fix readme

.gitattributes

@@ -144,3 +144,40 @@ data/t_rex.filter_unified.min_entity_4_max_predicate_25.train.jsonl filter=lfs d
 data/t_rex.filter_unified.min_entity_12_max_predicate_100.train.jsonl filter=lfs diff=lfs merge=lfs -text
 data/t_rex.filter_unified.min_entity_16_max_predicate_50.validation.jsonl filter=lfs diff=lfs merge=lfs -text
 data/t_rex.filter_unified.min_entity_12_max_predicate_10.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_100.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_50.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_25.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_50.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_100.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_50.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_50.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_10.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_25.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_50.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_100.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_100.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_50.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_10.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_100.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_10.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_25.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_100.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_25.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_50.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_50.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_10.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_10.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_25.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_10.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_100.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_10.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_25.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_50.train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.test.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_1_max_predicate_10.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_100.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_3_max_predicate_100.validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter_unified.min_entity_2_max_predicate_10.train.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -16,26 +16,30 @@ pretty_name: t_rex
 
 ## Dataset Summary
 This is the T-REX dataset proposed in [https://aclanthology.org/L18-1544/](https://aclanthology.org/L18-1544/).
-We split the raw T-REX dataset into train/validation/test split by the ratio of 70/15/15.
-
-| data                                           |   train |   validation |   test |   total |
-|:-----------------------------------------------|--------:|-------------:|-------:|--------:|
-| filter_unified.min_entity_4_max_predicate_100  |    7,790 |          865 |   2,113 |   10,768 |
-| filter_unified.min_entity_4_max_predicate_50   |    4,852 |          539 |   1,326 |    6,717 |
-| filter_unified.min_entity_4_max_predicate_25   |    2,874 |          319 |    768 |    3,961 |
-| filter_unified.min_entity_4_max_predicate_10   |    1,286 |          142 |    353 |    1,781 |
-| filter_unified.min_entity_8_max_predicate_100  |    6,117 |          679 |   1,647 |    8,443 |
-| filter_unified.min_entity_8_max_predicate_50   |    3,823 |          424 |   1,039 |    5,286 |
-| filter_unified.min_entity_8_max_predicate_25   |    2,319 |          257 |    640 |    3,216 |
-| filter_unified.min_entity_8_max_predicate_10   |    1,098 |          122 |    302 |    1,522 |
-| filter_unified.min_entity_12_max_predicate_100 |    5,186 |          576 |   1,429 |    7,191 |
-| filter_unified.min_entity_12_max_predicate_50  |    3,250 |          361 |    894 |    4,505 |
-| filter_unified.min_entity_12_max_predicate_25  |    1,971 |          218 |    531 |    2,720 |
-| filter_unified.min_entity_12_max_predicate_10  |     937 |          104 |    260 |    1,301 |
-| filter_unified.min_entity_16_max_predicate_100 |    4,690 |          521 |   1,270 |    6,481 |
-| filter_unified.min_entity_16_max_predicate_50  |    2,894 |          321 |    779 |    3,994 |
-| filter_unified.min_entity_16_max_predicate_25  |    1,734 |          192 |    463 |    2,389 |
-| filter_unified.min_entity_16_max_predicate_10  |     809 |           89 |    222 |    1,120 |
+The test split is universal across different version, which is manually checked by the author of [relbert/t_rex](https://huggingface.co/datasets/relbert/t_rex), 
+and the test split contains predicates that is not included in the train/validation split.
+The train/validation splits are created for each configuration by the ratio of 9:1. 
+The number of triples in test split is 121, and train/validation is summarized in the table below.
+
+| data                                          | train   |   validation | total   |
+|:----------------------------------------------|:--------|-------------:|:--------|
+| filter_unified.min_entity_1_max_predicate_100 | 7,075   |          787 | 7,862   |
+| filter_unified.min_entity_1_max_predicate_50  | 4,131   |          459 | 4,590   |
+| filter_unified.min_entity_1_max_predicate_25  | 2,358   |          262 | 2,620   |
+| filter_unified.min_entity_1_max_predicate_10  | 1,134   |          127 | 1,261   |
+| filter_unified.min_entity_2_max_predicate_100 | 4,873   |          542 | 5,415   |
+| filter_unified.min_entity_2_max_predicate_50  | 3,002   |          334 | 3,336   |
+| filter_unified.min_entity_2_max_predicate_25  | 1,711   |          191 | 1,902   |
+| filter_unified.min_entity_2_max_predicate_10  | 858     |           96 | 954     |
+| filter_unified.min_entity_3_max_predicate_100 | 3,659   |          407 | 4,066   |
+| filter_unified.min_entity_3_max_predicate_50  | 2,336   |          260 | 2,596   |
+| filter_unified.min_entity_3_max_predicate_25  | 1,390   |          155 | 1,545   |
+| filter_unified.min_entity_3_max_predicate_10  | 689     |           77 | 766     |
+| filter_unified.min_entity_4_max_predicate_100 | 2,995   |          333 | 3,328   |
+| filter_unified.min_entity_4_max_predicate_50  | 1,989   |          222 | 2,211   |
+| filter_unified.min_entity_4_max_predicate_25  | 1,221   |          136 | 1,357   |
+| filter_unified.min_entity_4_max_predicate_10  | 603     |           68 | 671     |
+
 
