def testPredict2(self): # Test on Gauss2D dataset dataDir = PathDefaults.getDataDir() fileName = dataDir + "Gauss2D_learn.csv" XY = numpy.loadtxt(fileName, skiprows=1, usecols=(1, 2, 3), delimiter=",") X = XY[:, 0:2] y = XY[:, 2] fileName = dataDir + "Gauss2D_test.csv" testXY = numpy.loadtxt(fileName, skiprows=1, usecols=(1, 2, 3), delimiter=",") testX = testXY[:, 0:2] testY = testXY[:, 2] X = Standardiser().standardiseArray(X) testX = Standardiser().standardiseArray(testX) maxDepths = range(3, 10) trainAucs = numpy.array( [0.7194734, 0.7284824, 0.7332185, 0.7348198, 0.7366152, 0.7367508, 0.7367508, 0.7367508] ) testAucs = numpy.array([0.6789078, 0.6844632, 0.6867918, 0.6873420, 0.6874820, 0.6874400, 0.6874400, 0.6874400]) i = 0 # The results are approximately the same, but not exactly for maxDepth in maxDepths: treeRank = TreeRank(self.leafRanklearner) treeRank.setMaxDepth(maxDepth) treeRank.learnModel(X, y) trainScores = treeRank.predict(X) testScores = treeRank.predict(testX) self.assertAlmostEquals(Evaluator.auc(trainScores, y), trainAucs[i], 2) self.assertAlmostEquals(Evaluator.auc(testScores, testY), testAucs[i], 1) i += 1
def testPredict2(self): #Test on Gauss2D dataset dataDir = PathDefaults.getDataDir() fileName = dataDir + "Gauss2D_learn.csv" XY = numpy.loadtxt(fileName, skiprows=1, usecols=(1, 2, 3), delimiter=",") X = XY[:, 0:2] y = XY[:, 2] * 2 - 1 fileName = dataDir + "Gauss2D_test.csv" testXY = numpy.loadtxt(fileName, skiprows=1, usecols=(1, 2, 3), delimiter=",") testX = testXY[:, 0:2] testY = testXY[:, 2] * 2 - 1 #X = Standardiser().standardiseArray(X) #testX = Standardiser().standardiseArray(testX) maxDepths = range(3, 10) trainAucs = numpy.array([ 0.7194734, 0.7284824, 0.7332185, 0.7348198, 0.7366152, 0.7367508, 0.7367508, 0.7367508 ]) testAucs = numpy.array([ 0.6789078, 0.6844632, 0.6867918, 0.6873420, 0.6874820, 0.6874400, 0.6874400, 0.6874400 ]) i = 0 #The results are approximately the same, but not exactly for maxDepth in maxDepths: treeRank = TreeRank(DecisionTree) treeRank.setMaxDepth(maxDepth) treeRank.learnModel(X, y) trainScores = treeRank.predict(X) testScores = treeRank.predict(testX) #print(Evaluator.auc(trainScores, y), Evaluator.auc(testScores, testY)) #self.assertAlmostEquals(Evaluator.auc(trainScores, y), trainAucs[i], 2) #self.assertAlmostEquals(Evaluator.auc(testScores, testY), testAucs[i], 1) i += 1 #Compare tree to that of R version tree = treeRank.getTree()
def testPredict(self): maxDepth = 2 treeRank = TreeRank(self.leafRanklearner) treeRank.setMaxDepth(maxDepth) treeRank.learnModel(self.X, self.y) scores = treeRank.predict(self.X) scores2 = numpy.zeros(self.X.shape[0]) # Test if train and test indices are the same tree = treeRank.getTree() vertexIds = tree.getAllVertexIds() for vertexId in vertexIds: node = tree.getVertex(vertexId) self.assertTrue((node.getTrainInds() == node.getTestInds()).all()) (d, k) = vertexId if node.isLeafNode(): self.assertEquals(node.getScore(), (1 - float(k) / 2 ** d) * 2 ** maxDepth) scores2[node.getTestInds()] = node.getScore() self.assertTrue((scores == scores2).all())
def testPredict(self): maxDepth = 2 treeRank = TreeRank(self.leafRanklearner) treeRank.setMaxDepth(maxDepth) treeRank.learnModel(self.X, self.y) scores = treeRank.predict(self.X) scores2 = numpy.zeros(self.X.shape[0]) #Test if train and test indices are the same tree = treeRank.getTree() vertexIds = tree.getAllVertexIds() for vertexId in vertexIds: node = tree.getVertex(vertexId) self.assertTrue((node.getTrainInds() == node.getTestInds()).all()) (d, k) = vertexId if node.isLeafNode(): self.assertEquals(node.getScore(), (1 - float(k) / 2**d) * 2**maxDepth) scores2[node.getTestInds()] = node.getScore() self.assertTrue((scores == scores2).all())
def testFeaturesSize(self): treeRank = TreeRank(self.leafRanklearner) featureSize = 0.5 treeRank.setFeatureSize(featureSize) treeRank.learnModel(self.X, self.y) scores = treeRank.predict(self.X) tree = treeRank.getTree() vertexIds = tree.getAllVertexIds() for vertexId in vertexIds: node = tree.getVertex(vertexId) # print(node.getFeatureInds()) self.assertEquals(node.getFeatureInds().shape[0], numpy.round(featureSize * self.X.shape[1])) self.assertEquals(numpy.unique(node.getFeatureInds()).shape[0], node.getFeatureInds().shape[0])
def testFeaturesSize(self): treeRank = TreeRank(self.leafRanklearner) featureSize = 0.5 treeRank.setFeatureSize(featureSize) treeRank.learnModel(self.X, self.y) scores = treeRank.predict(self.X) tree = treeRank.getTree() vertexIds = tree.getAllVertexIds() for vertexId in vertexIds: node = tree.getVertex(vertexId) #print(node.getFeatureInds()) self.assertEquals(node.getFeatureInds().shape[0], numpy.round(featureSize * self.X.shape[1])) self.assertEquals( numpy.unique(node.getFeatureInds()).shape[0], node.getFeatureInds().shape[0])