2023-09-28 17:47:39 +02:00
|
|
|
import numpy as np
|
|
|
|
|
import pandas as pd
|
|
|
|
|
import seaborn as sns
|
|
|
|
|
import matplotlib.pyplot as plt
|
|
|
|
|
|
|
|
|
|
"""
|
|
|
|
|
This script generates plots of sensibility for the hyperparameter "bandwidth".
|
|
|
|
|
Plots results for MAE, MRAE, and KLD
|
|
|
|
|
The rest of hyperparameters were set to their default values
|
|
|
|
|
"""
|
|
|
|
|
|
2023-11-03 09:54:36 +01:00
|
|
|
|
|
|
|
|
|
|
|
|
|
log_mrae = True
|
|
|
|
|
|
|
|
|
|
for method, param, xlim, xticks in [
|
|
|
|
|
('KDEy-ML', 'Bandwidth', (0.01, 0.2), np.linspace(0.01, 0.2, 20)),
|
|
|
|
|
('DM-HD', 'nbins', (2,32), list(range(2,10)) + list(range(10,34,2)))
|
|
|
|
|
]:
|
|
|
|
|
|
|
|
|
|
for dataset in ['tweet', 'lequa', 'uciml']:
|
|
|
|
|
|
|
|
|
|
if dataset == 'tweet':
|
|
|
|
|
df = pd.read_csv(f'../results/tweet/sensibility/{method}.csv', sep='\t')
|
|
|
|
|
ylim = (0.03, 0.21)
|
|
|
|
|
elif dataset == 'lequa':
|
|
|
|
|
df = pd.read_csv(f'../results/lequa/T1B/sensibility/{method}.csv', sep='\t')
|
|
|
|
|
ylim = (0.0125, 0.03)
|
|
|
|
|
elif dataset == 'uciml':
|
|
|
|
|
ylim = (0, 0.23)
|
|
|
|
|
df = pd.read_csv(f'../results/ucimulti/sensibility/{method}.csv', sep='\t')
|
|
|
|
|
|
|
|
|
|
for err in ['MAE']: #, 'MRAE']:
|
|
|
|
|
piv = df.pivot_table(index=param, columns='Dataset', values=err)
|
|
|
|
|
g = sns.lineplot(data=piv, markers=True, dashes=False)
|
|
|
|
|
g.set(xlim=xlim)
|
|
|
|
|
g.legend(loc="center left", bbox_to_anchor=(1, 0.5))
|
|
|
|
|
|
|
|
|
|
if log_mrae and err=='MRAE':
|
|
|
|
|
plt.yscale('log')
|
|
|
|
|
g.set_ylabel('log('+err+')')
|
|
|
|
|
else:
|
|
|
|
|
g.set_ylabel(err)
|
|
|
|
|
|
|
|
|
|
g.set_ylim(ylim)
|
|
|
|
|
g.set_xticks(xticks)
|
|
|
|
|
plt.xticks(rotation=90)
|
|
|
|
|
plt.grid()
|
|
|
|
|
plt.savefig(f'./sensibility_{method}_{dataset}_{err}.pdf', bbox_inches='tight')
|
|
|
|
|
plt.clf()
|
