mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-01 00:17:44 +03:00
270 lines
10 KiB
Cython
270 lines
10 KiB
Cython
# cython: profile=True
|
|
# cython: experimental_cpp_class_def=True
|
|
# cython: cdivision=True
|
|
# cython: infer_types=True
|
|
"""
|
|
MALT-style dependency parser
|
|
"""
|
|
from __future__ import unicode_literals
|
|
cimport cython
|
|
|
|
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
|
|
|
|
from libc.stdint cimport uint32_t, uint64_t
|
|
from libc.string cimport memset, memcpy
|
|
from libc.stdlib cimport rand
|
|
from libc.math cimport log, exp, isnan, isinf
|
|
import random
|
|
import os.path
|
|
from os import path
|
|
import shutil
|
|
import json
|
|
import math
|
|
|
|
from cymem.cymem cimport Pool, Address
|
|
from murmurhash.mrmr cimport real_hash64 as hash64
|
|
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
|
|
|
|
|
|
from util import Config
|
|
|
|
from thinc.linear.features cimport ConjunctionExtracter
|
|
from thinc.structs cimport FeatureC, ExampleC
|
|
|
|
from thinc.extra.search cimport Beam
|
|
from thinc.extra.search cimport MaxViolation
|
|
from thinc.extra.eg cimport Example
|
|
from thinc.extra.mb cimport Minibatch
|
|
|
|
from ..structs cimport TokenC
|
|
|
|
from ..tokens.doc cimport Doc
|
|
from ..strings cimport StringStore
|
|
|
|
from .transition_system cimport TransitionSystem, Transition
|
|
|
|
from ..gold cimport GoldParse
|
|
|
|
from . import _parse_features
|
|
from ._parse_features cimport CONTEXT_SIZE
|
|
from ._parse_features cimport fill_context
|
|
from .stateclass cimport StateClass
|
|
from .parser cimport Parser
|
|
|
|
|
|
DEBUG = False
|
|
def set_debug(val):
|
|
global DEBUG
|
|
DEBUG = val
|
|
|
|
|
|
def get_templates(name):
|
|
pf = _parse_features
|
|
if name == 'ner':
|
|
return pf.ner
|
|
elif name == 'debug':
|
|
return pf.unigrams
|
|
else:
|
|
return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
|
|
pf.tree_shape + pf.trigrams)
|
|
|
|
|
|
cdef int BEAM_WIDTH = 16
|
|
cdef weight_t BEAM_DENSITY = 0.001
|
|
|
|
cdef class BeamParser(Parser):
|
|
def __init__(self, *args, **kwargs):
|
|
self.beam_width = kwargs.get('beam_width', BEAM_WIDTH)
|
|
self.beam_density = kwargs.get('beam_density', BEAM_DENSITY)
|
|
Parser.__init__(self, *args, **kwargs)
|
|
|
|
cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil:
|
|
with gil:
|
|
self._parseC(tokens, length, nr_feat, self.moves.n_moves)
|
|
|
|
cdef int _parseC(self, TokenC* tokens, int length, int nr_feat, int nr_class) except -1:
|
|
cdef Beam beam = Beam(self.moves.n_moves, self.beam_width, min_density=self.beam_density)
|
|
# TODO: How do we handle new labels here? This increases nr_class
|
|
beam.initialize(self.moves.init_beam_state, length, tokens)
|
|
beam.check_done(_check_final_state, NULL)
|
|
if beam.is_done:
|
|
_cleanup(beam)
|
|
return 0
|
|
while not beam.is_done:
|
|
self._advance_beam(beam, None, False)
|
|
state = <StateClass>beam.at(0)
|
|
self.moves.finalize_state(state.c)
|
|
for i in range(length):
|
|
tokens[i] = state.c._sent[i]
|
|
_cleanup(beam)
|
|
|
|
def update(self, Doc tokens, GoldParse gold_parse, itn=0):
|
|
self.moves.preprocess_gold(gold_parse)
|
|
cdef Beam pred = Beam(self.moves.n_moves, self.beam_width)
|
|
pred.initialize(self.moves.init_beam_state, tokens.length, tokens.c)
|
|
pred.check_done(_check_final_state, NULL)
|
|
# Hack for NER
|
|
for i in range(pred.size):
|
|
stcls = <StateClass>pred.at(i)
|
|
self.moves.initialize_state(stcls.c)
|
|
|
|
cdef Beam gold = Beam(self.moves.n_moves, self.beam_width, min_density=0.0)
|
|
gold.initialize(self.moves.init_beam_state, tokens.length, tokens.c)
|
|
gold.check_done(_check_final_state, NULL)
|
|
violn = MaxViolation()
|
|
while not pred.is_done and not gold.is_done:
