import copy
import experiments.uci.uci_experiments as uci
import experiments.uci.uci_default_parameters as defaults

experiment_name = "uci_bbb_bo_wine"

lr = 0.005

optimizer_params = defaults.UCI_SMALL_BBB_PARAMETERS

optimizer_params["learning_rate"] = lr

save_params = {
    'metric_history': False,
    'objective_history': True,
    'model': False,
    'optimizer': False }

param_bounds = {'log_noise_prec': (0.8, 1.1), 'log_prior_prec': (0, 4)}

from sklearn.gaussian_process.kernels import Matern
gp_params = {'kernel': Matern(nu=2.5, length_scale=[0.06, 1.25]), 'alpha': 1e-2}

# Create the base experiment and register it.
uci_slang_run = uci.uci_bbb_bo.add_variant(experiment_name, optimizer_params=optimizer_params, param_bounds=param_bounds, gp_params=gp_params)



# Define the parameters of the experiment:
data_sets = ["wine" + str(i) for i in range(20)]

variants = []

for data_set in data_sets:
    params = copy.deepcopy(uci_slang_run.get_args())
    name = '{}'.format(data_set)
    variants.append(name)
    uci_slang_run.add_variant(name, data_set=data_set)

fa01a42597182f7ebb4aa2bebc185f3901db486b
<mask token>

<mask token>
for data_set in data_sets:
    params = copy.deepcopy(uci_slang_run.get_args())
    name = '{}'.format(data_set)
    variants.append(name)
    uci_slang_run.add_variant(name, data_set=data_set)

<mask token>
experiment_name = 'uci_bbb_bo_wine'
lr = 0.005
optimizer_params = defaults.UCI_SMALL_BBB_PARAMETERS
optimizer_params['learning_rate'] = lr
save_params = {'metric_history': False, 'objective_history': True, 'model':
    False, 'optimizer': False}
param_bounds = {'log_noise_prec': (0.8, 1.1), 'log_prior_prec': (0, 4)}
<mask token>
gp_params = {'kernel': Matern(nu=2.5, length_scale=[0.06, 1.25]), 'alpha': 0.01
    }
uci_slang_run = uci.uci_bbb_bo.add_variant(experiment_name,
    optimizer_params=optimizer_params, param_bounds=param_bounds, gp_params
    =gp_params)
data_sets = [('wine' + str(i)) for i in range(20)]
variants = []
for data_set in data_sets:
    params = copy.deepcopy(uci_slang_run.get_args())
    name = '{}'.format(data_set)
    variants.append(name)
    uci_slang_run.add_variant(name, data_set=data_set)

import copy
import experiments.uci.uci_experiments as uci
import experiments.uci.uci_default_parameters as defaults
experiment_name = 'uci_bbb_bo_wine'
lr = 0.005
optimizer_params = defaults.UCI_SMALL_BBB_PARAMETERS
optimizer_params['learning_rate'] = lr
save_params = {'metric_history': False, 'objective_history': True, 'model':
    False, 'optimizer': False}
param_bounds = {'log_noise_prec': (0.8, 1.1), 'log_prior_prec': (0, 4)}
from sklearn.gaussian_process.kernels import Matern
gp_params = {'kernel': Matern(nu=2.5, length_scale=[0.06, 1.25]), 'alpha': 0.01
    }
uci_slang_run = uci.uci_bbb_bo.add_variant(experiment_name,
    optimizer_params=optimizer_params, param_bounds=param_bounds, gp_params
    =gp_params)
data_sets = [('wine' + str(i)) for i in range(20)]
variants = []
for data_set in data_sets:
    params = copy.deepcopy(uci_slang_run.get_args())
    name = '{}'.format(data_set)
    variants.append(name)
    uci_slang_run.add_variant(name, data_set=data_set)

import copy
import experiments.uci.uci_experiments as uci
import experiments.uci.uci_default_parameters as defaults

experiment_name = "uci_bbb_bo_wine"

lr = 0.005

optimizer_params = defaults.UCI_SMALL_BBB_PARAMETERS

optimizer_params["learning_rate"] = lr

save_params = {
    'metric_history': False,
    'objective_history': True,
    'model': False,
    'optimizer': False }

param_bounds = {'log_noise_prec': (0.8, 1.1), 'log_prior_prec': (0, 4)}

from sklearn.gaussian_process.kernels import Matern
gp_params = {'kernel': Matern(nu=2.5, length_scale=[0.06, 1.25]), 'alpha': 1e-2}

# Create the base experiment and register it.
uci_slang_run = uci.uci_bbb_bo.add_variant(experiment_name, optimizer_params=optimizer_params, param_bounds=param_bounds, gp_params=gp_params)



# Define the parameters of the experiment:
data_sets = ["wine" + str(i) for i in range(20)]

variants = []

for data_set in data_sets:
    params = copy.deepcopy(uci_slang_run.get_args())
    name = '{}'.format(data_set)
    variants.append(name)
    uci_slang_run.add_variant(name, data_set=data_set)
