### Rule-based lemmatizer {id="lemmatizer-rule",model="morphologizer"}
When training pipelines that include a component that assigns part-of-speech
tags (a morphologizer or a tagger with a [POS mapping](#mappings-exceptions)), a
@ -194,7 +194,7 @@ information, without consulting the context of the token. The rule-based
lemmatizer also accepts list-based exception files. For English, these are
acquired from [WordNet](https://wordnet.princeton.edu/).
### Trainable lemmatizer
### Trainable lemmatizer {id="lemmatizer-train",model="trainable_lemmatizer"}
The [`EditTreeLemmatizer`](/api/edittreelemmatizer) can learn form-to-lemma
transformations from a training corpus that includes lemma annotations. This

72
website/docs/usage/training.mdx
View File

@ -11,7 +11,6 @@ menu:
- ['Custom Functions', 'custom-functions']
- ['Initialization', 'initialization']
- ['Data Utilities', 'data']
- ['Parallel Training', 'parallel-training']
- ['Internal API', 'api']
---
@ -1565,77 +1564,6 @@ token-based annotations like the dependency parse or entity labels, you'll need
to take care to adjust the `Example` object so its annotations match and remain
valid.
## Parallel & distributed training with Ray {id="parallel-training"}
> #### Installation
>
> ```bash
> $ pip install -U %%SPACY_PKG_NAME[ray]%%SPACY_PKG_FLAGS
> # Check that the CLI is registered
> $ python -m spacy ray --help
> ```
[Ray](https://ray.io/) is a fast and simple framework for building and running
**distributed applications**. You can use Ray to train spaCy on one or more
remote machines, potentially speeding up your training process. Parallel
training won't always be faster though – it depends on your batch size, models,
and hardware.
<Infobox variant="warning">
To use Ray with spaCy, you need the
[`spacy-ray`](https://github.com/explosion/spacy-ray) package installed.
Installing the package will automatically add the `ray` command to the spaCy
CLI.
</Infobox>
The [`spacy ray train`](/api/cli#ray-train) command follows the same API as
[`spacy train`](/api/cli#train), with a few extra options to configure the Ray
setup. You can optionally set the `--address` option to point to your Ray
cluster. If it's not set, Ray will run locally.
```bash
python -m spacy ray train config.cfg --n-workers 2
```
<Project id="integrations/ray">
Get started with parallel training using our project template. It trains a
simple model on a Universal Dependencies Treebank and lets you parallelize the
training with Ray.
</Project>
### How parallel training works {id="parallel-training-details"}
Each worker receives a shard of the **data** and builds a copy of the **model
and optimizer** from the [`config.cfg`](#config). It also has a communication
channel to **pass gradients and parameters** to the other workers. Additionally,
each worker is given ownership of a subset of the parameter arrays. Every
parameter array is owned by exactly one worker, and the workers are given a
mapping so they know which worker owns which parameter.
![Illustration of setup](/images/spacy-ray.svg)
As training proceeds, every worker will be computing gradients for **all** of
the model parameters. When they compute gradients for parameters they don't own,
they'll **send them to the worker** that does own that parameter, along with a
version identifier so that the owner can decide whether to discard the gradient.
Workers use the gradients they receive and the ones they compute locally to
update the parameters they own, and then broadcast the updated array and a new
version ID to the other workers.
This training procedure is **asynchronous** and **non-blocking**. Workers always
push their gradient increments and parameter updates, they do not have to pull
them and block on the result, so the transfers can happen in the background,
overlapped with the actual training work. The workers also do not have to stop
and wait for each other ("synchronize") at the start of each batch. This is very
useful for spaCy, because spaCy is often trained on long documents, which means
**batches can vary in size** significantly. Uneven workloads make synchronous
gradient descent inefficient, because if one batch is slow, all of the other
workers are stuck waiting for it to complete before they can continue.
## Internal training API {id="api"}
<Infobox variant="danger">

