Bayesian exploration from YAML¶

In [1]:

from xopt import Xopt

# set values if testing
import os

import warnings

warnings.filterwarnings("ignore")


SMOKE_TEST = os.environ.get("SMOKE_TEST")

YAML = """
generator:
    name: bayesian_exploration
    vocs:
        variables:
            x1: [0, 3.14159]
            x2: [0, 3.14159]
        objectives:
            y1: EXPLORE
        constraints:
            c1: [GREATER_THAN, 0]
            c2: [LESS_THAN, 0.5]
        constants: {a: dummy_constant}
evaluator:
    function: xopt.resources.test_functions.tnk.evaluate_TNK



"""

from xopt import Xopt # set values if testing import os import warnings warnings.filterwarnings("ignore") SMOKE_TEST = os.environ.get("SMOKE_TEST") YAML = """ generator: name: bayesian_exploration vocs: variables: x1: [0, 3.14159] x2: [0, 3.14159] objectives: y1: EXPLORE constraints: c1: [GREATER_THAN, 0] c2: [LESS_THAN, 0.5] constants: {a: dummy_constant} evaluator: function: xopt.resources.test_functions.tnk.evaluate_TNK """

In [2]:

X = Xopt.from_yaml(YAML)

# for testing purposes only
if SMOKE_TEST:
    X.generator.numerical_optimizer.n_restarts = 1
    X.generator.n_monte_carlo_samples = 1

X

X = Xopt.from_yaml(YAML) # for testing purposes only if SMOKE_TEST: X.generator.numerical_optimizer.n_restarts = 1 X.generator.n_monte_carlo_samples = 1 X

Out[2]:

            Xopt
________________________________
Version: 0.1.dev1+g6d6c7f050
Data size: 0
Config as YAML:
dump_file: null
evaluator:
  function: xopt.resources.test_functions.tnk.evaluate_TNK
  function_kwargs:
    raise_probability: 0
    random_sleep: 0
    sleep: 0
  max_workers: 1
  vectorized: false
generator:
  computation_time: null
  custom_objective: null
  fixed_features: null
  gp_constructor:
    covar_modules: {}
    custom_noise_prior: null
    mean_modules: {}
    name: standard
    train_config: null
    train_kwargs: null
    train_method: lbfgs
    train_model: true
    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: bayesian_exploration
  numerical_optimizer:
    discrete_max_batch_size: 2048
    discrete_max_choices: 4096
    max_iter: 1000
    max_time: 5.0
    mixed_max_discrete_configurations: 512
    n_restarts: 20
    name: LBFGS
  returns_id: false
  supports_batch_generation: true
  supports_constraints: true
  supports_discrete_variables: true
  supports_multi_objective: true
  supports_single_objective: true
  turbo_controller: null
  use_cuda: false
  vocs:
    constants:
      a:
        dtype: null
        type: Constant
        value: dummy_constant
    constraints:
      c1:
        dtype: null
        type: GreaterThanConstraint
        value: 0.0
      c2:
        dtype: null
        type: LessThanConstraint
        value: 0.5
    objectives:
      y1:
        dtype: null
        type: ExploreObjective
    observables: {}
    variables:
      x1:
        default_value: null
        domain:
        - 0.0
        - 3.14159
        dtype: null
        type: ContinuousVariable
      x2:
        default_value: null
        domain:
        - 0.0
        - 3.14159
        dtype: null
        type: ContinuousVariable
serialize_inline: false
serialize_torch: false
stopping_condition: null
strict: true

In [3]:

X.random_evaluate(5)

for i in range(5):
    print(f"step {i}")
    X.step()

X.random_evaluate(5) for i in range(5): print(f"step {i}") X.step()

step 0

step 1

step 2

step 3

step 4

In [4]:

print(X.data)

print(X.data)

         x1        x2               a        y1        y2         c1  \
0  0.549180  2.143294  dummy_constant  0.549180  2.143294   3.959657   
1  1.654162  2.914159  dummy_constant  1.654162  2.914159  10.268110   
2  2.246483  0.232013  dummy_constant  2.246483  0.232013   4.108091   
3  0.083061  2.918981  dummy_constant  0.083061  2.918981   7.437529   
4  0.668058  1.340336  dummy_constant  0.668058  1.340336   1.198784   
5  2.632330  1.173525  dummy_constant  2.632330  1.173525   7.215287   
6  0.000000  0.000000  dummy_constant  0.000000  0.000000  -1.100000   
7  0.963789  0.778770  dummy_constant  0.963789  0.778770   0.547511   
8  0.880936  0.781622  dummy_constant  0.880936  0.781622   0.329191   
9  1.064300  0.341910  dummy_constant  1.064300  0.341910   0.223827   

         c2  xopt_runtime  xopt_error  
0  2.702835      0.001316       False  
1  7.160253      0.000271       False  
2  3.122021      0.001229       False  
3  6.025306      0.000440       False  
4  0.734407      0.000272       False  
5  5.000467      0.005893       False  
6  0.500000      0.002732       False  
7  0.292813      0.005722       False  
8  0.224423      0.005611       False  
9  0.343426      0.002558       False  

In [5]:

# plot results
ax = X.data.plot("x1", "x2")
ax.set_aspect("equal")

# plot results ax = X.data.plot("x1", "x2") ax.set_aspect("equal")

In [6]:

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

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