spaCy/spacy/matcher/matcher.pyx

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# cython: infer_types=True, cython: profile=True
from typing import List, Iterable
from libcpp.vector cimport vector
from libc.stdint cimport int32_t, int8_t
from libc.string cimport memset, memcmp
from cymem.cymem cimport Pool
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from murmurhash.mrmr cimport hash64
import re
import srsly
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import warnings
from ..typedefs cimport attr_t
from ..structs cimport TokenC
from ..vocab cimport Vocab
from ..tokens.doc cimport Doc, get_token_attr_for_matcher
from ..tokens.span cimport Span
from ..tokens.token cimport Token
from ..tokens.morphanalysis cimport MorphAnalysis
from ..attrs cimport ID, attr_id_t, NULL_ATTR, ORTH, POS, TAG, DEP, LEMMA, MORPH, ENT_IOB
from ..schemas import validate_token_pattern
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from ..errors import Errors, MatchPatternError, Warnings
from ..strings import get_string_id
from ..attrs import IDS
DEF PADDING = 5
cdef class Matcher:
"""Match sequences of tokens, based on pattern rules.
DOCS: https://spacy.io/api/matcher
USAGE: https://spacy.io/usage/rule-based-matching
"""
def __init__(self, vocab, validate=True):
"""Create the Matcher.
vocab (Vocab): The vocabulary object, which must be shared with the
documents the matcher will operate on.
"""
self._extra_predicates = []
self._patterns = {}
self._callbacks = {}
self._filter = {}
self._extensions = {}
self._seen_attrs = set()
self.vocab = vocab
self.mem = Pool()
self.validate = validate
def __reduce__(self):
data = (self.vocab, self._patterns, self._callbacks)
return (unpickle_matcher, data, None, None)
def __len__(self):
"""Get the number of rules added to the matcher. Note that this only
returns the number of rules (identical with the number of IDs), not the
number of individual patterns.
RETURNS (int): The number of rules.
"""
return len(self._patterns)
def __contains__(self, key):
"""Check whether the matcher contains rules for a match ID.
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key (str): The match ID.
RETURNS (bool): Whether the matcher contains rules for this match ID.
"""
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return self.has_key(key)
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.
If the key exists, the patterns are appended to the previous ones, and
the previous on_match callback is replaced. The `on_match` callback
will receive the arguments `(matcher, doc, i, matches)`. You can also
set `on_match` to `None` to not perform any actions.
A pattern consists of one or more `token_specs`, where a `token_spec`
is a dictionary mapping attribute IDs to values, and optionally a
quantifier operator under the key "op". The available quantifiers are:
'!': Negate the pattern, by requiring it to match exactly 0 times.
'?': Make the pattern optional, by allowing it to match 0 or 1 times.
'+': Require the pattern to match 1 or more times.
'*': Allow the pattern to zero or more times.
'{n}': Require the pattern to match exactly _n_ times.
'{n,m}': Require the pattern to match at least _n_ but not more than _m_ times.
'{n,}': Require the pattern to match at least _n_ times.
'{,m}': Require the pattern to match at most _m_ times.
The + and * operators return all possible matches (not just the greedy
ones). However, the "greedy" argument can filter the final matches
by returning a non-overlapping set per key, either taking preference to
the first greedy match ("FIRST"), or the longest ("LONGEST").
Since spaCy v2.2.2, Matcher.add takes a list of patterns as the second
argument, and the on_match callback is an optional keyword argument.
🏷 Add Mypy check to CI and ignore all existing Mypy errors (#9167) * 🚨 Ignore all existing Mypy errors * 🏗 Add Mypy check to CI * Add types-mock and types-requests as dev requirements * Add additional type ignore directives * Add types packages to dev-only list in reqs test * Add types-dataclasses for python 3.6 * Add ignore to pretrain * 🏷 Improve type annotation on `run_command` helper The `run_command` helper previously declared that it returned an `Optional[subprocess.CompletedProcess]`, but it isn't actually possible for the function to return `None`. These changes modify the type annotation of the `run_command` helper and remove all now-unnecessary `# type: ignore` directives. * 🔧 Allow variable type redefinition in limited contexts These changes modify how Mypy is configured to allow variables to have their type automatically redefined under certain conditions. The Mypy documentation contains the following example: ```python def process(items: List[str]) -> None: # 'items' has type List[str] items = [item.split() for item in items] # 'items' now has type List[List[str]] ... ``` This configuration change is especially helpful in reducing the number of `# type: ignore` directives needed to handle the common pattern of: * Accepting a filepath as a string * Overwriting the variable using `filepath = ensure_path(filepath)` These changes enable redefinition and remove all `# type: ignore` directives rendered redundant by this change. * 🏷 Add type annotation to converters mapping * 🚨 Fix Mypy error in convert CLI argument verification * 🏷 Improve type annotation on `resolve_dot_names` helper * 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors` * 🏷 Add type annotations for more `Vocab` attributes * 🏷 Add loose type annotation for gold data compilation * 🏷 Improve `_format_labels` type annotation * 🏷 Fix `get_lang_class` type annotation * 🏷 Loosen return type of `Language.evaluate` * 🏷 Don't accept `Scorer` in `handle_scores_per_type` * 🏷 Add `string_to_list` overloads * 🏷 Fix non-Optional command-line options * 🙈 Ignore redefinition of `wandb_logger` in `loggers.py` * ➕ Install `typing_extensions` in Python 3.8+ The `typing_extensions` package states that it should be used when "writing code that must be compatible with multiple Python versions". Since SpaCy needs to support multiple Python versions, it should be used when newer `typing` module members are required. One example of this is `Literal`, which is available starting with Python 3.8. Previously SpaCy tried to import `Literal` from `typing`, falling back to `typing_extensions` if the import failed. However, Mypy doesn't seem to be able to understand what `Literal` means when the initial import means. Therefore, these changes modify how `compat` imports `Literal` by always importing it from `typing_extensions`. These changes also modify how `typing_extensions` is installed, so that it is a requirement for all Python versions, including those greater than or equal to 3.8. * 🏷 Improve type annotation for `Language.pipe` These changes add a missing overload variant to the type signature of `Language.pipe`. Additionally, the type signature is enhanced to allow type checkers to differentiate between the two overload variants based on the `as_tuple` parameter. Fixes #8772 * ➖ Don't install `typing-extensions` in Python 3.8+ After more detailed analysis of how to implement Python version-specific type annotations using SpaCy, it has been determined that by branching on a comparison against `sys.version_info` can be statically analyzed by Mypy well enough to enable us to conditionally use `typing_extensions.Literal`. This means that we no longer need to install `typing_extensions` for Python versions greater than or equal to 3.8! 🎉 These changes revert previous changes installing `typing-extensions` regardless of Python version and modify how we import the `Literal` type to ensure that Mypy treats it properly. * resolve mypy errors for Strict pydantic types * refactor code to avoid missing return statement * fix types of convert CLI command * avoid list-set confustion in debug_data * fix typo and formatting * small fixes to avoid type ignores * fix types in profile CLI command and make it more efficient * type fixes in projects CLI * put one ignore back * type fixes for render * fix render types - the sequel * fix BaseDefault in language definitions * fix type of noun_chunks iterator - yields tuple instead of span * fix types in language-specific modules * 🏷 Expand accepted inputs of `get_string_id` `get_string_id` accepts either a string (in which case it returns its ID) or an ID (in which case it immediately returns the ID). These changes extend the type annotation of `get_string_id` to indicate that it can accept either strings or IDs. * 🏷 Handle override types in `combine_score_weights` The `combine_score_weights` function allows users to pass an `overrides` mapping to override data extracted from the `weights` argument. Since it allows `Optional` dictionary values, the return value may also include `Optional` dictionary values. These changes update the type annotations for `combine_score_weights` to reflect this fact. * 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer` * 🏷 Fix redefinition of `wandb_logger` These changes fix the redefinition of `wandb_logger` by giving a separate name to each `WandbLogger` version. For backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` as `wandb_logger` for now. * more fixes for typing in language * type fixes in model definitions * 🏷 Annotate `_RandomWords.probs` as `NDArray` * 🏷 Annotate `tok2vec` layers to help Mypy * 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6 Also remove an import that I forgot to move to the top of the module 😅 * more fixes for matchers and other pipeline components * quick fix for entity linker * fixing types for spancat, textcat, etc * bugfix for tok2vec * type annotations for scorer * add runtime_checkable for Protocol * type and import fixes in tests * mypy fixes for training utilities * few fixes in util * fix import * 🐵 Remove unused `# type: ignore` directives * 🏷 Annotate `Language._components` * 🏷 Annotate `spacy.pipeline.Pipe` * add doc as property to span.pyi * small fixes and cleanup * explicit type annotations instead of via comment Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
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key (Union[str, int]): The match ID.
patterns (list): The patterns to add for the given key.
on_match (callable): Optional callback executed on match.
greedy (str): Optional filter: "FIRST" or "LONGEST".
"""
errors = {}
if on_match is not None and not hasattr(on_match, "__call__"):
raise ValueError(Errors.E171.format(arg_type=type(on_match)))
if patterns is None or not isinstance(patterns, List): # old API
raise ValueError(Errors.E948.format(arg_type=type(patterns)))
if greedy is not None and greedy not in ["FIRST", "LONGEST"]:
raise ValueError(Errors.E947.format(expected=["FIRST", "LONGEST"], arg=greedy))
for i, pattern in enumerate(patterns):
if len(pattern) == 0:
raise ValueError(Errors.E012.format(key=key))
if not isinstance(pattern, list):
raise ValueError(Errors.E178.format(pat=pattern, key=key))
if self.validate:
errors[i] = validate_token_pattern(pattern)
if any(err for err in errors.values()):
raise MatchPatternError(key, errors)
key = self._normalize_key(key)
for pattern in patterns:
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try:
specs = _preprocess_pattern(pattern, self.vocab,
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self._extensions, self._extra_predicates)
self.patterns.push_back(init_pattern(self.mem, key, specs))
for spec in specs:
for attr, _ in spec[1]:
self._seen_attrs.add(attr)
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except OverflowError, AttributeError:
raise ValueError(Errors.E154.format()) from None
self._patterns.setdefault(key, [])
self._callbacks[key] = on_match
self._filter[key] = greedy
self._patterns[key].extend(patterns)
def _require_patterns(self) -> None:
"""Raise a warning if this component has no patterns defined."""
if len(self) == 0:
warnings.warn(Warnings.W036.format(name="matcher"))
def remove(self, key):
"""Remove a rule from the matcher. A KeyError is raised if the key does
not exist.
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key (str): The ID of the match rule.
"""
norm_key = self._normalize_key(key)
if not norm_key in self._patterns:
raise ValueError(Errors.E175.format(key=key))
self._patterns.pop(norm_key)
self._callbacks.pop(norm_key)
cdef int i = 0
while i < self.patterns.size():
pattern_key = get_ent_id(self.patterns.at(i))
if pattern_key == norm_key:
self.patterns.erase(self.patterns.begin()+i)
else:
i += 1
def has_key(self, key):
"""Check whether the matcher has a rule with a given key.
key (string or int): The key to check.
RETURNS (bool): Whether the matcher has the rule.
"""
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return self._normalize_key(key) in self._patterns
def get(self, key, default=None):
"""Retrieve the pattern stored for a key.
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key (str / int): The key to retrieve.
RETURNS (tuple): The rule, as an (on_match, patterns) tuple.
