class TestSequenceLabeler(unittest.TestCase):
n_sample = 100
n_hidden = 768
dataset_path = os.path.join('Data', 'Sequence', 'reuters.xml')
processed_path = os.path.join('Data', 'Sequence', 'reuters.json')
@classmethod
def _download_reuters(cls):
"""
Download Stanford Sentiment Treebank to enso `data` directory
"""
path = Path(cls.dataset_path)
if not path.exists():
path.parent.mkdir(parents=True, exist_ok=True)
if not os.path.exists(cls.dataset_path):
url = "https://raw.githubusercontent.com/dice-group/n3-collection/master/reuters.xml"
r = requests.get(url)
with open(cls.dataset_path, "wb") as fp:
fp.write(r.content)
with codecs.open(cls.dataset_path, "r", "utf-8") as infile:
soup = bs(infile, "html5lib")
docs = []
docs_labels = []
for elem in soup.find_all("document"):
texts = []
labels = []
# Loop through each child of the element under "textwithnamedentities"
for c in elem.find("textwithnamedentities").children:
if type(c) == Tag:
if c.name == "namedentityintext":
label = "Named Entity" # part of a named entity
else:
label = "<PAD>" # irrelevant word
texts.append(c.text)
labels.append(label)
docs.append(texts)
docs_labels.append(labels)
with open(cls.processed_path, 'wt') as fp:
json.dump((docs, docs_labels), fp)
@classmethod
def setUpClass(cls):
cls._download_reuters()
def setUp(self):
self.save_file = 'tests/saved-models/test-save-load'
with open(self.processed_path, 'rt') as fp:
self.texts, self.labels = json.load(fp)
tf.reset_default_graph()
self.model = SequenceLabeler(batch_size=2,
max_length=256,
lm_loss_coef=0.0,
verbose=False)
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(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, 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_reasonable_predictions(self):
test_sequence = [
"I am a dog. A dog that's incredibly bright. I can talk, read, and write!"
]
path = os.path.join(os.path.dirname(__file__), "testdata.json")
# test ValueError raised when raw text is passed along with character idxs and doesn't match
with self.assertRaises(ValueError):
self.model.fit(["Text about a dog."], [[{
"start": 0,
"end": 5,
"text": "cat",
"label": "dog"
}]])
with open(path, "rt") as fp:
text, labels = json.load(fp)
self.model.finetune(text * 10, labels * 10)
predictions = self.model.predict(test_sequence)
self.assertTrue(1 <= len(predictions[0]) <= 3)
self.assertTrue(any(pred["text"] == "dog" for pred in predictions[0]))
self.model.config.subtoken_predictions = True
predictions = self.model.predict(test_sequence)
self.assertTrue(1 <= len(predictions[0]) <= 3)
self.assertTrue(any(pred["text"] == "dog" for pred in predictions[0]))
def test_chunk_long_sequences(self):
test_sequence = [
"I am a dog. A dog that's incredibly bright. I can talk, read, and write!"
