Generator Specs

Used to specify the generator function, its inputs and outputs, and user data.

Can be constructed and passed to libEnsemble as a Python class or a dictionary.

 1...
 2import numpy as np
 3from libensemble import GenSpecs
 4from generator import gen_random_sample
 5
 6...
 7
 8gen_specs = GenSpecs(
 9    gen_f=gen_random_sample,
10    out=[("x", float, (1,))],
11    user={
12        "lower": np.array([-3]),
13        "upper": np.array([3]),
14        "gen_batch_size": 5,
15    },
16)
17...
pydantic model libensemble.specs.GenSpecs
Fields:

 1...
 2import numpy as np
 3from generator import gen_random_sample
 4
 5...
 6
 7gen_specs = {
 8    "gen_f": gen_random_sample,
 9    "out": [("x", float, (1,))],
10    "user": {
11        "lower": np.array([-3]),
12        "upper": np.array([3]),
13        "gen_batch_size": 5,
14    },
15}

See also

  • test_uniform_sampling.py: the generator function uniform_random_sample in sampling.py will generate 500 random points uniformly over the 2D domain defined by gen_specs["ub"] and gen_specs["lb"].

    gen_specs = {
        "gen_f": uniform_random_sample,  # Function generating sim_f input
        "out": [("x", float, (2,))],  # Tell libE gen_f output, type, size
        "user": {
            "gen_batch_size": 500,  # Used by this specific gen_f
            "lb": np.array([-3, -2]),  # Used by this specific gen_f
            "ub": np.array([3, 2]),  # Used by this specific gen_f
        },
    }

See also

  • test_persistent_aposmm_nlopt.py shows an example where gen_specs["in"] is empty, but gen_specs["persis_in"] specifies values to return to the persistent generator.

  • test_persistent_aposmm_with_grad.py shows a similar example where an H0 is used to provide points from a previous run. In this case, gen_specs["in"] is populated to provide the generator with data for the initial points.

  • In some cases you might be able to give different (perhaps fewer) fields in "persis_in" than "in"; you may not need to give x for example, as the persistent generator already has x for those points. See more example uses of persis_in.

Note

  • In all interfaces, custom fields should only be placed in "user"

  • Generator "out" fields typically match Simulation "in" fields, and vice-versa.