mirror of https://github.com/explosion/spaCy.git
113 lines
3.5 KiB
Cython
113 lines
3.5 KiB
Cython
"""
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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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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
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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 ..tokens cimport Tokens, TokenC
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from .arc_eager cimport TransitionSystem, Transition
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from ._state cimport init_state, State, is_final, get_idx, get_s0, get_s1, get_n0, get_n1
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from . import _parse_features
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from ._parse_features cimport fill_context, CONTEXT_SIZE
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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]]
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second = words[s.stack[-1]]
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n0 = words[s.i]
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n1 = words[s.i + 1]
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return ' '.join((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 == 'zhang':
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return pf.unigrams, pf.arc_eager
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else:
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return pf.unigrams, (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 GreedyParser:
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def __init__(self, model_dir):
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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 = TransitionSystem(self.cfg.left_labels, self.cfg.right_labels)
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hasty_templ, full_templ = get_templates(self.cfg.features)
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self.model = Model(self.moves.n_moves, full_templ, model_dir)
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cpdef int parse(self, Tokens tokens) except -1:
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cdef:
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Transition guess
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uint64_t state_key
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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 = init_state(mem, tokens.data, tokens.length)
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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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self.moves.transition(state, &guess)
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return 0
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def train_sent(self, Tokens tokens, list gold_heads, list gold_labels):
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cdef:
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const Feature* feats
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const weight_t* scores
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Transition guess
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Transition gold
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cdef int n_feats
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cdef atom_t[CONTEXT_SIZE] context
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cdef Pool mem = Pool()
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cdef int* heads_array = <int*>mem.alloc(tokens.length, sizeof(int))
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cdef int* labels_array = <int*>mem.alloc(tokens.length, sizeof(int))
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cdef int i
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for i in range(tokens.length):
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heads_array[i] = gold_heads[i]
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labels_array[i] = self.moves.label_ids[gold_labels[i]]
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cdef State* state = init_state(mem, tokens.data, tokens.length)
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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(&guess, scores, state, heads_array, labels_array)
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self.model.update(context, guess.clas, best.clas, guess.cost)
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self.moves.transition(state, &guess)
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cdef int n_corr = 0
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for i in range(tokens.length):
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n_corr += (i + state.sent[i].head) == gold_heads[i]
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return n_corr
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