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Sampling ​

dmosopt implements various sampling strategies listed below:

You may also point to your custom implementations by specifying a Python import path.

Dynamic sampling ​

By default, the number of samples is pre-determined via the n_initial parameter. However, dmosopt supports dynamic sampling strategies that generate samples until custom criteria are met. To implement a dynamic sampling strategy, you can point dynamic_initial_sampling to a callable object (with optional parameters dynamic_initial_sampling_kwargs). The callable receives the filepath, iteration count, the sampler options, the evaluated samples up to this point, as well as an unevaluated next batch of samples. It must return a new set of samples or None to end the dynamic sampling process. For example:

python
from dmosopt.datatypes import EvalEntry

def dynamic_sampling(
    file_path: str,
    interation: int,
    evaluated_samples: list[EvalEntry], 
    next_samples: list, 
    sampler: dict[], # contains n_initial, maxiter, method, param_names, xlb, xub
    **kwargs
):
    done = ... # decide if sampling is complete based on `evaluated_samples`

    if done:
        # no more samples, sampling will be complete using `evaluated_samples`
        return

    # return the next set of samples 
    # (can be the unmodified `next_samples` or some custom set of samples)
    return next_samples
from dmosopt.datatypes import EvalEntry

def dynamic_sampling(
    file_path: str,
    interation: int,
    evaluated_samples: list[EvalEntry], 
    next_samples: list, 
    sampler: dict[], # contains n_initial, maxiter, method, param_names, xlb, xub
    **kwargs
):
    done = ... # decide if sampling is complete based on `evaluated_samples`

    if done:
        # no more samples, sampling will be complete using `evaluated_samples`
        return

    # return the next set of samples 
    # (can be the unmodified `next_samples` or some custom set of samples)
    return next_samples