Python jmoo_decision函数代码示例

 def __init__(prob, dataset_name, instances=100, features=20, nol=30, noc=0.3, tuning_precent=20):
     prob.name = "hpcc_kmeans_" + dataset_name
     prob.features = features
     prob.instances = instances
     prob.dataset_name = dataset_name
     prob.tuning_instances = sample(range(1, prob.instances + 1), int(prob.instances * tuning_precent / 100))
     prob.conv = noc
     prob.decisions = [jmoo_decision("k", 1, int(features ** 0.5)), jmoo_decision("number_of_loops", 1, nol)]
     prob.objectives = [jmoo_objective("convergence", True)]
     prob.is_binary = False

 def __init__(prob, numDecs=20, numObjs=2):
     prob.name = "DTLZ6_" + str(numDecs) + "_" + str(numObjs)
     names = ["x"+str(i+1) for i in range(numDecs)]
     lows =  [0.0 for i in range(numDecs)]
     ups =   [1.0 for i in range(numDecs)]
     prob.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in range(numDecs)]
     prob.objectives = [jmoo_objective("f" + str(i+1), True) for i in range(numObjs)]

 def __init__(prob):
     prob.name = "POM3Asanscomp"
     names = ["Culture", "Criticality", "Criticality Modifier", "Initial Known", "Inter-Dependency", "Dynamism", "Size", "Plan", "Team Size"]
     LOWS = [0.1, 0.82, 2,  0.40, 1,   1,  0, 0, 1]
     UPS  = [0.9, 1.20, 10, 0.70, 100, 50, 4, 5, 44]
     prob.decisions = [jmoo_decision(names[i], LOWS[i], UPS[i]) for i in range(len(names))]
     prob.objectives = [jmoo_objective("Cost", True, 0), jmoo_objective("Score", False, 0, 1), jmoo_objective("Idle", True, 0, 1)]

    def __init__(
        self,
        treatment,
        number=50,
        requirements=16,
        fraction=0.5,
        name="cpm_X264",
        filename="./Data/X264_AllMeasurements.csv",
    ):
        # def __init__(self, treatment, number=50, requirements=16, fraction=0.5, name="cpm_X264", filename="./Problems/CPM/Data/X264_AllMeasurements.csv"):

        self.name = name
        self.filename = filename
        # Setting up to create decisions
        names = ["x" + str(i + 1) for i in xrange(requirements)]
        lows = [0 for _ in xrange(requirements)]
        ups = [1 for _ in xrange(requirements)]
        # Generating decisions
        self.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in range(requirements)]
        # Generating Objectives (this is single objective)
        self.objectives = [jmoo_objective("f1", True)]
        # Read Data
        self.header, self.data = read_csv(self.filename, header=True)

        self.training_independent, self.training_dependent = self.get_training_data(method=treatment)
        global training_percent
        # print training_percent,
        from math import log

        # print "Length of training dataset: ", len(self.training_dependent), len(self.Data), (2*log(len(self.Data) * training_percent, 2))

        self.CART = tree.DecisionTreeRegressor()
        self.CART = self.CART.fit(self.training_independent, self.training_dependent)
        self.saved_time = (self.find_total_time() - sum(self.training_dependent)) / 10 ** 4

 def __init__(self, requirements, releases, clients, density, budget):
     self.name = (
         "NRP_"
         + str(requirements)
         + "_"
         + str(releases)
         + "_"
         + str(clients)
         + "_"
         + str(density)
         + "_"
         + str(budget)
     )
     names = ["x" + str(i + 1) for i in range(requirements)]  # |x_i + y_i|
     lows = [-1 for i in xrange(requirements)]
     ups = [(releases - 1) for _ in xrange(requirements)]
     self.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in range(requirements)]
     self.objectives = [jmoo_objective("f1", False)]  # , jmoo_objective("f2", False)]  # single objective nrp
     self.trequirements = requirements
     self.treleases = releases
     self.tclients = clients
     self.tdensity = density
     self.tbudget = budget
     self.requirement = None
     self.client = None
     self.release = None
     self.precedence = []
     self.generate_data()

    def __init__(self, treatment, requirements=9, name="CPM_APACHE", filename="./Data/Apache_AllMeasurements.csv"):
        self.name = name
        self.filename = filename

        if treatment is None:
            treatment = random_where
        elif treatment == 0:
            treatment = base_line

