spaCy/spacy/syntax/parser.pyx

145 lines
4.9 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 .arc_eager cimport TransitionSystem, Transition
from .arc_eager import OracleError
from ._state cimport init_state, State, is_final, get_idx, get_s0, get_s1, get_n0, get_n1
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]
return ' '.join((str(s.stack_len), third, second, top, '|', n0, n1))
def get_templates(name):
pf = _parse_features
if name == 'zhang':
return pf.unigrams, pf.arc_eager
else:
return pf.unigrams, (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, model_dir):
assert os.path.exists(model_dir) and os.path.isdir(model_dir)
self.cfg = Config.read(model_dir, 'config')
self.moves = TransitionSystem(self.cfg.left_labels, self.cfg.right_labels)
hasty_templ, full_templ = get_templates(self.cfg.features)
self.model = Model(self.moves.n_moves, full_templ, model_dir)
def __call__(self, Tokens tokens):
cdef:
Transition guess
uint64_t state_key
cdef atom_t[CONTEXT_SIZE] context
cdef int n_feats
cdef Pool mem = Pool()
cdef State* state = init_state(mem, tokens.data, tokens.length)
while not is_final(state):
fill_context(context, state)
scores = self.model.score(context)
guess = self.moves.best_valid(scores, state)
self.moves.transition(state, &guess)
# Messily tell Tokens object the string names of the dependency labels
dep_strings = [None] * len(self.moves.label_ids)
for label, id_ in self.moves.label_ids.items():
dep_strings[id_] = label
tokens._dep_strings = tuple(dep_strings)
tokens.is_parsed = True
# TODO: Clean this up.
tokens._py_tokens = [None] * tokens.length
return 0
def train_sent(self, Tokens tokens, list gold_heads, list gold_labels,
force_gold=False):
cdef:
const Feature* feats
const weight_t* scores
Transition guess
Transition gold
cdef int n_feats
cdef atom_t[CONTEXT_SIZE] context
cdef Pool mem = Pool()
cdef int* heads_array = <int*>mem.alloc(tokens.length, sizeof(int))
cdef int* labels_array = <int*>mem.alloc(tokens.length, sizeof(int))
cdef int i
for i in range(tokens.length):
if gold_heads[i] is None:
heads_array[i] = -1
labels_array[i] = -1
else:
heads_array[i] = gold_heads[i]
labels_array[i] = self.moves.label_ids[gold_labels[i]]
py_words = [t.orth_ for t in tokens]
py_moves = ['S', 'D', 'L', 'R', 'BS', 'BR']
history = []
#print py_words
cdef State* state = init_state(mem, tokens.data, tokens.length)
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(&guess, scores, state, heads_array, labels_array)
history.append((py_moves[best.move], print_state(state, py_words)))
self.model.update(context, guess.clas, best.clas, guess.cost)
if force_gold:
self.moves.transition(state, &best)
else:
self.moves.transition(state, &guess)
cdef int n_corr = 0
for i in range(tokens.length):
if gold_heads[i] != -1:
n_corr += (i + state.sent[i].head) == gold_heads[i]
if force_gold and n_corr != tokens.length:
#print py_words
#print gold_heads
#for move, state_str in history:
# print move, state_str
#for i in range(tokens.length):
# print py_words[i], py_words[i + state.sent[i].head], py_words[gold_heads[i]]
raise OracleError
return n_corr