# files now # log.write_files() # print an overview of the best individuals from each run for i, bi in enumerate(best_inds): print( f'Run {i}: difference = {fit(bi).objective}, bin weights = {bin_weights(weights, bi)}' ) # write summary logs for the whole experiment utils.summarize_experiment(OUT_DIR, EXP_ID) # read the summary log and plot the experiment evals, lower, mean, upper = utils.get_plot_data(OUT_DIR, EXP_ID) plt.figure(figsize=(12, 8)) utils.plot_experiment(evals, lower, mean, upper, legend_name='Default settings') plt.legend() plt.show() # you can also plot mutiple experiments at the same time using # utils.plot_experiments, e.g. if you have two experiments 'default' and # 'tuned' both in the 'partition' directory, you can call # utils.plot_experiments('partition', ['default', 'tuned'], # rename_dict={'default': 'Default setting'}) # the rename_dict can be used to make reasonable entries in the legend - # experiments that are not in the dict use their id (in this case, the # legend entries would be 'Default settings' and 'tuned')
f.write(f'{w} {b}\n') # if we used write_immediately = False, we would need to save the # files now # log.write_files() # print an overview of the best individuals from each run for i, bi in enumerate(best_inds): print( f'Run {i}: difference = {fit(bi).objective}, bin weights = {bin_weights(weights, bi)}' ) # write summary logs for the whole experiment utils.summarize_experiment(OUT_DIR, EXP_ID) # read the summary log and plot the experiment evals, lower, mean, upper = utils.get_plot_data(OUT_DIR, EXP_ID) plt.figure(figsize=(12, 8)) utils.plot_experiment(evals, lower, mean, upper, legend_name=EXP_ID) plt.legend() plt.show() # you can also plot mutiple experiments at the same time using # utils.plot_experiments, e.g. if you have two experiments 'default' and # 'tuned' both in the 'partition' directory, you can call # utils.plot_experiments('partition', ['default', 'tuned'], # rename_dict={'default': 'Default setting'}) # the rename_dict can be used to make reasonable entries in the legend - # experiments that are not in the dict use their id (in this case, the # legend entries would be 'Default settings' and 'tuned')