def main():
# Check input
if len(sys.argv) != 5:
print"usage: python run_ML_FW.py [train file] [setting file] [model folder] [test data folder]"
exit()
# Get environment variables
train_file = sys.argv[1]
setting_file = sys.argv[2]
model_folder = sys.argv[3]
test_data_folder = sys.argv[4]
tops = 10#int(sys.argv[5])
# Create model folder if it doesn't exist
if os.path.exists(model_folder):
shutil.rmtree(model_folder)
os.makedirs(model_folder)
# Read settings
print'reading setting ...'
ddict = utilities.read_setting(setting_file)
print'write setting ...'
file_name = '%s/setting.txt'%(model_folder)
utilities.write_setting(ddict, file_name)
# Read data for computing perplexities
print'read data for computing perplexities ...'
(wordids_1, wordcts_1, wordids_2, wordcts_2) = \
utilities.read_data_for_perpl(test_data_folder)
# Initialize the algorithm
print'initialize the algorithm ...'
ml_fw = ML_FW.MLFW(ddict['num_terms'], ddict['num_topics'], ddict['tau0'], ddict['kappa'], ddict['iter_infer'])
# Start
print'start!!!'
i = 0
while i < ddict['iter_train']:
i += 1
print'\n***iter_train:%d***\n'%(i)
datafp = open(train_file, 'r')
j = 0
while True:
j += 1
(wordids, wordcts) = utilities.read_minibatch_list_frequencies(datafp, ddict['batch_size'])
# Stop condition
if len(wordids) == 0:
break
#
print'---num_minibatch:%d---'%(j)
(time_e, time_m, theta) = ml_fw.static_online(ddict['batch_size'], wordids, wordcts)
# Compute sparsity
sparsity = utilities.compute_sparsity(theta, theta.shape[0], theta.shape[1], 't')
# Compute perplexities
LD2 = utilities.compute_perplexities_fw(ml_fw.beta, ddict['iter_infer'], \
wordids_1, wordcts_1, wordids_2, wordcts_2)
# Search top words of each topics
list_tops = utilities.list_top(ml_fw.beta, tops)
# Write files
utilities.write_file(i, j, ml_fw.beta, time_e, time_m, theta, sparsity, LD2, list_tops, tops, model_folder)
datafp.close()
# Write final model to file
file_name = '%s/beta_final.dat'%(model_folder)
utilities.write_topics(ml_fw.beta, file_name)
# Finish
print'done!!!'
def run(self):
# Initialize the algorithm
print 'initialize the algorithm ...'
online_vb = Online_VB.OnlineVB(
self.settings['num_docs'], self.settings['num_terms'],
self.settings['num_topics'], self.settings['alpha'],
self.settings['eta'], self.settings['tau0'],
self.settings['kappa'], self.settings['conv_infer'],
self.settings['iter_infer'])
# Start
print 'start!!!'
i = 0
while i < self.settings['iter_train']:
i += 1
print '\n***iter_train:%d***\n' % (i)
datafp = open(self.train_file, 'r')
j = 0
while True:
j += 1
(wordids, wordcts) = utilities.read_minibatch_list_frequencies(
datafp, self.settings['batch_size'])
# Stop condition
if len(wordids) == 0:
break
#
print '---num_minibatch:%d---' % (j)
(time_e, time_m,
theta) = online_vb.static_online(self.settings['batch_size'],
wordids, wordcts)
# Compute sparsity
sparsity = utilities.compute_sparsity(theta, theta.shape[0],
theta.shape[1], 't')
# Compute perplexities
LD2 = utilities.compute_perplexities_vb(
online_vb._lambda, self.settings['alpha'],
self.settings['eta'], self.settings['iter_infer'],
self.test_data)
# Search top words of each topics
list_tops = utilities.list_top(online_vb._lambda, self.tops)
# Write files
utilities.write_file(i, j, online_vb._lambda, time_e, time_m,
theta, sparsity, LD2, list_tops,
self.tops, self.model_folder)
datafp.close()
# Write settings
print 'write setting ...'
file_name = '%s/setting.txt' % (self.model_folder)
utilities.write_setting(self.settings, file_name)
# Write final model to file
print 'write final model ...'
file_name = '%s/beta_final.dat' % (self.model_folder)
utilities.write_topics(online_vb._lambda, file_name)
# Finish
print 'done!!!'
def run(self):
# Initialize the algorithm
print'initialize the algorithm ...'
new2_online_ope = New2Online_OPE.New2OnlineOPE(self.settings['num_docs'], self.settings['num_terms'],
self.settings['num_topics'], self.settings['alpha'],
self.settings['eta'], self.settings['tau0'],
self.settings['kappa'], self.settings['iter_infer'])
# Start
print'start!!!'
