Unify SARPlus.recommend_k_items #1644
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simonzhaoms
requested review from
miguelgfierro,
gramhagen,
anargyri,
loomlike and
wutaomsft
as code owners
simonzhaoms
changed the title
angusrtaylor
approved these changes
Feb 14, 2022
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| test: test Spark dataframe | ||
| top_k: top n items to return | ||
| remove_seen: remove items test users have already seen in the past from the recommended set | ||
| use_cache: use specified local directory stored in self.cache_path as cache for C++ based fast predictions | ||
| n_user_prediction_partitions: prediction partitions |
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Suggested change
| test: test Spark dataframe | |
| top_k: top n items to return | |
| remove_seen: remove items test users have already seen in the past from the recommended set | |
| use_cache: use specified local directory stored in self.cache_path as cache for C++ based fast predictions | |
| n_user_prediction_partitions: prediction partitions | |
| test (pySpark.DataFrame): test Spark dataframe. | |
| top_k (int): top n items to return. | |
| remove_seen (bool): remove items test users have already seen in the past from the recommended set. | |
| use_cache (bool): use specified local directory stored in `self.cache_path` as cache for C++ based fast predictions. | |
| n_user_prediction_partitions (int): prediction partitions. | |
| Returns: | |
| pySpark.DataFrame: Spark dataframe with recommended items. |
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Apart from the suggestion (@simonzhaoms please let me know if you can commit FYI @angusrtaylor).
One question, how much fast is the C++ version vs the Spark version. Maybe it would be good to explain what is happening a little bit. For example, the fast version caches to disk a intermediate computation using C++ and is around X% faster than the standard Spark version
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@miguelgfierro I'll make another PR to patch your suggestion
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Description
This PR unifies SARPlus.recommend_k_items() and SARPlus.recommend_k_items_slow suggested by @angusrtaylor in the comment
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staging branchand not tomain branch.