Skip to content

Commit

Permalink
Merge pull request #78 from SENG-499-Company2-B01/c1-integration
Browse files Browse the repository at this point in the history 
C1 integration
  • Loading branch information
@albie0019
albie0019 committed Aug 1, 2023
2 parents e78fe5d + c5acda4 commit bf0656c
Show file tree
Hide file tree
Showing 3 changed files with 20 additions and 95 deletions.
97 changes: 4 additions & 93 deletions src/apis/scheduler/cat_swarm.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,14 +4,11 @@
from datetime import datetime, timedelta
import numpy as np
import heapq
import math
import time
#Punishments
VERY_LOW_VALUE = -50000
ROOM_TOO_SMALL_PUNISHMENT = -10000
PROFESSOR_PREFERRED_COURSE_MATCH_PUNISHMENT = -1
PROFESSOR_MAXIMUM_COURSES_EXCEEDED_PUNISHMENT = -10000
COREQUISITE_COSCHEDULE_CONSTRAINT_PUNISHMENT = -10000 # Gets cumulated for every coreq mismatch pairs.
PROFESSOR_UNAVAILABLE_TIMEBLOCK = -5000
PROF_NOT_PENG_CLASS_REQ_PENG = -500

Expand All @@ -22,14 +19,11 @@

def fitness_room_assignments(classes, rooms, class_id, room_id, fitness):
assigned_class = classes[class_id]
#print(room_id)
assigned_room = rooms[room_id]
# Check if the room capacity is sufficient
# print(assigned_room['capacity'])
# print(assigned_class['pre_enroll'])
if assigned_room['capacity'] < assigned_class['pre_enroll']:
fitness += ROOM_TOO_SMALL_PUNISHMENT
#print(1)
# print("ROOM TOO SMALL")
else:
fitness += 1
return fitness
Expand All @@ -45,11 +39,8 @@ def preferred_course_match(professor, assigned_class, fitness):

if has_pref_flag == 1:
fitness += PROFESSOR_PREFERRED_COURSE_MATCH_REWARD
#print('nice')
else:
fitness += PROFESSOR_PREFERRED_COURSE_MATCH_PUNISHMENT
#print('not nice')
#print(2)
return fitness

def prof_maximum_courses_exceeded_constraint(professor, professor_assignments, professor_id, fitness):
Expand All @@ -65,47 +56,24 @@ def prof_maximum_courses_exceeded_constraint(professor, professor_assignments, p

if count_of_course_assignments > max_course_val and max_course_val > 0:
fitness += PROFESSOR_MAXIMUM_COURSES_EXCEEDED_PUNISHMENT
#print(3)
return fitness

def corequisite_coschedule_constraint(class_id, time_block, classes, class_timeslots, fitness):
course = classes[class_id]

#Find course corequisites
corequisite_list = course["corequisites"]
if corequisite_list == None or len(corequisite_list) == 0:
return fitness

for coreqs_subarray in corequisite_list:
#Iteratively go through each coreq and read corequisite_time_block
for coreq in coreqs_subarray:
coreqs_course = list(filter(lambda x: x["shorthand"] == coreq, classes))[0]
coreq_course_id = classes.index(coreqs_course)
corequisite_time_block = class_timeslots[coreq_course_id]

if corequisite_time_block == time_block:
fitness += COREQUISITE_COSCHEDULE_CONSTRAINT_PUNISHMENT
#print(4)
# print("PROF MAX COURSES")
return fitness

def prof_timepref_constraint(professor, class_timeslots, class_id, time_blocks,fitness):
#print(professor['timeblocks'].get(class_timeslots[class_id]))
#is assigned to a prefered timeblock
time_blocks_letter = chr(ord('@')+int(time_blocks))
if professor['timeblocks'].get(time_blocks_letter):
fitness += PROFESSOR_PREFERRED_TIMEBLOCK

else:
fitness += PROFESSOR_UNAVAILABLE_TIMEBLOCK
#print(5)
return fitness

#fitness function if a course requires a peng and check to see if professor has peng
def prof_peng_constraint(professor, assigned_class, fitness):

if assigned_class["peng"] == True and professor["peng"] == False:
fitness += PROF_NOT_PENG_CLASS_REQ_PENG
#print(6)
elif assigned_class["peng"] == True and professor["peng"] == True:
fitness += PROF_PENG_CLASS_REQ_PENG
return fitness
Expand All @@ -115,30 +83,19 @@ def evaluate_fitness(solution, professors, classes, rooms, time_blocks):
professor_assignments = solution['professor_assignments']
room_assignments = solution['room_assignments']
class_timeslots = solution['class_timeslots']
# print(solution)
# print(professors)
# print(professors[0])
#print(rooms)
# time.sleep(5)
# Initialize fitness score
fitness = 0

