mmas-3d-slots-27-no-incentive.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.112887Z","iopub.status.busy":"2022-08-21T11:15:56.112258Z","iopub.status.idle":"2022-08-21T11:15:56.117560Z","shell.execute_reply":"2022-08-21T11:15:56.116340Z","shell.execute_reply.started":"2022-08-21T11:15:56.112850Z"},"trusted":true},"outputs":[],"source":["import numpy as np \n","import pandas as pd "]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.127498Z","iopub.status.busy":"2022-08-21T11:15:56.126835Z","iopub.status.idle":"2022-08-21T11:15:56.151248Z","shell.execute_reply":"2022-08-21T11:15:56.150446Z","shell.execute_reply.started":"2022-08-21T11:15:56.127461Z"},"trusted":true},"outputs":[],"source":["students = pd.read_csv(\"data_set/students.csv\")\n","exams = pd.read_csv(\"data_set/exams.csv\")\n","enrolments = pd.read_csv(\"data_set/enrolements.csv\")\n","rooms = pd.read_csv(\"data_set/indexed_rooms.csv\")\n","slots= pd.read_csv(\"data_set/slots.csv\")"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.155472Z","iopub.status.busy":"2022-08-21T11:15:56.154614Z","iopub.status.idle":"2022-08-21T11:15:56.167616Z","shell.execute_reply":"2022-08-21T11:15:56.166653Z","shell.execute_reply.started":"2022-08-21T11:15:56.155437Z"},"trusted":true},"outputs":[],"source":["class Exam:\n","    def __init__(self, exam_code, name, duration, department_code):\n","        self.code = exam_code\n","        self.name = name\n","        self.duration = duration\n","        self.duration_min = 0\n","        self.dep_code = department_code\n","        self.conflicts = 0\n","        self.exam_conflicts = set()\n","        self.priority = 0\n","        self.slot = -1\n","        self.student_codes = []\n","        self.students = []\n","        self.rooms = []\n","        self.room_assigned = False\n","        self.access_requirement = False\n","        self.students_with_access_needs = []\n","        self.students_assigned = 0\n","\n","class Student:\n","    def __init__(self, student_code, course_code, accessibility_need):\n","        self.code = student_code\n","        self.course_code = course_code\n","        self.accessibility_need = accessibility_need\n","        self.exams = []\n","        self.exam_codes = []\n","\n","class Enrolment:\n","    def __init__(self, student_code, exam_code):\n","        self.student_code = student_code\n","        self.exam_code = exam_code\n","\n","class Room:\n","    def __init__(self, index, room_code, capacity, accessible):\n","        self.index = index\n","        self.code = room_code\n","        self.capacity = capacity\n","        self.status = False\n","        self.accessible = accessible\n","        self.seats_occupied = 0\n","class Slot:\n","    def __init__(self, index, date, day, type):\n","        self.index = index\n","        self.date = date\n","        self.day = day\n","        self.type = type\n","        self.exams = []\n","        self.student_count = 0"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.212490Z","iopub.status.busy":"2022-08-21T11:15:56.211851Z","iopub.status.idle":"2022-08-21T11:15:56.221457Z","shell.execute_reply":"2022-08-21T11:15:56.220559Z","shell.execute_reply.started":"2022-08-21T11:15:56.212454Z"},"trusted":true},"outputs":[],"source":["exam_objects = [Exam(**kwargs) for kwargs in exams.to_dict(orient='records')]\n","\n","student_objects = [Student(**kwargs) for kwargs in students.to_dict(orient='records')]\n","\n","enrolment_objects = [Enrolment(**kwargs) for kwargs in enrolments.to_dict(orient='records')]\n","\n","room_objects = [Room(**kwargs) for kwargs in rooms.to_dict(orient='records')]\n","\n","slot_objects = [Slot(**kwargs) for kwargs in slots.to_dict(orient='records')]"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.265179Z","iopub.status.busy":"2022-08-21T11:15:56.264411Z","iopub.status.idle":"2022-08-21T11:15:56.270114Z","shell.execute_reply":"2022-08-21T11:15:56.269163Z","shell.execute_reply.started":"2022-08-21T11:15:56.265137Z"},"trusted":true},"outputs":[],"source":["for