def mainline(train=False):
datadir = DIR["BASE"] + "data/"
if train is True:
featurefile = datadir + "train-features.txt"
xmldir = DIR["BASE"] + "demo/train/"
else:
featurefile = datadir + "test-features.txt"
xmldir = DIR["BASE"] + "demo/test/"
deleteFiles([featurefile])
# infile = xmldir + 'C08-1122-parscit-section.xml'
client_socket = getConnection()
for infile in glob(xmldir + "*.xml"):
try:
print infile + " is being processed."
if train is True:
generateTrainFeatures(client_socket, infile, featurefile)
else:
generateTestFeatures(client_socket, infile, featurefile)
except Exception as e:
print "Some Exception in the main pipeline"
print (str(type(e)))
print str(e)
logging.exception("Something awfull !!")
model = DIR["DATA"] + "sec-tfidf-model.txt"
if train is False:
# TESTING
outfile = DIR["DATA"] + "sec-tfidf-test-out.txt"
predictSvm(featurefile, model, outfile)
extractValues(outfile)
outstring = "Default values Test results"
analyze(featurefile, outfile, outstring=outstring)
pickleIt()
else:
# TRAINING
trainSvm(featurefile, model)
outfile = DIR["DATA"] + "sec-tfidf-train-out.txt"
predictSvm(featurefile, model, outfile)
outstring = "Default values"
analyze(featurefile, outfile, outstring=outstring)
def mainline(train=False):
datadir = DIR['BASE'] + "data/"
if train is True:
featurefile = datadir + 'train-features.txt'
xmldir = DIR['BASE'] + "demo/train/"
else:
featurefile = datadir + 'test-features.txt'
xmldir = DIR['BASE'] + "demo/test/"
deleteFiles([featurefile])
#infile = xmldir + 'C08-1122-parscit-section.xml'
client_socket = getConnection()
for infile in glob(xmldir + "*.xml"):
try:
print infile + " is being processed."
if train is True:
generateTrainFeatures(client_socket, infile, featurefile)
else:
generateTestFeatures(client_socket, infile, featurefile)
except Exception as e:
print "Some Exception in the main pipeline"
print(str(type(e)))
print str(e)
logging.exception("Something awfull !!")
model = DIR['DATA'] + "sec-tfidf-model.txt"
if train is False:
# TESTING
outfile = DIR['DATA'] + "sec-tfidf-test-out.txt"
predictSvm(featurefile, model, outfile)
extractValues(outfile)
outstring = "Default values Test results"
analyze(featurefile, outfile, outstring=outstring)
pickleIt()
else:
# TRAINING
trainSvm(featurefile, model)
outfile = DIR['DATA'] + "sec-tfidf-train-out.txt"
predictSvm(featurefile, model, outfile)
outstring = "Default values"
analyze(featurefile, outfile, outstring=outstring)
def pickleIt():
picklefile = DIR["DATA"] + "test-sentences-pickle"
deleteFiles([picklefile])
with open(picklefile, "wb") as pfile:
pickle.dump(test_data, pfile)
from random import choice
from analysis import analyze
from utilityFunctions import writeToFile, trainSvm, predictSvm, deleteFiles
picklefile = DIR['DATA'] + 'test-sentences-pickle'
with open(picklefile, 'rb') as pfile:
global data
data = pickle.load(pfile)
datadir = DIR['BASE'] + "data/"
model = DIR['DATA'] + "sec-tfidf-model-small.txt"
featurefile = datadir + 'features-small.txt'
outfile = DIR['DATA'] + "sec-tfidf-train-out-small.txt"
resfile = DIR['DATA'] + "sec-tfidf-result-small.txt"
deleteFiles([model, featurefile, outfile, resfile])
bucket = data.keys()
precision = []
recall = []
all_sets = []
#for i in range(07):
# train_set = list(bucket)
# test_set = []
# for k in range(11):
# curr = choice(train_set)
# test_set.append(curr)
# train_set.remove(curr)
def pickleIt():
picklefile = DIR['DATA'] + 'test-sentences-pickle'
deleteFiles([picklefile])
with open(picklefile, 'wb') as pfile:
pickle.dump(test_data, pfile)
from Config import DIR
from random import choice
from analysis import analyze
from utilityFunctions import writeToFile, trainSvm, predictSvm, deleteFiles
picklefile = DIR['DATA'] + 'test-sentences-pickle'
with open(picklefile, 'rb') as pfile:
global data
data = pickle.load(pfile)
datadir = DIR['BASE'] + "data/"
model = DIR['DATA'] + "sec-tfidf-model-small.txt"
featurefile = datadir + 'features-small.txt'
outfile = DIR['DATA'] + "sec-tfidf-train-out-small.txt"
resfile = DIR['DATA'] + "sec-tfidf-result-small.txt"
deleteFiles([model, featurefile, outfile, resfile])
bucket = data.keys()
precision = []
recall = []
all_sets = []
#for i in range(07):
# train_set = list(bucket)
# test_set = []
# for k in range(11):
# curr = choice(train_set)
# test_set.append(curr)
# train_set.remove(curr)