# Evaluate professor assignments
for class_id, professor_id in professor_assignments.items():
#print(professor_id)

professor = professors[professor_id]
assigned_class = classes[class_id]

for class_id, time_block in class_timeslots.items():
#print(class_timeslots[class_id])
time_block_num = class_timeslots[class_id]
#print(time_block_letter)
fitness = prof_timepref_constraint(professor, class_timeslots, class_id, time_block_num, fitness)
fitness = prof_peng_constraint(professor, assigned_class, fitness)
# # Check if the professor has preferences
# if professor['course_pref'] and assigned_class['course'] not in professor['course_pref']:
# fitness -= 1

fitness = preferred_course_match(professor, assigned_class, fitness)

Expand All @@ -155,18 +112,8 @@ def evaluate_fitness(solution, professors, classes, rooms, time_blocks):

# Evaluate room assignments
for class_id, room_id in room_assignments.items():
#print(room_id)

#print(rooms[room_id])
#time.sleep(5)
fitness = fitness_room_assignments(classes, rooms, class_id, room_id, fitness)

#Evaluate time_block Assignments
for class_id, time_block in class_timeslots.items():
time_block_num = class_timeslots[class_id]
time_block_letter = chr(ord('@')+int(time_block_num))
fitness = corequisite_coschedule_constraint(class_id, time_block_letter, classes, class_timeslots, fitness)

# Evaluate prof's clash fitness
# list(set()) just returns the unique values in a list.
assigned_profs = list(set(professor_assignments.values()))
Expand All @@ -187,7 +134,7 @@ def evaluate_fitness(solution, professors, classes, rooms, time_blocks):
# it means the prof_id was assigned to teach more courses at same timeblock
if(len(timeblock_for_courses_taught_by_prof) != len(list(set(timeblock_for_courses_taught_by_prof)))):
fitness += VERY_LOW_VALUE
print(7)
# print("DUPE COURSE AT TIME FOR PROF")
break

#fitness += random.randint(-10,10)
Expand All @@ -198,12 +145,8 @@ def update_cat_position(cat, population, best_solution, c1, c2, w, max_prof_rang
for i in range(len(cat['position'])):
r1 = random.random()
r2 = random.random()
# print(best_solution)
# print(cat['position'])
#print(cat['velocity'][i])
#cat['velocity'][i] = w * cat['velocity'][i] + c1 * r1 * (cat['best_position'][i] - cat['position'][i]) + c2 * r2 * (best_solution['best_position'][i] - cat['position'][i]) #best_solution['position'][i]
for j in range(len(cat['position'][i])):
#print(cat['velocity'][i][j])
cat['velocity'][i][j] = w * cat['velocity'][i][j] + c1 * r1 * (best_solution['best_position'][i][j] - cat['position'][i][j]) #best_solution['position'][i]


Expand All @@ -215,7 +158,6 @@ def update_cat_position(cat, population, best_solution, c1, c2, w, max_prof_rang
#cat['velocity'][i][j] = [max(min(number, range_val), -range_val) for number in list(cat['velocity'][i])]
cat['velocity'][i] = np.clip(cat['velocity'][i], -range_val, range_val).tolist()
cat['position'][i][j] = round(cat['position'][i][j] + cat['velocity'][i][j])
#print(cat['velocity'][i])
#Apply boundary constraints if needed
if cat['position'][i][j] < 0:
cat['position'][i][j] = 0
Expand Down Expand Up @@ -284,31 +226,13 @@ def cat_swarm_optimization(professors, classes, rooms, time_blocks, population_s
cat['best_position'] = cat['position']
population.append(solution)
cats.append(cat)
#print(cat['position'])
i=0
for solution in population:
if solution['fitness'] is None:
solution['fitness'] = evaluate_fitness(solution, professors, classes, rooms, time_blocks)
cats[i]['best_fitness'] = solution['fitness']
i += 1



# for _ in range(swarm_size):
# cat = {
# 'position': [random.randint(0, population_size - 1) for _ in range(population_size)],
# 'velocity': [random.uniform(-5, 5) for _ in range(population_size)],
# 'best_position': None,
# 'best_fitness': None
# }
# cat['best_position'] = cat['position']
# cats.append(cat)
# print(cat['velocity'])
# print(cat['position'])
#Evaluate fitness for each solution in population
# for solution in population:
# if solution['fitness'] is None:
# solution['fitness'] = evaluate_fitness(solution, professors, classes, rooms, time_blocks)

