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LocalSearch_IWCSP.py
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LocalSearch_IWCSP.py
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import random
import time
from itertools import combinations
import numpy as np
import argparse
import readInput
from LocalSearchProblem import LocalSearchProblem
class LocalSearch_IWCSP(LocalSearchProblem):
#name: filename
def __init__(self, name, file_path, tabu_list_maxsize, budget, heuristic = None, elicitation_strat = 'ALL'):
path = './'
xmlfile = file_path
incomp = open(path + 'output-Incomp'+'-'+name+'.txt', 'r')
oracle = open(path + 'oracle'+'-'+name+'.txt', 'r')
elicit = open(path + 'elicit'+'-'+name+'.txt', 'r')
self.input = readInput.ReadInput(xmlfile, path, name)
self.varList = (sorted(self.input.varList, reverse=True))
self.domainrange = self.input.nvalues
self.incompTable, variables, varDomain = self.input.readIncomp(incomp)
self.oracleTable = self.input.readOracle(oracle, self.domainrange)
self.elicitationTable = self.input.readElicitationCost(elicit, self.domainrange)
self.budget = budget
self.lbc = min(self.input.allcostList)
self.elicitation_number = 0
self.elicitation_cost = 0
self.elicitation_strat = elicitation_strat
self.tabu_list = []
self.tabu_list_maxsize = tabu_list_maxsize
self.heuristic = heuristic
self.best_val = float('inf')
self.current_assign = self.get_starting_assign()
#choose random starting assignment
def get_starting_assign(self):
starting_dict = {key: random.randint(1, self.domainrange) for key in self.varList}
scopes = self.incompTable.keys()
answer_list = list(starting_dict)
comb = [(str(x) + ' ' + str(y)) for idx, x in enumerate(answer_list) for y in answer_list[idx + 1: ]]
#iterate through constraints and update the incomplete table with elicited values
for scope in comb:
if scope in self.incompTable or scope[::-1] in self.incompTable:
if scope[::-1] in self.incompTable:
scope = scope[::-1]
scope_values = [int(i) for i in scope.split()]
row_cell = starting_dict[scope_values[0]] - 1
column_cell = starting_dict[scope_values[1]] - 1
if self.incompTable[scope][row_cell][column_cell] == '?':
elicited_value = self.oracleTable[scope][row_cell][column_cell]
self.incompTable[scope][row_cell][column_cell] = elicited_value
elicitation_cost = self.elicitationTable[scope][row_cell][column_cell]
self.elicitation_cost += int(elicitation_cost)
self.elicitation_number += 1
self.best_val = self.compute_preference(starting_dict)[0]
return starting_dict
#chooses variable that should be updated in local search
def choose_variable(self):
local_pref_dict = {key: [] for key in self.varList}
scopes = self.incompTable.keys()
answer_list = list(self.current_assign)
comb = [(str(x) + ' ' + str(y)) for idx, x in enumerate(answer_list) for y in answer_list[idx + 1: ]]
for scope in comb:
if scope in self.incompTable or scope[::-1] in self.incompTable:
if scope[::-1] in self.incompTable:
scope = scope[::-1]
scope_values = [int(i) for i in scope.split()]
row_cell = self.current_assign[scope_values[0]] - 1
column_cell = self.current_assign[scope_values[1]] - 1
if self.incompTable[scope][row_cell][column_cell] == '?':
constraint_value = 0
if self.heuristic == 'lbc':
constraint_value = self.lbc
else:
constraint_value = self.incompTable[scope][row_cell][column_cell]
local_pref_dict[scope_values[0]].append(int(constraint_value))
local_pref_dict[scope_values[1]].append(int(constraint_value))
best_variable = None
max_val = float('-inf')
for vars in self.tabu_list:
if vars in local_pref_dict:
del local_pref_dict[vars]
for key in local_pref_dict:
if sum(local_pref_dict[key]) > max_val:
best_variable = key
max_val = sum(local_pref_dict[key])
return best_variable
#chooses value for variable that should be updated in local search
def choose_value_for_variable(self, variable):
if (variable is None):
return
old_value = self.current_assign[variable]
preferences = {}
for value in range(1,self.domainrange+1):
if value != old_value:
new_assign = self.current_assign.copy()
new_assign[variable] = value
