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114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
'''Train a multi-label convolutional neural network text classifier,
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using the spacy.pipeline.TextCategorizer component. The model is then added
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to spacy.pipeline, and predictions are available at `doc.cats`.
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'''
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from __future__ import unicode_literals
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import plac
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import random
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import tqdm
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from thinc.neural.optimizers import Adam
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from thinc.neural.ops import NumpyOps
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import thinc.extra.datasets
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import spacy.lang.en
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from spacy.gold import GoldParse, minibatch
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from spacy.util import compounding
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from spacy.pipeline import TextCategorizer
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def train_textcat(tokenizer, textcat,
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train_texts, train_cats, dev_texts, dev_cats,
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n_iter=20):
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'''
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Train the TextCategorizer without associated pipeline.
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'''
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textcat.begin_training()
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optimizer = Adam(NumpyOps(), 0.001)
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train_docs = [tokenizer(text) for text in train_texts]
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train_gold = [GoldParse(doc, cats=cats) for doc, cats in
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zip(train_docs, train_cats)]
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train_data = zip(train_docs, train_gold)
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batch_sizes = compounding(4., 128., 1.001)
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for i in range(n_iter):
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losses = {}
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train_data = tqdm.tqdm(train_data, leave=False) # Progress bar
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for batch in minibatch(train_data, size=batch_sizes):
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docs, golds = zip(*batch)
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textcat.update(docs, golds, sgd=optimizer, drop=0.2,
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losses=losses)
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with textcat.model.use_params(optimizer.averages):
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scores = evaluate(tokenizer, textcat, dev_texts, dev_cats)
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yield losses['textcat'], scores
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def evaluate(tokenizer, textcat, texts, cats):
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docs = (tokenizer(text) for text in texts)
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tp = 1e-8 # True positives
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fp = 1e-8 # False positives
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fn = 1e-8 # False negatives
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tn = 1e-8 # True negatives
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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 score >= 0.5 and label in gold:
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tp += 1.
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elif score >= 0.5 and label not in gold:
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fp += 1.
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elif score < 0.5 and label not in gold:
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tn += 1
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if score < 0.5 and label in gold:
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fn += 1
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precis = tp / (tp + fp)
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recall = tp / (tp + fn)
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fscore = 2 * (precis * recall) / (precis + recall)
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return {'textcat_p': precis, 'textcat_r': recall, 'textcat_f': fscore}
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def load_data():
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# Partition off part of the train data --- avoid running experiments
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# against test.
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train_data, _ = thinc.extra.datasets.imdb()
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random.shuffle(train_data)
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texts, labels = zip(*train_data)
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cats = [(['POSITIVE'] if y else []) for y in labels]
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split = int(len(train_data) * 0.8)
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train_texts = texts[:split]
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train_cats = cats[:split]
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dev_texts = texts[split:]
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dev_cats = cats[split:]
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return (train_texts, train_cats), (dev_texts, dev_cats)
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def main(model_loc=None):
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nlp = spacy.lang.en.English()
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tokenizer = nlp.tokenizer
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textcat = TextCategorizer(tokenizer.vocab, labels=['POSITIVE'])
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print("Load IMDB data")
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(train_texts, train_cats), (dev_texts, dev_cats) = load_data()
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print("Itn.\tLoss\tP\tR\tF")
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progress = '{i:d} {loss:.3f} {textcat_p:.3f} {textcat_r:.3f} {textcat_f:.3f}'
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for i, (loss, scores) in enumerate(train_textcat(tokenizer, textcat,
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train_texts, train_cats,
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dev_texts, dev_cats, n_iter=20)):
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print(progress.format(i=i, loss=loss, **scores))
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# How to save, load and use
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nlp.pipeline.append(textcat)
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if model_loc is not None:
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nlp.to_disk(model_loc)
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nlp = spacy.load(model_loc)
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doc = nlp(u'This movie sucked!')
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print(doc.cats)
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if __name__ == '__main__':
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plac.call(main)
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