class Model extends PredictionModel[Vector, Model]
A Spark API compatible Scoring Code model. For instantiating use the Predictors
- Alphabetic
- By Inheritance
- Model
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
-
new
Model(modelBytes: Array[Byte], model: IPredictorInfo, modelId: String, _options: TimeSeriesOptions)
- modelBytes
byte array from a Scoring Code model file
- model
Scoring Code instance
- modelId
unique model id for the Scoring Code model
Type Members
- class TimeSeriesHistoricalPredictionsFunc extends (org.apache.spark.sql.Row) ⇒ TraversableOnce[org.apache.spark.sql.Row] with Serializable
- class TimeSeriesRealTimePredictionsFunc extends (org.apache.spark.sql.Row) ⇒ TraversableOnce[org.apache.spark.sql.Row] with Serializable
- class TimeSeriesSingleForecastPointPredictionsFunc extends (org.apache.spark.sql.Row) ⇒ TraversableOnce[org.apache.spark.sql.Row] with Serializable
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
$[T](param: Param[T]): T
- Attributes
- protected
- Definition Classes
- Params
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- var _options: TimeSeriesOptions
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
final
def
clear(param: Param[_]): Model.this.type
- Definition Classes
- Params
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
-
def
copy(extra: ParamMap): Model
- Definition Classes
- Model → Model → Transformer → PipelineStage → Params
-
def
copyValues[T <: Params](to: T, extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
- def dateTimeStringToEpoch(date: String, format: String): Long
- def dateTimeStringToEpochFunc(format: String): (String) ⇒ Long
-
final
def
defaultCopy[T <: Params](extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
explainParam(param: Param[_]): String
- Definition Classes
- Params
-
def
explainParams(): String
- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
-
final
val
featuresCol: Param[String]
- Definition Classes
- HasFeaturesCol
-
def
featuresDataType: DataType
- Attributes
- protected
- Definition Classes
- PredictionModel
-
final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
-
final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
final
def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
-
final
def
getLabelCol: String
- Definition Classes
- HasLabelCol
- def getModel(): IPredictorInfo
-
final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
-
def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
-
final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
- def getWindowLength(predictor: ITimeSeriesRegressionPredictor): Long
-
final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
-
def
hasParent: Boolean
- Definition Classes
- Model
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
-
def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
final
def
isDefined(param: Param[_]): Boolean
- Definition Classes
- Params
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
isSet(param: Param[_]): Boolean
- Definition Classes
- Params
-
def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
final
val
labelCol: Param[String]
- Definition Classes
- HasLabelCol
-
def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- var notSeerializableModel: IPredictorInfo
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @HotSpotIntrinsicCandidate()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @HotSpotIntrinsicCandidate()
-
def
numFeatures: Int
- Definition Classes
- PredictionModel
- Annotations
- @Since( "1.6.0" )
- def options: TimeSeriesOptions
- def options_=(options: TimeSeriesOptions): Unit
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[Model]
- Definition Classes
- Model
-
def
predict(features: Vector): Double
- Definition Classes
- Model → PredictionModel
-
final
val
predictionCol: Param[String]
- Definition Classes
- HasPredictionCol
-
final
def
set(paramPair: ParamPair[_]): Model.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): Model.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): Model.this.type
- Definition Classes
- Params
-
final
def
setDefault(paramPairs: ParamPair[_]*): Model.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): Model.this.type
- Attributes
- protected[ml]
- Definition Classes
- Params
-
def
setFeaturesCol(value: String): Model
- Definition Classes
- PredictionModel
-
def
setParent(parent: Estimator[Model]): Model
- Definition Classes
- Model
-
def
setPredictionCol(value: String): Model
- Definition Classes
- PredictionModel
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
def
transform(dataset: Dataset[_]): DataFrame
For the regression models it adds only one column with the prediction for it.
For the regression models it adds only one column with the prediction for it.
For the multi-class models it adds one column per class with probability for that class. e.g target_{CLASS_NAME}_PREDICTION => target_Iris-setosa_PREDICTION,target_Iris-versicolor_PREDICTION,target_Iris-virginica_PREDICTION
- dataset
input
- returns
output is an input DataFrame with additional columns with predictions.
- Definition Classes
- Model → PredictionModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
def
transformImpl(dataset: Dataset[_]): DataFrame
- Attributes
- protected
- Definition Classes
- PredictionModel
-
def
transformSchema(schema: StructType): StructType
- Definition Classes
- PredictionModel → PipelineStage
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
-
val
uid: String
- Definition Classes
- Model → Identifiable
-
def
validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
- Attributes
- protected
- Definition Classes
- PredictorParams
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
Deprecated Value Members
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] ) @Deprecated
- Deprecated