def testCvModelSelection(self):
graph = self.graph
lmbda = 0.01
alterRegressor = PrimalRidgeRegression(lmbda)
egoRegressor = PrimalRidgeRegression(lmbda)
predictor = EgoEdgeLabelPredictor(alterRegressor, egoRegressor)
paramList = [[0.1, 0.1], [0.2, 0.1], [0.1, 0.2]]
paramFunc = [egoRegressor.setLambda, alterRegressor.setLambda]
folds = 3
errorFunc = Evaluator.rootMeanSqError
meanErs, stdEr = predictor.cvModelSelection(graph, paramList, paramFunc, folds, errorFunc)
logging.debug(meanErs)
self.assertTrue(meanErs.shape[0] == len(paramList))
meanErs2 = meanErs
paramList = [[0.1, 0.1], [0.2, 0.1]]
meanErs, stdEr = predictor.cvModelSelection(graph, paramList, paramFunc, folds, errorFunc)
logging.debug(meanErs)
self.assertTrue(meanErs.shape[0] == len(paramList))
self.assertTrue((meanErs2[0:2] == meanErs).all())
paramList = [[0.1, 0.1]]
meanErs, stdEr = predictor.cvModelSelection(graph, paramList, paramFunc, folds, errorFunc)
logging.debug(meanErs)
self.assertTrue(meanErs.shape[0] == len(paramList))
self.assertTrue((meanErs2[0:1] == meanErs).all())
def testLoadParams(self):
try:
lmbda = 0.01
alterRegressor = PrimalRidgeRegression(lmbda)
egoRegressor = PrimalRidgeRegression(lmbda)
predictor = EgoEdgeLabelPredictor(alterRegressor, egoRegressor)
params = [0.1, 0.2]
paramFuncs = [egoRegressor.setLambda, alterRegressor.setLambda]
fileName = PathDefaults.getTempDir() + "tempParams.pkl"
predictor.saveParams(params, paramFuncs, fileName)
params2 = predictor.loadParams(fileName)
self.assertTrue(params2[0][0] == "apgl.predictors.PrimalRidgeRegression")
self.assertTrue(params2[0][1] == "setLambda")
self.assertTrue(params2[0][2] == 0.1)
self.assertTrue(params2[1][0] == "apgl.predictors.PrimalRidgeRegression")
self.assertTrue(params2[1][1] == "setLambda")
self.assertTrue(params2[1][2] == 0.2)
except IOError as e:
logging.warn(e)
def testLoadParams(self):
try:
lmbda = 0.01
alterRegressor = PrimalRidgeRegression(lmbda)
egoRegressor = PrimalRidgeRegression(lmbda)
predictor = EgoEdgeLabelPredictor(alterRegressor, egoRegressor)
params = [0.1, 0.2]
paramFuncs = [egoRegressor.setLambda, alterRegressor.setLambda]
fileName = PathDefaults.getTempDir() + "tempParams.pkl"
predictor.saveParams(params, paramFuncs, fileName)
params2 = predictor.loadParams(fileName)
self.assertTrue(
params2[0][0] == "apgl.predictors.PrimalRidgeRegression")
self.assertTrue(params2[0][1] == "setLambda")
self.assertTrue(params2[0][2] == 0.1)
self.assertTrue(
params2[1][0] == "apgl.predictors.PrimalRidgeRegression")
self.assertTrue(params2[1][1] == "setLambda")
self.assertTrue(params2[1][2] == 0.2)
except IOError as e:
logging.warn(e)
def testSaveParams(self):
try:
lmbda = 0.01
alterRegressor = PrimalRidgeRegression(lmbda)
egoRegressor = PrimalRidgeRegression(lmbda)
predictor = EgoEdgeLabelPredictor(alterRegressor, egoRegressor)
params = [0.1, 0.2]
paramFuncs = [egoRegressor.setLambda, alterRegressor.setLambda]
fileName = PathDefaults.getTempDir() + "tempParams.pkl"
predictor.saveParams(params, paramFuncs, fileName)
except IOError as e:
logging.warn(e)
def testSaveParams(self):
try:
lmbda = 0.01
alterRegressor = PrimalRidgeRegression(lmbda)
egoRegressor = PrimalRidgeRegression(lmbda)
predictor = EgoEdgeLabelPredictor(alterRegressor, egoRegressor)
params = [0.1, 0.2]
paramFuncs = [egoRegressor.setLambda, alterRegressor.setLambda]
fileName = PathDefaults.getTempDir() + "tempParams.pkl"
predictor.saveParams(params, paramFuncs, fileName)
except IOError as e:
logging.warn(e)
def testCvError(self):
graph = self.graph
lmbda = 0.01
alterRegressor = PrimalRidgeRegression(lmbda)
egoRegressor = PrimalRidgeRegression(lmbda)
predictor = EgoEdgeLabelPredictor(alterRegressor, egoRegressor)
paramList = [0.1, 0.1]
paramFunc = [egoRegressor.setLambda, alterRegressor.setLambda]
folds = 3
errorFunc = Evaluator.rootMeanSqError
meanEr, stdEr = predictor.cvError(graph, paramList, paramFunc, folds, errorFunc)
paramList = [[0.1, 0.1]]
meanErs, stdEr = predictor.cvModelSelection(graph, paramList, paramFunc, folds, errorFunc)
self.assertEquals(meanEr, meanErs[0])
def testCvError(self):
graph = self.graph
lmbda = 0.01
alterRegressor = PrimalRidgeRegression(lmbda)
egoRegressor = PrimalRidgeRegression(lmbda)
predictor = EgoEdgeLabelPredictor(alterRegressor, egoRegressor)
paramList = [0.1, 0.1]
paramFunc = [egoRegressor.setLambda, alterRegressor.setLambda]
folds = 3
errorFunc = Evaluator.rootMeanSqError
meanEr, stdEr = predictor.cvError(graph, paramList, paramFunc, folds,
errorFunc)
paramList = [[0.1, 0.1]]
meanErs, stdEr = predictor.cvModelSelection(graph, paramList,
paramFunc, folds,
errorFunc)
self.assertEquals(meanEr, meanErs[0])
def testCvModelSelection(self):
graph = self.graph
lmbda = 0.01
alterRegressor = PrimalRidgeRegression(lmbda)
egoRegressor = PrimalRidgeRegression(lmbda)
predictor = EgoEdgeLabelPredictor(alterRegressor, egoRegressor)
paramList = [[0.1, 0.1], [0.2, 0.1], [0.1, 0.2]]
paramFunc = [egoRegressor.setLambda, alterRegressor.setLambda]
folds = 3
errorFunc = Evaluator.rootMeanSqError
meanErs, stdEr = predictor.cvModelSelection(graph, paramList,
paramFunc, folds,
errorFunc)
logging.debug(meanErs)
self.assertTrue(meanErs.shape[0] == len(paramList))
meanErs2 = meanErs
paramList = [[0.1, 0.1], [0.2, 0.1]]
meanErs, stdEr = predictor.cvModelSelection(graph, paramList,
paramFunc, folds,
errorFunc)
logging.debug(meanErs)
self.assertTrue(meanErs.shape[0] == len(paramList))
self.assertTrue((meanErs2[0:2] == meanErs).all())
paramList = [[0.1, 0.1]]
meanErs, stdEr = predictor.cvModelSelection(graph, paramList,
paramFunc, folds,
errorFunc)
logging.debug(meanErs)
self.assertTrue(meanErs.shape[0] == len(paramList))
self.assertTrue((meanErs2[0:1] == meanErs).all())
