def predict(self, content):
if not hasattr(self, 'pipeline'):
raise ValueError("You have to load model first.")
# 1. 利用POS得到分词和pos tagging结果
pos_out = self.pos_tagger.predict(content)
# pos_out = ['这里/NN 是/VB 分词/NN 结果/NN'.split()]
# 2. 组建dataset
dataset = DataSet()
dataset.add_field('wp', pos_out)
dataset.apply(lambda x: ['<BOS>'] + [w.split('/')[0] for w in x['wp']],
new_field_name='words')
dataset.apply(lambda x: ['<BOS>'] + [w.split('/')[1] for w in x['wp']],
new_field_name='pos')
dataset.rename_field("words", "raw_words")
# 3. 使用pipeline
self.pipeline(dataset)
dataset.apply(lambda x: [str(arc) for arc in x['arc_pred']],
new_field_name='arc_pred')
dataset.apply(lambda x: [
arc + '/' + label
for arc, label in zip(x['arc_pred'], x['label_pred_seq'])
][1:],
new_field_name='output')
# output like: [['2/top', '0/root', '4/nn', '2/dep']]
return dataset.field_arrays['output'].content
def test_rename_field(self):
ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})
ds.rename_field("x", "xx")
self.assertTrue("xx" in ds)
self.assertFalse("x" in ds)
with self.assertRaises(KeyError):
ds.rename_field("yyy", "oo")
