def testEvaluateCvOuter(self):
folds = 3
rankBoost = RankBoost()
(bestParams, allMetrics, bestMetaDicts) = rankBoost.evaluateCvOuter(self.X, self.y, folds)
self.assertEquals(len(allMetrics[0]), folds)
self.assertEquals(len(allMetrics[2]), folds)
def testEvaluateCvOuter(self):
folds = 3
rankBoost = RankBoost()
(bestParams, allMetrics,
bestMetaDicts) = rankBoost.evaluateCvOuter(self.X, self.y, folds)
self.assertEquals(len(allMetrics[0]), folds)
self.assertEquals(len(allMetrics[2]), folds)
def testPredict(self):
rankBoost = RankBoost()
rankBoost.learnModel(self.X, self.y)
predY = rankBoost.predict(self.X)
self.assertTrue(
Evaluator.auc(predY, self.y) <= 1.0
and Evaluator.auc(predY, self.y) >= 0.0)
def testSetLearners(self):
rankBoost = RankBoost()
rankBoost.setIterations(10)
rankBoost.setLearners(20)
rankBoost.learnModel(self.X, self.y)
predY = rankBoost.predict(self.X)
def testSetLearners(self):
rankBoost = RankBoost()
rankBoost.setIterations(10)
rankBoost.setLearners(20)
rankBoost.learnModel(self.X, self.y)
predY = rankBoost.predict(self.X)
def testStr(self):
rankBoost = RankBoost()
def testLearnModel(self):
rankBoost = RankBoost()
rankBoost.learnModel(self.X, self.y)
def testInit(self):
rankBoost = RankBoost()
def testPredict(self):
rankBoost = RankBoost()
rankBoost.learnModel(self.X, self.y)
predY = rankBoost.predict(self.X)
self.assertTrue(Evaluator.auc(predY, self.y) <= 1.0 and Evaluator.auc(predY, self.y) >= 0.0)
def testLearnModel(self):
rankBoost = RankBoost()
rankBoost.learnModel(self.X, self.y)