def testVertexLabelPairs(self):
numVertices = 6
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.array([numpy.arange(0, 6)]).T)
graph = DenseGraph(vList, True)
graph.addEdge(0, 1, 0.1)
graph.addEdge(1, 3, 0.1)
graph.addEdge(0, 2, 0.2)
graph.addEdge(2, 3, 0.5)
graph.addEdge(0, 4, 0.1)
graph.addEdge(3, 4, 0.1)
tol = 10**-6
edges = graph.getAllEdges()
X = GraphUtils.vertexLabelPairs(graph, edges)
self.assertTrue(numpy.linalg.norm(X - edges) < tol )
X = GraphUtils.vertexLabelPairs(graph, edges[[5, 2, 1], :])
self.assertTrue(numpy.linalg.norm(X - edges[[5,2,1], :]) < tol )
#Try a bigger graph
numVertices = 6
numFeatures = 2
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.random.randn(numVertices, numFeatures))
graph = DenseGraph(vList, True)
graph.addEdge(0, 1, 0.1)
graph.addEdge(1, 3, 0.1)
edges = graph.getAllEdges()
X = GraphUtils.vertexLabelPairs(graph, edges)
self.assertTrue(numpy.linalg.norm(X[0, 0:numFeatures] - vList.getVertex(1)) < tol )
self.assertTrue(numpy.linalg.norm(X[0, numFeatures:numFeatures*2] - vList.getVertex(0)) < tol )
self.assertTrue(numpy.linalg.norm(X[1, 0:numFeatures] - vList.getVertex(3)) < tol )
self.assertTrue(numpy.linalg.norm(X[1, numFeatures:numFeatures*2] - vList.getVertex(1)) < tol )
#Try directed graphs
graph = DenseGraph(vList, False)
graph.addEdge(0, 1, 0.1)
graph.addEdge(1, 3, 0.1)
edges = graph.getAllEdges()
X = GraphUtils.vertexLabelPairs(graph, edges)
self.assertTrue(numpy.linalg.norm(X[0, 0:numFeatures] - vList.getVertex(0)) < tol )
self.assertTrue(numpy.linalg.norm(X[0, numFeatures:numFeatures*2] - vList.getVertex(1)) < tol )
self.assertTrue(numpy.linalg.norm(X[1, 0:numFeatures] - vList.getVertex(1)) < tol )
self.assertTrue(numpy.linalg.norm(X[1, numFeatures:numFeatures*2] - vList.getVertex(3)) < tol )
def learnModel(self, graph):
"""
Learn a prediction model based on considering all ego-alter pairs.
:param graph: The input graph to learn from.
:type graph: class:`apgl.graph.AbstractSingleGraph`
"""
logging.info("Learning model on graph of size " +
str(graph.getNumVertices()))
logging.info("Regressor: " + str(self.predictor))
edges = graph.getAllEdges()
if graph.isUndirected():
edges2 = numpy.c_[edges[:, 1], edges[:, 0]]
edges = numpy.r_[edges, edges2]
X = GraphUtils.vertexLabelPairs(graph, edges)
y = graph.getEdgeValues(edges)
#Now we need to solve least to find regressor of X onto y
logging.info("Number of vertex pairs " + str(X.shape))
gc.collect()
self.predictor.learnModel(X, y)
def predictEdges(self, graph, edges):
"""
Make prediction given the edges and given graph.
:param edges: A numpy array consisting of the edges to make predictions over.
"""
Parameter.checkInt(graph.getVertexList().getNumFeatures(), 1, float('inf'))
logging.info("Making prediction over " + str(edges.shape[0]) + " edges")
X = GraphUtils.vertexLabelPairs(graph, edges)
predY = self.predictor.predict(X)
return predY
def predictEdges(self, graph, edges):
"""
Make prediction given the edges and given graph.
:param edges: A numpy array consisting of the edges to make predictions over.
"""
Parameter.checkInt(graph.getVertexList().getNumFeatures(), 1,
float('inf'))
logging.info("Making prediction over " + str(edges.shape[0]) +
" edges")
X = GraphUtils.vertexLabelPairs(graph, edges)
predY = self.predictor.predict(X)
return predY
def learnModel(self, graph):
"""
Learn a prediction model based on considering all ego-alter pairs.
:param graph: The input graph to learn from.
:type graph: class:`apgl.graph.AbstractSingleGraph`
"""
logging.info("Learning model on graph of size " + str(graph.getNumVertices()))
logging.info("Regressor: " + str(self.predictor))
edges = graph.getAllEdges()
if graph.isUndirected():
edges2 = numpy.c_[edges[:, 1], edges[:, 0]]
edges = numpy.r_[edges, edges2]
X = GraphUtils.vertexLabelPairs(graph, edges)
y = graph.getEdgeValues(edges)
#Now we need to solve least to find regressor of X onto y
logging.info("Number of vertex pairs " + str(X.shape))
gc.collect()
self.predictor.learnModel(X, y)
def testVertexLabelPairs(self):
numVertices = 6
numFeatures = 1
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.array([numpy.arange(0, 6)]).T)
graph = DenseGraph(vList, True)
graph.addEdge(0, 1, 0.1)
graph.addEdge(1, 3, 0.1)
graph.addEdge(0, 2, 0.2)
graph.addEdge(2, 3, 0.5)
graph.addEdge(0, 4, 0.1)
graph.addEdge(3, 4, 0.1)
tol = 10**-6
edges = graph.getAllEdges()
X = GraphUtils.vertexLabelPairs(graph, edges)
self.assertTrue(numpy.linalg.norm(X - edges) < tol)
X = GraphUtils.vertexLabelPairs(graph, edges[[5, 2, 1], :])
self.assertTrue(numpy.linalg.norm(X - edges[[5, 2, 1], :]) < tol)
#Try a bigger graph
numVertices = 6
numFeatures = 2
vList = VertexList(numVertices, numFeatures)
vList.setVertices(numpy.random.randn(numVertices, numFeatures))
graph = DenseGraph(vList, True)
graph.addEdge(0, 1, 0.1)
graph.addEdge(1, 3, 0.1)
edges = graph.getAllEdges()
X = GraphUtils.vertexLabelPairs(graph, edges)
self.assertTrue(
numpy.linalg.norm(X[0, 0:numFeatures] - vList.getVertex(1)) < tol)
self.assertTrue(
numpy.linalg.norm(X[0, numFeatures:numFeatures * 2] -
vList.getVertex(0)) < tol)
self.assertTrue(
numpy.linalg.norm(X[1, 0:numFeatures] - vList.getVertex(3)) < tol)
self.assertTrue(
numpy.linalg.norm(X[1, numFeatures:numFeatures * 2] -
vList.getVertex(1)) < tol)
#Try directed graphs
graph = DenseGraph(vList, False)
graph.addEdge(0, 1, 0.1)
graph.addEdge(1, 3, 0.1)
edges = graph.getAllEdges()
X = GraphUtils.vertexLabelPairs(graph, edges)
self.assertTrue(
numpy.linalg.norm(X[0, 0:numFeatures] - vList.getVertex(0)) < tol)
self.assertTrue(
numpy.linalg.norm(X[0, numFeatures:numFeatures * 2] -
vList.getVertex(1)) < tol)
self.assertTrue(
numpy.linalg.norm(X[1, 0:numFeatures] - vList.getVertex(1)) < tol)
self.assertTrue(
numpy.linalg.norm(X[1, numFeatures:numFeatures * 2] -
vList.getVertex(3)) < tol)
