return rankingModel
if __name__ == '__main__':
print 'This program generates a results file containing Raw lads output postscored with the algorithm of choice. The discmodel is a supplied model, if necessary for the postscoring algorithm'
options = ArgLib.parse(['init', 'ppmstd', 'dtadir', 'lads', 'sequest', 'config', 'model', 'output', 'symbolmap'], optArgs=[{'opts': ('-D', '--discmodel'), 'attrs': {'type': 'string', 'dest': 'discmodel', 'help': 'Model used to calculate discriminant score'}}, {'opts': ('-P', '--pairconfig'), 'attrs': {'type': 'string', 'dest': 'pairconfig', 'help': 'Name of LADS Pair Configuration'}}, {'opts': ('-F', '--featurelist'), 'attrs': {'type': 'string', 'dest': 'featurelist', 'help': 'File containing pickled list of desired features (optional)'}}])
parent = os.path.abspath(os.pardir)
PNet = PN.ProbNetwork(options.config, options.model)
paramsDict = ArgLib.parseInitFile(options.init, options)
pairConfigurations = paramsDict['Pair Configurations']
LADSSeqInfo = GLFD.parseSequenceDTAsLogfile(options.lads)
with open(options.symbolmap, 'r') as fin:
symbolMap = pickle.load(fin)
seqMap = DataFile.generateSeqMap({'LADS Unit Test': 'LADS'}, symbolMap, paramsDict)
seqMap = seqMap['LADS Unit Test']
if options.featurelist:
with open(options.featurelist) as fin:
desired_feats = pickle.load(fin)
else:
desired_feats = None
heavySeqMaps = {}
for confName in pairConfigurations:
heavySeqMaps[confName] = copy.deepcopy(seqMap)
