def testModelSelectRBF(self):
folds = 3
rankSVM = RankSVM()
rankSVM.setKernel("rbf")
#logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
rankSVM.modelSelectRBF(self.X, self.y, folds)
def testSetC(self):
rankSVM = RankSVM()
rankSVM.setC(100.0)
rankSVM.learnModel(self.X, self.y)
predY = rankSVM.predict(self.X)
auc1 = Evaluator.auc(predY, self.y)
rankSVM.setC(0.1)
rankSVM.learnModel(self.X, self.y)
predY = rankSVM.predict(self.X)
auc2 = Evaluator.auc(predY, self.y)
self.assertTrue(auc1 != auc2)
def testEvaluateCvOuter(self):
folds = 3
rankSVM = RankSVM()
(bestParams, allMetrics, bestMetaDicts) = rankSVM.evaluateCvOuter(self.X, self.y, folds)
self.assertEquals(len(allMetrics[0]), folds)
self.assertEquals(len(allMetrics[2]), folds)
#for i in allMetrics[1]:
# print(i)
#Now try the RBF version
rankSVM.setKernel("rbf")
(bestParams, allMetrics, bestMetaDicts) = rankSVM.evaluateCvOuter(self.X, self.y, folds)
def testStr(self):
rankSVM = RankSVM()
def testPredict(self):
rankSVM = RankSVM()
rankSVM.learnModel(self.X, self.y)
predY = rankSVM.predict(self.X)
def testLearnModel(self):
rankSVM = RankSVM()
rankSVM.learnModel(self.X, self.y)
def testInit(self):
rankSVM = RankSVM()
