def add_complex_scores_to_model(artm_model,
n_top_tokens,
p_mass_threshold,
common_topics,
subject_topics,
class_name,
_debug_print=False):
if _debug_print:
print '[{}] adding scores'.format(datetime.now())
# subject
artm_model.scores.add(
artm.PerplexityScore(name='perplexity_score_subject',
dictionary=dictionary,
topic_names=subject_topics))
artm_model.scores.add(
artm.SparsityPhiScore(name='ss_phi_score_subject',
class_id=class_name,
topic_names=subject_topics))
artm_model.scores.add(
artm.SparsityThetaScore(name='ss_theta_score_subject',
topic_names=subject_topics))
artm_model.scores.add(
artm.TopicKernelScore(name='topic_kernel_score_subject',
class_id=class_name,
topic_names=subject_topics,
probability_mass_threshold=p_mass_threshold))
artm_model.scores.add(
artm.TopTokensScore(name='top_tokens_score_subject',
class_id=class_name,
topic_names=subject_topics,
num_tokens=n_top_tokens))
# common
artm_model.scores.add(
artm.PerplexityScore(name='perplexity_score_common',
dictionary=dictionary,
topic_names=common_topics))
artm_model.scores.add(
artm.SparsityPhiScore(name='ss_phi_score_common',
class_id=class_name,
topic_names=common_topics))
artm_model.scores.add(
artm.SparsityThetaScore(name='ss_theta_score_common',
topic_names=common_topics))
artm_model.scores.add(
artm.TopicKernelScore(name='topic_kernel_score_common',
class_id=class_name,
topic_names=common_topics,
probability_mass_threshold=p_mass_threshold))
artm_model.scores.add(
artm.TopTokensScore(name='top_tokens_score_common',
class_id=class_name,
topic_names=common_topics,
num_tokens=n_top_tokens))
def experiment(filename, tau_phi, tau_theta):
batch_vectorizer = artm.BatchVectorizer(data_path=filename, data_format='vowpal_wabbit',
target_folder='batches')
dictionary = batch_vectorizer.dictionary
topic_num = 30
tokens_num = 100
print("ARTM training")
topic_names = ['topic_{}'.format(i) for i in range(topic_num)]
model_artm = artm.ARTM(topic_names=topic_names, dictionary=dictionary, cache_theta=True)
model_plsa = artm.ARTM(topic_names=topic_names, cache_theta=True,
scores=[artm.PerplexityScore(name='PerplexityScore', dictionary=dictionary)])
model_lda = artm.LDA(num_topics=topic_num)
model_artm.scores.add(artm.PerplexityScore(name='perplexity_score', dictionary=dictionary))
model_artm.scores.add(artm.SparsityPhiScore(name='sparsity_phi_score'))
model_artm.scores.add(artm.SparsityThetaScore(name='sparsity_theta_score'))
model_artm.scores.add(artm.TopTokensScore(name='top_tokens_score', num_tokens=tokens_num))
model_artm.scores.add(artm.TopicKernelScore(name='topic_kernel_score', probability_mass_threshold=0.3))
model_artm.scores.add(artm.BackgroundTokensRatioScore(name='background_tokens_ratio_score'))
model_artm.scores.add(artm.ClassPrecisionScore(name='class_precision_score'))
model_artm.scores.add(artm.TopicMassPhiScore(name='topic_mass_phi_score'))
model_plsa.scores.add(artm.PerplexityScore(name='perplexity_score', dictionary=dictionary))
model_plsa.scores.add(artm.SparsityPhiScore(name='sparsity_phi_score'))
model_plsa.scores.add(artm.SparsityThetaScore(name='sparsity_theta_score'))
model_plsa.scores.add(artm.TopTokensScore(name='top_tokens_score'))
model_plsa.scores.add(artm.TopicKernelScore(name='topic_kernel_score', probability_mass_threshold=0.3))
model_plsa.scores.add(artm.BackgroundTokensRatioScore(name='background_tokens_ratio_score'))
model_plsa.scores.add(artm.ClassPrecisionScore(name='class_precision_score'))
model_plsa.scores.add(artm.TopicMassPhiScore(name='topic_mass_phi_score'))
model_artm.regularizers.add(artm.SmoothSparsePhiRegularizer(name='sparse_phi_regularizer'))
model_artm.regularizers.add(artm.SmoothSparseThetaRegularizer(name='sparse_theta_regularizer'))
model_artm.regularizers.add(artm.DecorrelatorPhiRegularizer(name='decorrelator_phi_regularizer'))
model_artm.regularizers['sparse_phi_regularizer'].tau = tau_phi
model_artm.regularizers['sparse_theta_regularizer'].tau = tau_theta
model_artm.regularizers['decorrelator_phi_regularizer'].tau = 1e+3
model_plsa.initialize(dictionary=dictionary)
model_artm.initialize(dictionary=dictionary)
model_lda.initialize(dictionary=dictionary)
passes = 100
model_plsa.fit_offline(batch_vectorizer=batch_vectorizer, num_collection_passes=passes)
model_artm.fit_offline(batch_vectorizer=batch_vectorizer, num_collection_passes=passes)
model_lda.fit_offline(batch_vectorizer=batch_vectorizer, num_collection_passes=passes)
print_measures(model_plsa, model_artm, model_lda)
def define_model(n_topics: int, dictionary: artm.Dictionary,
sparse_theta: float, sparse_phi: float,
decorrelator_phi: float) -> artm.artm_model.ARTM:
"""
Define the ARTM model.
:param n_topics: number of topics.
:param dictionary: batch vectorizer dictionary.
:param sparse_theta: sparse theta parameter.
:param sparse_phi: sparse phi Parameter.
:param decorrelator_phi: decorellator phi Parameter.
:return: ARTM model.
"""
print("Defining the model.")
topic_names = ["topic_{}".format(i) for i in range(1, n_topics + 1)]
model_artm = artm.ARTM(
topic_names=topic_names,
cache_theta=True,
scores=[
artm.PerplexityScore(name="PerplexityScore",
dictionary=dictionary),
artm.SparsityPhiScore(name="SparsityPhiScore"),
artm.SparsityThetaScore(name="SparsityThetaScore"),
artm.TopicKernelScore(name="TopicKernelScore",
probability_mass_threshold=0.3),
artm.TopTokensScore(name="TopTokensScore", num_tokens=15)
],
regularizers=[
artm.SmoothSparseThetaRegularizer(name="SparseTheta",
tau=sparse_theta),
artm.SmoothSparsePhiRegularizer(name="SparsePhi", tau=sparse_phi),
artm.DecorrelatorPhiRegularizer(name="DecorrelatorPhi",
tau=decorrelator_phi)
])
return model_artm
def init_model(self, dictionary_path=None):
"""dictionary_path: optional, used with pretrained model"""
self.dictionary = artm.Dictionary()
if dictionary_path is None:
self.dictionary.gather(data_path=self.batches_path)
self.dictionary.filter(min_tf=10, max_df_rate=0.1)
self.dictionary.save_text(
f"{self.dir_path}/dicts/dict_{self.name_dataset}.txt")
else:
self.dictionary.load_text(dictionary_path)
self.model = artm.ARTM(
num_topics=self.n_topics,
dictionary=self.dictionary,
show_progress_bars=True,
)
# scores
self.model.scores.add(
artm.PerplexityScore(name="PerplexityScore",
dictionary=self.dictionary))
self.model.scores.add(
artm.SparsityThetaScore(name="SparsityThetaScore"))
self.model.scores.add(artm.SparsityPhiScore(name="SparsityPhiScore"))
# regularizers
self.model.regularizers.add(
artm.SmoothSparsePhiRegularizer(name="SparsePhi", tau=-0.1))
self.model.regularizers.add(
artm.SmoothSparseThetaRegularizer(name="SparseTheta", tau=-0.5))
self.model.regularizers.add(
artm.DecorrelatorPhiRegularizer(name="DecorrelatorPhi", tau=1.5e5))
def set_scores(self):
self.model.scores.add(
artm.PerplexityScore(name='PerplexityScore',
dictionary=self.dictionary))
self.model.scores.add(
artm.SparsityPhiScore(name='SparsityPhiScore',
class_id='@default_class',
topic_names=self.specific))
self.model.scores.add(
artm.SparsityThetaScore(name='SparsityThetaScore',
topic_names=self.specific))
# Fraction of background words in the whole collection
self.model.scores.add(
artm.BackgroundTokensRatioScore(name='BackgroundTokensRatioScore',
class_id='@default_class'))
