Implemented hits@k metric for link-prediction #675
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classicsong
reviewed
Dec 6, 2023
| @@ -2076,6 +2076,14 @@ def eval_metric(self): | |||
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| return eval_metric | |||
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| @property | |||
| def eval_k(self): | |||
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Is it possible to have a list of ks instead of just 1 k?
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I think most of the LP/retrieval datasets only select one specific k.
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changed to eval_hit_k
| early_stop_strategy=EARLY_STOP_AVERAGE_INCREASE_STRATEGY): | ||
| eval_metric = ["mrr"] | ||
| early_stop_strategy=EARLY_STOP_AVERAGE_INCREASE_STRATEGY, | ||
| k=100): |
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It is better to define a hit@k evaluator.
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I think that will lead to a lot of duplicated codes. I saw GSgnnRegressionEvaluator allows multiple metrics. What's the reasoning for having evaluator vs metric objects?
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We have two choices:
- define a GSgnnRankingLPEvaluator class and both GSgnnMrrLPEvaluator and GSgnnHitKLPEvaluator will inherate from GSgnnRankingLPEvaluator.
- define a GSgnnRankingLPEvaluator class which implements mrr() and hit(), and rename it to GSgnnMrrLPEvaluator and GSgnnRankingLPEvaluator.
I would prefer the first one.
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Description of changes:
GSgnnMrrLPEvaluatorandGSgnnPerEtypeMrrLPEvaluatorto work with both mrr and hits@kBy submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.