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)
