mirror of https://github.com/explosion/spaCy.git
247 lines
7.8 KiB
Cython
247 lines
7.8 KiB
Cython
"""
|
|
MALT-style dependency parser
|
|
"""
|
|
from __future__ import unicode_literals
|
|
cimport cython
|
|
|
|
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
|
|
from cpython.exc cimport PyErr_CheckSignals
|
|
|
|
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 thinc.linear.avgtron cimport AveragedPerceptron
|
|
from thinc.linalg cimport VecVec
|
|
|
|
from util import Config
|
|
|
|
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
|
|
|
|
|
|
|
|
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 ParserModel(AveragedPerceptron):
|
|
cdef void set_featuresC(self, ExampleC* eg, StateClass stcls) except *:
|
|
fill_context(eg.atoms, stcls)
|
|
eg.nr_feat = self.extracter.set_features(eg.features, eg.atoms)
|
|
|
|
|
|
cdef class Parser:
|
|
def __init__(self, StringStore strings, transition_system, ParserModel 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 = ParserModel(templates)
|
|
if path.exists(path.join(model_dir, 'model')):
|
|
model.load(path.join(model_dir, 'model'))
|
|
return cls(strings, moves, model)
|
|
|
|
@classmethod
|
|
def load(cls, pkg_or_str_or_file, vocab):
|
|
# TODO
|
|
raise NotImplementedError(
|
|
"This should be here, but isn't yet =/. Use Parser.from_dir")
|
|
|
|
|
|
def __reduce__(self):
|
|
return (Parser, (self.moves.strings, self.moves, self.model), None, None)
|
|
|
|
def __call__(self, Doc tokens):
|
|
cdef StateClass stcls = StateClass.init(tokens.c, tokens.length)
|
|
self.moves.initialize_state(stcls)
|
|
|
|
cdef Pool mem = Pool()
|
|
cdef Example eg = Example(
|
|
nr_class=self.moves.n_moves,
|
|
nr_atom=CONTEXT_SIZE,
|
|
nr_feat=self.model.nr_feat)
|
|
while not stcls.is_final():
|
|
self.model.set_featuresC(&eg.c, stcls)
|
|
self.moves.set_valid(eg.c.is_valid, stcls)
|
|
self.model.set_scoresC(eg.c.scores, eg.c.features, eg.c.nr_feat)
|
|
|
|
guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
|
|
|
|
action = self.moves.c[guess]
|
|
if not eg.is_valid[guess]:
|
|
raise ValueError(
|
|
"Illegal action: %s" % self.moves.move_name(action.move, action.label)
|
|
)
|
|
|
|
action.do(stcls, action.label)
|
|
# Check for KeyboardInterrupt etc. Untested
|
|
PyErr_CheckSignals()
|
|
eg.reset_classes(eg.nr_class)
|
|
self.moves.finalize_state(stcls)
|
|
tokens.set_parse(stcls._sent)
|
|
|
|
def train(self, Doc tokens, GoldParse gold):
|
|
self.moves.preprocess_gold(gold)
|
|
cdef StateClass stcls = StateClass.init(tokens.c, tokens.length)
|
|
self.moves.initialize_state(stcls)
|
|
cdef Pool mem = Pool()
|
|
cdef Example eg = Example(
|
|
nr_class=self.moves.n_moves,
|
|
nr_atom=CONTEXT_SIZE,
|
|
nr_feat=self.model.nr_feat)
|
|
cdef weight_t loss = 0
|
|
cdef Transition action
|
|
while not stcls.is_final():
|
|
self.model.set_featuresC(&eg.c, stcls)
|
|
self.moves.set_costs(eg.c.is_valid, eg.c.costs, stcls, gold)
|
|
self.model.set_scoresC(eg.c.scores, eg.c.features, eg.c.nr_feat)
|
|
self.model.updateC(&eg.c)
|
|
guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
|
|
|
|
action = self.moves.c[eg.guess]
|
|
action.do(stcls, action.label)
|
|
loss += eg.costs[eg.guess]
|
|
eg.reset_classes(eg.nr_class)
|
|
return loss
|
|
|
|
def step_through(self, Doc doc):
|
|
return StepwiseState(self, doc)
|
|
|
|
def add_label(self, label):
|
|
for action in self.moves.action_types:
|
|
self.moves.add_action(action, label)
|
|
|
|
|
|
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.c, doc.length)
|
|
self.parser.moves.initialize_state(self.stcls)
|
|
self.eg = Example(
|
|
nr_class=self.parser.moves.n_moves,
|
|
nr_atom=CONTEXT_SIZE,
|
|
nr_feat=self.parser.model.nr_feat)
|
|
|
|
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.eg.reset()
|
|
self.parser.model.set_featuresC(&self.eg.c, self.stcls)
|
|
self.parser.moves.set_valid(self.eg.c.is_valid, self.stcls)
|
|
self.parser.model.set_scoresC(self.eg.c.scores,
|
|
self.eg.c.features, self.eg.c.nr_feat)
|
|
|
|
cdef Transition action = self.parser.moves.c[self.eg.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
|