def export(self, _training, _model, _out, _discretize=False):
FileHandler().createFolder("tmp")
tmpId = "_" + str(uuid.uuid1())
tmpFolder = "tmp/"
tmpTraining = "train" + tmpId + ".arff"
csv = CSV(_training)
csv.convertToARFF(tmpFolder + tmpTraining, False)
d = None
if _discretize:
d = csv.discretizeData()
attributes = csv.findAttributes(0)
weka = WEKA()
weka.folder = tmpFolder
weka.train(_model, tmpFolder + tmpTraining, tmpId)
data = "\n".join(FileHandler().read(tmpFolder + "raw" + tmpId + ".txt"))
FileHandler().checkFolder(_out)
weka.modelInterface.exportCode(data, csv, attributes, _out, _training, discretization=d)
FileHandler().deleteFiles([tmpFolder + tmpTraining, tmpFolder + "raw" + tmpId + ".txt"])
def run(self, _training, _models, _platforms):
R = ResultMatrix()
M = [];
for model in _models:
# run the cross validation to compute the model performance
M.append(model.toString())
e = Experiment(_training)
header, result = e.regression([model], 10)
R.add(header, result)
# train with the global training data and export code
training_arff = "tmp/recommend.arff"
csv = CSV()
csv.load(_training)
csv.convertToARFF(training_arff, False)
attributes = csv.findAttributes(0)
lAtt = len(attributes)-1
WEKA().train(model, training_arff, "0")
data = "\n".join(FileHandler().read("tmp/raw0.txt"))
codeFile = "recommend.c"
model.exportCode(data, csv, attributes, codeFile)
# complile platform-specific code
for platform in _platforms:
""
#print(model.toString() + " : " + platform.toString())
print(R.header, R.data)
print(M)