* 10
]
path = os.path.join(os.path.dirname(__file__), "testdata.json")
# test ValueError raised when raw text is passed along with character idxs and doesn't match
self.model.config.chunk_long_sequences = True
self.model.config.max_length = 18
with self.assertRaises(ValueError):
self.model.fit(["Text about a dog."], [[{
"start": 0,
"end": 5,
"text": "cat",
"label": "dog"
}]])
with open(path, "rt") as fp:
text, labels = json.load(fp)
self.model.finetune(text * 10, labels * 10)
predictions = self.model.predict(test_sequence)
print(test_sequence)
print(predictions)
print(len(predictions))
self.assertEqual(len(predictions[0]), 20)
self.assertTrue(any(pred["text"] == "dog" for pred in predictions[0]))
class TestSequenceLabeler(unittest.TestCase):
n_sample = 100
dataset_path = os.path.join('Data', 'Sequence', 'reuters.xml')
processed_path = os.path.join('Data', 'Sequence', 'reuters.json')
@classmethod
def _download_reuters(cls):
"""
Download Stanford Sentiment Treebank to enso `data` directory
"""
path = Path(cls.dataset_path)
if not path.exists():
path.parent.mkdir(parents=True, exist_ok=True)
if not os.path.exists(cls.dataset_path):
url = "https://raw.githubusercontent.com/dice-group/n3-collection/master/reuters.xml"
r = requests.get(url)
with open(cls.dataset_path, "wb") as fp:
fp.write(r.content)
with codecs.open(cls.dataset_path, "r", "utf-8") as infile:
soup = bs(infile, "html.parser")
docs = []
docs_labels = []
for elem in soup.find_all("document"):
texts = []
labels = []
# Loop through each child of the element under "textwithnamedentities"
for c in elem.find("textwithnamedentities").children:
if type(c) == Tag:
if c.name == "namedentityintext":
label = "Named Entity" # part of a named entity
else:
label = "<PAD>" # irrelevant word
texts.append(c.text)
labels.append(label)
docs.append(texts)
docs_labels.append(labels)
with open(cls.processed_path, 'wt') as fp:
json.dump((docs, docs_labels), fp)
@classmethod
def setUpClass(cls):
cls._download_reuters()
def default_config(self, **kwargs):
d = dict(
batch_size=2,
max_length=256,
lm_loss_coef=0.0,
val_size=0,
interpolate_pos_embed=False,
)
d.update(**kwargs)
return d
def setUp(self):
self.save_file = 'tests/saved-models/test-save-load'
random.seed(42)
np.random.seed(42)
with open(self.processed_path, 'rt') as fp:
self.texts, self.labels = json.load(fp)
self.model = SequenceLabeler(**self.default_config())
@pytest.mark.skipif(
SKIP_LM_TESTS,
reason="Bidirectional models do not yet support LM functions")
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,
none_value=self.model.config.pad_token)
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(self):
"""
Ensure model training does not error out
Ensure model returns predictions
Ensure class reweighting behaves as intended
"""
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, test_annotations = train_test_split(
texts, annotations, test_size=0.1, random_state=42)
reweighted_model = SequenceLabeler(**self.default_config(
class_weights={'Named Entity': 10.}))
reweighted_model.fit(train_texts, train_annotations)
reweighted_predictions = reweighted_model.predict(test_texts)
reweighted_token_recall = sequence_labeling_token_recall(
test_annotations, reweighted_predictions)
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)
self.assertGreater(reweighted_token_recall['Named Entity'],
token_recall['Named Entity'])
def test_cached_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,
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.config.chunk_long_sequences = True
self.model.config.max_length = 128
uncached_preds = self.model.predict(test_texts[:1])
with self.model.cached_predict():
start = time.time()
self.model.predict(test_texts[:1])
first = time.time()
self.model.predict(test_texts[:1])
second = time.time()
preds = self.model.predict(test_texts[:1])
assert len(preds) == 1
preds = self.model.predict(test_texts[:2])
assert len(preds) == 2
for uncached_pred, cached_pred in zip(uncached_preds, preds):
self.assertEqual(str(uncached_pred), str(cached_pred))
first_prediction_time = (first - start)
second_prediction_time = (second - first)
self.assertLess(second_prediction_time, first_prediction_time / 2.)
def test_reasonable_predictions(self):
test_sequence = [
"I am a dog. A dog that's incredibly bright. I can talk, read, and write!"
