def fit(self, dtm):
'''
Parallel version of the lda: the temporary topics are computed in
parallel for each document inside a mini-batch
'''
# Initialisation
num_docs, num_words = dtm.shape
topics = np.random.gamma(100., 1./100., (self.num_topics, num_words))
gamma = np.ones((num_docs, self.num_topics))
ExpELogBeta = np.zeros((self.num_topics, num_words))
topics_int = np.zeros((self.num_threads, self.num_topics, num_words))
num_batch = num_docs / self.batch_size
batches = np.array_split(
np.arange(num_docs, dtype=np.int32), num_batch)
for it_batch in range(num_batch):
ovi_cython.exp_digamma2d(topics, ExpELogBeta)
docs_thread = np.array_split(batches[it_batch], self.num_threads)
# vector of threads
threads = [None]*self.num_threads
for tid in range(self.num_threads):
threads[tid] = threading.Thread(target=self._worker_estep,
args=(docs_thread[tid], dtm,
topics_int[tid, :, :],
gamma, ExpELogBeta))
threads[tid].start()
for thread in threads:
thread.join()
# Synchronizing the topics_int
topics_int_tot = np.sum(topics_int, axis=0)
# Initialize the list of topics int for the next batch
topics_int[:, :, :] = 0
# M-step
indices = (np.sum(dtm[batches[it_batch], :], axis=0) > 0).astype(
np.int32)
ovi_cython.m_step(topics, topics_int_tot, indices, num_docs,
self.batch_size, self.tau, self.kappa, it_batch)
self.topics = topics
self.gamma = gamma
# Compute the perplexity of the trained model on the train data
self.perplexity_train = Evaluation._log_likelihood(self, gamma, dtm)
def transform(self, dtm):
'''
Transform dtm into gamma according to the previously trained model.
'''
if self.topics is None:
raise NameError('The model has not been trained yet')
# Initialisation
num_docs, num_words = dtm.shape
np.random.seed(0)
gamma = np.ones((num_docs, self.num_topics))
ExpELogBeta = np.zeros((self.num_topics, num_words))
topics_int = np.zeros((self.num_threads, self.num_topics, num_words))
num_batch = num_docs / self.batch_size
batches = np.array_split(
np.arange(num_docs, dtype=np.int32), num_batch)
for it_batch in range(num_batch):
ovi_cython.exp_digamma2d(self.topics, ExpELogBeta)
docs_thread = np.array_split(batches[it_batch], self.num_threads)
# vector of threads
threads = [None]*self.num_threads
for tid in range(self.num_threads):
threads[tid] = threading.Thread(target=self._worker_estep,
args=(docs_thread[tid], dtm,
topics_int[tid, :, :],
gamma, ExpELogBeta))
threads[tid].start()
for thread in threads:
thread.join()
return gamma
