Add example for training text classifier

examples/training/train_textcat.py

@@ -0,0 +1,101 @@
+from __future__ import unicode_literals
+import plac
+import random
+import tqdm
+
+from thinc.neural.optimizers import Adam
+from thinc.neural.ops import NumpyOps
+import thinc.extra.datasets
+
+import spacy.lang.en
+from spacy.gold import GoldParse, minibatch
+from spacy.util import compounding
+from spacy.pipeline import TextCategorizer
+
+
+def train_textcat(tokenizer, textcat,
+                  train_texts, train_cats, dev_texts, dev_cats,
+                  n_iter=20):
+    '''
+    Train the TextCategorizer without associated pipeline.
+    '''
+    textcat.begin_training()
+    optimizer = Adam(NumpyOps(), 0.001)
+    train_docs = [tokenizer(text) for text in train_texts]
+    train_gold = [GoldParse(doc, cats=cats) for doc, cats in
+                  zip(train_docs, train_cats)]
+    train_data = zip(train_docs, train_gold)
+    batch_sizes = compounding(4., 128., 1.001)
+    for i in range(n_iter):
+        losses = {}
+        for batch in minibatch(tqdm.tqdm(train_data, leave=False),
+                               size=batch_sizes):
+            docs, golds = zip(*batch)
+            textcat.update((docs, None), golds, sgd=optimizer, drop=0.2,
+                losses=losses)
+        with textcat.model.use_params(optimizer.averages):
+            scores = evaluate(tokenizer, textcat, dev_texts, dev_cats)
+        yield losses['textcat'], scores
+
+
+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 score >= 0.5 and label in gold:
+                tp += 1.
+            elif score >= 0.5 and label not in gold:
+                fp += 1.
+            elif score < 0.5 and label not in gold:
+                tn += 1
+            if score < 0.5 and label in gold:
+                fn += 1
+    precis = tp / (tp + fp)
+    recall = tp / (tp + fn)
+    fscore = 2 * (precis * recall) / (precis + recall)
+    return {'textcat_p': precis, 'textcat_r': recall, 'textcat_f': fscore}  
+
+
+def load_data():
+    # Partition off part of the train data --- avoid running experiments
+    # against test.
+    train_data, _ = thinc.extra.datasets.imdb()
+
+    random.shuffle(train_data)
+
+    texts, labels = zip(*train_data)
+    cats = [(['POSITIVE'] if y else []) for y in labels]
+    
+    split = int(len(train_data) * 0.8)
+
+    train_texts = texts[:split]
+    train_cats = cats[:split]
+    dev_texts = texts[split:]
+    dev_cats = cats[split:]
+    return (train_texts, train_cats), (dev_texts, dev_cats)
+
+
+def main():
+    nlp = spacy.lang.en.English()
+    tokenizer = nlp.tokenizer
+    textcat = TextCategorizer(tokenizer.vocab, labels=['POSITIVE'])
+
+    print("Load IMDB data")
+    (train_texts, train_cats), (dev_texts, dev_cats) = load_data()
+    
+    print("Itn.\tLoss\tP\tR\tF")
+    progress = '{i:d} {loss:.3f} {textcat_p:.3f} {textcat_r:.3f} {textcat_f:.3f}'
+
+    for i, (loss, scores) in enumerate(train_textcat(tokenizer, textcat,
+                                       train_texts, train_cats,
+                                       dev_texts, dev_cats, n_iter=20)):
+        print(progress.format(i=i, loss=loss, **scores))
+ 
+
+if __name__ == '__main__':
+    plac.call(main)