i in enrolment_objects:\n","    for j in student_objects:\n","        if j.code == i.student_code:\n","            j.exam_codes.append(i.exam_code)"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.320147Z","iopub.status.busy":"2022-08-21T11:15:56.319586Z","iopub.status.idle":"2022-08-21T11:15:56.325910Z","shell.execute_reply":"2022-08-21T11:15:56.324870Z","shell.execute_reply.started":"2022-08-21T11:15:56.320107Z"},"trusted":true},"outputs":[],"source":["for i in student_objects:\n","    for j in exam_objects:\n","        for k in i.exam_codes:\n","            if k == j.code:\n","                i.exams.append(j)"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.329582Z","iopub.status.busy":"2022-08-21T11:15:56.328870Z","iopub.status.idle":"2022-08-21T11:15:56.336655Z","shell.execute_reply":"2022-08-21T11:15:56.335758Z","shell.execute_reply.started":"2022-08-21T11:15:56.329544Z"},"trusted":true},"outputs":[],"source":["for i in enrolment_objects:\n","    for j in exam_objects:\n","        if j.code == i.exam_code:\n","            j.student_codes.append(i.student_code)"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.340238Z","iopub.status.busy":"2022-08-21T11:15:56.338075Z","iopub.status.idle":"2022-08-21T11:15:56.347590Z","shell.execute_reply":"2022-08-21T11:15:56.346579Z","shell.execute_reply.started":"2022-08-21T11:15:56.340210Z"},"trusted":true},"outputs":[],"source":["for i in exam_objects:\n","    for j in student_objects:\n","        for k in i.student_codes:\n","            if k == j.code:\n","                i.students.append(j)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["for i in exam_objects:\n","    for j in i.students:\n","        if j.accessibility_need == True:\n","            i.access_requirement = True\n","            i.students_with_access_needs.append(j)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.419625Z","iopub.status.busy":"2022-08-21T11:15:56.418914Z","iopub.status.idle":"2022-08-21T11:15:56.424602Z","shell.execute_reply":"2022-08-21T11:15:56.423510Z","shell.execute_reply.started":"2022-08-21T11:15:56.419597Z"},"trusted":true},"outputs":[],"source":["def getRoomKey(obj):\n","    return obj.capacity"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.505580Z","iopub.status.busy":"2022-08-21T11:15:56.505011Z","iopub.status.idle":"2022-08-21T11:15:56.511382Z","shell.execute_reply":"2022-08-21T11:15:56.510522Z","shell.execute_reply.started":"2022-08-21T11:15:56.505543Z"},"trusted":true},"outputs":[],"source":["def hhToSeconds(s):\n","    t = 0\n","    splitTime = s.split(':')\n","    t = 60 * int(splitTime[0]) + int(splitTime[1])\n","    return t"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.521027Z","iopub.status.busy":"2022-08-21T11:15:56.520774Z","iopub.status.idle":"2022-08-21T11:15:56.525661Z","shell.execute_reply":"2022-08-21T11:15:56.524671Z","shell.execute_reply.started":"2022-08-21T11:15:56.521003Z"},"trusted":true},"outputs":[],"source":["for i in exam_objects:\n","    i.duration_min = hhToSeconds(str(i.duration))"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.541041Z","iopub.status.busy":"2022-08-21T11:15:56.540376Z","iopub.status.idle":"2022-08-21T11:15:56.547739Z","shell.execute_reply":"2022-08-21T11:15:56.546792Z","shell.execute_reply.started":"2022-08-21T11:15:56.541012Z"},"trusted":true},"outputs":[],"source":["def degreeOfConflict(exam):\n","    s = set()\n","    for i in exam.students:\n","        for j in i.exams:\n","            if exam.code != j.code:\n","                s.add(j)\n","    exam.exam_conflicts = s\n","    return len(s)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.578952Z","iopub.status.busy":"2022-08-21T11:15:56.578412Z","iopub.status.idle":"2022-08-21T11:15:56.583344Z","shell.execute_reply":"2022-08-21T11:15:56.582450Z","shell.execute_reply.started":"2022-08-21T11:15:56.578925Z"},"trusted":true},"outputs":[],"source":["for