def create_rows(depth, C_set):
global TARGETS
row_value = 0
row_names = []
row_values = []
row_right_sides = []
row_senses = ""
num_features = get_num_features()
data_size = get_data_size()
num_leafs = 2**depth
num_nodes = num_leafs - 1
for r in range(data_size): #constraint (1)
for j in range(num_nodes):
col_names = [
"x_" + str(l) + "_" + str(r)
for l in get_left_leafs(j, num_nodes)
]
col_values = [-1 for l in get_left_leafs(j, num_nodes)]
for i in range(num_features):
for v in range(len(C_set[i])):
if get_feature_value(r, i) > C_set[i][v]:
col_names.append("rho_" + str(i) + "_" + str(j) + "_" +
str(v))
col_values.append(-1)
for j2 in get_parents(j, depth):
for i in range(num_features):
for v in range(len(C_set[i])):
if get_feature_value(
r, i) <= C_set[i][v] and j in get_left_nodes(
j2, num_nodes):
col_names.append("rho_" + str(i) + "_" + str(j2) +
"_" + str(v))
col_values.append(1)
if get_feature_value(
r, i) > C_set[i][v] and j in get_right_nodes(
j2, num_nodes):
col_names.append("rho_" + str(i) + "_" + str(j2) +
"_" + str(v))
col_values.append(1)
row_names.append("constraint_1_" + str(r) + "_" + str(j))
row_values.append([col_names, col_values])
row_right_sides.append(get_depth(j, num_nodes) - 2)
row_senses = row_senses + "L"
row_value = row_value + 1
for r in range(data_size): #constraint (1bis)
for j in range(num_nodes):
col_names = [
"x_" + str(l) + "_" + str(r)
for l in get_right_leafs(j, num_nodes)
]
col_values = [-1 for l in get_right_leafs(j, num_nodes)]
for i in range(num_features):
for v in range(len(C_set[i])):
if get_feature_value(r, i) <= C_set[i][v]:
col_names.append("rho_" + str(i) + "_" + str(j) + "_" +
str(v))
col_values.append(-1)
for j2 in get_parents(j, depth):
for i in range(num_features):
for v in range(len(C_set[i])):
if get_feature_value(
r, i) <= C_set[i][v] and j in get_left_nodes(
j2, num_nodes):
col_names.append("rho_" + str(i) + "_" + str(j2) +
"_" + str(v))
col_values.append(1)
if get_feature_value(
r, i) > C_set[i][v] and j in get_right_nodes(
j2, num_nodes):
col_names.append("rho_" + str(i) + "_" + str(j2) +
"_" + str(v))
col_values.append(1)
row_names.append("constraint_1bis_" + str(r) + "_" + str(j))
row_values.append([col_names, col_values])
row_right_sides.append(get_depth(j, num_nodes) - 2)
row_senses = row_senses + "L"
row_value = row_value + 1
for j in range(num_nodes): #constraint (2)
col_names, col_values = [], []
for i in range(num_features):
for v in range(len(C_set[i])):
col_names.append("rho_" + str(i) + "_" + str(j) + "_" + str(v))
col_values.append(1)
row_names.append("constraint_2_" + str(j))
row_values.append([col_names, col_values])
row_right_sides.append(1)
row_senses = row_senses + "E"
row_value = row_value + 1
for r in range(data_size): #constraint (3)
for l in range(num_leafs):
col_names, col_values = [], []
col_names.append("x_" + str(l) + "_" + str(r)) #x_{l,s}
col_values.append(1)
for t in range(get_num_targets()):
if TARGETS[t] == get_target(r):
col_names.append("prediction_type_" + str(l) + "_" +
str(t))
col_values.append(-1)
col_names.append("row_error_" + str(r))
col_values.append(-1)
row_names.append("constraint_3_" + str(r) + "_" + str(l))
row_values.append([col_names, col_values])
row_right_sides.append(0)
row_senses = row_senses + "L"
row_value = row_value + 1
for l in range(num_leafs): #constraint (4)
col_names = [
"prediction_type_" + str(l) + "_" + str(t)
