def validation_error(self, learner, criterion, **other_params):
error = 0
try:
for i in range(self.num_folds):
predictor = learner(self,
train=list(self.fold_complement(i)),
**other_params).learn()
error += sum(
error_example(predictor(example), self.target(example),
criterion) for example in self.fold(i))
except ValueError:
return float("inf") #infinity
return error / len(self.data)
def training_error(dataset, data_subset, to_optimize):
"""returns training error for dataset on to_optimize.
This assumes that we choose the best value for the optimization
criteria for dataset according to point_prediction
"""
select_dict = {"sum-of-squares":"mean", "sum_absolute":"median",
"logloss":"Laplace"} # arbitrary mapping. Perhaps wrong.
selection = select_dict[to_optimize]
predictor = point_prediction(dataset.target, data_subset, selection=selection)
error = sum(error_example(predictor(example),
dataset.target(example),
to_optimize)
for example in data_subset)
return error