143
website/docs/usage/v3-6.mdx Normal file
View File

@ -0,0 +1,143 @@
---
title: What's New in v3.6
teaser: New features and how to upgrade
menu:
- ['New Features', 'features']
- ['Upgrading Notes', 'upgrading']
---
## New features {id="features",hidden="true"}
spaCy v3.6 adds the new [`SpanFinder`](/api/spanfinder) component to the core
spaCy library and new trained pipelines for Slovenian.
### SpanFinder {id="spanfinder"}
The [`SpanFinder`](/api/spanfinder) component identifies potentially
overlapping, unlabeled spans by identifying span start and end tokens. It is
intended for use in combination with a component like
[`SpanCategorizer`](/api/spancategorizer) that may further filter or label the
spans. See our
[Spancat blog post](https://explosion.ai/blog/spancat#span-finder) for a more
detailed introduction to the span finder.
To train a pipeline with `span_finder` + `spancat`, remember to add
`span_finder` (and its `tok2vec` or `transformer` if required) to
`[training.annotating_components]` so that the `spancat` component can be
trained directly from its predictions:
```ini
[nlp]
pipeline = ["tok2vec","span_finder","spancat"]
[training]
annotating_components = ["tok2vec","span_finder"]
```
In practice it can be helpful to initially train the `span_finder` separately
before [sourcing](/usage/processing-pipelines#sourced-components) it (along with
its `tok2vec`) into the `spancat` pipeline for further training. Otherwise the
memory usage can spike for `spancat` in the first few training steps if the
`span_finder` makes a large number of predictions.
### Additional features and improvements {id="additional-features-and-improvements"}
- Language updates:
- Add initial support for Malay.
- Update Latin defaults to support noun chunks, update lexical/tokenizer
settings and add example sentences.
- Support `spancat_singlelabel` in `spacy debug data` CLI.
- Add `doc.spans` rendering to `spacy evaluate` CLI displaCy output.
- Support custom token/lexeme attribute for vectors.
- Add option to return scores separately keyed by component name with
`spacy evaluate --per-component`, `Language.evaluate(per_component=True)` and
`Scorer.score(per_component=True)`. This is useful when the pipeline contains
more than one of the same component like `textcat` that may have overlapping
scores keys.
- Typing updates for `PhraseMatcher` and `SpanGroup`.
## Trained pipelines {id="pipelines"}
### New trained pipelines {id="new-pipelines"}
v3.6 introduces new pipelines for Slovenian, which use the trainable lemmatizer
and [floret vectors](https://github.com/explosion/floret).
| Package | UPOS | Parser LAS | NER F |
| ------------------------------------------------- | ---: | ---------: | ----: |
| [`sl_core_news_sm`](/models/sl#sl_core_news_sm) | 96.9 | 82.1 | 62.9 |
| [`sl_core_news_md`](/models/sl#sl_core_news_md) | 97.6 | 84.3 | 73.5 |
| [`sl_core_news_lg`](/models/sl#sl_core_news_lg) | 97.7 | 84.3 | 79.0 |
| [`sl_core_news_trf`](/models/sl#sl_core_news_trf) | 99.0 | 91.7 | 90.0 |
### Pipeline updates {id="pipeline-updates"}
The English pipelines have been updated to improve handling of contractions with
various apostrophes and to lemmatize "get" as a passive auxiliary.
The Danish pipeline `da_core_news_trf` has been updated to use
[`vesteinn/DanskBERT`](https://huggingface.co/vesteinn/DanskBERT) with
performance improvements across the board.
## Notes about upgrading from v3.5 {id="upgrading"}
### SpanGroup spans are now required to be from the same doc {id="spangroup-spans"}
When initializing a `SpanGroup`, there is a new check to verify that all added
spans refer to the current doc. Without this check, it was possible to run into
string store or other errors.
One place this may crop up is when creating `Example` objects for training with
custom spans:
```diff
doc = Doc(nlp.vocab, words=tokens) # predicted doc
example = Example.from_dict(doc, {"ner": iob_tags})
# use the reference doc when creating reference spans
- span = Span(doc, 0, 5, "ORG")
+ span = Span(example.reference, 0, 5, "ORG")
example.reference.spans[spans_key] = [span]
```
### Pipeline package version compatibility {id="version-compat"}
> #### Using legacy implementations
>
> In spaCy v3, you'll still be able to load and reference legacy implementations
> via [`spacy-legacy`](https://github.com/explosion/spacy-legacy), even if the
> components or architectures change and newer versions are available in the
> core library.
When you're loading a pipeline package trained with an earlier version of spaCy
v3, you will see a warning telling you that the pipeline may be incompatible.
This doesn't necessarily have to be true, but we recommend running your
pipelines against your test suite or evaluation data to make sure there are no
unexpected results.
If you're using one of the [trained pipelines](/models) we provide, you should
run [`spacy download`](/api/cli#download) to update to the latest version. To
see an overview of all installed packages and their compatibility, you can run
[`spacy validate`](/api/cli#validate).
If you've trained your own custom pipeline and you've confirmed that it's still
working as expected, you can update the spaCy version requirements in the
[`meta.json`](/api/data-formats#meta):
```diff
- "spacy_version": ">=3.5.0,<3.6.0",
+ "spacy_version": ">=3.5.0,<3.7.0",
```
### Updating v3.5 configs
To update a config from spaCy v3.5 with the new v3.6 settings, run
[`init fill-config`](/api/cli#init-fill-config):
```cli
$ python -m spacy init fill-config config-v3.5.cfg config-v3.6.cfg
```
In many cases ([`spacy train`](/api/cli#train),
[`spacy.load`](/api/top-level#spacy.load)), the new defaults will be filled in
automatically, but you'll need to fill in the new settings to run
[`debug config`](/api/cli#debug) and [`debug data`](/api/cli#debug-data).

