Upload conversion.py

conversion.py

@@ -0,0 +1,97 @@
+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)
+
+def convert_xml_to_jsonl(path_to_dataset, dir, filename, train_split=None):
+    """
+    Utility function used for conversion of XML files from the dataset into JSON lines
+    
+    Params:
+        path_to_dataset (string): path to the folder containing the dataset (in XML 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 in JSON lines format in the specified location
+    """
+    data = []
+
+    # loop through all files in directory
+    for f in os.listdir(path_to_dataset):
+        if f.endswith('.xml'):
+            root = et.parse(os.path.join(path_to_dataset, f)).getroot()
+            # get question
+            question = root.find('questionText').text.replace('\n', ' ')
+            # get reference and student answers
+            ref_answers = [x for x in root.find('referenceAnswers')]
+            student_answers = [x for x in root.find('studentAnswers')]
+
+            if len(ref_answers) == 1:
+                # get reference answer and clear all spaces
+                ref_answer = ref_answers[0].text.strip()
+
+                # loop through all student answers and store the appropriate fields in a list
+                for answer in student_answers:
+                    response = answer.find('response').text.strip()
+                    score = float(answer.find('score').text)
+                    feedback = answer.find('response_feedback').text.strip()
+                    verification_feedback = answer.find('verification_feedback').text.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,
+                        '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 micro job dataset (german) to JSON lines
+    convert_xml_to_jsonl(
+        'data/training/german',
+        'data/json',
+        'saf-micro-job-german',
+        train_split=0.8)
+
+    convert_xml_to_jsonl(
+        'data/unseen_answers/german',
+        'data/json',
+        'saf-micro-job-german-unseen-answers')
+
+    convert_xml_to_jsonl(
+        'data/unseen_questions/german',
+        'data/json',
+        'saf-micro-job-german-unseen-questions')