dicee.evaluation.utils

Utility functions for evaluation module.

This module contains shared helper functions used across different evaluation components.

Functions

make_iterable_verbose(→ Iterable)

Wrap an iterable with tqdm progress bar if verbose is True.

compute_metrics_from_ranks(→ Dict[str, float])

Compute standard link prediction metrics from ranks.

compute_metrics_from_ranks_simple(→ Dict[str, float])

Compute link prediction metrics without scaling factor.

update_hits(→ None)

Update hits dictionary based on rank.

efficient_zero_grad(→ None)

Efficiently zero gradients using parameter.grad = None.

Module Contents

dicee.evaluation.utils.make_iterable_verbose(iterable_object: Iterable, verbose: bool, desc: str = 'Default', position: int = None, leave: bool = True) → Iterable

Wrap an iterable with tqdm progress bar if verbose is True.

Parameters:

  • iterable_object – The iterable to potentially wrap.

  • verbose – Whether to show progress bar.

  • desc – Description for the progress bar.

  • position – Position of the progress bar.

  • leave – Whether to leave the progress bar after completion.

Returns:

The original iterable or a tqdm-wrapped version.

dicee.evaluation.utils.compute_metrics_from_ranks(ranks: List[int], num_triples: int, hits_dict: Dict[int, List[float]], scale_factor: int = 1) → Dict[str, float]

Compute standard link prediction metrics from ranks.

Parameters:

  • ranks – List of ranks for each prediction.

  • num_triples – Total number of triples evaluated.

  • hits_dict – Dictionary mapping hit levels to lists of hits.

  • scale_factor – Factor to scale the denominator (e.g., 2 for head+tail).

Returns:

Dictionary containing H@1, H@3, H@10, and MRR metrics.

dicee.evaluation.utils.compute_metrics_from_ranks_simple(ranks: List[int], num_triples: int, hits_dict: Dict[int, List[float]]) → Dict[str, float]

Compute link prediction metrics without scaling factor.

Parameters:

  • ranks – List of ranks for each prediction.

  • num_triples – Total number of triples evaluated.

  • hits_dict – Dictionary mapping hit levels to lists of hits.

Returns:

Dictionary containing H@1, H@3, H@10, and MRR metrics.

dicee.evaluation.utils.update_hits(hits: Dict[int, List[float]], rank: int, hits_range: List[int] = None) → None

Update hits dictionary based on rank.

Parameters:

  • hits – Dictionary to update in-place.

  • rank – The rank to check against hit levels.

  • hits_range – List of hit levels to check (default: 1-10).

dicee.evaluation.utils.efficient_zero_grad(model) → None

Efficiently zero gradients using parameter.grad = None.

This is more efficient than optimizer.zero_grad() as it avoids memory operations.

See: https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html

Parameters:

model – PyTorch model to zero gradients for.