i in exam_objects:\n","    i.conflicts = degreeOfConflict(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.599177Z","iopub.status.busy":"2022-08-21T11:15:56.598419Z","iopub.status.idle":"2022-08-21T11:15:56.609290Z","shell.execute_reply":"2022-08-21T11:15:56.608395Z","shell.execute_reply.started":"2022-08-21T11:15:56.599141Z"},"trusted":true},"outputs":[],"source":["def partialExamOrdering():\n","    sup = np.zeros(len(exam_objects))\n","    for i in range(len(exam_objects)):\n","        for j in range(len(exam_objects)):\n","            if exam_objects[i].conflicts > exam_objects[j].conflicts:\n","                sup[i] = sup[i] + 1\n","                continue\n","            elif exam_objects[i].conflicts < exam_objects[j].conflicts:\n","                sup[j] = sup[j] + 1\n","                continue\n","            if len(exam_objects[i].students) > len(exam_objects[j].students):\n","                sup[i] = sup[i] + 1\n","                continue\n","            elif len(exam_objects[i].students) < len(exam_objects[j].students):\n","                sup[j] = sup[j] + 1\n","                continue\n","            if exam_objects[i].duration_min > exam_objects[j].duration_min:\n","                sup[i] = sup[i] + 1\n","                continue\n","            elif exam_objects[i].duration_min < exam_objects[j].duration_min:\n","                sup[j] = sup[j] + 1\n","                continue\n","            if len(exam_objects[i].students_with_access_needs) > len(exam_objects[j].students_with_access_needs):\n","                sup[i] = sup[i] + 1\n","                continue\n","            elif len(exam_objects[i].students_with_access_needs) < len(exam_objects[j].students_with_access_needs):\n","                sup[j] = sup[j] + 1\n","                continue\n","            if exam_objects[i].code > exam_objects[j].code:\n","                sup[j] = sup[j] + 1\n","                continue\n","            elif exam_objects[i].code < exam_objects[j].code:\n","                sup[i] = sup[i] + 1\n","                continue\n","    return sup                               "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.619740Z","iopub.status.busy":"2022-08-21T11:15:56.619476Z","iopub.status.idle":"2022-08-21T11:15:56.624988Z","shell.execute_reply":"2022-08-21T11:15:56.623771Z","shell.execute_reply.started":"2022-08-21T11:15:56.619715Z"},"trusted":true},"outputs":[],"source":["def totalConflictsForExams():\n","    partialExamOrder = partialExamOrdering()\n","    for i in range(len(exam_objects)):\n","        exam_objects[i].priority = partialExamOrder[i]  "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.649514Z","iopub.status.busy":"2022-08-21T11:15:56.647492Z","iopub.status.idle":"2022-08-21T11:15:56.653777Z","shell.execute_reply":"2022-08-21T11:15:56.652648Z","shell.execute_reply.started":"2022-08-21T11:15:56.649487Z"},"trusted":true},"outputs":[],"source":["def getKey(obj):\n","    return obj.priority"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.690465Z","iopub.status.busy":"2022-08-21T11:15:56.689631Z","iopub.status.idle":"2022-08-21T11:15:56.698989Z","shell.execute_reply":"2022-08-21T11:15:56.698132Z","shell.execute_reply.started":"2022-08-21T11:15:56.690429Z"},"trusted":true},"outputs":[],"source":["pheromoneMatrix = np.zeros((len(exam_objects), len(slot_objects), len(room_objects)))\n","\n","def updatePheromoneMatrix(t_min, t_max, EVAPORATION_RATE):\n","    for i in range(len(exam_objects)):\n","        for j in range(len(slot_objects)):\n","            for k in range(len(room_objects)):\n","                if exam_objects[i].slot.index == slot_objects[j].index and room_objects[k] in exam_objects[i].rooms:\n","                    pheromoneMatrix[i][j][k] = (1 - EVAPORATION_RATE) * pheromoneMatrix[i][j][k] + 1\n","                else:\n","                    pheromoneMatrix[i][j][k] = (1 - EVAPORATION_RATE) * pheromoneMatrix[i][j][k]\n","                if pheromoneMatrix[i][j][k] < t_min:\n","                    pheromoneMatrix[i][j][k] = t_min\n","                elif pheromoneMatrix[i][j][k] > t_max:\n","                    pheromoneMatrix[i][j][k] = t_max\n","    \n","def initPheromoneMatrix(exams, slots, rooms, value):\n","    for i in range(len(exams)):\n","        for j in range(len(slots)):\n","            for k in range(len(rooms)):\n","                pheromoneMatrix[i][j][k] = value\n","       "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.714814Z","iopub.status.busy":"2022-08-21T11:15:56.714062Z","iopub.status.idle":"2022-08-21T11:15:56.721370Z","shell.execute_reply":"2022-08-21T11:15:56.720455Z","shell.execute_reply.started":"2022-08-21T11:15:56.714780Z"},"trusted":true},"outputs":[],"source":["heuristicsMatrix = np.zeros((len(exam_objects), len(slot_objects), len(room_objects)))\n","\n","def initHeuristicsMatrix(exams, slots, rooms):\n","    for i in range(len(exams)):\n","        for j in range(len(slots)):\n","            for k in range(len(rooms)):\n","                heuristicsMatrix[i][j][k] = 1\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["roomCapacityMatrix = np.zeros((len(slot_objects), len(room_objects)))\n","\n","def initRoomCapacityMatrixMatrix(exams, slots, rooms):\n","    for j in range(len(slots)):\n","        for k in range(len(rooms)):\n","            roomCapacityMatrix[j][k] = room_objects[k].capacity\n","                \n","                    "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.759634Z","iopub.status.busy":"2022-08-21T11:15:56.759379Z","iopub.status.idle":"2022-08-21T11:15:56.765511Z","shell.execute_reply":"2022-08-21T11:15:56.764423Z","shell.execute_reply.started":"2022-08-21T11:15:56.759610Z"},"trusted":true},"outputs":[],"source":["def checkForHardConstraintViolations(exam, slot, room):\n","    score = 0\n","    for i in exam.exam_conflicts:\n","            if i.slot != -1:\n","                if i.slot.index == slot.index:\n","                    score += 4\n","                elif np.abs(i.slot.index - slot.index) < 2:\n","                    score += 4\n","    if slot.day == \"Saturday\" and slot.type == \"Evening\":\n","        score += 4\n","    if slot.type == \"Evening\" and exam.duration_min > 90:\n","        score += 4\n","    if len(exam.students) + slot.student_count > 2653:\n","        score += 4\n","    if len(exam.students) > room.capacity:\n","        score += 4\n","    if exam.access_requirement and room.accessible == False:\n","        score += 4\n","    return score\n","                    "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def numIncentive(exam, slot, room):\n","    score = 0\n","    if slot.type == \"Evening\" and exam.duration_min <= 90:\n","        score += 4\n","    return 0"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.780021Z","iopub.status.busy":"2022-08-21T11:15:56.779150Z","iopub.status.idle":"2022-08-21T11:15:56.785758Z","shell.execute_reply":"2022-08-21T11:15:56.784831Z","shell.execute_reply.started":"2022-08-21T11:15:56.779986Z"},"trusted":true},"outputs":[],"source":["def checkForSoftConstraintViolations(exam, slot, room):\n","    score = 0\n","    for i in exam.exam_conflicts:\n","        if i.slot != -1:\n","            if np.abs(i.slot.index - slot.index) < 3:\n","                score += 1\n","    return score"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.806028Z","iopub.status.busy":"2022-08-21T11:15:56.805349Z","iopub.status.idle":"2022-08-21T11:15:56.812233Z","shell.execute_reply":"2022-08-21T11:15:56.811320Z","shell.execute_reply.started":"2022-08-21T11:15:56.805991Z"},"trusted":true},"outputs":[],"source":["def findForbiddenSlots(exam):\n","    forbiddenSlots = set()\n","    forbiddenSlots.add(slot_objects[11])\n","    for i in exam.exam_conflicts:\n","        if i.slot != -1 and i.code != exam.code:\n","            forbiddenSlots.add(i.slot)\n","    