preferences[value] = self.compute_preference(new_assign)
#we want the value that minimizes the cost
best_value = None
min = float('inf')
for value in preferences:
if preferences[value][0] < min:
best_value = value
min = preferences[value][0]
elif (preferences[value][0] == min):
if (preferences[value][1] < preferences[best_value][1]):
best_value = value
min = preferences[value][0]
return best_value
#computes the preference of a given assignment
def compute_preference(self, assignment, elicit = False):
scopes = self.incompTable.keys()
answer_list = list(assignment)
comb = [(str(x) + ' ' + str(y)) for idx, x in enumerate(answer_list) for y in answer_list[idx + 1: ]]
preference_val = 0
count = 0
#array, dict for elicit constraint values
elicit_value_list = []
elicit_dict_value = {}
#array, dict for elicit cost values
elicit_cost_list = []
elicit_dict_cost = {}
for scope in comb:
if scope in self.incompTable or scope[::-1] in self.incompTable:
if scope[::-1] in self.incompTable:
scope = scope[::-1]
scope_values = [int(i) for i in scope.split()]
row_cell = assignment[scope_values[0]] - 1
column_cell = assignment[scope_values[1]] - 1
if (elicit):
if self.incompTable[scope][row_cell][column_cell] == '?':
#elicited values from oracle table
elicited_value = self.oracleTable[scope][row_cell][column_cell]
elicit_value_list.append(int(elicited_value))
elicit_dict_value[len(elicit_value_list)-1] = [scope, row_cell, column_cell]
#elicited costs
elicited_cost = self.elicitationTable[scope][row_cell][column_cell]
elicit_cost_list.append(int(elicited_cost))
elicit_dict_cost[len(elicit_cost_list)-1] = [scope, row_cell, column_cell]
else:
constraint_value = int(self.incompTable[scope][row_cell][column_cell])
preference_val += constraint_value
else:
if self.incompTable[scope][row_cell][column_cell] != '?':
constraint_value = int(self.incompTable[scope][row_cell][column_cell])
preference_val += constraint_value
else:
if self.heuristic == 'lbc':
preference_val += self.lbc
count += 1
if (elicit):
if (self.elicitation_cost + sum(elicit_cost_list)) < self.budget:
#We elicit all missing values
if (self.elicitation_strat == "ALL"):
index = 0
while ((index < len(elicit_value_list))):
scope = elicit_dict_cost[index][0]
row_cell = elicit_dict_cost[index][1]
column_cell = elicit_dict_cost[index][2]
#get elicitation cost
elicitation_cost = self.elicitationTable[scope][row_cell][column_cell]
self.elicitation_cost += int(elicitation_cost)
#get elicitated value
self.incompTable[scope][row_cell][column_cell] = self.oracleTable[scope][row_cell][column_cell]
self.elicitation_number += 1
constraint_value = int(self.incompTable[scope][row_cell][column_cell])
preference_val += constraint_value
index += 1
#We only elicit until we either have an optimal assignment, or the assignment turns out to be worse
if (self.elicitation_strat == "WW"):
sorted_indices = np.argsort(elicit_value_list)[::-1]
index = 0
while ((preference_val < self.best_val) and (index < len(sorted_indices))):
val = sorted_indices[index]
scope = elicit_dict_value[val][0]
row_cell = elicit_dict_value[val][1]
column_cell = elicit_dict_value[val][2]
#get elicitation cost
elicitation_cost = self.elicitationTable[scope][row_cell][column_cell]
self.elicitation_cost += int(elicitation_cost)
#get elicitated value
self.incompTable[scope][row_cell][column_cell] = self.oracleTable[scope][row_cell][column_cell]
self.elicitation_number += 1
constraint_value = int(self.incompTable[scope][row_cell][column_cell])
preference_val += constraint_value
index += 1
if (self.elicitation_strat == "BB"):
sorted_indices = np.argsort(elicit_value_list)
index = 0
while ((preference_val < self.best_val) and (index < len(sorted_indices))):
val = sorted_indices[index]
scope = elicit_dict_value[val][0]
row_cell = elicit_dict_value[val][1]
column_cell = elicit_dict_value[val][2]
#get elicitation cost
elicitation_cost = self.elicitationTable[scope][row_cell][column_cell]
self.elicitation_cost += int(elicitation_cost)
#get elicitated value
self.incompTable[scope][row_cell][column_cell] = self.oracleTable[scope][row_cell][column_cell]
