def __init__(self):
self.training_in, self.training_exp, self.test_in, self.test_exp = \
get_data(params['DATASET'])
self.n_vars = np.shape(self.test_in)[1]
if params['ERROR_METRIC'] == None:
self.error = mse
params['ERROR_METRIC'] = self.error
else:
self.error = params['ERROR_METRIC']
self.training_test = True
def __init__(self):
# Get training and test data
self.training_in, self.training_exp, self.test_in, self.test_exp = \
get_data(params['DATASET_TRAIN'], params['DATASET_TEST'])
# Find number of variables.
self.n_vars = np.shape(self.training_in)[0]
# Regression/classification-style problems use training and test data.
if params['DATASET_TEST']:
self.training_test = True
def __init__(self):
# Initialise base fitness function class.
super().__init__()
# Get training and test data
self.training_in, self.training_exp, self.test_in, self.test_exp = \
get_data(params['DATASET_TRAIN'], params['DATASET_TEST'])
# Find number of variables.
self.n_vars = np.shape(self.training_in)[0]
# Regression/classification-style problems use training and test data.
if params['DATASET_TEST']:
self.training_test = True
def __init__(self):
# Initialise base fitness function class.
super().__init__()
# Get training and test data
self.training_in, self.training_exp, self.test_in, self.test_exp = \
get_data(params['DATASET_TRAIN'], params['DATASET_TEST'])
# Find number of variables.
self.n_vars = np.shape(self.training_in)[0]
# Regression/classification-style problems use training and test data.
if params['DATASET_TEST']:
self.training_test = True
a = np.array([])
model = "ElasticNet int %.2f coefs %s" % (enet.intercept_,
pprint(enet.coef_))
yhat_train = enet.predict(train_X)
yhat_test = enet.predict(test_X)
return model, yhat_train, yhat_test
if __name__ == "__main__":
dataset_name = sys.argv[1]
if len(sys.argv) > 2:
metric = sys.argv[2]
else:
metric = "rmse"
s = "from .error_metric import " + metric + " as metric"
exec(s)
train_X, train_y, test_X, test_y = get_data(dataset_name)
train_X = train_X.T
test_X = test_X.T
methods = [fit_maj_class, fit_const, fit_lr, fit_enet]
for fit in methods:
model, train_yhat, test_yhat = fit(train_X, train_y, test_X)
error_train = metric(train_y, train_yhat)
error_test = metric(test_y, test_yhat)
print("%s %s %s train error %.2f test error %.2f" %
(metric.__name__, fit.__name__, model, error_train, error_test))
enet.fit(train_X, train_y)
model = "ElasticNet int %.2f coefs %s" % (enet.intercept_, pprint(enet.coef_))
yhat_train = enet.predict(train_X)
yhat_test = enet.predict(test_X)
return model, yhat_train, yhat_test
if __name__ == "__main__":
dataset_name = sys.argv[1]
if len(sys.argv) > 2:
metric = sys.argv[2]
else:
metric = "rmse"
s = "from .error_metric import " + metric + " as metric"
exec(s)
train_X, train_y, test_X, test_y = get_data(dataset_name)
train_X = train_X.T
test_X = test_X.T
methods = [fit_maj_class, fit_const, fit_lr, fit_enet]
for fit in methods:
model, train_yhat, test_yhat = fit(train_X, train_y, test_X)
error_train = metric(train_y, train_yhat)
error_test = metric(test_y, test_yhat)
print("%s %s %s train error %.2f test error %.2f" %
(metric.__name__, fit.__name__, model, error_train, error_test))