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Fix print statements in text classifier example

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Matthew Honnibal 2017-11-01 16:34:31 +01:00
parent 9f9439667b
commit dad8f09fba
1 changed files with 6 additions and 4 deletions
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10
examples/training/train_textcat.py
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@ -26,8 +26,9 @@ from spacy.pipeline import TextCategorizer
@plac.annotations( @plac.annotations(
model=("Model name. Defaults to blank 'en' model.", "option", "m", str), model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
output_dir=("Optional output directory", "option", "o", Path), output_dir=("Optional output directory", "option", "o", Path),
n_examples=("Number of texts to train from", "option", "N", int),
n_iter=("Number of training iterations", "option", "n", int)) n_iter=("Number of training iterations", "option", "n", int))
def main(model=None, output_dir=None, n_iter=20): def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
if model is not None: if model is not None:
nlp = spacy.load(model) # load existing spaCy model nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model) print("Loaded model '%s'" % model)
@ -50,7 +51,8 @@ def main(model=None, output_dir=None, n_iter=20):
# load the IMBD dataset # load the IMBD dataset
print("Loading IMDB data...") print("Loading IMDB data...")
(train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=2000) print("Using %d training examples" % n_texts)
(train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts)
train_docs = [nlp.tokenizer(text) for text in train_texts] train_docs = [nlp.tokenizer(text) for text in train_texts]
train_gold = [GoldParse(doc, cats=cats) for doc, cats in train_gold = [GoldParse(doc, cats=cats) for doc, cats in
zip(train_docs, train_cats)] zip(train_docs, train_cats)]
@ -65,14 +67,14 @@ def main(model=None, output_dir=None, n_iter=20):
for i in range(n_iter): for i in range(n_iter):
losses = {} losses = {}
# batch up the examples using spaCy's minibatch # batch up the examples using spaCy's minibatch
batches = minibatch(train_data, size=compounding(4., 128., 1.001)) batches = minibatch(train_data, size=compounding(4., 32., 1.001))
for batch in batches: for batch in batches:
docs, golds = zip(*batch) docs, golds = zip(*batch)
nlp.update(docs, golds, sgd=optimizer, drop=0.2, losses=losses) nlp.update(docs, golds, sgd=optimizer, drop=0.2, losses=losses)
with textcat.model.use_params(optimizer.averages): with textcat.model.use_params(optimizer.averages):
# evaluate on the dev data split off in load_data() # evaluate on the dev data split off in load_data()
scores = evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats) scores = evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats)
print('{0:.3f}\t{0:.3f}\t{0:.3f}\t{0:.3f}' # print a simple table print('{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}' # print a simple table
.format(losses['textcat'], scores['textcat_p'], .format(losses['textcat'], scores['textcat_p'],
scores['textcat_r'], scores['textcat_f'])) scores['textcat_r'], scores['textcat_f']))