def main():
from argument_parser import argument_parser
args = argument_parser().parse_args("-d TwitterMining -m 27017".split())
args.H = "localhost"
args = vars(args)
imgc = ImgCreator(**args)
# print imgc.web_query('android')
imgc.query(*sys.argv[1:])
imgc.close()
return 0
import os
import sys
from argument_parser import argument_parser
#_____________________________________________________________________________
if __name__ == "__main__":
#merge output files by chunks of a given size
#config file from command line argumet
args = sys.argv
args.pop(0)
config = args.pop(0)
parser = argument_parser()
parser.add_parameter("top")
parser.add_parameter("outlist")
parser.add_parameter("add_input", list)
parser.parse(config)
top = parser.get("top")
qlist = parser.get("add_input")
outlist = parser.get("outlist")
for q in qlist:
log_path = top + q[0] + "/logs"
cmd = "ls " + log_path + "/*.out"
logs = Popen(cmd, shell=True, stdout=PIPE,
stderr=PIPE).communicate()[0].split("\n")
from argument_parser import argument_parser from models import Trainer from utils import table_printer args = argument_parser() table_printer(args) # Initialize a trainer trainer = Trainer(args) # load edgelist and split into training, test and validation set trainer.setup_features() # Setup a Biological Network Embedding model trainer.setup_model() trainer.setup_training_data() trainer.fit() trainer.evaluate()
from default_values import default_values
from validate import validate_check
from make_directory import make_directory
from cuda_check import cuda_check
from make_models import make_models
from reading_data import reading_data
from train_GAN import train_GAN
from save_models import save_models
from evaluate import evaluate
from argument_parser import argument_parser
from time import time
if __name__ == "__main__":
"""Driver function."""
start = time()
conf_data = argument_parser()
validate_check(conf_data)
configured_parameters = default_values(conf_data)
print(configured_parameters)
configured_parameters = make_directory(configured_parameters)
configured_parameters = cuda_check(configured_parameters)
configured_parameters = make_models(configured_parameters)
configured_parameters = reading_data(configured_parameters)
setting_up_time = time() - start
time_step_2 = time()
configured_parameters = train_GAN(configured_parameters)
training_time = time() - time_step_2
configured_parameters = save_models(configured_parameters)
if conf_data['GAN_model']['seq'] == 0:
score = evaluate(conf_data)
def main():
parser = argument_parser.argument_parser()
parser.parse()
parser.organize()
# (self, cases, vfrac, tfrac, casefrac, mapsep)
caseman = gann_base.Caseman(parser.data_set_v, parser.vfrac_v,
parser.tfrac_v, parser.casefrac_v,
parser.mapbs_v)
# (self, dims, cman, afunc, ofunc, cfunc, optimizer, lrate, wrange, vint, mbs, usevsi, showint=None):
ann = gann_base.Gann(parser.dims_v,
caseman,
parser.afunc_v,
parser.ofunc_v,
parser.cfunc_v,
parser.optimizer_v,
parser.lrate_v,
parser.wrange_v,
parser.vint_v,
parser.mbs_v,
parser.usevsi_v,
showint=parser.steps_v - 1)
for layer in parser.dispw_v:
ann.add_grabvar(layer, type='wgt')
ann.gen_probe(layer, 'wgt', 'hist')
for layer in parser.dispb_v:
ann.add_grabvar(layer, type='bias')
ann.gen_probe(layer, 'bias', 'hist')
# run, then map
ann.run(steps=parser.steps_v,
sess=None,
continued=False,
bestk=parser.best1_v)
ann.remove_grabvars()
for layer in parser.maplayers_v:
if layer == 0:
ann.add_grabvar(layer, type='in', add_figure=False)
else:
ann.add_grabvar(layer - 1, type='out', add_figure=False)
res, labs = ann.do_mapping()
results = []
for i in range(len(res[0])):
l = np.array([r[i] for r in res])
l = l.reshape(l.shape[0], l.shape[2])
TFT.hinton_plot(l,
title="mapping test output of layer " +
str(parser.maplayers_v[i]))
results.append(l)
for i, r in enumerate(results):
# DENDOGRAM
# if parser.maplayers_v[i] in parser.mapdend_v:
if parser.best1_v:
TFT.dendrogram(r,
list(map(TFT.one_hot_to_int, labs)),
title="Dendrogram " + str(parser.maplayers_v[i]))
gann_base.PLT.show()
TFT.fireup_tensorboard('probeview')