"""
key = self._normalize_key(key)
if key not in self._patterns:
return default
return (self._callbacks[key], self._patterns[key])
def pipe(self, docs, batch_size=1000, return_matches=False, as_tuples=False):
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"""Match a stream of documents, yielding them in turn. Deprecated as of
spaCy v3.0.
"""
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warnings.warn(Warnings.W105.format(matcher="Matcher"), DeprecationWarning)
if as_tuples:
for doc, context in docs:
matches = self(doc)
if return_matches:
yield ((doc, matches), context)
else:
yield (doc, context)
else:
for doc in docs:
matches = self(doc)
if return_matches:
yield (doc, matches)
else:
yield doc
def __call__(self, object doclike, *, as_spans=False, allow_missing=False, with_alignments=False):
"""Find all token sequences matching the supplied pattern.
doclike (Doc or Span): The document to match over.
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as_spans (bool): Return Span objects with labels instead of (match_id,
start, end) tuples.
allow_missing (bool): Whether to skip checks for missing annotation for
attributes included in patterns. Defaults to False.
with_alignments (bool): Return match alignment information, which is
`List[int]` with length of matched span. Each entry denotes the
corresponding index of token pattern. If as_spans is set to True,
this setting is ignored.
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RETURNS (list): A list of `(match_id, start, end)` tuples,
describing the matches. A match tuple describes a span
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`doc[start:end]`. The `match_id` is an integer. If as_spans is set
to True, a list of Span objects is returned.
If with_alignments is set to True and as_spans is set to False,
A list of `(match_id, start, end, alignments)` tuples is returned.
"""
self._require_patterns()
if isinstance(doclike, Doc):
doc = doclike
length = len(doc)
elif isinstance(doclike, Span):
doc = doclike.doc
length = doclike.end - doclike.start
else:
raise ValueError(Errors.E195.format(good="Doc or Span", got=type(doclike).__name__))
# Skip alignments calculations if as_spans is set
if as_spans:
with_alignments = False
cdef Pool tmp_pool = Pool()
if not allow_missing:
for attr in (TAG, POS, MORPH, LEMMA, DEP):
if attr in self._seen_attrs and not doc.has_annotation(attr):
if attr == TAG:
pipe = "tagger"
elif attr in (POS, MORPH):
pipe = "morphologizer or tagger+attribute_ruler"
elif attr == LEMMA:
pipe = "lemmatizer"
elif attr == DEP:
pipe = "parser"
error_msg = Errors.E155.format(pipe=pipe, attr=self.vocab.strings.as_string(attr))
raise ValueError(error_msg)
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)
final_matches = []
pairs_by_id = {}
# For each key, either add all matches, or only the filtered,
# non-overlapping ones this `match` can be either (start, end) or
# (start, end, alignments) depending on `with_alignments=` option.
for key, *match in matches:
span_filter = self._filter.get(key)
if span_filter is not None:
pairs = pairs_by_id.get(key, [])
pairs.append(match)
pairs_by_id[key] = pairs
else:
final_matches.append((key, *match))
matched = <char*>tmp_pool.alloc(length, sizeof(char))
empty = <char*>tmp_pool.alloc(length, sizeof(char))
for key, pairs in pairs_by_id.items():
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
elif span_filter == "LONGEST":
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:
start, end = match[:2]
assert 0 <= start < end # Defend against segfaults
span_len = end-start
# If no tokens in the span have matched
if memcmp(&matched[start], &empty[start], span_len * sizeof(matched[0])) == 0:
final_matches.append((key, *match))
# Mark tokens that have matched
memset(&matched[start], 1, span_len * sizeof(matched[0]))
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if as_spans:
final_results = []
for key, start, end, *_ in final_matches:
if isinstance(doclike, Span):
start += doclike.start
end += doclike.start
final_results.append(Span(doc, start, end, label=key))
elif with_alignments:
# convert alignments List[Dict[str, int]] --> List[int]
# when multiple alignment (belongs to the same length) is found,
# keeps the alignment that has largest token_idx
final_results = []
for key, start, end, alignments in final_matches:
sorted_alignments = sorted(alignments, key=lambda x: (x['length'], x['token_idx']), reverse=False)
alignments = [0] * (end-start)
for align in sorted_alignments:
if align['length'] >= end-start:
continue
# Since alignments are sorted in order of (length, token_idx)
# this overwrites smaller token_idx when they have same length.
alignments[align['length']] = align['token_idx']
final_results.append((key, start, end, alignments))
final_matches = final_results # for callbacks
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else:
final_results = final_matches
# perform the callbacks on the filtered set of results
for i, (key, *_) in enumerate(final_matches):
on_match = self._callbacks.get(key, None)
if on_match is not None:
on_match(self, doc, i, final_matches)
return final_results
def _normalize_key(self, key):
if isinstance(key, str):
return self.vocab.strings.add(key)
else:
return key
def unpickle_matcher(vocab, patterns, callbacks):
matcher = Matcher(vocab)
for key, pattern in patterns.items():
callback = callbacks.get(key, None)
matcher.add(key, pattern, on_match=callback)
return matcher
cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, extensions=None, predicates=tuple(), bint with_alignments=0):
"""Find matches in a doc, with a compiled array of patterns. Matches are
returned as a list of (id, start, end) tuples or (id, start, end, alignments) tuples (if with_alignments != 0)
To augment the compiled patterns, we optionally also take two Python lists.
The "predicates" list contains functions that take a Python list and return a
boolean value. It's mostly used for regular expressions.
The "extensions" list contains functions that take a Python list and return
an attr ID. It's mostly used for extension attributes.
