init

.gitattributes

@@ -61,3 +61,5 @@ data/t_rex.filter.validation.jsonl filter=lfs diff=lfs merge=lfs -text
 data/t_rex.filter.test.jsonl filter=lfs diff=lfs merge=lfs -text
 data/t_rex.filter.train.jsonl filter=lfs diff=lfs merge=lfs -text
 data/t_rex.filter.jsonl filter=lfs diff=lfs merge=lfs -text
+data/stats.data_size.csv filter=lfs diff=lfs merge=lfs -text
+data/t_rex.filter.sample.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -37,20 +37,20 @@ we choose top-`max predicate` triples based on the frequency of the subject and
 
 - number of triples in each configuration
 
-|   min entity / max predicate |   10 |    25 |    50 |   100 |
-|-----------------------------:|-----:|------:|------:|------:|
-|                            1 | 5832 | 12075 | 20382 | 32986 |
-|                            2 | 5309 | 10973 | 18415 | 29438 |
-|                            3 | 4836 |  9943 | 16449 | 26001 |
-|                            4 | 4501 |  9245 | 15196 | 23936 |
-
-
-|   min entity                 |   predicate |
-|-----------------------------:|------------:|
-|                            1 |         659 |
-|                            2 |         603 |
-|                            3 |         557 |
-|                            4 |         516 |
+|   min entity / max predicate |   10 |   25 |    50 |   100 |
+|-----------------------------:|-----:|-----:|------:|------:|
+|                            4 | 4501 | 9245 | 15196 | 23936 |
+|                            8 | 3557 | 7291 | 11804 | 18699 |
+|                           12 | 3132 | 6346 | 10155 | 16115 |
+|                           16 | 2769 | 5559 |  9014 | 14499 |
+
+
+|   min entity | predicate |
+|-------------:|-----:|
+|            4 |  516 |
+|            8 |  409 |
+|           12 |  366 |
+|           16 |  321 |
 
 - distribution of entities
 

check_predicate.py

@@ -0,0 +1,10 @@
+import json
+import numpy as np
+import pandas as pd
+
+with open("data/t_rex.filter.jsonl") as f:
+    data = pd.DataFrame([json.loads(i) for i in f.read().split('\n') if len(i) > 0])
+tmp = data.groupby("predicate").sample(10, replace=True)
+tmp = tmp.drop_duplicates()
+tmp.to_csv("data/t_rex.filter.sample.csv", index=False)
+

create_split.py

@@ -1,10 +0,0 @@
-import json
-import numpy as np
-
-with open("data/t_rex.filter.jsonl") as f:
-    data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
-
-p, c = np.unique([i['predicate'] for i in data], return_counts=True)
-d = dict(zip(p.tolist(), c.tolist()))
-with open("data/t_rex.filter.predicate.json", 'w') as f:
-    json.dump(d, f)

data/stats.data_size.csv

@@ -1,5 +1,3 @@
-min entity / max predicate,10,25,50,100,predicate
-1,5832,12075,20382,32986,659
-2,5309,10973,18415,29438,603
-3,4836,9943,16449,26001,557
-4,4501,9245,15196,23936,516
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9957f1edf8f50f209ee22c5b2c99aedf8f432dfc6d92beb13541ac6d8d81c06
+size 164

data/t_rex.filter.sample.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6733c3707cdbd270a070fd94f5717495c0f6d4e452dc6d4d4986fab741b35f7
+size 6123997

filtering_purify.py

@@ -14,6 +14,8 @@ from datasets import Dataset
 
 parameters_min_e_freq = [4, 8, 12, 16]
 parameters_max_p_freq = [100, 50, 25, 10]
+assert len(parameters_min_e_freq) == 4
+assert len(parameters_max_p_freq) == 4
 sns.set_theme(style="whitegrid")
 
 # load filtered data
@@ -119,9 +121,9 @@ if __name__ == '__main__':
     df_p.columns = [f"min entity: {mef}, max predicate: {mpf}" for mef, mpf in candidates]
     fig, axes = plt.subplots(2, 2, constrained_layout=True)
     fig.suptitle('Predicate Distribution over Different Configurations')
-    for (x, y), mpf in zip([(0, 0), (0, 1), (1, 0), (1, 1)], [100, 50, 25, 10]):
-        _df = df_p[[f"min entity: {mef}, max predicate: {mpf}" for mef in [1, 2, 3, 4]]]
-        _df.columns = [f"min entity: {mef}" for mef in [1, 2, 3, 4]]
+    for (x, y), mpf in zip([(0, 0), (0, 1), (1, 0), (1, 1)], parameters_max_p_freq):
+        _df = df_p[[f"min entity: {mef}, max predicate: {mpf}" for mef in parameters_min_e_freq]]
+        _df.columns = [f"min entity: {mef}" for mef in parameters_min_e_freq]
         ax = sns.lineplot(ax=axes[x, y], data=_df, linewidth=1)
         if mpf != 100:
             ax.legend_.remove()
@@ -136,9 +138,9 @@ if __name__ == '__main__':
     df_e.columns = [f"min entity: {mef}, max predicate: {mpf}" for mef, mpf in candidates]
     fig, axes = plt.subplots(2, 2, constrained_layout=True)
     fig.suptitle('Entity Distribution over Different Configurations')
-    for (x, y), mpf in zip([(0, 0), (0, 1), (1, 0), (1, 1)], [100, 50, 25, 10]):
-        _df = df_e[[f"min entity: {mef}, max predicate: {mpf}" for mef in [1, 2, 3, 4]]]
-        _df.columns = [f"min entity: {mef}" for mef in [1, 2, 3, 4]]
+    for (x, y), mpf in zip([(0, 0), (0, 1), (1, 0), (1, 1)], parameters_max_p_freq):
+        _df = df_e[[f"min entity: {mef}, max predicate: {mpf}" for mef in parameters_min_e_freq]]
+        _df.columns = [f"min entity: {mef}" for mef in parameters_min_e_freq]
         ax = sns.lineplot(ax=axes[x, y], data=_df, linewidth=1)
         ax.set(xscale='log')
         if mpf != 100: