def test_save_load_language_model(self):
"""
Ensure saving + loading does not cause errors
Ensure saving + loading does not change predictions
"""
save_file = "tests/saved-models/test-save-load"
model = Classifier()
lm_out = model.generate_text("The quick brown fox", 6)
start_id = model.input_pipeline.text_encoder.start_token
start_token = model.input_pipeline.text_encoder.decoder[start_id]
self.assertNotIn(start_token, lm_out) # Non finetuned models do not use extra tokens
train_sample = self.dataset.sample(n=self.n_sample)
model.fit(train_sample.Text, train_sample.Target)
lm_out = model.generate_text("", 5)
self.assertIn(start_token, lm_out.lower())
self.assertEqual(type(lm_out), str)
model.save(save_file)
model = Classifier.load(save_file)
lm_out_2 = model.generate_text("Indico RULE")
self.assertEqual(type(lm_out_2), str)
self.assertIn("{}Indico RULE".format(start_token).lower(), lm_out_2.lower()) # Both of these models use extra toks
def test_language_model(self):
"""
Ensure saving + loading does not cause errors
Ensure saving + loading does not change predictions
"""
model = Classifier(verbose=False)
lm_out = model.generate_text("", max_length=5)
self.assertEqual(type(lm_out), str)
lm_out_2 = model.generate_text("Indico RULE")
self.assertEqual(type(lm_out_2), str)
self.assertIn('_start_Indico RULE'.lower(), lm_out_2)
def test_language_model(self):
"""
Ensure saving + loading does not cause errors
Ensure saving + loading does not change predictions
"""
model = Classifier()
lm_out = model.generate_text("", max_length=5)
self.assertEqual(type(lm_out), str)
lm_out_2 = model.generate_text("Indico RULE").lower()
self.assertEqual(type(lm_out_2), str)
start_id = model.input_pipeline.text_encoder.start
start_token = model.input_pipeline.text_encoder.decoder[start_id]
self.assertIn('{}Indico RULE'.format(start_token).lower(), lm_out_2.lower())
def test_save_load_language_model(self):
"""
Ensure saving + loading does not cause errors
Ensure saving + loading does not change predictions
"""
save_file = 'tests/saved-models/test-save-load'
model = Classifier(verbose=False)
train_sample = self.dataset.sample(n=self.n_sample)
model.fit(train_sample.Text, train_sample.Target)
lm_out = model.generate_text("", 5)
self.assertEqual(type(lm_out), str)
model.save(save_file)
model = Classifier.load(save_file)
lm_out_2 = model.generate_text("Indico RULE")
self.assertEqual(type(lm_out_2), str)
self.assertIn('_start_Indico RULE'.lower(), lm_out_2)
def test_save_load_language_model(self):
"""
Ensure saving + loading does not cause errors
Ensure saving + loading does not change predictions
"""
save_file = 'tests/saved-models/test-save-load'
model = Classifier()
train_sample = self.dataset.sample(n=self.n_sample)
model.fit(train_sample.Text, train_sample.Target)
lm_out = model.generate_text("", 5)
self.assertEqual(type(lm_out), str)
model.save(save_file)
model = Classifier.load(save_file)
lm_out_2 = model.generate_text("Indico RULE")
self.assertEqual(type(lm_out_2), str)
start_id = model.input_pipeline.text_encoder.start
start_token = model.input_pipeline.text_encoder.decoder[start_id]
self.assertIn('{}Indico RULE'.format(start_token).lower(), lm_out_2.lower())
def test_early_termination_lm(self):
model = Classifier(verbose=False)
# A dirty mock to make all model inferences output a hundred _classify_ tokens
def load_mock(*args, **kwargs):
model.sess = MagicMock()
model.sess.run = MagicMock(return_value=100 *
[model.encoder['_classify_']])
model.saver.initialize = load_mock
lm_out = model.generate_text()
self.assertEqual(lm_out, '_start__classify_')
def test_early_termination_lm(self):
model = Classifier(verbose=False)
# A dirty mock to make all model inferences output a hundred _classify_ tokens
fake_estimator = MagicMock()
model.get_estimator = lambda *args, **kwargs: fake_estimator
fake_estimator.predict = MagicMock(
return_value=iter([{
"GEN_TEXT": 100 * [ENCODER['_classify_']]
}]))
lm_out = model.generate_text()
self.assertEqual(lm_out, '_start__classify_')
def test_generate_text_stop_early(self):
model = Classifier()
# A dirty mock to make all model inferences output a hundred _classify_ tokens
fake_estimator = MagicMock()
model.get_estimator = lambda *args, **kwargs: (fake_estimator, [])
model.input_pipeline.text_encoder._lazy_init()
fake_estimator.predict = MagicMock(return_value=iter([{
PredictMode.GENERATE_TEXT:
100 * [model.input_pipeline.text_encoder["_classify_"]]
}]))
start_id = model.input_pipeline.text_encoder.start
start_token = model.input_pipeline.text_encoder.decoder[start_id]
lm_out = model.generate_text(use_extra_toks=True)
self.assertEqual(lm_out, "{}_classify_".format(start_token))