"""
cdef vector[PatternStateC] states
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 = []
if length == 0:
# avoid any processing or mem alloc if the document is empty
return output
if len(predicates) > 0:
predicate_cache = <int8_t*>mem.alloc(length * len(predicates), sizeof(int8_t))
if extensions is not None and len(extensions) >= 1:
nr_extra_attr = max(extensions.values()) + 1
extra_attr_values = <attr_t*>mem.alloc(length * nr_extra_attr, sizeof(attr_t))
else:
nr_extra_attr = 0
extra_attr_values = <attr_t*>mem.alloc(length, sizeof(attr_t))
for i, token in enumerate(doclike):
for name, index in extensions.items():
value = token._.get(name)
if isinstance(value, str):
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)
extra_attr_values += nr_extra_attr
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predicate_cache += len(predicates)
# Handle matches that end in 0-width patterns
finish_states(matches, states, align_matches, align_states, with_alignments)
seen = set()
for i in range(matches.size()):
match = (
matches[i].pattern_id,
matches[i].start,
matches[i].start+matches[i].length
)
# We need to deduplicate, because we could otherwise arrive at the same
# match through two paths, e.g. .?.? matching 'a'. Are we matching the
# first .?, or the second .? -- it doesn't matter, it's just one match.
# Skip 0-length matches. (TODO: fix algorithm)
if match not in seen and matches[i].length > 0:
if with_alignments != 0:
# since the length of align_matches equals to that of match, we can share same 'i'
output.append(match + (align_matches[i],))
else:
output.append(match)
seen.add(match)
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 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)
action = get_action(states[i], token.c, extra_attrs,
cached_py_predicates)
if action == REJECT:
continue
# Keep only a subset of states (the active ones). Index q is the
# states which are still alive. If we reject a state, we overwrite
# it in the states list, because q doesn't advance.
state = states[i]
states[q] = state
# Separate from states, performance is guaranteed for users who only need basic options (without alignments).
# `align_states` always corresponds to `states` 1:1.
if with_alignments != 0:
align_state = align_states[i]
align_states[q] = align_state
while action in (RETRY, RETRY_ADVANCE, RETRY_EXTEND):
# Update alignment before the transition of current state
# 'MatchAlignmentC' maps 'original token index of current pattern' to 'current matching length'
if with_alignments != 0:
align_states[q].push_back(MatchAlignmentC(states[q].pattern.token_idx, states[q].length))
if action == RETRY_EXTEND:
# This handles the 'extend'
new_states.push_back(
PatternStateC(pattern=states[q].pattern, start=state.start,
length=state.length+1))
if with_alignments != 0:
align_new_states.push_back(align_states[q])
if action == RETRY_ADVANCE:
# This handles the 'advance'
new_states.push_back(
PatternStateC(pattern=states[q].pattern+1, start=state.start,
length=state.length+1))
if with_alignments != 0:
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)
action = get_action(states[q], token.c, extra_attrs,
cached_py_predicates)
# Update alignment before the transition of current state
if with_alignments != 0:
align_states[q].push_back(MatchAlignmentC(states[q].pattern.token_idx, states[q].length))
if action == REJECT:
pass
elif action == ADVANCE:
states[q].pattern += 1
states[q].length += 1
q += 1
else:
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))
# `align_matches` always corresponds to `matches` 1:1
if with_alignments != 0:
align_matches.push_back(align_states[q])
elif action == MATCH_DOUBLE:
# 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))
# 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))
# `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))
# `align_matches` always corresponds to `matches` 1:1
if with_alignments != 0:
align_matches.push_back(align_states[q])
elif action == MATCH_EXTEND:
matches.push_back(
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])
states[q].length += 1
q += 1
states.resize(q)
for i in range(new_states.size()):
states.push_back(new_states[i])
# `align_states` always corresponds to `states` 1:1
if with_alignments != 0:
align_states.resize(q)
for i in range(align_new_states.size()):
align_states.push_back(align_new_states[i])
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.
for i in range(pattern.nr_py):
index = pattern.py_predicates[i]
if cache[index] == 0:
predicate = predicates[index]
result = predicate(token)
if result is True:
cache[index] = 1
elif result is False:
cache[index] = -1
elif result is None:
pass
else:
raise ValueError(Errors.E125.format(value=result))
cdef void finish_states(vector[MatchC]& matches, vector[PatternStateC]& states,
vector[vector[MatchAlignmentC]]& align_matches,
vector[vector[MatchAlignmentC]]& align_states,
bint with_alignments) except *:
"""Handle states that end in zero-width patterns."""
cdef PatternStateC state
cdef vector[MatchAlignmentC] align_state
for i in range(states.size()):
state = states[i]
if with_alignments != 0:
align_state = align_states[i]
while get_quantifier(state) in (ZERO_PLUS, ZERO_ONE):
# Update alignment before the transition of current state
if with_alignments != 0:
align_state.push_back(MatchAlignmentC(state.pattern.token_idx, state.length))
is_final = get_is_final(state)
if is_final:
ent_id = get_ent_id(state.pattern)
# `align_matches` always corresponds to `matches` 1:1
if with_alignments != 0:
align_matches.push_back(align_state)
matches.push_back(
MatchC(pattern_id=ent_id, start=state.start, length=state.length))
break
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:
"""We need to consider:
a) Does the token match the specification? [Yes, No]
b) What's the quantifier? [1, 0+, ?]
c) Is this the last specification? [final, non-final]
We can transition in the following ways:
a) Do we emit a match?
b) Do we add a state with (next state, next token)?
c) Do we add a state with (next state, same token)?
d) Do we add a state with (same state, next token)?
We'll code the actions as boolean strings, so 0000 means no to all 4,
1000 means match but no states added, etc.
2018-11-14 18:10:21 +00:00
1:
Yes, final:
1000
Yes, non-final:
0100
No, final:
0000
No, non-final
0000
0+:
Yes, final:
1001
Yes, non-final:
0011
No, final:
1000 (note: Don't include last token!)
