def iterate(cself, svm, obj_val):
cself.mention('Linearizing constraints...')
classifications = svm._predictions[bs.X_p:bs.X_p + bs.L_p]
V = get_V(classifications)
cself.mention('Computing slacks...')
# Difference is [1 - y_i*(w*phi(x_i) + b)]
pos_differences = 1.0 - classifications
neg_differences = 1.0 + classifications
# Slacks are positive differences only
pos_slacks = np.multiply(pos_differences > 0,
pos_differences)
neg_slacks = np.multiply(neg_differences > 0,
neg_differences)
all_slacks = np.hstack([pos_slacks, neg_slacks])
cself.mention('Linearizing...')
# Compute gradient across pairs
slack_grads = np.vstack([
_grad_softmin(pair, self.alpha) for pair in all_slacks
])
# Stack results into one column
slack_grads = np.vstack([
np.ones((bs.X_p, 1)), slack_grads[:, 0],
slack_grads[:, 1],
np.ones((bs.L_n, 1))
])
# Update QP
qp.update_H(D * V * K * V.T * D)
qp.update_ub(np.multiply(ub, slack_grads))
# Re-solve
cself.mention('Solving QP...')
alphas, obj = qp.solve(self.verbose)
new_svm = MICA(kernel=self.kernel,
gamma=self.gamma,
p=self.p,
verbose=self.verbose,
sv_cutoff=self.sv_cutoff)
new_svm._X = self._X
new_svm._y = self._y
new_svm._V = V
new_svm._alphas = alphas
new_svm._objective = obj
new_svm._compute_separator(K)
new_svm._K = K
if cself.check_tolerance(obj_val, obj):
return None, new_svm
return {'svm': new_svm, 'obj_val': obj}, None
def iterate(cself, svm, obj_val):
cself.mention('Linearizing constraints...')
classifications = svm._predictions[bs.X_p: bs.X_p + bs.L_p]
V = get_V(classifications)
cself.mention('Computing slacks...')
# Difference is [1 - y_i*(w*phi(x_i) + b)]
pos_differences = 1.0 - classifications
neg_differences = 1.0 + classifications
# Slacks are positive differences only
pos_slacks = np.multiply(pos_differences > 0, pos_differences)
neg_slacks = np.multiply(neg_differences > 0, neg_differences)
all_slacks = np.hstack([pos_slacks, neg_slacks])
cself.mention('Linearizing...')
# Compute gradient across pairs
slack_grads = np.vstack([_grad_softmin(pair, self.alpha)
for pair in all_slacks])
# Stack results into one column
slack_grads = np.vstack([np.ones((bs.X_p, 1)),
slack_grads[:, 0],
slack_grads[:, 1],
np.ones((bs.L_n, 1))])
# Update QP
qp.update_H(D * V * K * V.T * D)
qp.update_ub(np.multiply(ub, slack_grads))
# Re-solve
cself.mention('Solving QP...')
alphas, obj = qp.solve(self.verbose)
new_svm = MICA(kernel=self.kernel, gamma=self.gamma, p=self.p,
verbose=self.verbose, sv_cutoff=self.sv_cutoff)
new_svm._X = self._X
new_svm._y = self._y
new_svm._V = V
new_svm._alphas = alphas
new_svm._objective = obj
new_svm._compute_separator(K)
new_svm._K = K
if cself.check_tolerance(obj_val, obj):
return None, new_svm
return {'svm': new_svm, 'obj_val': obj}, None
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
"""
self._bags = map(np.asmatrix, bags)
bs = BagSplitter(self._bags, np.asmatrix(y).reshape((-1, 1)))
self._X = np.vstack([
bs.pos_instances, bs.pos_instances, bs.pos_instances,
bs.neg_instances
])
self._y = np.vstack([
np.matrix(np.ones((bs.X_p + bs.L_p, 1))),
-np.matrix(np.ones((bs.L_p + bs.L_n, 1)))
])
if self.scale_C:
C = self.C / float(len(self._bags))
else:
C = self.C
# Setup SVM and adjust constraints
_, _, f, A, b, lb, ub = self._setup_svm(self._y, self._y, C)
ub[:bs.X_p] *= (float(bs.L_n) / float(bs.X_p))
ub[bs.X_p:bs.X_p + 2 * bs.L_p] *= (float(bs.L_n) / float(bs.L_p))
K = kernel_by_name(self.kernel, gamma=self.gamma, p=self.p)(self._X,
self._X)
D = spdiag(self._y)
ub0 = np.matrix(ub)
ub0[bs.X_p:bs.X_p + 2 * bs.L_p] *= 0.5
def get_V(pos_classifications):
eye_n = bs.L_n + 2 * bs.L_p
top = np.zeros((bs.X_p, bs.L_p))
for row, (i, j) in enumerate(slices(bs.pos_groups)):
top[row, i:j] = _grad_softmin(-pos_classifications[i:j],
self.alpha).flat
return sp.bmat([[sp.coo_matrix(top), None],
[None, sp.eye(eye_n, eye_n)]])
V0 = get_V(np.matrix(np.zeros((bs.L_p, 1))))
qp = IterativeQP(D * V0 * K * V0.T * D, f, A, b, lb, ub0)
best_obj = float('inf')
best_svm = None
for rr in range(self.restarts + 1):
if rr == 0:
if self.verbose:
print 'Non-random start...'
