codefuse-ai
diff --git a/‎.gitignore
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diff --git a/‎src/data/ data_load.py
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diff --git a/‎src/data/data_preprocess.py
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+68Lines changed: 68 additions & 0 deletions b/‎src/data/data_preprocess.py
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@@ -1,6 +1,7 @@
 **/__pycache__
 .DS_Store
 data/
+!src/data/
 .pyc
 __pycache__
 start_job.py
 
@@ -0,0 +1,51 @@
+import json
+import jsonlines
+import os
+import pandas as pd
+
+from loguru import logger
+
+from src.hparams.evaluate_args import EvaluateArguments
+from src.data.data_preprocess import preprocess
+
+
+def load_all_dataset(eval_args: EvaluateArguments):
+    '''
+    Load all eval dataset
+    '''
+    # get fp for eval dataset
+    dataset_name_list = eval_args.eval_dataset_list
+    eval_dataset_fp_conf_path = eval_args.eval_dataset_fp_conf_path
+
+    with open(eval_dataset_fp_conf_path, 'r') as f:
+        dataset_fn_dict = json.load(f)
+
+    data_dir = eval_args.data_path
+
+    logger.info(dataset_name_list)
+    if len(dataset_name_list) == 1 and dataset_name_list[0] == 'all':
+        dataset_name_list = dataset_fn_dict.keys()
+        dataset_fp_list = [data_dir + os.path.sep + eval_args.eval_language + os.path.sep + eval_args.eval_dataset_type + os.path.sep + dataset_fn_dict[i] for i in dataset_name_list]
+
+    logger.info('Start load and preprocess dataset')
+    all_dataset = {}
+    for dataset_name in dataset_name_list:
+        dataset_fp = data_dir + os.path.sep + eval_args.eval_language + os.path.sep + eval_args.eval_dataset_type + os.path.sep + dataset_fn_dict[dataset_name]
+        df = pd.read_csv(dataset_fp)
+
+        # Read dev data if doing few-shot test
+        df_dev = None
+        if eval_args.k_shot > 0:
+            dev_dataset_fp = data_dir + os.path.sep + eval_args.eval_language + os.path.sep + 'dev' + os.path.sep + dataset_fn_dict[dataset_name]
+            df_dev = pd.read_csv(dev_dataset_fp)
+
+        all_dataset[dataset_name] = preprocess(df, eval_args, df_dev=df_dev)
+        logger.info('Load success, dataset_name={}, dataset_file_path={}, dataset question count={}'.format(dataset_name, 
+                                                                                                            dataset_fp, 
+                                                                                                            len(all_dataset[dataset_name])))
+    return all_dataset
+    
+if __name__ == '__main__':
+    a = os.path.split(os.path.realpath(__file__))[0]
+    b = os.path.abspath(os.path.dirname(a)+os.path.sep+"../data")
+    logger.debug(b)
@@ -0,0 +1,68 @@
+import pandas as pd
+from loguru import logger
+
+
+def preprocess(df: pd.DataFrame, eval_args, df_dev: pd.DataFrame = None):
+    '''
+    Preprocess df and generate final dict
+    '''
+    question_prompt = '''以下是关于开发运维领域的单项选择题，请选出其中的正确答案。请直接输出选项。\n'''
+
+    if eval_args.k_shot > 0 and df_dev is not None:
+        # uppercase to lowercase
+        df_dev.rename(columns={
+            'Question': 'question',
+            'Answer': 'answer'
+        }, inplace=True)
+
+        prefix = ''
+
+        for idx in range(eval_args.k_shot):
+            question = df_dev['question'].iloc[idx]
+            prefix = prefix + question_prompt + '问题：' + question + '\n'
+
+            for option in ['A', 'B', 'C', 'D']:
+                if df_dev[option].iloc[idx]:
+                    prefix += '{}. {}\n'.format(option, df_dev[option].iloc[idx])
+            prefix += '答案：{}\n'.format(df_dev['answer'].iloc[idx].strip().upper())
+        prefix = prefix + question_prompt
+        res = preprocess_question(df, prefix)
+    else:
+        res = preprocess_question(df, question_prompt)
+    
+    return res
+
+def preprocess_question(df: pd.DataFrame, prefix: str = ''):
+    '''
+    Preprocess df and generate final dict
+    '''
+    res = []
+
+    # uppercase to lowercase
+    df.rename(columns={
+        'Question': 'question',
+        'Answer': 'answer'
+    }, inplace=True)
+
+    for idx in range(df.shape[0]):
+        to_append = {
+            'question': df['question'].iloc[idx],
+            'options': [],
+            'answer': df['answer'].iloc[idx].strip().upper()
+        }
+        question = df['question'].iloc[idx]
+
+        query = prefix + '''问题：{question}\n'''.format(question=question)
+
+        for option in ['A', 'B', 'C', 'D']:
+            if df[option].iloc[idx]:
+                to_append['options'].append(option)
+                to_append[option] = df[option].iloc[idx]
+                to_add = '{}. {}\n'.format(option, df[option].iloc[idx])
+                query += to_add
+        
+        to_add = '答案：'
+        query += to_add
+        to_append['query'] = query
+        res.append(to_append)
+    return res