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Copy pathlearn_class_bin.py
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2432 lines (1943 loc) · 96.7 KB
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import cplex
from cplex.exceptions import CplexError
import sys
import csv
import math
import getopt
import subprocess
import os
import importlib
import imp
import ntpath
import regtrees as tr
from learn_tree_funcs2 import *
# boolean relaxing all binary and integer variables to be continuous
relaxation = 0
# boolean whether node constraints are relaxed using penalties
relax_node_constraints = 0
# boolean whether to use scores or errors
use_score = 0
# computes all predictions in addition to errors
extra_error_obj = 0
# fix first x layers of tree using CART
fix_layers = 0
# make all non-decision variables boolean
bool_decision = 0
count_max = -1
maxi_depth = 10
forest_bounds = -1
# priority ordering
PRIO = []
FIXED_NODES = dict()
ITER_VARS = dict()
ITER_LEAF = dict()
# create the ILP variables, add to VARS dictionary
def create_variables(depth):
global VARS, PRIO
var_value = 0
VARS = dict()
PRIO = []
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
for l in range(num_leafs):
for l2 in range(num_leafs):
VARS["pair_leafsize_" + str(l) + "_" + str(l2)] = var_value
var_names.append("#" + str(var_value))
var_types = var_types + "C"
var_lb.append(0)
var_ub.append(data_size)
var_obj.append(0.01)
var_value = var_value + 1
# leaf l predicts type s, boolean
for s in range(get_num_targets()):
for l in range(num_leafs):
VARS["prediction_type_" + str(s) + "_" + str(l)] = var_value
PRIO.append((var_value,10,0))
var_names.append("#" + str(var_value))
if not relaxation:
var_types = var_types + "B"
else:
var_types = var_types + "C"
var_lb.append(0)
var_ub.append(1)
var_obj.append(0)
var_value = var_value + 1
for n in range(num_nodes):
# node n had a boolean test on feature f, boolean
for f in range(num_features):
VARS["node_feature_" + str(n) + "_" + str(f)] = var_value
PRIO.append((var_value,101 - 2*get_num_parents(n,num_nodes),0))
var_names.append("#" + str(var_value))
if not relaxation:
var_types = var_types + "B"
else:
var_types = var_types + "C"
var_lb.append(0)
var_ub.append(1)
var_obj.append(0)
var_value = var_value + 1
for n in range(num_nodes):
for i in range(1+int(math.log(max(1,get_max_num_constants())) / math.log(2.))):
#for i in range(get_max_num_constants()-1):
VARS["node_constant_bin_" + str(n) + "_" + str(i)] = var_value
PRIO.append((var_value,100 - 2*get_num_parents(n,num_nodes),0))
var_names.append("#" + str(var_value))
if not relaxation:
var_types = var_types + "B"
else:
var_types = var_types + "C"
var_lb.append(0)
var_ub.append(1)
var_obj.append(0)
var_value = var_value + 1
if relax_node_constraints:
for n in range(num_nodes):
for f in range(num_features):
VARS["node_feature_error_" + str(n) + "_" + str(f)] = var_value
var_names.append("#" + str(var_value))
if not bool_decision:
var_types = var_types + "C"
else:
var_types = var_types + "I"
var_lb.append(0)
var_ub.append(data_size)
var_obj.append(1)
var_value = var_value + 1
for n in range(num_nodes):
for i in range(1+int(math.log(get_max_num_constants()) / math.log(2.))):
VARS["node_constant_error_" + str(n) + "_" + str(i)] = var_value
var_names.append("#" + str(var_value))
if not bool_decision:
var_types = var_types + "C"
else:
var_types = var_types + "I"
var_lb.append(0)
var_ub.append(data_size)
var_obj.append(1)
var_value = var_value + 1
if not use_score:
for d in range(data_size):
for l in range(num_leafs):
VARS["leaf_" + str(d) + "_" + str(l)] = var_value
var_names.append("#" + str(var_value))
if not bool_decision:
var_types = var_types + "C"
else:
var_types = var_types + "B"
var_lb.append(0)
var_ub.append(1)
var_obj.append(0)
var_value = var_value + 1
for d in range(data_size):
for l in range(num_leafs):
VARS["row_error_" + str(d) + "_" + str(l)] = var_value
var_names.append("#" + str(var_value))
if not bool_decision:
var_types = var_types + "C"
else:
var_types = var_types + "B"
var_lb.append(0)
var_ub.append(1)
var_obj.append(1)
var_value = var_value + 1
if extra_error_obj != 1:
for s in range(get_num_targets()):
for l in range(num_leafs):
VARS["error_leaf_" + str(s) + "_" + str(l)] = var_value
var_names.append("#" + str(var_value))
if not bool_decision:
var_types = var_types + "C"
else:
var_types = var_types + "I"
var_lb.append(0)
#var_ub.append(0)
var_ub.append(max([sum([1 for d in range(data_size) if get_target(d) != TARGETS[s2]]) for s2 in range(get_num_targets())]))
var_obj.append(1)
var_value = var_value + 1
else:
