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tabusearch_submit.py
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tabusearch_submit.py
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import argparse
import logging
import math
import os
import time
from copy import deepcopy
from random import choice, randint, random
import itertools
import glob
import numpy as np
################################# Tabu Search ######################################################
from abc import ABCMeta, abstractmethod
from copy import deepcopy
from collections import deque
from numpy import argmax
class TabuSearch:
"""
Conducts tabu search
"""
__metaclass__ = ABCMeta
cur_steps = None
neighborhood_size = None
tabu_size = None
tabu_list = None
initial_state = None
current = None
best = None
max_steps = None
max_score = None
def __init__(self, initial_state, tabu_tenure, max_steps, neighborhood_size,
constraints = [0, 1, 2, 3], print_interval=100, max_score=None):
"""
:param initial_state: initial state, should implement __eq__ or __cmp__
:param tabu_size: number of states to keep in tabu list
:param max_steps: maximum number of steps to run algorithm for
:param max_score: score to stop algorithm once reached
"""
self.initial_state = initial_state
if isinstance(tabu_tenure, int) and tabu_tenure > 0:
self.tabu_size = tabu_tenure
else:
raise TypeError('Tabu size must be a positive integer')
if isinstance(max_steps, int) and max_steps > 0:
self.max_steps = max_steps
else:
raise TypeError('Maximum steps must be a positive integer')
if isinstance(neighborhood_size, int) and tabu_tenure > 0:
self.neighborhood_size = neighborhood_size
else:
raise TypeError('Neighborhood size must be a positive integer')
if isinstance(print_interval, int):
self.print_interval = print_interval
else:
raise TypeError('Interval must be a positive integer')
self.constraints = constraints
if max_score is not None:
if isinstance(max_score, (int, float)):
self.max_score = float(max_score)
else:
raise TypeError('Maximum score must be a numeric type')
def __str__(self):
return ('TABU SEARCH: \n' +
'CURRENT STEPS: %d \n' +
'CURRENT SCORE: %d \n' +
'BEST SCORE: %f \n' +
'BEST MEMBER: %s \n\n') % \
(self.cur_steps, self._score(self.current), self._score(self.best), str(self.best))
def __repr__(self):
return self.__str__()
def _clear(self):
"""
Resets the variables that are altered on a per-run basis of the algorithm
:return: None
"""
self.cur_steps = 0
self.tabu_list = deque(maxlen=self.tabu_size)
self.current = self.initial_state
self.best = self.initial_state
@abstractmethod
def _score(self, state):
"""
Returns objective function value of a state
:param state: a state
:return: objective function value of state
"""
pass
@abstractmethod
def _neighborhood(self):
"""
Returns list of all members of neighborhood of current state, given self.current
:return: list of members of neighborhood, changed attributes
"""
pass
def _best(self, neighborhood, attribute_change):
"""
Finds the best member of a neighborhood
:param neighborhood: a neighborhood
:return: best member of neighborhood
"""
indices = argmax([self._score(x) for x in neighborhood])
return neighborhood[indices], attribute_change[indices]
def run(self, verbose=True):
"""
Conducts tabu search
:param verbose: indicates whether or not to print progress regularly
:return: best state and objective function value of best state
"""
self._clear()
for i in range(self.max_steps):
self.cur_steps += 1
if ((i + 1) % self.print_interval == 0) and verbose:
print(self)
neighborhood, attribute_change = self._neighborhood()
neighborhood_best, attribute_change_best = self._best(neighborhood, attribute_change)
while True:
if all([x in self.tabu_list for x in attribute_change]):
print("TERMINATING - NO SUITABLE NEIGHBORS")
return self.best, self._score(self.best)
if attribute_change_best in self.tabu_list:
# aspriration criteria
if self._score(neighborhood_best) > self._score(self.best):
self.tabu_list.append(attribute_change_best)
self.best = deepcopy(neighborhood_best)
break
else:
neighborhood.remove(neighborhood_best)
attribute_change.remove(attribute_change_best)
neighborhood_best, attribute_change_best = self._best(neighborhood, attribute_change)
