def fit(self, documents):
'''
Implement a parallel version of CGS over documents
This requires storing a copy of sum_K and topics to avoid conflicts
There's a synchronization period over which to reconcile the global
sum_K/topics with the thread's local
Additional feature is setting split_words = True
This splits V into num_threads regions and provides locking mechanisms
to update topics. This avoids the need to store the local topics and synchronize
in case the corpus is very large, however the extra locking may hinder speedups
'''
# topic -> words distribution
topics = np.zeros((self.num_topics, documents.shape[1]), dtype=np.float)
# documents -> topic distribution
gamma = np.zeros((documents.shape[0], self.num_topics), dtype=np.float)
# sum of types per topic
sum_K = np.zeros((self.num_topics), dtype=np.dtype("i"))
# sampling distributions
sampling = np.zeros(
(documents.shape[0], documents.shape[1], np.max(documents)), dtype=np.dtype("i"))
self._pCGS(documents, topics, gamma, sum_K,
sampling)
assert np.sum(sum_K) == np.sum(documents), "Sum_K not synced: {}, {}".format(
np.sum(sum_K), np.sum(documents))
assert np.sum(topics) == np.sum(documents), "topics not synced: {}, {}".format(
np.sum(topics), np.sum(documents))
self.topics = topics
self.gamma = gamma
self.sum_K = sum_K
# Compute the perplexity of the trained model on the train data
self.perplexity_train = Evaluation._log_likelihood(self, gamma, documents)
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)