def __init__( self, treatment, number=50, requirements=18, name="CPM_BDBC", filename="./Data/BDBC_AllMeasurements.csv" ): # 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 = east_west_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) self.training_independent, self.training_dependent = self.get_training_data(method=treatment) global training_percent # print "inside: ", 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, 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, 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__(self, treatment, number=50, requirements=18, name="CPM_BDBC", filename="./Data/BDBC_AllMeasurements.csv"): # 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 = east_west_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) self.training_independent, self.training_dependent = self.get_training_data( method=treatment) global training_percent # print "inside: ", 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, 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__(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, treatment, number=50, requirements=16, fraction=0.5, name="cpm_X264", filename="./Data/X264_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 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__(self, treatment, number=50, requirements=18, name="CPM_BDBC", filename="./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) 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__(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__(self, treatment, number=50, requirements=9, name="CPM_APACHE", filename="./Problems/CPM/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 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)
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, 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)
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 if treatment is None: treatment = random_where elif treatment == 0: treatment = base_line # 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) self.CART = tree.DecisionTreeRegressor() self.CART = self.CART.fit(self.training_independent, self.training_dependent)
def __init__(self, requirements=9, fraction=0.5, name="CPM_APACHE", filename="./Problems/CPM/Data/Apache_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)