for l in range(num_leafs):
for s1 in range(get_num_targets()):
for s2 in range(get_num_targets()):
VARS["target_count_" + str(l) + "_" + str(s1) + "_" + str(s2)] = var_value
var_names.append("#" + str(var_value))
if not bool_decision:
var_types = var_types + "C"
else:
var_types = var_types + "I"
var_lb.append(0)
var_ub.append(get_data_size())
if s1 != s2:
var_obj.append(1)
else:
var_obj.append(0)
var_value = var_value + 1
if use_score:
for d in range(data_size):
for l in range(num_leafs):
VARS["score_" + str(d) + "_" + str(l)] = var_value
var_names.append("#" + str(var_value))
if not bool_decision:
var_types = var_types + "C"
else:
var_types = var_types + "B"
var_lb.append(0)
var_ub.append(1)
var_obj.append(-1)
var_value = var_value + 1
for d in range(data_size):
VARS["scoreC_" + str(d)] = var_value
var_names.append("#" + str(var_value))
if not bool_decision:
var_types = var_types + "C"
else:
var_types = var_types + "B"
var_lb.append(1)
var_ub.append(1)
var_obj.append(1)
var_value = var_value + 1
return var_names, var_types, var_lb, var_ub, var_obj
BIN_MAP = dict()
def get_binbin_ranges(min, max):
if max <= min:
return []
if max - min <= 1:
return [[[], [min, min], [max, max]]]
if min == 0 and max in BIN_MAP:
return BIN_MAP[max]
#print min, max
mid = int(float(max - min) / 2.0)
#if mid - min >= max - mid - 1:
# mid = mid - 1
result = [[[], [min, min + mid], [min + mid + 1, max]]]
for i in get_binbin_ranges(min, min + mid):
result.extend([[[0] + i[0], i[1], i[2]]])
for i in get_binbin_ranges(min + mid + 1, max):
result.extend([[[1] + i[0], i[1], i[2]]])
if min == 0:
BIN_MAP[max] = result
return result
def create_score_binbin_rows(depth, row_value):
num_features = get_num_features()
data_size = get_data_size()
row_names = []
row_values = []
row_right_sides = []
row_senses = ""
num_leafs = 2**depth
num_nodes = num_leafs-1
for d in range(data_size):
col_names = [VARS["score_" + str(d) + "_" + str(l)] for l in range(num_leafs)]
col_values = [1 for l in range(num_leafs)]
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(1)
row_senses = row_senses + "L"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
f = FIXED_NODES[n][0]
v = FIXED_NODES[n][1]
lower_leafs = get_right_leafs(n, num_nodes)
col_names = []
col_values = []
for d in range(data_size):
if get_feature_value(d,f) <= v:
col_names.extend([VARS["score_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(0)
row_senses = row_senses + "L"
row_value = row_value + 1
continue
for f in range(num_features):
lower_leafs = get_right_leafs(n, num_nodes)
#for bin in get_binbin_ranges(0, get_num_constants(f)-1):
# print bin
for bin in get_binbin_ranges(0, get_num_constants(f)-1):
col_names = []
col_values = []
#print "testing: [",get_constant_val(f, bin[1][0]),",",get_constant_val(f, bin[2][0]),"]"
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) >= get_constant_val(f, bin[1][0]) and get_feature_value(d,f) <= get_constant_val(f, bin[2][0]):
col_names.extend([VARS["score_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
num_bin = 0
for i in range(len(bin[0])):
#if bin[0][i] is 0:
# col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
# col_values.extend([max_val])
# num_bin = num_bin + 1
if bin[0][i] is 1:
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
col_values.extend([-max_val])
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([-max_val])
if relax_node_constraints:
col_names.extend([VARS["node_constant_error_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(max_val + max_val * num_bin)
row_senses = row_senses + "L"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
f = FIXED_NODES[n][0]
v = FIXED_NODES[n][1]
lower_leafs = get_left_leafs(n, num_nodes)
col_names = []
col_values = []
for d in range(data_size):
if get_feature_value(d,f) >= v:
col_names.extend([VARS["score_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(0)
row_senses = row_senses + "L"
row_value = row_value + 1
continue
for f in range(num_features):
lower_leafs = get_left_leafs(n, num_nodes)
for bin in get_binbin_ranges(0, get_num_constants(f)-1):
col_names = []
col_values = []
#print "testing: [",get_constant_val(f, bin[1][1]),",",get_constant_val(f, bin[2][1]),"]"
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) >= get_constant_val(f, bin[1][1]) and get_feature_value(d,f) <= get_constant_val(f, bin[2][1]):
col_names.extend([VARS["score_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
num_bin = 0
for i in range(len(bin[0])):
if bin[0][i] is 0:
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
col_values.extend([max_val])
num_bin = num_bin + 1
#if bin[0][i] is 1:
