def fit(self, bags, y):
"""
@param bags : a sequence of n bags; each bag is an m-by-k array-like
object containing m instances with k features
@param y : an array-like object of length n containing -1/+1 labels
"""
def transform(mx):
"""
Transform into np.matrix if array/list
ignore scipy.sparse matrix
"""
if issparse(mx):
return mx.todense()
return np.asmatrix(mx)
self._bags = [transform(bag) for bag in bags]
y = np.asmatrix(y).reshape((-1, 1))
bs = BagSplitter(self._bags, y)
best_obj = float('inf')
best_svm = None
for rr in range(self.restarts + 1):
if rr == 0:
if self.verbose:
print('Non-random start...')
pos_bag_avgs = np.vstack([np.average(bag, axis=0) for bag in bs.pos_bags])
else:
if self.verbose:
print('Random restart %d of %d...' % (rr, self.restarts))
pos_bag_avgs = np.vstack([rand_convex(len(bag)) * bag for bag in bs.pos_bags])
intial_instances = np.vstack([bs.neg_instances, pos_bag_avgs])
classes = np.vstack([-np.ones((bs.L_n, 1)),
np.ones((bs.X_p, 1))])
# Setup SVM and QP
if self.scale_C:
C = self.C / float(len(intial_instances))
else:
C = self.C
setup = self._setup_svm(intial_instances, classes, C)
K = setup[0]
qp = IterativeQP(*setup[1:])
# Fix Gx <= h
neg_cons = spzeros(bs.X_n, bs.L_n)
for b, (l, u) in enumerate(slices(bs.neg_groups)):
neg_cons[b, l:u] = 1.0
pos_cons = speye(bs.X_p)
bot_left = spzeros(bs.X_p, bs.L_n)
top_right = spzeros(bs.X_n, bs.X_p)
half_cons = sparse([[neg_cons, bot_left],
[top_right, pos_cons]])
qp.G = sparse([-speye(bs.X_p + bs.L_n), half_cons])
qp.h = cvxmat(np.vstack([np.zeros((bs.X_p + bs.L_n, 1)),
C * np.ones((bs.X_p + bs.X_n, 1))]))
# Precompute kernel for all positive instances
kernel = kernel_by_name(self.kernel, gamma=self.gamma, p=self.p)
K_all = kernel(bs.instances, bs.instances)
neg_selectors = np.array(range(bs.L_n))
class MISVMCCCP(CCCP):
def bailout(cself, svm, selectors, instances, K):
return svm
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
cccp = MISVMCCCP(verbose=self.verbose, svm=None, selectors=None,
instances=intial_instances, K=K, max_iters=self.max_iters)
svm = cccp.solve()
if svm is not None:
obj = float(svm._objective)
if obj < best_obj:
best_svm = svm
best_obj = obj
if best_svm is not None:
self._X = best_svm._X
self._y = best_svm._y
self._alphas = best_svm._alphas
self._objective = best_svm._objective
self._compute_separator(best_svm._K)
def fit(self, bags, y):
"""
@param bags : a sequence of n bags; each bag is an m-by-k array-like
object containing m instances with k features
@param y : an array-like object of length n containing -1/+1 labels
"""
def transform(mx):
"""
Transform into np.matrix if array/list
ignore scipy.sparse matrix
"""
if issparse(mx):
return mx.todense()
return np.asmatrix(mx)
self._bags = [transform(bag) for bag in bags]
y = np.asmatrix(y).reshape((-1, 1))
bs = BagSplitter(self._bags, y)
best_obj = float('inf')
best_svm = None
for rr in range(self.restarts + 1):
if rr == 0:
if self.verbose:
print 'Non-random start...'
pos_bag_avgs = np.vstack([np.average(bag, axis=0) for bag in bs.pos_bags])
else:
if self.verbose:
print 'Random restart %d of %d...' % (rr, self.restarts)
pos_bag_avgs = np.vstack([rand_convex(len(bag)) * bag for bag in bs.pos_bags])
intial_instances = np.vstack([bs.neg_instances, pos_bag_avgs])
classes = np.vstack([-np.ones((bs.L_n, 1)),
np.ones((bs.X_p, 1))])
# Setup SVM and QP
if self.scale_C:
C = self.C / float(len(intial_instances))
else:
C = self.C
setup = self._setup_svm(intial_instances, classes, C)
K = setup[0]
qp = IterativeQP(*setup[1:])
# Fix Gx <= h
neg_cons = spzeros(bs.X_n, bs.L_n)
for b, (l, u) in enumerate(slices(bs.neg_groups)):
neg_cons[b, l:u] = 1.0
pos_cons = speye(bs.X_p)
bot_left = spzeros(bs.X_p, bs.L_n)
top_right = spzeros(bs.X_n, bs.X_p)
half_cons = sparse([[neg_cons, bot_left],
[top_right, pos_cons]])
qp.G = sparse([-speye(bs.X_p + bs.L_n), half_cons])
qp.h = cvxmat(np.vstack([np.zeros((bs.X_p + bs.L_n, 1)),
C * np.ones((bs.X_p + bs.X_n, 1))]))
# Precompute kernel for all positive instances
kernel = kernel_by_name(self.kernel, gamma=self.gamma, p=self.p)
K_all = kernel(bs.instances, bs.instances)
neg_selectors = np.array(range(bs.L_n))
class MISVMCCCP(CCCP):
def bailout(cself, svm, selectors, instances, K):
return svm
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
cccp = MISVMCCCP(verbose=self.verbose, svm=None, selectors=None,
instances=intial_instances, K=K, max_iters=self.max_iters)
svm = cccp.solve()
if svm is not None:
obj = float(svm._objective)
if obj < best_obj:
best_svm = svm
best_obj = obj
if best_svm is not None:
self._X = best_svm._X
self._y = best_svm._y
self._alphas = best_svm._alphas
self._objective = best_svm._objective
self._compute_separator(best_svm._K)
