bingo.local_optimizers package#

Submodules#

bingo.local_optimizers.local_opt_fitness module#

Fitness evaluation with local optimization

This module contains the implementation of a fitness function wrapper that will perform local optimization of a Chromosome as necessary using a LocalOptimizer before evaluating it.

class bingo.local_optimizers.local_opt_fitness.LocalOptFitnessFunction(fitness_function, optimizer)#

Bases: FitnessFunction

Fitness function wrapper for individuals that want local optimization

A class for fitness evaluation of individuals that may or may not need local optimization before evaluation.

Parameters:

  • fitness_function (FitnessFunction) – A FitnessFunction for evaluating the fitness of a Chromosome.

  • optimizer (LocalOptimizer) – An optimizer that will perform local optimization on a Chromosome before evaluation as needed.

eval_count#

the number of evaluations that have been performed by the wrapped fitness function

Type:

int

training_data#

data that can be used in the wrapped fitness function

Type:

TrainingData

property eval_count#

the number of evaluations that have been performed

Type:

int

property training_data#

data that can be used in fitness evaluations

Type:

TrainingData

bingo.local_optimizers.local_optimizer module#

This module contains the abstract definition of an optimizer that can be used for local optimization of a Chromosome.

class bingo.local_optimizers.local_optimizer.LocalOptimizer#

Bases: object

An abstract base class for optimizing a Chromosome.

abstract property objective_fn#

function to minimize, must take a Chromosome as input and return a number

abstract property options#

optimizer’s options

Type:

dict

bingo.local_optimizers.normalized_marginal_likelihood module#

Normalized marginal likelihood calculation using SMCPy

This module contains the implementation of a fitness function wrapper that will perform probabilistic local optimization of a Chromosome using SMCPy. The normaized marginal likelihood from the SMC optimization is returned.

class bingo.local_optimizers.normalized_marginal_likelihood.NormalizedMarginalLikelihood(fitness_function, deterministic_optimizer, log_scale=True, **kwargs)#

Bases: FitnessFunction

Normalized marginal likelihood calculation using SMCPy

A class for fitness evaluation of individuals that have local optimization parameters

Parameters:

  • fitness_function (FitnessFunction) – A FitnessFunction for evaluating the fitness of a Chromosome.

  • deterministic_optimizer (LocalOptimizer) – An optimizer that will perform deterministic local optimization on a Chromosome. Used in proposals of SmcpyOptimizer

  • **kwargs – other keyword arguments are passed to the SmcpyOptimizer initialization

eval_count#

the number of evaluations that have been performed by the wrapped fitness function

Type:

int

training_data#

data that can be used in the wrapped fitness function

Type:

TrainingData

property eval_count#

the number of evaluations that have been performed

Type:

int

property training_data#

data that can be used in fitness evaluations

Type:

TrainingData

bingo.local_optimizers.scipy_optimizer module#

Local optimization using scipy

Specifies ScipyOptimizer which is a class for local optimization of `Chromosome`s using scipy’s minimize or root methods. Also specifies ROOT_SET, a set of methods that will use scipy’s root method; MINIMIZE_SET, a set of methods that will use scipy’s minimize method; and JACOBIAN_SET, a set of methods that will use jacobian information.

class bingo.local_optimizers.scipy_optimizer.ScipyOptimizer(objective_fn, **options)#

Bases: LocalOptimizer

An optimizer that uses scipy.minimize or scipy.root for local optimization

A class for optimizing the parameters of a Chromosome using either scipy.minimize or scipy.root depending on the method specified.

Parameters:

  • objective_fn – A function to minimize which can be evaluated by passing in a Chromosome.

  • options –

    Additional arguments for optimization. e.g. (…, tol=1e-8, options={“maxiter”: 1000})

    e.g. param_init_bounds: iterable

    [low, high) bounds that are used to initialize params, formatted as an iterable defaults to [-10000, 10000)

    e.g. method: string

    method to use for optimization (e.g. BFGS, lm, etc.) defaults to BFGS

    e.g. tol: float

    tolerance used for method defaults to 1e-6

    e.g. optionsdict

    method-specific options (e.g. maxiter, ftol, etc.)

objective_fn#

A function to minimize which can be evaluated by passing in a Chromosome

options#

Additional arguments for clo options

Type:

dict

Raises:

  • KeyError – method must be a method supported by scipy

  • TypeError – objective_function must suit the specified method

property objective_fn#

function to minimize, must take a Chromosome as input and return a number

property options#

optimizer options (e.g. param_init_bounds, method, tol, options, etc.)

Type:

dict

bingo.local_optimizers.smcpy_optimizer module#

A module for probabilistic calibration of parameters.

Probabilistic calibration of model parameters can be useful in cases where data is sparse and/or noisy. Using a calibration of this type can allow for a better estimate of true fitness while being a bit more robust to overfitting.

class bingo.local_optimizers.smcpy_optimizer.SmcpyOptimizer(objective_fn, deterministic_optimizer, num_particles=150, mcmc_steps=12, ess_threshold=0.75, std=None, num_multistarts=1, uniformly_weighted_proposal=True)#

Bases: LocalOptimizer

An optimizer that uses SMCPy for probabilistic parameter calibration

A class for probabilistic parameter calibration for the parameters of a Chromosome using SMCPy

Parameters:

  • objective_fn (VectorBasedFunction, VectorGradientMixin) – A VectorBasedFunction with VectorGradientMixin (e.g., ExplicitRegression). It should produce a vector where the target value is 0.

  • deterministic_optimizer (LocalOptimizer) – A deterministic local optimizer e.g., ScipyOptimizer

  • num_particles (int) – The number of particles to use in the SMC approximation

  • mcmc_steps (int) – The number of MCMC steps to perform with each SMC update

  • ess_threshold (float (0-1)) – The effective sample size (ratio) below which SMC particles will be resampled

  • std (float) – (Optional) The fixed noise level, if it is known

  • num_multistarts (int) – (Optional) The number of deterministic optimizations performed when developing the SMC proposal

  • uniformly_weighted_proposal (bool) – Whether to equally weight particles in the proposal. Default True.

objective_fn#

A function to minimize which can be evaluated by passing in a Chromosome

options#

Additional arguments for clo options

Type:

dict

property eval_count#

the number of evaluations that have been performed

Type:

int

evaluate_model(params, individual)#

Evaluate an individual given a set of parameters

Parameters:

  • params (numpy array) – parameters for which to evaluate the individual

  • individual (Equation) – individual for which to evaluate fitness

Returns:

numpy array

Return type:

fitness vector outputs for the individual w/ the params

property objective_fn#

A VectorBasedFunction with VectorGradientMixin (e.g., ExplicitRegression). It should produce a vector where the target value is 0.

property options#

optimizer’s options

Type:

dict

property training_data#

Training data used in objective function

Module contents#