def main():
parser = OptionParser(usage='train and evaluate ML model for DDI classification based on the DDI corpus')
parser.add_option("-f", "--file", dest="file", action="store", default="pairs.pickle",
help="Pickle file to load/store the data")
parser.add_option("-d", "--dir", action="store", dest="dir", type = "string", default="DDICorpus/Test/DDIextraction/MedLine/",
help="Corpus directory with XML files")
parser.add_option("--reload", action="store_true", default=False, dest="reload",
help="Reload corpus")
parser.add_option("--log", action="store", dest="loglevel", type = "string", default="WARNING",
help="Log level")
parser.add_option("--logfile", action="store", dest="logfile", type="string", default="kernel.log",
help="Log file")
parser.add_option("--nfolds", action="store", dest="nfolds", type="int", default=10,
help="Number of cross-validation folds")
parser.add_option("--action", action="store", dest="action", type="string", default="cv",
help="cv, train, test, or classify")
parser.add_option("--kernel", action="store", dest="kernel", type="string", default="slk",
help="slk, svmtk")
(options, args) = parser.parse_args()
numeric_level = getattr(logging, options.loglevel.upper(), None)
while len(logging.root.handlers) > 0:
logging.root.removeHandler(logging.root.handlers[-1])
logging.basicConfig(level=numeric_level, format='%(asctime)s %(levelname)s %(message)s')
#logging.getLogger().setLevel(numeric_level)
logging.debug("debug test")
logging.info("info test")
logging.warning("warning test")
if options.file in os.listdir(os.getcwd()) and not options.reload:
print "loading corpus pickle", options.file
docs = pickle.load(open(options.file, 'rb'))
else:
print "loading corpus", options.dir
docs = relations.loadCorpus(options.dir)
pickle.dump(docs, open(options.file, 'wb'))
def main():
parser = OptionParser(
usage=
'train and evaluate ML model for DDI classification based on the DDI corpus'
)
parser.add_option("-f",
"--file",
dest="file",
action="store",
default="pairs.pickle",
help="Pickle file to load/store the data")
parser.add_option("-d",
"--dir",
action="store",
dest="dir",
type="string",
default="DDICorpus/Test/DDIextraction/MedLine/",
help="Corpus directory with XML files")
parser.add_option("--reload",
action="store_true",
default=False,
dest="reload",
help="Reload corpus")
parser.add_option("--log",
action="store",
dest="loglevel",
type="string",
default="WARNING",
help="Log level")
parser.add_option("--logfile",
action="store",
dest="logfile",
type="string",
default="kernel.log",
help="Log file")
parser.add_option("--nfolds",
action="store",
dest="nfolds",
type="int",
default=10,
help="Number of cross-validation folds")
parser.add_option("--action",
action="store",
dest="action",
type="string",
default="cv",
help="cv, train, test, or classify")
parser.add_option("--kernel",
action="store",
dest="kernel",
type="string",
default="slk",
help="slk, svmtk")
(options, args) = parser.parse_args()
numeric_level = getattr(logging, options.loglevel.upper(), None)
while len(logging.root.handlers) > 0:
logging.root.removeHandler(logging.root.handlers[-1])
logging.basicConfig(level=numeric_level,
format='%(asctime)s %(levelname)s %(message)s')
#logging.getLogger().setLevel(numeric_level)
logging.debug("debug test")
logging.info("info test")
logging.warning("warning test")
if options.file in os.listdir(os.getcwd()) and not options.reload:
print "loading corpus pickle", options.file
docs = pickle.load(open(options.file, 'rb'))
else:
print "loading corpus", options.dir
docs = relations.loadCorpus(options.dir)
pickle.dump(docs, open(options.file, 'wb'))
#build_data_frame(docs)
#if 'parsetree' not in docs['info']:
# for doc in docs:
# for s in docs[doc]:
# docs[doc][s]['parsetree'] = gettree(docs[doc][s]['tokens'])
# docs['info'].append('parsetree')
#trainEvaluatePairs(docs, nfolds=options.nfolds)
if options.kernel == 'slk':
generatejSREdata(docs, options.action + '_pairs.txt')
if options.action == 'train':
trainjSRE(options.kernel + '_' + options.action + '_pairs.txt')
elif options.action == 'test':
testjSRE(options.kernel + '_' + options.action + '_pairs.txt',
options.kernel + '_' + "test_results.txt")
elif options.kernel == 'svmtk':
generateSVMTKdata(docs)
if options.action == 'train':
trainSVMTK(options.kernel + '_' + options.action + '_pairs.txt')
elif options.action == 'test':
testSVMTK(options.kernel + '_' + options.action + '_pairs.txt',
options.kernel + '_' + "test_results.txt")
generateSVMTKdata(docs)
