mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 10:26:35 +03:00
134 lines
5.0 KiB
Python
134 lines
5.0 KiB
Python
#!/usr/bin/env python
|
|
# coding: utf8
|
|
"""Train a multi-label convolutional neural network text classifier on the
|
|
IMDB dataset, using the TextCategorizer component. The dataset will be loaded
|
|
automatically via Thinc's built-in dataset loader. The model is added to
|
|
spacy.pipeline, and predictions are available via `doc.cats`. For more details,
|
|
see the documentation:
|
|
* Training: https://spacy.io/usage/training
|
|
|
|
Compatible with: spaCy v2.0.0+
|
|
"""
|
|
from __future__ import unicode_literals, print_function
|
|
import plac
|
|
import random
|
|
from pathlib import Path
|
|
import thinc.extra.datasets
|
|
|
|
import spacy
|
|
from spacy.util import minibatch, compounding
|
|
|
|
|
|
@plac.annotations(
|
|
model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
|
|
output_dir=("Optional output directory", "option", "o", Path),
|
|
n_texts=("Number of texts to train from", "option", "t", int),
|
|
n_iter=("Number of training iterations", "option", "n", int))
|
|
def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
|
|
if model is not None:
|
|
nlp = spacy.load(model) # load existing spaCy model
|
|
print("Loaded model '%s'" % model)
|
|
else:
|
|
nlp = spacy.blank('en') # create blank Language class
|
|
print("Created blank 'en' model")
|
|
|
|
# add the text classifier to the pipeline if it doesn't exist
|
|
# nlp.create_pipe works for built-ins that are registered with spaCy
|
|
if 'textcat' not in nlp.pipe_names:
|
|
textcat = nlp.create_pipe('textcat')
|
|
nlp.add_pipe(textcat, last=True)
|
|
# otherwise, get it, so we can add labels to it
|
|
else:
|
|
textcat = nlp.get_pipe('textcat')
|
|
|
|
# add label to text classifier
|
|
textcat.add_label('POSITIVE')
|
|
|
|
# load the IMDB dataset
|
|
print("Loading IMDB data...")
|
|
(train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts)
|
|
print("Using {} examples ({} training, {} evaluation)"
|
|
.format(n_texts, len(train_texts), len(dev_texts)))
|
|
train_data = list(zip(train_texts,
|
|
[{'cats': cats} for cats in train_cats]))
|
|
|
|
# get names of other pipes to disable them during training
|
|
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'textcat']
|
|
with nlp.disable_pipes(*other_pipes): # only train textcat
|
|
optimizer = nlp.begin_training()
|
|
print("Training the model...")
|
|
print('{:^5}\t{:^5}\t{:^5}\t{:^5}'.format('LOSS', 'P', 'R', 'F'))
|
|
for i in range(n_iter):
|
|
losses = {}
|
|
# batch up the examples using spaCy's minibatch
|
|
batches = minibatch(train_data, size=compounding(4., 32., 1.001))
|
|
for batch in batches:
|
|
texts, annotations = zip(*batch)
|
|
nlp.update(texts, annotations, sgd=optimizer, drop=0.2,
|
|
losses=losses)
|
|
with textcat.model.use_params(optimizer.averages):
|
|
# evaluate on the dev data split off in load_data()
|
|
scores = evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats)
|
|
print('{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}' # print a simple table
|
|
.format(losses['textcat'], scores['textcat_p'],
|
|
scores['textcat_r'], scores['textcat_f']))
|
|
|
|
# test the trained model
|
|
test_text = "This movie sucked"
|
|
doc = nlp(test_text)
|
|
print(test_text, doc.cats)
|
|
|
|
if output_dir is not None:
|
|
output_dir = Path(output_dir)
|
|
if not output_dir.exists():
|
|
output_dir.mkdir()
|
|
nlp.to_disk(output_dir)
|
|
print("Saved model to", output_dir)
|
|
|
|
# test the saved model
|
|
print("Loading from", output_dir)
|
|
nlp2 = spacy.load(output_dir)
|
|
doc2 = nlp2(test_text)
|
|
print(test_text, doc2.cats)
|
|
|
|
|
|
def load_data(limit=0, split=0.8):
|
|
"""Load data from the IMDB dataset."""
|
|
# Partition off part of the train data for evaluation
|
|
train_data, _ = thinc.extra.datasets.imdb()
|
|
random.shuffle(train_data)
|
|
train_data = train_data[-limit:]
|
|
texts, labels = zip(*train_data)
|
|
cats = [{'POSITIVE': bool(y)} for y in labels]
|
|
split = int(len(train_data) * split)
|
|
return (texts[:split], cats[:split]), (texts[split:], cats[split:])
|
|
|
|
|
|
def evaluate(tokenizer, textcat, texts, cats):
|
|
docs = (tokenizer(text) for text in texts)
|
|
tp = 1e-8 # True positives
|
|
fp = 1e-8 # False positives
|
|
fn = 1e-8 # False negatives
|
|
tn = 1e-8 # True negatives
|
|
for i, doc in enumerate(textcat.pipe(docs)):
|
|
gold = cats[i]
|
|
for label, score in doc.cats.items():
|
|
if label not in gold:
|
|
continue
|
|
if score >= 0.5 and gold[label] >= 0.5:
|
|
tp += 1.
|
|
elif score >= 0.5 and gold[label] < 0.5:
|
|
fp += 1.
|
|
elif score < 0.5 and gold[label] < 0.5:
|
|
tn += 1
|
|
elif score < 0.5 and gold[label] >= 0.5:
|
|
fn += 1
|
|
precision = tp / (tp + fp)
|
|
recall = tp / (tp + fn)
|
|
f_score = 2 * (precision * recall) / (precision + recall)
|
|
return {'textcat_p': precision, 'textcat_r': recall, 'textcat_f': f_score}
|
|
|
|
|
|
if __name__ == '__main__':
|
|
plac.call(main)
|