mirror of https://github.com/explosion/spaCy.git
168 lines
6.6 KiB
Cython
168 lines
6.6 KiB
Cython
from collections import defaultdict
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import numpy
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cimport numpy as np
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from thinc.api import chain
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from thinc.neural.util import to_categorical, copy_array, get_array_module
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from .. import util
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from .pipes import Pipe
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from ..language import component
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from .._ml import Tok2Vec, build_morphologizer_model
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from .._ml import link_vectors_to_models, zero_init, flatten
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from .._ml import create_default_optimizer
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from ..errors import Errors, TempErrors
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from ..tokens.doc cimport Doc
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from ..vocab cimport Vocab
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from ..morphology cimport Morphology
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@component("morphologizer", assigns=["token.morph", "token.pos"])
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class Morphologizer(Pipe):
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@classmethod
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def Model(cls, **cfg):
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if cfg.get('pretrained_dims') and not cfg.get('pretrained_vectors'):
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raise ValueError(TempErrors.T008)
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class_map = Morphology.create_class_map()
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return build_morphologizer_model(class_map.field_sizes, **cfg)
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def __init__(self, vocab, model=True, **cfg):
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self.vocab = vocab
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self.model = model
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self.cfg = dict(sorted(cfg.items()))
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self.cfg.setdefault('cnn_maxout_pieces', 2)
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self._class_map = self.vocab.morphology.create_class_map()
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@property
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def labels(self):
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return self.vocab.morphology.tag_names
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@property
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def tok2vec(self):
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if self.model in (None, True, False):
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return None
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else:
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return chain(self.model.tok2vec, flatten)
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def __call__(self, doc):
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features, tokvecs = self.predict([doc])
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self.set_annotations([doc], features, tensors=tokvecs)
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return doc
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def pipe(self, stream, batch_size=128, n_threads=-1):
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for docs in util.minibatch(stream, size=batch_size):
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docs = list(docs)
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features, tokvecs = self.predict(docs)
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self.set_annotations(docs, features, tensors=tokvecs)
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yield from docs
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def predict(self, docs):
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if not any(len(doc) for doc in docs):
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# Handle case where there are no tokens in any docs.
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n_labels = self.model.nO
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guesses = [self.model.ops.allocate((0, n_labels)) for doc in docs]
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tokvecs = self.model.ops.allocate((0, self.model.tok2vec.nO))
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return guesses, tokvecs
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tokvecs = self.model.tok2vec(docs)
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scores = self.model.softmax(tokvecs)
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return scores, tokvecs
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def set_annotations(self, docs, batch_scores, tensors=None):
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if isinstance(docs, Doc):
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docs = [docs]
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cdef Doc doc
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cdef Vocab vocab = self.vocab
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offsets = [self._class_map.get_field_offset(field)
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for field in self._class_map.fields]
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for i, doc in enumerate(docs):
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doc_scores = batch_scores[i]
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doc_guesses = scores_to_guesses(doc_scores, self.model.softmax.out_sizes)
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# Convert the neuron indices into feature IDs.
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doc_feat_ids = numpy.zeros((len(doc), len(self._class_map.fields)), dtype='i')
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for j in range(len(doc)):
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for k, offset in enumerate(offsets):
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if doc_guesses[j, k] == 0:
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doc_feat_ids[j, k] = 0
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else:
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doc_feat_ids[j, k] = offset + doc_guesses[j, k]
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# Get the set of feature names.
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feats = {self._class_map.col2info[f][2] for f in doc_feat_ids[j]}
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if "NIL" in feats:
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feats.remove("NIL")
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# Now add the analysis, and set the hash.
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doc.c[j].morph = self.vocab.morphology.add(feats)
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if doc[j].morph.pos != 0:
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doc.c[j].pos = doc[j].morph.pos
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def update(self, examples, drop=0., sgd=None, losses=None):
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if losses is not None and self.name not in losses:
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losses[self.name] = 0.
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docs = [self._get_doc(ex) for ex in examples]
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tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
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loss, d_tag_scores = self.get_loss(examples, tag_scores)
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bp_tag_scores(d_tag_scores, sgd=sgd)
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if losses is not None:
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losses[self.name] += loss
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def get_loss(self, examples, scores):
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guesses = []
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for doc_scores in scores:
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guesses.append(scores_to_guesses(doc_scores, self.model.softmax.out_sizes))
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guesses = self.model.ops.xp.vstack(guesses)
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scores = self.model.ops.xp.vstack(scores)
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if not isinstance(scores, numpy.ndarray):
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scores = scores.get()
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if not isinstance(guesses, numpy.ndarray):
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guesses = guesses.get()
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cdef int idx = 0
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# Do this on CPU, as we can't vectorize easily.
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target = numpy.zeros(scores.shape, dtype='f')
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field_sizes = self.model.softmax.out_sizes
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for example in examples:
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doc = example.doc
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gold = example.gold
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for t, features in enumerate(gold.morphology):
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if features is None:
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target[idx] = scores[idx]
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else:
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gold_fields = {}
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for feature in features:
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field = self._class_map.feat2field[feature]
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gold_fields[field] = self._class_map.feat2offset[feature]
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for field in self._class_map.fields:
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field_id = self._class_map.field2id[field]
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col_offset = self._class_map.field2col[field]
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if field_id in gold_fields:
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target[idx, col_offset + gold_fields[field_id]] = 1.
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else:
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target[idx, col_offset] = 1.
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#print(doc[t])
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#for col, info in enumerate(self._class_map.col2info):
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# print(col, info, scores[idx, col], target[idx, col])
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idx += 1
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target = self.model.ops.asarray(target, dtype='f')
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scores = self.model.ops.asarray(scores, dtype='f')
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d_scores = scores - target
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loss = (d_scores**2).sum()
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docs = [self._get_doc(ex) for ex in examples]
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d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
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return float(loss), d_scores
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def use_params(self, params):
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with self.model.use_params(params):
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yield
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def scores_to_guesses(scores, out_sizes):
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xp = get_array_module(scores)
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guesses = xp.zeros((scores.shape[0], len(out_sizes)), dtype='i')
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offset = 0
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for i, size in enumerate(out_sizes):
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slice_ = scores[:, offset : offset + size]
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col_guesses = slice_.argmax(axis=1)
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guesses[:, i] = col_guesses
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offset += size
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return guesses
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