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) | |