smash.default_bayesian_optimize_options

smash.default_bayesian_optimize_options(model, mapping='uniform', optimizer=None)

Default bayesian optimize options of Model.

Parameters:

modelModel

Primary data structure of the hydrological model smash.

mappingstr, default ‘uniform’

Type of mapping. Should be one of

• 'uniform'

• 'distributed'

• 'multi-linear'

• 'multi-polynomial'

Hint

See the Mapping section

optimizerstr or None, default None

Name of optimizer. Should be one of

• 'sbs' ('uniform' mapping only)

• 'lbfgsb' ('uniform', 'distributed', 'multi-linear' or 'multi-polynomial' mapping only)

Note

If not given, a default optimizer will be set depending on the optimization mapping:

• mapping = 'uniform'; optimizer = 'sbs'

• mapping = 'distributed', 'multi-linear', or 'multi-polynomial'; optimizer = 'lbfgsb'

Hint

See the Optimization Algorithm section

Returns:

optimize_optionsdict[str, Any]

Dictionary containing optimization options for fine-tuning the optimization process. The specific keys returned depend on the chosen mapping and optimizer. This dictionary can be directly pass to the optimize_options argument of the optimize method bayesian_optimize (or Model.bayesian_optimize).

Examples

>>> from smash.factory import load_dataset
>>> setup, mesh = load_dataset("cance")
>>> model = smash.Model(setup, mesh)

Get the default bayesian optimization options for 'uniform' mapping

>>> opt_u = smash.default_bayesian_optimize_options(model, mapping="uniform")
>>> opt_u
{
    'parameters': ['cp', 'ct', 'kexc', 'llr', 'sg0', 'sg1'],
    'bounds': {
        'cp': (1e-06, 1000.0),
        'ct': (1e-06, 1000.0),
        'kexc': (-50, 50),
        'llr': (1e-06, 1000.0),
        'sg0': (1e-06, 1000.0),
        'sg1': (1e-06, 10.0)
    },
    'control_tfm': 'sbs',
    'termination_crit': {'maxiter': 50},
}

Directly pass this dictionary to the optimize_options argument of the optimize method bayesian_optimize (or Model.bayesian_optimize). It’s equivalent to set optimize_options to None (which is the default value)

>>> model_u = smash.bayesian_optimize(model, mapping="uniform", optimize_options=opt_u)
</> Bayesian Optimize
    At iterate      0    nfg =     1    J =     77.049133    ddx = 0.64
    At iterate      1    nfg =    68    J =      2.584603    ddx = 0.64
    At iterate      2    nfg =   135    J =      2.324317    ddx = 0.32
    At iterate      3    nfg =   202    J =      2.304130    ddx = 0.08
    At iterate      4    nfg =   269    J =      2.262191    ddx = 0.08
    At iterate      5    nfg =   343    J =      2.260251    ddx = 0.01
    At iterate      6    nfg =   416    J =      2.258220    ddx = 0.00
    CONVERGENCE: DDX < 0.01

Get the default bayesian optimization options for a different mapping

>>> opt_ml = smash.default_bayesian_optimize_options(model, mapping="multi-linear")
>>> opt_ml
{
    'parameters': ['cp', 'ct', 'kexc', 'llr', 'sg0', 'sg1'],
    'bounds': {
        'cp': (1e-06, 1000.0),
        'ct': (1e-06, 1000.0),
        'kexc': (-50, 50),
        'llr': (1e-06, 1000.0),
        'sg0': (1e-06, 1000.0),
        'sg1': (1e-06, 10.0)
    },
    'control_tfm': 'normalize',
    'descriptor': {
        'cp': array(['slope', 'dd'], dtype='<U5'),
        'ct': array(['slope', 'dd'], dtype='<U5'),
        'kexc': array(['slope', 'dd'], dtype='<U5'),
        'llr': array(['slope', 'dd'], dtype='<U5')
    },
    'termination_crit': {'maxiter': 100, 'factr': 1000000.0, 'pgtol': 1e-12},
}

Customize the bayesian optimize options and optimize the Model

>>> opt_ml["bounds"]["cp"] = (1, 2000)
>>> opt_ml["bounds"]["sg0"] = (1e-3, 100)
>>> opt_ml["descriptor"]["cp"] = "slope"
>>> opt_ml["termination_crit"]["maxiter"] = 10
>>> model.bayesian_optimize(mapping="multi-linear", optimize_options=opt_ml)
</> Bayesian Optimize
    At iterate      0    nfg =     1    J =     77.049095    |proj g| =    147.958771
    At iterate      1    nfg =     2    J =      6.694370    |proj g| =      4.301311
    At iterate      2    nfg =     3    J =      6.527157    |proj g| =      3.754591
    At iterate      3    nfg =     4    J =      5.088758    |proj g| =      2.261085
    At iterate      4    nfg =     5    J =      4.736641    |proj g| =      1.637916
    At iterate      5    nfg =     6    J =      4.421250    |proj g| =      1.300914
    At iterate      6    nfg =     7    J =      4.284939    |proj g| =      1.345769
    At iterate      7    nfg =     8    J =      4.196455    |proj g| =      1.179024
    At iterate      8    nfg =     9    J =      4.139528    |proj g| =      1.159659
    At iterate      9    nfg =    10    J =      4.099973    |proj g| =      1.042880
    At iterate     10    nfg =    11    J =      4.027408    |proj g| =      1.142047
    STOP: TOTAL NO. OF ITERATION EXCEEDS LIMIT

The optimization process was terminated after 10 iterations, the maximal value we defined.