def test_training_with_saved_data(data_dir):
handler = TextHandler(data_dir + "gnews/GoogleNews.txt")
handler.prepare() # create vocabulary and training data
# load BERT data
with open(data_dir + "gnews/bert_embeddings_gnews", "rb") as filino:
training_bert = pickle.load(filino)
training_dataset = CTMDataset(handler.bow, training_bert,
handler.idx2token)
ctm = CTM(input_size=len(handler.vocab),
bert_input_size=768,
num_epochs=1,
inference_type="combined",
n_components=5)
ctm.fit(training_dataset) # run the model
print(ctm.get_topics(2))
ctm.get_doc_topic_distribution(training_dataset)
assert isclose(np.sum(ctm.get_topic_word_distribution()[0]),
1,
rel_tol=1e-5,
abs_tol=0.0)
def test_training_all_classes_ctm(data_dir):
handler = TextHandler(data_dir + "sample_text_document")
handler.prepare() # create vocabulary and training data
train_bert = bert_embeddings_from_file(
data_dir + 'sample_text_document', "distiluse-base-multilingual-cased")
training_dataset = CTMDataset(handler.bow, train_bert, handler.idx2token)
ctm = CTM(input_size=len(handler.vocab),
bert_input_size=512,
num_epochs=1,
inference_type="combined",
n_components=5)
ctm.fit(training_dataset) # run the model
topics = ctm.get_topic_lists(2)
assert len(topics) == 5
thetas = ctm.get_doc_topic_distribution(training_dataset)
assert len(thetas) == len(train_bert)
ctm = ZeroShotTM(input_size=len(handler.vocab),
bert_input_size=512,
num_epochs=1,
n_components=5)
ctm.fit(training_dataset) # run the model
topics = ctm.get_topic_lists(2)
assert len(topics) == 5
thetas = ctm.get_doc_topic_distribution(training_dataset)
assert len(thetas) == len(train_bert)
ctm = CombinedTM(input_size=len(handler.vocab),
bert_input_size=512,
num_epochs=1,
n_components=5)
ctm.fit(training_dataset) # run the model
topics = ctm.get_topic_lists(2)
assert len(topics) == 5
thetas = ctm.get_doc_topic_distribution(training_dataset)
assert len(thetas) == len(train_bert)
with open(data_dir + 'sample_text_document') as filino:
data = filino.readlines()
handler = TextHandler(sentences=data)
handler.prepare() # create vocabulary and training data
train_bert = bert_embeddings_from_list(
data, "distiluse-base-multilingual-cased")
training_dataset = CTMDataset(handler.bow, train_bert, handler.idx2token)
ctm = CTM(input_size=len(handler.vocab),
bert_input_size=512,
num_epochs=1,
inference_type="combined",
n_components=5)
ctm.fit(training_dataset) # run the model
topics = ctm.get_topic_lists(2)
assert len(topics) == 5
thetas = ctm.get_doc_topic_distribution(training_dataset)
assert len(thetas) == len(train_bert)
qt = QuickText("distiluse-base-multilingual-cased",
text_for_bow=data,
text_for_bert=data)
dataset = qt.load_dataset()
ctm = ZeroShotTM(input_size=len(qt.vocab),
bert_input_size=512,
num_epochs=1,
n_components=5)
ctm.fit(dataset) # run the model
topics = ctm.get_topic_lists(2)
assert len(topics) == 5
qt_from_conf = QuickText("distiluse-base-multilingual-cased", None, None)
qt_from_conf.load_configuration(qt.bow, qt.data_bert, qt.vocab,
qt.idx2token)
dataset = qt_from_conf.load_dataset()
ctm = ZeroShotTM(input_size=len(qt.vocab),
bert_input_size=512,
num_epochs=1,
n_components=5)
ctm.fit(dataset) # run the model
topics = ctm.get_topic_lists(2)
assert len(topics) == 5