]
path = os.path.join(os.path.dirname(__file__), "testdata.json")
# test ValueError raised when raw text is passed along with character idxs and doesn't match
with self.assertRaises(ValueError):
self.model.fit(["Text about a dog."], [[{
"start": 0,
"end": 5,
"text": "cat",
"label": "dog"
}]])
with open(path, "rt") as fp:
text, labels = json.load(fp)
self.model.fit(text * 10, labels * 10)
predictions = self.model.predict(test_sequence)
self.assertTrue(1 <= len(predictions[0]) <= 3)
self.assertTrue(
any(pred["text"].strip() == "dog" for pred in predictions[0]))
predictions = self.model.predict(test_sequence)
self.assertTrue(1 <= len(predictions[0]) <= 3)
self.assertTrue(
any(pred["text"].strip() == "dog" for pred in predictions[0]))
def test_chunk_long_sequences(self):
test_sequence = [
"I am a dog. A dog that's incredibly bright. I can talk, read, and write! "
* 10
]
path = os.path.join(os.path.dirname(__file__), "testdata.json")
# test ValueError raised when raw text is passed along with character idxs and doesn't match
self.model.config.chunk_long_sequences = True
self.model.config.max_length = 18
with self.assertRaises(ValueError):
self.model.fit(["Text about a dog."], [[{
"start": 0,
"end": 5,
"text": "cat",
"label": "dog"
}]])
with open(path, "rt") as fp:
text, labels = json.load(fp)
self.model.finetune(text * 10, labels * 10)
predictions = self.model.predict(test_sequence)
self.assertEqual(len(predictions[0]), 20)
self.assertTrue(
any(pred["text"].strip() == "dog" for pred in predictions[0]))
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)
class TestSequenceLabeler(unittest.TestCase):
n_sample = 100
dataset_path = os.path.join(
'Data', 'Sequence', 'reuters.xml'
)
processed_path = os.path.join('Data', 'Sequence', 'reuters.json')
@classmethod
def _download_reuters(cls):
"""
Download Stanford Sentiment Treebank to enso `data` directory
"""
path = Path(cls.dataset_path)
if not path.exists():
path.parent.mkdir(parents=True, exist_ok=True)
if not os.path.exists(cls.dataset_path):
url = "https://raw.githubusercontent.com/dice-group/n3-collection/master/reuters.xml"
r = requests.get(url)
with open(cls.dataset_path, "wb") as fp:
fp.write(r.content)
with codecs.open(cls.dataset_path, "r", "utf-8") as infile:
soup = bs(infile, "html.parser")
docs = []
docs_labels = []
for elem in soup.find_all("document"):
texts = []
labels = []
# Loop through each child of the element under "textwithnamedentities"
for c in elem.find("textwithnamedentities").children:
if type(c) == Tag:
if c.name == "namedentityintext":
label = "Named Entity" # part of a named entity
else:
label = "<PAD>" # irrelevant word
texts.append(c.text)
labels.append(label)
docs.append(texts)
docs_labels.append(labels)
with open(cls.processed_path, 'wt') as fp:
json.dump((docs, docs_labels), fp)
@classmethod
def setUpClass(cls):
cls._download_reuters()
def default_config(self, **kwargs):
d = dict(
batch_size=2,
max_length=256,
lm_loss_coef=0.0,
val_size=0,
interpolate_pos_embed=False,
)
d.update(**kwargs)
return d
def setUp(self):
self.save_file = 'tests/saved-models/test-save-load'
random.seed(42)
np.random.seed(42)
with open(self.processed_path, 'rt') as fp:
self.texts, self.labels = json.load(fp)
self.model = SequenceLabeler(
**default_config()
)
def test_fit_predict(self):
"""
Ensure model training does not error out
Ensure model returns predictions
Ensure class reweighting behaves as intended
"""
raw_docs = ["".join(text) for text in self.texts]
texts, annotations = finetune_to_indico_sequence(
raw_docs,
self.texts,
self.labels,
encoder=self.model.input_pipeline.text_encoder,
none_value=self.model.config.pad_token
)
train_texts, test_texts, train_annotations, test_annotations = train_test_split(
texts, annotations, test_size=0.1
)
reweighted_model = SequenceLabeler(
**default_config(class_weights={'Named Entity': 10.})
)
reweighted_model.fit(train_texts, train_annotations)
reweighted_predictions = reweighted_model.predict(test_texts)
reweighted_token_recall = sequence_labeling_token_recall(test_annotations, reweighted_predictions)
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)
self.assertGreater(reweighted_token_recall['Named Entity'], token_recall['Named Entity'])
