44 lines
2.0 KiB
Scala
44 lines
2.0 KiB
Scala
package com.twitter.timelines.prediction.features.simcluster
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import com.twitter.dal.personal_data.thriftjava.PersonalDataType.SemanticcoreClassification
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import com.twitter.ml.api.Feature
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import com.twitter.ml.api.Feature.Continuous
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import com.twitter.timelines.data_processing.ml_util.aggregation_framework.conversion.CombineCountsBase
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import scala.collection.JavaConverters._
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object SimclustersScoresFeatures extends CombineCountsBase {
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override def topK: Int = 2
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override def hardLimit: Option[Int] = Some(20)
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val prefix = s"recommendations.sim_clusters_scores"
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val TOPIC_CONSUMER_TWEET_EMBEDDING_Cs = new Continuous(
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s"$prefix.localized_topic_consumer_tweet_embedding_cosine_similarity",
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Set(SemanticcoreClassification).asJava)
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val TOPIC_PRODUCER_TWEET_EMBEDDING_Cs = new Continuous(
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s"$prefix.topic_producer_tweet_embedding_cosine_similarity",
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Set(SemanticcoreClassification).asJava)
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val USER_TOPIC_CONSUMER_TWEET_EMBEDDING_COSINE_SIM = new Continuous(
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s"$prefix.user_interested_in_localized_topic_consumer_embedding_cosine_similarity",
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Set(SemanticcoreClassification).asJava)
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val USER_TOPIC_CONSUMER_TWEET_EMBEDDING_DOT_PRODUCT = new Continuous(
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s"$prefix.user_interested_in_localized_topic_consumer_embedding_dot_product",
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Set(SemanticcoreClassification).asJava)
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val USER_TOPIC_PRODUCER_TWEET_EMBEDDING_COSINE_SIM = new Continuous(
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s"$prefix.user_interested_in_localized_topic_producer_embedding_cosine_similarity",
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Set(SemanticcoreClassification).asJava)
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val USER_TOPIC_PRODUCER_TWEET_EMBEDDING_DOT_PRODUCT = new Continuous(
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s"$prefix.user_interested_in_localized_topic_producer_embedding_dot_product",
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Set(SemanticcoreClassification).asJava)
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override def precomputedCountFeatures: Seq[Feature[_]] =
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Seq(
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TOPIC_CONSUMER_TWEET_EMBEDDING_Cs,
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TOPIC_PRODUCER_TWEET_EMBEDDING_Cs,
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USER_TOPIC_CONSUMER_TWEET_EMBEDDING_COSINE_SIM,
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USER_TOPIC_CONSUMER_TWEET_EMBEDDING_DOT_PRODUCT,
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USER_TOPIC_PRODUCER_TWEET_EMBEDDING_COSINE_SIM,
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USER_TOPIC_PRODUCER_TWEET_EMBEDDING_DOT_PRODUCT
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)
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}
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