def test_func():
# Set some constants
data_path = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
dictionary_name = 'dictionary'
pwt = 'pwt'
nwt = 'nwt'
rwt = 'rwt'
docword = 'docword.kos.txt'
vocab = 'vocab.kos.txt'
smsp_phi_tau = -0.2
smsp_theta_tau = -0.1
decor_phi_tau = 1000000
num_topics = 10
num_inner_iterations = 10
num_outer_iterations = 8
perplexity_tol = 0.001
expected_perplexity_value_on_iteration = {
0: 6703.161,
1: 2426.277,
2: 2276.476,
3: 1814.072,
4: 1742.911,
5: 1637.142,
6: 1612.946,
7: 1581.725
}
sparsity_tol = 0.001
expected_phi_sparsity_value_on_iteration = {
0: 0.059,
1: 0.120,
2: 0.212,
3: 0.306,
4: 0.380,
5: 0.438,
6: 0.483,
7: 0.516
}
expected_theta_sparsity_value_on_iteration = {
0: 0.009,
1: 0.036,
2: 0.146,
3: 0.239,
4: 0.278,
5: 0.301,
6: 0.315,
7: 0.319
}
batches_folder = tempfile.mkdtemp()
try:
# Create the instance of low-level API and master object
lib = artm.wrapper.LibArtm()
# Parse collection from disk
lib.ArtmParseCollection({
'format':
constants.CollectionParserConfig_Format_BagOfWordsUci,
'docword_file_path':
os.path.join(data_path, docword),
'vocab_file_path':
os.path.join(data_path, vocab),
'target_folder':
batches_folder
})
# Create master component and scores
scores = {
'Perplexity': messages.PerplexityScoreConfig(),
'SparsityPhi': messages.SparsityPhiScoreConfig()
}
master = mc.MasterComponent(lib, scores=scores)
master.create_score('SparsityTheta',
messages.SparsityThetaScoreConfig())
master.create_score('TopTokens', messages.TopTokensScoreConfig())
# Create collection dictionary and import it
master.gather_dictionary(dictionary_target_name=dictionary_name,
data_path=batches_folder,
vocab_file_path=os.path.join(
data_path, vocab))
# Configure basic regularizers
master.create_regularizer(name='SmoothSparsePhi',
config=messages.SmoothSparsePhiConfig(),
tau=0.0)
master.create_regularizer(name='SmoothSparseTheta',
config=messages.SmoothSparseThetaConfig(),
tau=0.0)
master.create_regularizer(name='DecorrelatorPhi',
config=messages.DecorrelatorPhiConfig(),
tau=0.0)
# Initialize model
master.initialize_model(
model_name=pwt,
topic_names=['topic_{}'.format(i) for i in xrange(num_topics)],
dictionary_name=dictionary_name)
for iter in xrange(num_outer_iterations):
# Invoke one scan of the collection, regularize and normalize Phi
master.clear_score_cache()
master.process_batches(pwt=pwt,
nwt=nwt,
num_inner_iterations=num_inner_iterations,
batches_folder=batches_folder,
regularizer_name=['SmoothSparseTheta'],
regularizer_tau=[smsp_theta_tau])
master.regularize_model(pwt, nwt, rwt,
['SmoothSparsePhi', 'DecorrelatorPhi'],
[smsp_phi_tau, decor_phi_tau])
master.normalize_model(pwt, nwt, rwt)
# Retrieve scores
perplexity_score = master.get_score('Perplexity')
sparsity_phi_score = master.get_score('SparsityPhi')
sparsity_theta_score = master.get_score('SparsityTheta')
# Assert and print scores
print_string = 'Iter#{0}'.format(iter)
print_string += ': Perplexity = {0:.3f}'.format(
perplexity_score.value)
print_string += ', Phi sparsity = {0:.3f}'.format(
sparsity_phi_score.value)
print_string += ', Theta sparsity = {0:.3f}'.format(
sparsity_theta_score.value)
print print_string
assert abs(
perplexity_score.value -
expected_perplexity_value_on_iteration[iter]) < perplexity_tol
assert abs(
sparsity_phi_score.value -
expected_phi_sparsity_value_on_iteration[iter]) < sparsity_tol
assert abs(sparsity_theta_score.value -
expected_theta_sparsity_value_on_iteration[iter]
) < sparsity_tol
# Retrieve and print top tokens score
top_tokens_score = master.get_score('TopTokens')
print 'Top tokens per topic:'
top_tokens_triplets = zip(
top_tokens_score.topic_index,
zip(top_tokens_score.token, top_tokens_score.weight))
for topic_index, group in itertools.groupby(
top_tokens_triplets, key=lambda (topic_index, _): topic_index):
print_string = 'Topic#{0} : '.format(topic_index)
for _, (token, weight) in group:
print_string += ' {0}({1:.3f})'.format(token, weight)
print print_string
finally:
shutil.rmtree(batches_folder)
def test_func():
# Set some constants
data_path = os.environ.get('BIGARTM_UNITTEST_DATA')
dictionary_name = 'dictionary'
pwt = 'pwt'
nwt = 'nwt'
docword = 'docword.kos.txt'
vocab = 'vocab.kos.txt'
smsp_phi_tau = -0.2
smsp_theta_tau = -0.1
decor_phi_tau = 1000000
num_topics = 10
num_document_passes = 10
num_outer_iterations = 8
perplexity_tol = 0.001
expected_perplexity_value_on_iteration = {
0: 6703.161,
1: 2426.277,
2: 2276.476,
3: 1814.072,
4: 1742.911,
5: 1637.142,
6: 1612.946,
7: 1581.725
}
sparsity_tol = 0.001
expected_phi_sparsity_value_on_iteration = {
0: 0.059,
1: 0.120,
2: 0.212,
3: 0.306,
4: 0.380,
5: 0.438,
6: 0.483,
7: 0.516
