73 lines
3.3 KiB
Scala
73 lines
3.3 KiB
Scala
package com.twitter.home_mixer.functional_component.feature_hydrator
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import com.twitter.home_mixer.model.HomeFeatures.FavoritedByUserIdsFeature
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import com.twitter.home_mixer.model.HomeFeatures.PerspectiveFilteredLikedByUserIdsFeature
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import com.twitter.product_mixer.component_library.model.candidate.TweetCandidate
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import com.twitter.product_mixer.core.feature.Feature
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import com.twitter.product_mixer.core.feature.featuremap.FeatureMap
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import com.twitter.product_mixer.core.feature.featuremap.FeatureMapBuilder
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import com.twitter.product_mixer.core.functional_component.feature_hydrator.BulkCandidateFeatureHydrator
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import com.twitter.product_mixer.core.model.common.CandidateWithFeatures
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import com.twitter.product_mixer.core.model.common.identifier.FeatureHydratorIdentifier
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import com.twitter.product_mixer.core.pipeline.PipelineQuery
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import com.twitter.product_mixer.core.util.OffloadFuturePools
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import com.twitter.stitch.Stitch
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import com.twitter.stitch.timelineservice.TimelineService
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import com.twitter.stitch.timelineservice.TimelineService.GetPerspectives
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import com.twitter.timelineservice.thriftscala.PerspectiveType
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import com.twitter.timelineservice.thriftscala.PerspectiveType.Favorited
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import javax.inject.Inject
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import javax.inject.Singleton
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/**
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* Filter out unlike edges from liked-by tweets
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* Useful if the likes come from a cache and because UTEG does not fully remove unlike edges.
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*/
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@Singleton
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class PerspectiveFilteredSocialContextFeatureHydrator @Inject() (timelineService: TimelineService)
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extends BulkCandidateFeatureHydrator[PipelineQuery, TweetCandidate] {
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override val identifier: FeatureHydratorIdentifier =
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FeatureHydratorIdentifier("PerspectiveFilteredSocialContext")
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override val features: Set[Feature[_, _]] = Set(PerspectiveFilteredLikedByUserIdsFeature)
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private val MaxCountUsers = 10
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private val favoritePerspectiveSet: Set[PerspectiveType] = Set(Favorited)
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override def apply(
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query: PipelineQuery,
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candidates: Seq[CandidateWithFeatures[TweetCandidate]]
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): Stitch[Seq[FeatureMap]] = OffloadFuturePools.offloadStitch {
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val engagingUserIdtoTweetId = candidates.flatMap { candidate =>
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candidate.features
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.getOrElse(FavoritedByUserIdsFeature, Seq.empty).take(MaxCountUsers)
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.map(favoritedBy => favoritedBy -> candidate.candidate.id)
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}
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val queries = engagingUserIdtoTweetId.map {
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case (userId, tweetId) =>
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GetPerspectives.Query(userId = userId, tweetId = tweetId, types = favoritePerspectiveSet)
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}
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Stitch.collect(queries.map(timelineService.getPerspective)).map { perspectiveResults =>
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val validUserIdTweetIds: Set[(Long, Long)] =
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queries
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.zip(perspectiveResults)
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.collect { case (query, perspective) if perspective.favorited => query }
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.map(query => (query.userId, query.tweetId))
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.toSet
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candidates.map { candidate =>
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val perspectiveFilteredFavoritedByUserIds: Seq[Long] = candidate.features
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.getOrElse(FavoritedByUserIdsFeature, Seq.empty).take(MaxCountUsers)
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.filter { userId => validUserIdTweetIds.contains((userId, candidate.candidate.id)) }
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FeatureMapBuilder()
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.add(PerspectiveFilteredLikedByUserIdsFeature, perspectiveFilteredFavoritedByUserIds)
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.build()
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}
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}
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}
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}
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