Storage¶
It is important to be able to store analysis results efficiently, easily accessible, and portable across systems. For this aim, pyPESTO allows to store results in efficient, portable HDF5 files. Further, optimization trajectories can be stored using various backends, including HDF5 and CSV.
In the following, describe the file formats.
For detailed information on usage, consult the doc/example/hdf5_storage.ipynb
notebook, and the API documentation for the pypesto.objective.history and
pypesto.storage modules.
pyPESTO Problem¶
+ /problem/
- Attributes:
- filled by objective.get_config()
- ...
- lb [float n_par]
- ub [float n_par]
- lb_full [float n_par_full]
- ub_full [float n_par_full]
- dim [int]
- dim_full [int]
- x_fixed_values [float (n_par_full-n_par)]
- x_fixed_indices [int (n_par_full-n_par)]
- x_free_indices [int n_par]
- x_names [str n_par_full]
Parameter estimation¶
Parameter estimation settings¶
Parameter estimation settings are saved in /optimization/settings.
Parameter estimation results¶
Parameter estimation results are saved in /optimization/results/.
Results per local optimization¶
Results of the $n’th multistart a saved in the format
+ /optimization/results/$n/
- fval: [float]
Objective function value of best iteration
- x: [float n_par_full]
Parameter set of best iteration
- grad: [float n_par_full]
Gradient of objective function at point x
- hess: [float n_par_full x n_par_full]
Hessian matrix of objective function at point x
- n_fval: [int]
Total number of objective function evaluations
- n_grad: [int]
Number of gradient evaluations
- n_hess: [int]
Number of Hessian evaluations
- x0: [float n_par_full]
Initial parameter set
- fval0: [float]
Objective function value at starting parameters
- exitflag: [str] Some exit flag
- time: [float] Execution time
- message: [str] Some exit message
Trace per local optimization¶
When objective function call histories are saved to HDF5, they are under
/optimization/results/$n/trace/.
+ /optimization/results/$n/trace/
- fval: [float n_iter]
Objective function value of best iteration
- x: [float n_iter x n_par_full]
Parameter set of best iteration
- grad: [float n_iter x n_par_full]
Gradient of objective function at point x
- hess: [float n_iter x n_par_full x n_par_full]
Hessian matrix of objective function at point x
- time: [float n_iter] Executition time
- chi2: [float n_iter x ...]
- schi2: [float n_iter x ...]