fix readme

add_new_analogy.py

@@ -14,81 +14,81 @@ 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")
-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 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"]
-                })
-    print(len(analogy_data))
-    os.makedirs("dataset/conceptnet_relational_similarity", exist_ok=True)
-    with open(f"dataset/conceptnet_relational_similarity/{s if s == 'test' else '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 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"]
+#                 })
+#     print(len(analogy_data))
+#     os.makedirs("dataset/conceptnet_relational_similarity", exist_ok=True)
+#     with open(f"dataset/conceptnet_relational_similarity/{s if s == 'test' else 'valid'}.jsonl", "w") as f:
+#         f.write("\n".join([json.dumps(i) for i in analogy_data]))