# 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')