From e43b3f8aa36c198b95c1e46bea2eaf3893b13dc3 Mon Sep 17 00:00:00 2001
From: YurenHao0426 <blackhao0426@gmail.com>
Date: Wed, 17 Dec 2025 04:29:37 -0600
Subject: Initial commit (clean history)

---
 scripts/split_train_test.py | 76 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 scripts/split_train_test.py

(limited to 'scripts/split_train_test.py')

diff --git a/scripts/split_train_test.py b/scripts/split_train_test.py
new file mode 100644
index 0000000..ccb4cb1
--- /dev/null
+++ b/scripts/split_train_test.py
@@ -0,0 +1,76 @@
+import json
+import os
+import random
+
+INPUT_FILE = "data/finetune/preference_extractor_450k.jsonl"
+TRAIN_FILE = "data/finetune/train_llama_factory.json"
+TEST_FILE = "data/finetune/test_llama_factory.json"
+TEST_SIZE = 1000
+
+SYSTEM_INSTRUCTION = (
+    "Extract user preferences from the query into JSON format based on the PreferenceList schema. "
+    "If no preferences are found, return {\"preferences\": []}."
+)
+
+def split_and_convert():
+    if not os.path.exists(INPUT_FILE):
+        print(f"Error: {INPUT_FILE} not found.")
+        return
+
+    print(f"Reading {INPUT_FILE}...")
+    all_data = []
+    
+    with open(INPUT_FILE, "r", encoding="utf-8") as f:
+        for line in f:
+            if line.strip():
+                item = json.loads(line)
+                # Convert to LLaMA-Factory format immediately
+                record = {
+                    "instruction": SYSTEM_INSTRUCTION,
+                    "input": item["input"],
+                    "output": item["output"]
+                }
+                all_data.append(record)
+    
+    print(f"Total records: {len(all_data)}")
+    
+    # Shuffle
+    random.seed(42) # Fixed seed for reproducibility
+    random.shuffle(all_data)
+    
+    # Split
+    test_data = all_data[:TEST_SIZE]
+    train_data = all_data[TEST_SIZE:]
+    
+    print(f"Train size: {len(train_data)}")
+    print(f"Test size:  {len(test_data)}")
+    
+    # Save Train
+    print(f"Saving train set to {TRAIN_FILE}...")
+    with open(TRAIN_FILE, "w", encoding="utf-8") as f:
+        json.dump(train_data, f, indent=2, ensure_ascii=False)
+        
+    # Save Test
+    print(f"Saving test set to {TEST_FILE}...")
+    with open(TEST_FILE, "w", encoding="utf-8") as f:
+        json.dump(test_data, f, indent=2, ensure_ascii=False)
+
+    print("Done!")
+    
+    # Update dataset_info advice
+    print("\nUpdate dataset_info.json with:")
+    info = {
+        "preference_extractor_train": {
+            "file_name": "train_llama_factory.json",
+            "columns": {"prompt": "instruction", "query": "input", "response": "output"}
+        },
+        "preference_extractor_test": {
+            "file_name": "test_llama_factory.json",
+            "columns": {"prompt": "instruction", "query": "input", "response": "output"}
+        }
+    }
+    print(json.dumps(info, indent=2))
+
+if __name__ == "__main__":
+    split_and_convert()
+
-- 
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