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+g46cc86a6b
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_contextual_variables: true
  supports_discrete_variables: true
  supports_multi_objective: true
  supports_no_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        c2  \
0  0.596286  0.256373  dummy_constant  0.596286  0.256373 -0.676441  0.068625   
1  1.490711  2.012031  dummy_constant  1.490711  2.012031  5.341743  3.267744   
2  2.307613  2.003695  dummy_constant  2.307613  2.003695  8.296845  5.528563   
3  1.637783  1.517861  dummy_constant  1.637783  1.517861  3.904142  2.330592   
4  2.657714  0.599857  dummy_constant  2.657714  0.599857  6.514980  4.665703   
5  0.000000  2.582827  dummy_constant  0.000000  2.582827  5.570994  4.588167   
6  0.000000  0.111089  dummy_constant  0.000000  0.111089 -1.087659  0.401252   
7  0.958514  0.340364  dummy_constant  0.958514  0.340364 -0.033341  0.235719   
8  0.968388  0.992126  dummy_constant  0.968388  0.992126  0.823960  0.461575   
9  0.803133  0.953018  dummy_constant  0.803133  0.953018  0.532564  0.297115   

   xopt_runtime  xopt_error  
0      0.003036       False  
1      0.000275       False  
2      0.000263       False  
3      0.000265       False  
4      0.000269       False  
5      0.005077       False  
6      0.001464       False  
7      0.005866       False  
8      0.006000       False  
9      0.003152       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)