else:
self.tabu_list.append(attribute_change_best)
self.current = neighborhood_best
if self._score(self.current) > self._score(self.best):
self.best = deepcopy(self.current)
break
# print(self.tabu_list)
if self.max_score is not None and self._score(self.best) >= self.max_score:
if verbose:
print("TERMINATING - REACHED MAXIMUM SCORE")
return self.best, self._score(self.best)
if verbose:
print("TERMINATING - REACHED MAXIMUM STEPS")
return self.best, self._score(self.best)
##################################################################################################
# Constraint
# assignments (Class, Subject, Start time, Teacher)
# Thời gian bắt đầu, kết thúc phải cùng buổi
def check_same_session_time(assignments):
time_violation = 0
for assignment in assignments:
if(assignment[2]) == 0:
continue
# start_time // 6 - end_time // 6 = 0
start_time_session = (assignment[2] - 1) // 6
end_time_session = ((assignment[2] + subject_periods[assignment[1]]) - 2) // 6
if start_time_session != end_time_session:
# return False
time_violation += 1
# return True
return time_violation
# Các lớp-môn mà cùng giáo viên dạy không trùng lịch
def check_teacher_schedule_conflicts(assignments):
time_violation = 0
# Mang luu trang thai cua cac giao vien tu tiet 1-60
teacher_periods = np.zeros((T + 1, 61), dtype=int)
for assignment in assignments:
if assignment[3] == 0:
continue
start_time = assignment[2]
end_time = (assignment[2] + subject_periods[assignment[1]]) - 1
teacher = assignment[3]
if np.all(teacher_periods[teacher, start_time: end_time + 1] == 0):
teacher_periods[teacher, start_time: end_time + 1] = 1
else:
# return False
time_violation += 1
# return True
return time_violation
# Các môn của cùng lớp không trùng lịch
def check_class_schedule_conflicts(assignments):
time_violation = 0
# Mang luu trang thai cua cac lop tu tiet 1-60
classtable = np.zeros((N + 1, 61), dtype=int)
for assignment in assignments:
if assignment[2] == 0:
continue
class_n = assignment[0]
start_time = assignment[2]
end_time = (assignment[2] + subject_periods[assignment[1]]) - 1
if np.all(classtable[class_n, start_time: end_time + 1] == 0):
classtable[class_n, start_time: end_time + 1] = 1
else:
# return False
time_violation += 1
# return True
return time_violation
# Thời gian kết thúc không vượt quá 60 tiết
def check_end_time_limit(assignments):
time_violation = 0
for assignment in assignments:
# start_time // 6 - end_time // 6 = 0
# start_time_session = assignment[2]
end_time_session = ((assignment[2] + subject_periods[assignment[1]]) - 1)
if end_time_session > 60:
# return False
time_violation += 1
# return True
return time_violation
# Change assignment
def change_teacher(assignment):
assignable_teacher = subject_teachers[assignment[1]]
if len(assignable_teacher) == 0:
return True
if len(assignable_teacher) == 1 and assignment[3] != 0:
return
new_teacher = assignment[3]
while new_teacher == assignment[3]:
new_teacher = choice(assignable_teacher)
assignment[3] = new_teacher
def change_time(assignment):
new_start_time = assignment[2]
subject = assignment[1]
while new_start_time == assignment[2]:
new_start_time = choice(subject_times[subject])
assignment[2] = new_start_time
def change_both(assignment):
if change_teacher(assignment) is not None:
return
change_time(assignment)
CHANGE_STRATEGY = [change_teacher, change_time, change_both]
# Algorithms
class Algorithm(TabuSearch):
"""
Tries to get a randomly-generated classtable
"""
def _neighborhood(self):
neighborhood = []
attribute_change = []
for _ in range(self.neighborhood_size):
neighbor = deepcopy(self.current)
# Find neighbor by randomly change start_time or teacher or both in one random class-subject
choice = randint(0, len(neighbor) - 1)
candidate = neighbor[choice]
if candidate[2] * candidate[3] == 0:
change_strategy_choice = 2
else:
change_strategy_choice = randint(0, 2)
CHANGE_STRATEGY[change_strategy_choice](candidate)
neighborhood.append(neighbor)
# attribute_change.append((choice, candidate[2], candidate[3]))
attribute_change.append((choice))
return neighborhood, attribute_change
def _score(self, assignments):
score = 0
# Penalty
if 0 in self.constraints:
