def get():
config = configuration_parser.parse()
exec('max_depth=' + config.get(__name__, 'max_depth'),locals(),globals())
min_samples_split = config.getint(__name__, 'min_samples_split')
min_samples_leaf = config.getint(__name__, 'min_samples_leaf')
criterion = config.get(__name__, 'split criterion')
return tree.DecisionTreeRegressor(criterion=criterion,max_depth=max_depth,min_samples_leaf=min_samples_leaf,
min_samples_split=min_samples_split)
def get():
config = configuration_parser.parse()
alpha = config.getfloat(__name__, 'alpha')
coef0 = config.getint(__name__, 'coef0')
degree = config.getint(__name__, 'degree')
gamma = config.getfloat(__name__, 'gamma')
kernel = config.get(__name__, 'kernel')
model = KernelRidge(alpha=alpha, coef0=coef0, degree=degree, gamma=gamma, kernel=kernel, kernel_params=None)
return model
def get():
config = configuration_parser.parse()
exec('max_depth=' + config.get(__name__, 'max_depth'), locals(), globals())
min_samples_split = config.getint(__name__, 'min_samples_split')
min_samples_leaf = config.getint(__name__, 'min_samples_leaf')
criterion = config.get(__name__, 'split criterion')
return tree.DecisionTreeRegressor(criterion=criterion,
max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
min_samples_split=min_samples_split)
def get():
config = configuration_parser.parse()
minmax = ast.literal_eval(config.get(__name__, 'minmax'))
size = ast.literal_eval(config.get(__name__, 'size'))
epochs = config.getint(__name__, 'epochs')
show = config.getboolean(__name__, 'show')
goal = config.getfloat(__name__, 'goal')
exec('transf = [nl.trans.' + config.get(__name__, 'transfer_function') + '()]*len(size)',locals(),globals())
train = config.get(__name__, 'training_algorithm')
return model(nl.net.newff(minmax,size,transf),train,epochs,show,goal)
def get():
config = configuration_parser.parse()
estimators = config.getint(__name__, 'estimators')
lr = config.getfloat(__name__, 'learning rate')
loss = config.get(__name__, 'loss function')
exec('max_depth=' + config.get(__name__, 'max_depth'), locals(), globals())
min_samples_split = config.getint(__name__, 'min_samples_split')
min_samples_leaf = config.getint(__name__, 'min_samples_leaf')
return AdaBoostRegressor(DecisionTreeRegressor(max_depth=max_depth,min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf),n_estimators=estimators,loss=loss,
learning_rate=lr)
def get():
config = configuration_parser.parse()
estimators = config.getint(__name__, 'estimators')
lr = config.getfloat(__name__, 'learning rate')
loss = config.get(__name__, 'loss function')
exec('max_depth=' + config.get(__name__, 'max_depth'), locals(), globals())
min_samples_split = config.getint(__name__, 'min_samples_split')
min_samples_leaf = config.getint(__name__, 'min_samples_leaf')
return AdaBoostRegressor(DecisionTreeRegressor(
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf),
n_estimators=estimators,
loss=loss,
learning_rate=lr)
def get():
config = configuration_parser.parse()
estimators = config.getint(__name__, 'estimators')
exec('max_depth = ' + config.get(__name__, 'max_depth'),locals(),globals())
min_samples_split = config.getint(__name__, 'min_samples_split')
min_samples_leaf = config.getint(__name__, 'min_samples_leaf')
exec('max_leaf_nodes=' + config.get(__name__, 'max_leaf_nodes'),locals(),globals())
jobs = config.getint(__name__, 'jobs')
model = RandomForestRegressor(n_estimators=estimators,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes,
n_jobs=jobs)
return model
def get():
config = configuration_parser.parse()
estimators = config.getint(__name__, 'estimators')
exec('max_depth = ' + config.get(__name__, 'max_depth'), locals(),
globals())
min_samples_split = config.getint(__name__, 'min_samples_split')
min_samples_leaf = config.getint(__name__, 'min_samples_leaf')
exec('max_leaf_nodes=' + config.get(__name__, 'max_leaf_nodes'), locals(),
globals())
jobs = config.getint(__name__, 'jobs')
model = RandomForestRegressor(n_estimators=estimators,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes,
n_jobs=jobs)
return model
def get():
config = configuration_parser.parse()
alpha = config.getfloat(__name__, 'alpha')
gamma = config.getfloat(__name__, 'gamma')
kernel = config.get(__name__, 'kernel')
return KernelRidge(alpha=alpha, gamma=gamma, kernel=kernel)
def get():
config = configuration_parser.parse()
alpha = config.getfloat(__name__, 'alpha')
gamma = config.getfloat(__name__, 'gamma')
kernel = config.get(__name__, 'kernel')
return KernelRidge(alpha=alpha,gamma=gamma,kernel=kernel)
import configuration_parser
import importlib
import data_parser
import matplotlib
import sys
import numpy as np
if len(sys.argv) > 1:
config = configuration_parser.parse(sys.argv[1])
else:
config = configuration_parser.parse('default.conf')
parameter_names = ['model', 'data_path', 'save_path', 'Y', 'X', 'lwr_data_path', 'weights']
all_tests = config.get('AllTests', 'test_cases').split(',')
for case_name in all_tests:
parameter_values = []
for parameter in parameter_names:
if parameter == 'weights':
if config.has_option(case_name, parameter):
parameter_values.append(config.getboolean(case_name, parameter))
else:
parameter_values.append(config.getboolean('AllTests', parameter))
else:
if config.has_option(case_name, parameter):
parameter_values.append(config.get(case_name, parameter))
else:
parameter_values.append(config.get('AllTests', parameter))
model, data_path, save_path, y_data, x_data, lwr_data_path, weights = parameter_values
import configuration_parser
import importlib
import data_parser
import matplotlib
import sys
import numpy as np
if len(sys.argv) > 1:
config = configuration_parser.parse(sys.argv[1])
else:
config = configuration_parser.parse('default.conf')
parameter_names = ['model', 'data_path', 'save_path', 'Y', 'X', 'lwr_data_path', 'weights']
all_tests = config.get('AllTests', 'test_cases').split(',')
for case_name in all_tests:
parameter_values = []
for parameter in parameter_names:
if parameter == 'weights':
if config.has_option(case_name, parameter):
parameter_values.append(config.getboolean(case_name, parameter))
else:
parameter_values.append(config.getboolean('AllTests', parameter))
else:
if config.has_option(case_name, parameter):
parameter_values.append(config.get(case_name, parameter))
else:
parameter_values.append(config.get('AllTests', parameter))
model, data_path, save_path, y_data, x_data, lwr_data_path, weights = parameter_values