smefit.optimize package

class smefit.optimize.Optimizer(results_path, loaded_datasets, coefficients, use_quad, single_parameter_fits, use_multiplicative_prescription, external_chi2=None, rgemat=None, rge_dict=None)[source]

Bases: object

Common interface for Chi2 profile, NS and MC and A optimizers.

Parameters:

  • results_path (pathlib.path) – path to result folder

  • loaded_datasets (DataTuple,) – dataset tuple

  • coefficients (smefit.coefficients.CoefficientManager) – instance of CoefficientManager with all the relevant coefficients to fit

  • use_quad (bool) – if True includes also HO correction

  • single_parameter_fits (bool) – True for single parameter fits

  • use_multiplicative_prescription – if True uses the multiplicative prescription for the EFT corrections.

  • external_chi2 (dict) – dict of external chi2

  • rgemat (numpy.ndarray) – solution matrix of the RGE

  • rge_dict (dict) – dictionary with the RGE input parameter options

chi2_func(use_replica=False, print_log=True)[source]

Wrap the math:chi^2 in a function for the optimizer. Pass noise and data info as args. Log the math:chi^2 value and values of the coefficients.

Returns:

current_chi2 – computed \(\chi^2\)

Return type:

np.ndarray

dump_posterior(posterior_file, values)[source]
property free_parameters

Returns the free parameters entering fit

generate_chi2_table(chi2_dict, chi2_tot)[source]

Generate log \(\chi^2\) table

load_external_chi2(external_chi2)[source]

Loads the external chi2 modules

Parameters:

external_chi2 (dict) – dict of external chi2s, with the name of the function object as key and the path to the external script as value

Returns:

ext_chi2_modules – List of external chi2 objects that can be evaluated by passing a coefficients instance

Return type:

list

print_rate = 500

Submodules

smefit.optimize.analytic module

Solve the linear plolem to get the best analytic bounds.

class smefit.optimize.analytic.ALOptimizer(loaded_datasets, coefficients, result_path, result_ID, single_parameter_fits, n_samples)[source]

Bases: Optimizer

Optimizer specification for the linear analytic solution.

Parameters:

  • loaded_datasets (smefit.loader.DataTuple,) – dataset tuple

  • coefficients (smefit.coefficients.CoefficientManager) – instance of CoefficientManager with all the relevant coefficients to fit

  • result_path (pathlib.Path) – path to result folder

  • result_ID (str) – result name

  • single_parameter_fits (bool) – True for individual scan fits

  • n_samples – number of replica to sample

classmethod from_dict(config)[source]

Create object from theory dictionary.

Parameters:

config (dict) – configuration dictionary

Returns:

cls – created object

Return type:

Optimizer

log_result(coeff_best, coeff_covmat)[source]

Log a table with solution.

run_sampling()[source]

Run sapmling accordying to the analytic solution.

save(samples)[source]

Save samples to json inside a dictionary: {coff: [replicas values]}.

Parameters:

samples (np.array) – raw samples with shape (n_samples, n_free_param)

smefit.optimize.analytic.is_semi_pos_def(x)[source]

Check is a matrix is positive-semidefinite.

smefit.optimize.mc module

Fitting the Wilson coefficients with MC.

class smefit.optimize.mc.MCOptimizer(loaded_datasets, coefficients, result_path, use_quad, result_ID, replica, single_parameter_fits, use_bounds, minimizer_specs, use_multiplicative_prescription, external_chi2=None)[source]

Bases: Optimizer

Optimizer specification for MC.

Parameters:

  • loaded_datasets (smefit.loader.DataTuple) – dataset tuple

  • coefficients (smefit.coefficients.CoefficientManager) – instance of CoefficientManager with all the relevant coefficients to fit

  • result_path (pathlib.Path) – path to result folder

  • use_quad (bool) – If True use also HO corrections

  • result_ID (str) – result name

  • single_parameter_fits (bool) – True for individual scan fits

  • use_multiplicative_prescription (bool) – if True uses the multiplicative prescription for the EFT corrections

  • replica (int) – replica number

  • use_bounds (bool) – If true start the minimization with the specified values of min and max for each coeffient

  • minimizer_specs (dict) –

    minimizer options. The allowed optrions are:

    Args:

    • mc_minimiser: minimizer alogrithm: ‘cma’, ‘dual_annealing’, ‘trust-constr’.

    • maxiter: number of maximium iterations.

    • restarts: only for cma, number of restarts (< 9).

    • initial_temp: only for dual_annealing.