# Kernel characteristics
self.model.scores.add(
artm.TopicKernelScore(name='TopicKernelScore',
class_id='@default_class',
topic_names=self.specific,
probability_mass_threshold=0.5,
dictionary=self.dictionary))
# Looking at top tokens
self.model.scores.add(
artm.TopTokensScore(name='TopTokensScore',
class_id='@default_class',
num_tokens=100))
def experiment_enviroment(request):
""" """
with warnings.catch_warnings():
warnings.filterwarnings(action="ignore", message=W_DIFF_BATCHES_1)
dataset = Dataset('tests/test_data/test_dataset.csv')
dictionary = dataset.get_dictionary()
model_artm = artm.ARTM(
num_processors=1,
num_topics=5,
cache_theta=True,
num_document_passes=1,
dictionary=dictionary,
scores=[artm.PerplexityScore(name='PerplexityScore', )],
)
model_artm.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model_artm.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'))
ex_score = ScoreExample()
tm = TopicModel(model_artm,
model_id='new_id',
custom_scores={'example_score': ex_score})
# experiment starts without model
experiment = Experiment(tm,
experiment_id="test_cube_creator",
save_path="tests/experiments")
return tm, dataset, experiment, dictionary
def init_hierarchical_model(class_ids):
score = [artm.PerplexityScore(name='perplexity_words', class_ids=['body']),
artm.PerplexityScore(name='perplexity_bigrams', class_ids=['bigrams'])]
top_tokens = [artm.TopTokensScore(name='top_words', num_tokens=15, class_id='body'),
artm.TopTokensScore(name='top_bigrams', num_tokens=10, class_id='bigrams')]
sparsity = [artm.SparsityThetaScore(name='sparsity_theta', eps=1e-6),
artm.SparsityPhiScore(name='sparsity_phi_words', class_id='words', eps=1e-6),
artm.SparsityPhiScore(name='sparsity_phi_bigrams', class_id='bigrams', eps=1e-6)]
regularizers = [artm.DecorrelatorPhiRegularizer(tau=0, class_ids=['body'], name='decorr_words'),
artm.DecorrelatorPhiRegularizer(tau=0, class_ids=['bigram'], name='decorr_bigrams'),
artm.DecorrelatorPhiRegularizer(tau=0, class_ids=['categories'], name='decorr_categories'),
artm.SmoothSparseThetaRegularizer(tau=0, name='sparsity_theta'),
artm.SmoothSparsePhiRegularizer(tau=0, class_ids=['body'], name='sparsity_words'),
artm.SmoothSparsePhiRegularizer(tau=0, class_ids=['bigram'], name='sparsity_bigrams')]
hmodel = artm.hARTM(class_ids=class_ids,
cache_theta=True,
reuse_theta=True,
scores=score + top_tokens + sparsity,
regularizers=regularizers,
theta_columns_naming='title')
return hmodel
def create_and_learn_ARTM_decorPhi_modal(name="",
topic_number=750,
num_collection_passes=1,
weigths=[1., 1., 1., 1.],
decorTau=1.0):
batch_vectorizer_train = None
batch_vectorizer_train = artm.BatchVectorizer(data_path='./' + name,
data_format='vowpal_wabbit',
target_folder='folder' +
name)
dictionary = artm.Dictionary()
dictionary.gather(data_path=batch_vectorizer_train.data_path)
topic_names = ['topic_{}'.format(i) for i in range(topic_number)]
model = artm.ARTM(topic_names=topic_names,
class_ids={
'@text': weigths[0],
'@first': weigths[1],
'@second': weigths[2],
'@third': weigths[3]
},
cache_theta=True,
theta_columns_naming='title',
scores=[
artm.PerplexityScore(name='PerplexityScore',
dictionary=dictionary)
])
model.regularizers.add(
artm.DecorrelatorPhiRegularizer(
name='DecorrelatorPhi_modals',
tau=decorTau,
class_ids=['@first', '@second', '@third']))
model.initialize(dictionary=dictionary)
model.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'))
model.scores.add(
artm.TopicKernelScore(name='TopicKernelScore',
class_id='@text',
probability_mass_threshold=0.3))
model.scores.add(
artm.TopTokensScore(name='TopTokensScore',
num_tokens=6,
class_id='@text'))
model.scores.add(
artm.SparsityPhiScore(name='sparsity_phi_score', class_id='@third'))
model.num_document_passes = 1
model.fit_offline(batch_vectorizer=batch_vectorizer_train,
num_collection_passes=num_collection_passes)
theta_train = model.transform(batch_vectorizer=batch_vectorizer_train)
return model, theta_train
def experiment_enviroment(request):
""" """
with warnings.catch_warnings():
warnings.filterwarnings(action="ignore", message=W_DIFF_BATCHES_1)
dataset = Dataset('tests/test_data/test_dataset.csv')
dictionary = dataset.get_dictionary()
model_artm = artm.ARTM(
num_processors=3,
num_topics=5,
cache_theta=True,
num_document_passes=1,
dictionary=dictionary,
scores=[artm.PerplexityScore(name='PerplexityScore')])
model_artm.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model_artm.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'))
ex_score = ScoreExample()
tm = TopicModel(model_artm,
model_id='new_id',
custom_scores={'example_score': ex_score})
experiment = Experiment(tm,
experiment_id="test_pipeline",
save_path="tests/experiments")
cube_settings = [{
'CubeCreator': {
'num_iter': 10,
'parameters': [
{
'name': 'seed',
'values': [82019, 322],
},
],
'reg_search': 'grid',
'separate_thread': USE_MULTIPROCESS,
},
'selection': [
'model.seed = 82019 and PerplexityScore -> min COLLECT 2',
]
}, {
'RegularizersModifierCube': {
'num_iter': 10,
'regularizer_parameters': {
"regularizer": artm.regularizers.SmoothSparsePhiRegularizer(),
"tau_grid": [0.1, 0.5, 1, 5, 10]
},
'reg_search': 'grid',
'use_relative_coefficients': False,
'separate_thread': USE_MULTIPROCESS,
},
'selection': [
'PerplexityScore -> max COLLECT 2',
]
}]
return tm, dataset, experiment, dictionary, cube_settings
def create_topic_model(self, topic_model_name: str,
batch_vectorizer: artm.BatchVectorizer,
dictionary: artm.Dictionary) -> artm.ARTM:
topic_model = artm.ARTM(num_topics=self.number_of_topics,
dictionary=dictionary,
cache_theta=False)
topic_model.scores.add(
artm.PerplexityScore(name='perplexity_score',
dictionary=dictionary))
topic_model.scores.add(
artm.SparsityPhiScore(name='sparsity_phi_score'))
topic_model.scores.add(
artm.SparsityThetaScore(name='sparsity_theta_score'))
topic_model.num_document_passes = 5
topic_model.num_processors = max(1, os.cpu_count() - 1)
topic_model.regularizers.add(
artm.SmoothSparsePhiRegularizer(name='sparse_phi_regularizer'))
topic_model.regularizers.add(
artm.SmoothSparseThetaRegularizer(name='sparse_theta_regularizer'))
topic_model.regularizers.add(
artm.DecorrelatorPhiRegularizer(
name='decorrelator_phi_regularizer'))
topic_model.regularizers['sparse_phi_regularizer'].tau = -1.0
topic_model.regularizers['sparse_theta_regularizer'].tau = -0.5
topic_model.regularizers['decorrelator_phi_regularizer'].tau = 1e+5
best_score = None
keyword_extraction_logger.info(
'epoch perplexity_score sparsity_phi_score sparsity_theta_score'
)
for restart_index in range(10):
topic_model.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=3)
if best_score is None:
best_score = topic_model.score_tracker[
'perplexity_score'].last_value
else:
if best_score > topic_model.score_tracker[
'perplexity_score'].last_value:
best_score = topic_model.score_tracker[
'perplexity_score'].last_value
self.save_topic_model(topic_model, topic_model_name)
keyword_extraction_logger.info(
'{0:5} {1:16.9} {2:18.9} {3:20.9}'.format(
(restart_index + 1) * 3,
topic_model.score_tracker['perplexity_score'].last_value,
topic_model.score_tracker['sparsity_phi_score'].last_value,
topic_model.score_tracker['sparsity_theta_score'].
