spaCy/spacy/syntax/parser.pyx

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# cython: profile=True
# cython: experimental_cpp_class_def=True
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"""
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
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from libc.string cimport memset, memcpy
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import random
import os.path
from os import path
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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
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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
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from ..tokens cimport Tokens, TokenC
from ..strings cimport StringStore
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from .arc_eager cimport TransitionSystem, Transition
from .transition_system import OracleError
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from ..gold cimport GoldParse
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from . import _parse_features
from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
from .stateclass cimport StateClass
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DEBUG = False
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def set_debug(val):
global DEBUG
DEBUG = val
def get_templates(name):
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pf = _parse_features
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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)
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cdef class Parser:
def __init__(self, StringStore strings, model_dir, transition_system):
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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)
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def __call__(self, Tokens tokens):
if tokens.length == 0:
return 0
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:
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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():
#print stcls.print_state(words)
fill_context(context, stcls)
scores = self.model.score(context)
guess = self.moves.best_valid(scores, stcls)
guess.do(stcls, guess.label)
self.moves.finalize_state(stcls)
tokens.set_parse(stcls._sent)
cdef int _beam_parse(self, Tokens tokens) except -1:
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cdef Beam beam = Beam(self.moves.n_moves, self.cfg.beam_width)
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)
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):
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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]
while not stcls.is_final():
fill_context(context, stcls)
scores = self.model.score(context)
guess = self.moves.best_valid(scores, stcls)
best = self.moves.best_gold(scores, stcls, gold)
cost = guess.get_cost(stcls, &gold.c, guess.label)
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):
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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)
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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()
while not pred.is_done and not gold.is_done:
self._advance_beam(pred, gold_parse, False)
self._advance_beam(gold, gold_parse, True)
violn.check(pred, gold)
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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)
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return pred.loss
def _advance_beam(self, Beam beam, GoldParse gold, bint follow_gold):
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:
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for i in range(beam.size):
stcls = <StateClass>beam.at(i)
self.moves.set_costs(beam.costs[i], stcls, gold)
if follow_gold:
n_true = 0
for j in range(self.moves.n_moves):
beam.is_valid[i][j] *= beam.costs[i][j] == 0
n_true += beam.is_valid[i][j]
assert n_true >= 1
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)
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)