return forbiddenSlots"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.832895Z","iopub.status.busy":"2022-08-21T11:15:56.830880Z","iopub.status.idle":"2022-08-21T11:15:56.837133Z","shell.execute_reply":"2022-08-21T11:15:56.836205Z","shell.execute_reply.started":"2022-08-21T11:15:56.832867Z"},"trusted":true},"outputs":[],"source":["def numAdditionalViolations(exam, slot, room):\n","    score = 0\n","    score += checkForHardConstraintViolations(exam, slot, room)\n","    score += checkForSoftConstraintViolations(exam, slot, room)\n","    return score"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.855512Z","iopub.status.busy":"2022-08-21T11:15:56.854979Z","iopub.status.idle":"2022-08-21T11:15:56.861314Z","shell.execute_reply":"2022-08-21T11:15:56.860474Z","shell.execute_reply.started":"2022-08-21T11:15:56.855476Z"},"trusted":true},"outputs":[],"source":["def pheroHeuristicSummation(examIndex, slotIndex, roomIndex, alpha, beta):\n","    sum = 0\n","    for j in range(len(slot_objects)):\n","        for k in range(len(room_objects)):\n","            if j != slotIndex and k != roomIndex:\n","                sum += (pheromoneMatrix[examIndex][j][k]**alpha) * (heuristicsMatrix[examIndex][j][k]**beta)\n","    return sum"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.912448Z","iopub.status.busy":"2022-08-21T11:15:56.911854Z","iopub.status.idle":"2022-08-21T11:15:56.921353Z","shell.execute_reply":"2022-08-21T11:15:56.920301Z","shell.execute_reply.started":"2022-08-21T11:15:56.912405Z"},"trusted":true},"outputs":[],"source":["def chooseSlot(examIndex, exam, slots, rooms, alpha, beta):\n","    slot = 0\n","    room = []\n","    desirability = np.zeros((len(slots), len(rooms)))\n","    forbiddenSlots = list(findForbiddenSlots(exam))\n","    for i in range(len(slots)):\n","        for k in range(len(rooms)):\n","            if roomCapacityMatrix[i][k] <= 0:\n","                heuristicsMatrix[examIndex][i][k] = 0.000000000000000000001\n","            else:\n","                heuristicsMatrix[examIndex][i][k] = (1 + numIncentive(exam, slots[i], rooms[k])) / (1 + numAdditionalViolations(exam, slots[i], rooms[k]))\n","            desirability[i][k] = ((pheromoneMatrix[examIndex][i][k]**alpha) * (heuristicsMatrix[examIndex][i][k]**beta))\n","            for j in forbiddenSlots:\n","                if slot_objects[i].index == j.index:\n","                    desirability[i][k] = -1\n","                    continue\n","    argSortedList = np.flip(np.dstack(np.unravel_index(np.argsort(desirability.ravel()), desirability.shape))[0])\n","    slot = argSortedList[0][1]\n","    r = argSortedList[0][0]\n","    val = desirability[slot][r]\n","    availableSlots = []\n","    for i in argSortedList:\n","        if desirability[i[1]][i[0]] == desirability[slot][r]:\n","            availableSlots.append(i[1])\n","    rand = np.random.randint(0, len(availableSlots))\n","    slot = availableSlots[rand]\n","    for index in argSortedList:\n","        if len(exam.students) - exam.students_assigned == 0:\n","            break\n","        if index[1] == slot:\n","            room_ind = index[0]\n","            index_room = rooms[room_ind]\n","            room.append(index_room)\n","            cap = roomCapacityMatrix[slot][room_ind]\n","            if len(exam.students) - exam.students_assigned >= cap:\n","                exam.students_assigned += cap\n","                np.asmatrix(roomCapacityMatrix)\n","                roomCapacityMatrix[slot, room_ind] = 0\n","            elif len(exam.students) - exam.students_assigned < cap:\n","                exam.students_assigned += len(exam.students) - exam.students_assigned\n","                np.asmatrix(roomCapacityMatrix)\n","                roomCapacityMatrix[slot, room_ind] -= exam.students_assigned\n","    return