last_value))
del topic_model
return self.load_topic_model(
artm.ARTM(num_topics=self.number_of_topics,
dictionary=dictionary,
cache_theta=False), topic_model_name)
def _get_corpus_model(self,
corpus_vector_spaced,
clustering_method='artm'):
if 'gensim' == clustering_method:
return self._get_model_LSI(corpus_vector_spaced)
elif 'sklearn' == clustering_method:
return self._get_model_LDA(corpus_vector_spaced)
elif 'artm' == clustering_method:
batch_vectorizer = corpus_vector_spaced['batch_vectorizer']
dictionary = corpus_vector_spaced['dictionary']
topic_names = [
'topic_{}'.format(i) for i in range(self.num_of_clusters)
]
model_artm = artm.ARTM(
topic_names=topic_names,
cache_theta=True,
scores=[
artm.PerplexityScore(name='PerplexityScore',
dictionary=dictionary)
],
regularizers=[
artm.SmoothSparseThetaRegularizer(name='SparseTheta',
tau=-0.15)
])
model_artm.scores.add(
artm.SparsityPhiScore(name='SparsityPhiScore'))
model_artm.scores.add(
artm.SparsityThetaScore(name='SparsityThetaScore'))
model_artm.scores.add(
artm.TopicKernelScore(name='TopicKernelScore',
probability_mass_threshold=0.3))
model_artm.scores.add(artm.TopTokensScore(name='TopTokensScore',
num_tokens=10),
overwrite=True)
model_artm.regularizers.add(
artm.SmoothSparsePhiRegularizer(name='SparsePhi', tau=-0.1))
model_artm.regularizers['SparseTheta'].tau = -0.2
model_artm.regularizers.add(
artm.DecorrelatorPhiRegularizer(name='DecorrelatorPhi',
tau=1.5e+5))
model_artm.num_document_passes = 1
model_artm.initialize(dictionary)
model_artm.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=30)
return model_artm.transform(batch_vectorizer=batch_vectorizer).T
def create_thematic_model(checked_list, num_topics, num_tokens, phi_tau,
theta_tau, decorr_tau):
""" Create a thematic model """
gluing_bag_of_words(checked_list)
batch_vectorizer = artm.BatchVectorizer(data_path=COLLECTION_PATH,
data_format='vowpal_wabbit',
target_folder=TARGET_FOLDER,
batch_size=len(checked_list))
dictionary = artm.Dictionary(data_path=TARGET_FOLDER)
model = artm.ARTM(
num_topics=num_topics,
num_document_passes=len(checked_list),
dictionary=dictionary,
regularizers=[
artm.SmoothSparsePhiRegularizer(name='sparse_phi_regularizer',
tau=phi_tau),
artm.SmoothSparseThetaRegularizer(name='sparse_theta_regularizer',
tau=theta_tau),
artm.DecorrelatorPhiRegularizer(
name='decorrelator_phi_regularizer', tau=decorr_tau),
],
scores=[
artm.PerplexityScore(name='perplexity_score',
dictionary=dictionary),
artm.SparsityPhiScore(name='sparsity_phi_score'),
artm.SparsityThetaScore(name='sparsity_theta_score'),
artm.TopTokensScore(name='top_tokens_score', num_tokens=num_tokens)
])
model.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=len(checked_list))
top_tokens = model.score_tracker['top_tokens_score']
topic_dictionary = OrderedDict()
for topic_name in model.topic_names:
list_name = []
for (token, weight) in zip(top_tokens.last_tokens[topic_name],
top_tokens.last_weights[topic_name]):
list_name.append(token + '-' + str(round(weight, 3)))
topic_dictionary[str(topic_name)] = list_name
return model.score_tracker[
'perplexity_score'].last_value, model.score_tracker[
'sparsity_phi_score'].last_value, model.score_tracker[
'sparsity_theta_score'].last_value, topic_dictionary
def add_scores_to_model(current_dictionary, artm_model, n_top_tokens,
p_mass_threshold):
artm_model.scores.add(
artm.PerplexityScore(name='perplexity_score',
use_unigram_document_model=False,
dictionary=current_dictionary))
artm_model.scores.add(
artm.SparsityPhiScore(name='sparsity_phi_score', class_id='ngramm'))
artm_model.scores.add(artm.SparsityThetaScore(name='sparsity_theta_score'))
artm_model.scores.add(
artm.TopicKernelScore(name='topic_kernel_score',
class_id='ngramm',
probability_mass_threshold=p_mass_threshold))
artm_model.scores.add(
artm.TopTokensScore(name='top_tokens_score',
class_id='ngramm',
num_tokens=n_top_tokens))
def create_and_learn_PLSA(name="", topic_number=750, num_collection_passes=1):
batch_vectorizer_train = None
batch_vectorizer_train = artm.BatchVectorizer(data_path='./' + name,
data_format='vowpal_wabbit',
target_folder='folder' +
name)
dictionary = artm.Dictionary()
dictionary.gather(data_path=batch_vectorizer_train.data_path)
topic_names = ['topic_{}'.format(i) for i in range(topic_number)]
model_plsa = artm.ARTM(topic_names=topic_names,
class_ids={
'@text': 1.0,
'@first': 1.0,
'@second': 1.0,
'@third': 1.0
},
cache_theta=True,
theta_columns_naming='title',
scores=[
artm.PerplexityScore(name='PerplexityScore',
dictionary=dictionary)
])
model_plsa.initialize(dictionary=dictionary)
model_plsa.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model_plsa.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'))
model_plsa.scores.add(
artm.TopicKernelScore(name='TopicKernelScore',
class_id='@text',
probability_mass_threshold=0.3))
model_plsa.scores.add(
artm.TopTokensScore(name='TopTokensScore',
num_tokens=6,
class_id='@text'))
model_plsa.num_document_passes = 1
model_plsa.fit_offline(batch_vectorizer=batch_vectorizer_train,
num_collection_passes=num_collection_passes)
theta_train = model_plsa.transform(batch_vectorizer=batch_vectorizer_train)
return model_plsa, theta_train
def pipeline_plsa_bigartm(lines,
TOPIC_NUMBER,
ngram_range,
topnwords,
LOGS_DATA_PATH="plsa.txt",
TARGET_FOLDER="plsa"):
make_file(lines, ngram_range, LOGS_DATA_PATH)
batch_vectorizer = artm.BatchVectorizer(data_path=LOGS_DATA_PATH,
data_format='vowpal_wabbit',
target_folder=TARGET_FOLDER)
model_artm = artm.ARTM(num_topics=TOPIC_NUMBER, cache_theta=True)
model_artm.initialize(dictionary=batch_vectorizer.dictionary)
model_artm.regularizers.add(
artm.SmoothSparsePhiRegularizer(name='SparsePhi', tau=0.05))
model_artm.regularizers.add(
artm.DecorrelatorPhiRegularizer(name='DecorrelatorPhi', tau=1.5e+5))
model_artm.regularizers.add(
artm.SmoothSparseThetaRegularizer(name='SparseTheta', tau=-0.01))
model_artm.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model_artm.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'))
model_artm.scores.add(artm.TopTokensScore(name='TopTokensScore',
num_tokens=topnwords),
overwrite=True)
model_artm.scores.add(
artm.PerplexityScore(name='PerplexityScore',
dictionary=batch_vectorizer.dictionary))
model_artm.num_document_passes = 2
model_artm.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=15)
topic_names = {}
for topic_name in model_artm.topic_names:
topic_names[topic_name] = model_artm.score_tracker[
'TopTokensScore'].last_tokens[topic_name]
#return label_after_bigarm(model_artm), topic_names
return "nothing, sorry", topic_names
def create_model_with_background(dictionary, num_tokens, num_document_passes):
sm_phi_tau = 0.0001 * 1e-4
sp_phi_tau = -0.0001 * 1e-4
decor_phi_tau = 1
specific_topics = ['topic {}'.format(i) for i in range(1, 20)]
topic_names = specific_topics + ["background"]
scores = [
artm.PerplexityScore(name='PerplexityScore', dictionary=dictionary),
artm.TopTokensScore(
name='TopTokensScore', num_tokens=10, class_id='plain_text'
), # web version of Palmetto works only with <= 10 tokens
artm.SparsityPhiScore(name='SparsityPhiScore'),