self.elicitation_number += 1
constraint_value = int(self.incompTable[scope][row_cell][column_cell])
preference_val += constraint_value
index += 1
if (self.elicitation_strat == "BM"):
elicit_combined_list = [elicit_value_list[i]+elicit_cost_list[i] for i in range(len(elicit_value_list))]
sorted_indices = np.argsort(elicit_combined_list)
index = 0
while ((preference_val < self.best_val) and (index < len(sorted_indices))):
val = sorted_indices[index]
scope = elicit_dict_value[val][0]
row_cell = elicit_dict_value[val][1]
column_cell = elicit_dict_value[val][2]
#get elicitation cost
elicitation_cost = self.elicitationTable[scope][row_cell][column_cell]
self.elicitation_cost += int(elicitation_cost)
#get elicitated value
self.incompTable[scope][row_cell][column_cell] = self.oracleTable[scope][row_cell][column_cell]
self.elicitation_number += 1
constraint_value = int(self.incompTable[scope][row_cell][column_cell])
preference_val += constraint_value
index += 1
if (self.elicitation_strat == "MM"):
sorted_indices = np.argsort(elicit_cost_list)
index = 0
while ((preference_val < self.best_val) and (index < len(sorted_indices))):
val = sorted_indices[index]
scope = elicit_dict_value[val][0]
row_cell = elicit_dict_value[val][1]
column_cell = elicit_dict_value[val][2]
#get elicitation cost
elicitation_cost = self.elicitationTable[scope][row_cell][column_cell]
self.elicitation_cost += int(elicitation_cost)
#get elicitated value
self.incompTable[scope][row_cell][column_cell] = self.oracleTable[scope][row_cell][column_cell]
self.elicitation_number += 1
constraint_value = int(self.incompTable[scope][row_cell][column_cell])
preference_val += constraint_value
index += 1
if (self.elicitation_strat == "WM"):
elicit_combined_list = [elicit_value_list[i]+elicit_cost_list[i] for i in range(len(elicit_value_list))]
sorted_indices = np.argsort(elicit_combined_list)
sorted_indices = sorted_indices[::-1]
index = 0
while ((preference_val < self.best_val) and (index < len(sorted_indices))):
val = sorted_indices[index]
scope = elicit_dict_value[val][0]
row_cell = elicit_dict_value[val][1]
column_cell = elicit_dict_value[val][2]
#get elicitation cost
elicitation_cost = self.elicitationTable[scope][row_cell][column_cell]
self.elicitation_cost += int(elicitation_cost)
#get elicitated value
self.incompTable[scope][row_cell][column_cell] = self.oracleTable[scope][row_cell][column_cell]
self.elicitation_number += 1
constraint_value = int(self.incompTable[scope][row_cell][column_cell])
preference_val += constraint_value
index += 1
if (self.elicitation_strat == "BW"):
elicit_combined_list = [elicit_value_list[i]+elicit_cost_list[i] for i in range(len(elicit_value_list))]
sorted_indices = np.argsort(elicit_combined_list)
first = 0
switch = 0
last = len(sorted_indices) - 1
while ((preference_val < self.best_val) and (first <= last)):
if ((switch % 2) == 0):
index = first
first += 1
else:
index = last
last -= 1
val = sorted_indices[index]
scope = elicit_dict_value[val][0]
row_cell = elicit_dict_value[val][1]
column_cell = elicit_dict_value[val][2]
#get elicitation cost
elicitation_cost = self.elicitationTable[scope][row_cell][column_cell]
self.elicitation_cost += int(elicitation_cost)
#get elicitated value
self.incompTable[scope][row_cell][column_cell] = self.oracleTable[scope][row_cell][column_cell]
self.elicitation_number += 1
constraint_value = int(self.incompTable[scope][row_cell][column_cell])
preference_val += constraint_value
else:
#right now return this however we should come up with a new elicitation strategy
return([float('inf'), 0])
return ([preference_val, count])
#updates assignment
def update_assign(self, variable, value):
new_assign = self.current_assign.copy()
new_assign[variable] = value
if (variable is None):
self.tabu_list.append(variable)
if (len(self.tabu_list) > self.tabu_list_maxsize):
self.tabu_list.pop(0)
return
new_val = self.compute_preference(new_assign, elicit = True)[0]
#compares two lists using lexigraphical ordering
if new_val < self.best_val:
#print ("changed variable!")