        # Setting up to create decisions (This is something specific from the JMOO framework
        names = ["x" + str(i + 1) for i in xrange(requirements)]
        lows = [0 for _ in xrange(requirements)]
        ups = [1 for _ in xrange(requirements)]

        # Generating decisions
        self.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in range(requirements)]
        # Generating Objectives (this is single objective)
        self.objectives = [jmoo_objective("f1", True)]

        # Read Data
        self.header, self.data = read_csv(self.filename, header=True)

        self.training_independent, self.training_dependent = self.get_training_data(method=treatment)
        self.CART = tree.DecisionTreeRegressor()
        self.CART = self.CART.fit(self.training_independent, self.training_dependent)
        self.saved_time = (self.find_total_time() - sum(self.training_dependent))/10**4

    def __init__(prob):

        super(zdt1, prob).__init__()
        prob.name = "ZDT1"
        names = ["x" + str(i+1) for i in range(30)]
        prob.decisions = [jmoo_decision(names[i], 0, 1) for i in range(len(names))]
        prob.objectives = [jmoo_objective("f1", True), jmoo_objective("f2", True)]

    def __init__(self, treatment, requirements=9, name="CPM_APACHE", filename="./data/Apache_AllMeasurements.csv"):
    # def __init__(self, treatment, number=50, requirements=9, name="CPM_APACHE", filename="./Problems/CPM/data/Apache_AllMeasurements.csv"):

        self.name = name
        self.filename = filename
        self.no_of_clusters = 0
        # Setting up to create decisions
        names = ["x"+str(i+1) for i in xrange(requirements)]
        lows = [0 for _ in xrange(requirements)]
        ups = [1 for _ in xrange(requirements)]
        # Generating decisions
        self.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in range(requirements)]
        # Generating Objectives (this is single objective)
        self.objectives = [jmoo_objective("f1", True)]
        # Read data
        self.header, self.data = read_csv(self.filename, header=True)

        self.training_independent, self.training_dependent,  = self.get_training_data(method=treatment)
        global training_percent
        from math import log, ceil
        # # print training_percent,
        # print "=" * 20
        # print "Reduced data: ", self.training_dependent
        # print "total run time: ", sum(self.training_dependent)
        # print "totol total run time: ", self.find_total_time()
        # print "sadsadsa time: ", self.find_total_time() - sum(self.training_dependent)
        # print "Saving Percentage: ", (sum(self.training_dependent)/self.find_total_time()) *100
        # print "Length of self.data: ", len(self.data)
        # print treatment.__name__


        print "Length of training dataset: ", len(self.training_dependent), len(self.data), (2*log(len(self.data) * training_percent, 2))
        self.CART = tree.DecisionTreeRegressor()
        self.CART = self.CART.fit(self.training_independent, self.training_dependent)
        self.saved_time = (self.find_total_time() - sum(self.training_dependent))/10**4

 def __init__(prob, numDecs=10, numObjs=3):
     super(car_impact, prob).__init__()
     prob.name = "Car_Impact_" + str(numDecs) + "_" + str(numObjs)
     names = ["x"+str(i+1) for i in range(numDecs)]
     lows =  [0.0 for i in range(numDecs)]
     ups =   [1.0 for i in range(numDecs)]
     prob.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in range(numDecs)]
     prob.objectives = [jmoo_objective("f" + str(i+1), True) for i in range(numObjs)]

 def __init__(prob, numDecs=10, numObjs=2):
     super(c2_convex_dtlz2, prob).__init__()
     prob.name = "Convex_DTLZ2_" + str(numDecs) + "_" + str(numObjs)
     names = ["x"+str(i+1) for i in range(numDecs)]
     lows =  [0.0 for i in range(numDecs)]
     ups =   [1.0 for i in range(numDecs)]
     prob.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in range(numDecs)]
     prob.objectives = [jmoo_objective("f" + str(i+1), True) for i in range(numObjs)]