artm.SparsityThetaScore(name='SparsityThetaScore'),
artm.TopicKernelScore(name='TopicKernelScore',
probability_mass_threshold=0.3,
class_id='plain_text')
]
model = artm.ARTM(topic_names=specific_topics + ["background"],
regularizers=[],
cache_theta=True,
scores=scores,
class_ids={'plain_text': 1.0})
model.regularizers.add(
artm.SmoothSparsePhiRegularizer(name='SparsePhi',
tau=-sp_phi_tau,
topic_names=specific_topics))
model.regularizers.add(
artm.SmoothSparsePhiRegularizer(name='SmoothPhi',
tau=sm_phi_tau,
topic_names=["background"]))
# model.regularizers.add(artm.DecorrelatorPhiRegularizer(name='DecorrelatorPhi', tau=decor_phi_tau))
model.initialize(dictionary=dictionary)
model.num_document_passes = num_document_passes
return model
def create_model(dictionary, num_tokens, num_document_passes):
tn = ['topic {}'.format(i) for i in range(1, 20)]
scores = [
artm.PerplexityScore(name='PerplexityScore', dictionary=dictionary),
artm.TopTokensScore(
name='TopTokensScore', num_tokens=10
), # web version of Palmetto works only with <= 10 tokens
artm.SparsityPhiScore(name='SparsityPhiScore'),
artm.SparsityThetaScore(name='SparsityThetaScore')
]
model = artm.ARTM(topic_names=tn,
regularizers=[],
cache_theta=True,
scores=scores)
model.initialize(dictionary=dictionary)
model.num_document_passes = num_document_passes
return model
def add_scores_to_model(artm_model,
dictionary,
n_top_tokens,
p_mass_threshold,
class_name,
_debug_print=False):
if _debug_print:
print '[{}] adding scores'.format(datetime.now())
artm_model.scores.add(
artm.PerplexityScore(name='perplexity_score', dictionary=dictionary))
artm_model.scores.add(
artm.SparsityPhiScore(name='ss_phi_score', class_id=class_name))
artm_model.scores.add(artm.SparsityThetaScore(name='ss_theta_score'))
artm_model.scores.add(
artm.TopicKernelScore(name='topic_kernel_score',
class_id=class_name,
probability_mass_threshold=p_mass_threshold))
artm_model.scores.add(
artm.TopTokensScore(name='top_tokens_score',
class_id=class_name,
num_tokens=n_top_tokens))
def init_score_tracker(model_artm, my_dictionary, class_id='text'):
model_artm.scores.add(artm.PerplexityScore(name='PerplexityScore',
dictionary=my_dictionary),
overwrite=True)
model_artm.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore',
class_id=class_id),
overwrite=True)
model_artm.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'),
overwrite=True)
model_artm.scores.add(artm.TopTokensScore(name="top_words",
num_tokens=200,
class_id=class_id),
overwrite=True)
model_artm.scores.add(artm.TopicKernelScore(
name='TopicKernelScore',
class_id=class_id,
probability_mass_threshold=0.6),
overwrite=True)
print('Scores are set!')
def train():
try:
logger.info("TRAINIG BEGIN")
batch_vectorizer_mono = artm.BatchVectorizer(
data_path="./opt/vw/prod/dataset1.vw",
data_format='vowpal_wabbit',
target_folder="./opt/vw/prod/dataset_batch")
logger.info("Batches stage completed")
model_artm = artm.ARTM(
num_topics=NUM_TOPICS,
num_processors=THREADS,
theta_columns_naming='title',
# show_progress_bars=True,
theta_name="prod_theta",
class_ids={'@modal': 1},
cache_theta=True)
dictionary = artm.Dictionary()
dictionary.gather(data_path=batch_vectorizer_mono.data_path)
dictionary.filter(min_df_rate=0.01, min_tf=10, inplace=True)
model_artm.initialize(dictionary=dictionary)
logger.info("Dictionary stage completed")
model_artm.scores.add(
artm.SparsityPhiScore(name='sparsity_phi_score',
class_id='@modal'))
model_artm.scores.add(
artm.SparsityThetaScore(name='sparsity_theta_score'))
model_artm.scores.add(
artm.TopTokensScore(name='top_tokens_score', class_id="@modal"))
model_artm.scores.add(
artm.PerplexityScore(name='perplexity_score',
class_ids={'@modal': 1}))
model_artm.regularizers.add(
artm.DecorrelatorPhiRegularizer(name='decorrelator_phi_lab',
tau=1.0e+5,
class_ids=['@modal']))
model_artm.num_document_passes = 1
ITERATIONS = [8, 8, 8]
logger.info("Model initialization stage completed")
model_artm.fit_offline(batch_vectorizer=batch_vectorizer_mono,
num_collection_passes=ITERATIONS[0])
logger.info("1/3 trainig stage")
model_artm.regularizers.add(
artm.SmoothSparseThetaRegularizer(name='sparse_theta_regularizer',
tau=-1.5))
model_artm.fit_offline(batch_vectorizer=batch_vectorizer_mono,
num_collection_passes=ITERATIONS[1])
logger.info("2/3 trainig stage")
model_artm.fit_offline(batch_vectorizer=batch_vectorizer_mono,
num_collection_passes=ITERATIONS[2])
logger.info("3/3 trainig stage")
logger.info("Training is completed")
# logger.info("Sparsity_phi_score: {}".format(model_artm.score_tracker['sparsity_phi_score'].value))
# logger.info("Sparsity_theta_score".format(model_artm.score_tracker['sparsity_theta_score'].value))
logger.info("Perplexity_score".format(
model_artm.score_tracker['perplexity_score'].value[-1]))
# delete tm_model_sources folder
if os.path.isdir('./opt/tm_model/tm_model_sources'):
shutil.rmtree("./opt/tm_model/tm_model_sources")
logger.warning("Tm_model folder was deleted")
model_artm.dump_artm_model("./opt/tm_model/tm_model_sources")
logger.info("TM model is saved")
except Exception as error:
logger.critical(
"During training critical error occured | {0}".format(error))
def test_func():
# constants
num_tokens = 11
probability_mass_threshold = 0.9
sp_reg_tau = -0.1
decor_tau = 1.5e+5
decor_rel_tau = 0.3
num_collection_passes = 15
num_document_passes = 1
num_topics = 15
vocab_size = 6906
num_docs = 3430
data_path = os.environ.get('BIGARTM_UNITTEST_DATA')
batches_folder = tempfile.mkdtemp()
sp_zero_eps = 0.001
sparsity_phi_value = [
0.034, 0.064, 0.093, 0.120, 0.145, 0.170, 0.194, 0.220, 0.246, 0.277,
0.312, 0.351, 0.390, 0.428, 0.464
]
sparsity_phi_rel_value = [
0.442, 0.444, 0.444, 0.446, 0.448, 0.449, 0.458, 0.468, 0.476, 0.488,
0.501, 0.522, 0.574, 0.609, 0.670
]
sparsity_theta_value = [0.0] * num_collection_passes
perp_zero_eps = 2.0
perplexity_value = [
6873, 2590, 2685, 2578, 2603, 2552, 2536, 2481, 2419, 2331, 2235, 2140,
2065, 2009, 1964
]
perplexity_rel_value = [
6873, 2667, 2458, 2323, 2150, 2265, 2015, 1967, 1807, 1747, 1713, 1607,
1632, 1542, 1469
]
top_zero_eps = 0.0001
top_tokens_num_tokens = [num_tokens * num_topics] * num_collection_passes
top_tokens_topic_0_tokens = [
u'party', u'state', u'campaign', u'tax', u'political', u'republican',
u'senate', u'candidate', u'democratic', u'court', u'president'
]
top_tokens_topic_0_weights = [
0.0209, 0.0104, 0.0094, 0.0084, 0.0068, 0.0067, 0.0065, 0.0058, 0.0053,
0.0053, 0.0051
]
ker_zero_eps = 0.02
topic_kernel_topic_0_contrast = 0.96
topic_kernel_topic_0_purity = 0.014
topic_kernel_topic_0_size = 18.0
topic_kernel_average_size = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13, 0.6, 1.6, 3.53, 7.15, 12.6,
20.4, 29.06
]
topic_kernel_average_contrast = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12, 0.31, 0.7, 0.96, 0.96, 0.96,
0.96, 0.97
]
topic_kernel_average_purity = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.015, 0.017, 0.02,
0.03, 0.04, 0.05
]
len_last_document_ids = 10
try:
batch_vectorizer = artm.BatchVectorizer(data_path=data_path,
data_format='bow_uci',
