def testCvModelSelection(self):
numVertices = 10
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
graph.addEdge(0, 1)
graph.addEdge(0, 2)
graph.addEdge(0, 3)
graph.addEdge(1, 2)
graph.addEdge(3, 4)
graph.addEdge(5, 6)
graph.addEdge(4, 6)
graph.addEdge(9, 8)
graph.addEdge(9, 7)
graph.addEdge(9, 6)
windowSize = 3
predictor = RandomEdgePredictor(windowSize)
folds = 5
paramList = [[1, 2], [2, 1], [12, 1]]
paramFunc = [predictor.setC, predictor.setD]
errors = predictor.cvModelSelection(graph, paramList, paramFunc, folds)
self.assertTrue(errors.shape[0] == len(paramList))
for i in range(errors.shape[0]):
self.assertTrue(errors[i]>= 0 and errors[i]<= 1)
def testIndicesFromScores(self):
numVertices = 10
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
graph = SparseGraph(vList)
graph.addEdge(0, 1)
graph.addEdge(0, 2)
graph.addEdge(0, 3)
graph.addEdge(1, 2)
graph.addEdge(3, 4)
graph.addEdge(5, 6)
graph.addEdge(4, 6)
graph.addEdge(9, 8)
graph.addEdge(9, 7)
graph.addEdge(9, 6)
windowSize = 10
predictor = RandomEdgePredictor(windowSize)
predictor.learnModel(graph)
scores = numpy.random.randn(numVertices)
ind = 0
p, s = predictor.indicesFromScores(ind , scores)
self.assertTrue(p.shape[0] == windowSize)
self.assertTrue(s.shape[0] == windowSize)
infIndices = p[numpy.nonzero(s==-float('Inf'))]
self.assertTrue((numpy.sort(infIndices) == numpy.sort(graph.neighbours(ind))).all())