def test_cached_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,
encoder=self.model.input_pipeline.text_encoder,
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)
with self.model.cached_predict():
self.model.predict(test_texts)
self.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,
encoder=self.model.input_pipeline.text_encoder,
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)
class TestSequenceLabelerTextCNN(TestModelBase):
n_sample = 100
dataset_path = os.path.join("Data", "Sequence", "reuters.xml")
processed_path = os.path.join("Data", "Sequence", "reuters.json")
base_model = TextCNN
@classmethod
def _download_reuters(cls):
"""
Download Reuters to test directory
"""
path = Path(cls.dataset_path)
if not path.exists():
path.parent.mkdir(parents=True, exist_ok=True)
if not os.path.exists(cls.dataset_path):
url = "https://raw.githubusercontent.com/dice-group/n3-collection/master/reuters.xml"
r = requests.get(url)
with open(cls.dataset_path, "wb") as fp:
fp.write(r.content)
with codecs.open(cls.dataset_path, "r", "utf-8") as infile:
soup = bs(infile, "html.parser")
docs = []
docs_labels = []
for elem in soup.find_all("document"):
texts = []
labels = []
# Loop through each child of the element under "textwithnamedentities"
for c in elem.find("textwithnamedentities").children:
if type(c) == Tag:
if c.name == "namedentityintext":
label = "Named Entity" # part of a named entity
else:
label = "<PAD>" # irrelevant word
texts.append(c.text)
labels.append(label)
docs.append(texts)
docs_labels.append(labels)
with open(cls.processed_path, "wt") as fp:
json.dump((docs, docs_labels), fp)
@classmethod
def setUpClass(cls):
cls._download_reuters()
def setUp(self):
self.save_file = "tests/saved-models/test-save-load"
random.seed(42)
np.random.seed(42)
with open(self.processed_path, "rt") as fp:
self.texts, self.labels = json.load(fp)
self.model = SequenceLabeler(**self.default_config())
def test_fit_predict(self):
"""
Ensure model training does not error out
Ensure model returns predictions
Ensure class reweighting behaves as intended
"""
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, test_annotations = train_test_split(
texts, annotations, test_size=0.1)
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)
def test_cached_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,
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)
with self.model.cached_predict():
self.model.predict(test_texts)
self.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(**self.default_config(
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)
class TestSequenceLabeler(unittest.TestCase):
n_sample = 100
n_hidden = 768
dataset_path = os.path.join(
'Data', 'Sequence', 'reuters.xml'
)
processed_path = os.path.join('Data', 'Sequence', 'reuters.json')
@classmethod
def _download_reuters(cls):
"""
Download Stanford Sentiment Treebank to enso `data` directory
"""
path = Path(cls.dataset_path)
if not path.exists():
path.parent.mkdir(parents=True, exist_ok=True)
if not os.path.exists(cls.dataset_path):
url = "https://raw.githubusercontent.com/dice-group/n3-collection/master/reuters.xml"
r = requests.get(url)
with open(cls.dataset_path, "wb") as fp:
fp.write(r.content)
with codecs.open(cls.dataset_path, "r", "utf-8") as infile:
soup = bs(infile, "html5lib")
docs = []
docs_labels = []
for elem in soup.find_all("document"):
texts = []
labels = []
# Loop through each child of the element under "textwithnamedentities"
for c in elem.find("textwithnamedentities").children:
if type(c) == Tag:
if c.name == "namedentityintext":
label = "Named Entity" # part of a named entity
else:
label = "<PAD>" # irrelevant word
texts.append(c.text)
labels.append(label)
docs.append(texts)
docs_labels.append(labels)
with open(cls.processed_path, 'wt') as fp:
json.dump((docs, docs_labels), fp)
@classmethod
def setUpClass(cls):
cls._download_reuters()
def setUp(self):
self.save_file = 'tests/saved-models/test-save-load'
with open(self.processed_path, 'rt') as fp:
self.texts, self.labels = json.load(fp)
tf.reset_default_graph()
self.model = SequenceLabeler(batch_size=2, max_length=256, verbose=False)
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)