collection_name='kos',
target_folder=batches_folder)
dictionary = artm.Dictionary()
dictionary.gather(data_path=batch_vectorizer.data_path)
model = artm.ARTM(
topic_names=['topic_{}'.format(i) for i in range(num_topics)],
dictionary=dictionary.name,
cache_theta=True)
model.regularizers.add(
artm.SmoothSparsePhiRegularizer(name='SparsePhi', tau=sp_reg_tau))
model.regularizers.add(
artm.DecorrelatorPhiRegularizer(name='DecorrelatorPhi',
tau=decor_tau))
model.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'))
model.scores.add(
artm.PerplexityScore(name='PerplexityScore',
use_unigram_document_model=False,
dictionary=dictionary))
model.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model.scores.add(
artm.TopTokensScore(name='TopTokensScore', num_tokens=num_tokens))
model.scores.add(
artm.TopicKernelScore(
name='TopicKernelScore',
probability_mass_threshold=probability_mass_threshold))
model.scores.add(artm.ThetaSnippetScore(name='ThetaSnippetScore'))
model.num_document_passes = num_document_passes
model.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=num_collection_passes)
for i in range(num_collection_passes):
assert abs(model.score_tracker['SparsityPhiScore'].value[i] -
sparsity_phi_value[i]) < sp_zero_eps
for i in range(num_collection_passes):
assert abs(model.score_tracker['SparsityThetaScore'].value[i] -
sparsity_theta_value[i]) < sp_zero_eps
for i in range(num_collection_passes):
assert abs(model.score_tracker['PerplexityScore'].value[i] -
perplexity_value[i]) < perp_zero_eps
for i in range(num_collection_passes):
assert model.score_tracker['TopTokensScore'].num_tokens[
i] == top_tokens_num_tokens[i]
for i in range(num_tokens):
assert model.score_tracker['TopTokensScore'].last_tokens[
model.topic_names[0]][i] == top_tokens_topic_0_tokens[i]
assert abs(model.score_tracker['TopTokensScore'].last_weights[
model.topic_names[0]][i] -
top_tokens_topic_0_weights[i]) < top_zero_eps
assert len(model.score_tracker['TopicKernelScore'].last_tokens[
model.topic_names[0]]) > 0
assert abs(topic_kernel_topic_0_contrast -
model.score_tracker['TopicKernelScore'].last_contrast[
model.topic_names[0]]) < ker_zero_eps
assert abs(topic_kernel_topic_0_purity -
model.score_tracker['TopicKernelScore'].last_purity[
model.topic_names[0]]) < ker_zero_eps
assert abs(topic_kernel_topic_0_size -
model.score_tracker['TopicKernelScore'].last_size[
model.topic_names[0]]) < ker_zero_eps
for i in range(num_collection_passes):
assert abs(
model.score_tracker['TopicKernelScore'].average_size[i] -
topic_kernel_average_size[i]) < ker_zero_eps
assert abs(
model.score_tracker['TopicKernelScore'].average_contrast[i] -
topic_kernel_average_contrast[i]) < ker_zero_eps
assert abs(
model.score_tracker['TopicKernelScore'].average_purity[i] -
topic_kernel_average_purity[i]) < ker_zero_eps
model.fit_online(batch_vectorizer=batch_vectorizer)
info = model.info
assert info is not None
assert len(info.config.topic_name) == num_topics
assert len(info.score) >= len(model.score_tracker)
assert len(info.regularizer) == len(model.regularizers.data)
assert len(info.cache_entry) > 0
temp = model.score_tracker['ThetaSnippetScore'].last_document_ids
assert len_last_document_ids == len(temp)
assert len(model.score_tracker['ThetaSnippetScore'].last_snippet[
temp[0]]) == num_topics
phi = model.get_phi()
assert phi.shape == (vocab_size, num_topics)
theta = model.get_theta()
assert theta.shape == (num_topics, num_docs)
assert model.library_version.count('.') == 2 # major.minor.patch
# test relative coefficients for Phi matrix regularizers
model = artm.ARTM(num_topics=num_topics,
dictionary=dictionary.name,
cache_theta=False)
model.regularizers.add(
artm.DecorrelatorPhiRegularizer(name='DecorrelatorPhi',
tau=decor_rel_tau))
model.regularizers['DecorrelatorPhi'].gamma = 0.0
model.scores.add(
artm.PerplexityScore(name='PerplexityScore',
use_unigram_document_model=False,
dictionary=dictionary))
model.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model.num_document_passes = num_document_passes
model.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=num_collection_passes)
for i in range(num_collection_passes):
assert abs(model.score_tracker['SparsityPhiScore'].value[i] -
sparsity_phi_rel_value[i]) < sp_zero_eps
for i in range(num_collection_passes):
assert abs(model.score_tracker['PerplexityScore'].value[i] -
perplexity_rel_value[i]) < perp_zero_eps
finally:
shutil.rmtree(batches_folder)
def topic_model_clf(X, y, topic_num=30):
labels_decreasing_size_order = list(y.value_counts().index)
(X_train, X_test, y_train, y_test) = train_test_split(X,
y,
test_size=0.2,
stratify=y,
random_state=42)
file_train = 'temp_files/X_train.txt'
file_test = 'temp_files/X_test.txt'
temp_df = pd.DataFrame()
temp_df['text'] = X_train
temp_df['class_label'] = y_train
write_vw(temp_df, X_train.index, file_train)
temp_df = pd.DataFrame()
temp_df['text'] = X_test
write_vw(temp_df, X_test.index, file_test)
if len(glob.glob(os.path.join('batches_train*.batch'))) < 1:
batch_vectorizer_train = artm.BatchVectorizer(
data_path=file_train,
data_format='vowpal_wabbit',
target_folder='batches_train',
gather_dictionary=True)
else:
batch_vectorizer_train = artm.BatchVectorizer(
data_path='batches_train',
data_format='batches',
gather_dictionary=True)
if len(glob.glob(os.path.join('batches_test' + '*.batch'))) < 1:
batch_vectorizer_test = artm.BatchVectorizer(
data_path=file_test,
data_format='vowpal_wabbit',
target_folder='batches_test',
gather_dictionary=True)
else:
batch_vectorizer_test = artm.BatchVectorizer(data_path='batches_test',
data_format='batches',
gather_dictionary=True)
model = artm.ARTM(num_topics=topic_num,
class_ids={
'@text': 5.0,
'@class_label': 100.0
},
cache_theta=True,
dictionary=batch_vectorizer_train.dictionary,
theta_columns_naming='title')
scores = [
artm.PerplexityScore(name='Perplexity',
dictionary=batch_vectorizer_train.dictionary,
class_ids=['@text']),
artm.SparsityPhiScore(name='SparsityPhiText', class_id='@text'),
artm.SparsityPhiScore(name='SparsityPhiClasses',
class_id='@class_label'),
artm.SparsityThetaScore(name='SparsityTheta'),
artm.TopicKernelScore(name='TopicKernelText',
probability_mass_threshold=0.1,
class_id='@text'),
artm.TopTokensScore(name='TopTokensText',
class_id='@text',
num_tokens=20),
artm.TopTokensScore(name='TopTokensClasses',
class_id='@class_label',
num_tokens=10)
]
regularizers = [
artm.DecorrelatorPhiRegularizer(name='DeccorText',
class_ids=['@text'],
tau=10000),
artm.SmoothSparsePhiRegularizer(name='SmoothPhiText',
class_ids=['@text'],
tau=0),
artm.SmoothSparsePhiRegularizer(name='SmoothPhiClasses',
class_ids=['@class_label'],
tau=-1),
# artm.SmoothSparsePhiRegularizer(name='SmoothBackgroundPhi', tau=100, topic_names=['background_topic']),
artm.SmoothSparseThetaRegularizer(name='SmoothTheta', tau=-1.5),
# artm.SmoothSparseThetaRegularizer(name='SmoothBackgroundTheta', tau=100, topic_names=['background_topic'])
]
for r in regularizers:
model.regularizers.add(r)
for s in scores:
model.scores.add(s)
for i in tqdm(range(35)):
model.fit_offline(batch_vectorizer=batch_vectorizer_train,
