def getCythonLearner(self):
if self.loss == "tanh":
learnerCython = MaxAUCTanh(self.k, self.lmbdaU, self.lmbdaV,
self.normalise, self.numAucSamples,
self.numRowSamples, self.startAverage,
self.rho)
elif self.loss == "hinge":
learnerCython = MaxAUCHinge(self.k, self.lmbdaU, self.lmbdaV,
self.normalise, self.numAucSamples,
self.numRowSamples, self.startAverage,
self.rho)
elif self.loss == "square":
learnerCython = MaxAUCSquare(self.k, self.lmbdaU, self.lmbdaV,
self.normalise, self.numAucSamples,
self.numRowSamples, self.startAverage,
self.rho)
elif self.loss == "logistic":
learnerCython = MaxAUCLogistic(self.k, self.lmbdaU, self.lmbdaV,
self.normalise, self.numAucSamples,
self.numRowSamples,
self.startAverage, self.rho)
elif self.loss == "sigmoid":
learnerCython = MaxAUCSigmoid(self.k, self.lmbdaU, self.lmbdaV,
self.normalise, self.numAucSamples,
self.numRowSamples,
self.startAverage, self.rho)
else:
raise ValueError("Unknown objective: " + self.loss)
learnerCython.eta = self.eta
learnerCython.printStep = self.printStep
learnerCython.maxNormU = self.maxNormU
learnerCython.maxNormV = self.maxNormV
return learnerCython
def getCythonLearner(self):
if self.loss == "tanh":
learnerCython = MaxAUCTanh(
self.k,
self.lmbdaU,
self.lmbdaV,
self.normalise,
self.numAucSamples,
self.numRowSamples,
self.startAverage,
self.rho,
)
elif self.loss == "hinge":
learnerCython = MaxAUCHinge(
self.k,
self.lmbdaU,
self.lmbdaV,
self.normalise,
self.numAucSamples,
self.numRowSamples,
self.startAverage,
self.rho,
)
elif self.loss == "square":
learnerCython = MaxAUCSquare(
self.k,
self.lmbdaU,
self.lmbdaV,
self.normalise,
self.numAucSamples,
self.numRowSamples,
self.startAverage,
self.rho,
)
elif self.loss == "logistic":
learnerCython = MaxAUCLogistic(
self.k,
self.lmbdaU,
self.lmbdaV,
self.normalise,
self.numAucSamples,
self.numRowSamples,
self.startAverage,
self.rho,
)
elif self.loss == "sigmoid":
learnerCython = MaxAUCSigmoid(
self.k,
self.lmbdaU,
self.lmbdaV,
self.normalise,
self.numAucSamples,
self.numRowSamples,
self.startAverage,
self.rho,
)
else:
raise ValueError("Unknown objective: " + self.loss)
learnerCython.eta = self.eta
learnerCython.printStep = self.printStep
learnerCython.maxNormU = self.maxNormU
learnerCython.maxNormV = self.maxNormV
return learnerCython
