mirror of https://github.com/explosion/spaCy.git
127 lines
3.9 KiB
Cython
127 lines
3.9 KiB
Cython
# cython: profile=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 cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
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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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import sys
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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.api cimport Example, ExampleC
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from ..structs cimport TokenC
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from ..tokens.doc cimport Doc
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from ..strings cimport StringStore
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from .transition_system import OracleError
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from .transition_system cimport TransitionSystem, Transition
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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 fill_context
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from .stateclass cimport StateClass
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from .._ml cimport arg_max_if_true
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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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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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elif name.startswith('embed'):
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return (pf.words, pf.tags, pf.labels)
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else:
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return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
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pf.tree_shape + pf.trigrams)
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def ParserFactory(transition_system):
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return lambda strings, dir_: Parser(strings, dir_, transition_system)
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cdef class Parser:
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def __init__(self, StringStore strings, model_dir, transition_system):
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if not os.path.exists(model_dir):
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print >> sys.stderr, "Warning: No model found at", model_dir
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elif not os.path.isdir(model_dir):
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print >> sys.stderr, "Warning: model path:", model_dir, "is not a directory"
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else:
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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, Doc tokens):
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cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
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self.moves.initialize_state(stcls)
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cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
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self.model.n_feats, self.model.n_feats)
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with nogil:
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self.parse(stcls, eg.c)
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tokens.set_parse(stcls._sent)
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cdef void parse(self, StateClass stcls, ExampleC eg) nogil:
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while not stcls.is_final():
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memset(eg.scores, 0, eg.nr_class * sizeof(weight_t))
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self.moves.set_valid(<bint*>eg.is_valid, stcls)
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fill_context(eg.atoms, stcls)
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self.model.set_scores(eg.scores, eg.atoms)
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eg.guess = arg_max_if_true(eg.scores, eg.is_valid, self.model.n_classes)
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self.moves.c[eg.guess].do(stcls, self.moves.c[eg.guess].label)
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self.moves.finalize_state(stcls)
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def train(self, Doc tokens, GoldParse gold):
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self.moves.preprocess_gold(gold)
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cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
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self.moves.initialize_state(stcls)
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cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
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self.model.n_feats, self.model.n_feats)
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cdef weight_t loss = 0
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words = [w.orth_ for w in tokens]
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cdef Transition G
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while not stcls.is_final():
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memset(eg.c.scores, 0, eg.c.nr_class * sizeof(weight_t))
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self.moves.set_costs(<bint*>eg.c.is_valid, eg.c.costs, stcls, gold)
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fill_context(eg.c.atoms, stcls)
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self.model.train(eg)
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G = self.moves.c[eg.c.guess]
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self.moves.c[eg.c.guess].do(stcls, self.moves.c[eg.c.guess].label)
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loss += eg.c.loss
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return loss
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