The module includes performance metrics for neural graph embeddings models, along with model selection routines, negatives generation, and an implementation of the learning-to-rank-based evaluation protocol used in literature.


Learning-to-rank metrics to evaluate the performance of neural graph embedding models.

rank_score(y_true, y_pred[, pos_lab])

Rank of a triple


Mean Reciprocal Rank (MRR)


Mean Rank (MR)

hits_at_n_score(ranks, n)


Negatives Generation

Negatives generation routines. These are corruption strategies based on the Local Closed-World Assumption (LCWA).

generate_corruptions_for_eval(X, …[, …])

Generate corruptions for evaluation.

generate_corruptions_for_fit(X[, …])

Generate corruptions for training.

Evaluation & Model Selection

Functions to evaluate the predictive power of knowledge graph embedding models, and routines for model selection.

evaluate_performance(X, model[, …])

Evaluate the performance of an embedding model.

select_best_model_ranking(model_class, …)

Model selection routine for embedding models via either grid search or random search.

Helper Functions

Utilities and support functions for evaluation procedures.

train_test_split_no_unseen(X[, test_size, …])

Split into train and test sets.


Create string-IDs mappings for entities and relations.

to_idx(X, ent_to_idx, rel_to_idx)

Convert statements (triples) into integer IDs.