i = 0
while i < self.settings['iter_train']:
i += 1
print'\n***iter_train:%d***\n'%(i)
datafp = open(self.train_file, 'r')
j = 0
while True:
j += 1
(wordids, wordcts) = utilities.read_minibatch_list_frequencies(datafp, self.settings['batch_size'])
# Stop condition
if len(wordids) == 0:
break
#
print'---num_minibatch:%d---'%(j)
(time_e, time_m, theta) = new2_online_ope.static_online(wordids, wordcts)
# Compute sparsity
sparsity = utilities.compute_sparsity(theta, theta.shape[0], theta.shape[1], 't')
# Compute perplexities
LD2 = utilities.compute_perplexities_vb(new2_online_ope._lambda, self.settings['alpha'], self.settings['eta'],
self.settings['iter_infer'], self.test_data)
# Search top words of each topics
list_tops = utilities.list_top(new2_online_ope._lambda, self.tops)
# Write files
utilities.write_file(i, j, new2_online_ope._lambda, time_e, time_m, theta, sparsity, LD2, list_tops, self.tops,
self.model_folder)
datafp.close()
# Write settings
print'write setting ...'
file_name = '%s/setting.txt'%(self.model_folder)
utilities.write_setting(self.settings, file_name)
# Write final model to file
print'write final model ...'
file_name = '%s/beta_final.dat'%(self.model_folder)
utilities.write_topics(new2_online_ope._lambda, file_name)
# Finish
print'done!!!'
def main():
# Check input
if len(sys.argv) != 5:
print "usage: python run_Online_FW.py [train file] [setting file] [model folder] [test data folder]"
exit()
# Get environment variables
train_file = sys.argv[1]
setting_file = sys.argv[2]
model_folder = sys.argv[3]
test_data_folder = sys.argv[4]
tops = 10 # int(sys.argv[5])
# Create model folder if it doesn't exist
if os.path.exists(model_folder):
shutil.rmtree(model_folder)
os.makedirs(model_folder)
# Read settings
print "reading setting ..."
ddict = utilities.read_setting(setting_file)
print "write setting ..."
file_name = "%s/setting.txt" % (model_folder)
utilities.write_setting(ddict, file_name)
# Read data for computing perplexities
print "read data for computing perplexities ..."
(wordids_1, wordcts_1, wordids_2, wordcts_2) = utilities.read_data_for_perpl(test_data_folder)
# Initialize the algorithm
print "initialize the algorithm ..."
online_fw = Online_FW.OnlineFW(
ddict["num_docs"],
ddict["num_terms"],
ddict["num_topics"],
ddict["eta"],
ddict["tau0"],
ddict["kappa"],
ddict["iter_infer"],
)
# Start
print "start!!!"
i = 0
while i < ddict["iter_train"]:
i += 1
print "\n***iter_train:%d***\n" % (i)
datafp = open(train_file, "r")
j = 0
while True:
j += 1
(wordids, wordcts) = utilities.read_minibatch_list_frequencies(datafp, ddict["batch_size"])
# Stop condition
if len(wordids) == 0:
break
#
print "---num_minibatch:%d---" % (j)
(time_e, time_m, theta) = online_fw.static_online(ddict["batch_size"], wordids, wordcts)
# Compute sparsity
sparsity = utilities.compute_sparsity(theta, theta.shape[0], theta.shape[1], "t")
# Compute perplexities
LD2 = utilities.compute_perplexities_fw(
online_fw._lambda, ddict["iter_infer"], wordids_1, wordcts_1, wordids_2, wordcts_2
)
# Search top words of each topics
list_tops = utilities.list_top(online_fw._lambda, tops)
# Write files
utilities.write_file(
i, j, online_fw._lambda, time_e, time_m, theta, sparsity, LD2, list_tops, tops, model_folder
)
datafp.close()
# Write final model to file
file_name = "%s/lambda_final.dat" % (model_folder)
utilities.write_topics(online_fw._lambda, file_name)
# Finish
print "done!!!"
def main():
# Check input
if len(sys.argv) != 5:
print(
"usage: python run_ML_OPE.py [train file] [setting file] [model folder] [test data folder]"
)
exit()
# Get environment variables
train_file = sys.argv[1]
setting_file = sys.argv[2]
model_folder = sys.argv[3]
test_data_folder = sys.argv[4]
# Create model folder if it doesn't exist
if os.path.exists(model_folder):
shutil.rmtree(model_folder)
os.makedirs(model_folder)
# Read settings
print('reading setting ...')
ddict = utilities.read_setting(setting_file)
print('write setting ...')
file_name = '%s/setting.txt' % (model_folder)
utilities.write_setting(ddict, file_name)
# Read data for computing perplexities
print('read data for computing perplexities ...')
