def iterate(cself, svm, classes):
cself.mention('Training SVM...')
D = spdiag(classes)
qp.update_H(D * K * D)
qp.update_Aeq(classes.T)
alphas, obj = qp.solve(cself.verbose)
# Construct SVM from solution
svm = SVM(kernel=self.kernel, gamma=self.gamma, p=self.p,
verbose=self.verbose, sv_cutoff=self.sv_cutoff)
svm._X = bs.instances
svm._y = classes
svm._alphas = alphas
svm._objective = obj
svm._compute_separator(K)
svm._K = K
cself.mention('Recomputing classes...')
p_conf = svm._predictions[-bs.L_p:]
pos_classes = np.vstack([_update_classes(part)
for part in
partition(p_conf, bs.pos_groups)])
new_classes = np.vstack([-np.ones((bs.L_n, 1)), pos_classes])
class_changes = round(np.sum(np.abs(classes - new_classes) / 2))
cself.mention('Class Changes: %d' % class_changes)
if class_changes == 0:
return None, svm
return {'svm': svm, 'classes': new_classes}, None
def iterate(cself, svm, classes):
cself.mention('Training SVM...')
D = spdiag(classes)
qp.update_H(D * K * D)
qp.update_Aeq(classes.T)
alphas, obj = qp.solve(cself.verbose)
# Construct SVM from solution
svm = SVM(kernel=self.kernel, gamma=self.gamma, p=self.p,
verbose=self.verbose, sv_cutoff=self.sv_cutoff)
svm._X = bs.instances
svm._y = classes
svm._alphas = alphas
svm._objective = obj
svm._compute_separator(K)
svm._K = K
cself.mention('Recomputing classes...')
p_conf = svm._predictions[-bs.L_p:]
pos_classes = np.vstack([_update_classes(part)
for part in
partition(p_conf, bs.pos_groups)])
new_classes = np.vstack([-np.ones((bs.L_n, 1)), pos_classes])
class_changes = round(np.sum(np.abs(classes - new_classes) / 2))
cself.mention('Class Changes: %d' % class_changes)
if class_changes == 0:
return None, svm
return {'svm': svm, 'classes': new_classes}, None
def iterate(cself, svm, selectors, instances, K):
cself.mention('Training SVM...')
alphas, obj = qp.solve(cself.verbose)
# Construct SVM from solution
svm = SVM(kernel=self.kernel,
gamma=self.gamma,
p=self.p,
verbose=self.verbose,
sv_cutoff=self.sv_cutoff)
svm._X = instances
svm._y = classes
svm._alphas = alphas
svm._objective = obj
svm._compute_separator(K)
svm._K = K
cself.mention('Recomputing classes...')
p_confs = svm.predict(bs.pos_instances)
pos_selectors = bs.L_n + np.array([
l + np.argmax(p_confs[l:u])
for l, u in slices(bs.pos_groups)
])
new_selectors = np.hstack([neg_selectors, pos_selectors])
if selectors is None:
sel_diff = len(new_selectors)
else:
sel_diff = np.nonzero(new_selectors -
selectors)[0].size
cself.mention('Selector differences: %d' % sel_diff)
if sel_diff == 0:
return None, svm
elif sel_diff > 5:
# Clear results to avoid a
# bad starting point in
# the next iteration
qp.clear_results()
cself.mention('Updating QP...')
indices = (new_selectors, )
K = K_all[indices].T[indices].T
D = spdiag(classes)
qp.update_H(D * K * D)
return {
'svm': svm,
'selectors': new_selectors,
'instances': bs.instances[indices],
'K': K
}, None
def iterate(cself, svm, selectors, instances, K):
cself.mention('Training SVM...')
alphas, obj = qp.solve(cself.verbose)
# Construct SVM from solution
svm = SVM(kernel=self.kernel, gamma=self.gamma, p=self.p,
verbose=self.verbose, sv_cutoff=self.sv_cutoff)
svm._X = instances
svm._y = classes
svm._alphas = alphas
svm._objective = obj
svm._compute_separator(K)
svm._K = K
cself.mention('Recomputing classes...')
p_confs = svm.predict(bs.pos_instances)
pos_selectors = bs.L_n + np.array([l + np.argmax(p_confs[l:u])
for l, u in slices(bs.pos_groups)])
new_selectors = np.hstack([neg_selectors, pos_selectors])
if selectors is None:
sel_diff = len(new_selectors)
else:
sel_diff = np.nonzero(new_selectors - selectors)[0].size
cself.mention('Selector differences: %d' % sel_diff)
if sel_diff == 0:
return None, svm
elif sel_diff > 5:
# Clear results to avoid a
# bad starting point in
# the next iteration
qp.clear_results()
cself.mention('Updating QP...')
indices = (new_selectors,)
K = K_all[indices].T[indices].T
D = spdiag(classes)
qp.update_H(D * K * D)
return {'svm': svm, 'selectors': new_selectors,
'instances': bs.instances[indices], 'K': K}, None