mirror of https://github.com/explosion/spaCy.git
167 lines
6.6 KiB
Cython
167 lines
6.6 KiB
Cython
cimport numpy as np
|
|
|
|
import numpy
|
|
from collections import defaultdict
|
|
from thinc.api import chain, list2array, to_categorical, get_array_module
|
|
from thinc.util import copy_array
|
|
|
|
from ..tokens.doc cimport Doc
|
|
from ..vocab cimport Vocab
|
|
from ..morphology cimport Morphology
|
|
|
|
from .. import util
|
|
from ..language import component
|
|
from ..util import link_vectors_to_models, create_default_optimizer
|
|
from ..errors import Errors, TempErrors
|
|
from .pipes import Pipe
|
|
|
|
|
|
@component("morphologizer", assigns=["token.morph", "token.pos"])
|
|
class Morphologizer(Pipe):
|
|
|
|
def __init__(self, vocab, model, **cfg):
|
|
self.vocab = vocab
|
|
self.model = model
|
|
self.cfg = dict(sorted(cfg.items()))
|
|
self._class_map = self.vocab.morphology.create_class_map() # Morphology.create_class_map() ?
|
|
|
|
@property
|
|
def labels(self):
|
|
return self.vocab.morphology.tag_names
|
|
|
|
@property
|
|
def tok2vec(self):
|
|
if self.model in (None, True, False):
|
|
return None
|
|
else:
|
|
return chain(self.model.get_ref("tok2vec"), list2array())
|
|
|
|
def __call__(self, doc):
|
|
features, tokvecs = self.predict([doc])
|
|
self.set_annotations([doc], features, tensors=tokvecs)
|
|
return doc
|
|
|
|
def pipe(self, stream, batch_size=128, n_threads=-1):
|
|
for docs in util.minibatch(stream, size=batch_size):
|
|
docs = list(docs)
|
|
features, tokvecs = self.predict(docs)
|
|
self.set_annotations(docs, features, tensors=tokvecs)
|
|
yield from docs
|
|
|
|
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None,
|
|
**kwargs):
|
|
self.set_output(len(self.labels))
|
|
self.model.initialize()
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
return sgd
|
|
|
|
def predict(self, docs):
|
|
if not any(len(doc) for doc in docs):
|
|
# Handle case where there are no tokens in any docs.
|
|
n_labels = self.model.get_dim("nO")
|
|
guesses = [self.model.ops.alloc((0, n_labels)) for doc in docs]
|
|
tokvecs = self.model.ops.alloc((0, self.model.get_ref("tok2vec").get_dim("nO")))
|
|
return guesses, tokvecs
|
|
tokvecs = self.model.get_ref("tok2vec")(docs)
|
|
scores = self.model.get_ref("softmax")(tokvecs)
|
|
return scores, tokvecs
|
|
|
|
def set_annotations(self, docs, batch_scores, tensors=None):
|
|
if isinstance(docs, Doc):
|
|
docs = [docs]
|
|
cdef Doc doc
|
|
cdef Vocab vocab = self.vocab
|
|
offsets = [self._class_map.get_field_offset(field)
|
|
for field in self._class_map.fields]
|
|
for i, doc in enumerate(docs):
|
|
doc_scores = batch_scores[i]
|
|
doc_guesses = scores_to_guesses(doc_scores, self.model.get_ref("softmax").attrs["nOs"])
|
|
# Convert the neuron indices into feature IDs.
|
|
doc_feat_ids = numpy.zeros((len(doc), len(self._class_map.fields)), dtype='i')
|
|
for j in range(len(doc)):
|
|
for k, offset in enumerate(offsets):
|
|
if doc_guesses[j, k] == 0:
|
|
doc_feat_ids[j, k] = 0
|
|
else:
|
|
doc_feat_ids[j, k] = offset + doc_guesses[j, k]
|
|
# Get the set of feature names.
|
|
feats = {self._class_map.col2info[f][2] for f in doc_feat_ids[j]}
|
|
if "NIL" in feats:
|
|
feats.remove("NIL")
|
|
# Now add the analysis, and set the hash.
|
|
doc.c[j].morph = self.vocab.morphology.add(feats)
|
|
if doc[j].morph.pos != 0:
|
|
doc.c[j].pos = doc[j].morph.pos
|
|
|
|
def update(self, examples, drop=0., sgd=None, losses=None):
|
|
if losses is not None and self.name not in losses:
|
|
losses[self.name] = 0.
|
|
|
|
docs = [self._get_doc(ex) for ex in examples]
|
|
tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
|
|
loss, d_tag_scores = self.get_loss(examples, tag_scores)
|
|
bp_tag_scores(d_tag_scores, sgd=sgd)
|
|
|
|
if losses is not None:
|
|
losses[self.name] += loss
|
|
|
|
def get_loss(self, examples, scores):
|
|
guesses = []
|
|
for doc_scores in scores:
|
|
guesses.append(scores_to_guesses(doc_scores, self.model.get_ref("softmax").attrs["nOs"]))
|
|
guesses = self.model.ops.xp.vstack(guesses)
|
|
scores = self.model.ops.xp.vstack(scores)
|
|
if not isinstance(scores, numpy.ndarray):
|
|
scores = scores.get()
|
|
if not isinstance(guesses, numpy.ndarray):
|
|
guesses = guesses.get()
|
|
cdef int idx = 0
|
|
# Do this on CPU, as we can't vectorize easily.
|
|
target = numpy.zeros(scores.shape, dtype='f')
|
|
field_sizes = self.model.get_ref("softmax").attrs["nOs"]
|
|
for example in examples:
|
|
doc = example.doc
|
|
gold = example.gold
|
|
for t, features in enumerate(gold.morphology):
|
|
if features is None:
|
|
target[idx] = scores[idx]
|
|
else:
|
|
gold_fields = {}
|
|
for feature in features:
|
|
field = self._class_map.feat2field[feature]
|
|
gold_fields[field] = self._class_map.feat2offset[feature]
|
|
for field in self._class_map.fields:
|
|
field_id = self._class_map.field2id[field]
|
|
col_offset = self._class_map.field2col[field]
|
|
if field_id in gold_fields:
|
|
target[idx, col_offset + gold_fields[field_id]] = 1.
|
|
else:
|
|
target[idx, col_offset] = 1.
|
|
#print(doc[t])
|
|
#for col, info in enumerate(self._class_map.col2info):
|
|
# print(col, info, scores[idx, col], target[idx, col])
|
|
idx += 1
|
|
target = self.model.ops.asarray(target, dtype='f')
|
|
scores = self.model.ops.asarray(scores, dtype='f')
|
|
d_scores = scores - target
|
|
loss = (d_scores**2).sum()
|
|
docs = [self._get_doc(ex) for ex in examples]
|
|
d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
|
|
return float(loss), d_scores
|
|
|
|
def use_params(self, params):
|
|
with self.model.use_params(params):
|
|
yield
|
|
|
|
def scores_to_guesses(scores, out_sizes):
|
|
xp = get_array_module(scores)
|
|
guesses = xp.zeros((scores.shape[0], len(out_sizes)), dtype='i')
|
|
offset = 0
|
|
for i, size in enumerate(out_sizes):
|
|
slice_ = scores[:, offset : offset + size]
|
|
col_guesses = slice_.argmax(axis=1)
|
|
guesses[:, i] = col_guesses
|
|
offset += size
|
|
return guesses
|