def createModel(args):
"""
Return a classification model of the appropriate type. The model could be any
supported subclass of ClassficationModel based on args.
"""
if args.modelName == "htm":
# Instantiate the HTM model
model = ClassificationModelHTM(networkConfig=getNetworkConfig(
args.networkConfigPath),
inputFilePath=None,
retina=args.retina,
verbosity=args.verbosity,
numLabels=2,
prepData=False,
modelDir="tempdir")
elif args.modelName == "keywords":
# Instantiate the keywords model
model = ClassificationModelKeywords(verbosity=args.verbosity,
numLabels=2,
k=9,
modelDir="tempdir")
elif args.modelName == "docfp":
# Instantiate the document fingerprint model
model = ClassificationModelDocumentFingerprint(
verbosity=args.verbosity, retina=args.retina, numLabels=2, k=3)
else:
raise RuntimeError("Unknown model type: " + args.modelName)
return model
def initModel(self, trial=0):
"""
Load or instantiate the classification model. Assumes network data is
already setup.
"""
if self.loadPath:
with open(self.loadPath, "rb") as f:
self.model = pkl.load(f)
# TODO: uncomment once we can save TPRegion; do we need this?
# networkFile = self.model.network
# self.model.network = Network(networkFile)
print "Model loaded from \'{0}\'.".format(self.loadPath)
else:
print "Creating HTM classification model..."
self.model = ClassificationModelHTM(
self.networkConfig,
self.dataFiles[trial],
retinaScaling=self.retinaScaling,
retina=self.retina,
apiKey=self.apiKey,
verbosity=self.verbosity,
numLabels=self.numClasses,
modelDir=self.modelDir,
prepData=False)