 ### Filtering to Remove Noise
 
@@ -95,7 +99,24 @@ An example looks as follows.
 }
 ```
 
-### Citation Information
+## Reproduce the Dataset
+
+```shell
+git clone https://huggingface.co/datasets/relbert/t_rex
+cd t_rex
+mkdir data_raw
+cd data_raw
+cd data_raw
+wget https://figshare.com/ndownloader/files/8760241
+unzip 8760241
+cd ../
+python process.py
+python unify_predicate.py
+python filtering_purify.py
+python create_split.py
+```
+
+## Citation Information
 ```
 @inproceedings{elsahar2018t,
   title={T-rex: A large scale alignment of natural language with knowledge base triples},

check_split.py

@@ -3,7 +3,7 @@ from itertools import product
 
 import pandas as pd
 
-parameters_min_e_freq = [4, 8, 12, 16]
+parameters_min_e_freq = [1, 2, 3, 4]
 parameters_max_p_freq = [100, 50, 25, 10]
 
 stats = []
@@ -12,15 +12,16 @@ for min_e_freq, max_p_freq in product(parameters_min_e_freq, parameters_max_p_fr
         train = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
     with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.validation.jsonl") as f:
         validation = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
-    with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.test.jsonl") as f:
-        test = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
     stats.append({
         "data": f"filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}",
         "train": len(train),
-        "validation": len(validation),
-        "test": len(test)
+        "validation": len(validation)
     })
 
 df = pd.DataFrame(stats)
-df['total'] = df['train'] + df['validation'] + df['test']
-print(df.to_markdown(index=False))
+df['total'] = df['train'] + df['validation']
+df.loc[:, 'total'] = df['total'].map('{:,d}'.format)
+df.loc[:, 'train'] = df['train'].map('{:,d}'.format)
+df.loc[:, 'validation'] = df['validation'].map('{:,d}'.format)
+
+print(df.to_markdown(index=False))

create_split.py

@@ -0,0 +1,66 @@
+import json
+import os
+from itertools import product
+
+import pandas as pd
+from random import shuffle, seed
+
+
+parameters_min_e_freq = [1, 2, 3, 4]
+parameters_max_p_freq = [100, 50, 25, 10]
+
+
+def get_test_predicate(_data):
+    tmp_df = pd.DataFrame(_data)
+    predicates_count = tmp_df.groupby("predicate")['text'].count().sort_values(ascending=False).to_dict()
+    total_num = sum(predicates_count.values())
+    pre_k = list(predicates_count.keys())
+    seed(42)
+    shuffle(pre_k)
+    predicates_train = []
+    for k in pre_k:
+        predicates_train.append(k)
+        if sum([predicates_count[i] for i in predicates_train]) > total_num * 0.8:
+            break
+    predicates_test = sorted([i for i in pre_k if i not in predicates_train])
+    return predicates_test
+
+
+if not os.path.exists("data/t_rex.filter_unified.test.jsonl"):
+    with open(f"data/t_rex.filter_unified.min_entity_{max(parameters_min_e_freq)}_max_predicate_{min(parameters_max_p_freq)}.jsonl") as f:
+        data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
+    pred_test = get_test_predicate(data)
+    data_test = [i for i in data if i['predicate'] in pred_test]
+    f_writer = open("data/t_rex.filter_unified.test.jsonl", 'w')
+    for n, i in enumerate(data_test):
+        print(f"\n[{n+1}/{len(data_test)}]")
+        print(f"{json.dumps(i, indent=4)}")
+        flag = input(">>> (enter to add to test)")
+        if flag == '':
+            f_writer.write(json.dumps(i) + '\n')
+    f_writer.close()
+
+with open("data/t_rex.filter_unified.test.jsonl") as f:
+    data_test = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
+    test_predicate = set([i['predicate'] for i in data_test])
+
+
+seed(42)
+for min_e_freq, max_p_freq in product(parameters_min_e_freq, parameters_max_p_freq):
+    with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.jsonl") as f:
+        data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
+    data = [i for i in data if i['predicate'] not in test_predicate]
+    shuffle(data)
+    data_train = data[:int(len(data) * 0.9)]
+    data_valid = data[int(len(data) * 0.9):]
+    with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.train.jsonl", "w") as f:
+        f.write('\n'.join([json.dumps(i) for i in data_train]))
+    with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.validation.jsonl", "w") as f:
+        f.write('\n'.join([json.dumps(i) for i in data_valid]))
+
+
+#
+# # make test split
+# with open(f"data/t_rex.filter_unified.jsonl") as f:
+#     data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
+# train_data, validation_data, test_data = create_split(data)

data/stats.data_size.csv

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data/stats.predicate_size.csv

@@ -1,5 +1,5 @@
 min entity,10
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+3,208
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-8,168
-12,146
-16,123

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