def get_validation_cost(self, thetas):
validation_cost = testerror_elastic_net(
self.data.X_validate,
self.data.y_validate,
thetas
)
return validation_cost
def get_validation_cost(self, lambdas):
# if any are not positive, then just return max value
for l in lambdas:
if l <= 0:
return self.MAX_COST
betas = self.problem_wrapper.solve(lambdas, quick_run=True)
validation_cost = testerror_elastic_net(self.data.X_validate,
self.data.y_validate, betas)
self.log("validation_cost %f" % validation_cost)
return validation_cost
def get_validation_cost(self, lambdas):
# if any are not positive, then just return max value
for l in lambdas:
if l <= 0:
return self.MAX_COST
betas = self.problem_wrapper.solve(lambdas, quick_run=True)
validation_cost = testerror_elastic_net(
self.data.X_validate,
self.data.y_validate,
betas
)
self.log("validation_cost %f" % validation_cost)
return validation_cost
def get_validation_cost(self, thetas):
validation_cost = testerror_elastic_net(self.data.X_validate,
self.data.y_validate, thetas)
return validation_cost
def get_validate_cost(self, model_params):
return testerror_elastic_net(
self.data.X_validate,
self.data.y_validate,
model_params
)