# best_solution = max(population, key=lambda x: x['fitness'])
# Main optimization loop
Expand All @@ -329,7 +253,6 @@ def cat_swarm_optimization(professors, classes, rooms, time_blocks, population_s
# for solution in population:
# if solution['fitness'] is None:
# solution['fitness'] = evaluate_fitness(solution, professors, classes, rooms, time_blocks)
#print(population)
# Update best solution found
# best_solution = max(population, key=lambda x: x['fitness'])
# fitness_scores = []
Expand All @@ -347,17 +270,12 @@ def cat_swarm_optimization(professors, classes, rooms, time_blocks, population_s
best_solution_trace = {'fitness':None}

for cat in cats:
#print(population)
#print(best_solution)
#print(cat)

#cat seek
cat_copies = []
if cat['cat_seek'] == True:

for _ in range(smp):
cat_copies.append(copy.deepcopy(cat))
#print(cat_copies)
cat_solutions =[]
for cat_num in range(len(cat_copies)-1):
for _ in range(cdc):
Expand All @@ -367,7 +285,6 @@ def cat_swarm_optimization(professors, classes, rooms, time_blocks, population_s

cat_copies[cat_num]['position'][tmp][posi] = int(cat_copies[cat_num]['position'][tmp][posi] + (cat_copies[cat_num]['position'][tmp][posi] * random.choice([-1,1]) * srd))
cat_copies[cat_num]['position'][tmp] = np.clip(cat_copies[cat_num]['position'][tmp], 0, ranges[tmp]).tolist()
# print(cat_copies[cat_num]['position'][tmp])


cat_solution = copy.deepcopy(population[i])
Expand All @@ -379,8 +296,6 @@ def cat_swarm_optimization(professors, classes, rooms, time_blocks, population_s
for class_id, time_id in cat_solution['class_timeslots'].items():
cat_solution['class_timeslots'][class_id] = cat_copies[cat_num]['position'][2][class_id]

#print(cat_solution)
#time.sleep(2)
cat_solution['fitness'] = evaluate_fitness(cat_solution, professors, classes, rooms, time_blocks)
cat_solutions.append(cat_solution)

Expand Down Expand Up @@ -431,8 +346,6 @@ def cat_swarm_optimization(professors, classes, rooms, time_blocks, population_s
else:
cat_solution2 = {}
# Update position and velocity for each cat
# print(best_cats[0])
# time.sleep(5)
cat = update_cat_position(cat, population, best_cats[0], cc1, cc1, ww, max_prof_range, max_room_range, max_timeblock_range)


Expand Down Expand Up @@ -460,8 +373,6 @@ def cat_swarm_optimization(professors, classes, rooms, time_blocks, population_s

# # Update population with new solutions generated by cats
# for cat in cats:
# #print(cat)
# #print(population)
# new_solution = copy.deepcopy(population[int(cat['position'][0])])
# for i in range(1, population_size):
# for class_id, professor_id in new_solution['professor_assignments'].items():
Expand Down Expand Up @@ -560,7 +471,7 @@ def main(input_profs, input_courses, input_classrooms):
room_seats = input_classrooms[room_num]["capacity"]

time_letter = best_solution['class_timeslots'][i]
time_letter = chr(ord("@")+int(time_letter))
time_letter = chr(ord("@")+int(time_letter + 1))
for key in input_timeblocks[time_letter].keys():
days.append(key)
start_time = input_timeblocks[time_letter][days[0]]["start"]
Expand Down
15 changes: 14 additions & 1 deletion src/apis/views.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -21,4 +21,17 @@ def generate(request):
}
return Response(schedule, status=status.HTTP_200_OK)
except Exception as e:
return Response(e, status=status.HTTP_400_BAD_REQUEST)
return Response(e, status=status.HTTP_400_BAD_REQUEST)

@api_view(['POST'])
def schedule(request):
algs1_request = request.data
professors = algs1_request["professors"]
courses = algs1_request["courses"]
classrooms = algs1_request["classrooms"]

scheduled_courses = main(professors, courses, classrooms)
schedule = {
"schedule": scheduled_courses
}
return Response(schedule, status=status.HTTP_200_OK)
3 changes: 2 additions & 1 deletion src/src/urls.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -16,8 +16,9 @@
"""
from django.contrib import admin
from django.urls import path
from apis.views import generate
from apis.views import generate, schedule

urlpatterns = [
path("generate", generate),
path("schedule", schedule),
]

0 comments on commit bf0656c

Please sign in to comment.