def update(self, corpus):
"""
Train the model with new documents, by EM-iterating over `corpus` until
the topics converge (or until the maximum number of allowed iterations
is reached). `corpus` must be an iterable (repeatable stream of documents),
The E-step is distributed into the several processes.
This update also supports updating an already trained model (`self`)
with new documents from `corpus`; the two models are then merged in
proportion to the number of old vs. new documents. This feature is still
experimental for non-stationary input streams.
For stationary input (no topic drift in new documents), on the other hand,
this equals the online update of Hoffman et al. and is guaranteed to
converge for any `decay` in (0.5, 1.0>.
"""
# rho is the "speed" of updating, decelerating over time
rho = lambda: pow(1.0 + self.num_updates / self.chunksize, -self.decay)
try:
lencorpus = len(corpus)
except:
logger.warning("input corpus stream has no len(); counting documents")
lencorpus = sum(1 for _ in corpus)
if lencorpus == 0:
logger.warning("LdaMulticore.update() called with an empty corpus")
return
self.state.numdocs += lencorpus
if not self.batch:
updatetype = "online"
updateafter = self.chunksize * self.workers
else:
updatetype = "batch"
updateafter = lencorpus
evalafter = min(lencorpus, (self.eval_every * updateafter or 0))
updates_per_pass = max(1, lencorpus / updateafter)
logger.info("running %s LDA training, %s topics, %i passes over the"
" supplied corpus of %i documents, updating every %i documents,"
" evaluating every ~%i documents, iterating %ix with a convergence threshold of %f",
updatetype, self.num_topics, self.passes, lencorpus, updateafter, evalafter,
self.iterations, self.gamma_threshold)
if updates_per_pass * self.passes < 10:
logger.warning("too few updates, training might not converge; consider "
"increasing the number of passes or iterations to improve accuracy")
def worker_e_step(input_queue, result_queue):
"""
Perform E-step for each (chunk_no, chunk, model) 3-tuple from the
input queue, placing the resulting state into the result queue.
"""
logger.debug("worker process entering E-step loop")
while True:
logger.debug("getting a new job")
chunk_no, chunk, worker_lda = input_queue.get()
logger.debug("processing chunk #%i of %i documents", chunk_no, len(chunk))
worker_lda.state.reset()
worker_lda.do_estep(chunk) # TODO: auto-tune alpha?
del chunk
logger.debug("processed chunk, queuing the result")
result_queue.put(worker_lda.state)
del worker_lda # free up some memory
logger.debug("result put")
job_queue = Queue(maxsize=2 * self.workers)
result_queue = Queue()
logger.info("training LDA model using %i processes", self.workers)
pool = Pool(self.workers, worker_e_step, (job_queue, result_queue,))
for pass_ in xrange(self.passes):
queue_size, reallen = [0], 0
other = LdaState(self.eta, self.state.sstats.shape)
def process_result_queue(force=False):
"""
Clear the result queue, merging all intermediate results, and update the
LDA model if necessary.
"""
merged_new = False
while not result_queue.empty():
other.merge(result_queue.get())
queue_size[0] -= 1
merged_new = True
if (force and merged_new and queue_size[0] == 0) or (not self.batch and (other.numdocs >= updateafter)):
self.do_mstep(rho(), other)
other.reset()
if self.eval_every is not None and ((force and queue_size[0] == 0) or (self.eval_every != 0 and (self.num_updates / updateafter) % self.eval_every == 0)):
self.log_perplexity(chunk, total_docs=lencorpus)
chunk_stream = utils.grouper(corpus, self.chunksize, as_numpy=True)
for chunk_no, chunk in enumerate(chunk_stream):
reallen += len(chunk) # keep track of how many documents we've processed so far
# put the chunk into the workers' input job queue
chunk_put = False
while not chunk_put:
try:
job_queue.put((chunk_no, chunk, self), block=False, timeout=0.1)
chunk_put = True
queue_size[0] += 1
logger.info('PROGRESS: pass %i, dispatched chunk #%i = '
'documents up to #%i/%i, outstanding queue size %i',
pass_, chunk_no, chunk_no * self.chunksize + len(chunk), lencorpus, queue_size[0])
except Full:
# in case the input job queue is full, keep clearing the
# result queue, to make sure we don't deadlock
process_result_queue()
process_result_queue()
#endfor single corpus pass
# wait for all outstanding jobs to finish
while queue_size[0] > 0:
process_result_queue(force=True)
if reallen != lencorpus:
raise RuntimeError("input corpus size changed during training (don't use generators as input)")
#endfor entire update
pool.close()
def update(self, corpus, chunks_as_numpy=False):
"""Train the model with new documents, by EM-iterating over `corpus` until the topics converge
(or until the maximum number of allowed iterations is reached).
