init

stats.py

@@ -101,8 +101,13 @@ if __name__ == '__main__':
         e_dist_full.append(e_dist)
         data_size_full.append(data_size)
         config.append([min_e_freq, max_p_freq])
-        with open(f"data/t_rex.clean.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.jsonl", 'w') as f:
-            f.write('\n'.join([json.dumps(i) for i in new_data]))
+        # save data
+        for s in ['train', 'validation', 'test']:
+            new_data_s = [i for i in new_data if i['split'] == s]
+            for i in new_data_s:
+                i.pop('split')
+            with open(f"data/t_rex.clean.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.{s}.jsonl", 'w') as f:
+                f.write('\n'.join([json.dumps(i) for i in new_data_s]))
 
     # check statistics
     print("- Data Size")
@@ -119,20 +124,15 @@ if __name__ == '__main__':
     # plot predicate distribution
     df_p = pd.DataFrame([dict(enumerate(sorted(p.values(), reverse=True))) for p in p_dist_full]).T
     df_p.columns = [f"min entity: {mef}, max predicate: {mpf}" for mef, mpf in candidates]
-    plt.figure()
     fig, axes = plt.subplots(2, 2, constrained_layout=True)
     fig.suptitle('Predicate Distribution over Different Configurations')
     for (x, y), mpf in zip([(0, 0), (0, 1), (1, 0), (1, 1)], [100, 50, 25, 10]):
-        # fig = plt.figure()
         _df = df_p[[f"min entity: {mef}, max predicate: {mpf}" for mef in [1, 2, 3, 4]]]
         _df.columns = [f"min entity: {mef}" for mef in [1, 2, 3, 4]]
         ax = sns.lineplot(ax=axes[x, y], data=_df, linewidth=1)
         if mpf != 100:
             ax.legend_.remove()
         axes[x, y].set_title(f'max predicate: {mpf}')
-        # ax.set(xlabel='unique predicates sorted by frequency', ylabel='number of triples', title='Predicate Distribution (max predicate: {mpf})')
-        # ax.get_figure().savefig(f"data/stats.predicate_distribution.max_predicate_{mpf}.png", bbox_inches='tight')
-        # ax.get_figure().clf()
     fig.supxlabel('unique predicates sorted by frequency')
     fig.supylabel('number of triples')
     fig.savefig("data/stats.predicate_distribution.png", bbox_inches='tight')