if __name__ == '__main__': # Parameters scenarios = [] nb_obs_list = [] nb_fit = 1000 # Load the object that will handle the simulation simu = Simulations(nb_fit, scenarios, nb_obs_list) # Fit many estimators to this simulation estimator_types = [] for estimator_type in estimator_types: simu.fit(estimator_type) # Comparison of the diverse estimator # Compare all the estimator on a global graph (one graph per scenario) # On each graph the X axis should be the number of obs # the Y graph should the error simu.visualize_mean_test_error_graph(estimator_types, scenarios, nb_obs_list) # the other possible view, is to have one graph per number of observations # on the X axis should the name of the different estimator # on the y axis their error # Plot the same graph for the train/test error # For a single scenario, and a single obs (we give a plot detailing all the estimation steps that enabled to get # the result) simu.visualize_comparison_graph(estimator_types, scenario, nb_obs) # Analyse the result of a single estimator # Or all the result could be recorded in a matrix, with scenario as line, and nb_observaitons as columns # with the mean value (and the std in parenthesis) # (on the border on this matrix we should have the mean value) # for example, the first columns should be the mean of the other column for the same line simu.visualize_mean_test_error_matrix(estimator_type, scenarios, nb_obs_list) # simu.visualize