<jupyter_start><jupyter_text>Validation<jupyter_code>train, test = train_test_split(range(data_transformed.shape[0]), test_size=0.5, random_state=42)
supposion = list(map(lambda x: x[1], predicted_aranged[len(test):]))
y_true = np.zeros(data_transformed.shape[0])
y_true[train] = 1
y_predicted = np.zeros(data_transformed.shape[0])
y_predicted[supposion] = 1
roc_auc_score(y_true, y_predicted)
data_transformed.shape[0]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14253897550111358, 'fraction_numerical': 0.024498886414253896, 'mean_word_length': 12.235294117647058, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '591037', 'license': 'MIT', 'n_tokens_mistral': 186, 'n_tokens_neox': 176, 'n_words': 21, 'path': 'CreditCard/Whitebox.ipynb', 'repo_name': 'Private-ML/gan', 'size': '449'}