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import os |
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import numpy as np |
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import pandas as pd |
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import pickle |
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import math |
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import random |
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""" |
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Dataset url: https://github.com/koc-lab/law-turk/tree/main/data/constitutional/deep |
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Paper url: https://www.sciencedirect.com/science/article/abs/pii/S0306457321001692 |
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""" |
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def prepare_data(): |
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label_dict = {' İhlal' : 0, ' İhlal Olmadığı' : 1} |
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with open('old_dataset/constitutional_tokenized.law' ,'rb') as pickle_file: |
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tokenized = pickle.load(pickle_file) |
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with open('old_dataset/constitutional_labels.law' ,'rb') as pickle_file: |
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labels = pickle.load(pickle_file) |
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tokenized_lower = [] |
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for text in tokenized: |
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temp_text = "" |
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for word in text: |
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temp_text += word.lower() + " " |
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tokenized_lower.append(temp_text) |
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train_ratio = 0.70 |
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val_ratio = 0.15 |
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list_indices = [] |
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for i, lbl in enumerate(labels): |
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if lbl in label_dict: |
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list_indices.append(i) |
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random.Random(13).shuffle(list_indices) |
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new_length = len(list_indices) |
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train_idx = math.floor(new_length * train_ratio) |
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val_idx = math.floor(new_length * (train_ratio + val_ratio)) |
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train_indices = list_indices[0:train_idx] |
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val_indices = list_indices[train_idx : val_idx] |
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test_indices = list_indices[val_idx:] |
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train_list = [] |
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val_list = [] |
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test_list = [] |
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for ind in train_indices: |
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train_list.append(tokenized_lower[ind]) |
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for ind in val_indices: |
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val_list.append(tokenized_lower[ind]) |
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for ind in test_indices: |
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test_list.append(tokenized_lower[ind]) |
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train_labels = [] |
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val_labels = [] |
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test_labels = [] |
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count = 0 |
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for ind in train_indices: |
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if labels[ind] == " İhlal": |
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train_labels.append("Violation") |
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else: |
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train_labels.append("No violation") |
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for ind in val_indices: |
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if labels[ind] == " İhlal": |
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val_labels.append("Violation") |
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else: |
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val_labels.append("No violation") |
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for ind in test_indices: |
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if labels[ind] == " İhlal": |
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test_labels.append("Violation") |
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else: |
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test_labels.append("No violation") |
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print(count) |
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train_split = np.concatenate((np.expand_dims(train_list, axis=1), np.expand_dims(train_labels, axis=1)), axis=1) |
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val_split = np.concatenate((np.expand_dims(val_list, axis=1), np.expand_dims(val_labels, axis=1)), axis=1) |
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test_split = np.concatenate((np.expand_dims(test_list, axis=1), np.expand_dims(test_labels, axis=1)), axis=1) |
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return pd.DataFrame(train_split, columns=["Text", "Label"]), pd.DataFrame(val_split, columns=["Text", "Label"]), pd.DataFrame(test_split, columns=["Text", "Label"]) |
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train_split, val_split, test_split = prepare_data() |
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train_split.to_json(os.path.join("train.jsonl"), lines=True, orient="records", force_ascii=False) |
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val_split.to_json(os.path.join("validation.jsonl"), lines=True, orient="records", force_ascii=False) |
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test_split.to_json(os.path.join("test.jsonl"), lines=True, orient="records", force_ascii=False) |