for t in range(get_num_targets())
]
col_values = [1 for t in range(get_num_targets())]
row_names.append("constraint_4_" + str(l))
row_values.append([col_names, col_values])
row_right_sides.append(1)
row_senses = row_senses + "E"
row_value = row_value + 1
for r in range(data_size): #constraint (4)
col_names = ["x_" + str(l) + "_" + str(r) for l in range(num_leafs)]
col_values = [1 for l in range(num_leafs)]
row_names.append("constraint_4_" + str(l))
row_values.append([col_names, col_values])
row_right_sides.append(1)
row_senses = row_senses + "E"
row_value = row_value + 1
return row_names, row_values, row_right_sides, row_senses
def create_variables_pricing_all_at_once(depth, master_prob):
var_value = 0
var_names = []
var_types = ""
var_lb = []
var_ub = []
var_obj = []
num_features = get_num_features()
data_size = get_data_size()
num_leafs = 2**depth
num_nodes = num_leafs - 1
#compute useful sums of dual values
A_i_l, B_i_l = [[] for l in range(num_leafs)], [[]
for l in range(num_leafs)]
for leaf in range(num_leafs):
for i in range(num_features):
s = 0
for j in range(num_nodes):
left_leaves = get_left_leafs(j, num_nodes)
if leaf in left_leaves:
s = s + master_prob.solution.get_dual_values(
"constraint_15_" + str(i) + "_" + str(j) + "_" +
str(leaf))
#print(s)
if s > 0:
print(s)
input("STOP B")
B_i_l[leaf].append(-s)
for i in range(num_features):
s = 0
for j in range(num_nodes):
right_leaves = get_right_leafs(j, num_nodes)
if leaf in right_leaves:
s = s + master_prob.solution.get_dual_values(
"constraint_16_" + str(i) + "_" + str(j) + "_" +
str(leaf))
#print(s)
if s > 0:
print(s)
input("STOP A")
A_i_l[leaf].append(-s)
print("SUM DUALS :", A_i_l, " ", B_i_l)
# z_{r,l}, main decision variables
for leaf in range(num_leafs):
#print("SUM0",sum([duals[constraint_indicators[2] + r2] + duals[constraint_indicators[4] + r2*num_leafs] for r2 in [0, 1, 4, 8, 11, 15, 16, 17, 18, 19, 21, 23, 24, 27, 28, 29]])+duals[constraint_indicators[3]])
#print("SUM1",sum([duals[constraint_indicators[2] + r3] + duals[constraint_indicators[4] + r3*num_leafs + 1] for r3 in [2, 3, 5, 6, 7, 9, 10, 12, 13, 14, 20, 22, 25, 26]])+duals[constraint_indicators[3]+1])
for r in range(data_size):
var_names.append("row_" + str(leaf) + "_" + str(r))
var_types = var_types + "B"
var_lb.append(0)
var_ub.append(1)
#print(r,leaf,"C_{r,l} ",duals[constraint_indicators[2] + r] + duals[constraint_indicators[4] + r*num_leafs + leaf])
var_obj.append(
master_prob.solution.get_dual_values("constraint_17_" +
str(r)) +
master_prob.solution.get_dual_values("constraint_19_" +
str(r) + "_" + str(leaf)))
var_value = var_value + 1
# kappa_{i,r,l}, indicate the min feature of row r
for leaf in range(num_leafs):
for i in range(num_features):
for r in range(data_size):
var_names.append("kappa_" + str(leaf) + "_" + str(r) + "_" +
str(i))
var_types = var_types + "B"
var_lb.append(0)
var_ub.append(1)
var_obj.append(A_i_l[leaf][i] * get_feature_value(r, i))
var_value = var_value + 1
# omega_{i,r,l}, indicate the max feature of row r
for leaf in range(num_leafs):
for i in range(num_features):
for r in range(data_size):
var_names.append("omega_" + str(leaf) + "_" + str(r) + "_" +
str(i))
var_types = var_types + "B"
var_lb.append(0)
var_ub.append(1)
var_obj.append(-B_i_l[leaf][i] * get_feature_value(r, i))
var_value = var_value + 1
return var_names, var_types, var_lb, var_ub, var_obj