# Train on instances
alphas, obj = qp.solve(self.verbose)
else:
if self.verbose:
print 'Random restart %d of %d...' % (rr, self.restarts)
alphas = np.matrix(
[uniform(0.0, 1.0) for i in xrange(len(lb))]).T
obj = Objective(0.0, 0.0)
svm = MICA(kernel=self.kernel,
gamma=self.gamma,
p=self.p,
verbose=self.verbose,
sv_cutoff=self.sv_cutoff)
svm._X = self._X
svm._y = self._y
svm._V = V0
svm._alphas = alphas
svm._objective = obj
svm._compute_separator(K)
svm._K = K
class missCCCP(CCCP):
def bailout(cself, svm, obj_val):
return svm
def iterate(cself, svm, obj_val):
cself.mention('Linearizing constraints...')
classifications = svm._predictions[bs.X_p:bs.X_p + bs.L_p]
V = get_V(classifications)
cself.mention('Computing slacks...')
# Difference is [1 - y_i*(w*phi(x_i) + b)]
pos_differences = 1.0 - classifications
neg_differences = 1.0 + classifications
# Slacks are positive differences only
pos_slacks = np.multiply(pos_differences > 0,
pos_differences)
neg_slacks = np.multiply(neg_differences > 0,
neg_differences)
all_slacks = np.hstack([pos_slacks, neg_slacks])
cself.mention('Linearizing...')
# Compute gradient across pairs
slack_grads = np.vstack([
_grad_softmin(pair, self.alpha) for pair in all_slacks
])
# Stack results into one column
slack_grads = np.vstack([
np.ones((bs.X_p, 1)), slack_grads[:, 0],
slack_grads[:, 1],
np.ones((bs.L_n, 1))
])
# Update QP
qp.update_H(D * V * K * V.T * D)
qp.update_ub(np.multiply(ub, slack_grads))
# Re-solve
cself.mention('Solving QP...')
