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Allow zero costs and no triangle rule when updating edit costs in MPG.

v0.2.x
jajupmochi 5 years ago
parent
34e82bfce8
commit
85ad732a88
1 changed files with 236 additions and 25 deletions
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  1. +236
    -25
      gklearn/preimage/median_preimage_generator.py

+ 236
- 25
gklearn/preimage/median_preimage_generator.py View File

@@ -39,6 +39,8 @@ class MedianPreimageGenerator(PreimageGenerator):
self.__max_itrs_without_update = 3
self.__epsilon_residual = 0.01
self.__epsilon_ec = 0.1
self.__allow_zeros = False
self.__triangle_rule = True
# values to compute.
self.__runtime_optimize_ec = None
self.__runtime_generate_preimage = None
@@ -79,6 +81,8 @@ class MedianPreimageGenerator(PreimageGenerator):
self.__epsilon_ec = kwargs.get('epsilon_ec', 0.1)
self.__gram_matrix_unnorm = kwargs.get('gram_matrix_unnorm', None)
self.__runtime_precompute_gm = kwargs.get('runtime_precompute_gm', None)
self.__allow_zeros = kwargs.get('allow_zeros', False)
self.__triangle_rule = kwargs.get('triangle_rule', True)
def run(self):
@@ -382,7 +386,8 @@ class MedianPreimageGenerator(PreimageGenerator):

def __update_ecc(self, nb_cost_mat, dis_k_vec, rw_constraints='inequality'):
# if self.__ds_name == 'Letter-high':
if self.__ged_options['edit_cost'] == 'LETTER':
if self.__ged_options['edit_cost'] == 'LETTER':
raise Exception('Cannot compute for cost "LETTER".')
pass
# # method 1: set alpha automatically, just tune c_vir and c_eir by
# # LMS using cvxpy.
@@ -461,32 +466,34 @@ class MedianPreimageGenerator(PreimageGenerator):
# edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]]
# edit_costs_new = np.array(edit_costs_new)
# residual = np.sqrt(prob.value)
if rw_constraints == 'inequality':
# c_vs <= c_vi + c_vr.
if not self.__triangle_rule and self.__allow_zeros:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif rw_constraints == '2constraints':
# c_vs <= c_vi + c_vr and c_vi == c_vr, c_ei == c_er.
elif self.__triangle_rule and self.__allow_zeros:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0,
np.array([1.0, -1.0, 0.0, 0.0, 0.0]).T@x == 0.0,
np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0]
constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01,
np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif rw_constraints == 'no-constraint':
# no constraint.
elif not self.__triangle_rule and not self.__allow_zeros:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
@@ -508,11 +515,36 @@ class MedianPreimageGenerator(PreimageGenerator):
# edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]]
# edit_costs_new = np.array(edit_costs_new)
# residual = np.sqrt(prob.value)
elif self.__triangle_rule and not self.__allow_zeros:
# c_vs <= c_vi + c_vr.
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif rw_constraints == '2constraints': # @todo: rearrange it later.
# c_vs <= c_vi + c_vr and c_vi == c_vr, c_ei == c_er.
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0,
np.array([1.0, -1.0, 0.0, 0.0, 0.0]).T@x == 0.0,
np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
edit_costs_new = x.value
residual = np.sqrt(prob.value)

elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC':
is_n_attr = np.count_nonzero(nb_cost_mat[:,2])
is_e_attr = np.count_nonzero(nb_cost_mat[:,5])
if self.__ds_name == 'SYNTHETICnew':
if self.__ds_name == 'SYNTHETICnew': # @todo: rearrenge this later.
# nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]]
nb_cost_mat_new = nb_cost_mat[:,[2,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
@@ -529,7 +561,149 @@ class MedianPreimageGenerator(PreimageGenerator):
np.array([0.0])))
residual = np.sqrt(prob.value)
elif rw_constraints == 'inequality':
elif not self.__triangle_rule and self.__allow_zeros:
if is_n_attr and is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif is_n_attr and not is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = np.concatenate((x.value, np.array([0.0])))
residual = np.sqrt(prob.value)
elif not is_n_attr and is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:]))
residual = np.sqrt(prob.value)
else:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]),
x.value[2:], np.array([0.0])))
residual = np.sqrt(prob.value)
elif self.__triangle_rule and self.__allow_zeros:
if is_n_attr and is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01,
np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0,
np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif is_n_attr and not is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 0.0, 1.0]).T@x >= 0.01,
np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = np.concatenate((x.value, np.array([0.0])))
residual = np.sqrt(prob.value)
elif not is_n_attr and is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:]))
residual = np.sqrt(prob.value)
else:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]),
x.value[2:], np.array([0.0])))
residual = np.sqrt(prob.value)
elif not self.__triangle_rule and not self.__allow_zeros:
if is_n_attr and is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif is_n_attr and not is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = np.concatenate((x.value, np.array([0.0])))
residual = np.sqrt(prob.value)
elif not is_n_attr and is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:]))
residual = np.sqrt(prob.value)
else:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]),
x.value[2:], np.array([0.0])))
residual = np.sqrt(prob.value)
elif self.__triangle_rule and not self.__allow_zeros:
# c_vs <= c_vi + c_vr.
if is_n_attr and is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]]
@@ -572,17 +746,54 @@ class MedianPreimageGenerator(PreimageGenerator):
edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]),
x.value[2:], np.array([0.0])))
residual = np.sqrt(prob.value)

elif self.__ged_options['edit_cost'] == 'CONSTANT': # @todo: node/edge may not labeled.
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0,
np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
if not self.__triangle_rule and self.__allow_zeros:
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif self.__triangle_rule and self.__allow_zeros:
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01,
np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0,
np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif not self.__triangle_rule and not self.__allow_zeros:
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif self.__triangle_rule and not self.__allow_zeros:
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0,
np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
else:
raise Exception('The edit cost "', self.__ged_options['edit_cost'], '" is not supported for update progress.')
# # method 1: simple least square method.
# edit_costs_new, residual, _, _ = np.linalg.lstsq(nb_cost_mat, dis_k_vec,
# rcond=None)


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