mirror of
https://github.com/explosion/spaCy.git
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247 lines
8.7 KiB
Cython
247 lines
8.7 KiB
Cython
# cython: profile=True
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# cython: experimental_cpp_class_def=True
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"""
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MALT-style dependency parser
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"""
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from __future__ import unicode_literals
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cimport cython
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from libc.stdint cimport uint32_t, uint64_t
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from libc.string cimport memset, memcpy
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import random
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import os.path
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from os import path
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import shutil
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import json
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from cymem.cymem cimport Pool, Address
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from murmurhash.mrmr cimport hash64
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from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
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from util import Config
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from thinc.features cimport Extractor
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from thinc.features cimport Feature
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from thinc.features cimport count_feats
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from thinc.learner cimport LinearModel
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from thinc.search cimport Beam
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from thinc.search cimport MaxViolation
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from ..tokens cimport Tokens, TokenC
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from ..strings cimport StringStore
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from .arc_eager cimport TransitionSystem, Transition
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from .transition_system import OracleError
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from ._state cimport State, new_state, copy_state, is_final, push_stack, get_left, get_n0
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from ..gold cimport GoldParse
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from . import _parse_features
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from ._parse_features cimport CONTEXT_SIZE
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from ._parse_features cimport _new_fill_context as fill_context
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#from ._parse_features cimport fill_context
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DEBUG = False
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def set_debug(val):
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global DEBUG
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DEBUG = val
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cdef unicode print_state(State* s, list words):
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words = list(words) + ['EOL']
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top = words[s.stack[0]] + '_%d' % s.sent[s.stack[0]].head
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second = words[s.stack[-1]] + '_%d' % s.sent[s.stack[-1]].head
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third = words[s.stack[-2]] + '_%d' % s.sent[s.stack[-2]].head
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n0 = words[s.i] if s.i < len(words) else 'EOL'
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n1 = words[s.i + 1] if s.i+1 < len(words) else 'EOL'
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if s.ents_len:
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ent = '%s %d-%d' % (s.ent.label, s.ent.start, s.ent.end)
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else:
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ent = '-'
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return ' '.join((ent, str(s.stack_len), third, second, top, '|', n0, n1))
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def get_templates(name):
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pf = _parse_features
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if name == 'ner':
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return pf.ner
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elif name == 'debug':
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return pf.unigrams
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else:
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return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s0_n1 + pf.n0_n1 + \
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pf.tree_shape + pf.trigrams)
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cdef class Parser:
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def __init__(self, StringStore strings, model_dir, transition_system):
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assert os.path.exists(model_dir) and os.path.isdir(model_dir)
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self.cfg = Config.read(model_dir, 'config')
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self.moves = transition_system(strings, self.cfg.labels)
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templates = get_templates(self.cfg.features)
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self.model = Model(self.moves.n_moves, templates, model_dir)
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def __call__(self, Tokens tokens):
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if tokens.length == 0:
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return 0
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if self.cfg.get('beam_width', 1) < 1:
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self._greedy_parse(tokens)
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else:
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self._beam_parse(tokens)
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def train(self, Tokens tokens, GoldParse gold):
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self.moves.preprocess_gold(gold)
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if self.cfg.beam_width < 1:
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return self._greedy_train(tokens, gold)
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else:
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return self._beam_train(tokens, gold)
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cdef int _greedy_parse(self, Tokens tokens) except -1:
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cdef atom_t[CONTEXT_SIZE] context
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cdef int n_feats
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cdef Pool mem = Pool()
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cdef State* state = new_state(mem, tokens.data, tokens.length)
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self.moves.initialize_state(state)
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cdef Transition guess
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while not is_final(state):
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fill_context(context, state)
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scores = self.model.score(context)
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guess = self.moves.best_valid(scores, state)
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guess.do(state, guess.label)
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self.moves.finalize_state(state)
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tokens.set_parse(state.sent)
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cdef int _beam_parse(self, Tokens tokens) except -1:
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cdef Beam beam = Beam(self.moves.n_moves, self.cfg.beam_width)
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beam.initialize(_init_state, tokens.length, tokens.data)
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beam.check_done(_check_final_state, NULL)
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while not beam.is_done:
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self._advance_beam(beam, None, False)
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state = <State*>beam.at(0)
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self.moves.finalize_state(state)
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tokens.set_parse(state.sent)
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def _greedy_train(self, Tokens tokens, GoldParse gold):
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cdef Pool mem = Pool()
