"""
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
import sys
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.api cimport Example, ExampleC
from ..structs cimport TokenC
from ..tokens.doc cimport Doc
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
from .._ml cimport arg_max_if_true
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
elif name.startswith('embed'):
return (pf.words, pf.tags, pf.labels)
else:
return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
pf.tree_shape + pf.trigrams)
def ParserFactory(transition_system):
return lambda strings, dir_: Parser(strings, dir_, transition_system)
cdef class Parser:
def __init__(self, StringStore strings, transition_system, model):
self.moves = transition_system
self.model = model
@classmethod
def from_dir(cls, model_dir, strings, transition_system):
if not os.path.exists(model_dir):
print >> sys.stderr, "Warning: No model found at", model_dir
elif not os.path.isdir(model_dir):
print >> sys.stderr, "Warning: model path:", model_dir, "is not a directory"
cfg = Config.read(model_dir, 'config')
moves = transition_system(strings, cfg.labels)
templates = get_templates(cfg.features)
model = Model(moves.n_moves, templates, model_dir)
return cls(strings, moves, model)
def __call__(self, Doc tokens):
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
self.moves.initialize_state(stcls)
cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
self.model.n_feats, self.model.n_feats)
self.parse(stcls, eg.c)
tokens.set_parse(stcls._sent)
cdef void predict(self, StateClass stcls, ExampleC* eg) nogil:
memset(eg.scores, 0, eg.nr_class * sizeof(weight_t))
self.moves.set_valid(eg.is_valid, stcls)
fill_context(eg.atoms, stcls)
self.model.set_scores(eg.scores, eg.atoms)
eg.guess = arg_max_if_true(eg.scores, eg.is_valid, self.model.n_classes)
cdef void parse(self, StateClass stcls, ExampleC eg) nogil:
while not stcls.is_final():
self.predict(stcls, &eg)
if not eg.is_valid[eg.guess]:
break
self.moves.c[eg.guess].do(stcls, self.moves.c[eg.guess].label)
self.moves.finalize_state(stcls)
def train(self, Doc tokens, GoldParse gold):
self.moves.preprocess_gold(gold)
cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
self.moves.initialize_state(stcls)
cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
self.model.n_feats, self.model.n_feats)
cdef weight_t loss = 0
words = [w.orth_ for w in tokens]
cdef Transition G
while not stcls.is_final():
memset(eg.c.scores, 0, eg.c.nr_class * sizeof(weight_t))
self.moves.set_costs(eg.c.is_valid, eg.c.costs, stcls, gold)
fill_context(eg.c.atoms, stcls)
self.model.train(eg)
G = self.moves.c[eg.c.guess]
self.moves.c[eg.c.guess].do(stcls, self.moves.c[eg.c.guess].label)
loss += eg.c.loss
return loss
def step_through(self, Doc doc):
return StepwiseState(self, doc)
cdef class StepwiseState:
cdef readonly StateClass stcls
cdef readonly Example eg
cdef readonly Doc doc
cdef readonly Parser parser
def __init__(self, Parser parser, Doc doc):
self.parser = parser
self.doc = doc
self.stcls = StateClass.init(doc.data, doc.length)
self.parser.moves.initialize_state(self.stcls)
self.eg = Example(self.parser.model.n_classes, CONTEXT_SIZE,
self.parser.model.n_feats, self.parser.model.n_feats)
def __enter__(self):
return self
def __exit__(self, type, value, traceback):
self.finish()
@property
def is_final(self):
return self.stcls.is_final()
@property
def stack(self):
return self.stcls.stack
@property
def queue(self):
return self.stcls.queue
@property
def heads(self):
return [self.stcls.H(i) for i in range(self.stcls.length)]
@property
def deps(self):
return [self.doc.vocab.strings[self.stcls._sent[i].dep]
for i in range(self.stcls.length)]
def predict(self):
self.parser.predict(self.stcls, &self.eg.c)
action = self.parser.moves.c[self.eg.c.guess]
return self.parser.moves.move_name(action.move, action.label)
def transition(self, action_name):
moves = {'S': 0, 'D': 1, 'L': 2, 'R': 3}
if action_name == '_':
action_name = self.predict()
action = self.parser.moves.lookup_transition(action_name)
elif action_name == 'L' or action_name == 'R':
self.predict()
move = moves[action_name]
clas = _arg_max_clas(self.eg.c.scores, move, self.parser.moves.c,
self.eg.c.nr_class)
action = self.parser.moves.c[clas]
else:
action = self.parser.moves.lookup_transition(action_name)
action.do(self.stcls, action.label)
def finish(self):
if self.stcls.is_final():
self.parser.moves.finalize_state(self.stcls)
self.doc.set_parse(self.stcls._sent)
cdef int _arg_max_clas(const weight_t* scores, int move, const Transition* actions,
int nr_class) except -1:
cdef weight_t score = 0
cdef int mode = -1
cdef int i
for i in range(nr_class):
if actions[i].move == move and (mode == -1 or scores[i] >= score):
mode = i
score = scores[i]
return mode