mirror of
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222 lines
7.5 KiB
Python
222 lines
7.5 KiB
Python
"""This script is experimental.
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Try pre-training the CNN component of the text categorizer using a cheap
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language modelling-like objective. Specifically, we load pretrained vectors
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(from something like word2vec, GloVe, FastText etc), and use the CNN to
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predict the tokens' pretrained vectors. This isn't as easy as it sounds:
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we're not merely doing compression here, because heavy dropout is applied,
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including over the input words. This means the model must often (50% of the time)
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use the context in order to predict the word.
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To evaluate the technique, we're pre-training with the 50k texts from the IMDB
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corpus, and then training with only 100 labels. Note that it's a bit dirty to
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pre-train with the development data, but also not *so* terrible: we're not using
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the development labels, after all --- only the unlabelled text.
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"""
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import plac
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import random
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import spacy
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import thinc.extra.datasets
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from spacy.util import minibatch, use_gpu, compounding
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from spacy._ml import Tok2Vec
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from spacy.pipeline import TextCategorizer
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import numpy
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def load_texts(limit=0):
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train, dev = thinc.extra.datasets.imdb()
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train_texts, train_labels = zip(*train)
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dev_texts, dev_labels = zip(*train)
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train_texts = list(train_texts)
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dev_texts = list(dev_texts)
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random.shuffle(train_texts)
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random.shuffle(dev_texts)
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if limit >= 1:
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return train_texts[:limit]
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else:
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return list(train_texts) + list(dev_texts)
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def load_textcat_data(limit=0):
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"""Load data from the IMDB dataset."""
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# Partition off part of the train data for evaluation
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train_data, eval_data = thinc.extra.datasets.imdb()
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random.shuffle(train_data)
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train_data = train_data[-limit:]
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texts, labels = zip(*train_data)
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eval_texts, eval_labels = zip(*eval_data)
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cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
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eval_cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in eval_labels]
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return (texts, cats), (eval_texts, eval_cats)
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def prefer_gpu():
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used = spacy.util.use_gpu(0)
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if used is None:
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return False
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else:
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import cupy.random
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cupy.random.seed(0)
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return True
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def build_textcat_model(tok2vec, nr_class, width):
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from thinc.v2v import Model, Softmax, Maxout
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from thinc.api import flatten_add_lengths, chain
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from thinc.t2v import Pooling, sum_pool, mean_pool, max_pool
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from thinc.misc import Residual, LayerNorm
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from spacy._ml import logistic, zero_init
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with Model.define_operators({">>": chain}):
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model = (
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tok2vec
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>> flatten_add_lengths
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>> Pooling(mean_pool)
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>> Softmax(nr_class, width)
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)
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model.tok2vec = tok2vec
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return model
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def block_gradients(model):
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from thinc.api import wrap
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def forward(X, drop=0.0):
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Y, _ = model.begin_update(X, drop=drop)
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return Y, None
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return wrap(forward, model)
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def create_pipeline(width, embed_size, vectors_model):
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print("Load vectors")
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nlp = spacy.load(vectors_model)
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print("Start training")
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textcat = TextCategorizer(
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nlp.vocab,
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labels=["POSITIVE", "NEGATIVE"],
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model=build_textcat_model(
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Tok2Vec(width=width, embed_size=embed_size), 2, width
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),
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)
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nlp.add_pipe(textcat)
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return nlp
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def train_tensorizer(nlp, texts, dropout, n_iter):
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# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
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import tqdm
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tensorizer = nlp.create_pipe("tensorizer")
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nlp.add_pipe(tensorizer)
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optimizer = nlp.begin_training()
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for i in range(n_iter):
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losses = {}
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for i, batch in enumerate(minibatch(tqdm.tqdm(texts))):
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docs = [nlp.make_doc(text) for text in batch]
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tensorizer.update(docs, None, losses=losses, sgd=optimizer, drop=dropout)
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print(losses)
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return optimizer
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def train_textcat(nlp, n_texts, n_iter=10):
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# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
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import tqdm
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textcat = nlp.get_pipe("textcat")
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tok2vec_weights = textcat.model.tok2vec.to_bytes()
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(train_texts, train_cats), (dev_texts, dev_cats) = load_textcat_data(limit=n_texts)
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print(
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"Using {} examples ({} training, {} evaluation)".format(
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n_texts, len(train_texts), len(dev_texts)
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)
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)
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train_data = list(zip(train_texts, [{"cats": cats} for cats in train_cats]))
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# get names of other pipes to disable them during training
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
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with nlp.disable_pipes(*other_pipes): # only train textcat
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optimizer = nlp.begin_training()
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textcat.model.tok2vec.from_bytes(tok2vec_weights)
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print("Training the model...")
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print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
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for i in range(n_iter):
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losses = {"textcat": 0.0}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(tqdm.tqdm(train_data), size=2)
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for batch in batches:
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texts, annotations = zip(*batch)
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nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
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with textcat.model.use_params(optimizer.averages):
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# evaluate on the dev data split off in load_data()
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scores = evaluate_textcat(nlp.tokenizer, textcat, dev_texts, dev_cats)
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print(
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"{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}".format( # print a simple table
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losses["textcat"],
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scores["textcat_p"],
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scores["textcat_r"],
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scores["textcat_f"],
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)
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)
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def evaluate_textcat(tokenizer, textcat, texts, cats):
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docs = (tokenizer(text) for text in texts)
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tp = 1e-8
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fp = 1e-8
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tn = 1e-8
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fn = 1e-8
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for i, doc in enumerate(textcat.pipe(docs)):
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gold = cats[i]
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for label, score in doc.cats.items():
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if label not in gold:
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continue
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if score >= 0.5 and gold[label] >= 0.5:
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tp += 1.0
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elif score >= 0.5 and gold[label] < 0.5:
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fp += 1.0
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elif score < 0.5 and gold[label] < 0.5:
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tn += 1
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elif score < 0.5 and gold[label] >= 0.5:
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fn += 1
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precision = tp / (tp + fp)
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recall = tp / (tp + fn)
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f_score = 2 * (precision * recall) / (precision + recall)
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return {"textcat_p": precision, "textcat_r": recall, "textcat_f": f_score}
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@plac.annotations(
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width=("Width of CNN layers", "positional", None, int),
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embed_size=("Embedding rows", "positional", None, int),
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pretrain_iters=("Number of iterations to pretrain", "option", "pn", int),
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train_iters=("Number of iterations to pretrain", "option", "tn", int),
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train_examples=("Number of labelled examples", "option", "eg", int),
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vectors_model=("Name or path to vectors model to learn from"),
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)
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def main(
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width,
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embed_size,
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vectors_model,
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pretrain_iters=30,
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train_iters=30,
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train_examples=1000,
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):
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random.seed(0)
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numpy.random.seed(0)
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use_gpu = prefer_gpu()
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print("Using GPU?", use_gpu)
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nlp = create_pipeline(width, embed_size, vectors_model)
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print("Load data")
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texts = load_texts(limit=0)
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print("Train tensorizer")
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optimizer = train_tensorizer(nlp, texts, dropout=0.2, n_iter=pretrain_iters)
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print("Train textcat")
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train_textcat(nlp, train_examples, n_iter=train_iters)
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if __name__ == "__main__":
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plac.call(main)
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