num_collection_passes=1)
p_cd = model.transform(batch_vectorizer=batch_vectorizer_test,
predict_class_id='@class_label')
# пооптимизируем это место
y_pred = p_cd.idxmax(axis=0).astype(int)[[str(x)
for x in X_test.index]].values
# y_pred = p_cd[[str(x) for x in X_test.index]].idxmax(axis=0).values
# metrics_visualization(target_pred=y_pred, target_true=y_test,
# top_tokens_class=model.score_tracker['TopTokensClasses'],
# top_tokens_text=model.score_tracker['TopTokensText'],
# score_tracker=model.score_tracker,
# scores_names=['Perplexity', 'SparsityPhiClasses',
# 'SparsityPhiText', 'SparsityTheta'])
print('Accuracy_score: {}'.format(accuracy_score(y_test, y_pred)))
plt.hist(y_pred, color='g', label='pred')
plt.hist(y_test, color='b', alpha=0.7, label='true')
plt.title('Topic Model')
plt.show()
# print(classification_report(y_test, y_pred, labels=labels_decreasing_size_order))
create_confusion_matrix(y_test,
y_pred,
labels=labels_decreasing_size_order).savefig(
'../../reports/topic_model_conf_matrix.png')
micro_roc_auc = roc_auc_score(label_binarize(y_test,
classes=list(range(0, 17))),
p_cd.T,
average='micro')
macro_roc_auc = roc_auc_score(label_binarize(y_test,
classes=list(range(0, 17))),
p_cd.T,
average='macro')
macro_f1 = f1_score(y_test, y_pred, average='macro')
micro_f1 = f1_score(y_test, y_pred, average='micro')
log_loss_score = log_loss(y_test, p_cd.T)
return (micro_roc_auc, macro_roc_auc, micro_f1, macro_f1, log_loss_score,
precision_recall_fscore_support(
y_test, y_pred, labels=labels_decreasing_size_order))
batch_vectorizer = artm.BatchVectorizer(data_path='lemmed.txt', data_format='vowpal_wabbit', target_folder='batches')
dictionary = batch_vectorizer.dictionary
topic_num = 10
tokens_num = 100
print("ARTM training")
topic_names = ['topic_{}'.format(i) for i in range(topic_num)]
model_artm = artm.ARTM(topic_names=topic_names, dictionary=dictionary, cache_theta=True)
model_plsa = artm.ARTM(topic_names=topic_names, cache_theta=True,
scores=[artm.PerplexityScore(name='PerplexityScore', dictionary=dictionary)])
model_lda = artm.LDA(num_topics=topic_num)
model_artm.scores.add(artm.PerplexityScore(name='perplexity_score', dictionary=dictionary))
model_artm.scores.add(artm.SparsityPhiScore(name='sparsity_phi_score'))
model_artm.scores.add(artm.SparsityThetaScore(name='sparsity_theta_score'))
model_artm.scores.add(artm.TopTokensScore(name='top_tokens_score', num_tokens=tokens_num))
model_artm.scores.add(artm.TopicKernelScore(name='topic_kernel_score', probability_mass_threshold=0.3))
model_artm.scores.add(artm.BackgroundTokensRatioScore(name='background_tokens_ratio_score'))
model_artm.scores.add(artm.ClassPrecisionScore(name='class_precision_score'))
model_artm.scores.add(artm.TopicMassPhiScore(name='topic_mass_phi_score'))
model_plsa.scores.add(artm.PerplexityScore(name='perplexity_score', dictionary=dictionary))
model_plsa.scores.add(artm.SparsityPhiScore(name='sparsity_phi_score'))
model_plsa.scores.add(artm.SparsityThetaScore(name='sparsity_theta_score'))
model_plsa.scores.add(artm.TopTokensScore(name='top_tokens_score'))
model_plsa.scores.add(artm.TopicKernelScore(name='topic_kernel_score', probability_mass_threshold=0.3))
model_plsa.scores.add(artm.BackgroundTokensRatioScore(name='background_tokens_ratio_score'))
model_plsa.scores.add(artm.ClassPrecisionScore(name='class_precision_score'))
model_plsa.scores.add(artm.TopicMassPhiScore(name='topic_mass_phi_score'))
coherence_score = artm.TopTokensScore(name='TopTokensCoherenceScore',
dictionary=cooc_dict,
num_tokens=15)
model_artm = artm.LDA(num_topics=N_TOPICS)
model_artm._internal_model.scores.add(
artm.TopTokensScore(name="top_words", num_tokens=10))
model_artm._internal_model.scores.add(coherence_score)
model_artm._internal_model.scores.add(
artm.PerplexityScore(name='perplexity_score', dictionary=bv.dictionary))
model_artm._internal_model.scores.add(
artm.SparsityPhiScore(name='sparsity_phi_score'))
model_artm._internal_model.scores.add(
artm.SparsityThetaScore(name='sparsity_theta_score'))
model_artm.initialize(dictionary=dictionary)
print("Initializing time: {}".format(time.time() - start))
start = time.time()
model_artm.fit_offline(bv, num_collection_passes=N_PASSES)
print("Training time: {}".format(time.time() - start))
if os.path.isdir(SAVE_DIR):
shutil.rmtree(SAVE_DIR)
model_artm._internal_model.dump_artm_model(SAVE_DIR)
# model_artm = artm.load_artm_model(SAVE_DIR)
for topic_name in model_artm._internal_model.topic_names:
print("".center(25, "*"))
artm.SmoothSparseThetaRegularizer(
name='SparseTheta', tau=-0.15)
],
cache_theta=True)
if not os.path.isfile(filename + '/dictionary.dict'):
dictionary.gather(data_path=batch_vectorizer.data_path)
dictionary.save(dictionary_path=filename + '/dictionary.dict')
dictionary.load(dictionary_path=(filename + '/dictionary.dict'))
dictionary.load(dictionary_path=(filename + '/dictionary.dict'))
model_artm.initialize(dictionary=dictionary)
model_artm.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model_artm.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'))
model_artm.scores.add(
artm.TopicKernelScore(name='TopicKernelScore',
probability_mass_threshold=0.3))
model_artm.regularizers.add(
artm.SmoothSparsePhiRegularizer(name='SparsePhi', tau=-0.1))
model_artm.regularizers.add(
artm.DecorrelatorPhiRegularizer(name='DecorrelatorPhi', tau=1.5e+5))
model_artm.regularizers.add(
artm.TopicSelectionThetaRegularizer(name='TopicSelection', tau=0.25))
model_artm.regularizers['SparsePhi'].tau = -0.5
model_artm.regularizers['SparseTheta'].tau = -0.5
model_artm.regularizers['DecorrelatorPhi'].tau = 1e+5
def calc_coeffs():
batch_vectorizer = artm.BatchVectorizer(data_path='lemmed.txt', data_format='vowpal_wabbit',
target_folder='batches')
dictionary = batch_vectorizer.dictionary
topic_num = 10
topic_names = ['topic_{}'.format(i) for i in range(topic_num)]
model_artm = artm.ARTM(topic_names=topic_names, dictionary=dictionary, cache_theta=True)
model_artm.scores.add(artm.PerplexityScore(name='perplexity_score', dictionary=dictionary))
model_artm.scores.add(artm.SparsityPhiScore(name='sparsity_phi_score'))
model_artm.scores.add(artm.SparsityThetaScore(name='sparsity_theta_score'))
model_artm.scores.add(artm.TopTokensScore(name='top_tokens_score'))
model_artm.scores.add(artm.TopicKernelScore(name='topic_kernel_score', probability_mass_threshold=0.3))
model_artm.regularizers.add(artm.SmoothSparsePhiRegularizer(name='sparse_phi_regularizer'))
model_artm.regularizers.add(artm.SmoothSparseThetaRegularizer(name='sparse_theta_regularizer'))
model_artm.regularizers.add(artm.DecorrelatorPhiRegularizer(name='decorrelator_phi_regularizer'))
best_tau_phi = -5.0
best_tau_theta = -5.0
best_perplexity = 1000000
print("Started parameters choosing")
for i in range(-20, 20, 5):
for j in range(-20, 20, 5):
model_artm.regularizers['sparse_phi_regularizer'].tau = (i / 10.0)
model_artm.regularizers['sparse_theta_regularizer'].tau = (j / 10.0)
model_artm.fit_offline(batch_vectorizer=batch_vectorizer, num_collection_passes=100)
if model_artm.score_tracker['perplexity_score'].last_value < best_perplexity:
best_perplexity = model_artm.score_tracker['perplexity_score'].last_value
best_tau_phi = (i / 10.0)
best_tau_theta = (j / 10.0)
print(best_perplexity, " ", best_tau_phi, " ", best_tau_theta)
print("RESULT 1 ", best_perplexity, " ", best_tau_phi, " ", best_tau_theta)
for i in range(int(10 * best_tau_phi) - 5, int(10 * best_tau_phi) + 5, 1):
for j in range(int(10 * best_tau_theta) - 5, int(10 * best_tau_theta) + 5, 1):
model_artm.regularizers['sparse_phi_regularizer'].tau = (i / 10.0)
model_artm.regularizers['sparse_theta_regularizer'].tau = (j / 10.0)
model_artm.fit_offline(batch_vectorizer=batch_vectorizer, num_collection_passes=100)
if model_artm.score_tracker['perplexity_score'].last_value < best_perplexity:
best_perplexity = model_artm.score_tracker['perplexity_score'].last_value
best_tau_phi = (i / 10.0)
best_tau_theta = (j / 10.0)
print(best_perplexity, " ", best_tau_phi, " ", best_tau_theta)
print("RESULT 2 ", best_perplexity, " ", best_tau_phi, " ", best_tau_theta)
for i in range(int(100 * best_tau_phi) - 10, int(100 * best_tau_phi) + 10, 1):
for j in range(int(100 * best_tau_theta) - 10, int(100 * best_tau_theta) + 10, 1):
model_artm.regularizers['sparse_phi_regularizer'].tau = (i / 100.0)
model_artm.regularizers['sparse_theta_regularizer'].tau = (j / 100.0)
model_artm.fit_offline(batch_vectorizer=batch_vectorizer, num_collection_passes=100)
if model_artm.score_tracker['perplexity_score'].last_value < best_perplexity:
best_perplexity = model_artm.score_tracker['perplexity_score'].last_value
best_tau_phi = (i / 100.0)
best_tau_theta = (j / 100.0)
print(best_perplexity, " ", best_tau_phi, " ", best_tau_theta)
print("RESULT 3 ", best_perplexity, " ", best_tau_phi, " ", best_tau_theta)
return {"tau_phi": best_tau_phi, "tau_theta": best_tau_theta}
def test_func():
# constants
dictionary_name = 'dictionary'
num_tokens = 11
probability_mass_threshold = 0.9
sp_reg_tau = -0.1
decor_tau = 1.5e+5
num_collection_passes = 15
num_document_passes = 1
num_topics = 15
vocab_size = 6906
num_docs = 3430
data_path = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
batches_folder = tempfile.mkdtemp()
sp_zero_eps = 0.001
sparsity_phi_value = [
0.034, 0.064, 0.093, 0.120, 0.145, 0.170, 0.194, 0.220, 0.246, 0.277,
0.312, 0.351, 0.390, 0.428, 0.464
]
sparsity_theta_value = [0.0] * num_collection_passes
perp_zero_eps = 2.0
perplexity_value = [
6873, 2590, 2685, 2578, 2603, 2552, 2536, 2481, 2419, 2331, 2235, 2140,
2065, 2009, 1964
]
top_zero_eps = 0.0001
top_tokens_num_tokens = [num_tokens * num_topics] * num_collection_passes
top_tokens_topic_0_tokens = [
u'party', u'state', u'campaign', u'tax', u'political', u'republican',
u'senate', u'candidate', u'democratic', u'court', u'president'
]
top_tokens_topic_0_weights = [
0.0209, 0.0104, 0.0094, 0.0084, 0.0068, 0.0067, 0.0065, 0.0058, 0.0053,
0.0053, 0.0051
]
ker_zero_eps = 0.01
topic_kernel_topic_0_contrast = 0.96
topic_kernel_topic_0_purity = 0.014
topic_kernel_topic_0_size = 18.0
topic_kernel_average_size = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13, 0.53, 1.6, 3.33, 7.13, 12.067,
19.53, 27.8
]
topic_kernel_average_contrast = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12, 0.25, 0.7, 0.96, 0.96, 0.96,
0.96, 0.97
]
topic_kernel_average_purity = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.015, 0.017, 0.02,
0.03, 0.04, 0.05
]
len_last_document_ids = 10
try:
data_path = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
batch_vectorizer = None
batch_vectorizer = artm.BatchVectorizer(data_path=data_path,
data_format='bow_uci',
collection_name='kos',
target_folder=batches_folder)
model = artm.ARTM(
topic_names=['topic_{}'.format(i) for i in xrange(num_topics)],
cache_theta=True)
model.gather_dictionary(dictionary_name, batch_vectorizer.data_path)
model.initialize(dictionary_name=dictionary_name)
model.regularizers.add(
artm.SmoothSparsePhiRegularizer(name='SparsePhi', tau=sp_reg_tau))
model.regularizers.add(
artm.DecorrelatorPhiRegularizer(name='DecorrelatorPhi',
tau=decor_tau))
model.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'))
model.scores.add(
artm.PerplexityScore(name='PerplexityScore',
use_unigram_document_model=False,
dictionary_name=dictionary_name))
model.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model.scores.add(
artm.TopTokensScore(name='TopTokensScore', num_tokens=num_tokens))
model.scores.add(
artm.TopicKernelScore(
name='TopicKernelScore',
probability_mass_threshold=probability_mass_threshold))
model.scores.add(artm.ThetaSnippetScore(name='ThetaSnippetScore'))
model.num_document_passes = num_document_passes
model.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=num_collection_passes)
for i in xrange(num_collection_passes):
assert abs(model.score_tracker['SparsityPhiScore'].value[i] -
sparsity_phi_value[i]) < sp_zero_eps
for i in xrange(num_collection_passes):
assert abs(model.score_tracker['SparsityThetaScore'].value[i] -
sparsity_theta_value[i]) < sp_zero_eps
for i in xrange(num_collection_passes):
assert abs(model.score_tracker['PerplexityScore'].value[i] -
perplexity_value[i]) < perp_zero_eps
for i in xrange(num_collection_passes):
assert model.score_tracker['TopTokensScore'].num_tokens[
i] == top_tokens_num_tokens[i]
for i in xrange(num_tokens):
assert model.score_tracker['TopTokensScore'].last_tokens[
model.topic_names[0]][i] == top_tokens_topic_0_tokens[i]
assert abs(model.score_tracker['TopTokensScore'].last_weights[
model.topic_names[0]][i] -
top_tokens_topic_0_weights[i]) < top_zero_eps
assert len(model.score_tracker['TopicKernelScore'].last_tokens[
model.topic_names[0]]) > 0
assert abs(topic_kernel_topic_0_contrast -
model.score_tracker['TopicKernelScore'].last_contrast[
model.topic_names[0]]) < ker_zero_eps
assert abs(topic_kernel_topic_0_purity -
model.score_tracker['TopicKernelScore'].last_purity[
model.topic_names[0]]) < ker_zero_eps
assert abs(topic_kernel_topic_0_size -
model.score_tracker['TopicKernelScore'].last_size[
model.topic_names[0]]) < ker_zero_eps
for i in xrange(num_collection_passes):
assert abs(
model.score_tracker['TopicKernelScore'].average_size[i] -
topic_kernel_average_size[i]) < ker_zero_eps
assert abs(
model.score_tracker['TopicKernelScore'].average_contrast[i] -
topic_kernel_average_contrast[i]) < ker_zero_eps
assert abs(
model.score_tracker['TopicKernelScore'].average_purity[i] -
topic_kernel_average_purity[i]) < ker_zero_eps
model.fit_online(batch_vectorizer=batch_vectorizer)
info = model.info
assert info is not None
assert len(info.config.topic_name) == num_topics
assert len(info.score) == len(model.score_tracker)
assert len(info.regularizer) == len(model.regularizers.data)
assert len(info.cache_entry) > 0
temp = model.score_tracker['ThetaSnippetScore'].last_document_ids
assert len_last_document_ids == len(temp)
assert len(model.score_tracker['ThetaSnippetScore'].last_snippet[
temp[0]]) == num_topics
phi = model.get_phi()
assert phi.shape == (vocab_size, num_topics)
theta = model.get_theta()
assert theta.shape == (num_topics, num_docs)
finally:
shutil.rmtree(batches_folder)
