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solver_2OPT_sortGA_input.py
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solver_2OPT_sortGA_input.py
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from parse import read_input_file, write_output_file
import os
import random
import math
from exp_utils import get_logger
from Task import Task
import datetime
import numpy as np
import pickle
import time
from get_rank import get_rank
total_start = time.time()
# random.seed(123)
work_dir = "./logs"
now = datetime.datetime.now()
logging = get_logger(os.path.join(work_dir, now.strftime('%Y-%m-%d %H%M%S') + ' log.txt'))
total_benefit = 0
# Load optimal output
opt_dict = {}
if os.path.exists("optimum_output.pickle"):
with open("optimum_output.pickle", "rb") as f:
opt_dict = pickle.load(f)
def solve(tasks, input_path):
"""
Args:
tasks: list[Task], list of igloos to polish
Returns:
output: list of igloos in order of polishing
"""
############################################## CONFIG ##############################################
global opt_dict
MAX_TIME = 1440
opt = opt_dict.get(input_path, [None, float('-inf')])
best_plan = opt[0]
best_plan_benefit = opt[1]
# best_plan_benefit = 0
temp_best_plan = []
temp_best_benefit = 0
improvement = 0
####################################################################################################
epoch_idx = 0
def fitness(output_tasks, tasks):
assert len(output_tasks) == len(set(output_tasks)), "output_tasks contain duplicates!"
MAX_TIME = 1440
time_cum = 0
benefit_cum = 0
idx = 0
while idx < len(output_tasks) and time_cum + tasks[output_tasks[idx] - 1].duration <= MAX_TIME:
id = output_tasks[idx] - 1
time_cum = time_cum + tasks[id].duration
if time_cum <= tasks[id].deadline:
benefit_cum += tasks[id].perfect_benefit
else:
benefit_cum += tasks[id].perfect_benefit * math.exp(-0.0170 * (time_cum - tasks[id].deadline))
idx += 1
return benefit_cum
def postprocessing(output_tasks, tasks):
idx = 0
MAX_TIME = 1440
time_cum = 0
processed_output_taskId = []
while idx < len(output_tasks) and time_cum + tasks[output_tasks[idx] - 1].duration <= MAX_TIME:
id = output_tasks[idx] - 1
time_cum = time_cum + tasks[id].duration
processed_output_taskId.append(tasks[id].task_id)
idx += 1
# print(fitness(processed_output_taskId))
return processed_output_taskId
same = 0
count = 0
############################## Initial Input ################################################
# curr_output_tasks = [i for i in range(1, len(tasks)+1)]
# random.shuffle(curr_output_tasks)
tasks_greedy = sorted(tasks, key = lambda task: (round(-task.perfect_benefit / task.duration, 1), task.deadline))
curr_output_tasks = [task.task_id for task in tasks_greedy]
############################## TO CHANGE ####################################################
early_abort_epoch = 5
start = time.time()
while True:
curr_benefit = fitness(curr_output_tasks, tasks)
exit_curr_loop = False
i, j, k = random.sample(range(1, len(tasks)), 3)
curr_output_tasks[i], curr_output_tasks[j], curr_output_tasks[k] = curr_output_tasks[k], curr_output_tasks[i], curr_output_tasks[j]
while True:
curr_benefit = fitness(curr_output_tasks, tasks)
for i in range(len(tasks)):
for j in range(i+1, len(tasks)):
less_raito = tasks[curr_output_tasks[j]-1].get_benefit_over_duration_ratio() < tasks[curr_output_tasks[i]-1].get_benefit_over_duration_ratio()
later_ddl = tasks[curr_output_tasks[j]-1].deadline > tasks[curr_output_tasks[i]-1].deadline
if less_raito and later_ddl:
continue
new_output_task = curr_output_tasks[:]
new_output_task[i], new_output_task[j] = new_output_task[j], new_output_task[i]
new_benefit = fitness(new_output_task, tasks)
if new_benefit > curr_benefit:
curr_output_tasks = new_output_task[:]
curr_benefit = new_benefit
exit_curr_loop = True
break
if exit_curr_loop:
break
if exit_curr_loop == False:
break
epoch_idx += 1
exit_curr_loop = False
if curr_benefit > temp_best_benefit:
temp_best_benefit = curr_benefit
temp_best_plan = curr_output_tasks[:]
same = 0
else:
same += 1
if same > early_abort_epoch:
break
# end = time.time()
# elapsed = end - start
count = count + 1
# print(f"{count}. epoch: {epoch_idx} benefit: {curr_benefit} time: {elapsed} best: {temp_best_benefit}")
epoch_idx = 0
end = time.time()
elapsed = end - start
best_plan_benefit = fitness(best_plan, tasks)
improvement = max(temp_best_benefit - best_plan_benefit, 0)
if improvement > 0:
best_plan_benefit = temp_best_benefit
best_plan = postprocessing(temp_best_plan[:], tasks)
opt_dict[input_path] = (best_plan[:], best_plan_benefit)
print(f" Overall best: {best_plan_benefit} Curr best: {temp_best_benefit} Improved: {improvement} Time: {elapsed}")
return best_plan, best_plan_benefit
inputs_categories = ["large", "small", "medium"]
optimized_input = { "small-5.in", "small-54.in","small-57.in", "small-75.in",
"small-111.in", "small-266.in",
"medium-69.in", "medium-75.in", "medium-77.in", "medium-85.in",
"medium-111.in", "medium-132.in", "medium-139.in", "medium-192.in", "medium-267.in",
"large-5.in", "large-57.in", "large-69.in", "large-75.in",
"large-132.in" , "large-139.in", "large-192.in" }
_, not_first, ten_plus, twenty_plus = get_rank(team_name="22222222222222222222222222222222")
task_idx = 1
while True:
# for inputs_category in inputs_categories:
# for file_name in os.listdir(os.path.join('inputs/', inputs_category)):
for file_name in not_first:
if file_name[0] == ".":
continue
if (file_name in optimized_input):
continue
input_path = 'inputs/' + file_name.split('-')[0] + "/" + file_name + ".in"
print(f"{task_idx}. {file_name}")
output_path = 'outputs/' + file_name.split('-')[0] + "/" + file_name + '.out'
tasks = read_input_file(input_path)
output, benefit = solve(tasks, input_path)
total_benefit = total_benefit + benefit
with open('optimum_output.pickle', 'wb') as f:
pickle.dump(opt_dict, f)
write_output_file(output_path, output)
task_idx += 1
total_end = time.time()
total_elapsed = total_end - total_start
print(f"DONE! Total Benifit: {total_benefit} Total Time: {total_elapsed}")
total_benefit = 0
# task_idx = 1
# while True:
# total_benefit = 0
# inputs_category = "small"
# file_name = "small-27.in"
# input_path = 'inputs/' + inputs_category + "/" + file_name
# print(f"task {task_idx}: {input_path}")
# output_path = 'outputs/' + inputs_category + "/" + file_name[:-3] + '.out'
# tasks = read_input_file(input_path)
# output, benefit= solve(tasks, input_path)
# total_benefit = total_benefit + benefit
# with open('optimum_output.pickle', 'wb') as f:
# pickle.dump(opt_dict, f)
# write_output_file(output_path, output)
# task_idx += 1
# Here's an example of how to run your solver.
# if __name__ == '__main__':
# for input_path in os.listdir('inputs/'):
# output_path = 'outputs/' + input_path[:-3] + '.out'
# tasks = read_input_file(input_path)
# output = solve(tasks)
# write_output_file(output_path, output)