def testPredictEdges(self):
numVertices = 10
numFeatures = 3
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.random.rand(numVertices, numFeatures))
graph = SparseGraph(vList, False)
graph.addEdge(0, 1)
graph.addEdge(0, 2)
graph.addEdge(0, 3)
graph.addEdge(0, 4)
graph.addEdge(0, 5)
graph.addEdge(1, 2)
graph.addEdge(1, 3)
graph.addEdge(1, 4)
graph.addEdge(1, 5)
graph.addEdge(2, 3)
graph.addEdge(3, 4)
X = GraphUtils.vertexLabelPairs(graph, graph.getAllEdges())
c = numpy.random.rand(numFeatures * 2)
logging.debug(("c=" + str(c)))
y = numpy.dot(X, c)
graph.addEdges(graph.getAllEdges(), y)
lmbda = 0.001
tol = 10 ** -1
regressor = PrimalRidgeRegression(lmbda)
predictor = FlatEdgeLabelPredictor(regressor)
predictor.learnModel(graph)
w = regressor.getWeights()
logging.debug(("w=" + str(w)))
self.assertTrue(numpy.linalg.norm(w - c) < tol)
predY = predictor.predictEdges(graph, graph.getAllEdges())
logging.debug((numpy.linalg.norm(predY - y)))
self.assertTrue(numpy.linalg.norm(predY - y) < tol)
# Now try on new edges
testEdges = numpy.array([[2, 3], [4, 5], [6, 7]])
testX = GraphUtils.vertexLabelPairs(graph, testEdges)
testY = numpy.dot(testX, c)
graph.addEdges(testEdges, testY)
predY = predictor.predictEdges(graph, testEdges)
logging.debug((numpy.linalg.norm(predY - testY)))
self.assertTrue(numpy.linalg.norm(predY - testY) < 0.3)
def testPredictEdges(self):
numVertices = 10
numFeatures = 3
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.random.rand(numVertices, numFeatures))
graph = SparseGraph(vList, False)
graph.addEdge(0, 1)
graph.addEdge(0, 2)
graph.addEdge(0, 3)
graph.addEdge(0, 4)
graph.addEdge(0, 5)
graph.addEdge(1, 2)
graph.addEdge(1, 3)
graph.addEdge(1, 4)
graph.addEdge(1, 5)
graph.addEdge(2, 3)
graph.addEdge(3, 4)
X = GraphUtils.vertexLabelPairs(graph, graph.getAllEdges())
c = numpy.random.rand(numFeatures * 2)
logging.debug(("c=" + str(c)))
y = numpy.dot(X, c)
graph.addEdges(graph.getAllEdges(), y)
lmbda = 0.001
tol = 10**-1
regressor = PrimalRidgeRegression(lmbda)
predictor = FlatEdgeLabelPredictor(regressor)
predictor.learnModel(graph)
w = regressor.getWeights()
logging.debug(("w=" + str(w)))
self.assertTrue(numpy.linalg.norm(w - c) < tol)
predY = predictor.predictEdges(graph, graph.getAllEdges())
logging.debug((numpy.linalg.norm(predY - y)))
self.assertTrue(numpy.linalg.norm(predY - y) < tol)
#Now try on new edges
testEdges = numpy.array([[2, 3], [4, 5], [6, 7]])
testX = GraphUtils.vertexLabelPairs(graph, testEdges)
testY = numpy.dot(testX, c)
graph.addEdges(testEdges, testY)
predY = predictor.predictEdges(graph, testEdges)
logging.debug((numpy.linalg.norm(predY - testY)))
self.assertTrue(numpy.linalg.norm(predY - testY) < 0.3)
def testLearnModel(self):
numVertices = 10
numFeatures = 5
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.random.rand(numVertices, numFeatures))
graph = SparseGraph(vList, False)
#Create a graph with 2 ego networks
graph.addEdge(0, 1, 0.1)
graph.addEdge(0, 2, 0.2)
graph.addEdge(0, 3, 0.3)
graph.addEdge(4, 5, 0.5)
graph.addEdge(4, 6, 0.6)
graph.addEdge(4, 7, 0.7)
lmbda = 0.01
regressor = PrimalRidgeRegression(lmbda)
logging.debug("Running prediction")
predictor = FlatEdgeLabelPredictor(regressor)
predictor.learnModel(graph)
#Try using an undirected graph
graph = SparseGraph(vList, True)
#Create a graph with 2 ego networks
graph.addEdge(0, 1, 0.1)
graph.addEdge(0, 2, 0.2)
graph.addEdge(0, 3, 0.3)
graph.addEdge(4, 5, 0.5)
graph.addEdge(4, 6, 0.6)
graph.addEdge(4, 7, 0.7)
lmbda = 0.01
regressor = PrimalRidgeRegression(lmbda)
logging.debug("Running prediction")
predictor = FlatEdgeLabelPredictor(regressor)
predictor.learnModel(graph)
def testLearnModel(self):
numVertices = 10
numFeatures = 5
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.random.rand(numVertices, numFeatures))
graph = SparseGraph(vList, False)
# Create a graph with 2 ego networks
graph.addEdge(0, 1, 0.1)
graph.addEdge(0, 2, 0.2)
graph.addEdge(0, 3, 0.3)
graph.addEdge(4, 5, 0.5)
graph.addEdge(4, 6, 0.6)
graph.addEdge(4, 7, 0.7)
lmbda = 0.01
regressor = PrimalRidgeRegression(lmbda)
logging.debug("Running prediction")
predictor = FlatEdgeLabelPredictor(regressor)
predictor.learnModel(graph)
# Try using an undirected graph
graph = SparseGraph(vList, True)
# Create a graph with 2 ego networks
graph.addEdge(0, 1, 0.1)
graph.addEdge(0, 2, 0.2)
graph.addEdge(0, 3, 0.3)
graph.addEdge(4, 5, 0.5)
graph.addEdge(4, 6, 0.6)
graph.addEdge(4, 7, 0.7)
lmbda = 0.01
regressor = PrimalRidgeRegression(lmbda)
logging.debug("Running prediction")
predictor = FlatEdgeLabelPredictor(regressor)
predictor.learnModel(graph)
