def run(self):
s = open("log_batch.txt","a")
s.write("acquire\n")
s.close()
threadLimiter.acquire()
try:
self.output_dir = output_dir_prefix
self.output_dir = self.output_dir + '_a' + str(self.alg_type)
self.output_dir = self.output_dir + '_s' + str(self.eta0)
self.output_dir = self.output_dir + '_t' + str(self.t0)
self.output_dir = self.output_dir + '_l' + str(self.lambda0)
# sampling methods
self.output_dir = self.output_dir + '_p' + str(self.svrg_pSamples)
self.output_dir = self.output_dir + '_o' + str(self.svrg_outer_pSamples)
self.output_dir = self.output_dir + '_e' + str(self.npasses)
self.output_dir = self.output_dir + '_n' + str(self.nsamplesperpass)
self.output_dir = self.output_dir + '_h' + str(self.hessian_pSamples)
#self.output_dir = self.output_dir + '_b1000'
print self.output_dir
overwrite_dir = False
existing_dir = os.access(self.output_dir, os.F_OK)
self.cmd = ''
# Create output directory if it does not exist.
if not existing_dir or overwrite_dir:
if not existing_dir:
print 'mkdir ' + self.output_dir
if exec_cmd:
os.mkdir(self.output_dir)
# Copy config file
self.cmd = 'cp ' + input_file_fullpath + ' ' + self.output_dir
print self.cmd
if exec_cmd:
os.system(self.cmd)
# Change directory
print 'cd ' + self.output_dir
if exec_cmd:
os.chdir(self.output_dir)
configIO.write(input_file,'alg_type', self.alg_type)
configIO.write(input_file,'eta0', self.eta0)
configIO.write(input_file,'T0', self.t0)
configIO.write(input_file,'lambda', self.lambda0)
configIO.write(input_file,'svrg_pSamples', self.svrg_pSamples)
configIO.write(input_file,'svrg_outer_pSamples', self.svrg_outer_pSamples)
configIO.write(input_file,'npasses', self.npasses)
configIO.write(input_file,'nsamplesperpass', self.nsamplesperpass)
configIO.write(input_file,'hessian_psamples', self.hessian_pSamples)
datapath_rel = configIO.read(input_file,'datapath')
configIO.write(input_file,'datapath', data_path + datapath_rel)
configIO.write(input_file,'logdir', '')
# bsub -We 100:00 java
#self.cmd = 'bsub -We 100:00 -R rusage[mem=10000] java -cp .:' + lib_path + ':' + data_path + 'src/ optimize ' + input_file
self.cmd = 'java -cp .:' + lib_path + ':' + data_path + 'src/ optimize ' + input_file
if redirect_output:
self.cmd = self.cmd + ' > out.txt 2>&1'
print self.cmd
if exec_cmd:
os.system(self.cmd)
finally:
s = open("log_batch.txt","a")
s.write("release " + self.cmd + "\n")
s.close()
threadLimiter.release()
filename_max_score = ''
scores = [0]
idx_score = 0
# Check if configuration file exists
config_file_pattern = _dir[0:10]
config_filename = ''
for i in os.listdir(_dir):
if i[0:len(config_file_pattern
)] != config_file_pattern or i[len(i) - 3:len(i)] != 'txt':
continue
config_filename = _dir + '/' + i
stepForOutputFiles = 1
if config_filename != '':
stepForOutputFiles_str = configIO.read(config_filename,
'stepForOutputFiles')
stepForOutputFiles = int(stepForOutputFiles_str)
# Check if score file exists
filename = ''
for i in os.listdir(output_dir):
if i[0:len(score_filename)] != score_filename:
continue
filename = output_dir + i
if filename != '':
f = open(filename)
lines = f.readlines()
if sorting_index == -1:
if _dir.find('MSRC') != -1:
def plotWeights(dir_names, output_filename, max_delta_time = -1, dir_name_to_exclude = ''):
current_time = time.time()
ldirs = os.listdir('.')
