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test_zzz_ner_v1.py
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test_zzz_ner_v1.py
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#!/usr/bin/env python3
"""
Tests of ktrain text classification flows
"""
import os
from unittest import TestCase, main, skip
import IPython
import numpy as np
import testenv
os.environ["DISABLE_V2_BEHAVIOR"] = "1"
import ktrain
from ktrain import text as txt
class TestNERClassification(TestCase):
def setUp(self):
TDATA = "resources/conll2003/train.txt"
(trn, val, preproc) = txt.entities_from_txt(TDATA, use_char=True)
self.trn = trn
self.val = val
self.preproc = preproc
def test_ner(self):
wv_url = (
"https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.vec.gz"
)
model = txt.sequence_tagger("bilstm-crf", self.preproc, wv_path_or_url=wv_url)
learner = ktrain.get_learner(
model, train_data=self.trn, val_data=self.val, batch_size=128
)
lr = 0.01
hist = learner.fit(lr, 1)
# test training results
# self.assertAlmostEqual(max(hist.history['lr']), lr)
self.assertGreater(learner.validate(), 0.65)
# test top losses
obs = learner.top_losses(n=1)
self.assertIn(obs[0][0], list(range(len(self.val.x))))
learner.view_top_losses(n=1)
# test weight decay
self.assertEqual(learner.get_weight_decay(), None)
learner.set_weight_decay(1e-2)
self.assertAlmostEqual(learner.get_weight_decay(), 1e-2)
# test load and save model
learner.save_model("/tmp/test_model")
learner.load_model("/tmp/test_model")
# test predictor
SENT = "There is a man named John Smith."
p = ktrain.get_predictor(learner.model, self.preproc)
self.assertEqual(p.predict(SENT)[-2][1], "I-PER")
p.save("/tmp/test_predictor")
p = ktrain.load_predictor("/tmp/test_predictor")
self.assertEqual(p.predict(SENT)[-2][1], "I-PER")
if __name__ == "__main__":
main()