 def __init__(self, name,  objnum = 3):
     self.name = name
     assert(if_exists(name) is True), "Check the filename"
     self.url = "./Problems/Feature_Models/References/" + name + ".xml"
     spl_cost_data = "./Problems/Feature_Models/Cost/" + name + ".cost"
     self.ft = load_ft_url(self.url)
     self.ft.load_cost(spl_cost_data)
     lows = [0 for _ in xrange(len(self.ft.leaves))]
     ups = [1 for _ in xrange(len(self.ft.leaves))]
     names = ["x"+str(i) for i in xrange(len(self.ft.leaves))]
     self.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in xrange(len(self.ft.leaves))]
     self.objectives = [jmoo_objective("fea", True), jmoo_objective("conVio", True), jmoo_objective("Cost", True)]

 def __init__(self, requirements=18, fraction=0.5, name="CPM_BDBC", filename="./Problems/CPM/Data/BDBC_AllMeasurements.csv"):
     self.name = name
     self.filename = filename
     names = ["x"+str(i+1) for i in xrange(requirements)]
     lows = [0 for _ in xrange(requirements)]
     ups = [1 for _ in xrange(requirements)]
     self.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in range(requirements)]
     self.objectives = [jmoo_objective("f1", True)]
     self.data = read_csv(self.filename)
     self.testing_independent, self.testing_dependent = [], []
     self.training_independent, self.training_dependent = self.get_training_data(fraction)
     self.CART = tree.DecisionTreeRegressor()
     self.CART = self.CART.fit(self.training_independent, self.training_dependent)

 def __init__(self, tree, obj_funcs=None, **settings):
     jmoo_problem.__init__(self)
     if not obj_funcs:
         obj_funcs = [eval_softgoals, eval_goals, eval_coverage]
     self.name = tree.name
     self.obj_funcs = obj_funcs
     self._tree = tree
     self.roots = self._tree.get_roots()
     self.bases = self._tree.get_bases()
     obj_names = [func.__name__.split("_")[1] for func in obj_funcs]
     dec_names = [base.id for base in self.bases]
     self.decisions = [jmoo_decision(dec_names[i], f, t) for i in range(len(dec_names))]
     self.objectives = [jmoo_objective(obj_names[i], True) for i in range(len(obj_names))]
     self.chain = set()
     self.is_percent = settings.get("obj_is_percent", True)

 def __init__(self, name, valid_solutions=False, objnum=3, is_binary=False):
     self.name = name
     self.valid_solutions = valid_solutions
     assert(if_exists(name) is True), "Check the filename"
     self.url = "./Problems/Feature_Models/References/" + name + ".xml"
     spl_cost_data = "./Problems/Feature_Models/Cost/" + name + ".cost"
     self.ft = load_ft_url(self.url)
     self.ft.load_cost(spl_cost_data)
     lows = [0 for _ in xrange(len(self.ft.leaves))]
     ups = [1 for _ in xrange(len(self.ft.leaves))]
     names = ["x"+str(i) for i in xrange(len(self.ft.leaves))]
     self.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in xrange(len(self.ft.leaves))]
     self.objectives = [jmoo_objective("number_of_features", True),
                        jmoo_objective("constrained_violated", True),
                        jmoo_objective("cost", True)]
     self.is_binary = True

    def __init__(self, treatment, number=50, requirements=18, name="CPM_BDBC", filename="./Problems/CPM//Data/BDBC_AllMeasurements.csv"):

        self.name = name
        self.filename = filename
        if treatment is None: treatment = random_where
        elif treatment == 0: treatment = base_line
        names = ["x"+str(i+1) for i in xrange(requirements)]
        lows = [0 for _ in xrange(requirements)]
        ups = [1 for _ in xrange(requirements)]
        self.decisions = [jmoo_decision(names[i], lows[i], ups[i]) for i in range(requirements)]
        self.objectives = [jmoo_objective("f1", True)]
        self.header, self.data = read_csv(self.filename, header=True)
        print "Length of Data: ", len(self.data)

        self.training_independent, self.training_dependent = self.get_training_data(method=treatment)
        self.CART = tree.DecisionTreeRegressor()
        self.CART = self.CART.fit(self.training_independent, self.training_dependent)