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example_bayesian_optimization_runner

Example Bayesian optimization runner

ExampleBayesianOptimizationRunner 

ExampleBayesianOptimizationRunner(*args, **kwargs)

Bases: Runner

ExampleBayesianOptimizationRunner: a simple Runner that implements a 1D double Gaussian with a background slope to test the BayesianOptimizationSampler.

kwargs

model_parameters - vector of model parameters [a, g11, g12, g13, g21, g22, g23]: y = ax + g11np.exp(-(x - g13)2/g12) + g21*np.exp(-(x - g23)2/g22)

Returns a dictionary of containing the output and True for success. Future developments can include synthetic failure implementations.

Source code in src/enchanted_surrogates/runners/example_bayesian_optimization_runner.py 
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def __init__(self, *args, **kwargs):
    self.model_parameters = kwargs.get('model_parameters', 
                                       [0.2, 1.0, 0.001, 0.2, 
                                        0.6, 0.01, 0.7])

single_code_run 

single_code_run(run_dir, params=None)

Execute a single example run in run_dir and return a simple results dictionary.

Source code in src/enchanted_surrogates/runners/example_bayesian_optimization_runner.py 
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def single_code_run(self, run_dir: str, params: dict = None) -> dict:
    """
    Execute a single example run in `run_dir` and
    return a simple results dictionary.
    """

    # Ensure run_dir exists
    os.makedirs(run_dir, exist_ok=True)

    if params is None:
        params = {}

    outfile = os.path.join(run_dir, "output.txt")

    x = params['x']
    x = float(x)

    a   = self.model_parameters[0]
    g11 = self.model_parameters[1]
    g12 = self.model_parameters[2]
    g13 = self.model_parameters[3]
    g21 = self.model_parameters[4]
    g22 = self.model_parameters[5]
    g23 = self.model_parameters[6]

    y = a*x 
    y += self.gfunc(x, g1=g11, g2=g12, g3=g13)
    y += self.gfunc(x, g1=g21, g2=g22, g3=g23)

    with open(outfile, 'a') as f:
        result = y
        f.write(str(result))

    result = {"output": y, "success": True}

    return result