No, non-final:
0010
?:
Yes, final:
1000
Yes, non-final:
0100
No, final:
1000 (note: Don't include last token!)
No, non-final:
0010
Possible combinations: 1000, 0100, 0000, 1001, 0110, 0011, 0010,
2018-11-14 18:10:21 +00:00
We'll name the bits "match", "advance", "retry", "extend"
REJECT = 0000
MATCH = 1000
ADVANCE = 0100
RETRY = 0010
MATCH_EXTEND = 1001
RETRY_ADVANCE = 0110
RETRY_EXTEND = 0011
MATCH_REJECT = 2000 # Match, but don't include last token
MATCH_DOUBLE = 3000 # Match both with and without last token
Problem: If a quantifier is matching, we're adding a lot of open partials
"""
cdef int8_t is_match
is_match = get_is_match(state, token, extra_attrs, predicate_matches)
quantifier = get_quantifier(state)
is_final = get_is_final(state)
if quantifier == ZERO:
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
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
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
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
spec = state.pattern
if spec.nr_attr > 0:
for attr in spec.attrs[:spec.nr_attr]:
if get_token_attr_for_matcher(token, attr.attr) != attr.value:
return 0
for i in range(spec.nr_extra_attr):
if spec.extra_attrs[i].value != extra_attrs[spec.extra_attrs[i].index]:
return 0
return True
cdef inline int8_t get_is_final(PatternStateC state) nogil:
if state.pattern[1].quantifier == FINAL_ID:
return 1
else:
return 0
cdef inline int8_t get_quantifier(PatternStateC state) nogil:
return state.pattern.quantifier
💫 Port master changes over to develop (#2979) * Create aryaprabhudesai.md (#2681) * Update _install.jade (#2688) Typo fix: "models" -> "model" * Add FAC to spacy.explain (resolves #2706) * Remove docstrings for deprecated arguments (see #2703) * When calling getoption() in conftest.py, pass a default option (#2709) * When calling getoption() in conftest.py, pass a default option This is necessary to allow testing an installed spacy by running: pytest --pyargs spacy * Add contributor agreement * update bengali token rules for hyphen and digits (#2731) * Less norm computations in token similarity (#2730) * Less norm computations in token similarity * Contributor agreement * Remove ')' for clarity (#2737) Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know. * added contributor agreement for mbkupfer (#2738) * Basic support for Telugu language (#2751) * Lex _attrs for polish language (#2750) * Signed spaCy contributor agreement * Added polish version of english lex_attrs * Introduces a bulk merge function, in order to solve issue #653 (#2696) * Fix comment * Introduce bulk merge to increase performance on many span merges * Sign contributor agreement * Implement pull request suggestions * Describe converters more explicitly (see #2643) * Add multi-threading note to Language.pipe (resolves #2582) [ci skip] * Fix formatting * Fix dependency scheme docs (closes #2705) [ci skip] * Don't set stop word in example (closes #2657) [ci skip] * Add words to portuguese language _num_words (#2759) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Update Indonesian model (#2752) * adding e-KTP in tokenizer exceptions list * add exception token * removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception * add tokenizer exceptions list * combining base_norms with norm_exceptions * adding norm_exception * fix double key in lemmatizer * remove unused import on punctuation.py * reformat stop_words to reduce number of lines, improve readibility * updating tokenizer exception * implement is_currency for lang/id * adding orth_first_upper in tokenizer_exceptions * update the norm_exception list * remove bunch of abbreviations * adding contributors file * Fixed spaCy+Keras example (#2763) * bug fixes in keras example * created contributor agreement * Adding French hyphenated first name (#2786) * Fix typo (closes #2784) * Fix typo (#2795) [ci skip] Fixed typo on line 6 "regcognizer --> recognizer" * Adding basic support for Sinhala language. (#2788) * adding Sinhala language package, stop words, examples and lex_attrs. * Adding contributor agreement * Updating contributor agreement * Also include lowercase norm exceptions * Fix error (#2802) * Fix error ValueError: cannot resize an array that references or is referenced by another array in this way. Use the resize function * added spaCy Contributor Agreement * Add charlax's contributor agreement (#2805) * agreement of contributor, may I introduce a tiny pl languge contribution (#2799) * Contributors agreement * Contributors agreement * Contributors agreement * Add jupyter=True to displacy.render in documentation (#2806) * Revert "Also include lowercase norm exceptions" This reverts commit 70f4e8adf37cfcfab60be2b97d6deae949b30e9e. * Remove deprecated encoding argument to msgpack * Set up dependency tree pattern matching skeleton (#2732) * Fix bug when too many entity types. Fixes #2800 * Fix Python 2 test failure * Require older msgpack-numpy * Restore encoding arg on msgpack-numpy * Try to fix version pin for msgpack-numpy * Update Portuguese Language (#2790) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols * Extended punctuation and norm_exceptions in the Portuguese language * Correct error in spacy universe docs concerning spacy-lookup (#2814) * Update Keras Example for (Parikh et al, 2016) implementation (#2803) * bug fixes in keras example * created contributor agreement * baseline for Parikh model * initial version of parikh 2016 implemented * tested asymmetric models * fixed grevious error in normalization * use standard SNLI test file * begin to rework parikh example * initial version of running example * start to document the new version * start to document the new version * Update Decompositional Attention.ipynb * fixed calls to similarity * updated the README * import sys package duh * simplified indexing on mapping word to IDs * stupid python indent error * added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround * Fix typo (closes #2815) [ci skip] * Update regex version dependency * Set version to 2.0.13.dev3 * Skip seemingly problematic test * Remove problematic test * Try previous version of regex * Revert "Remove problematic test" This reverts commit bdebbef45552d698d390aa430b527ee27830f11b. * Unskip test * Try older version of regex * 💫 Update training examples and use minibatching (#2830) <!