dicee.evaluation.literal_prediction
===================================

.. py:module:: dicee.evaluation.literal_prediction

.. autoapi-nested-parse::

   Literal prediction evaluation functions.

   This module provides functions for evaluating literal/attribute prediction
   performance of knowledge graph embedding models.



Functions
---------

.. autoapisummary::

   dicee.evaluation.literal_prediction.evaluate_literal_prediction


Module Contents
---------------

.. py:function:: evaluate_literal_prediction(kge_model, eval_file_path: str = None, store_lit_preds: bool = True, eval_literals: bool = True, loader_backend: str = 'pandas', return_attr_error_metrics: bool = False) -> Optional[pandas.DataFrame]

   Evaluate trained literal prediction model on a test file.

   Evaluates the literal prediction capabilities of a KGE model by
   computing MAE and RMSE metrics for each attribute.

   :param kge_model: Trained KGE model with literal prediction capability.
   :param eval_file_path: Path to the evaluation file containing test literals.
   :param store_lit_preds: If True, stores predictions to CSV file.
   :param eval_literals: If True, evaluates and prints error metrics.
   :param loader_backend: Backend for loading dataset ('pandas' or 'rdflib').
   :param return_attr_error_metrics: If True, returns the metrics DataFrame.

   :returns: DataFrame with per-attribute MAE and RMSE if return_attr_error_metrics
             is True, otherwise None.

   :raises RuntimeError: If the KGE model doesn't have a trained literal model.
   :raises AssertionError: If model is invalid or test set has no valid data.

   .. rubric:: Example

   >>> from dicee import KGE
   >>> from dicee.evaluation import evaluate_literal_prediction
   >>> model = KGE(path="pretrained_model")
   >>> metrics = evaluate_literal_prediction(
   ...     model,
   ...     eval_file_path="test_literals.csv",
   ...     return_attr_error_metrics=True
   ... )
   >>> print(metrics)