a.txt

I write a Python code that recommends games for a specific user.
I have a data "recdata.csv" with columns "uid" - user id, "id" - game id, "owned" - if game is owned by user.
I have another data "gamesdata.csv" where I have columns "id" - game id, "title" - game title.
Write a function that gets user id, finds his owned games in recdata.csv, turns those "id" of games into titles from gamesdata.csv and returns a list of those titles.

Then I need another function that is modified version of this:
def print_user_games_and_predictions(
    user_id: int,
    user_item_matrix: pd.DataFrame,
    reduced_matrix: np.ndarray,
    model: nn.Module,
    transformer: TruncatedSVD,
    scaler: MinMaxScaler,
    # rec_data_file: str,
    games_data_file: str,
    n_recommendations: int = 5,
) -> None:
    # Load the games data and create a dictionary to map game ids to titles
    games_data = pd.read_csv(games_data_file)
    id_to_title = games_data.set_index("id")["title"].to_dict()

    # Get the index of the user_id in the matrix
    user_index = user_item_matrix.index.get_loc(user_id)
    user_reduced_vector = reduced_matrix[user_index]

    # Get the model predictions in the reduced space
    with torch.no_grad():
        user_reduced_vector_tensor = torch.tensor(
            user_reduced_vector, dtype=torch.float32
        ).unsqueeze(0)
        predicted_reduced_vector_tensor = model(user_reduced_vector_tensor)
        predicted_reduced_vector = predicted_reduced_vector_tensor.numpy().squeeze()

    # Inverse transformations: denormalize and inverse SVD
    denormalized_predicted_vector = scaler.inverse_transform([predicted_reduced_vector])
    reconstructed_predicted_vector = transformer.inverse_transform(
        denormalized_predicted_vector
    ).squeeze()

    # Get the actual and predicted games for the user
    user_actual_games = user_item_matrix.loc[user_id]
    user_predicted_games = pd.Series(
        reconstructed_predicted_vector, index=user_actual_games.index
    )
    actual_games_sorted = user_actual_games.sort_values(ascending=False)
    predicted_games_sorted = user_predicted_games.sort_values(ascending=False)

    # Save the interacted games and top 5 recommended games to a single file
    with open("9-NN-recs.txt", "w") as f:
        f.write(f"User id: {user_id}\n\n")

        f.write("Interacted games:\n")
        for game_id, owned in actual_games_sorted.iteritems():
            if owned == 1.0:
                if game_id in id_to_title:
                    f.write(f"{id_to_title[game_id]} (id: {game_id})\n")
                else:
                    f.write(f"Game not found in 'gamesdata.csv' (id: {game_id})\n")

        f.write("\nTop 5 recommended games:\n")
        for game_id, score in predicted_games_sorted.head(
            n_recommendations
        ).iteritems():
            if game_id in id_to_title:
                f.write(f"{id_to_title[game_id]} (id: {game_id}, score: {score})\n")
            else:
                f.write(
                    f"Game not found in 'gamesdata.csv' (id: {game_id}, score: {score})\n"
                )