Train the model with new documents, by EM-iterating over the corpus until the topics converge, or until
the maximum number of allowed iterations is reached. `corpus` must be an iterable. The E step is distributed
into the several processes.
Notes
-----
This update also supports updating an already trained model (`self`)
with new documents from `corpus`; the two models are then merged in
proportion to the number of old vs. new documents. This feature is still
experimental for non-stationary input streams.
For stationary input (no topic drift in new documents), on the other hand,
this equals the online update of Hoffman et al. and is guaranteed to
converge for any `decay` in (0.5, 1.0>.
Parameters
----------
corpus : {iterable of list of (int, float), scipy.sparse.csc}, optional
Stream of document vectors or sparse matrix of shape (`num_terms`, `num_documents`) used to update the
model.
chunks_as_numpy : bool
Whether each chunk passed to the inference step should be a np.ndarray or not. Numpy can in some settings
turn the term IDs into floats, these will be converted back into integers in inference, which incurs a
performance hit. For distributed computing it may be desirable to keep the chunks as `numpy.ndarray`.
"""
try:
lencorpus = len(corpus)
except TypeError:
logger.warning("input corpus stream has no len(); counting documents")
lencorpus = sum(1 for _ in corpus)
if lencorpus == 0:
logger.warning("LdaMulticore.update() called with an empty corpus")
return
self.state.numdocs += lencorpus
if not self.batch:
updatetype = "online"
updateafter = self.chunksize * self.workers
else:
updatetype = "batch"
updateafter = lencorpus
evalafter = min(lencorpus, (self.eval_every or 0) * updateafter)
updates_per_pass = max(1, lencorpus / updateafter)
logger.info(
"running %s LDA training, %s topics, %i passes over the supplied corpus of %i documents, "
"updating every %i documents, evaluating every ~%i documents, "
"iterating %ix with a convergence threshold of %f",
updatetype, self.num_topics, self.passes, lencorpus, updateafter,
evalafter, self.iterations, self.gamma_threshold
)
if updates_per_pass * self.passes < 10:
logger.warning(
"too few updates, training might not converge; "
"consider increasing the number of passes or iterations to improve accuracy"
)
job_queue = Queue(maxsize=2 * self.workers)
result_queue = Queue()
# rho is the "speed" of updating; TODO try other fncs
# pass_ + num_updates handles increasing the starting t for each pass,
# while allowing it to "reset" on the first pass of each update
def rho():
return pow(self.offset + pass_ + (self.num_updates / self.chunksize), -self.decay)
logger.info("training LDA model using %i processes", self.workers)
pool = Pool(self.workers, worker_e_step, (job_queue, result_queue,))
for pass_ in range(self.passes):
queue_size, reallen = [0], 0
other = LdaState(self.eta, self.state.sstats.shape)
def process_result_queue(force=False):
"""
Clear the result queue, merging all intermediate results, and update the
LDA model if necessary.
"""
merged_new = False
while not result_queue.empty():
other.merge(result_queue.get())
queue_size[0] -= 1
merged_new = True
if (force and merged_new and queue_size[0] == 0) or (not self.batch and (other.numdocs >= updateafter)):
self.do_mstep(rho(), other, pass_ > 0)
other.reset()
if self.eval_every is not None \
and ((force and queue_size[0] == 0)
or (self.eval_every != 0 and (self.num_updates / updateafter) % self.eval_every == 0)):
self.log_perplexity(chunk, total_docs=lencorpus)
chunk_stream = utils.grouper(corpus, self.chunksize, as_numpy=chunks_as_numpy)
for chunk_no, chunk in enumerate(chunk_stream):
reallen += len(chunk) # keep track of how many documents we've processed so far
# put the chunk into the workers' input job queue
chunk_put = False
while not chunk_put:
try:
job_queue.put((chunk_no, chunk, self), block=False, timeout=0.1)
chunk_put = True
queue_size[0] += 1
logger.info(
"PROGRESS: pass %i, dispatched chunk #%i = documents up to #%i/%i, "
"outstanding queue size %i",
pass_, chunk_no, chunk_no * self.chunksize + len(chunk), lencorpus, queue_size[0]
)
except queue.Full:
# in case the input job queue is full, keep clearing the
# result queue, to make sure we don't deadlock
process_result_queue()
process_result_queue()
# endfor single corpus pass
# wait for all outstanding jobs to finish
while queue_size[0] > 0:
process_result_queue(force=True)
if reallen != lencorpus:
raise RuntimeError("input corpus size changed during training (don't use generators as input)")
# endfor entire update
pool.terminate()
