Ejemplos de simulation_results_to_tables en Python

Ejemplo n.º 1

Archivo: sim_100_sparse_t_error_num_comp_pls.py Proyecto: alvaromc317/adaptive-sparse-group-lasso-paper-simulations

    dict(model_solver=asgl_model, weight_calculator=asgl_wc_pca_d_7),
    dict(model_solver=asgl_model, weight_calculator=asgl_wc_pca_d_8),
    dict(model_solver=asgl_model, weight_calculator=asgl_wc_pca_d_9),
    dict(model_solver=asgl_model, weight_calculator=asgl_wc_pca_d_10)
]

results = gf.automatic_simulator(data_param=data_param,
                                 model_selection_param=model_selection_param,
                                 model_param=model_param,
                                 data_generator=data_generator,
                                 n_repetitions=n_repetitions,
                                 folder=folder)

########################################################################################################################

results = af.simulation_results_to_tables(results=results,
                                          from_file=False,
                                          table_format='row_models')

sorting = [
    'lasso', 'sgl', 'asgl_pca_d_0.1', 'asgl_pca_d_0.2', 'asgl_pca_d_0.3',
    'asgl_pca_d_0.4', 'asgl_pca_d_0.5', 'asgl_pca_d_0.6', 'asgl_pca_d_0.7',
    'asgl_pca_d_0.8', 'asgl_pca_d_0.9', 'asgl_pca_d_1'
]
gc.boxplot_creator_by_metric_var_pct(results=results,
                                     interesting_metrics=['final_error'],
                                     figsize=(33, 10),
                                     sorting=sorting)

########################################################################################################################

Ejemplo n.º 2

Archivo: genetic_data_scheetz.py Proyecto: alvaromc317/adaptive-sparse-group-lasso-paper-simulations

                                model_param=model_param,
                                n_repetitions=n_repetitions,
                                folder=folder)

########################################################################################################################
"""
Processing the results obtained
"""

route = 'D:/Documentos_2/GoogleDrive/Doctorado/Tesis/Codigo/Python/Quantile_regression/simulation_results/'
fnamer2 = '2019-05-07--10-31-51--real_dataset--n=120--p=3734--standardized--cols--tau5.json'
fnamer3 = '2019-05-11--00-03-53--real_dataset--n=120--p=3734--standardized--cols--tau3.json'
fnamer4 = '2019-05-15--17-30-16--real_dataset--n=120--p=3734--standardized--cols--tau7.json'

results5 = af.simulation_results_to_tables(results=route + fnamer2,
                                           from_file=True,
                                           table_format='row_models')
results3 = af.simulation_results_to_tables(results=route + fnamer3,
                                           from_file=True,
                                           table_format='row_models')
results7 = af.simulation_results_to_tables(results=route + fnamer4,
                                           from_file=True,
                                           table_format='row_models')

gc.boxplot_creator_by_metric(
    results=results,
    interesting_metrics=['final_error', 'non_zero_pred_beta'],
    figsize=(25, 10),
    sorting=['lasso', 'sgl', 'asgl_pca_d', 'asgl_pls_d'])

significance = gc.variables_probability_heatmap(