Upper Confidence Bound BO¶

In [1]:

from xopt import Xopt

# Ignore all warnings
import warnings

warnings.filterwarnings("ignore")

from xopt import Xopt # Ignore all warnings import warnings warnings.filterwarnings("ignore")

The Xopt object can be instantiated from a JSON or YAML file, or a dict, with the proper structure.

Here we will make one

In [2]:

# Make a proper input file.
YAML = """
generator:
  name: upper_confidence_bound
  beta: 0.1

evaluator:
  function: xopt.resources.test_functions.sinusoid_1d.evaluate_sinusoid

vocs:
  variables:
    x1: [0, 6.28]
  objectives:
    y1: 'MINIMIZE'
"""

# Make a proper input file. YAML = """ generator: name: upper_confidence_bound beta: 0.1 evaluator: function: xopt.resources.test_functions.sinusoid_1d.evaluate_sinusoid vocs: variables: x1: [0, 6.28] objectives: y1: 'MINIMIZE' """

In [3]:

X = Xopt.from_yaml(YAML)
X

X = Xopt.from_yaml(YAML) X

Out[3]:

            Xopt
________________________________
Version: 2.6.7.dev55+g7aa2f3618.d20250930
Data size: 0
Config as YAML:
dump_file: null
evaluator:
  function: xopt.resources.test_functions.sinusoid_1d.evaluate_sinusoid
  function_kwargs: {}
  max_workers: 1
  vectorized: false
generator:
  beta: 0.1
  computation_time: null
  custom_objective: null
  fixed_features: null
  gp_constructor:
    covar_modules: {}
    custom_noise_prior: null
    mean_modules: {}
    name: standard
    trainable_mean_keys: []
    transform_inputs: true
    use_cached_hyperparameters: false
    use_low_noise_prior: false
  max_travel_distances: null
  model: null
  n_candidates: 1
  n_interpolate_points: null
  n_monte_carlo_samples: 128
  name: upper_confidence_bound
  numerical_optimizer:
    max_iter: 2000
    max_time: 5.0
    n_restarts: 20
    name: LBFGS
  supports_batch_generation: true
  supports_constraints: true
  supports_single_objective: true
  turbo_controller: null
  use_cuda: false
max_evaluations: null
serialize_inline: false
serialize_torch: false
strict: true
vocs:
  constants: {}
  constraints: {}
  objectives:
    y1: MINIMIZE
  observables: []
  variables:
    x1:
    - 0.0
    - 6.28

Run Optimization¶

In [4]:

X.random_evaluate(3)
for i in range(5):
    print(i)
    X.step()

X.random_evaluate(3) for i in range(5): print(i) X.step()

0

1
2
3

4

View output data¶

In [5]:

X.data

X.data

Out[5]:

	x1	y1	c1	xopt_runtime	xopt_error
0	5.218640	-0.874569	-19.165903	0.000012	False
1	0.311845	0.306815	-5.613417	0.000005	False
2	4.188778	-0.866019	-19.026173	0.000004	False
3	4.876270	-0.986602	-18.954942	0.000011	False
4	4.819940	-0.994222	-18.712480	0.000010	False
5	4.706106	-0.999980	-18.500770	0.000010	False
6	4.709298	-0.999995	-18.500186	0.000009	False
7	4.710414	-0.999998	-18.500076	0.000009	False

Visualize model¶

In [6]:

fig, ax = X.generator.visualize_model(n_grid=100)

fig, ax = X.generator.visualize_model(n_grid=100)