self.current_assign[variable] = value
self.best_val = new_val
else:
if variable not in self.tabu_list:
#print ("added tabu")
self.tabu_list.append(variable)
if (len(self.tabu_list) > self.tabu_list_maxsize):
self.tabu_list.pop(0)
def solve(self, iterations = 0, p = 0.00):
for i in range(0, iterations):
if (self.elicitation_cost > self.budget):
break
random_step_chance = random.random()
if (random_step_chance > p):
var = self.choose_variable()
else:
var = random.choice(self.varList)
value = self.choose_value_for_variable(var)
self.update_assign(var, value)
return self.current_assign
def define_parser():
parser = argparse.ArgumentParser(description='Process Arguments For Local Search.')
parser.add_argument('--iterations', type=int,
help='an integer for the number of iterations')
parser.add_argument('--budget', type=int,
help='an integer for the budget')
parser.add_argument('--flag', type=int,
help='a flag to use elicitation cost')
parser.add_argument('--original', type=int,
help='a flag to go back to the original proble. We can only use BB, WW, and ALL elicitation strategies')
parser.add_argument('--strategy', type=str,
help='an elicitation strategy to use')
parser.add_argument('--filepath', type=str,
help='the filepath to a problem')
return (parser)
def main():
preferences = []
runtimes = []
elicitation_cost = []
elicitation_numbers = []
parser = define_parser()
args = parser.parse_args()
iterations = args.iterations
elicitation_strat = args.strategy
filepath = args.filepath
if (args.flag == 1):
budget = args.budget
else:
budget = float('inf')
if (args.original == 1):
if (elicitation_strat != "WW" and elicitation_strat != "ALL"
and elicitation_strat != "BB" and elicitation_strat != "BW"):
elicitation_strat = "ALL"
budget = float('inf')
runs = 10
for i in range(0,runs):
start = time.time()
LSP = LocalSearch_IWCSP(name = '1', file_path = filepath, tabu_list_maxsize = 1000, elicitation_strat = elicitation_strat, budget = budget)
LSP.solve(iterations = iterations, p = 0.20)
end = time.time()
runtime = end - start
preferences.append(LSP.compute_preference(LSP.current_assign)[0])
runtimes.append(runtime)
elicitation_cost.append(LSP.elicitation_cost)
elicitation_numbers.append(LSP.elicitation_number)
print ('number of runs: ' + str(runs))
print ('Elicitation Strategy: ' + elicitation_strat)
print ('preference: ' + str((sum(preferences)/ len(preferences))))
print ('runtime: ' + str((sum(runtimes)/ len(runtimes))))
print ('elicitation_cost: ' + str((sum(elicitation_cost)/ len(elicitation_cost))))
print ('number of elicitations: ' + str((sum(elicitation_numbers)/ len(elicitation_numbers))))
print ('final assignment: ' + str(LSP.current_assign))
if __name__ == "__main__":
main()