ndirs = len(ldirs)
fig_idx = 0
list_objs = []
for dir_name in dir_names:
for _dir in ldirs:
if not os.path.isdir(_dir):
continue
#if _dir[0:len(dir_name)] != dir_name:
# continue
m = re.search(dir_name, _dir)
if m is None:
continue
#else:
# print 'Match: ' + _dir
if dir_name_to_exclude != '':
m = re.search(dir_name_to_exclude, _dir)
if m is not None:
continue
if max_delta_time > 0:
delta_time = current_time - os.path.getmtime(_dir)
if delta_time > max_delta_time:
continue
fullpath = _dir + '/parameter_vector0/'
lfiles = os.listdir(fullpath)
nfiles = len(lfiles)
# Check if configuration file exists
config_file_pattern = _dir[0:10]
config_filename = ''
for i in os.listdir(_dir):
if i[0:len(config_file_pattern)] != config_file_pattern or i[len(i)-3:len(i)] != 'txt':
continue
config_filename = _dir + '/' + i
stepForParameterFiles = int(configIO.read(config_filename, 'stepForParameterFiles'))
# plot last one only
for i in range(nfiles-1, nfiles):
fileId = i * stepForParameterFiles
weight_filename = fullpath + 'iteration_' + str(fileId) + '.txt'
#print 'weight ' + weight_filename
if os.access(weight_filename, os.F_OK):
w = readColumnFromFile(0, weight_filename)
w = w[8:len(w)]
#fig = figure()
#fig = figure(num=None, figsize=(16, 12), dpi=80, facecolor='w', edgecolor='k')
fig = figure(num=None, figsize=(30, 14), dpi=160, facecolor='w', edgecolor=None)
ax = fig.add_subplot(1,1,1)
ind = range(0,len(w))
ax.bar(ind, w,align='center', edgecolor="none",linewidth=0.0)
#ax.bar(ind, w,align='center')
#ax.set_xticks(ind)
savefig(output_filename)
clf
max_score = 0
filename_max_score = ''
scores = [0]
idx_score = 0
# Check if configuration file exists
config_file_pattern = _dir[0:10]
config_filename = ''
for i in os.listdir(_dir):
if i[0:len(config_file_pattern)] != config_file_pattern or i[len(i)-3:len(i)] != 'txt':
continue
config_filename = _dir + '/' + i
stepForOutputFiles = 1
if config_filename != '':
stepForOutputFiles_str = configIO.read(config_filename, 'stepForOutputFiles')
stepForOutputFiles = int(stepForOutputFiles_str)
# Check if score file exists
filename = ''
for i in os.listdir(output_dir):
if i[0:len(score_filename)] != score_filename:
continue
filename = output_dir + i
if filename != '':
f = open(filename)
lines = f.readlines()
if sorting_index == -1:
if _dir.find('MSRC') != -1:
best_training_loss = tokens[score_col_idx]
cmd = pathname + '/sortScore.py -e 1 -s 0 -o 1 -a svgr_cl -d ' + _dir
out = commands.getoutput(cmd)
tokens = out.split('\t\t')
if len(tokens) > 2:
best_training_loss = tokens[score_col_idx]
cmd = pathname + '/sortScore.py -e 1 -s 0 -o 1 -a saga -d ' + _dir
out = commands.getoutput(cmd)
tokens = out.split('\t\t')
if len(tokens) > 2:
best_training_loss = tokens[score_col_idx]
# Read attributes from config file
eta0 = configIO.read(config_filename,'eta0')
T0 = configIO.read(config_filename,'T0')
nsamplesperpass = configIO.read(config_filename,'nsamplesperpass')
svgr_pSamples = configIO.read(config_filename,'svgr_pSamples')
svgr_outer_pSamples = configIO.read(config_filename,'svgr_outer_pSamples')
hessian_psamples = configIO.read(config_filename,'hessian_psamples')
alg_type = configIO.read(config_filename,'alg_type')
alambda = configIO.read(config_filename,'lambda')
if idx != 0:
fdata.write(',')
fdata.write('{')
fdata.write('"name" : "' + _dir + '", ')
fdata.write('"link" : "' + options.output_name + str(idx) + '", ')
fdata.write('"alg_type" : "' + alg_type + '", ')
def plotWeights(dir_names,
output_filename,
max_delta_time=-1,
dir_name_to_exclude=''):
current_time = time.time()
ldirs = os.listdir('.')
