spacy_sentiment_classifier.py

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 0 is <=3 else 1
for label in self.label_cols:
    textcat.add_label(label)

print("Loading data...")
limit = None
(train_texts, train_cats), (dev_texts, dev_cats) = self.load_train(limit=None, split=.95)
print("Using {} examples ({} training, {} evaluation)"
      .format(limit, len(train_texts), len(dev_texts)))
train_texts = [str(doc) for doc in train_texts]
dev_texts = [str(doc) for doc in dev_texts]
train_data = list(zip(train_texts,
                      [{'cats': cats} for cats in train_cats]))

n_iter = 10
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_eval = self.evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats)
            scores_train = self.evaluate(nlp.tokenizer, textcat, train_texts, train_cats)

        # should log these somewhere
        print('Train {0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}'  # print a simple table
              .format(losses['textcat'], scores_train['textcat_p'],
                      scores_train['textcat_r'], scores_train['textcat_f']))
        print('Eval {0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}'  # print a simple table
              .format(losses['textcat'], scores_eval['textcat_p'],
                      scores_eval['textcat_r'], scores_eval['textcat_f']))
# dump to disk so we can load it later. 
nlp.to_disk('appclass')