def test_rank_aic(self):
global table
ranked_table = ac.rank_aic(table, aic_column_name='AIC Value', inplace=False)
table = ranked_table
assert len(table)
for tax in trace_AX.values():
tax.set_xticks(tax.get_xticks()[::2])
tax.set_yticks(tax.get_yticks()[::2])
tax.tick_params(axis='both', labelsize=16)
tax.xaxis.label.set_size(fontsize=16)
tax.yaxis.label.set_size(fontsize=16)
plt.tight_layout(pad=1.0) # set spacing between figures
#Rank models
table = ac.calculate_aic(data=sampler_args['data'],
models=sampler_args['models'],
priors=sampler_args['priors'],
params=accepted.iloc[-10:])
ranked_table = ac.rank_aic(table, inplace=False)
print('\nRanked AIC table:\n', ranked_table.head())
#export the ranked table into csv file inside the output folder
ranked_table.to_csv(output_folder / 'ranked_table.csv')
best = ranked_table.iloc[0]
best_row_index = best['row']
best_model_num = best['model_num']
new_settings = config_data[best_model_num]
new_settings['settings_name'] = 'LogicGate_OR_bestfitted'
new_settings['parameters'] = cf.get_params_for_model(
models=sampler_args['models'],
trace=accepted,
model_num=best_model_num,
labels=labels,
titles=titles,
legend_args=legend_args,
figs=None,
AX=None)
'''
6. Model Ranking with AIC Calculation
'''
table = ac.calculate_aic(data=sampler_args['data'],
models=sampler_args['models'],
priors=sampler_args['priors'],
params=posterior)
ranked_table = ac.rank_aic(table,
aic_column_name='AIC Value',
inplace=False)
'''
rank_aic accepts a DataFrame containing AIC values indexed under
aic_column_name. It then sorts the DataFrame and adds columns for the normalized
AIC and the evidence for that model. The original columns in the input
DataFrame remain untouched.
rank_aic isn't picky about what indices and columns you use as long as your
DataFrame has its AIC values indexed under the argument aic_column_name.
'''
'''
7. Saving the Results
'''
'''
Saving the models and settings allows us to reuse them without having to