init

fewshot_link_prediction.py

@@ -5,7 +5,7 @@ import datasets
 logger = datasets.logging.get_logger(__name__)
 _DESCRIPTION = """Few shots link prediction dataset. """
 _NAME = "fewshot_link_prediction"
-_VERSION = "0.0.2"
+_VERSION = "0.0.3"
 _CITATION = """
 @inproceedings{xiong-etal-2018-one,
     title = "One-Shot Relational Learning for Knowledge Graphs",

stats.py

@@ -1,17 +1,18 @@
 import pandas as pd
 from datasets import load_dataset
 
-data = load_dataset("fewshot_link_prediction", "nell", split='test')
-df = data.to_pandas()
+for _type in ['nell', 'nell_filter']:
+    data = load_dataset("fewshot_link_prediction", _type, split='test')
+    df = data.to_pandas()
 
-print("\nEntity Types")
-tail = df.groupby("tail_type")['relation'].count().to_dict()
-head = df.groupby("head_type")['relation'].count().to_dict()
-k = set(list(tail.keys()) + list(head.keys()))
-df_types = pd.DataFrame([{"entity_type": _k, "tail": tail[_k] if _k in tail else 0, "head": head[_k] if _k in head else 0} for _k in k])
-print(df_types.to_markdown(index=False))
+    print(f"\nEntity Types ({_type})")
+    tail = df.groupby("tail_type")['relation'].count().to_dict()
+    head = df.groupby("head_type")['relation'].count().to_dict()
+    k = set(list(tail.keys()) + list(head.keys()))
+    df_types = pd.DataFrame([{"entity_type": _k, "tail": tail[_k] if _k in tail else 0, "head": head[_k] if _k in head else 0} for _k in k])
+    print(df_types.to_markdown(index=False))
 
-print("\nRelation Types")
-print(df.groupby("relation")['relation'].count().to_markdown())
+    print(f"\nRelation Types ({_type})")
+    print(df.groupby("relation")['relation'].count().to_markdown())