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