slots[slot], room"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.933688Z","iopub.status.busy":"2022-08-21T11:15:56.933118Z","iopub.status.idle":"2022-08-21T11:15:56.940801Z","shell.execute_reply":"2022-08-21T11:15:56.939730Z","shell.execute_reply.started":"2022-08-21T11:15:56.933651Z"},"trusted":true},"outputs":[],"source":["def evaluateHardConstraints():\n","    countHard = 0\n","    examsChecked = []\n","    index = 0\n","    for i in slot_objects:\n","        if i.student_count > 2653:\n","            countHard += 1\n","    for i in room_objects:\n","        if i.seats_occupied > i.capacity:\n","            countHard += 1\n","    while index < len(exam_objects):\n","        exam = exam_objects[index]\n","        flag = False\n","        for j in exam.exam_conflicts:\n","            for k in examsChecked:\n","                if k == j:\n","                    flag = True\n","            if flag:\n","                continue\n","            else:\n","                if exam.slot.index == j.slot.index:\n","                        countHard += 1\n","                elif np.abs(exam.slot.index - j.slot.index) < 2:\n","                    countHard += 1\n","        if exam.slot.day == \"Saturday\" and exam.slot.type == \"Evening\":\n","            countHard += 1\n","        if exam.slot.type == \"Evening\" and exam.duration_min > 90:\n","            countHard += 1\n","        if exam.access_requirement:\n","            for i in list(exam.rooms)[0]:\n","                if i.accessible == False:\n","                    countHard += 1\n","        index += 1\n","        examsChecked.append(exam)\n","    return countHard"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.971491Z","iopub.status.busy":"2022-08-21T11:15:56.970008Z","iopub.status.idle":"2022-08-21T11:15:56.976775Z","shell.execute_reply":"2022-08-21T11:15:56.975722Z","shell.execute_reply.started":"2022-08-21T11:15:56.971453Z"},"trusted":true},"outputs":[],"source":["def evaluateSoftConstraints():\n","    countSoft = 0\n","    examsChecked = []\n","    index = 0\n","    while index  < len(exam_objects):\n","        exam = exam_objects[index]\n","        flag = False\n","        for j in exam.exam_conflicts:\n","            for k in examsChecked:\n","                if  k == j:\n","                    flag = True\n","            if flag:\n","                continue\n","            else:\n","                if np.abs(exam.slot.index - j.slot.index) < 3:\n","                    countSoft += 1\n","        index += 1\n","        examsChecked.append(exam)\n","    return countSoft"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:56.982353Z","iopub.status.busy":"2022-08-21T11:15:56.981429Z","iopub.status.idle":"2022-08-21T11:15:56.988871Z","shell.execute_reply":"2022-08-21T11:15:56.987963Z","shell.execute_reply.started":"2022-08-21T11:15:56.982316Z"},"trusted":true},"outputs":[],"source":["def evaluate():\n","    metricHardConstraints = 0\n","    metricSoftConstraints = 0\n","    metricHardConstraints += evaluateHardConstraints()\n","    metricSoftConstraints += evaluateSoftConstraints()\n","    return metricSoftConstraints, metricHardConstraints"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def getMapKey(obj):\n","    return obj.capacity"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["room_max_capacity = room_objects[0]\n","for i in room_objects:\n","    if room_max_capacity.capacity < i.capacity:\n","        room_max_capacity = i"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:57.265972Z","iopub.status.busy":"2022-08-21T11:15:57.265642Z","iopub.status.idle":"2022-08-21T11:15:57.271455Z","shell.execute_reply":"2022-08-21T11:15:57.270505Z","shell.execute_reply.started":"2022-08-21T11:15:57.265943Z"},"trusted":true},"outputs":[],"source":["def assignSlotsForSolution(solution):\n","    for i in slot_objects:\n","        i.exams = []\n","        i.student_count = 0\n","    for i in exam_objects:\n","        i.rooms = []\n","    for i in solution:\n","        list(i)[0].slot = list(i)[1]\n","        list(i)[0].rooms.append(list(i)[2])\n","        