upload australian data

australian.dat

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australian_test.csv

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data/test.parquet

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+size 26394

data/train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b86a40d7e179ab03608b76eb6a09b52f3f23215037f22c4feb426866e0a6707e
+size 66747

data/valid.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d33cc8a8ffb3f25b8dfacfa6572961dd90418ea46a7fec98d80a61405687068
+size 17969

prepocess.py

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+import random
+import pandas as pd
+import json
+
+#####config
+name = "australian.dat"
+feature_size = 14 + 1   # Target_index = -1
+train_size, dev_size, test_size = 0.7, 0.1, 0.2
+
+if train_size + dev_size + test_size != 1:
+    print("sample size wrong!!!")
+
+#####function
+def process_table(data):
+    data_tmp = []
+    prompt = 'Assess the creditworthiness of a customer using the following table attributes for financial status. ' \
+             'Respond with either \'good\' or \'bad\'. And all the table attribute names including 8 categorical ' \
+             'attributes and 6 numerical attributes and values have been changed to meaningless symbols to ' \
+             'protect confidentiality of the data. For instance, \'The client has attributes: A1: 0, A2: 21.67, ' \
+             'A3: 11.5, A4: 1, A5: 5, A6: 3, A7: 0, A8: 1, A9: 1, A10: 11, A11: 1, A12: 2, A13: 0, A14: 1.\', ' \
+             'should be classified as \'good\'. \nText: '
+
+    for j in range(len(data)):
+        text = 'The client has attributes:'
+        for i in range(len(data[0]) - 1):
+            sy = '.' if i == len(data[0]) - 2 else ','
+            text = text + f' A{i + 1}: ' + str(data[j][i]) + sy
+        answer = 'good' if data[j][-1] == 1 else 'bad'
+        gold = 0 if data[j][-1] == 1 else 1
+        # '1' is good (307) and '0' is bad (383)
+        data_tmp.append({'id': j, "query": prompt + text + ' \n Answer:', 'answer': answer, "choices": ["good", "bad"],
+                         "gold": gold, 'text': text})
+    return data_tmp
+
+
+def json_save(data, dataname, out_jsonl=False):
+    data_tmp = process_table(data)
+    if out_jsonl:
+        with open('{}.jsonl'.format(dataname), 'w') as f:
+            for i in data_tmp:
+                json.dump(i, f)
+                f.write('\n')
+            print('-----------')
+            print(f"{dataname} write done")
+        f.close()
+    df = pd.DataFrame(data_tmp)
+    # 保存为 Parquet 文件
+    parquet_file_path = f'data/{dataname}.parquet'
+    df.to_parquet(parquet_file_path, index=False)
+    return data_tmp
+
+
+#####process
+data = pd.read_csv(name, sep=' ', names=[i for i in range(feature_size)]).values.tolist()
+
+random.seed(10086)
+
+train_ind = random.sample([i for i in range(len(data))], int(len(data) * train_size))
+train_data = [data[i] for i in train_ind]
+
+index_left = list(filter(lambda x: x not in train_ind, [i for i in range(len(data))]))
+dev__ind = random.sample(index_left, int(len(data) * dev_size))
+dev_data = [data[i] for i in dev__ind]
+
+index_left = list(filter(lambda x: x not in train_ind + dev__ind, [i for i in range(len(data))]))
+test_data = [data[i] for i in index_left]
+
+columns = [i for i in range(feature_size)]
+ss_data = pd.DataFrame(test_data, columns=columns)
+ss_data.to_csv('australian_test.csv', index=False)
+
+test_prompt_data = json_save(test_data, 'test')
+train_prompt_data = json_save(train_data, 'train')
+dev_prompt_data = json_save(dev_data, 'valid')