def cvModelSelection(self, graph, paramList, paramFunc, folds, errorFunc):
"""
ParamList is a list of lists of parameters and paramFunc
is a list of the corresponding functions to call with the parameters
as arguments. Note that a parameter can also be a tuple which is expanded
out before the function is called.
e.g.
paramList = [[1, 2], [2, 1], [12, 1]]
paramFunc = [predictor.setC, predictor.setD]
"""
inds = Sampling.crossValidation(folds, graph.getNumEdges())
errors = numpy.zeros((len(paramList), folds))
allEdges = graph.getAllEdges()
for i in range(len(paramList)):
paramSet = paramList[i]
logging.debug("Using paramSet=" + str(paramSet))
for j in range(len(paramSet)):
if type(paramSet[j]) == tuple:
paramFunc[j](*paramSet[j])
else:
paramFunc[j](paramSet[j])
predY = numpy.zeros(0)
y = numpy.zeros(0)
j = 0
for (trainInds, testInds) in inds:
trainEdges = allEdges[trainInds, :]
testEdges = allEdges[testInds, :]
trainGraph = SparseGraph(graph.getVertexList(),
graph.isUndirected())
trainGraph.addEdges(trainEdges,
graph.getEdgeValues(trainEdges))
testGraph = SparseGraph(graph.getVertexList(),
graph.isUndirected())
testGraph.addEdges(testEdges, graph.getEdgeValues(testEdges))
self.learnModel(trainGraph)
predY = self.predictEdges(testGraph, testGraph.getAllEdges())
y = testGraph.getEdgeValues(testGraph.getAllEdges())
#Note that the order the edges is different in testGraphs as
#opposed to graph when calling getAllEdges()
errors[i, j] = errorFunc(y, predY)
j = j + 1
logging.info("Error of current fold: " +
str(numpy.mean(errors[i, :])))
meanErrors = numpy.mean(errors, 1)
strErrors = numpy.std(errors, 1)
return meanErrors, strErrors
def cvModelSelection(self, graph, paramList, paramFunc, folds, errorFunc):
"""
ParamList is a list of lists of parameters and paramFunc
is a list of the corresponding functions to call with the parameters
as arguments. Note that a parameter can also be a tuple which is expanded
out before the function is called.
e.g.
paramList = [[1, 2], [2, 1], [12, 1]]
paramFunc = [predictor.setC, predictor.setD]
"""
inds = Sampling.crossValidation(folds, graph.getNumEdges())
errors = numpy.zeros((len(paramList), folds))
allEdges = graph.getAllEdges()
for i in range(len(paramList)):
paramSet = paramList[i]
logging.debug("Using paramSet=" + str(paramSet))
for j in range(len(paramSet)):
if type(paramSet[j]) == tuple:
paramFunc[j](*paramSet[j])
else:
paramFunc[j](paramSet[j])
predY = numpy.zeros(0)
y = numpy.zeros(0)
j = 0
for (trainInds, testInds) in inds:
trainEdges = allEdges[trainInds, :]
testEdges = allEdges[testInds, :]
trainGraph = SparseGraph(graph.getVertexList(), graph.isUndirected())
trainGraph.addEdges(trainEdges, graph.getEdgeValues(trainEdges))
testGraph = SparseGraph(graph.getVertexList(), graph.isUndirected())
testGraph.addEdges(testEdges, graph.getEdgeValues(testEdges))
self.learnModel(trainGraph)
predY = self.predictEdges(testGraph, testGraph.getAllEdges())
y = testGraph.getEdgeValues(testGraph.getAllEdges())
#Note that the order the edges is different in testGraphs as
#opposed to graph when calling getAllEdges()
errors[i, j] = errorFunc(y, predY)
j = j+1
logging.info("Error of current fold: " + str(numpy.mean(errors[i, :])))
meanErrors = numpy.mean(errors, 1)
strErrors = numpy.std(errors, 1)
return meanErrors, strErrors
