hnn_core.optimization.Optimizer#

class hnn_core.optimization.Optimizer(initial_net, tstop, constraints, set_params, initial_params=None, solver='bayesian', obj_fun='dipole_rmse', max_iter=200)[source]#

Methods

`fit`(**obj_fun_kwargs)	Runs optimization routine.
`plot_convergence`([ax, show])	Convergence plot.

__repr__()[source]#: Return repr(self).

fit(**obj_fun_kwargs)[source]#

Runs optimization routine.

Parameters:

targetinstance of Dipole (Required if obj_fun=’dipole_rmse’): A dipole object with experimental data.
n_trialsint (Optional if obj_fun=’dipole_rmse’): Number of trials to simulate and average.
f_bandslist of tuples (Required if obj_fun=’maximize_psd’): Lower and higher limit for each frequency band.
relative_bandpowerlist of float | float (Required if obj_fun=’maximize_psd’): Weight for each frequency band in f_bands. If a single float is provided, the same weight is applied to all frequency bands.
sigma0float| array-like (Only used if solver=’cma’): Initial standard deviation of CME-ES algorithm. If float, sigma0 is scaled by bounds defined in the constraints for each parameter. If array-like, The length of sigma0 must equal the length of constraints. Default: 0.25
popsizeint (Only used if solver=’cma’): Number of parameter samples simulated per epoch. Default: 16
n_jobsint (Only used if solver=’cma’): The number of jobs to start in parallel. If None, then 1 trial will be started without parallelism.
tolfunfloat: Termination criteria. Stops if the range of the best objective function values of the last 10 + ((30 * n_parameters) / popsize) generations and all function values of the recent generation is below tolfun. Default 0.01.
dtfloat: The integration time step of h.CVode (ms). Default: 0.025 ms
scale_factorfloat, optional: The dipole scale factor.
smooth_window_lenfloat, optional: The smooth window length.
seedint, optional (Only used if solver=’cma’): Optional seed for random number generator of optimizer.
verbosebool: If True, print build steps and simulation progress to console. Default: True.

Notes

When defining sigma0 for CMA-ES as a float, the sigma0 applied to each parameter is calculated as sigma0 * (upper_bound - lower_bound) based on the constraints. It is recommended to choose a sigma0 such that the optimum is expected to lie within about initial_params +- 3*sigma0. A smaller sigma0 searches closer to initial_params.

When defining popsize for CMA-ES, it is recommended to increase popsize relative to the number of parameters being optimized (N). 4+3*log(N)

plot_convergence(ax=None, show=True)[source]#