def looCV(self):
""" Calculate leave one out metrics """
# remember original solution
optimal_w = copy.deepcopy(self.w)
optimal_b = copy.deepcopy(self.b)
optimal_dual_solution = copy.deepcopy(self.dual_solution)
# preparation of sorting
sort_dual = self.dual_solution
# sort indices --> zero weights do not need any changing and
# low weights are less relevant for changes
sorted_indices = map(list, [numpy.argsort(sort_dual)])[0]
sorted_indices.reverse()
prediction_vectors = []
using_initial_solution = True
for index in sorted_indices:
d_i = self.dual_solution[index]
# delete each index from the current observation
if d_i == 0 and using_initial_solution:
# no change in classifier necessary
pass
else:
# set weight to zero and track the corresponding changes
self.reduce_dual_weight(index)
# reiterate till convergence but skip current index
temp_iter = self.iterations
self.iteration_loop(self.M, reduced_indices=[index])
self.iterations += temp_iter
using_initial_solution = False
prediction_vectors.append((
self._execute(numpy.atleast_2d(self.samples[index])),
self.classes[self.labels[index]]))
self.loo_metrics = BinaryClassificationDataset.calculate_metrics(
prediction_vectors,
ir_class=self.classes[1],
sec_class=self.classes[0])
#undo changes
self.b = optimal_b
self.w = optimal_w
self.dual_solution = optimal_dual_solution
def looCV(self):
""" Calculate leave one out metrics """
# remember original solution
optimal_w = copy.deepcopy(self.w)
optimal_b = copy.deepcopy(self.b)
optimal_dual_solution = copy.deepcopy(self.dual_solution)
# preparation of sorting
sort_dual = self.dual_solution
# sort indices --> zero weights do not need any changing and
# low weights are less relevant for changes
sorted_indices = map(list, [numpy.argsort(sort_dual)])[0]
sorted_indices.reverse()
prediction_vectors = []
using_initial_solution = True
for index in sorted_indices:
d_i = self.dual_solution[index]
# delete each index from the current observation
if d_i == 0 and using_initial_solution:
# no change in classifier necessary
pass
else:
# set weight to zero and track the corresponding changes
self.reduce_dual_weight(index)
# reiterate till convergence but skip current index
temp_iter = self.iterations
self.iteration_loop(self.M, reduced_indices=[index])
self.iterations += temp_iter
using_initial_solution = False
prediction_vectors.append(
(self._execute(numpy.atleast_2d(self.samples[index])),
self.classes[self.labels[index]]))
self.loo_metrics = BinaryClassificationDataset.calculate_metrics(
prediction_vectors,
ir_class=self.classes[1],
sec_class=self.classes[0])
#undo changes
self.b = optimal_b
self.w = optimal_w
self.dual_solution = optimal_dual_solution
