init

README.md

@@ -42,6 +42,8 @@ We first remove triples that contain either of subject or object with the occurr
 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.
 
+- number of triples in each configuration
+
 | min entity/max predicate |   10 |    25 |    50 |   100 |
 |-------------------------|-----|------|------|------|
 |                        1 | 6,052 | 12,295 | 20,602 | 33,206 |
@@ -49,8 +51,21 @@ If the number of triples in a predicate is higher than `max predicate`, we choos
 |                        3 | 4,986 | 10,093 | 16,599 | 26,151 |
 |                        4 | 4,640 |  9,384 | 15,335 | 24,075 |
 
+- number of predicates in each configuration
+
+|   min entity / max predicate |   10 |   25 |   50 |   100 |
+|-----------------------------:|-----:|-----:|-----:|------:|
+|                            1 |  818 |  818 |  818 |   818 |
+|                            2 |  726 |  726 |  726 |   726 |
+|                            3 |  665 |  665 |  665 |   665 |
+|                            4 |  614 |  614 |  614 |   614 |
+
+- distribution of entities
+
 <img src="https://huggingface.co/datasets/relbert/t_rex/resolve/main/data/stats.entity_distribution.png" alt="" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
+- distribution of predicates
+
 <img src="https://huggingface.co/datasets/relbert/t_rex/resolve/main/data/stats.predicate_distribution.png" alt="" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
 

data/stats.predicate_size.csv

@@ -0,0 +1,5 @@
+min entity / max predicate,10,25,50,100
+1,818,818,818,818
+2,726,726,726,726
+3,665,665,665,665
+4,614,614,614,614

stats.py

@@ -102,6 +102,12 @@ if __name__ == '__main__':
     df_size.index.name = "min entity / max predicate"
     df_size.to_csv("data/stats.data_size.csv")
     print(df_size.to_markdown())
+    df_size = pd.DataFrame(
+        [{"min entity": mef, "max predicate": mpf, "freq": len(x)} for x, (mef, mpf) in zip(p_dist_full, candidates)])
+    df_size = df_size.pivot(index="min entity", columns="max predicate", values="freq")
+    df_size.index.name = "min entity / max predicate"
+    df_size.to_csv("data/stats.predicate_size.csv")
+    print(df_size.to_markdown())
 
     # plot predicate distribution
     df_p = pd.DataFrame([dict(enumerate(sorted(p.values(), reverse=True))) for p in p_dist_full]).T