      The initial temperature, use higher values to facilitates a wider search of the energy landscape, allowing dual_annealing to escape local minima that it is trapped in. Default value is 5230. Range is (0.01, 5.e4].

    • restart_temp_ratio: only for dual_annealing.

      During the annealing process, temperature is decreasing, when it reaches initial_temp * restart_temp_ratio, the reannealing process is triggered. Default value of the ratio is 2e-5. Range is (0, 1).

    See also cma.fmin, scipy.optimize.minimize, scipy.optimize.dual_annealing.

chi2_func_mc(params, print_log=True)[source]

Wrap the chi2 in a function for the optimizer. Pass noise and data info as args. Log the chi2 value and values of the coefficients.

Parameters:

params (np.ndarray) – noise and data info

Returns:

chi2 value

Return type:

np.ndarray

classmethod from_dict(config)[source]

Create object from theory dictionary. The default minimizer is trust-constr.

The minimizer options have to be specified with:

` mc_minimiser: 'cma' maxiter: 100000 restarts: 0 `

Parameters:

config (dict) – configuration dictionary

Returns:

created object

Return type:

Optimizer

get_status(chi2)[source]
run_sampling()[source]

Run the minimization with MC.

save()[source]

Save MC replicas to json inside a dictionary: {coff: replica values}

Parameters:

result (dict) – result dictionary

smefit.optimize.ultranest module

Fitting the Wilson coefficients with NS

class smefit.optimize.ultranest.USOptimizer(loaded_datasets, coefficients, result_path, use_quad, result_ID, single_parameter_fits, pairwise_fits, use_multiplicative_prescription, live_points=500, lepsilon=0.001, target_evidence_unc=0.5, target_post_unc=0.5, frac_remain=0.01, store_raw=False, vectorized=False, float64=False, external_chi2=None, rgemat=None, rge_dict=None)[source]

Bases: Optimizer

Optimizer specification for Ultra nest.

Parameters:

  • loaded_datasets (smefit.loader.DataTuple,) – dataset tuple

  • coefficients (smefit.coefficients.CoefficientManager) – instance of CoefficientManager with all the relevant coefficients to fit

  • result_path (pathlib.Path) – path to result folder

  • use_quad (bool) – If True use also HO corrections

  • result_ID (str) – result name

  • single_parameter_fits (bool) – True for single parameter fits

  • pairwise_fits (bool) – True for pairwise parameter fits

  • use_multiplicative_prescription (bool) – if True uses the multiplicative prescription for the EFT corrections

  • live_points (int) – number of NS live points

  • lepsilon (float) – sampling tollerance. Terminate when live point likelihoods are all the same, within Lepsilon tolerance. Increase this when your likelihood function is inaccurate,

  • target_evidence_unc (float) – target evidence uncertainty.

  • target_post_unc (float) – target posterior uncertainty.

  • frac_remain (float) – integrate until this fraction of the integral is left in the remainder. Set to a higher number (0.5) if you know the posterior is simple.

  • store_raw (bool) – if True store the result to eventually resume the job

  • vectorized (bool) – if True use jax vectorization

  • float64 (bool) – if True use float64 precision

  • external_chi2 (dict) – dict of external chi2

  • rgemat (numpy.ndarray) – solution matrix of the RGE

  • rge_dict (dict) – dictionary with the RGE input parameter options

chi2_func_ns(params)[source]

Compute the chi2 function for NS. It is simplified with respect to the one in the Optimizer class, so that it can be compiled with jax.jit. :param params: Wilson coefficients :type params: jnp.ndarray

compute_fixed_coeff(constrain, param_dict)[source]

Compute the fixed coefficient.

flat_prior(hypercube)[source]

Update the prior function.

Parameters:

hypercube (np.ndarray) – hypercube prior

Returns:

flat_prior – updated hypercube prior

Return type:

np.ndarray

classmethod from_dict(config)[source]

Create object from theory dictionary.

Parameters:

config (dict) – configuration dictionary

Returns:

cls – created object

Return type:

Optimizer

gaussian_loglikelihood(params)[source]

Multi gaussian log likelihood function.

Parameters:

params (np.ndarray) – params prior

Returns:

-0.5 * chi2 – multi gaussian log likelihood

Return type:

np.ndarray

print_rate = 5000
produce_all_params(params)[source]

Produce all parameters from the free parameters.

Parameters:

params (jnp.ndarray) – free parameters

Returns:

all_params – all parameters

Return type:

jnp.ndarray

run_sampling()[source]

Run the minimization with Ultra nest.

save(result)[source]

Save NS replicas to json inside a dictionary: {coff: [replicas values]}.

Parameters:

result (dict) – result dictionary