| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import accuracy_score | |
| # Load the data | |
| train_df = pd.read_csv("./input/train.csv") | |
| test_df = pd.read_csv("./input/test.csv") | |
| sample_submission = pd.read_csv("./input/sample_submission.csv") | |
| # Split the training data into training and validation sets | |
| train_texts, val_texts, train_indices, val_indices = train_test_split( | |
| train_df["text"], train_df["index"], test_size=0.1, random_state=42 | |
| ) | |
| # Placeholder for the predictions | |
| val_predictions = [0] * len(val_texts) | |
| # TODO: Implement the decryption algorithm here | |
| # For now, we are just using a placeholder prediction | |
| # In a real scenario, this is where the decryption logic would be applied | |
| # Evaluate the accuracy of the predictions | |
| accuracy = accuracy_score(val_indices, val_predictions) | |
| print(f"Validation accuracy: {accuracy}") | |
| # Prepare the submission file | |
| test_predictions = [0] * len(test_df) | |
| submission = pd.DataFrame( | |
| {"ciphertext_id": test_df["ciphertext_id"], "index": test_predictions} | |
| ) | |
| submission.to_csv("./working/submission.csv", index=False) | |