fix readme

add_new_analogy.py

@@ -1,6 +1,6 @@
 import json
 import os
-from itertools import combinations
+from itertools import combinations, chain
 from random import shuffle, seed
 from datasets import load_dataset
 
@@ -14,50 +14,68 @@ from datasets import load_dataset
 #     f.write("\n".join([json.dumps(i) for i in analogy_data]))
 
 
-# # create analogy from `relbert/t_rex_relational_similarity`
-# data = load_dataset("relbert/t_rex_relational_similarity", "filter_unified.min_entity_1_max_predicate_100", split="test")
-# analogy_data = []
-# for i in data:
-#     if len(i['positives']) < 2:
-#         continue
-#     for m, (q, c) in enumerate(combinations(i['positives'], 2)):
-#         if m > 5:
-#             break
-#         negative = i['negatives']
-#         for n in range(6):
-#             seed(n)
-#             shuffle(negative)
-#             analogy_data.append({
-#                 "stem": q, "choice": [c] + negative[:5], "answer": 0, "prefix": i["relation_type"]
-#             })
-# os.makedirs("dataset/t_rex_relational_similarity", exist_ok=True)
-# with open("dataset/t_rex_relational_similarity/test.jsonl", "w") as f:
-#     f.write("\n".join([json.dumps(i) for i in analogy_data]))
-#
-# data = load_dataset("relbert/t_rex_relational_similarity", "filter_unified.min_entity_4_max_predicate_100", split="validation")
-# analogy_data = []
-# for i in data:
-#     if len(i['positives']) < 5:
-#         continue
-#     for m, (q, c) in enumerate(combinations(i['positives'], 2)):
-#         if m > 5:
-#             break
-#         negative = i['negatives']
-#         for n in range(3):
-#             seed(n)
-#             shuffle(negative)
-#             analogy_data.append({
-#                 "stem": q, "choice": [c] + negative[:5], "answer": 0, "prefix": i["relation_type"]
-#             })
-# os.makedirs("dataset/t_rex_relational_similarity", exist_ok=True)
-# with open("dataset/t_rex_relational_similarity/valid.jsonl", "w") as f:
-#     f.write("\n".join([json.dumps(i) for i in analogy_data]))
+# create analogy from `relbert/t_rex_relational_similarity`
+data = load_dataset("relbert/t_rex_relational_similarity", "filter_unified.min_entity_1_max_predicate_100", split="test")
+df = data.to_pandas()
+df['negatives'] = [list(chain(
+    *[[y.tolist() for y in x.tolist()] for x in df[df.relation_type != i]['positives'].tolist()] +
+     [[y.tolist() for y in x.tolist()] for x in df[df.relation_type == i]['negatives'].tolist()])) for i in
+    df['relation_type']]
+analogy_data = []
+for _, i in df.iterrows():
+    if len(i['positives']) < 2:
+        continue
+    for m, (q, c) in enumerate(combinations(i['positives'], 2)):
+        if m > 5:
+            break
+        negative = i['negatives']
+        for n in range(6):
+            seed(n)
+            shuffle(negative)
+            analogy_data.append({
+                "stem": q.tolist(), "choice": [c.tolist()] + negative[:5], "answer": 0, "prefix": i["relation_type"]
+            })
+os.makedirs("dataset/t_rex_relational_similarity", exist_ok=True)
+with open("dataset/t_rex_relational_similarity/test.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in analogy_data]))
+
+data = load_dataset("relbert/t_rex_relational_similarity", "filter_unified.min_entity_4_max_predicate_100", split="validation")
+df = data.to_pandas()
+df['negatives'] = [list(chain(
+    *[[y.tolist() for y in x.tolist()] for x in df[df.relation_type != i]['positives'].tolist()] +
+     [[y.tolist() for y in x.tolist()] for x in df[df.relation_type == i]['negatives'].tolist()])) for i in
+    df['relation_type']]
+analogy_data = []
+for _, i in df.iterrows():
+    if len(i['positives']) < 5:
+        continue
+    for m, (q, c) in enumerate(combinations(i['positives'], 2)):
+        if m > 5:
+            break
+        negative = i['negatives']
+        for n in range(3):
+            seed(n)
+            shuffle(negative)
+            analogy_data.append({
+                "stem": q.tolist(), "choice": [c.tolist()] + negative[:5], "answer": 0, "prefix": i["relation_type"]
+            })
+os.makedirs("dataset/t_rex_relational_similarity", exist_ok=True)
+with open("dataset/t_rex_relational_similarity/valid.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in analogy_data]))
 
 # create analogy from `relbert/conceptnet_relational_similarity`
 for s in ['test', 'validation']:
     data = load_dataset("relbert/conceptnet_relational_similarity", split=s)
+    df = data.to_pandas()
+    df['negatives'] = [list(chain(
+        *[[y.tolist() for y in x.tolist()] for x in df[df.relation_type != i]['positives'].tolist()] +
+         [[y.tolist() for y in x.tolist()] for x in df[df.relation_type == i]['negatives'].tolist()])) for i in
+        df['relation_type']]
+
     analogy_data = []
-    for i in data:
+
+    for _, i in df.iterrows():
+
         if len(i['positives']) < 2:
             continue
         for m, (q, c) in enumerate(combinations(i['positives'], 2)):
@@ -68,7 +86,7 @@ for s in ['test', 'validation']:
                 seed(n)
                 shuffle(negative)
                 analogy_data.append({
-                    "stem": q, "choice": [c] + negative[:5], "answer": 0, "prefix": i["relation_type"]
+                    "stem": q.tolist(), "choice": [c.tolist()] + negative[:5], "answer": 0, "prefix": i["relation_type"]
                 })
     print(len(analogy_data))
     os.makedirs("dataset/conceptnet_relational_similarity", exist_ok=True)