list(i)[1].exams.append(list(i)[0])\n","        list(i)[1].student_count += len(list(i)[0].students)\n","        #list(i)[2].seats_occupied += len(list(i)[0].students)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["t_max = 5.0\n","t_min = 0.0019\n","MAX_ITER = 30"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:15:57.289950Z","iopub.status.busy":"2022-08-21T11:15:57.289637Z","iopub.status.idle":"2022-08-21T11:16:22.416529Z","shell.execute_reply":"2022-08-21T11:16:22.415556Z","shell.execute_reply.started":"2022-08-21T11:15:57.289923Z"},"trusted":true},"outputs":[],"source":["def aco(EVAPORATION_RATE, numAnts, alpha, beta):\n","    index = 0\n","    totalConflictsForExams()\n","    sortedExamsByPriority = sorted(exam_objects, key=getKey, reverse=True)\n","    #sortedExamsByPriority = exam_objects\n","    initPheromoneMatrix(sortedExamsByPriority, slot_objects, room_objects, t_max)\n","    evaluationSoftMap = []\n","    evaluationHardMap = []\n","    A_gb = set()\n","    prevScore = 0\n","    while index < MAX_ITER:\n","        A_ib = set()\n","        prevScoreAnt = 0\n","        for k in range(numAnts):\n","            initHeuristicsMatrix(sortedExamsByPriority, slot_objects, room_objects)\n","            initRoomCapacityMatrixMatrix(sortedExamsByPriority, slot_objects, room_objects)\n","            A = set()\n","            chosenSlot = 0\n","            for i in range(len(exam_objects)):\n","                exam_objects[i].slot = -1\n","                exam_objects[i].rooms = []\n","                exam_objects[i].students_assigned = 0\n","            for i in slot_objects:\n","                i.student_count = 0\n","            for i in room_objects:\n","                i.seats_occupied = 0\n","                i.seats_remaining = i.capacity\n","            for i in range(len(exam_objects)):\n","                chosenSlot = chooseSlot(i,sortedExamsByPriority[i], slot_objects, room_objects, alpha, beta)\n","                sortedExamsByPriority[i].slot = chosenSlot[0]\n","                sortedExamsByPriority[i].rooms.append(chosenSlot[1])\n","                chosenSlot[0].student_count += len(sortedExamsByPriority[i].students)\n","                A.add((sortedExamsByPriority[i], chosenSlot[0], tuple(chosenSlot[1])))\n","            assignSlotsForSolution(A)\n","            scoreAl = evaluate()[0] *4 + evaluate()[1]\n","            if A_ib != set():\n","                A_ib = A if scoreAl < prevScoreAnt else A_ib\n","                prevScoreAnt = scoreAl if scoreAl < prevScoreAnt else prevScoreAnt\n","            else:\n","                A_ib = A\n","                prevScoreAnt = scoreAl\n","        assignSlotsForSolution(A_ib)\n","        scoreA_ibl = evaluate()[0]*4 + evaluate()[1]\n","        if A_gb != set():\n","            A_gb = A_ib if scoreA_ibl < prevScore else A_gb\n","            prevScore = scoreA_ibl if scoreA_ibl < prevScore else prevScore\n","        else:\n","            A_gb = A_ib\n","            prevScore = scoreA_ibl\n","        assignSlotsForSolution(A_gb)\n","        updatePheromoneMatrix(t_min, t_max, EVAPORATION_RATE)\n","        evaluationSoftMap.append((index, evaluate()[0]))\n","        evaluationHardMap.append((index, evaluate()[1]))\n","        index += 1\n","    return evaluationSoftMap, evaluationHardMap, A_gb, prevScore"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["aco1 = aco(0.2, 3, 1.0, 2.0)\n","aco2 = aco(0.2, 3, 1.0, 4.0)\n","aco3 = aco(0.2, 6, 1.0, 2.0)\n","aco4 = aco(0.2, 6, 1.0, 4.0)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["scores = [aco1,aco2,aco3,aco4]\n","print(\"ACO1: \" + str(aco1[3]))\n","print(\"ACO2: \" + str(aco2[3]))\n","print(\"ACO3: \" + str(aco3[3]))\n","print(\"ACO4: \" + str(aco4[3]))\n","\n","min = scores[0]\n","for i in scores:\n","    if min[3] < i[3]:\n","        min = i\n","\n","print(\"Best model