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+g3385ef356
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:
    max_iter: 2000
    max_time: 5.0
    n_restarts: 20
    name: LBFGS
  returns_id: false
  supports_batch_generation: true
  supports_constraints: 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        c2  \
0  1.472321  0.074442  dummy_constant  1.472321  0.074442  1.104196  1.126507   
1  0.304019  3.068013  dummy_constant  0.304019  3.068013  8.506083  6.633097   
2  1.731476  0.676010  dummy_constant  1.731476  0.676010  2.360317  1.547513   
3  1.856030  0.643523  dummy_constant  1.856030  0.643523  2.800247  1.859416   
4  2.241388  1.641485  dummy_constant  2.241388  1.641485  6.795483  4.335421   
5  0.000000  0.000000  dummy_constant  0.000000  0.000000 -1.100000  0.500000   
6  3.141590  0.000000  dummy_constant  3.141590  0.000000  8.769588  7.227998   
7  0.728417  0.860487  dummy_constant  0.728417  0.860487  0.246879  0.182126   
8  1.081259  0.798299  dummy_constant  1.081259  0.798299  0.879515  0.426844   
9  0.499710  0.965958  dummy_constant  0.499710  0.965958  0.161406  0.217117   

   xopt_runtime  xopt_error  
0      0.002917       False  
1      0.000146       False  
2      0.000130       False  
3      0.000122       False  
4      0.000125       False  
5      0.006124       False  
6      0.005397       False  
7      0.001317       False  
8      0.000224       False  
9      0.000140       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)