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87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
from __future__ import unicode_literals
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from __future__ import print_function
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import plac
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from pathlib import Path
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import random
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import spacy.en
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import model
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try:
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import cPickle as pickle
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except ImportError:
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import pickle
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def read_data(nlp, data_dir):
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for subdir, label in (('pos', 1), ('neg', 0)):
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for filename in (data_dir / subdir).iterdir():
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text = filename.open().read()
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doc = nlp(text)
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yield doc, label
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def partition(examples, split_size):
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examples = list(examples)
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random.shuffle(examples)
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n_docs = len(examples)
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split = int(n_docs * split_size)
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return examples[:split], examples[split:]
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class Dataset(object):
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def __init__(self, nlp, data_dir, batch_size=24):
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self.batch_size = batch_size
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self.train, self.dev = partition(read_data(nlp, Path(data_dir)), 0.8)
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print("Read %d train docs" % len(self.train))
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print("Pos. Train: ", sum(eg[1] == 1 for eg in self.train))
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print("Read %d dev docs" % len(self.dev))
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print("Neg. Dev: ", sum(eg[1] == 1 for eg in self.dev))
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def batches(self, data):
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for i in range(0, len(data), self.batch_size):
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yield data[i : i + self.batch_size]
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def model_writer(out_dir, name):
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def save_model(epoch, params):
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out_path = out_dir / name.format(epoch=epoch)
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pickle.dump(params, out_path.open('wb'))
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return save_model
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@plac.annotations(
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data_dir=("Data directory", "positional", None, Path),
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vocab_size=("Number of words to fine-tune", "option", "w", int),
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n_iter=("Number of iterations (epochs)", "option", "i", int),
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vector_len=("Size of embedding vectors", "option", "e", int),
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hidden_len=("Size of hidden layers", "option", "H", int),
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depth=("Depth", "option", "d", int),
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drop_rate=("Drop-out rate", "option", "r", float),
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rho=("Regularization penalty", "option", "p", float),
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batch_size=("Batch size", "option", "b", int),
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out_dir=("Model directory", "positional", None, Path)
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)
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def main(data_dir, out_dir, n_iter=10, vector_len=300, vocab_size=20000,
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hidden_len=300, depth=3, drop_rate=0.3, rho=1e-4, batch_size=24):
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print("Loading")
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nlp = spacy.en.English(parser=False)
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dataset = Dataset(nlp, data_dir / 'train', batch_size)
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print("Training")
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network = model.train(dataset, vector_len, hidden_len, 2, vocab_size, depth,
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drop_rate, rho, n_iter,
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model_writer(out_dir, 'model_{epoch}.pickle'))
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score = model.Scorer()
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print("Evaluating")
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for doc, label in read_data(nlp, data_dir / 'test'):
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word_ids, embeddings = model.get_words(doc, 0.0, vocab_size)
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guess = network.forward(word_ids, embeddings)
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score += guess == label
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print(score)
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if __name__ == '__main__':
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plac.call(main)
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