|
|
# We search separately here, to allow for ambiguity in the gold parse.
|
|
self._advance_beam(pred, gold_parse, False)
|
|
self._advance_beam(gold, gold_parse, True)
|
|
violn.check_crf(pred, gold)
|
|
if pred.loss > 0 and pred.min_score > (gold.score + self.model.time):
|
|
break
|
|
else:
|
|
# The non-monotonic oracle makes it difficult to ensure final costs are
|
|
# correct. Therefore do final correction
|
|
for i in range(pred.size):
|
|
if is_gold(<StateClass>pred.at(i), gold_parse, self.moves.strings):
|
|
pred._states[i].loss = 0.0
|
|
elif pred._states[i].loss == 0.0:
|
|
pred._states[i].loss = 1.0
|
|
violn.check_crf(pred, gold)
|
|
if pred.size < 1:
|
|
raise Exception("No candidates", tokens.length)
|
|
if gold.size < 1:
|
|
raise Exception("No gold", tokens.length)
|
|
if pred.loss == 0:
|
|
self.model.update_from_histories(self.moves, tokens, [(0.0, [])])
|
|
elif True:
|
|
#_check_train_integrity(pred, gold, gold_parse, self.moves)
|
|
histories = list(zip(violn.p_probs, violn.p_hist)) + \
|
|
list(zip(violn.g_probs, violn.g_hist))
|
|
self.model.update_from_histories(self.moves, tokens, histories, min_grad=0.001**(itn+1))
|
|
else:
|
|
self.model.update_from_histories(self.moves, tokens,
|
|
[(1.0, violn.p_hist[0]), (-1.0, violn.g_hist[0])])
|
|
_cleanup(pred)
|
|
_cleanup(gold)
|
|
return pred.loss
|
|
|
|
def _advance_beam(self, Beam beam, GoldParse gold, bint follow_gold):
|
|
cdef atom_t[CONTEXT_SIZE] context
|
|
cdef Pool mem = Pool()
|
|
features = <FeatureC*>mem.alloc(self.model.nr_feat, sizeof(FeatureC))
|
|
if False:
|
|
mb = Minibatch(self.model.widths, beam.size)
|
|
for i in range(beam.size):
|
|
stcls = <StateClass>beam.at(i)
|
|
if stcls.c.is_final():
|
|
nr_feat = 0
|
|
else:
|
|
nr_feat = self.model.set_featuresC(context, features, stcls.c)
|
|
self.moves.set_valid(beam.is_valid[i], stcls.c)
|
|
mb.c.push_back(features, nr_feat, beam.costs[i], beam.is_valid[i], 0)
|
|
self.model(mb)
|
|
for i in range(beam.size):
|
|
memcpy(beam.scores[i], mb.c.scores(i), mb.c.nr_out() * sizeof(beam.scores[i][0]))
|
|
else:
|
|
for i in range(beam.size):
|
|
stcls = <StateClass>beam.at(i)
|
|
if not stcls.is_final():
|
|
nr_feat = self.model.set_featuresC(context, features, stcls.c)
|
|
self.moves.set_valid(beam.is_valid[i], stcls.c)
|
|
self.model.set_scoresC(beam.scores[i], features, nr_feat)
|
|
if gold is not None:
|
|
n_gold = 0
|
|
lines = []
|
|
for i in range(beam.size):
|
|
stcls = <StateClass>beam.at(i)
|
|
if not stcls.c.is_final():
|
|
self.moves.set_costs(beam.is_valid[i], beam.costs[i], stcls, gold)
|
|
if follow_gold:
|
|
for j in range(self.moves.n_moves):
|
|
if beam.costs[i][j] >= 1:
|
|
beam.is_valid[i][j] = 0
|
|
lines.append((stcls.B(0), stcls.B(1),
|
|
stcls.B_(0).ent_iob, stcls.B_(1).ent_iob,
|
|
stcls.B_(1).sent_start,
|
|
j,
|
|
beam.is_valid[i][j], 'set invalid',
|
|
