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)