def main():
parser = OptionParser(
usage=
'train and evaluate ML model for DDI classification based on the DDI corpus'
)
parser.add_option("-f",
"--file",
dest="file",
action="store",
default="pairs.pickle",
help="Pickle file to load/store the data")
parser.add_option("-d",
"--dir",
action="store",
dest="dir",
type="string",
default="DDICorpus/Test/DDIextraction/MedLine/",
help="Corpus directory with XML files")
parser.add_option("--reload",
action="store_true",
default=False,
dest="reload",
help="Reload corpus")
parser.add_option("--log",
action="store",
dest="loglevel",
type="string",
default="WARNING",
help="Log level")
parser.add_option("--logfile",
action="store",
dest="logfile",
type="string",
default="kernel.log",
help="Log file")
parser.add_option("--nfolds",
action="store",
dest="nfolds",
type="int",
default=10,
help="Number of cross-validation folds")
parser.add_option("--action",
action="store",
dest="action",
type="string",
default="cv",
help="cv, train, test, or classify")
parser.add_option("--kernel",
action="store",
dest="kernel",
type="string",
default="slk",
help="slk, svmtk")
(options, args) = parser.parse_args()
numeric_level = getattr(logging, options.loglevel.upper(), None)
while len(logging.root.handlers) > 0:
logging.root.removeHandler(logging.root.handlers[-1])
logging.basicConfig(level=numeric_level,
format='%(asctime)s %(levelname)s %(message)s')
#logging.getLogger().setLevel(numeric_level)
logging.debug("debug test")
logging.info("info test")
logging.warning("warning test")
if options.file in os.listdir(os.getcwd()) and not options.reload:
print "loading corpus pickle", options.file
docs = pickle.load(open(options.file, 'rb'))
else:
print "loading corpus", options.dir
docs = relations.loadCorpus(options.dir)
pickle.dump(docs, open(options.file, 'wb'))
def main():
parser = OptionParser(usage='train and evaluate ML model for DDI classification based on the DDI corpus')
parser.add_option("-f", "--file", dest="file", action="store", default="pairs.pickle",
help="Pickle file to load/store the data")
parser.add_option("-d", "--dir", action="store", dest="dir", type = "string", default="DDICorpus/Test/DDIextraction/MedLine/",
help="Corpus directory with XML files")
parser.add_option("--reload", action="store_true", default=False, dest="reload",
help="Reload corpus")
parser.add_option("--log", action="store", dest="loglevel", type = "string", default="WARNING",
help="Log level")
parser.add_option("--logfile", action="store", dest="logfile", type="string", default="kernel.log",
help="Log file")
parser.add_option("--nfolds", action="store", dest="nfolds", type="int", default=10,
help="Number of cross-validation folds")
parser.add_option("--action", action="store", dest="action", type="string", default="cv",
help="cv, train, test, or classify")
parser.add_option("--kernel", action="store", dest="kernel", type="string", default="slk",
help="slk, svmtk")
(options, args) = parser.parse_args()
numeric_level = getattr(logging, options.loglevel.upper(), None)
while len(logging.root.handlers) > 0:
logging.root.removeHandler(logging.root.handlers[-1])
logging.basicConfig(level=numeric_level, format='%(asctime)s %(levelname)s %(message)s')
#logging.getLogger().setLevel(numeric_level)
logging.debug("debug test")
logging.info("info test")
logging.warning("warning test")
if options.file in os.listdir(os.getcwd()) and not options.reload:
print "loading corpus pickle", options.file
docs = pickle.load(open(options.file, 'rb'))
else:
print "loading corpus", options.dir
docs = relations.loadCorpus(options.dir)
pickle.dump(docs, open(options.file, 'wb'))
#build_data_frame(docs)
#if 'parsetree' not in docs['info']:
# for doc in docs:
# for s in docs[doc]:
# docs[doc][s]['parsetree'] = gettree(docs[doc][s]['tokens'])
# docs['info'].append('parsetree')
#trainEvaluatePairs(docs, nfolds=options.nfolds)
if options.kernel == 'slk':
generatejSREdata(docs, options.action + '_pairs.txt')
if options.action == 'train':
trainjSRE(options.kernel + '_' + options.action + '_pairs.txt')
elif options.action == 'test':
testjSRE(options.kernel + '_' +options.action + '_pairs.txt', options.kernel + '_' + "test_results.txt")
elif options.kernel == 'svmtk':
generateSVMTKdata(docs)
if options.action == 'train':
trainSVMTK(options.kernel + '_' +options.action + '_pairs.txt')
elif options.action == 'test':
testSVMTK(options.kernel + '_' +options.action + '_pairs.txt', options.kernel + '_' + "test_results.txt")
generateSVMTKdata(docs)