beam.costs[i][j], self.moves.c[j].move, self.moves.c[j].label))
|
|
n_gold += 1 if beam.is_valid[i][j] else 0
|
|
if follow_gold and n_gold == 0:
|
|
raise Exception("No gold")
|
|
if follow_gold:
|
|
beam.advance(_transition_state, NULL, <void*>self.moves.c)
|
|
else:
|
|
beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
|
|
beam.check_done(_check_final_state, NULL)
|
|
|
|
|
|
# These are passed as callbacks to thinc.search.Beam
|
|
cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
|
|
dest = <StateClass>_dest
|
|
src = <StateClass>_src
|
|
moves = <const Transition*>_moves
|
|
dest.clone(src)
|
|
moves[clas].do(dest.c, moves[clas].label)
|
|
|
|
|
|
cdef int _check_final_state(void* _state, void* extra_args) except -1:
|
|
return (<StateClass>_state).is_final()
|
|
|
|
|
|
def _cleanup(Beam beam):
|
|
for i in range(beam.width):
|
|
Py_XDECREF(<PyObject*>beam._states[i].content)
|
|
Py_XDECREF(<PyObject*>beam._parents[i].content)
|
|
|
|
|
|
cdef hash_t _hash_state(void* _state, void* _) except 0:
|
|
state = <StateClass>_state
|
|
if state.c.is_final():
|
|
return 1
|
|
else:
|
|
return state.c.hash()
|
|
|
|
|
|
def _check_train_integrity(Beam pred, Beam gold, GoldParse gold_parse, TransitionSystem moves):
|
|
for i in range(pred.size):
|
|
if not pred._states[i].is_done or pred._states[i].loss == 0:
|
|
continue
|
|
state = <StateClass>pred.at(i)
|
|
if is_gold(state, gold_parse, moves.strings) == True:
|
|
for dep in gold_parse.orig_annot:
|
|
print(dep[1], dep[3], dep[4])
|
|
print("Cost", pred._states[i].loss)
|
|
for j in range(gold_parse.length):
|
|
print(gold_parse.orig_annot[j][1], state.H(j), moves.strings[state.safe_get(j).dep])
|
|
acts = [moves.c[clas].move for clas in pred.histories[i]]
|
|
labels = [moves.c[clas].label for clas in pred.histories[i]]
|
|
print([moves.move_name(move, label) for move, label in zip(acts, labels)])
|
|
raise Exception("Predicted state is gold-standard")
|
|
for i in range(gold.size):
|
|
if not gold._states[i].is_done:
|
|
continue
|
|
state = <StateClass>gold.at(i)
|
|
if is_gold(state, gold_parse, moves.strings) == False:
|
|
print("Truth")
|
|
for dep in gold_parse.orig_annot:
|
|
print(dep[1], dep[3], dep[4])
|
|
print("Predicted good")
|
|
for j in range(gold_parse.length):
|
|
print(gold_parse.orig_annot[j][1], state.H(j), moves.strings[state.safe_get(j).dep])
|
|
raise Exception("Gold parse is not gold-standard")
|
|
|
|
|
|
def is_gold(StateClass state, GoldParse gold, StringStore strings):
|
|
predicted = set()
|
|
truth = set()
|
|
for i in range(gold.length):
|
|
if gold.cand_to_gold[i] is None:
|
|
continue
|
|
if state.safe_get(i).dep:
|
|
predicted.add((i, state.H(i), strings[state.safe_get(i).dep]))
|
|
else:
|
|
predicted.add((i, state.H(i), 'ROOT'))
|
|
id_, word, tag, head, dep, ner = gold.orig_annot[gold.cand_to_gold[i]]
|
|
truth.add((id_, head, dep))
|
|
return truth == predicted
|
|
|