--- a
+++ b/paper/Benchmarking with real and synthetic datasets/plot_benchmark.py
@@ -0,0 +1,60 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+dat_NB = pd.read_csv('result/result_VITAE_NB.csv',
+                        index_col=0)#.drop(['type'], axis=1)
+dat_NB = dat_NB[dat_NB.method == 'modified_map']
+dat_NB.method = 'VITAE_NB'
+
+dat_Gaussian = pd.read_csv('result/result_VITAE_Gaussian.csv',
+                        index_col=0)#.drop(['type'], axis=1)
+dat_Gaussian = dat_Gaussian[dat_Gaussian.method == 'modified_map']
+dat_Gaussian.method = 'VITAE_Gauss'
+
+dat_other = pd.read_csv('result/result_other_methods.csv',
+                        index_col=0)#.drop(['type'], axis=1)
+
+dat = pd.concat([dat_NB, dat_Gaussian, dat_other])
+
+sources = ['dyngen','our model','real']
+scores = ['GED score','IM score','ARI','GRI','PDT score']
+cmaps = ['YlOrRd_r', 'YlGn_r', 'RdPu_r', 'PuBu_r', 'BuGn_r']
+
+rotation_xticklabels = 25
+
+sns.set(font_scale=1.5, rc={'axes.facecolor':(0.85,0.85,0.85), 'xtick.labelsize':14, 'ytick.labelsize':11})
+fig, ax = plt.subplots(3, 5, gridspec_kw={'height_ratios':[6, 2, 8], 'width_ratios' :[1,1,1,1,1]}, figsize = (20,10))
+for i in range(5):
+    for j in range(3):
+        vmin = dat[scores[i]].min()
+        vmax = dat[scores[i]].max() 
+        dat_t = dat[dat.source == sources[j]]
+        dat_t = dat_t[['data','method',scores[i]]].pivot('data', 'method', scores[i])
+        if  j == 0:
+            ax[j][i].set_title(scores[i], fontweight='bold', fontsize = 18)
+        if (j == 2) & (i == 0):
+            sns.heatmap(dat_t, ax = ax[j][i], cbar = True, 
+                cbar_kws={"orientation": "horizontal", "pad": 0.2}, vmin=vmin, vmax=vmax, cmap = cmaps[i])
+            ax[j][i].set_xticklabels(ax[j][i].get_xticklabels(), rotation=rotation_xticklabels, ha="center")
+            # ax[j][i].set_yticklabels(ax[j][i].get_yticklabels(), fontsize=12)#rotation=30)
+        elif i == 0:
+            sns.heatmap(dat_t, ax = ax[j][i], xticklabels=False, cbar = False, vmin=vmin, vmax=vmax, cmap = cmaps[i])
+            # ax[j][i].set_yticklabels(ax[j][i].get_yticklabels(), rotation=30)
+        elif j == 2:
+            sns.heatmap(dat_t, ax = ax[j][i], yticklabels=False, cbar = True, 
+                cbar_kws={"orientation": "horizontal", "pad": 0.2}, vmin=vmin, vmax=vmax, cmap = cmaps[i])
+            ax[j][i].set_xticklabels(ax[j][i].get_xticklabels(), rotation=rotation_xticklabels, ha="center")
+        else:
+            sns.heatmap(dat_t, ax = ax[j][i], xticklabels=False, yticklabels=False, cbar = False, vmin=vmin, vmax=vmax, cmap = cmaps[i])
+        if i == 4:
+            ax[j][i].set_ylabel(sources[j], rotation=270, fontweight='bold', fontsize = 18, labelpad=20)
+            ax[j][i].yaxis.set_label_position("right")
+        else:
+            ax[j][i].set_ylabel(None)
+        ax[j][i].set_xlabel(None)
+        
+plt.tight_layout()
+plt.subplots_adjust(wspace=0.2, hspace=0.1)
+fig.savefig('result/comp_heatmap.pdf', bbox_inches='tight')
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