init

README.md

@@ -40,7 +40,8 @@ We apply filtering to keep triples with alpha-numeric subject and object, as wel
 We reduce the size of the dataset by applying filtering based on the number of predicates and entities in the triples.
 We first remove triples that contain either of subject or object with the occurrence in the dataset that is lower than `min entity`.
 Then, we reduce the number triples in each predicate to be less than `max predicate`. 
-If the number of triples in a predicate is higher than `max predicate`, we choose top-`max predicate` triples in terms of the frequency of the subject and the object.
+If the number of triples in a predicate is higher than `max predicate`, 
+we choose top-`max predicate` triples based on the frequency of the subject and the object, or random sampling.
 
 - number of triples in each configuration
 

stats.py

@@ -1,3 +1,6 @@
+"""
+TODO: 
+"""
 import json
 from itertools import product
 
@@ -12,11 +15,15 @@ sns.set_theme(style="whitegrid")
 
 # load filtered data
 tmp = []
+splits = []
 for s in ['train', 'validation', 'test']:
     with open(f"data/t_rex.filter.{s}.jsonl") as f:
-        tmp += [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
+        _tmp = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
+        tmp += _tmp
+        splits += [s] * len(_tmp)
 data = Dataset.from_list(tmp)
 df_main = data.to_pandas()
+df_main['split'] = splits
 
 
 def is_entity(token):
@@ -29,8 +36,7 @@ def filtering(row, min_freq: int = 3, target: str = "subject"):
     return row[target] >= min_freq
 
 
-def main(min_entity_freq, max_pairs_predicate, min_pairs_predicate: int = 1,
-         return_stats: bool = True, random_sampling: bool = True):
+def main(min_entity_freq, max_pairs_predicate, min_pairs_predicate: int = 1, random_sampling: bool = True):
 
     df = df_main.copy()
 
@@ -67,17 +73,17 @@ def main(min_entity_freq, max_pairs_predicate, min_pairs_predicate: int = 1,
             [g if len(g) <= max_pairs_predicate else g.sort_values(by='count_sum', ascending=False).head(max_pairs_predicate) for _, g in
              df_filter.groupby("predicate") if len(g) >= min_pairs_predicate])
 
-    if not return_stats:
-        df_balanced.pop("count_subject")
-        df_balanced.pop("count_object")
-        df_balanced.pop("count_sum")
-        return [i.to_dict() for _, i in df_balanced]
+
+    df_balanced.pop("count_subject")
+    df_balanced.pop("count_object")
+    df_balanced.pop("count_sum")
+    target_data = [i.to_dict() for _, i in df_balanced]
 
     # return distribution
     predicate_dist = df_balanced.groupby("predicate")['text'].count().sort_values(ascending=False).to_dict()
     entity, count = np.unique(df_balanced['object'].tolist() + df_balanced['subject'].tolist(), return_counts=True)
     entity_dist = dict(list(zip(entity.tolist(), count.tolist())))
-    return predicate_dist, entity_dist, len(df_balanced)
+    return predicate_dist, entity_dist, len(df_balanced), target_data
 
 
 if __name__ == '__main__':
@@ -89,7 +95,7 @@ if __name__ == '__main__':
 
     # run filtering with different configs
     for min_e_freq, max_p_freq in candidates:
-        p_dist, e_dist, data_size = main(min_entity_freq=min_e_freq, max_pairs_predicate=max_p_freq)
+        p_dist, e_dist, data_size, new_data = main(min_entity_freq=min_e_freq, max_pairs_predicate=max_p_freq)
         p_dist_full.append(p_dist)
         e_dist_full.append(e_dist)
         data_size_full.append(data_size)