--- Provide a general summary of your changes in the title. --> ## Description Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results. ### Types of change enhancements ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Visual C++ link updated (#2842) (closes #2841) [ci skip] * New landing page * Add contribution agreement * Correcting lang/ru/examples.py (#2845) * Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement * Correct some grammatical inaccuracies in lang\ru\examples.py * Move contributor agreement to separate file * Set version to 2.0.13.dev4 * Add Persian(Farsi) language support (#2797) * Also include lowercase norm exceptions * Remove in favour of https://github.com/explosion/spaCy/graphs/contributors * Rule-based French Lemmatizer (#2818) <!--- Provide a general summary of your changes in the title. --> ## Description <!--- Use this section to describe your changes. If your changes required testing, include information about the testing environment and the tests you ran. If your test fixes a bug reported in an issue, don't forget to include the issue number. If your PR is still a work in progress, that's totally fine – just include a note to let us know. --> Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class. ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> - Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version. - Add several files containing exhaustive list of words for each part of speech - Add some lemma rules - Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX - Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned - Modify the lemmatize function to check in lookup table as a last resort - Init files are updated so the model can support all the functionalities mentioned above - Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [X] I have submitted the spaCy Contributor Agreement. - [X] I ran the tests, and all new and existing tests passed. - [X] My changes don't require a change to the documentation, or if they do, I've added all required information. * Set version to 2.0.13 * Fix formatting and consistency * Update docs for new version [ci skip] * Increment version [ci skip] * Add info on wheels [ci skip] * Adding "This is a sentence" example to Sinhala (#2846) * Add wheels badge * Update badge [ci skip] * Update README.rst [ci skip] * Update murmurhash pin * Increment version to 2.0.14.dev0 * Update GPU docs for v2.0.14 * Add wheel to setup_requires * Import prefer_gpu and require_gpu functions from Thinc * Add tests for prefer_gpu() and require_gpu() * Update requirements and setup.py * Workaround bug in thinc require_gpu * Set version to v2.0.14 * Update push-tag script * Unhack prefer_gpu * Require thinc 6.10.6 * Update prefer_gpu and require_gpu docs [ci skip] * Fix specifiers for GPU * Set version to 2.0.14.dev1 * Set version to 2.0.14 * Update Thinc version pin * Increment version * Fix msgpack-numpy version pin * Increment version * Update version to 2.0.16 * Update version [ci skip] * Redundant ')' in the Stop words' example (#2856) <!--- Provide a general summary of your changes in the title. --> ## Description <!--- Use this section to describe your changes. If your changes required testing, include information about the testing environment and the tests you ran. If your test fixes a bug reported in an issue, don't forget to include the issue number. If your PR is still a work in progress, that's totally fine – just include a note to let us know. --> ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [ ] I have submitted the spaCy Contributor Agreement. - [ ] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information. * Documentation improvement regarding joblib and SO (#2867) Some documentation improvements ## Description 1. Fixed the dead URL to joblib 2. Fixed Stack Overflow brand name (with space) ### Types of change Documentation ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * raise error when setting overlapping entities as doc.ents (#2880) * Fix out-of-bounds access in NER training The helper method state.B(1) gets the index of the first token of the buffer, or -1 if no such token exists. Normally this is safe because we pass this to functions like state.safe_get(), which returns an empty token. Here we used it directly as an array index, which is not okay! This error may have been the cause of out-of-bounds access errors during training. Similar errors may still be around, so much be hunted down. Hunting this one down took a long time...I printed out values across training runs and diffed, looking for points of divergence between runs, when no randomness should be allowed. * Change PyThaiNLP Url (#2876) * Fix missing comma * Add example showing a fix-up rule for space entities * Set version to 2.0.17.dev0 * Update regex version * Revert "Update regex version" This reverts commit 62358dd867d15bc6a475942dff34effba69dd70a. * Try setting older regex version, to align with conda * Set version to 2.0.17 * Add spacy-js to universe [ci-skip] * Add spacy-raspberry to universe (closes #2889) * Add script to validate universe json [ci skip] * Removed space in docs + added contributor indo (#2909) * - removed unneeded space in documentation * - added contributor info * Allow input text of length up to max_length, inclusive (#2922) * Include universe spec for spacy-wordnet component (#2919) * feat: include universe spec for spacy-wordnet component * chore: include spaCy contributor agreement * Minor formatting changes [ci skip] * Fix image [ci skip] Twitter URL doesn't work on live site * Check if the word is in one of the regular lists specific to each POS (#2886) * 💫 Create random IDs for SVGs to prevent ID clashes (#2927) Resolves #2924. ## Description Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.) ### Types of change bug fix ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Fix typo [ci skip] * fixes symbolic link on py3 and windows (#2949) * fixes symbolic link on py3 and windows during setup of spacy using command python -m spacy link en_core_web_sm en closes #2948 * Update spacy/compat.py Co-Authored-By: cicorias <cicorias@users.noreply.github.com> * Fix formatting * Update universe [ci skip] * Catalan Language Support (#2940) * Catalan language Support * Ddding Catalan to documentation * Sort languages alphabetically [ci skip] * Update tests for pytest 4.x (#2965) <!