spaCy/spacy/syntax/parser.pyx

120 lines
3.5 KiB
Cython

"""
MALT-style dependency parser
"""
from __future__ import unicode_literals
cimport cython
from libc.stdint cimport uint32_t, uint64_t
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
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 ..tokens cimport Tokens, TokenC
from ..strings cimport StringStore
from .arc_eager cimport TransitionSystem, Transition
from .transition_system import OracleError
from ._state cimport new_state, State, is_final, get_idx, get_s0, get_s1, get_n0, get_n1
from .conll cimport GoldParse
from . import _parse_features
from ._parse_features cimport fill_context, CONTEXT_SIZE
DEBUG = False
def set_debug(val):
global DEBUG
DEBUG = val
cdef unicode print_state(State* s, list words):
words = list(words) + ['EOL']
top = words[s.stack[0]] + '_%d' % s.sent[s.stack[0]].head
second = words[s.stack[-1]] + '_%d' % s.sent[s.stack[-1]].head
third = words[s.stack[-2]] + '_%d' % s.sent[s.stack[-2]].head
n0 = words[s.i]
n1 = words[s.i + 1]
if s.ents_len:
ent = '%s %d-%d' % (s.ent.label, s.ent.start, s.ent.end)
else:
ent = '-'
return ' '.join((ent, str(s.stack_len), third, second, top, '|', n0, n1))
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 GreedyParser:
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 tokens.length == 0:
return 0
cdef atom_t[CONTEXT_SIZE] context
cdef int n_feats
cdef Pool mem = Pool()
cdef State* state = new_state(mem, tokens.data, tokens.length)
self.moves.first_state(state)
cdef Transition guess
while not is_final(state):
fill_context(context, state)
scores = self.model.score(context)
guess = self.moves.best_valid(scores, state)
guess.do(&guess, state)
tokens.set_parse(state.sent)
return 0
def train(self, Tokens tokens, GoldParse gold):
self.moves.preprocess_gold(gold)
cdef Pool mem = Pool()
cdef State* state = new_state(mem, tokens.data, tokens.length)
self.moves.first_state(state)
cdef int cost
cdef const Feature* feats
cdef const weight_t* scores
cdef Transition guess
cdef Transition best
cdef atom_t[CONTEXT_SIZE] context
while not is_final(state):
fill_context(context, state)
scores = self.model.score(context)
guess = self.moves.best_valid(scores, state)
best = self.moves.best_gold(scores, state, gold)
cost = guess.get_cost(&guess, state, gold)
self.model.update(context, guess.clas, best.clas, cost)
guess.do(&guess, state)