score:\", min[3])\n","assignSlotsForSolution(min[2])"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:16:22.418964Z","iopub.status.busy":"2022-08-21T11:16:22.418591Z","iopub.status.idle":"2022-08-21T11:16:22.627263Z","shell.execute_reply":"2022-08-21T11:16:22.626212Z","shell.execute_reply.started":"2022-08-21T11:16:22.418927Z"},"trusted":true},"outputs":[],"source":["hard_aco1 = pd.DataFrame(aco1[1], columns=['Iterations', 'Violations'])\n","hard_aco2 = pd.DataFrame(aco2[1], columns=['Iterations', 'Violations'])\n","hard_aco3 = pd.DataFrame(aco3[1], columns=['Iterations', 'Violations'])\n","hard_aco4 = pd.DataFrame(aco4[1], columns=['Iterations', 'Violations'])\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.legend.Legend at 0x1e25a640250>"]},"execution_count":53,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1152x576 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["ax = hard_aco1.plot(x='Iterations', y='Violations', figsize=(16, 8), label=\"ACO1\")\n","hard_aco2.plot(ax=ax, x='Iterations', y='Violations', label=\"ACO2\")\n","hard_aco3.plot(ax=ax, x='Iterations', y='Violations', label=\"ACO3\")\n","hard_aco4.plot(ax=ax, x='Iterations', y='Violations', label=\"ACO4\")\n","ax.legend(loc=\"upper right\")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-21T11:16:22.629229Z","iopub.status.busy":"2022-08-21T11:16:22.628887Z","iopub.status.idle":"2022-08-21T11:16:22.842846Z","shell.execute_reply":"2022-08-21T11:16:22.841958Z","shell.execute_reply.started":"2022-08-21T11:16:22.629185Z"},"trusted":true},"outputs":[],"source":["soft_aco1 = pd.DataFrame(aco1[0], columns=['Iterations', 'Violations'])\n","soft_aco2 = pd.DataFrame(aco2[0], columns=['Iterations', 'Violations'])\n","soft_aco3 = pd.DataFrame(aco3[0], columns=['Iterations', 'Violations'])\n","soft_aco4 = pd.DataFrame(aco4[0], columns=['Iterations', 'Violations'])\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.legend.Legend at 0x1e25a643a00>"]},"execution_count":54,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1152x576 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["ax = soft_aco1.plot(x='Iterations', y='Violations', figsize=(16, 8), label=\"ACO1\")\n","soft_aco2.plot(ax=ax, x='Iterations', y='Violations', label=\"ACO2\")\n","soft_aco3.plot(ax=ax, x='Iterations', y='Violations', label=\"ACO3\")\n","soft_aco4.plot(ax=ax, x='Iterations', y='Violations', label=\"ACO4\")\n","ax.legend(loc=\"upper right\")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["studentArr = []\n","for i in student_objects:\n","    for j in i.exams:\n","        roomArr = []\n","        capArr = []\n","        for k in list(j.rooms)[0]:\n","            roomArr.append(k.code)\n","            capArr.append(k.capacity)\n","        studentArr.append([i.code, j.code, j.slot.index, j.slot.day, j.slot.type, j.duration, len(j.students), roomArr,capArr])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pd.DataFrame(studentArr, columns=[\"student_code\", \"exam_code\", \"slot_index\", \"day\", \"type\", \"duration\", \"total_students\", \"rooms\", \"rooms_capacity\" ]).to_csv('output_log/student_output_6.csv')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["examArr = []\n","for i in exam_objects:\n","    roomArr = []\n","    capArr = []\n","    for j in list(i.rooms)[0]:\n","        roomArr.append(j.code)\n","        capArr.append(j.capacity)\n","    examArr.append([i.code, i.slot.index, i.slot.day, i.slot.type, i.duration, len(i.students), roomArr, capArr, i.access_requirement])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pd.DataFrame(examArr, columns=[\"exam_code\", \"slot_index\", \"day\", \"type\", \"duration\", \"total_students\", \"rooms\", \"rooms_capacity\", \"special_requirement\" ]).to_csv('output_log/exam_output_6.csv')"]}],"metadata":{"kernelspec":{"display_name":"Python 3.10.6 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.6"},"vscode":{"interpreter":{"hash":"95cdb06e919eab5e2c554174537356ac9b55200d1eb6f880dc25de04343a18ea"}}},"nbformat":4,"nbformat_minor":4}