def main():
"""
Downloads and analyzes a bunch of random Wikipedia articles using
online VB for LDA.
"""
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
# The number of documents to analyze each iteration
D = 1000
batchsize = 100
# The number of topics
K = 30
# Our vocabulary
vocab = file('./com_all_words.txt').readlines()
W = len(vocab)
# Initialize the algorithm with alpha=1/K, eta=1/K, tau_0=1024, kappa=0.7
olda = onlineldavb.OnlineLDA(vocab, K, D, 1. / K, 1. / K, 1024., 0.7)
# Run until we've seen D documents. (Feel free to interrupt *much*
# sooner than this.)
for iteration in range(D / batchsize / size): # slaves
# Download some articles
docset = []
linecache.clearcache()
startpoint = iteration * batchsize * size + batchsize * rank + 1
if startpoint > D: # search to the end
break # stop
# get the paper keywords in batches
for i in range(batchsize):
f1 = open('com_all_key.txt', 'r')
docset.append(
linecache.getline('com_all_key.txt', min(D, startpoint))[:-1])
startpoint = startpoint + 1
# Give them to online LDA
(gamma, bound) = olda.update_lambda(docset)
# Compute an estimate of held-out perplexity
(wordids, wordcts) = onlineldavb.parse_doc_list(docset, olda._vocab)
perwordbound = bound * len(docset) / (D * sum(map(sum, wordcts)))
print '%d: rho_t = %f, held-out perplexity estimate = %f' % \
(iteration+1, olda._rhot, numpy.exp(-perwordbound))
# Save lambda, the parameters to the variational distributions
# over topics, and gamma, the parameters to the variational
# distributions over topic weights for the articles analyzed in
# the last iteration.
gammas = comm.gather(gamma, root=0)
lambdas = comm.gather(olda._lambda, root=0)
if rank == 0:
gamma_result = numpy.vstack((x for x in gammas))
lambda_result = numpy.vstack((x for x in lambdas))
numpy.savetxt('lambda_parallel.dat', olda._lambda)
numpy.savetxt('gamma_parallel.dat', gamma)
def main():
"""
Downloads and analyzes a bunch of random Wikipedia articles using
online VB for LDA.
"""
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
# The number of documents to analyze each iteration
D = 1000
batchsize = 100
# The number of topics
K = 30
# Our vocabulary
vocab = file('./com_all_words.txt').readlines()
W = len(vocab)
# Initialize the algorithm with alpha=1/K, eta=1/K, tau_0=1024, kappa=0.7
olda = onlineldavb.OnlineLDA(vocab, K, D, 1./K, 1./K, 1024., 0.7)
# Run until we've seen D documents. (Feel free to interrupt *much*
# sooner than this.)
for iteration in range(D/batchsize/size): # slaves
# Download some articles
docset = []
linecache.clearcache()
startpoint = iteration * batchsize * size + batchsize * rank + 1
if startpoint > D: # search to the end
break # stop
# get the paper keywords in batches
for i in range(batchsize):
f1 = open('com_all_key.txt','r')
docset.append(linecache.getline('com_all_key.txt', min(D, startpoint))[:-1])
startpoint = startpoint + 1
# Give them to online LDA
(gamma, bound) = olda.update_lambda(docset)
# Compute an estimate of held-out perplexity
(wordids, wordcts) = onlineldavb.parse_doc_list(docset, olda._vocab)
perwordbound = bound * len(docset) / (D * sum(map(sum, wordcts)))
print '%d: rho_t = %f, held-out perplexity estimate = %f' % \
(iteration+1, olda._rhot, numpy.exp(-perwordbound))
# Save lambda, the parameters to the variational distributions
# over topics, and gamma, the parameters to the variational
# distributions over topic weights for the articles analyzed in
# the last iteration.
gammas = comm.gather(gamma, root = 0)
lambdas = comm.gather(olda._lambda, root = 0)
if rank == 0:
gamma_result = numpy.vstack((x for x in gammas))
lambda_result = numpy.vstack((x for x in lambdas))
numpy.savetxt('lambda_parallel.dat', olda._lambda)
numpy.savetxt('gamma_parallel.dat', gamma)
def main():
"""
Downloads and analyzes a bunch of random Wikipedia articles using
online VB for LDA.
"""
# The number of documents to analyze each iteration
batchsize = 64
# The total number of documents in Wikipedia
D = 3.3e6
# The number of topics
K = 100
# How many documents to look at
if (len(sys.argv) < 2):
documentstoanalyze = int(D/batchsize)
else:
documentstoanalyze = int(sys.argv[1])
# Our vocabulary
vocab = file('./dictnostops.txt').readlines()
W = len(vocab)
# Initialize the algorithm with alpha=1/K, eta=1/K, tau_0=1024, kappa=0.7
olda = onlineldavb.OnlineLDA(vocab, K, D, 1./K, 1./K, 1024., 0.7)
# Run until we've seen D documents. (Feel free to interrupt *much*
# sooner than this.)
for iteration in range(0, documentstoanalyze):
# Download some articles
(docset, articlenames) = \
wikirandom.get_random_wikipedia_articles(batchsize)
# Give them to online LDA
(gamma, bound) = olda.update_lambda(docset)
# Compute an estimate of held-out perplexity
(wordids, wordcts) = onlineldavb.parse_doc_list(docset, olda._vocab)
perwordbound = bound * len(docset) / (D * sum(map(sum, wordcts)))
print '%d: rho_t = %f, held-out perplexity estimate = %f' % \
(iteration, olda._rhot, numpy.exp(-perwordbound))
# Save lambda, the parameters to the variational distributions
# over topics, and gamma, the parameters to the variational
# distributions over topic weights for the articles analyzed in
# the last iteration.
if (iteration % 10 == 0):
numpy.savetxt('lambda-%d.dat' % iteration, olda._lambda)
numpy.savetxt('gamma-%d.dat' % iteration, gamma)
def main():
"""
Downloads and analyzes a bunch of random Wikipedia articles using
online VB for LDA.
"""
# The number of documents to analyze each iteration
batchsize = 64
# The total number of documents in Wikipedia
D = 3.3e6
# The number of topics
K = 100
# How many documents to look at
if (len(sys.argv) < 2):
documentstoanalyze = int(D / batchsize)
else:
documentstoanalyze = int(sys.argv[1])
# Our vocabulary
vocab = file('./dictnostops.txt').readlines()
W = len(vocab)
# Initialize the algorithm with alpha=1/K, eta=1/K, tau_0=1024, kappa=0.7
olda = onlineldavb.OnlineLDA(vocab, K, D, 1. / K, 1. / K, 1024., 0.7)
# Run until we've seen D documents. (Feel free to interrupt *much*
# sooner than this.)
for iteration in range(0, documentstoanalyze):
# Download some articles
(docset, articlenames) = \
wikirandom.get_random_wikipedia_articles(batchsize)
# Give them to online LDA
(gamma, bound) = olda.update_lambda(docset)
# Compute an estimate of held-out perplexity
(wordids, wordcts) = onlineldavb.parse_doc_list(docset, olda._vocab)
perwordbound = bound * len(docset) / (D * sum(map(sum, wordcts)))
print '%d: rho_t = %f, held-out perplexity estimate = %f' % \
(iteration, olda._rhot, numpy.exp(-perwordbound))
# Save lambda, the parameters to the variational distributions
# over topics, and gamma, the parameters to the variational
# distributions over topic weights for the articles analyzed in
# the last iteration.
if (iteration % 10 == 0):
numpy.savetxt('lambda-%d.dat' % iteration, olda._lambda)
numpy.savetxt('gamma-%d.dat' % iteration, gamma)
def main():
"""
Downloads and analyzes a bunch of random Wikipedia articles using
online VB for LDA.
"""
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
# The number of documents to analyze each iteration
D = 3.3e6
batchsize = 10
# The number of topics
K = 100
# Our vocabulary
vocab = file('./dictnostops.txt').readlines()
W = len(vocab)
# Initialize the algorithm with alpha=1/K, eta=1/K, tau_0=1024, kappa=0.7
olda = onlineldavb.OnlineLDA(vocab, K, D, 1./K, 1./K, 1024., 0.7)
# Run until we've seen D documents. (Feel free to interrupt *much*
# sooner than this.)
for iteration in range(3): # slaves
# Download some articles
(docset, articlenames) = \
wikirandom.get_random_wikipedia_articles(batchsize)
# Give them to online LDA
(gamma, bound) = olda.update_lambda(docset)
# Compute an estimate of held-out perplexity
(wordids, wordcts) = onlineldavb.parse_doc_list(docset, olda._vocab)
perwordbound = bound * len(docset) / (D * sum(map(sum, wordcts)))
print '%d: rho_t = %f, held-out perplexity estimate = %f' % \
(iteration+1, olda._rhot, numpy.exp(-perwordbound))
# Save lambda, the parameters to the variational distributions
# over topics, and gamma, the parameters to the variational
# distributions over topic weights for the articles analyzed in
# the last iteration.
gammas = comm.gather(gamma, root = 0)
lambdas = comm.gather(olda._lambda, root = 0)
if rank == 0:
gamma_result = numpy.vstack((x for x in gammas))
lambda_result = numpy.vstack((x for x in lambdas))
numpy.savetxt('lambda_parallel.dat', olda._lambda)
numpy.savetxt('gamma_parallel.dat', gamma)