9
website/meta/languages.json
View File

@ -222,7 +222,9 @@
},
{
"code": "la",
"name": "Latin"
"name": "Latin",
"example": "In principio creavit Deus caelum et terram.",
"has_examples": true
},
{
"code": "lb",
@ -339,7 +341,10 @@
},
{
"code": "sl",
"name": "Slovenian"
"name": "Slovenian",
"example": "France Prešeren je umrl 8. februarja 1849 v Kranju",
"has_examples": true,
"models": ["sl_core_news_sm", "sl_core_news_md", "sl_core_news_lg", "sl_core_news_trf"]
},
{
"code": "sq",

3
website/meta/sidebars.json
View File

@ -14,7 +14,8 @@
{ "text": "New in v3.2", "url": "/usage/v3-2" },
{ "text": "New in v3.3", "url": "/usage/v3-3" },
{ "text": "New in v3.4", "url": "/usage/v3-4" },
{ "text": "New in v3.5", "url": "/usage/v3-5" }
{ "text": "New in v3.5", "url": "/usage/v3-5" },
{ "text": "New in v3.6", "url": "/usage/v3-6" }
]
},
{

2
website/meta/site.json
View File

@ -27,7 +27,7 @@
"indexName": "spacy"
},
"binderUrl": "explosion/spacy-io-binder",
"binderVersion": "3.5",
"binderVersion": "3.6",
"sections": [
{ "id": "usage", "title": "Usage Documentation", "theme": "blue" },
{ "id": "models", "title": "Models Documentation", "theme": "blue" },

2
website/meta/universe.json
View File

@ -4376,7 +4376,7 @@
"code_example": [
"import spacy",
"",
"nlp = spacy.load(\"en_core_web_sm\", disable=[\"ner\"])",
"nlp = spacy.load(\"en_core_web_sm\", exclude=[\"ner\"])",
"nlp.add_pipe(\"span_marker\", config={\"model\": \"tomaarsen/span-marker-roberta-large-ontonotes5\"})",
"",
"text = \"\"\"Cleopatra VII, also known as Cleopatra the Great, was the last active ruler of the \\",

4
website/src/components/code.js
View File

@ -13,6 +13,8 @@ import 'prismjs/components/prism-json.min.js'
import 'prismjs/components/prism-markdown.min.js'
import 'prismjs/components/prism-python.min.js'
import 'prismjs/components/prism-yaml.min.js'
import 'prismjs/components/prism-docker.min.js'
import 'prismjs/components/prism-r.min.js'
import { isString } from './util'
import Link, { OptionalLink } from './link'
@ -172,7 +174,7 @@ const convertLine = ({ line, prompt, lang }) => {
return handlePromot({ lineFlat, prompt })
}
return lang === 'none' || !lineFlat ? (
return lang === 'none' || !lineFlat || !(lang in Prism.languages) ? (
lineFlat
) : (
<span

4
website/src/templates/index.js
View File

@ -58,8 +58,8 @@ const AlertSpace = ({ nightly, legacy }) => {
}
const navAlert = (
<Link to="/usage/v3-5" noLinkLayout>
<strong>💥 Out now:</strong> spaCy v3.5
<Link to="/usage/v3-6" noLinkLayout>
<strong>💥 Out now:</strong> spaCy v3.6
</Link>
)