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68 lines
3.3 KiB
Scala
68 lines
3.3 KiB
Scala
package com.twitter.product_mixer.component_library.scorer.cortex
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import com.twitter.finagle.Http
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import com.twitter.product_mixer.component_library.module.http.FinagleHttpClientModule.FinagleHttpClientModule
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import com.twitter.product_mixer.component_library.scorer.common.ManagedModelClient
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import com.twitter.product_mixer.component_library.scorer.common.ModelSelector
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import com.twitter.product_mixer.core.feature.datarecord.BaseDataRecordFeature
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import com.twitter.product_mixer.core.feature.datarecord.TensorDataRecordCompatible
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import com.twitter.product_mixer.core.feature.featuremap.datarecord.FeaturesScope
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import com.twitter.product_mixer.core.functional_component.scorer.Scorer
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import com.twitter.product_mixer.core.model.common.UniversalNoun
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import com.twitter.product_mixer.core.model.common.identifier.ScorerIdentifier
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import com.twitter.product_mixer.core.pipeline.PipelineQuery
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import javax.inject.Inject
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import javax.inject.Named
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import javax.inject.Singleton
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@Singleton
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class CortexManagedInferenceServiceDataRecordScorerBuilder @Inject() (
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@Named(FinagleHttpClientModule) httpClient: Http.Client) {
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/**
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* Builds a configurable Scorer to call into your desired DataRecord-backed Cortex Managed ML Model Service.
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*
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* If your service does not bind an Http.Client implementation, add
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* [[com.twitter.product_mixer.component_library.module.http.FinagleHttpClientModule]]
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* to your server module list
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*
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* @param scorerIdentifier Unique identifier for the scorer
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* @param modelPath MLS path to model
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* @param modelSignature Model Signature Key
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* @param modelSelector [[ModelSelector]] for choosing the model name, can be an anon function.
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* @param candidateFeatures Desired candidate level feature store features to pass to the model.
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* @param resultFeatures Desired candidate level feature store features to extract from the model.
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* Since the Cortex Managed Platform always returns tensor values, the
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* feature must use a [[TensorDataRecordCompatible]].
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* @tparam Query Type of pipeline query.
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* @tparam Candidate Type of candidates to score.
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* @tparam QueryFeatures type of the query level features consumed by the scorer.
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* @tparam CandidateFeatures type of the candidate level features consumed by the scorer.
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* @tparam ResultFeatures type of the candidate level features returned by the scorer.
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*/
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def build[
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Query <: PipelineQuery,
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Candidate <: UniversalNoun[Any],
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QueryFeatures <: BaseDataRecordFeature[Query, _],
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CandidateFeatures <: BaseDataRecordFeature[Candidate, _],
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ResultFeatures <: BaseDataRecordFeature[Candidate, _] with TensorDataRecordCompatible[_]
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](
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scorerIdentifier: ScorerIdentifier,
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modelPath: String,
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modelSignature: String,
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modelSelector: ModelSelector[Query],
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queryFeatures: FeaturesScope[QueryFeatures],
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candidateFeatures: FeaturesScope[CandidateFeatures],
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resultFeatures: Set[ResultFeatures]
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): Scorer[Query, Candidate] =
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new CortexManagedDataRecordScorer(
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identifier = scorerIdentifier,
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modelSignature = modelSignature,
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modelSelector = modelSelector,
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modelClient = ManagedModelClient(httpClient, modelPath),
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queryFeatures = queryFeatures,
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candidateFeatures = candidateFeatures,
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resultFeatures = resultFeatures
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)
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}
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