Upload conversion.py

conversion.py

@@ -0,0 +1,130 @@
+import os
+import string
+import math
+import random
+import xml.etree.ElementTree as et
+import jsonlines
+import uuid
+import pandas as pd
+
+# set random seed for shuffling
+random.seed(1)
+
+# column names of the reference answers file
+FILE_NUMBER_COL = 'file_number'
+REFERENCE_ANSWER_COL = 'reference_answer'
+
+# column names of the files with the data
+QUESTION_COL = 'Frage'
+ANSWER_COL = 'Antwort'
+SCORE_COL = 'Score'
+ERROR_CLASS_COL = 'Fehlerklasse'
+FEEDBACK_COL = 'Feedback'
+
+# labels for verification_feedback
+CORRECT_LABEL = 'Correct'
+PARTIALLY_CORRECT_LABEL = 'Partially correct'
+INCORRECT_LABEL = 'Incorrect'
+
+def convert_xlsx_to_jsonl(
+    path_to_dataset,
+    path_to_reference_answers_file,
+    dir,
+    filename,
+    train_split=None):
+    """
+    Utility function used for conversion of .xlsx files from the dataset into JSON lines
+    
+    Params:
+        path_to_dataset (string): path to the folder containing the dataset (in .xlsx format)
+        path_to_reference_answers_file (string): path to the folder containing the reference answers (in .xlsx format)
+        dir (string): name of the directory where the JSON lines file will be stored
+        filename (string): name of the JSON lines file that will store the dataset
+        train_split (float or None): if not None, defines which percentage of the dataset to use for the train and validation splits
+    
+    Returns:
+        None: the file is saved JSON lines format in the specified location
+    """
+    def return_verification_feedback(score):
+        if math.isclose(score, 1.0):
+            return CORRECT_LABEL
+        elif math.isclose(score, 0.0):
+            return INCORRECT_LABEL
+        else:
+            return PARTIALLY_CORRECT_LABEL
+    
+    data = []
+    
+    # get reference answers from file
+    reference_answers_df = pd.read_excel(path_to_reference_answers_file)
+    # the keys of the dictionary are the number of the files padded with zeroes
+    # so that it has two digits, and the values are the reference answers themselves
+    reference_answers = {
+        f'{row[FILE_NUMBER_COL]:02}': row[REFERENCE_ANSWER_COL].strip() 
+        for _, row in reference_answers_df.iterrows()}
+    
+    # loop through all files in directory
+    for f in os.listdir(path_to_dataset):
+        if f.endswith('.xlsx'):
+            # read file
+            file_df = pd.read_excel(os.path.join(path_to_dataset, f))
+            # get question
+            question = file_df[QUESTION_COL].iat[0].strip()
+            # get reference answer based on file name
+            ref_answer = reference_answers[f.split('.')[0]]
+
+            # loop through all rows and store the appropriate fields in a list
+            for _, row in file_df.iterrows():
+                response = row[ANSWER_COL].strip()
+                score = float(row[SCORE_COL])
+                feedback = str(row[FEEDBACK_COL]).strip()
+                verification_feedback = return_verification_feedback(score)
+                error_class = row[ERROR_CLASS_COL].strip()
+                
+                # create dictionary with the appropriate fields
+                data.append({
+                    'id': uuid.uuid4().hex, # generate unique id in HEX format
+                    'question': question,
+                    'reference_answer': ref_answer,
+                    'provided_answer': response,
+                    'answer_feedback': feedback,
+                    'verification_feedback': verification_feedback,
+                    'error_class': error_class,
+                    'score': score
+                })
+   
+    if not os.path.exists(dir):
+        print('Creating directory where JSON file will be stored\n')
+        os.makedirs(dir)
+    
+    if train_split is None:
+        with jsonlines.open(f'{os.path.join(dir, filename)}.jsonl', 'w') as writer:
+            writer.write_all(data)
+    else:
+        # shuffle data and divide it into train and validation splits
+        random.shuffle(data)
+        train_data = data[: int(train_split * (len(data) - 1))]
+        val_data = data[int(train_split * (len(data) - 1)) :]
+
+        # write JSON lines file with train data
+        with jsonlines.open(f'{os.path.join(dir, filename)}-train.jsonl', 'w') as writer:
+            writer.write_all(train_data)
+        
+        # write JSON lines file with validation data
+        with jsonlines.open(f'{os.path.join(dir, filename)}-validation.jsonl', 'w') as writer:
+            writer.write_all(val_data)
+
+if __name__ == '__main__':
+    # convert legal domain dataset (german) to JSON lines
+    convert_xlsx_to_jsonl(
+        'data/training', 'data/reference_answers.xlsx',
+        'data/json', 'saf-legal-domain-german',
+        train_split=0.8)
+
+    convert_xlsx_to_jsonl(
+        'data/unseen_answers', 'data/reference_answers.xlsx',
+        'data/json', 'saf-legal-domain-german-unseen-answers')
+
+    convert_xlsx_to_jsonl(
+        'data/unseen_questions', 'data/reference_answers.xlsx',
+        'data/json', 'saf-legal-domain-german-unseen-questions')