--- Provide a general summary of your changes in the title. --> ## Description - [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize)) - [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here) ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Fix regex pin to harmonize with conda (#2964) * Update README.rst * Fix bug where Vocab.prune_vector did not use 'batch_size' (#2977) Fixes #2976 * Fix typo * Fix typo * Remove duplicate file * Require thinc 7.0.0.dev2 Fixes bug in gpu_ops that would use cupy instead of numpy on CPU * Add missing import * Fix error IDs * Fix tests
2018-11-29 15:30:29 +00:00
cdef TokenPatternC* init_pattern(Pool mem, attr_t entity_id, object token_specs) except NULL:
pattern = <TokenPatternC*>mem.alloc(len(token_specs) + 1, sizeof(TokenPatternC))
cdef int i, index
for i, (quantifier, spec, extensions, predicates, token_idx) in enumerate(token_specs):
pattern[i].quantifier = quantifier
# Ensure attrs refers to a null pointer if nr_attr == 0
if len(spec) > 0:
pattern[i].attrs = <AttrValueC*>mem.alloc(len(spec), sizeof(AttrValueC))
pattern[i].nr_attr = len(spec)
for j, (attr, value) in enumerate(spec):
pattern[i].attrs[j].attr = attr
pattern[i].attrs[j].value = value
if len(extensions) > 0:
pattern[i].extra_attrs = <IndexValueC*>mem.alloc(len(extensions), sizeof(IndexValueC))
for j, (index, value) in enumerate(extensions):
pattern[i].extra_attrs[j].index = index
pattern[i].extra_attrs[j].value = value
pattern[i].nr_extra_attr = len(extensions)
if len(predicates) > 0:
pattern[i].py_predicates = <int32_t*>mem.alloc(len(predicates), sizeof(int32_t))
for j, index in enumerate(predicates):
pattern[i].py_predicates[j] = index
pattern[i].nr_py = len(predicates)
pattern[i].key = hash64(pattern[i].attrs, pattern[i].nr_attr * sizeof(AttrValueC), 0)
pattern[i].token_idx = token_idx
i = len(token_specs)
# Use quantifier to identify final ID pattern node (rather than previous
# uninitialized quantifier == 0/ZERO + nr_attr == 0 + non-zero-length attrs)
pattern[i].quantifier = FINAL_ID
pattern[i].attrs = <AttrValueC*>mem.alloc(1, sizeof(AttrValueC))
pattern[i].attrs[0].attr = ID
pattern[i].attrs[0].value = entity_id
pattern[i].nr_attr = 1
pattern[i].nr_extra_attr = 0
pattern[i].nr_py = 0
pattern[i].token_idx = -1
return pattern
cdef attr_t get_ent_id(const TokenPatternC* pattern) nogil:
while pattern.quantifier != FINAL_ID:
2017-05-20 11:54:53 +00:00
pattern += 1
id_attr = pattern[0].attrs[0]
if id_attr.attr != ID:
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with gil:
raise ValueError(Errors.E074.format(attr=ID, bad_attr=id_attr.attr))
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return id_attr.value
def _preprocess_pattern(token_specs, vocab, extensions_table, extra_predicates):
"""This function interprets the pattern, converting the various bits of
syntactic sugar before we compile it into a struct with init_pattern.
We need to split the pattern up into four parts:
* Normal attribute/value pairs, which are stored on either the token or lexeme,
can be handled directly.
* Extension attributes are handled specially, as we need to prefetch the
values from Python for the doc before we begin matching.
* Extra predicates also call Python functions, so we have to create the
functions and store them. So we store these specially as well.
* Extension attributes that have extra predicates are stored within the
extra_predicates.
* Token index that this pattern belongs to.
"""
tokens = []
string_store = vocab.strings
for token_idx, spec in enumerate(token_specs):
if not spec:
# Signifier for 'any token'
tokens.append((ONE, [(NULL_ATTR, 0)], [], [], token_idx))
continue
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if not isinstance(spec, dict):
raise ValueError(Errors.E154.format())
ops = _get_operators(spec)
attr_values = _get_attr_values(spec, string_store)
extensions = _get_extensions(spec, string_store, extensions_table)
predicates = _get_extra_predicates(spec, extra_predicates, vocab)
for op in ops:
tokens.append((op, list(attr_values), list(extensions), list(predicates), token_idx))
return tokens
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def _get_attr_values(spec, string_store):
attr_values = []
for attr, value in spec.items():
input_attr = attr
if isinstance(attr, str):
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attr = attr.upper()
if attr == '_':
continue
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elif attr == "OP":
continue
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if attr == "TEXT":
attr = "ORTH"
if attr == "IS_SENT_START":
attr = "SENT_START"
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attr = IDS.get(attr)
if isinstance(value, str):
if attr == ENT_IOB and value in Token.iob_strings():
value = Token.iob_strings().index(value)
else:
value = string_store.add(value)
elif isinstance(value, bool):
value = int(value)
elif isinstance(value, int):
pass
elif isinstance(value, dict):
continue
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else:
raise ValueError(Errors.E153.format(vtype=type(value).__name__))
if attr is not None:
attr_values.append((attr, value))
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else:
# should be caught in validation
raise ValueError(Errors.E152.format(attr=input_attr))