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cdef State* state = new_state(mem, tokens.data, tokens.length)
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self.moves.initialize_state(state)
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cdef int cost
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cdef const Feature* feats
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cdef const weight_t* scores
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cdef Transition guess
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cdef Transition best
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cdef atom_t[CONTEXT_SIZE] context
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loss = 0
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while not is_final(state):
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fill_context(context, state)
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scores = self.model.score(context)
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guess = self.moves.best_valid(scores, state)
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best = self.moves.best_gold(scores, state, gold)
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cost = guess.get_cost(state, &gold.c, guess.label)
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self.model.update(context, guess.clas, best.clas, cost)
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guess.do(state, guess.label)
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loss += cost
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return loss
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def _beam_train(self, Tokens tokens, GoldParse gold_parse):
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cdef Beam pred = Beam(self.moves.n_moves, self.cfg.beam_width)
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pred.initialize(_init_state, tokens.length, tokens.data)
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pred.check_done(_check_final_state, NULL)
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cdef Beam gold = Beam(self.moves.n_moves, self.cfg.beam_width)
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gold.initialize(_init_state, tokens.length, tokens.data)
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gold.check_done(_check_final_state, NULL)
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violn = MaxViolation()
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while not pred.is_done and not gold.is_done:
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self._advance_beam(pred, gold_parse, False)
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self._advance_beam(gold, gold_parse, True)
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violn.check(pred, gold)
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if pred.loss >= 1:
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counts = {clas: {} for clas in range(self.model.n_classes)}
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self._count_feats(counts, tokens, violn.g_hist, 1)
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self._count_feats(counts, tokens, violn.p_hist, -1)
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else:
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counts = {}
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self.model._model.update(counts)
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return pred.loss
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def _advance_beam(self, Beam beam, GoldParse gold, bint follow_gold):
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cdef atom_t[CONTEXT_SIZE] context
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cdef State* state
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cdef int i, j, cost
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cdef bint is_valid
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cdef const Transition* move
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for i in range(beam.size):
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state = <State*>beam.at(i)
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if not is_final(state):
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fill_context(context, state)
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self.model.set_scores(beam.scores[i], context)
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self.moves.set_valid(beam.is_valid[i], state)
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if gold is not None:
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for i in range(beam.size):
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state = <State*>beam.at(i)
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self.moves.set_costs(beam.costs[i], state, gold)
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if follow_gold:
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for j in range(self.moves.n_moves):
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beam.is_valid[i][j] *= beam.costs[i][j] == 0
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beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
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beam.check_done(_check_final_state, NULL)
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def _count_feats(self, dict counts, Tokens tokens, list hist, int inc):
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cdef atom_t[CONTEXT_SIZE] context
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cdef Pool mem = Pool()
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cdef State* state = new_state(mem, tokens.data, tokens.length)
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self.moves.initialize_state(state)
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cdef class_t clas
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cdef int n_feats
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for clas in hist:
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fill_context(context, state)
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feats = self.model._extractor.get_feats(context, &n_feats)
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count_feats(counts[clas], feats, n_feats, inc)
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self.moves.c[clas].do(state, self.moves.c[clas].label)
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# These are passed as callbacks to thinc.search.Beam
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cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
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dest = <State*>_dest
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src = <const State*>_src
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moves = <const Transition*>_moves
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copy_state(dest, src)
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moves[clas].do(dest, moves[clas].label)
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cdef void* _init_state(Pool mem, int length, void* tokens) except NULL:
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state = new_state(mem, <const TokenC*>tokens, length)
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push_stack(state)
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return state
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cdef int _check_final_state(void* state, void* extra_args) except -1:
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return is_final(<State*>state)
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cdef hash_t _hash_state(void* _state, void* _) except 0:
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state = <const State*>_state
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cdef atom_t[10] rep
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rep[0] = state.stack[0] if state.stack_len >= 1 else 0
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rep[1] = state.stack[-1] if state.stack_len >= 2 else 0
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rep[2] = state.stack[-2] if state.stack_len >= 3 else 0
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rep[3] = state.i
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rep[4] = state.sent[state.stack[0]].l_kids if state.stack_len >= 1 else 0
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rep[5] = state.sent[state.stack[0]].r_kids if state.stack_len >= 1 else 0
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rep[6] = state.sent[state.stack[0]].dep if state.stack_len >= 1 else 0
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rep[7] = state.sent[state.stack[-1]].dep if state.stack_len >= 2 else 0
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if get_left(state, get_n0(state), 1) != NULL:
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rep[8] = get_left(state, get_n0(state), 1).dep
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else:
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rep[8] = 0
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rep[9] = state.sent[state.i].l_kids
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return hash64(rep, sizeof(atom_t) * 10, 0)
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