def test_model_output_lda_tomotopy(data_dir):
dataset = Dataset()
dataset.load_custom_dataset_from_folder(data_dir + '/M10')
num_topics = 3
model = LDATOMOTO(num_topics=num_topics, alpha=0.1)
output = model.train_model(dataset)
assert 'topics' in output.keys()
assert 'topic-word-matrix' in output.keys()
assert 'test-topic-document-matrix' in output.keys()
# check topics format
assert type(output['topics']) == list
assert len(output['topics']) == num_topics
# check topic-word-matrix format
assert type(output['topic-word-matrix']) == np.ndarray
assert output['topic-word-matrix'].shape == (num_topics,
len(dataset.get_vocabulary()))
# check topic-document-matrix format
assert type(output['topic-document-matrix']) == np.ndarray
assert output['topic-document-matrix'].shape == (
num_topics, len(dataset.get_partitioned_corpus()[0]))
# check test-topic-document-matrix format
assert type(output['test-topic-document-matrix']) == np.ndarray
assert output['test-topic-document-matrix'].shape == (
num_topics, len(dataset.get_partitioned_corpus()[2]))
def test_model_output_nmf(data_dir):
dataset = Dataset()
dataset.load_custom_dataset_from_folder(data_dir + '/M10')
num_topics = 3
model = NMF(num_topics=num_topics,
w_max_iter=10,
h_max_iter=10,
use_partitions=True)
output = model.train_model(dataset)
assert 'topics' in output.keys()
assert 'topic-word-matrix' in output.keys()
assert 'test-topic-document-matrix' in output.keys()
# check topics format
assert type(output['topics']) == list
assert len(output['topics']) == num_topics
# check topic-word-matrix format
assert type(output['topic-word-matrix']) == np.ndarray
assert output['topic-word-matrix'].shape == (num_topics,
len(dataset.get_vocabulary()))
# check topic-document-matrix format
assert type(output['topic-document-matrix']) == np.ndarray
assert output['topic-document-matrix'].shape == (
num_topics, len(dataset.get_partitioned_corpus()[0]))
# check test-topic-document-matrix format
assert type(output['test-topic-document-matrix']) == np.ndarray
assert output['test-topic-document-matrix'].shape == (
num_topics, len(dataset.get_partitioned_corpus()[2]))
def test_model_output_ctm_combined(data_dir):
dataset = Dataset()
dataset.load_custom_dataset_from_folder(data_dir + '/M10')
num_topics = 3
model = CTM(num_topics=num_topics, num_epochs=5, inference_type='combined')
output = model.train_model(dataset)
assert 'topics' in output.keys()
assert 'topic-word-matrix' in output.keys()
assert 'test-topic-document-matrix' in output.keys()
# check topics format
assert type(output['topics']) == list
assert len(output['topics']) == num_topics
# check topic-word-matrix format
assert type(output['topic-word-matrix']) == np.ndarray
assert output['topic-word-matrix'].shape == (num_topics,
len(dataset.get_vocabulary()))
# check topic-document-matrix format
assert type(output['topic-document-matrix']) == np.ndarray
assert output['topic-document-matrix'].shape == (
num_topics, len(dataset.get_partitioned_corpus()[0]))
# check test-topic-document-matrix format
assert type(output['test-topic-document-matrix']) == np.ndarray
assert output['test-topic-document-matrix'].shape == (
num_topics, len(dataset.get_partitioned_corpus()[2]))
def test_model_output_prodlda_not_partitioned(data_dir):
dataset = Dataset()
dataset.load_custom_dataset_from_folder(data_dir + '/M10')
num_topics = 3
model = ProdLDA(num_topics=num_topics, num_epochs=5, use_partitions=False)
output = model.train_model(dataset)
assert 'topics' in output.keys()
assert 'topic-word-matrix' in output.keys()
assert 'test-topic-document-matrix' not in output.keys()
# check topics format
assert type(output['topics']) == list
assert len(output['topics']) == num_topics
# check topic-word-matrix format
assert type(output['topic-word-matrix']) == np.ndarray
assert output['topic-word-matrix'].shape == (num_topics, len(dataset.get_vocabulary()))
# check topic-document-matrix format
assert type(output['topic-document-matrix']) == np.ndarray
assert output['topic-document-matrix'].shape == (num_topics, len(dataset.get_corpus()))