# col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
# col_values.extend([-max_val])
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([max_val])
if relax_node_constraints:
col_names.extend([VARS["node_constant_error_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(2*max_val + max_val * num_bin)
row_senses = row_senses + "L"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
continue
for f in range(num_features):
lower_leafs = get_right_leafs(n, num_nodes)
col_names = []
col_values = []
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) <= get_min_constant_val(f):
col_names.extend([VARS["score_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
if relax_node_constraints:
col_names.extend([VARS["node_feature_error_" + str(n) + "_" + str(f)]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(max_val)
row_senses = row_senses + "L"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
continue
for f in range(num_features):
lower_leafs = get_left_leafs(n, num_nodes)
col_names = []
col_values = []
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) >= get_max_constant_val(f):
col_names.extend([VARS["score_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
if relax_node_constraints:
col_names.extend([VARS["node_feature_error_" + str(n) + "_" + str(f)]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(max_val)
row_senses = row_senses + "L"
row_value = row_value + 1
return row_names, row_values, row_right_sides, row_senses, row_value
def create_error_binbin_rows(depth, row_value):
num_features = get_num_features()
data_size = get_data_size()
row_names = []
row_values = []
row_right_sides = []
row_senses = ""
num_leafs = 2**depth
num_nodes = num_leafs-1
for d in range(data_size):
col_names = [VARS["leaf_" + str(d) + "_" + str(l)] for l in range(num_leafs)]
col_values = [1 for l in range(num_leafs)]
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(1)
row_senses = row_senses + "E"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
f = FIXED_NODES[n][0]
v = FIXED_NODES[n][1]
lower_leafs = get_right_leafs(n, num_nodes)
col_names = []
col_values = []
for d in range(data_size):
if get_feature_value(d,f) <= v:
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(0)
row_senses = row_senses + "L"
row_value = row_value + 1
continue
for f in range(num_features):
lower_leafs = get_right_leafs(n, num_nodes)
#for bin in get_binbin_ranges(0, get_num_constants(f)-1):
# print bin
for bin in get_binbin_ranges(0, get_num_constants(f)-1):
col_names = []
col_values = []
#print "testing: [",get_constant_val(f, bin[1][0]),",",get_constant_val(f, bin[2][0]),"]"
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) >= get_constant_val(f, bin[1][0]) and get_feature_value(d,f) <= get_constant_val(f, bin[2][0]):
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
#for v in range(bin[1][0], bin[2][0]):
#col_names.extend([VARS["leafnum_" + str(l) + "_" + str(f) + "_" + str(v)] for l in lower_leafs])
#col_values.extend([1 for l in lower_leafs])
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
num_bin = 0
for i in range(len(bin[0])):
#if bin[0][i] is 0:
# col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
# col_values.extend([max_val])
# num_bin = num_bin + 1
if bin[0][i] is 1:
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
col_values.extend([-max_val])
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([-max_val])
if relax_node_constraints:
col_names.extend([VARS["node_constant_error_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(max_val + max_val * num_bin)
row_senses = row_senses + "L"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
f = FIXED_NODES[n][0]
v = FIXED_NODES[n][1]
lower_leafs = get_left_leafs(n, num_nodes)
col_names = []
col_values = []
for d in range(data_size):
if get_feature_value(d,f) >= v:
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(0)
row_senses = row_senses + "L"
row_value = row_value + 1
continue
for f in range(num_features):
lower_leafs = get_left_leafs(n, num_nodes)
for bin in get_binbin_ranges(0, get_num_constants(f)-1):
col_names = []
col_values = []
#print "testing: [",get_constant_val(f, bin[1][1]),",",get_constant_val(f, bin[2][1]),"]"
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) >= get_constant_val(f, bin[1][1]) and get_feature_value(d,f) <= get_constant_val(f, bin[2][1]):
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
#for v in range(bin[1][1], bin[2][1]):
# col_names.extend([VARS["leafnum_" + str(l) + "_" + str(f) + "_" + str(v)] for l in lower_leafs])
# col_values.extend([1 for l in lower_leafs])
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
