init

README.md

@@ -45,18 +45,20 @@ 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 | 6,052 | 12,295 | 20,602 | 33,206 |
-|                        2 | 5,489 | 11,153 | 18,595 | 29,618 |
-|                        3 | 4,986 | 10,093 | 16,599 | 26,151 |
-|                        4 | 4,640 |  9,384 | 15,335 | 24,075 |
+|   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 |
 
-- number of predicates in each configuration
 
-|   min entity  |   1 |   2 |   3 |4|
-|-----------------------------:|-----:|-----:|-----:|------:|
-|   number of unique predicates                          |  818 |  726 |  665 |   614 |
+|   min entity                 |   predicate |
+|-----------------------------:|------------:|
+|                            1 |         659 |
+|                            2 |         603 |
+|                            3 |         557 |
+|                            4 |         516 |
 
 - distribution of entities
 
@@ -64,7 +66,7 @@ we choose top-`max predicate` triples based on the frequency of the subject and
 
 - distribution of predicates
 
-<img src="https://huggingface.co/datasets/relbert/t_rex/resolve/main/data/stats.predicate_distribution.max_predicate_100.png" alt="" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+<img src="https://huggingface.co/datasets/relbert/t_rex/resolve/main/data/stats.predicate_distribution.png" alt="" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
 
 ## Dataset Structure

data/stats.data_size.csv

@@ -1,5 +1,5 @@
-min entity / max predicate,10,25,50,100
-1,6052,12295,20602,33206
-2,5489,11153,18595,29618
-3,4986,10093,16599,26151
-4,4640,9384,15335,24075
+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

data/stats.predicate_size.csv

@@ -1,5 +0,0 @@
-min entity / max predicate,10,25,50,100
-1,818,818,818,818
-2,726,726,726,726
-3,665,665,665,665
-4,614,614,614,614

stats.py

@@ -1,5 +1,6 @@
 """
-TODO: 
+TODO: save the data with different config
+TODO: get stats for the frequency based selection
 """
 import json
 from itertools import product
@@ -36,7 +37,7 @@ def filtering(row, min_freq: int = 3, target: str = "subject"):
     return row[target] >= min_freq
 
 
-def main(min_entity_freq, max_pairs_predicate, min_pairs_predicate: int = 1, random_sampling: bool = True):
+def main(min_entity_freq, max_pairs_predicate, min_pairs_predicate: int = 3, random_sampling: bool = True):
 
     df = df_main.copy()
 
@@ -77,7 +78,7 @@ def main(min_entity_freq, max_pairs_predicate, min_pairs_predicate: int = 1, ran
     df_balanced.pop("count_subject")
     df_balanced.pop("count_object")
     df_balanced.pop("count_sum")
-    target_data = [i.to_dict() for _, i in df_balanced]
+    target_data = [i.to_dict() for _, i in df_balanced.iterrows()]
 
     # return distribution
     predicate_dist = df_balanced.groupby("predicate")['text'].count().sort_values(ascending=False).to_dict()
@@ -95,37 +96,47 @@ if __name__ == '__main__':
 
     # run filtering with different configs
     for min_e_freq, max_p_freq in candidates:
-        p_dist, e_dist, data_size, new_data = main(min_entity_freq=min_e_freq, max_pairs_predicate=max_p_freq)
+        p_dist, e_dist, data_size, new_data = main(min_entity_freq=min_e_freq, max_pairs_predicate=max_p_freq, random_sampling=False)
         p_dist_full.append(p_dist)
         e_dist_full.append(e_dist)
         data_size_full.append(data_size)
         config.append([min_e_freq, max_p_freq])
+        with open(f"data/t_rex.clean.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.jsonl", 'w') as f:
+            f.write('\n'.join([json.dumps(i) for i in new_data]))
 
     # check statistics
     print("- Data Size")
     df_size = pd.DataFrame([{"min entity": mef, "max predicate": mpf, "freq": x} for x, (mef, mpf) in zip(data_size_full, candidates)])
     df_size = df_size.pivot(index="min entity", columns="max predicate", values="freq")
     df_size.index.name = "min entity / max predicate"
-    df_size.to_csv("data/stats.data_size.csv")
-    print(df_size.to_markdown())
-    df_size = pd.DataFrame(
+    df_size_p = pd.DataFrame(
         [{"min entity": mef, "max predicate": mpf, "freq": len(x)} for x, (mef, mpf) in zip(p_dist_full, candidates)])
-    df_size = df_size.pivot(index="min entity", columns="max predicate", values="freq")
-    df_size.index.name = "min entity / max predicate"
-    df_size.to_csv("data/stats.predicate_size.csv")
+    df_size_p = df_size_p.pivot(index="max predicate", columns="min entity", values="freq")
+    df_size['predicate'] = df_size_p.loc[10]
+    df_size.to_csv("data/stats.data_size.csv")
     print(df_size.to_markdown())
 
     # plot predicate distribution
     df_p = pd.DataFrame([dict(enumerate(sorted(p.values(), reverse=True))) for p in p_dist_full]).T
     df_p.columns = [f"min entity: {mef}, max predicate: {mpf}" for mef, mpf in candidates]
-    for mpf in [100, 50, 25, 10]:
-        fig = plt.figure()
-        _df_p = df_p[[f"min entity: {mef}, max predicate: {mpf}" for mef in [1, 2, 3, 4]]]
-        _df_p.columns = [f"min entity: {mef}" for mef in [1, 2, 3, 4]]
-        ax = sns.lineplot(data=_df_p, linewidth=2.5)
-        ax.set(xlabel='unique predicates sorted by frequency', ylabel=f'number of triples', title='Predicate Distribution (max predicate: {mpf})')
-        ax.get_figure().savefig(f"data/stats.predicate_distribution.max_predicate_{mpf}.png", bbox_inches='tight')
-        ax.get_figure().clf()
+    plt.figure()
+    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]):
+        # fig = plt.figure()
+        _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]]
+        ax = sns.lineplot(ax=axes[x, y], data=_df, linewidth=1)
+        if mpf != 100:
+            ax.legend_.remove()
+        axes[x, y].set_title(f'max predicate: {mpf}')
+        # ax.set(xlabel='unique predicates sorted by frequency', ylabel='number of triples', title='Predicate Distribution (max predicate: {mpf})')
+        # ax.get_figure().savefig(f"data/stats.predicate_distribution.max_predicate_{mpf}.png", bbox_inches='tight')
+        # ax.get_figure().clf()
+    fig.supxlabel('unique predicates sorted by frequency')
+    fig.supylabel('number of triples')
+    fig.savefig("data/stats.predicate_distribution.png", bbox_inches='tight')
+    fig.clf()
 
     # plot entity distribution
     df_e = pd.DataFrame([dict(enumerate(sorted(e.values(), reverse=True))) for e in e_dist_full]).T
@@ -135,7 +146,7 @@ if __name__ == '__main__':
     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]]
-        ax = sns.lineplot(ax=axes[x, y], data=_df, linewidth=1.5)
+        ax = sns.lineplot(ax=axes[x, y], data=_df, linewidth=1)
         ax.set(xscale='log')
         if mpf != 100:
             ax.legend_.remove()
@@ -143,3 +154,4 @@ if __name__ == '__main__':
     fig.supxlabel('unique entities sorted by frequency')
     fig.supylabel('number of triples')
     fig.savefig("data/stats.entity_distribution.png", bbox_inches='tight')
+    fig.clf()