spaCy/spacy/syntax/beam_parser.pyx

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"""
MALT-style dependency parser
"""
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# cython: profile=True
# cython: experimental_cpp_class_def=True
# cython: cdivision=True
# cython: infer_types=True
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# coding: utf-8
from __future__ import unicode_literals, print_function
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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
from libc.stdlib cimport rand
from libc.math cimport log, exp, isnan, isinf
from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport real_hash64 as hash64
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
from thinc.linear.features cimport ConjunctionExtracter
from thinc.structs cimport FeatureC, ExampleC
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from thinc.extra.search cimport Beam, MaxViolation
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from thinc.extra.eg cimport Example
from thinc.extra.mb cimport Minibatch
from ..structs cimport TokenC
from ..tokens.doc cimport Doc
from ..strings cimport StringStore
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 .parser cimport Parser
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
else:
return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
pf.tree_shape + pf.trigrams)
cdef int BEAM_WIDTH = 16
cdef weight_t BEAM_DENSITY = 0.001
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cdef class BeamParser(Parser):
def __init__(self, *args, **kwargs):
self.beam_width = kwargs.get('beam_width', BEAM_WIDTH)
self.beam_density = kwargs.get('beam_density', BEAM_DENSITY)
Parser.__init__(self, *args, **kwargs)
cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil:
with gil:
self._parseC(tokens, length, nr_feat, self.moves.n_moves)
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cdef int _parseC(self, TokenC* tokens, int length, int nr_feat, int nr_class) except -1:
cdef Beam beam = Beam(self.moves.n_moves, self.beam_width, min_density=self.beam_density)
# TODO: How do we handle new labels here? This increases nr_class
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beam.initialize(self.moves.init_beam_state, length, tokens)
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beam.check_done(_check_final_state, NULL)
if beam.is_done:
_cleanup(beam)
return 0
while not beam.is_done:
self._advance_beam(beam, None, False)
state = <StateClass>beam.at(0)
self.moves.finalize_state(state.c)
for i in range(length):
tokens[i] = state.c._sent[i]
_cleanup(beam)
def update(self, Doc tokens, GoldParse gold_parse, itn=0):
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self.moves.preprocess_gold(gold_parse)
cdef Beam pred = Beam(self.moves.n_moves, self.beam_width)
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pred.initialize(self.moves.init_beam_state, tokens.length, tokens.c)
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pred.check_done(_check_final_state, NULL)
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# Hack for NER
for i in range(pred.size):
stcls = <StateClass>pred.at(i)
self.moves.initialize_state(stcls.c)
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cdef Beam gold = Beam(self.moves.n_moves, self.beam_width, min_density=0.0)
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gold.initialize(self.moves.init_beam_state, tokens.length, tokens.c)
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gold.check_done(_check_final_state, NULL)
violn = MaxViolation()
while not pred.is_done and not gold.is_done:
# We search separately here, to allow for ambiguity in the gold parse.
self._advance_beam(pred, gold_parse, False)
self._advance_beam(gold, gold_parse, True)
violn.check_crf(pred, gold)
if pred.loss > 0 and pred.min_score > (gold.score + self.model.time):
break
else:
# The non-monotonic oracle makes it difficult to ensure final costs are
# correct. Therefore do final correction
for i in range(pred.size):
if self.moves.is_gold_parse(<StateClass>pred.at(i), gold_parse):
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pred._states[i].loss = 0.0
elif pred._states[i].loss == 0.0:
pred._states[i].loss = 1.0
violn.check_crf(pred, gold)
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if pred.size < 1:
raise Exception("No candidates", tokens.length)
if gold.size < 1:
raise Exception("No gold", tokens.length)
if pred.loss == 0:
self.model.update_from_histories(self.moves, tokens, [(0.0, [])])
elif True:
#_check_train_integrity(pred, gold, gold_parse, self.moves)
histories = list(zip(violn.p_probs, violn.p_hist)) + \
list(zip(violn.g_probs, violn.g_hist))
self.model.update_from_histories(self.moves, tokens, histories, min_grad=0.001**(itn+1))
else:
self.model.update_from_histories(self.moves, tokens,
[(1.0, violn.p_hist[0]), (-1.0, violn.g_hist[0])])
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_cleanup(pred)
_cleanup(gold)
return pred.loss
def _advance_beam(self, Beam beam, GoldParse gold, bint follow_gold):
cdef atom_t[CONTEXT_SIZE] context
cdef Pool mem = Pool()
features = <FeatureC*>mem.alloc(self.model.nr_feat, sizeof(FeatureC))
if False:
mb = Minibatch(self.model.widths, beam.size)
for i in range(beam.size):
stcls = <StateClass>beam.at(i)
if stcls.c.is_final():
nr_feat = 0
else:
nr_feat = self.model.set_featuresC(context, features, stcls.c)
self.moves.set_valid(beam.is_valid[i], stcls.c)
mb.c.push_back(features, nr_feat, beam.costs[i], beam.is_valid[i], 0)
self.model(mb)
for i in range(beam.size):
memcpy(beam.scores[i], mb.c.scores(i), mb.c.nr_out() * sizeof(beam.scores[i][0]))
else:
for i in range(beam.size):
stcls = <StateClass>beam.at(i)
if not stcls.is_final():
nr_feat = self.model.set_featuresC(context, features, stcls.c)
self.moves.set_valid(beam.is_valid[i], stcls.c)
self.model.set_scoresC(beam.scores[i], features, nr_feat)
if gold is not None:
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n_gold = 0
lines = []
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for i in range(beam.size):
stcls = <StateClass>beam.at(i)
if not stcls.c.is_final():
self.moves.set_costs(beam.is_valid[i], beam.costs[i], stcls, gold)
if follow_gold:
for j in range(self.moves.n_moves):
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if beam.costs[i][j] >= 1:
beam.is_valid[i][j] = 0
lines.append((stcls.B(0), stcls.B(1),
stcls.B_(0).ent_iob, stcls.B_(1).ent_iob,
stcls.B_(1).sent_start,
j,
beam.is_valid[i][j], 'set invalid',
beam.costs[i][j], self.moves.c[j].move, self.moves.c[j].label))
n_gold += 1 if beam.is_valid[i][j] else 0
if follow_gold and n_gold == 0:
raise Exception("No gold")
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if follow_gold:
beam.advance(_transition_state, NULL, <void*>self.moves.c)
else:
beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
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beam.check_done(_check_final_state, NULL)
# 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.c, moves[clas].label)
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:
state = <StateClass>_state
if state.c.is_final():
return 1
else:
return state.c.hash()
def _check_train_integrity(Beam pred, Beam gold, GoldParse gold_parse, TransitionSystem moves):
for i in range(pred.size):
if not pred._states[i].is_done or pred._states[i].loss == 0:
continue
state = <StateClass>pred.at(i)
if moves.is_gold_parse(state, gold_parse) == True:
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for dep in gold_parse.orig_annot:
print(dep[1], dep[3], dep[4])
print("Cost", pred._states[i].loss)
for j in range(gold_parse.length):
print(gold_parse.orig_annot[j][1], state.H(j), moves.strings[state.safe_get(j).dep])
acts = [moves.c[clas].move for clas in pred.histories[i]]
labels = [moves.c[clas].label for clas in pred.histories[i]]
print([moves.move_name(move, label) for move, label in zip(acts, labels)])
raise Exception("Predicted state is gold-standard")
for i in range(gold.size):
if not gold._states[i].is_done:
continue
state = <StateClass>gold.at(i)
if moves.is_gold(state, gold_parse) == False:
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print("Truth")
for dep in gold_parse.orig_annot:
print(dep[1], dep[3], dep[4])
print("Predicted good")
for j in range(gold_parse.length):
print(gold_parse.orig_annot[j][1], state.H(j), moves.strings[state.safe_get(j).dep])
raise Exception("Gold parse is not gold-standard")