num_bin = 0
for i in range(len(bin[0])):
if bin[0][i] is 0:
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
col_values.extend([max_val])
num_bin = num_bin + 1
#if bin[0][i] is 1:
# col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
# col_values.extend([-max_val])
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([max_val])
if relax_node_constraints:
col_names.extend([VARS["node_constant_error_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(2*max_val + max_val * num_bin)
row_senses = row_senses + "L"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
continue
for f in range(num_features):
left_leafs = get_left_leafs(n, num_nodes)
right_leafs = get_right_leafs(n, num_nodes)
col_names = []
col_values = []
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) >= get_max_constant_val(f):
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in left_leafs])
col_values.extend([1 for l in left_leafs])
max_val = max_val + 1
if get_feature_value(d,f) <= get_min_constant_val(f):
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in right_leafs])
col_values.extend([1 for l in right_leafs])
max_val = max_val + 1
#col_names.extend([VARS["leafmax_" + str(l) + "_" + str(f)] for l in lower_leafs])
#col_values.extend([1 for l in lower_leafs])
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
if relax_node_constraints:
col_names.extend([VARS["node_feature_error_" + str(n) + "_" + str(f)]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(max_val)
row_senses = row_senses + "L"
row_value = row_value + 1
return row_names, row_values, row_right_sides, row_senses, row_value
def create_error_binbin_rows2(depth, row_value):
num_features = get_num_features()
data_size = get_data_size()
row_names = []
row_values = []
row_right_sides = []
row_senses = ""
num_leafs = 2**depth
num_nodes = num_leafs-1
for d in range(data_size):
col_names = [VARS["leaf_" + str(d) + "_" + str(l)] for l in range(num_leafs)]
col_values = [1 for l in range(num_leafs)]
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(1)
row_senses = row_senses + "E"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
f = FIXED_NODES[n][0]
v = FIXED_NODES[n][1]
lower_leafs = get_right_leafs(n, num_nodes)
col_names = []
col_values = []
for d in range(data_size):
if get_feature_value(d,f) <= v:
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(0)
row_senses = row_senses + "L"
row_value = row_value + 1
continue
for f in range(num_features):
lower_leafs = get_right_leafs(n, num_nodes)
#for bin in get_binbin_ranges(0, get_num_constants(f)-1):
# print bin
for bin in get_binbin_ranges(0, get_num_constants(f)-1):
col_names = []
col_values = []
#print "testing: [",get_constant_val(f, bin[1][0]),",",get_constant_val(f, bin[2][0]),"]"
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) >= get_constant_val(f, bin[1][0]) and get_feature_value(d,f) <= get_constant_val(f, bin[2][0]):
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
#for v in range(bin[1][0], bin[2][0]):
#col_names.extend([VARS["leafnum_" + str(l) + "_" + str(f) + "_" + str(v)] for l in lower_leafs])
#col_values.extend([1 for l in lower_leafs])
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
num_bin = 0
for i in range(len(bin[0])):
#if bin[0][i] is 0:
# col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
# col_values.extend([max_val])
# num_bin = num_bin + 1
if bin[0][i] is 1:
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
col_values.extend([-max_val])
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([-max_val])
if relax_node_constraints:
col_names.extend([VARS["node_constant_error_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(max_val + max_val * num_bin)
row_senses = row_senses + "L"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
f = FIXED_NODES[n][0]
v = FIXED_NODES[n][1]
lower_leafs = get_left_leafs(n, num_nodes)
col_names = []
col_values = []
for d in range(data_size):
if get_feature_value(d,f) >= v:
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(0)
row_senses = row_senses + "L"
row_value = row_value + 1
continue
for f in range(num_features):
lower_leafs = get_left_leafs(n, num_nodes)
for bin in get_binbin_ranges(0, get_num_constants(f)-1):
col_names = []
col_values = []
#print "testing: [",get_constant_val(f, bin[1][1]),",",get_constant_val(f, bin[2][1]),"]"
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) >= get_constant_val(f, bin[1][1]) and get_feature_value(d,f) <= get_constant_val(f, bin[2][1]):
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
#for v in range(bin[1][1], bin[2][1]):
