Sensitivity module

Sampler base

class emsa.sensitivity.sampler_base.SamplerBase(sim_object, variable_params=None)

Bases: ABC

Base class for performing sampling-based simulations.

This abstract class defines the common structure and interface for samplers used to explore parameter combinations and collect simulation results.

Parameters:

sim_object – The simulation object representing the underlying simulation model.

sim_object

The simulation object representing the underlying simulation model.

lhs_bounds_dict

The boundaries for Latin Hypercube Sampling (LHS) parameter ranges.

Type:

dict

run_sampling()

Runs the sampling-based simulation to explore different parameter combinations and collect simulation results.

get_lhs_table()
get_sim_output(lhs_table: ndarray)
abstract run()
save_output(output, output_name: str, filename: str)
emsa.sensitivity.sampler_base.create_latin_table(n_of_samples, lower, upper)

Sensitivity model base

class emsa.sensitivity.sensitivity_model_base.SensitivityModelBase(sim_object)

Bases: EpidemicModelBase, ABC

Base class for implementing epidemic models with the capacity to run sample based simulations for sensitivity analysis.

generate_3D_matrices(samples: Tensor)
get_basic_ode()
get_initial_values()
get_matrix_from_lhs(lhs_dict: dict, matrix_name: str)
static get_mul_method(tensor: Tensor)
emsa.sensitivity.sensitivity_model_base.get_lhs_dict(params: list, lhs_table: Tensor, params_col_idx: dict) → dict
emsa.sensitivity.sensitivity_model_base.get_params_col_idx(sampled_params_boundaries: dict)

PRCC calculator

emsa.sensitivity.prcc.get_prcc_values(lhs_output_table)

Calculates the Partial Rank Correlation Coefficient (PRCC) values for the last column of an ndarray based on the preceding columns.

Parameters:

lhs_output_table (ndarray) – Input ndarray containing the LHS samples and simulation results.

Returns:

PRCC values for the last column.

Return type:

ndarray