path = 'C:\\NIVC\\Nivc_BigARTM_corpus\\unary_comm\\'
subd = "golosislamacom"
batch_vectorizer = artm.BatchVectorizer(data_path=path + "\\" + subd + "\\" +
"batches_pos",
data_format='batches')
modelPLSA = artm.ARTM(topic_names=['topic_{}'.format(i) for i in xrange(100)],
scores=[
artm.PerplexityScore(
name='PerplexityScore',
use_unigram_document_model=False,
dictionary=batch_vectorizer.dictionary,
class_ids=["text"]),
artm.SparsityPhiScore(name='SparsityPhiScore',
class_id="text"),
artm.SparsityThetaScore(name='SparsityThetaScore'),
artm.TopicKernelScore(name='TopicKernelScore',
probability_mass_threshold=0.3,
class_id="text"),
artm.TopTokensScore(name='TopTokensScore',
num_tokens=100,
class_id="text")
],
cache_theta=True)
modelPLSA.initialize(dictionary=batch_vectorizer.dictionary)
modelPLSA.num_document_passes = 5
modelPLSA.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=30)
def test_func():
# constants
num_tokens = 15
alpha = 0.01
beta = 0.02
num_collection_passes = 15
num_document_passes = 1
num_topics = 15
vocab_size = 6906
num_docs = 3430
zero_eps = 0.001
data_path = os.environ.get('BIGARTM_UNITTEST_DATA')
batches_folder = tempfile.mkdtemp()
try:
batch_vectorizer = artm.BatchVectorizer(data_path=data_path,
data_format='bow_uci',
collection_name='kos',
target_folder=batches_folder)
dictionary = artm.Dictionary()
dictionary.gather(data_path=batch_vectorizer.data_path)
model_artm = artm.ARTM(num_topics=num_topics,
dictionary=dictionary,
cache_theta=True,
reuse_theta=True)
model_artm.regularizers.add(
artm.SmoothSparsePhiRegularizer(name='SparsePhi', tau=beta))
model_artm.regularizers.add(
artm.SmoothSparseThetaRegularizer(name='SparseTheta', tau=alpha))
model_artm.scores.add(
artm.SparsityThetaScore(name='SparsityThetaScore'))
model_artm.scores.add(
artm.PerplexityScore(name='PerplexityScore',
dictionary=dictionary))
model_artm.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))
model_artm.scores.add(
artm.TopTokensScore(name='TopTokensScore', num_tokens=num_tokens))
model_lda = artm.LDA(num_topics=num_topics,
alpha=alpha,
beta=beta,
dictionary=dictionary,
cache_theta=True)
model_lda.initialize(dictionary=dictionary)
model_artm.num_document_passes = num_document_passes
model_lda.num_document_passes = num_document_passes
model_artm.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=num_collection_passes)
model_lda.fit_offline(batch_vectorizer=batch_vectorizer,
num_collection_passes=num_collection_passes)
for i in range(num_collection_passes):
assert abs(model_artm.score_tracker['SparsityPhiScore'].value[i] -
model_lda.sparsity_phi_value[i]) < zero_eps
for i in range(num_collection_passes):
assert abs(
model_artm.score_tracker['SparsityThetaScore'].value[i] -
model_lda.sparsity_theta_value[i]) < zero_eps
for i in range(num_collection_passes):
assert abs(model_artm.score_tracker['PerplexityScore'].value[i] -
model_lda.perplexity_value[i]) < zero_eps
lda_tt = model_lda.get_top_tokens(num_tokens=num_tokens)
assert len(lda_tt) == num_topics
for i in range(num_topics):
for j in range(num_tokens):
assert model_artm.score_tracker['TopTokensScore'].last_tokens[
model_artm.topic_names[i]][j] == lda_tt[i][j]
lda_tt = model_lda.get_top_tokens(num_tokens=num_tokens,
with_weights=True)
for i in range(num_tokens):
assert abs(model_artm.score_tracker['TopTokensScore'].last_weights[
model_artm.topic_names[0]][i] - lda_tt[0][i][1]) < zero_eps
model_lda.fit_online(batch_vectorizer=batch_vectorizer)
phi = model_lda.phi_
assert phi.shape == (vocab_size, num_topics)
theta = model_lda.get_theta()
assert theta.shape == (num_topics, num_docs)
assert model_lda.library_version.count('.') == 2 # major.minor.patch
model_lda = artm.LDA(num_topics=num_topics,
alpha=alpha,
beta=([0.1] * num_topics),
dictionary=dictionary,
cache_theta=True)
assert model_lda._internal_model.regularizers.size() == num_topics + 1
finally:
shutil.rmtree(batches_folder)
def test_func():
data_path = os.environ.get('BIGARTM_UNITTEST_DATA')
batches_folder = tempfile.mkdtemp()
dump_folder = tempfile.mkdtemp()
try:
batch_vectorizer = artm.BatchVectorizer(data_path=data_path,
data_format='bow_uci',
collection_name='kos',
target_folder=batches_folder)
model_1 = artm.ARTM(num_processors=7,
cache_theta=True,
num_document_passes=5,
reuse_theta=True,
seed=10,
num_topics=15,
class_ids={'@default_class': 1.0},
theta_name='THETA',
dictionary=batch_vectorizer.dictionary)
model_2 = artm.ARTM(num_processors=7,
cache_theta=False,
num_document_passes=5,
reuse_theta=False,
seed=10,
num_topics=15,
class_ids={'@default_class': 1.0},
dictionary=batch_vectorizer.dictionary)
for model in [model_1, model_2]:
model.scores.add(
artm.PerplexityScore(name='perp',
dictionary=batch_vectorizer.dictionary))
model.scores.add(artm.SparsityThetaScore(name='sp_theta', eps=0.1))
model.scores.add(artm.TopTokensScore(name='top_tok',
num_tokens=10))
model.scores.add(
artm.SparsityPhiScore(name='sp_nwt',
model_name=model.model_nwt))
model.scores.add(
artm.TopicKernelScore(name='kernel',
topic_names=model.topic_names[0:5],
probability_mass_threshold=0.4))
topic_pairs = {}
for topic_name_1 in model.topic_names:
for topic_name_2 in model.topic_names:
if topic_name_1 not in topic_pairs:
topic_pairs[topic_name_1] = {}
topic_pairs[topic_name_1][
topic_name_2] = numpy.random.randint(0, 3)
model.regularizers.add(
artm.DecorrelatorPhiRegularizer(name='decor',
tau=100000.0,
topic_pairs=topic_pairs))
model.regularizers.add(
artm.SmoothSparsePhiRegularizer(
name='smsp_phi',
tau=-0.5,
gamma=0.3,
dictionary=batch_vectorizer.dictionary))
model.regularizers.add(
artm.SmoothSparseThetaRegularizer(name='smsp_theta',
tau=0.1,
doc_topic_coef=[2.0] *
model.num_topics))
model.regularizers.add(
artm.SmoothPtdwRegularizer(name='sm_ptdw', tau=0.1))
# learn first model and dump it on disc
model.fit_offline(batch_vectorizer, num_collection_passes=10)
model.fit_online(batch_vectorizer, update_every=1)
model.dump_artm_model(os.path.join(dump_folder, 'target'))
params = {}
with open(os.path.join(dump_folder, 'target', 'parameters.json'),
'r') as fin:
params = json.load(fin)
_assert_json_params(params)
# create second model from the dump and check the results are equal
model_new = artm.load_artm_model(
os.path.join(dump_folder, 'target'))
_assert_params_equality(model, model_new)
_assert_scores_equality(model, model_new)
_assert_regularizers_equality(model, model_new)
_assert_score_values_equality(model, model_new)
_assert_matrices_equality(model, model_new)
# continue learning of both models
model.fit_offline(batch_vectorizer, num_collection_passes=3)
model.fit_online(batch_vectorizer, update_every=1)
model_new.fit_offline(batch_vectorizer, num_collection_passes=3)
model_new.fit_online(batch_vectorizer, update_every=1)
# check new results are also equal
_assert_params_equality(model, model_new)
_assert_scores_equality(model, model_new)
_assert_regularizers_equality(model, model_new)
_assert_score_values_equality(model, model_new)
_assert_matrices_equality(model, model_new)
shutil.rmtree(os.path.join(dump_folder, 'target'))
finally:
shutil.rmtree(batches_folder)
shutil.rmtree(dump_folder)