# col_names.extend([VARS["leafnum_" + str(l) + "_" + str(f) + "_" + str(v)] for l in lower_leafs])
# col_values.extend([1 for l in lower_leafs])
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
num_bin = 0
for i in range(len(bin[0])):
if bin[0][i] is 0:
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
col_values.extend([max_val])
num_bin = num_bin + 1
#if bin[0][i] is 1:
# col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(i)]])
# col_values.extend([-max_val])
col_names.extend([VARS["node_constant_bin_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([max_val])
if relax_node_constraints:
col_names.extend([VARS["node_constant_error_" + str(n) + "_" + str(len(bin[0]))]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(2*max_val + max_val * num_bin)
row_senses = row_senses + "L"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
continue
for f in range(num_features):
lower_leafs = get_right_leafs(n, num_nodes)
col_names = []
col_values = []
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) <= get_min_constant_val(f):
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
#col_names.extend([VARS["leafmin_" + str(l) + "_" + str(f)] for l in lower_leafs])
#col_values.extend([1 for l in lower_leafs])
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
if relax_node_constraints:
col_names.extend([VARS["node_feature_error_" + str(n) + "_" + str(f)]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(max_val)
row_senses = row_senses + "L"
row_value = row_value + 1
for n in range(num_nodes):
if n in FIXED_NODES:
continue
for f in range(num_features):
lower_leafs = get_left_leafs(n, num_nodes)
col_names = []
col_values = []
max_val = 0
for d in range(data_size):
if get_feature_value(d,f) >= get_max_constant_val(f):
col_names.extend([VARS["leaf_" + str(d) + "_" + str(l)] for l in lower_leafs])
col_values.extend([1 for l in lower_leafs])
max_val = max_val + 1
#col_names.extend([VARS["leafmax_" + str(l) + "_" + str(f)] for l in lower_leafs])
#col_values.extend([1 for l in lower_leafs])
col_names.extend([VARS["node_feature_" + str(n) + "_" + str(f)]])
col_values.extend([max_val])
if relax_node_constraints:
col_names.extend([VARS["node_feature_error_" + str(n) + "_" + str(f)]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(max_val)
row_senses = row_senses + "L"
row_value = row_value + 1
return row_names, row_values, row_right_sides, row_senses, row_value
for d in range(data_size):
for l in range(num_leafs):
col_names = [VARS["leaf_" + str(d) + "_" + str(l)]]
col_values = [1]
col_names.extend([VARS["leaf_div_" + str(l)]])
col_values.extend([-1])
col_names.extend([VARS["row_div_" + str(d) + "_" + str(l)]])
col_values.extend([1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(1)
row_senses = row_senses + "L"
row_value = row_value + 1
for d in range(data_size):
for l in range(num_leafs):
col_names = [VARS["leaf_" + str(d) + "_" + str(l)]]
col_values = [1]
col_names.extend([VARS["leaf_div_" + str(l)]])
col_values.extend([1])
col_names.extend([VARS["row_div_" + str(d) + "_" + str(l)]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(1)
row_senses = row_senses + "L"
row_value = row_value + 1
for d in range(data_size):
for l in range(num_leafs):
col_names = [VARS["leaf_" + str(d) + "_" + str(l)]]
col_values = [-1]
col_names.extend([VARS["row_div_" + str(d) + "_" + str(l)]])
col_values.extend([1])
row_names.append("#" + str(row_value))
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):
col_names = []
col_values = []
col_names.extend([VARS["leaf_prob_" + str(l)]])
col_values.extend([1])
col_names.extend([VARS["row_div_" + str(d) + "_" + str(l)] for d in range(data_size)])
col_values.extend([-1 for d in range(data_size)])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(0)
row_senses = row_senses + "E"
row_value = row_value + 1
for d in range(data_size):
for l in range(num_leafs):
col_names = [VARS["leaf_" + str(d) + "_" + str(l)]]
col_values = [1]
col_names.extend([VARS["leaf_prob_" + str(l)]])
col_values.extend([-1])
col_names.extend([VARS["row_prob_" + str(d) + "_" + str(l)]])
col_values.extend([1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])
row_right_sides.append(1)
row_senses = row_senses + "L"
row_value = row_value + 1
for d in range(data_size):
for l in range(num_leafs):
col_names = [VARS["leaf_" + str(d) + "_" + str(l)]]
col_values = [1]
col_names.extend([VARS["leaf_prob_" + str(l)]])
col_values.extend([1])
col_names.extend([VARS["row_prob_" + str(d) + "_" + str(l)]])
col_values.extend([-1])
row_names.append("#" + str(row_value))
row_values.append([col_names,col_values])