mirror of https://github.com/explosion/spaCy.git
Add text-classification hook to pipeline
This commit is contained in:
Matthew Honnibal
parent
7ea50182a5
commit
a231b56d40
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@ -42,10 +42,89 @@ from .compat import json_dumps
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from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
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from ._ml import rebatch, Tok2Vec, flatten, get_col, doc2feats
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from ._ml import build_text_classifier
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from .parts_of_speech import X
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class TokenVectorEncoder(object):
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class BaseThincComponent(object):
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name = None
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@classmethod
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def Model(cls, *shape, **kwargs):
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raise NotImplementedError
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def __init__(self, vocab, model=True, **cfg):
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raise NotImplementedError
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def __call__(self, doc):
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scores = self.predict([doc])
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self.set_annotations([doc], scores)
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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 cytoolz.partition_all(batch_size, stream):
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docs = list(docs)
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scores = self.predict(docs)
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self.set_annotations(docs, scores)
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yield from docs
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def predict(self, docs):
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raise NotImplementedError
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def set_annotations(self, docs, scores):
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raise NotImplementedError
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def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
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raise NotImplementedError
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def get_loss(self, docs, golds, scores):
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raise NotImplementedError
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def begin_training(self, gold_tuples, pipeline=None):
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token_vector_width = pipeline[0].model.nO
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if self.model is True:
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self.model = self.Model(1, token_vector_width)
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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 to_bytes(self, **exclude):
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serialize = OrderedDict((
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('model', lambda: self.model.to_bytes()),
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('vocab', lambda: self.vocab.to_bytes())
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))
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return util.to_bytes(serialize, exclude)
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def from_bytes(self, bytes_data, **exclude):
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if self.model is True:
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self.model = self.Model()
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deserialize = OrderedDict((
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('model', lambda b: self.model.from_bytes(b)),
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('vocab', lambda b: self.vocab.from_bytes(b))
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))
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util.from_bytes(bytes_data, deserialize, exclude)
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return self
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def to_disk(self, path, **exclude):
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serialize = OrderedDict((
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('model', lambda p: p.open('wb').write(self.model.to_bytes())),
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('vocab', lambda p: self.vocab.to_disk(p))
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))
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util.to_disk(path, serialize, exclude)
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def from_disk(self, path, **exclude):
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if self.model is True:
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self.model = self.Model()
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deserialize = OrderedDict((
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('model', lambda p: self.model.from_bytes(p.open('rb').read())),
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('vocab', lambda p: self.vocab.from_disk(p))
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))
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util.from_disk(path, deserialize, exclude)
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return self
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class TokenVectorEncoder(BaseThincComponent):
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"""Assign position-sensitive vectors to tokens, using a CNN or RNN."""
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name = 'tensorizer'
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@ -155,51 +234,8 @@ class TokenVectorEncoder(object):
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if self.model is True:
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self.model = self.Model()
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def use_params(self, params):
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"""Replace weights of models in the pipeline with those provided in the
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params dictionary.
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params (dict): A dictionary of parameters keyed by model ID.
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"""
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with self.model.use_params(params):
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yield
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def to_bytes(self, **exclude):
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serialize = OrderedDict((
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('model', lambda: self.model.to_bytes()),
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('vocab', lambda: self.vocab.to_bytes())
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))
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return util.to_bytes(serialize, exclude)
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def from_bytes(self, bytes_data, **exclude):
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if self.model is True:
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self.model = self.Model()
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deserialize = OrderedDict((
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('model', lambda b: self.model.from_bytes(b)),
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('vocab', lambda b: self.vocab.from_bytes(b))
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))
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util.from_bytes(bytes_data, deserialize, exclude)
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return self
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def to_disk(self, path, **exclude):
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serialize = OrderedDict((
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('model', lambda p: p.open('wb').write(self.model.to_bytes())),
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('vocab', lambda p: self.vocab.to_disk(p))
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))
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util.to_disk(path, serialize, exclude)
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def from_disk(self, path, **exclude):
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if self.model is True:
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self.model = self.Model()
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deserialize = OrderedDict((
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('model', lambda p: self.model.from_bytes(p.open('rb').read())),
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('vocab', lambda p: self.vocab.from_disk(p))
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))
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util.from_disk(path, deserialize, exclude)
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return self
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class NeuralTagger(object):
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class NeuralTagger(BaseThincComponent):
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name = 'tagger'
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def __init__(self, vocab, model=True):
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self.vocab = vocab
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@ -252,7 +288,6 @@ class NeuralTagger(object):
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loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
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d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
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return d_tokvecs
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def get_loss(self, docs, golds, scores):
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@ -423,7 +458,7 @@ class NeuralLabeller(NeuralTagger):
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return float(loss), d_scores
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class SimilarityHook(object):
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class SimilarityHook(BaseThincComponent):
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"""
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Experimental
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@ -477,48 +512,65 @@ class SimilarityHook(object):
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if self.model is True:
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self.model = self.Model(pipeline[0].model.nO)
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def use_params(self, params):
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"""Replace weights of models in the pipeline with those provided in the
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params dictionary.
