CS224W: Added filtered evaluation setting to KGEModel

[ananjan-nandi-9]

The original implementation of KGEModel simply calculates the rank of the gold tail for any given query by considering all entities in the dataset as the candidate tail set. However, most Knowledge Graph Completion literature Bordes et al, 2013 performs evaluation in a filtered setting. For any query (h,r,?), we first filter out all t from the candidate tail set such that (h,r,t) is present in the training, validation, or test split, with the exception of the gold answer to the query. The rank is then calculated on this filtered candidate set.

This PR introduces an option to turn on this mode of evaluation. If the 'filtered' argument is set to True, and a list of tails present in the training, validation, or test set for any (head,relation) pair is passed in the 'neighbors' argument, the evaluation is performed in the filtered setting. Specifically, if (h,r,t) is present in the dataset, t is present in neighbors[h][r].

[codecov]

Codecov Report

Attention: 8 lines in your changes are missing coverage. Please review.

Comparison is base (ef13fd3) 89.87% compared to head (bf0c40b) 89.18%.
Report is 3 commits behind head on master.

Files	Patch %	Lines
torch_geometric/nn/kge/base.py	33.33%	8 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #8644      +/-   ##
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- Coverage   89.87%   89.18%   -0.69%     
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  Files         481      481              
  Lines       30745    30750       +5     
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- Hits        27631    27425     -206     
- Misses       3114     3325     +211     

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