mirror of https://github.com/explosion/spaCy.git
263 lines
9.4 KiB
Cython
263 lines
9.4 KiB
Cython
# cython: profile=True
|
|
# cython: experimental_cpp_class_def=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
|
|
import random
|
|
import os.path
|
|
from os import path
|
|
import shutil
|
|
import json
|
|
|
|
from cymem.cymem cimport Pool, Address
|
|
from murmurhash.mrmr cimport hash64
|
|
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
|
|
|
|
|
|
from util import Config
|
|
|
|
from thinc.features cimport Extractor
|
|
from thinc.features cimport Feature
|
|
from thinc.features cimport count_feats
|
|
|
|
from thinc.learner cimport LinearModel
|
|
|
|
from thinc.search cimport Beam
|
|
from thinc.search cimport MaxViolation
|
|
|
|
from ..tokens cimport Tokens, TokenC
|
|
from ..strings cimport StringStore
|
|
|
|
|
|
from .transition_system import OracleError
|
|
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
|
|
|
|
|
|
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.s0_n1 + pf.n0_n1 + \
|
|
pf.tree_shape + pf.trigrams)
|
|
|
|
|
|
cdef class Parser:
|
|
def __init__(self, StringStore strings, model_dir, transition_system):
|
|
assert os.path.exists(model_dir) and os.path.isdir(model_dir)
|
|
self.cfg = Config.read(model_dir, 'config')
|
|
self.moves = transition_system(strings, self.cfg.labels)
|
|
templates = get_templates(self.cfg.features)
|
|
self.model = Model(self.moves.n_moves, templates, model_dir)
|
|
|
|
def __call__(self, Tokens tokens):
|
|
if self.cfg.get('beam_width', 1) < 1:
|
|
self._greedy_parse(tokens)
|
|
else:
|
|
self._beam_parse(tokens)
|
|
|
|
def train(self, Tokens tokens, GoldParse gold):
|
|
self.moves.preprocess_gold(gold)
|
|
if self.cfg.beam_width < 1:
|
|
return self._greedy_train(tokens, gold)
|
|
else:
|
|
return self._beam_train(tokens, gold)
|
|
|
|
cdef int _greedy_parse(self, Tokens tokens) except -1:
|
|
cdef atom_t[CONTEXT_SIZE] context
|
|
cdef int n_feats
|
|
cdef Pool mem = Pool()
|
|
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
|
|
self.moves.initialize_state(stcls)
|
|
cdef Transition guess
|
|
words = [w.orth_ for w in tokens]
|
|
while not stcls.is_final():
|
|
fill_context(context, stcls)
|
|
scores = self.model.score(context)
|
|
guess = self.moves.best_valid(scores, stcls)
|
|
#print self.moves.move_name(guess.move, guess.label), stcls.print_state(words)
|
|
guess.do(stcls, guess.label)
|
|
assert stcls._s_i >= 0
|
|
self.moves.finalize_state(stcls)
|
|
tokens.set_parse(stcls._sent)
|
|
|
|
cdef int _beam_parse(self, Tokens tokens) except -1:
|
|
cdef Beam beam = Beam(self.moves.n_moves, self.cfg.beam_width)
|
|
words = [w.orth_ for w in tokens]
|
|
beam.initialize(_init_state, tokens.length, tokens.data)
|
|
beam.check_done(_check_final_state, NULL)
|
|
while not beam.is_done:
|
|
self._advance_beam(beam, None, False, words)
|
|
state = <StateClass>beam.at(0)
|
|
self.moves.finalize_state(state)
|
|
tokens.set_parse(state._sent)
|
|
_cleanup(beam)
|
|
|
|
def _greedy_train(self, Tokens tokens, GoldParse gold):
|
|
cdef Pool mem = Pool()
|
|
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
|
|
self.moves.initialize_state(stcls)
|
|
|
|
cdef int cost
|
|
cdef const Feature* feats
|
|
cdef const weight_t* scores
|
|
cdef Transition guess
|
|
cdef Transition best
|
|
cdef atom_t[CONTEXT_SIZE] context
|
|
loss = 0
|
|
words = [w.orth_ for w in tokens]
|
|
history = []
|
|
while not stcls.is_final():
|
|
assert stcls._s_i >= 0
|
|
fill_context(context, stcls)
|
|
scores = self.model.score(context)
|
|
guess = self.moves.best_valid(scores, stcls)
|
|
try:
|
|
best = self.moves.best_gold(scores, stcls, gold)
|
|
except:
|
|
history.append((self.moves.move_name(guess.move, guess.label), '!', stcls.print_state(words)))
|
|
for i, word in enumerate(words):
|
|
print gold.orig_annot[i]
|
|
print '\n'.join('\t'.join(s) for s in history)
|
|
print words[gold.c.heads[stcls.S(0)]]