alphas, obj = qp.solve(self.verbose)
new_svm = MICA(kernel=self.kernel,
gamma=self.gamma,
p=self.p,
verbose=self.verbose,
sv_cutoff=self.sv_cutoff)
new_svm._X = self._X
new_svm._y = self._y
new_svm._V = V
new_svm._alphas = alphas
new_svm._objective = obj
new_svm._compute_separator(K)
new_svm._K = K
if cself.check_tolerance(obj_val, obj):
return None, new_svm
return {'svm': new_svm, 'obj_val': obj}, None
cccp = missCCCP(verbose=self.verbose,
svm=svm,
obj_val=None,
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._V = best_svm._V
self._alphas = best_svm._alphas
self._objective = best_svm._objective
self._compute_separator(best_svm._K)
self._bag_predictions = self.predict(self._bags)
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
"""
self._bags = map(np.asmatrix, bags)
bs = BagSplitter(self._bags,
np.asmatrix(y).reshape((-1, 1)))
self._X = np.vstack([bs.pos_instances,
bs.pos_instances,
bs.pos_instances,
bs.neg_instances])
self._y = np.vstack([np.matrix(np.ones((bs.X_p + bs.L_p, 1))),
-np.matrix(np.ones((bs.L_p + bs.L_n, 1)))])
if self.scale_C:
C = self.C / float(len(self._bags))
else:
C = self.C
# Setup SVM and adjust constraints
_, _, f, A, b, lb, ub = self._setup_svm(self._y, self._y, C)
ub[:bs.X_p] *= (float(bs.L_n) / float(bs.X_p))
ub[bs.X_p: bs.X_p + 2 * bs.L_p] *= (float(bs.L_n) / float(bs.L_p))
K = kernel_by_name(self.kernel, gamma=self.gamma, p=self.p)(self._X, self._X)
D = spdiag(self._y)
ub0 = np.matrix(ub)
ub0[bs.X_p: bs.X_p + 2 * bs.L_p] *= 0.5
def get_V(pos_classifications):
eye_n = bs.L_n + 2 * bs.L_p
top = np.zeros((bs.X_p, bs.L_p))
for row, (i, j) in enumerate(slices(bs.pos_groups)):
top[row, i:j] = _grad_softmin(-pos_classifications[i:j], self.alpha).flat
return sp.bmat([[sp.coo_matrix(top), None],
[None, sp.eye(eye_n, eye_n)]])
V0 = get_V(np.matrix(np.zeros((bs.L_p, 1))))
qp = IterativeQP(D * V0 * K * V0.T * D, f, A, b, lb, ub0)
best_obj = float('inf')
best_svm = None
for rr in range(self.restarts + 1):
if rr == 0:
if self.verbose:
print 'Non-random start...'
# Train on instances
alphas, obj = qp.solve(self.verbose)
else:
if self.verbose:
print 'Random restart %d of %d...' % (rr, self.restarts)
alphas = np.matrix([uniform(0.0, 1.0) for i in xrange(len(lb))]).T
obj = Objective(0.0, 0.0)
svm = MICA(kernel=self.kernel, gamma=self.gamma, p=self.p,
verbose=self.verbose, sv_cutoff=self.sv_cutoff)
svm._X = self._X
svm._y = self._y
svm._V = V0
svm._alphas = alphas
svm._objective = obj
svm._compute_separator(K)
svm._K = K
class missCCCP(CCCP):
def bailout(cself, svm, obj_val):
return svm
def iterate(cself, svm, obj_val):
cself.mention('Linearizing constraints...')
classifications = svm._predictions[bs.X_p: bs.X_p + bs.L_p]
V = get_V(classifications)
cself.mention('Computing slacks...')
# Difference is [1 - y_i*(w*phi(x_i) + b)]
pos_differences = 1.0 - classifications
neg_differences = 1.0 + classifications
# Slacks are positive differences only
pos_slacks = np.multiply(pos_differences > 0, pos_differences)
neg_slacks = np.multiply(neg_differences > 0, neg_differences)
all_slacks = np.hstack([pos_slacks, neg_slacks])
cself.mention('Linearizing...')
# Compute gradient across pairs
slack_grads = np.vstack([_grad_softmin(pair, self.alpha)
for pair in all_slacks])
# Stack results into one column
slack_grads = np.vstack([np.ones((bs.X_p, 1)),
slack_grads[:, 0],
slack_grads[:, 1],
np.ones((bs.L_n, 1))])
# Update QP
qp.update_H(D * V * K * V.T * D)
qp.update_ub(np.multiply(ub, slack_grads))
# Re-solve
cself.mention('Solving QP...')
alphas, obj = qp.solve(self.verbose)
new_svm = MICA(kernel=self.kernel, gamma=self.gamma, p=self.p,
verbose=self.verbose, sv_cutoff=self.sv_cutoff)
new_svm._X = self._X
new_svm._y = self._y
new_svm._V = V
new_svm._alphas = alphas
new_svm._objective = obj
new_svm._compute_separator(K)
new_svm._K = K
if cself.check_tolerance(obj_val, obj):
return None, new_svm
return {'svm': new_svm, 'obj_val': obj}, None
cccp = missCCCP(verbose=self.verbose, svm=svm, obj_val=None,
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._V = best_svm._V
self._alphas = best_svm._alphas
self._objective = best_svm._objective
self._compute_separator(best_svm._K)
self._bag_predictions = self.predict(self._bags)