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params (dict): A dictionary of parameters keyed by model ID.
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"""
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with self.model.use_params(params):
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yield
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class TextClassifier(BaseThincComponent):
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name = 'text-classifier'
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def to_bytes(self, **exclude):
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serialize = OrderedDict((
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('model', lambda: self.model.to_bytes()),
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('vocab', lambda: self.vocab.to_bytes())
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))
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return util.to_bytes(serialize, exclude)
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@classmethod
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def Model(cls, nr_class, width=64, **cfg):
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return build_text_classifier(nr_class, width, **cfg)
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def from_bytes(self, bytes_data, **exclude):
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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.labels = cfg.get('labels', ['LABEL'])
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def __call__(self, doc):
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scores = self.predict([doc])
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self.set_annotations([doc], scores)
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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 cytoolz.partition_all(batch_size, stream):
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docs = list(docs)
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scores = self.predict(docs)
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self.set_annotations(docs, scores)
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yield from docs
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def predict(self, docs):
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scores = self.model(docs)
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scores = self.model.ops.asarray(scores)
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return scores
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def set_annotations(self, docs, scores):
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for i, doc in enumerate(docs):
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for j, label in self.labels:
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doc.cats[label] = float(scores[i, j])
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def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
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docs, tensors = docs_tensors
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scores, bp_scores = self.model.begin_update(docs, drop=drop)
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loss, d_scores = self.get_loss(docs, golds, scores)
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d_tensors = bp_scores(d_scores, sgd=sgd)
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if losses is not None:
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losses.setdefault(self.name, 0.0)
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losses[self.name] += loss
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return d_tensors
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def get_loss(self, docs, golds, scores):
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truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')
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for i, gold in enumerate(golds):
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for j, label in enumerate(self.labels):
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truths[i, j] = label in gold.cats
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truths = self.model.ops.asarray(truths)
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d_scores = (scores-truths) / scores.shape[0]
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mean_square_error = ((scores-truths)**2).sum(axis=1).mean()
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return mean_square_error, d_scores
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def begin_training(self, gold_tuples, pipeline=None):
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token_vector_width = pipeline[0].model.nO
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if self.model is True:
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self.model = self.Model()
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deserialize = OrderedDict((
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('model', lambda b: self.model.from_bytes(b)),
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('vocab', lambda b: self.vocab.from_bytes(b))
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))
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util.from_bytes(bytes_data, deserialize, exclude)
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return self
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def to_disk(self, path, **exclude):
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serialize = OrderedDict((
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('model', lambda p: p.open('wb').write(self.model.to_bytes())),
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('vocab', lambda p: self.vocab.to_disk(p))
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))
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util.to_disk(path, serialize, exclude)
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def from_disk(self, path, **exclude):
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if self.model is True:
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self.model = self.Model()
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deserialize = OrderedDict((
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('model', lambda p: self.model.from_bytes(p.open('rb').read())),
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('vocab', lambda p: self.vocab.from_disk(p))
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))
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util.from_disk(path, deserialize, exclude)
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return self
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self.model = self.Model(len(self.labels), token_vector_width)
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cdef class EntityRecognizer(LinearParser):
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@ -569,6 +621,14 @@ cdef class NeuralEntityRecognizer(NeuralParser):
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nr_feature = 6
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def predict_confidences(self, docs):
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tensors = [d.tensor for d in docs]
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samples = []
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for i in range(10):
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states = self.parse_batch(docs, tensors, drop=0.3)
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for state in states:
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samples.append(self._get_entities(state))
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def __reduce__(self):
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return (NeuralEntityRecognizer, (self.vocab, self.moves, self.model), None, None)
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