mirror of https://github.com/explosion/spaCy.git
129 lines
4.2 KiB
Python
129 lines
4.2 KiB
Python
# coding: utf-8
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from random import shuffle
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import numpy as np
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from spacy._ml import zero_init, create_default_optimizer
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from spacy.cli.pretrain import get_cossim_loss
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from thinc.v2v import Model
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from thinc.api import chain
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from thinc.neural._classes.affine import Affine
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class EntityEncoder:
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"""
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Train the embeddings of entity descriptions to fit a fixed-size entity vector (e.g. 64D).
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This entity vector will be stored in the KB, and context vectors will be trained to be similar to them.
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"""
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DROP = 0
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EPOCHS = 5
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STOP_THRESHOLD = 0.04
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BATCH_SIZE = 1000
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def __init__(self, nlp, input_dim, desc_width):
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self.nlp = nlp
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self.input_dim = input_dim
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self.desc_width = desc_width
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def apply_encoder(self, description_list):
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if self.encoder is None:
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raise ValueError("Can not apply encoder before training it")
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print("Encoding", len(description_list), "entities")
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batch_size = 100000
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start = 0
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stop = min(batch_size, len(description_list))
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encodings = []
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while start < len(description_list):
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docs = list(self.nlp.pipe(description_list[start:stop]))
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doc_embeddings = [self._get_doc_embedding(doc) for doc in docs]
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enc = self.encoder(np.asarray(doc_embeddings))
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encodings.extend(enc.tolist())
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start = start + batch_size
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stop = min(stop + batch_size, len(description_list))
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print("encoded :", len(encodings))
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return encodings
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def train(self, description_list, to_print=False):
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processed, loss = self._train_model(description_list)
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if to_print:
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print("Trained on", processed, "entities across", self.EPOCHS, "epochs")
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print("Final loss:", loss)
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def _train_model(self, description_list):
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# TODO: when loss gets too low, a 'mean of empty slice' warning is thrown by numpy
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self._build_network(self.input_dim, self.desc_width)
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processed = 0
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loss = 1
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descriptions = description_list.copy() # copy this list so that shuffling does not affect other functions
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for i in range(self.EPOCHS):
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shuffle(descriptions)
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batch_nr = 0
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start = 0
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stop = min(self.BATCH_SIZE, len(descriptions))
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while loss > self.STOP_THRESHOLD and start < len(descriptions):
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batch = []
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for descr in descriptions[start:stop]:
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doc = self.nlp(descr)
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doc_vector = self._get_doc_embedding(doc)
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batch.append(doc_vector)
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loss = self._update(batch)
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print(i, batch_nr, loss)
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processed += len(batch)
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batch_nr += 1
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start = start + self.BATCH_SIZE
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stop = min(stop + self.BATCH_SIZE, len(descriptions))
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return processed, loss
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@staticmethod
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def _get_doc_embedding(doc):
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indices = np.zeros((len(doc),), dtype="i")
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for i, word in enumerate(doc):
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if word.orth in doc.vocab.vectors.key2row:
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indices[i] = doc.vocab.vectors.key2row[word.orth]
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else:
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indices[i] = 0
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word_vectors = doc.vocab.vectors.data[indices]
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doc_vector = np.mean(word_vectors, axis=0) # TODO: min? max?
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return doc_vector
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def _build_network(self, orig_width, hidden_with):
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with Model.define_operators({">>": chain}):
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# very simple encoder-decoder model
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self.encoder = (
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Affine(hidden_with, orig_width)
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)
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self.model = self.encoder >> zero_init(Affine(orig_width, hidden_with, drop_factor=0.0))
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self.sgd = create_default_optimizer(self.model.ops)
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def _update(self, vectors):
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predictions, bp_model = self.model.begin_update(np.asarray(vectors), drop=self.DROP)
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loss, d_scores = self._get_loss(scores=predictions, golds=np.asarray(vectors))
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bp_model(d_scores, sgd=self.sgd)
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return loss / len(vectors)
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@staticmethod
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def _get_loss(golds, scores):
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loss, gradients = get_cossim_loss(scores, golds)
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return loss, gradients
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