def test_compute_token_relevance_matrix():
# Arrange
config = fixture.fixture_lda_config()
sampler = fixture.fixture_sampler()
beta = config.getBeta(config_default.BETA_DEFAULT)
vlambda = config.getLambda(config_default.LAMBDA_DEFAULT)
type_topic_counts = pypclda.get_token_topic_matrix(sampler)
n_types = len(type_topic_counts)
n_topics = len(type_topic_counts[0])
# Act
token_relevance_matrix_python = pypclda.compute_token_relevance_matrix(
type_topic_counts, beta, vlambda)
# Assert
token_relevance_matrix_java = __java_compute_token_relevance_matrix(
n_types, n_topics, type_topic_counts, beta, vlambda)
token_relevance_matrix_java = np.array(
[list(x) for x in token_relevance_matrix_java])
assert np.allclose(token_relevance_matrix_java,
token_relevance_matrix_python,
rtol=1e-10)
def test_get_top_relevance_topic_tokens2():
"""Tests a "port" of cc.mallet.util.LDAUtils.getTopRelevanceWords
"""
# Arrange
n_top_tokens = 20
sampler = fixture.fixture_sampler()
config = fixture.fixture_lda_config()
# Act
top_token_relevance_python = pypclda.get_top_relevance_topic_tokens2(
sampler, config, n_top_tokens)
# Assert
type_topic_count_matrix = sampler.getTypeTopicMatrix()
java_words = cc.mallet.util.LDAUtils().getTopRelevanceWords(
n_top_tokens, len(type_topic_count_matrix),
len(type_topic_count_matrix[0]), type_topic_count_matrix,
config.getBeta(config_default.BETA_DEFAULT),
config.getLambda(config_default.LAMBDA_DEFAULT), sampler.getAlphabet())
python_words = [[w[0] for w in row] for row in top_token_relevance_python]
java_words = [list(x) for x in java_words]
assert len(python_words) == len(java_words)
assert len(python_words[0]) == len(java_words[0])
def test_load_lda_sampler():
expected_sampler_type = "cc.mallet.topics.PolyaUrnSpaliasLDA"
config = fixture.fixture_lda_config()
sampler_folder = str(config.getSavedSamplerDirectory(""))
sampler = pypclda.load_lda_sampler(
config, stored_dir=config.getSavedSamplerDirectory(""))
assert sampler is not None
assert expected_sampler_type == sampler.getClass().getName()
def test_sample_pclda():
sampler_type = "cc.mallet.topics.PolyaUrnSpaliasLDA"
config = fixture.fixture_lda_config()
dataset = fixture.fixture_dataset(config)
sampler = pypclda.sample_pclda(config,
dataset,
iterations=2000,
sampler_type=sampler_type,
testset=None,
save_sampler=True)
assert sampler is not None
def test_get_top_relevance_topic_tokens():
"""Tests call to cc.mallet.util.LDAUtils.getTopRelevanceWords
TODO: fix equality test of word (different sort order when value the same)
"""
n_top_words = 20
sampler = fixture.fixture_sampler()
config = fixture.fixture_lda_config()
relevances = pypclda.get_top_topic_word_relevances(sampler,
config,
n_top_words=n_top_words)
assert relevances is not None
assert int(sampler.getNoTopics()) == len(relevances)
assert n_top_words == len(relevances[0])
assert relevances is not None
def test_compute_token_probabilities_given_topic():
# Arrange
lda_util = cc.mallet.util.LDAUtils()
config = fixture.fixture_lda_config()
sampler = fixture.fixture_sampler()
beta = config.getBeta(config_default.BETA_DEFAULT)
type_topic_counts = pypclda.get_token_topic_matrix(sampler)
# Act
word_probs_python = pypclda.compute_token_probabilities_given_topic(
type_topic_counts, beta)
# Assert
word_probs_java = lda_util.calcWordProbGivenTopic(type_topic_counts, beta)
assert np.allclose(word_probs_java, word_probs_python, rtol=1e-05)
"""
def test_compute_distinctiveness_matrix():
# Arrange
config = fixture.fixture_lda_config()
sampler = fixture.fixture_sampler()
token_topic_count_matrix = sampler.getTypeTopicMatrix()
beta = config.getBeta(config_default.BETA_DEFAULT)
p_w_k = pypclda.compute_token_probabilities_given_topic(
token_topic_count_matrix, beta)
p_w = pypclda.compute_token_probabilities(token_topic_count_matrix, beta)
# Act
python_matrix = pypclda.compute_distinctiveness_matrix(p_w_k, p_w)
# Assert
java_matrix = cc.mallet.util.LDAUtils.calcWordDistinctiveness(p_w_k, p_w)
java_matrix = np.array([list(x) for x in java_matrix])
assert np.allclose(java_matrix, python_matrix, rtol=1e-10)
