def test_fit_lm_only(self):
"""
Ensure model training does not error out
Ensure model returns predictions
"""
raw_docs = ["".join(text) for text in self.texts]
texts, annotations = finetune_to_indico_sequence(
raw_docs, self.texts, self.labels)
train_texts, test_texts, train_annotations, test_annotations = train_test_split(
texts, annotations, test_size=0.1)
self.model.fit(train_texts)
self.model.fit(train_texts, train_annotations)
predictions = self.model.predict(test_texts)
probas = self.model.predict_proba(test_texts)
self.assertIsInstance(probas, list)
self.assertIsInstance(probas[0], list)
self.assertIsInstance(probas[0][0], dict)
self.assertIsInstance(probas[0][0]['confidence'], dict)
token_precision = sequence_labeling_token_precision(
test_annotations, predictions)
token_recall = sequence_labeling_token_recall(test_annotations,
predictions)
overlap_precision = sequence_labeling_overlap_precision(
test_annotations, predictions)
overlap_recall = sequence_labeling_overlap_recall(
test_annotations, predictions)
self.assertIn('Named Entity', token_precision)
self.assertIn('Named Entity', token_recall)
self.assertIn('Named Entity', overlap_precision)
self.assertIn('Named Entity', overlap_recall)
self.model.save(self.save_file)
model = SequenceLabeler.load(self.save_file)
predictions = model.predict(test_texts)
def test_fit_predict_multi_model(self):
"""
Ensure model training does not error out
Ensure model returns predictions
"""
self.model = SequenceLabeler(batch_size=2,
max_length=256,
lm_loss_coef=0.0,
multi_label_sequences=True)
raw_docs = ["".join(text) for text in self.texts]
texts, annotations = finetune_to_indico_sequence(
raw_docs,
self.texts,
self.labels,
none_value=self.model.config.pad_token)
train_texts, test_texts, train_annotations, _ = train_test_split(
texts, annotations, test_size=0.1)
self.model.fit(train_texts, train_annotations)
self.model.predict(test_texts)
probas = self.model.predict_proba(test_texts)
self.assertIsInstance(probas, list)
self.assertIsInstance(probas[0], list)
self.assertIsInstance(probas[0][0], dict)
self.assertIsInstance(probas[0][0]['confidence'], dict)
self.model.save(self.save_file)
model = SequenceLabeler.load(self.save_file)
model.predict(test_texts)
def setUpClass(cls):
cls._download_data()
#dataset preparation
cls.classifier_dataset = pd.read_csv(cls.classifier_dataset_path, nrows=cls.n_sample * 10)
path = os.path.join(os.path.dirname(__file__), "data", "testdata.json")
with open(path, 'rt') as fp:
cls.texts, cls.labels = json.load(fp)
cls.animals = ["dog", "cat", "horse", "cow", "pig", "sheep", "goat", "chicken", "guinea pig", "donkey", "turkey", "duck", "camel", "goose", "llama", "rabbit", "fox"]
cls.numbers = ["one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen"]
#train and save sequence labeler for later use
try:
cls.s = SequenceLabeler.load(cls.sequence_labeler_path, **cls.default_seq_config(cls))
except FileNotFoundError:
cls.s = SequenceLabeler(**cls.default_seq_config(cls))
cls.s.fit(cls.texts * 10, cls.labels * 10)
cls.s.save(cls.sequence_labeler_path)
#train and save classifier for later use
train_sample = cls.classifier_dataset.sample(n=cls.n_sample*10)
try:
cls.cl = Classifier.load(cls.classifier_path)
except FileNotFoundError:
cls.cl = Classifier(**cls.default_config(cls))
cls.cl.fit(train_sample.Text, train_sample.Target)
cls.cl.save(cls.classifier_path)
if cls.do_comparison:
#train and save comparison regressor for use
cls.cr = ComparisonRegressor()
n_per = 150
similar = []
different = []
for dataset in [cls.animals, cls.numbers]:
for i in range(n_per // 2):
similar.append([random.choice(dataset), random.choice(dataset)])
for i in range(n_per):
different.append([random.choice(cls.animals), random.choice(cls.numbers)])
targets = np.asarray([1] * len(similar) + [0] * len(different))
data = similar + different
cls.x_tr, cls.x_te, cls.t_tr, cls.t_te = train_test_split(data, targets, test_size=0.3, random_state=42)
try:
cls.cr = ComparisonRegressor.load(cls.comparison_regressor_path, **cls.default_config(cls))
except FileNotFoundError:
cls.cr = ComparisonRegressor(**cls.default_config(cls))
cls.cr.fit(cls.x_tr, cls.t_tr)
cls.cr.save(cls.comparison_regressor_path)
def test_fit_predict(self):
"""
Ensure model training does not error out
Ensure model returns predictions
"""
raw_docs = ["".join(text) for text in self.texts]
texts, annotations = finetune_to_indico_sequence(raw_docs, self.texts, self.labels)
train_texts, test_texts, train_annotations, test_annotations = train_test_split(texts, annotations)
self.model.fit(train_texts, train_annotations)
predictions = self.model.predict(test_texts)
self.model.save(self.save_file)
model = SequenceLabeler.load(self.save_file)
predictions = model.predict(test_texts)
