contrib/azureml_designer_modules/entries/map_entry.py [8:68]:
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    load_data_frame_from_directory,
    save_data_frame_to_directory,
)
from recommenders.evaluation.python_evaluation import map_at_k


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--rating-true", help="True DataFrame.")
    parser.add_argument("--rating-pred", help="Predicted DataFrame.")
    parser.add_argument(
        "--col-user", type=str, help="A string parameter with column name for user."
    )
    parser.add_argument(
        "--col-item", type=str, help="A string parameter with column name for item."
    )
    parser.add_argument(
        "--col-rating", type=str, help="A string parameter with column name for rating."
    )
    parser.add_argument(
        "--col-prediction",
        type=str,
        help="A string parameter with column name for prediction.",
    )
    parser.add_argument(
        "--relevancy-method",
        type=str,
        help="method for determining relevancy ['top_k', 'by_threshold'].",
    )
    parser.add_argument("--k", type=int, help="number of top k items per user.")
    parser.add_argument(
        "--threshold", type=float, help="threshold of top items per user."
    )
    parser.add_argument("--score-result", help="Result of the computation.")

    args, _ = parser.parse_known_args()

    rating_true = load_data_frame_from_directory(args.rating_true).data
    rating_pred = load_data_frame_from_directory(args.rating_pred).data

    col_user = args.col_user
    col_item = args.col_item
    col_rating = args.col_rating
    col_prediction = args.col_prediction
    relevancy_method = args.relevancy_method
    k = args.k
    threshold = args.threshold

    logger.debug(f"Received parameters:")
    logger.debug(f"User:       {col_user}")
    logger.debug(f"Item:       {col_item}")
    logger.debug(f"Rating:     {col_rating}")
    logger.debug(f"Prediction: {col_prediction}")
    logger.debug(f"Relevancy:  {relevancy_method}")
    logger.debug(f"K:          {k}")
    logger.debug(f"Threshold:  {threshold}")

    logger.debug(f"Rating True path: {args.rating_true}")
    logger.debug(f"Shape of loaded DataFrame: {rating_true.shape}")
    logger.debug(f"Rating Pred path: {args.rating_pred}")
    logger.debug(f"Shape of loaded DataFrame: {rating_pred.shape}")
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contrib/azureml_designer_modules/entries/ndcg_entry.py [8:68]:
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    load_data_frame_from_directory,
    save_data_frame_to_directory,
)
from recommenders.evaluation.python_evaluation import ndcg_at_k


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--rating-true", help="True DataFrame.")
    parser.add_argument("--rating-pred", help="Predicted DataFrame.")
    parser.add_argument(
        "--col-user", type=str, help="A string parameter with column name for user."
    )
    parser.add_argument(
        "--col-item", type=str, help="A string parameter with column name for item."
    )
    parser.add_argument(
        "--col-rating", type=str, help="A string parameter with column name for rating."
    )
    parser.add_argument(
        "--col-prediction",
        type=str,
        help="A string parameter with column name for prediction.",
    )
    parser.add_argument(
        "--relevancy-method",
        type=str,
        help="method for determining relevancy ['top_k', 'by_threshold'].",
    )
    parser.add_argument("--k", type=int, help="number of top k items per user.")
    parser.add_argument(
        "--threshold", type=float, help="threshold of top items per user."
    )
    parser.add_argument("--score-result", help="Result of the computation.")

    args, _ = parser.parse_known_args()

    rating_true = load_data_frame_from_directory(args.rating_true).data
    rating_pred = load_data_frame_from_directory(args.rating_pred).data

    col_user = args.col_user
    col_item = args.col_item
    col_rating = args.col_rating
    col_prediction = args.col_prediction
    relevancy_method = args.relevancy_method
    k = args.k
    threshold = args.threshold

    logger.debug(f"Received parameters:")
    logger.debug(f"User:       {col_user}")
    logger.debug(f"Item:       {col_item}")
    logger.debug(f"Rating:     {col_rating}")
    logger.debug(f"Prediction: {col_prediction}")
    logger.debug(f"Relevancy:  {relevancy_method}")
    logger.debug(f"K:          {k}")
    logger.debug(f"Threshold:  {threshold}")

    logger.debug(f"Rating True path: {args.rating_true}")
    logger.debug(f"Shape of loaded DataFrame: {rating_true.shape}")
    logger.debug(f"Rating Pred path: {args.rating_pred}")
    logger.debug(f"Shape of loaded DataFrame: {rating_pred.shape}")
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