def test_case4(self):
"""Testing the calculate rank weights function when c=0 and n=0"""
rankWeights = Solution.calcRankWeights(c=0,n=0)
expected = {}
self.assertEqual(rankWeights,expected)
def setUp(self):
self.students, self.supervisors = createRandomData(4, 8, 18)
# Generating rank weights for c = 0.5
self.rankWeights = Solution.calcRankWeights()
#Creating fitness cache
self.dummySolution = Solution()
self.fitnessCache = {}
for sup in self.supervisors:
supervisorKeywords = self.supervisors[sup].getKeywords()
for stu in self.students:
studentKeywords = self.students[stu].getKeywords()
f_stu, f_sup = self.dummySolution.kw_similarity(
studentKeywords, supervisorKeywords, self.rankWeights)
self.fitnessCache[str((stu, sup))] = (f_stu, f_sup)
#Setting up the GA Object
self.mutationOp = mutate
self.selectionOp = tournamentSelection
self.crossoverOp = crossover
self.geneticAlgorithm = GeneticAlgorithm(
self.students, self.supervisors, self.fitnessCache,
self.rankWeights, self.mutationOp, self.crossoverOp,
self.selectionOp)
#Parameters for GA
self.numberOfGenerations = 20
self.populationSize = 10
self.mutationProbability = 0.1
self.swapProbability = 0.3
self.transferProbability = 0.5
#Initializing population
self.population = self.geneticAlgorithm.initializePopulation(
self.populationSize)
self.maxFst = max(self.population, key=lambda x: x.getFst()).getFst()
self.minFst = min(self.population, key=lambda x: x.getFst()).getFst()
self.maxFsup = max(self.population,
key=lambda x: x.getFsup()).getFsup()
self.minFsup = min(self.population,
key=lambda x: x.getFsup()).getFsup()
def test_case2(self):
"""Testing the calculate Rank Weights function when c = 1"""
rankWeights = Solution.calcRankWeights(c=1)
result = True
expected = 0.2
for i in rankWeights:
if rankWeights[i] != expected:
result = False
break
self.assertTrue(result)
def test_case3(self):
"""Testing the calculate rank weights function when c = 0.3"""
rankWeights = Solution.calcRankWeights(c=0.3)
result = round(sum(rankWeights.values()),15)
expected = {1: 0.701705143, 2: 0.210511543, 3: 0.063153463, 4: 0.0189460389, 5: 0.005683812}
result2 = True
for i in rankWeights:
if round(rankWeights[i],8) != round(expected[i],8):
result2 = False
break
self.assertEqual((result,result2),(1,True))
def setUp(self):
#Creating a list of students and supervisors
topicNames, topicPaths, topicIDs, levels = parseFile(
"pystsup/test/acm.txt")
self.supervisors = {}
self.students = {}
stuID = "student"
supID = "supervisor"
stuList1 = [
"MapReduce-based systems", "Multidimensional range search",
"Open source software", "Data mining", "Online shopping"
]
stuList2 = [
"Heuristic function construction", "Multi-agent systems",
"Open source software", "Data mining", "Speech recognition"
]
stuList3 = [
"Multi-agent systems", "Speech recognition",
"Heuristic function construction", "Data mining",
"Object identification"
]
stuList4 = [
"Multi-agent systems", "Intelligent agents", "Speech recognition",
"Object identification", "Heuristic function construction"
]
supList1 = [
"Multi-agent systems", "Intelligent agents",
"MapReduce-based systems", "Object identification",
"Heuristic function construction"
]
supList2 = [
"Open source software", "Data mining", "Speech recognition",
"Object identification", "Heuristic function construction"
]
supList3 = [
"Multi-agent systems", "Intelligent agents", "Speech recognition",
"Object identification", "Heuristic function construction"
]
supList = [supList1, supList2, supList3]
supQuota = [2, 2, 1]
stuList = [stuList1, stuList2, stuList3, stuList4]
for i in range(3):
toAdd = []
sup_list = {}
rank = 0
for kw in supList[i]:
rank += 1
sup_list[rank] = [
kw, getPath(kw, topicNames, topicPaths, topicIDs)
]
sup = supID + str(i + 1)
quota = supQuota[i]
supervisorObject = Supervisor(sup, sup_list, quota)
self.supervisors[sup] = supervisorObject
for i in range(4):
toAdd = []
stu_list = {}
rank = 0
for kw in stuList[i]:
rank += 1
stu_list[rank] = [
kw, getPath(kw, topicNames, topicPaths, topicIDs)
]
stu = stuID + str(i + 1)
studentObject = Student(stu, stu_list)
self.students[stu] = studentObject
# Generating rank weights for c = 0.5
self.rankWeights = Solution.calcRankWeights()
#Creating fitness cache
self.dummySolution = Solution()
self.fitnessCache = {}
for sup in self.supervisors:
supervisorKeywords = self.supervisors[sup].getKeywords()
for stu in self.students:
studentKeywords = self.students[stu].getKeywords()
f_stu, f_sup = self.dummySolution.kw_similarity(
studentKeywords, supervisorKeywords, self.rankWeights)
self.fitnessCache[str((stu, sup))] = (f_stu, f_sup)
# Creating two graphs and solutions
self.graph1 = BipartiteGraph()
self.graph2 = BipartiteGraph()
self.graph1.addEdge("supervisor1", "student2")
self.graph1.addEdge("supervisor2", "student4")
self.graph1.addEdge("supervisor3", "student1")
self.graph1.addEdge("supervisor1", "student3")
self.solution1 = Solution(self.graph1)
self.graph2.addEdge("supervisor1", "student2")
self.graph2.addEdge("supervisor2", "student1")
self.graph2.addEdge("supervisor3", "student4")
self.graph2.addEdge("supervisor1", "student3")
self.solution2 = Solution(self.graph2)