|
|
print words[gold.c.heads[stcls.B(0)]]
|
|
self.moves.set_valid(self.moves._is_valid, stcls)
|
|
raise
|
|
cost = guess.get_cost(stcls, &gold.c, guess.label)
|
|
history.append((self.moves.move_name(guess.move, guess.label), str(cost), stcls.print_state(words)))
|
|
self.model.update(context, guess.clas, best.clas, cost)
|
|
guess.do(stcls, guess.label)
|
|
loss += cost
|
|
return loss
|
|
|
|
def _beam_train(self, Tokens tokens, GoldParse gold_parse):
|
|
cdef Beam pred = Beam(self.moves.n_moves, self.cfg.beam_width)
|
|
pred.initialize(_init_state, tokens.length, tokens.data)
|
|
pred.check_done(_check_final_state, NULL)
|
|
cdef Beam gold = Beam(self.moves.n_moves, self.cfg.beam_width)
|
|
gold.initialize(_init_state, tokens.length, tokens.data)
|
|
gold.check_done(_check_final_state, NULL)
|
|
|
|
violn = MaxViolation()
|
|
words = [w.orth_ for w in tokens]
|
|
while not pred.is_done and not gold.is_done:
|
|
self._advance_beam(pred, gold_parse, False, words)
|
|
self._advance_beam(gold, gold_parse, True, words)
|
|
violn.check(pred, gold)
|
|
if pred.loss >= 1:
|
|
counts = {clas: {} for clas in range(self.model.n_classes)}
|
|
self._count_feats(counts, tokens, violn.g_hist, 1)
|
|
self._count_feats(counts, tokens, violn.p_hist, -1)
|
|
else:
|
|
counts = {}
|
|
self.model._model.update(counts)
|
|
_cleanup(pred)
|
|
_cleanup(gold)
|
|
return pred.loss
|
|
|
|
def _advance_beam(self, Beam beam, GoldParse gold, bint follow_gold, words):
|
|
cdef atom_t[CONTEXT_SIZE] context
|
|
cdef int i, j, cost
|
|
cdef bint is_valid
|
|
cdef const Transition* move
|
|
for i in range(beam.size):
|
|
stcls = <StateClass>beam.at(i)
|
|
if not stcls.is_final():
|
|
fill_context(context, stcls)
|
|
self.model.set_scores(beam.scores[i], context)
|
|
self.moves.set_valid(beam.is_valid[i], stcls)
|
|
if gold is not None:
|
|
for i in range(beam.size):
|
|
stcls = <StateClass>beam.at(i)
|
|
if not stcls.is_final():
|
|
self.moves.set_costs(beam.costs[i], stcls, gold)
|
|
if follow_gold:
|
|
for j in range(self.moves.n_moves):
|
|
beam.is_valid[i][j] *= beam.costs[i][j] == 0
|
|
beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
|
|
beam.check_done(_check_final_state, NULL)
|
|
|
|
def _count_feats(self, dict counts, Tokens tokens, list hist, int inc):
|
|
cdef atom_t[CONTEXT_SIZE] context
|
|
cdef Pool mem = Pool()
|
|
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
|
|
self.moves.initialize_state(stcls)
|
|
|
|
cdef class_t clas
|
|
cdef int n_feats
|
|
for clas in hist:
|
|
fill_context(context, stcls)
|
|
feats = self.model._extractor.get_feats(context, &n_feats)
|
|
count_feats(counts[clas], feats, n_feats, inc)
|
|
self.moves.c[clas].do(stcls, self.moves.c[clas].label)
|
|
|
|
|
|
# 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, moves[clas].label)
|
|
|
|
|
|
cdef void* _init_state(Pool mem, int length, void* tokens) except NULL:
|
|
cdef StateClass st = StateClass.init(<const TokenC*>tokens, length)
|
|
st.fast_forward()
|
|
Py_INCREF(st)
|
|
return <void*>st
|
|
|
|
|
|
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:
|
|
return <hash_t>_state
|
|
|
|
#state = <const State*>_state
|
|
#cdef atom_t[10] rep
|
|
|
|
#rep[0] = state.stack[0] if state.stack_len >= 1 else 0
|
|
#rep[1] = state.stack[-1] if state.stack_len >= 2 else 0
|
|
#rep[2] = state.stack[-2] if state.stack_len >= 3 else 0
|
|
#rep[3] = state.i
|
|
#rep[4] = state.sent[state.stack[0]].l_kids if state.stack_len >= 1 else 0
|
|
#rep[5] = state.sent[state.stack[0]].r_kids if state.stack_len >= 1 else 0
|
|
#rep[6] = state.sent[state.stack[0]].dep if state.stack_len >= 1 else 0
|
|
#rep[7] = state.sent[state.stack[-1]].dep if state.stack_len >= 2 else 0
|
|
#if get_left(state, get_n0(state), 1) != NULL:
|
|
# rep[8] = get_left(state, get_n0(state), 1).dep
|
|
#else:
|
|
# rep[8] = 0
|
|
#rep[9] = state.sent[state.i].l_kids
|
|
#return hash64(rep, sizeof(atom_t) * 10, 0)
|