ndirs = len(ldirs)
fig_idx = 0
list_objs = []
for dir_name in dir_names:
for _dir in ldirs:
if not os.path.isdir(_dir):
continue
#if _dir[0:len(dir_name)] != dir_name:
# continue
m = re.search(dir_name, _dir)
if m is None:
continue
#else:
# print 'Match: ' + _dir
if dir_name_to_exclude != '':
m = re.search(dir_name_to_exclude, _dir)
if m is not None:
continue
if max_delta_time > 0:
delta_time = current_time - os.path.getmtime(_dir)
if delta_time > max_delta_time:
continue
fullpath = _dir + '/parameter_vector0/'
lfiles = os.listdir(fullpath)
nfiles = len(lfiles)
# Check if configuration file exists
config_file_pattern = _dir[0:10]
config_filename = ''
for i in os.listdir(_dir):
if i[0:len(config_file_pattern)] != config_file_pattern or i[
len(i) - 3:len(i)] != 'txt':
continue
config_filename = _dir + '/' + i
stepForParameterFiles = int(
configIO.read(config_filename, 'stepForParameterFiles'))
# plot last one only
for i in range(nfiles - 1, nfiles):
fileId = i * stepForParameterFiles
weight_filename = fullpath + 'iteration_' + str(
fileId) + '.txt'
#print 'weight ' + weight_filename
if os.access(weight_filename, os.F_OK):
w = readColumnFromFile(0, weight_filename)
w = w[8:len(w)]
#fig = figure()
#fig = figure(num=None, figsize=(16, 12), dpi=80, facecolor='w', edgecolor='k')
fig = figure(num=None,
figsize=(30, 14),
dpi=160,
facecolor='w',
edgecolor=None)
ax = fig.add_subplot(1, 1, 1)
ind = range(0, len(w))
ax.bar(ind,
w,
align='center',
edgecolor="none",
linewidth=0.0)
#ax.bar(ind, w,align='center')
#ax.set_xticks(ind)
savefig(output_filename)
clf
best_training_loss = tokens[score_col_idx]
cmd = pathname + '/sortScore.py -e 1 -s 0 -o 1 -a svgr_cl -d ' + _dir
out = commands.getoutput(cmd)
tokens = out.split('\t\t')
if len(tokens) > 2:
best_training_loss = tokens[score_col_idx]
cmd = pathname + '/sortScore.py -e 1 -s 0 -o 1 -a saga -d ' + _dir
out = commands.getoutput(cmd)
tokens = out.split('\t\t')
if len(tokens) > 2:
best_training_loss = tokens[score_col_idx]
# Read attributes from config file
eta0 = configIO.read(config_filename, 'eta0')
T0 = configIO.read(config_filename, 'T0')
nsamplesperpass = configIO.read(config_filename, 'nsamplesperpass')
svgr_pSamples = configIO.read(config_filename, 'svgr_pSamples')
svgr_outer_pSamples = configIO.read(config_filename, 'svgr_outer_pSamples')
hessian_psamples = configIO.read(config_filename, 'hessian_psamples')
alg_type = configIO.read(config_filename, 'alg_type')
alambda = configIO.read(config_filename, 'lambda')
if idx != 0:
fdata.write(',')
fdata.write('{')
fdata.write('"name" : "' + _dir + '", ')
fdata.write('"link" : "' + options.output_name + str(idx) + '", ')
fdata.write('"alg_type" : "' + alg_type + '", ')