session_violations = check_same_session_time(assignments)
else :
session_violations = 0
if 1 in self.constraints:
class_violations = check_class_schedule_conflicts(assignments)
else:
class_violations = 0
if 2 in self.constraints:
teacher_violations = check_teacher_schedule_conflicts(assignments)
else:
teacher_violations = 0
if 3 in self.constraints:
endtimelimit_violations = check_end_time_limit(assignments)
else:
endtimelimit_violations = 0
score -= 100 * (session_violations + class_violations + teacher_violations + endtimelimit_violations)
# Score
for assignment in assignments:
if assignment[2] and assignment[3]:
score += 1
return score
def initialize_state(N, class_subjects, prob = 0.3):
assignments = []
for class_n in range(1, N + 1):
subjects = class_subjects[class_n]
for subject in subjects:
assignments.append([class_n, subject, 0, 0])
if random() < prob:
change_both(assignments[-1])
return assignments
def get_maximum_score(N, class_subjects):
result = initialize_state(N, class_subjects, prob = 1.0)
score = 0
final_result = []
for r in result:
if r[2] and r[3]:
final_result.append(r)
score += 1
return score
def print_final_result(result):
score = 0
final_result = []
for r in result:
if r[2] and r[3]:
final_result.append(r)
score += 1
print(score)
for r in final_result:
print(r[0], r[1], r[2], r[3])
def write_final_result(result, filename):
score = 0
final_result = []
for r in result:
if r[2] and r[3]:
final_result.append(r)
score += 1
with open(filename, "w") as f:
f.writelines(str(score) + "\n")
for r in final_result:
f.writelines(f"{r[0]} {r[1]} {r[2]} {r[3]}\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--tabu_tenure', type=int, default=105, help='tabu tenure size')
parser.add_argument('--max_steps', type=int, default=500, help='max number of steps')
parser.add_argument('--neighborhood_size', type=int, default=70, help='neighborhood size')
parser.add_argument('--constraints', nargs='+', type=int, default=[1, 2], help='constraints for evaluating score:\n 0 - session constraint \n 1 - class schedule constraint \n 2 - teacher schedule constraint')
parser.add_argument('--verbose', action='store_true', help='print progress')
parser.add_argument('--interval', type=int, default=100, help='set interval of printing results')
parser.add_argument('--score', action='store_true', help='print score')
parser.add_argument('--time', action='store_true', help='print total running time')
parser.add_argument('--keyboard', action='store_true', help='input is obtained from the keyboard')
parser.add_argument('--file_path', type=str, default='input.txt', help='input is obtained from the file')
opt = parser.parse_args()
# Read from keyboard
T, N, M = map(int, input().split())
class_subjects = []
class_subjects.append([])
for _ in range(N):
subjects = list(map(int, input().split()))
class_subjects.append(subjects[:-1])
teacher_subjects = []
teacher_subjects.append([])
for _ in range(T):
subjects = list(map(int, input().split()))
teacher_subjects.append(subjects[:-1])
subject_periods = list(map(int, input().split()))
subject_periods.insert(0, [])
# Make subject teachers list
subject_teachers = [[] for i in range(M + 1)]
for teacher, subjects in enumerate(teacher_subjects):
for subject in subjects:
subject_teachers[subject].append(teacher)
# Make choiceable time for each subject
subject_times = [[i for i in range(1, 61)] for i in range(M + 1)]
six_multiples = [s * 6 for s in range(1, 11)]
for subject, subject_period in enumerate(subject_periods):
if subject == 0:
continue
for s in six_multiples:
for i in range(subject_periods[subject] - 1):
subject_times[subject].remove(s - i)
# Initialize algorithm
if True:
max_score = get_maximum_score(N, class_subjects)
print("MAX_SCORE: ", max_score)
else:
max_score = None
start_time = time.time()
# Initialize algorithm
algorithm = Algorithm(initialize_state(N, class_subjects), opt.tabu_tenure,
opt.max_steps, opt.neighborhood_size,
constraints=opt.constraints, print_interval=opt.interval, max_score=max_score)
result, score = algorithm.run(verbose=opt.verbose)
print_final_result(result)
if opt.score:
print(f"SCORE: {score}")
if opt.time:
print(f"Total run time: {time.time() - start_time}s")