Update process.py

process.py

@@ -7,8 +7,8 @@ import pandas as pd
 from datasets import load_dataset
 
 
-def process(name, split, output):
-    data = load_dataset("relbert/t_rex", name, split=split)
+def process(split, output):
+    data = load_dataset("relbert/t_rex", split=split)
     df = data.to_pandas()
     df.pop('text')
     df.pop('title')
@@ -22,43 +22,9 @@ def process(name, split, output):
         f.write('\n'.join([json.dumps(i) for i in rel_sim_data]))
 
 
-parameters_min_e_freq = [1, 2, 3, 4]
-parameters_max_p_freq = [100, 50, 25, 10]
 os.makedirs("data", exist_ok=True)
 
-for min_e_freq, max_p_freq in product(parameters_min_e_freq, parameters_max_p_freq):
-    for s in ['train', 'validation']:
-        process(
-            name=f"filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}",
-            split=s,
-            output=f"data/filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.{s}.jsonl")
+for s in ['train', 'validation', 'test']:
+    process(split=s, output=f"data/filter_unified.{s}.jsonl")
 
-process(
-    name=f"filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}",
-    split='test',
-    output=f"data/filter_unified.test.jsonl")
-
-
-stats = []
-for min_e_freq, max_p_freq in product(parameters_min_e_freq, parameters_max_p_freq):
-    stats_tmp = {"data": f"filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}"}
-    for s in ['train', 'validation']:
-        with open(f"data/filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.{s}.jsonl") as f:
-            tmp = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
-            stats_tmp[f'num of relation types ({s})'] = len(tmp)
-            stats_tmp[f'average num of positive pairs ({s})'] = round(mean([len(i['positives']) for i in tmp]))
-            stats_tmp[f'average num of negative pairs ({s})'] = round(mean([len(i['negatives']) for i in tmp]))
-    stats.append(stats_tmp)
-df_stats = pd.DataFrame(stats)
-df_stats.index = df_stats.pop('data')
-print(df_stats.to_markdown())
-
-stats_tmp = {}
-with open("data/filter_unified.test.jsonl") as f:
-    tmp = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
-    stats_tmp[f'num of relation types (test)'] = len(tmp)
-    stats_tmp[f'average num of positive pairs (test)'] = round(mean([len(i['positives']) for i in tmp]))
-    stats_tmp[f'average num of negative pairs (test)'] = round(mean([len(i['negatives']) for i in tmp]))
-df_stats_test = pd.DataFrame([stats_tmp])
-print(df_stats_test.to_markdown(index=False))