(wordids_1, wordcts_1, wordids_2, wordcts_2) = \
utilities.read_data_for_perpl(test_data_folder)
# ============================================= TILL HERE OKAY [0] =============================================
# Initialize the algorithm
print('initialize the algorithm ...')
ml_ope = ML_OPE.MLOPE(ddict['num_terms'], ddict['num_topics'],
ddict['alpha'], ddict['tau0'], ddict['kappa'],
ddict['iter_infer'])
# Start
print('start!!!')
i = 0
list_tops = []
while i < ddict['iter_train']:
i += 1
print('\n***iter_train:%d***\n' % (i))
datafp = open(train_file, 'r')
j = 0
while True:
j += 1
(wordids, wordcts) = utilities.read_minibatch_list_frequencies(
datafp, ddict['batch_size'])
# Stop condition
if len(wordids) == 0:
break
#
print('---num_minibatch:%d---' % (j))
(time_e, time_m,
theta) = ml_ope.static_online(ddict['batch_size'], wordids,
wordcts)
# ========================= TILL HERE OKAY [1] ======================================
# Compute sparsity
sparsity = utilities.compute_sparsity(theta, theta.shape[0],
theta.shape[1], 't')
# print(sparsity) # for Testing Sparsity of 1st theta
# print(theta[0,:]) # for Testing Sparsity of 1st theta
# Compute perplexities
# LD2 = utilities.compute_perplexities_vb(ml_ope.beta, ddict['alpha'], ddict['eta'], ddict['iter_infer'], \
# wordids_1, wordcts_1, wordids_2, wordcts_2)
LD2 = None
# Saving previous list_tops for diff_list_tops() below
prev_list_tops = list_tops
# Search top words of each topics
list_tops = utilities.list_top(ml_ope.beta, ddict['tops'])
# TODO: add [last 25% avg diff count] to new file to compare later with other settings
# Calculate and print difference between old and current list_tops
utilities.diff_list_tops(list_tops, prev_list_tops, i)
# Write files
utilities.write_file(i, j, ml_ope.beta, time_e, time_m, theta,
sparsity, LD2, list_tops, model_folder)
datafp.close()
# Write final model to file
file_name = '%s/beta_final.dat' % (model_folder)
utilities.write_topics(ml_ope.beta, file_name)
# Finish
print('done!!!')
def main():
# Check input
if len(sys.argv) != 5:
print "usage: python run_ML_FW.py [train file] [setting file] [model folder] [test data folder]"
exit()
# Get environment variables
train_file = sys.argv[1]
setting_file = sys.argv[2]
model_folder = sys.argv[3]
test_data_folder = sys.argv[4]
tops = 10 #int(sys.argv[5])
# Create model folder if it doesn't exist
if os.path.exists(model_folder):
shutil.rmtree(model_folder)
os.makedirs(model_folder)
# Read settings
print 'reading setting ...'
ddict = utilities.read_setting(setting_file)
print 'write setting ...'
file_name = '%s/setting.txt' % (model_folder)
utilities.write_setting(ddict, file_name)
# Read data for computing perplexities
print 'read data for computing perplexities ...'
(wordids_1, wordcts_1, wordids_2, wordcts_2) = \
utilities.read_data_for_perpl(test_data_folder)
# Initialize the algorithm
print 'initialize the algorithm ...'
ml_fw = ML_FW.MLFW(ddict['num_terms'], ddict['num_topics'], ddict['tau0'],
ddict['kappa'], ddict['iter_infer'])
# Start
print 'start!!!'
i = 0
while i < ddict['iter_train']:
i += 1
print '\n***iter_train:%d***\n' % (i)
datafp = open(train_file, 'r')
j = 0
while True:
j += 1
(wordids, wordcts) = utilities.read_minibatch_list_frequencies(
datafp, ddict['batch_size'])
# Stop condition
if len(wordids) == 0:
break
#
print '---num_minibatch:%d---' % (j)
(time_e, time_m,
theta) = ml_fw.static_online(ddict['batch_size'], wordids,
wordcts)
# Compute sparsity
sparsity = utilities.compute_sparsity(theta, theta.shape[0],
theta.shape[1], 't')
# Compute perplexities
LD2 = utilities.compute_perplexities_fw(ml_fw.beta, ddict['iter_infer'], \
wordids_1, wordcts_1, wordids_2, wordcts_2)
# Search top words of each topics
list_tops = utilities.list_top(ml_fw.beta, tops)
# Write files
utilities.write_file(i, j, ml_fw.beta, time_e, time_m, theta,
sparsity, LD2, list_tops, tops, model_folder)
datafp.close()
# Write final model to file
file_name = '%s/beta_final.dat' % (model_folder)
utilities.write_topics(ml_fw.beta, file_name)
# Finish
print 'done!!!'