def pointsESLStudents(groupAssignments):
global totalPES
totalPES = 0
pointWeight = load_csv.weightMaxESLStudents
maxESL = load_csv.maxESLStudents
groupESL = [0] * len(
load_csv.projectIDs) # Initializing an empty array to 0's
groupSize = [0] * len(load_csv.projectIDs)
prints.debug(
f"\n\n========pointsESLStudents========\n{groupESL} gESL Before \n{groupSize} gSize Before"
)
# groupESL[i] is the number of ESL students on team i
for i in range(len(load_csv.studentID)):
if load_csv.studentESL[i]:
groupESL[groupAssignments[i]] += 1 # ESL students per team
groupSize[groupAssignments[i]] += 1 # Students per team
prints.debug(f"{groupESL} gESL After \n{groupSize} gSize After")
# Awarding points
for i in range(len(groupESL)):
if groupESL[i] <= maxESL and groupSize[i] >= load_csv.minTeamSize[i]:
totalPES += pointWeight
return totalPES
def pointsMaxLowGPAStudents(groupAssignments):
global totalPML
totalPML = 0
weightPML = load_csv.weightMaxLowGPAStudents
minGPA = load_csv.lowGPAThreshold
maxLow = load_csv.maxLowGPAStudents
# initialize maxLowGroup - counts how many per group do not meet minGPA constraint
maxLowGroup = [0] * len(load_csv.projectIDs)
# initialize groupSize - counts how many students there are in each group
groupSize = [0] * len(load_csv.projectIDs)
# loop for counting maxLowGPA students and group sizes
for i in range(len(load_csv.studentID)):
if load_csv.studentGPA[i] < minGPA:
maxLowGroup[groupAssignments[i]] += 1
groupSize[groupAssignments[i]] += 1
prints.debug("========pointsMaxLowGPAStudents========")
prints.debug(f"groups with lowGPA students: {maxLowGroup}")
prints.debug(f"groups with # of studetns: {groupSize}")
# iterates through maxLowGroup for points - also ignores empty groups
for i in range(len(maxLowGroup)):
if maxLowGroup[i] <= maxLow and groupSize[i] >= load_csv.minTeamSize[i]:
prints.debug(
f"group: {load_csv.projectIDs[i]} satisfies the condition!")
totalPML += weightPML
return totalPML
def outputCSV(studentFileData, outputFileName, optimalSolution):
# optimalSolution is a numpy array
# Matching projectID's to their indexed number in optimalSolution
for i in range(len(optimalSolution)):
optimalSolution[i] = load_csv.projectIDs[int(optimalSolution[i])]
studentFileData[
'assignment'] = '' # If there's no 'Assignment' column, create its header
assignments = pd.DataFrame({'assignment':
optimalSolution}) # Creating the dataframe
studentFileData.update(
assignments) # Appending the dataframe assignments to studentFileData
studentFileData = studentFileData.astype(
{"assignment": int}) # Treats assignment outputs as ints
prints.debug(f"\n========outputCSV========\n\n{studentFileData} After"
) # After changes output
# Writes/Overwrites to a csv named outputFileName
studentFileData.to_csv(outputFileName, index=False, float_format="%.3f")
def pointsStudentChoice(groupAssignments):
global totalPSC
totalPSC = 0
# maxNumChoices = load_csv.numStudentChoices
maxScore = load_csv.weightStudentChoice1
prints.debug(
f"\n\n========pointsStudentChoice========\n{groupAssignments}")
# This outputs the score for each student's choice's based on their actual assignment
# Students = rows (y), ProjectChoices = columns (x)
for y in range(len(load_csv.studentID)):
for x in range(len(load_csv.studentChoiceN.columns)):
# If NaN is detected gives score based on position x then breaks
prints.debug(
f"ID {load_csv.studentID.iat[y]}, Assignment {groupAssignments[y]}, Choice {load_csv.studentChoiceN.iat[y, x]}"
)
if pd.isna(load_csv.studentChoiceN.iat[y, x]):
totalPSC += math.ceil(maxScore / (2**x))
prints.debug(f"Score {totalPSC}")
break
# If an assignment match is detected gives score based on position x then breaks
else:
if load_csv.studentChoiceN.iat[y, x] == groupAssignments[y]:
if load_csv.studentChoiceN.iat[y,
0] == groupAssignments[y]:
totalPSC += maxScore
prints.debug(f"Score {totalPSC}")
break
totalPSC += math.ceil(maxScore / (2**x))
prints.debug(f"Score {totalPSC}")
break
return totalPSC
def pointsAvoid(groupAssignments):
global totalPSA
totalPSA = 0
weightPSA = load_csv.weightAvoid
# initializes bad - counts how many studentAvoid matches are found
bad = 0
prints.debug("========pointsAvoid========")
for i in range(load_csv.numStudents):
# skips student with empty avoid value
if pd.isna(load_csv.studentAvoid[i]) is False:
# stores studentAvoid data to 'avoid'
avoid = int(load_csv.studentAvoid[i])
prints.debug("======student loop======")
prints.debug(f"student: {[i]} avoid: {avoid}")
prints.debug(f"student group: {groupAssignments[i]}")
prints.debug(f"avoid group: {groupAssignments[avoid]}")
# identifies studentAvoid match within in same group
if groupAssignments[i] == groupAssignments[avoid]:
prints.debug("studentAvoid match found")
bad += 1
prints.debug(f"bad: {bad}")
totalPSA -= (weightPSA * bad)
return totalPSA
def pointsTeamSize(groupAssignments):
global totalPTS
totalPTS = 0
weightMinPTS = load_csv.weightMinTeamSize
weightMaxPTS = load_csv.weightMaxTeamSize
# initialize groupSize - counts how many students there are in each group
groupSize = [0] * len(load_csv.projectIDs)
# loop for counting group size
for i in range(len(load_csv.studentID)):
groupSize[groupAssignments[i]] += 1
prints.debug("========pointsTeamSize========")
prints.debug(f"group size: {groupSize}")
prints.debug(f"min size: {load_csv.minTeamSize}")
prints.debug(f"max size: {load_csv.maxTeamSize}")
# iterates through groupSize to identify group meeting the size constraints
for i in range(len(groupSize)):
if load_csv.minTeamSize[i] - 1 > groupSize[i] and groupSize[i] > 0:
totalPTS -= weightMinPTS
prints.debug(
f"group{[i]} teamSize:{groupSize[i]} points:{totalPTS}")
continue
if load_csv.minTeamSize[i] <= groupSize[i]:
totalPTS += weightMinPTS
if groupSize[i] > load_csv.maxTeamSize[i] + 2:
x = groupSize[i] - load_csv.maxTeamSize[i] - 1
totalPTS -= (weightMaxPTS * x)
prints.debug(
f"group{[i]} teamSize:{groupSize[i]} points:{totalPTS}")
continue
if groupSize[i] > load_csv.maxTeamSize[i] + 1:
totalPTS -= weightMaxPTS
prints.debug(
f"group{[i]} teamSize:{groupSize[i]} points:{totalPTS}")
continue
if groupSize[i] <= load_csv.maxTeamSize[i]:
totalPTS += weightMaxPTS
prints.debug(f"group{[i]} teamSize:{groupSize[i]} points:{totalPTS}")
return totalPTS