object
CtrFeatures
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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def
addCtrProviderToModel(model: TFullModel, ctrsContext: CtrsContext, quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool]): TFullModel
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def
addCtrsAsEstimated(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool], params: TrainingParamsTrait, oneHotMaxSize: Int): (Pool, Array[Pool], CtrsContext)
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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def
downloadSubsetOfQuantizedFeatures(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool], quantizedFeaturesIndices: QuantizedFeaturesIndices, selectedFlatFeaturesIndices: Set[Int], localExecutor: TLocalExecutor): (TQuantizedObjectsDataProviderPtr, TVector_TQuantizedObjectsDataProviderPtr)
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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def
getCatFeatureFlatIndicesForCtrs(pool: Pool, oneHotMaxSize: Int): Array[Int]
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final
def
getClass(): Class[_]
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def
getDatasetWithIdsAndIds(df: DataFrame): (DataFrame, Array[Long])
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def
getLearnTarget(pool: Pool): Array[Float]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
uploadAndMerge(spark: SparkSession, schema: StructType, aggregateData: DataFrame, ids: Array[Long], estimatedData: TQuantizedObjectsDataProviderPtr): DataFrame
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
Inherited from AnyRef
Inherited from Any