}
expected_theta_sparsity_value_on_iteration = {
0: 0.009,
1: 0.036,
2: 0.146,
3: 0.239,
4: 0.278,
5: 0.301,
6: 0.315,
7: 0.319
}
expected_perplexity_value_online = 1572.268
expected_phi_sparsity_value_online = 0.528
expected_theta_sparsity_value_online = 0.320
batches_folder = tempfile.mkdtemp()
try:
# Create the instance of low-level API and master object
lib = artm.wrapper.LibArtm()
# Parse collection from disk
lib.ArtmParseCollection({
'format':
constants.CollectionParserConfig_CollectionFormat_BagOfWordsUci,
'docword_file_path':
os.path.join(data_path, docword),
'vocab_file_path':
os.path.join(data_path, vocab),
'target_folder':
batches_folder
})
# Create master component and scores
scores = {
'Perplexity': messages.PerplexityScoreConfig(),
'SparsityPhi': messages.SparsityPhiScoreConfig()
}
master = mc.MasterComponent(lib,
scores=scores,
num_document_passes=num_document_passes)
master.create_score('SparsityTheta',
messages.SparsityThetaScoreConfig())
master.create_score('TopTokens', messages.TopTokensScoreConfig())
# Create collection dictionary and import it
master.gather_dictionary(dictionary_target_name=dictionary_name,
data_path=batches_folder,
vocab_file_path=os.path.join(
data_path, vocab))
# Configure basic regularizers
master.create_regularizer(name='SmoothSparsePhi',
config=messages.SmoothSparsePhiConfig(),
tau=0.0)
master.create_regularizer(name='SmoothSparseTheta',
config=messages.SmoothSparseThetaConfig(),
tau=0.0)
master.create_regularizer(name='DecorrelatorPhi',
config=messages.DecorrelatorPhiConfig(),
tau=decor_phi_tau)
master.reconfigure_regularizer(name='SmoothSparsePhi',
tau=smsp_phi_tau)
master.reconfigure_regularizer(name='SmoothSparseTheta',
tau=smsp_theta_tau)
# Initialize model
master.initialize_model(
model_name=pwt,
topic_names=['topic_{}'.format(i) for i in range(num_topics)],
dictionary_name=dictionary_name)
for iter in range(num_outer_iterations):
master.fit_offline(batches_folder=batches_folder,
num_collection_passes=1)
# Retrieve scores
perplexity_score = master.get_score('Perplexity')
sparsity_phi_score = master.get_score('SparsityPhi')
sparsity_theta_score = master.get_score('SparsityTheta')
# Assert and print scores
print_string = 'Iter#{0}'.format(iter)
print_string += ': Perplexity = {0:.3f}'.format(
perplexity_score.value)
print_string += ', Phi sparsity = {0:.3f}'.format(
sparsity_phi_score.value)
print_string += ', Theta sparsity = {0:.3f}'.format(
sparsity_theta_score.value)
print(print_string)
assert abs(
perplexity_score.value -
expected_perplexity_value_on_iteration[iter]) < perplexity_tol
assert abs(
sparsity_phi_score.value -
expected_phi_sparsity_value_on_iteration[iter]) < sparsity_tol
assert abs(sparsity_theta_score.value -
expected_theta_sparsity_value_on_iteration[iter]
) < sparsity_tol
perplexity_scores = master.get_score_array('Perplexity')
assert len(perplexity_scores) == (iter + 1)
# proceed one online iteration
batch_filenames = glob.glob(os.path.join(batches_folder, '*.batch'))
master.fit_online(batch_filenames=batch_filenames,
update_after=[4],
apply_weight=[0.5],
decay_weight=[0.5])
# Retrieve scores
perplexity_score = master.get_score('Perplexity')
sparsity_phi_score = master.get_score('SparsityPhi')
sparsity_theta_score = master.get_score('SparsityTheta')
# Assert and print scores
print_string = 'Iter Online'
print_string += ': Perplexity = {0:.3f}'.format(perplexity_score.value)
print_string += ', Phi sparsity = {0:.3f}'.format(
sparsity_phi_score.value)
print_string += ', Theta sparsity = {0:.3f}'.format(
sparsity_theta_score.value)
print(print_string)
assert abs(perplexity_score.value -
expected_perplexity_value_online) < perplexity_tol
assert abs(sparsity_phi_score.value -
expected_phi_sparsity_value_online) < sparsity_tol
assert abs(sparsity_theta_score.value -
expected_theta_sparsity_value_online) < sparsity_tol
# Retrieve and print top tokens score
top_tokens_score = master.get_score('TopTokens')
print('Top tokens per topic:')
top_tokens_triplets = zip(
top_tokens_score.topic_index,
zip(top_tokens_score.token, top_tokens_score.weight))
for topic_index, group in itertools.groupby(
top_tokens_triplets, key=lambda triplet: triplet[0]):
print_string = 'Topic#{0} : '.format(topic_index)
for _, (token, weight) in group:
print_string += ' {0}({1:.3f})'.format(token, weight)
print(print_string)
master.clear_score_array_cache()
master.fit_online(batch_filenames=batch_filenames,
update_after=[1, 2, 3, 4],
apply_weight=[0.5, 0.5, 0.5, 0.5],
decay_weight=[0.5, 0.5, 0.5, 0.5])
perplexity_scores = master.get_score_array('Perplexity')
assert len(perplexity_scores) == 4
finally:
shutil.rmtree(batches_folder)