return attr_values
# These predicate helper classes are used to match the REGEX, IN, >= etc
# extensions to the matcher introduced in #3173.
2020-07-12 12:03:23 +00:00
class _RegexPredicate:
operators = ("REGEX",)
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None):
self.i = i
self.attr = attr
self.value = re.compile(value)
self.predicate = predicate
self.is_extension = is_extension
self.key = (attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
def __call__(self, Token token):
if self.is_extension:
value = token._.get(self.attr)
else:
value = token.vocab.strings[get_token_attr_for_matcher(token.c, self.attr)]
return bool(self.value.search(value))
class _SetPredicate:
operators = ("IN", "NOT_IN", "IS_SUBSET", "IS_SUPERSET", "INTERSECTS")
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None):
self.i = i
self.attr = attr
self.vocab = vocab
if self.attr == MORPH:
# normalize morph strings
self.value = set(self.vocab.morphology.add(v) for v in value)
else:
self.value = set(get_string_id(v) for v in value)
self.predicate = predicate
self.is_extension = is_extension
self.key = (attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
def __call__(self, Token token):
if self.is_extension:
value = token._.get(self.attr)
else:
value = get_token_attr_for_matcher(token.c, self.attr)
if self.predicate in ("IN", "NOT_IN"):
if isinstance(value, (str, int)):
value = get_string_id(value)
else:
return False
elif self.predicate in ("IS_SUBSET", "IS_SUPERSET", "INTERSECTS"):
# ensure that all values are enclosed in a set
if self.attr == MORPH:
# break up MORPH into individual Feat=Val values
value = set(get_string_id(v) for v in MorphAnalysis.from_id(self.vocab, value))
elif isinstance(value, (str, int)):
value = set((get_string_id(value),))
elif isinstance(value, Iterable) and all(isinstance(v, (str, int)) for v in value):
value = set(get_string_id(v) for v in value)
else:
return False
if self.predicate == "IN":
return value in self.value
elif self.predicate == "NOT_IN":
return value not in self.value
elif self.predicate == "IS_SUBSET":
return value <= self.value
elif self.predicate == "IS_SUPERSET":
return value >= self.value
elif self.predicate == "INTERSECTS":
return bool(value & self.value)
def __repr__(self):
return repr(("SetPredicate", self.i, self.attr, self.value, self.predicate))
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class _ComparisonPredicate:
operators = ("==", "!=", ">=", "<=", ">", "<")
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None):
self.i = i
self.attr = attr
self.value = value
self.predicate = predicate
self.is_extension = is_extension
self.key = (attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
def __call__(self, Token token):
if self.is_extension:
value = token._.get(self.attr)
else:
value = get_token_attr_for_matcher(token.c, self.attr)
if self.predicate == "==":
return value == self.value
if self.predicate == "!=":
return value != self.value
elif self.predicate == ">=":
return value >= self.value
elif self.predicate == "<=":
return value <= self.value
elif self.predicate == ">":
return value > self.value
elif self.predicate == "<":
return value < self.value
def _get_extra_predicates(spec, extra_predicates, vocab):
predicate_types = {
"REGEX": _RegexPredicate,
"IN": _SetPredicate,
"NOT_IN": _SetPredicate,
"IS_SUBSET": _SetPredicate,
"IS_SUPERSET": _SetPredicate,
"INTERSECTS": _SetPredicate,
"==": _ComparisonPredicate,
"!=": _ComparisonPredicate,
">=": _ComparisonPredicate,
"<=": _ComparisonPredicate,
">": _ComparisonPredicate,
"<": _ComparisonPredicate,
}
seen_predicates = {pred.key: pred.i for pred in extra_predicates}
output = []
for attr, value in spec.items():
if isinstance(attr, str):
if attr == "_":
output.extend(
_get_extension_extra_predicates(
value, extra_predicates, predicate_types,
seen_predicates))
continue
elif attr.upper() == "OP":
continue
if attr.upper() == "TEXT":
attr = "ORTH"
attr = IDS.get(attr.upper())
if isinstance(value, dict):
processed = False
value_with_upper_keys = {k.upper(): v for k, v in value.items()}
for type_, cls in predicate_types.items():
if type_ in value_with_upper_keys:
predicate = cls(len(extra_predicates), attr, value_with_upper_keys[type_], type_, vocab=vocab)
# Don't create a redundant predicates.
# This helps with efficiency, as we're caching the results.
if predicate.key in seen_predicates:
output.append(seen_predicates[predicate.key])
else:
extra_predicates.append(predicate)
output.append(predicate.i)
seen_predicates[predicate.key] = predicate.i
processed = True
if not processed:
warnings.warn(Warnings.W035.format(pattern=value))
return output
def _get_extension_extra_predicates(spec, extra_predicates, predicate_types,
seen_predicates):
output = []
for attr, value in spec.items():
if isinstance(value, dict):
for type_, cls in predicate_types.items():
if type_ in value:
key = (attr, type_, srsly.json_dumps(value[type_], sort_keys=True))
if key in seen_predicates:
output.append(seen_predicates[key])
else:
predicate = cls(len(extra_predicates), attr, value[type_], type_,
is_extension=True)
extra_predicates.append(predicate)
output.append(predicate.i)
seen_predicates[key] = predicate.i
return output
def _get_operators(spec):
# Support 'syntactic sugar' operator '+', as combination of ONE, ZERO_PLUS
lookup = {"*": (ZERO_PLUS,), "+": (ONE, ZERO_PLUS),
"?": (ZERO_ONE,), "1": (ONE,), "!": (ZERO,)}
# Fix casing
spec = {key.upper(): values for key, values in spec.items()
if isinstance(key, str)}
if "OP" not in spec:
return (ONE,)
elif spec["OP"] in lookup:
return lookup[spec["OP"]]
#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)
# {,m} --> {0,m} min of zero, max of m ZERO_ONE,(m)
# {n,} --> {n,∞} min of n, max of inf ONE,(n),ZERO_PLUS
min_max = spec["OP"][1:-1]
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
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))
# if n is empty string, zero would be used
head = tuple(ONE for __ in range(int(n or 0)))
tail = tuple(ZERO_ONE for __ in range(int(m) - int(n or 0))) if m else (ZERO_PLUS,)
return head + tail
else:
keys = ", ".join(lookup.keys()) + ", {n}, {n,m}, {n,}, {,m} where n and m are integers and n <= m "
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raise ValueError(Errors.E011.format(op=spec["OP"], opts=keys))
def _get_extensions(spec, string_store, name2index):
attr_values = []
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if not isinstance(spec.get("_", {}), dict):
raise ValueError(Errors.E154.format())
for name, value in spec.get("_", {}).items():
if isinstance(value, dict):
# Handle predicates (e.g. "IN", in the extra_predicates, not here.
continue
if isinstance(value, str):
value = string_store.add(value)
if name not in name2index:
name2index[name] = len(name2index)
attr_values.append((name2index[name], value))
return attr_values