Packages

class DateTimeModel extends Model

returns

DateTimeModel object

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Instance Constructors

  1. new DateTimeModel(effectiveFeatureDerivationWindowStart: Option[Int], backtests: Option[Seq[Backtest]], trainingDuration: Option[String], dataSelectionMethod: Option[String], parentModelId: Option[String], holdoutStatus: Option[String], modelFamily: Option[String], windowsBasisUnit: Option[String], forecastWindowStart: Option[Int], timeWindowSamplePct: Option[Double], samplingMethod: Option[String], modelNumber: Option[Int], effectiveFeatureDerivationWindowEnd: Option[Int], trainingInfo: TrainingInfo, forecastWindowEnd: Option[Int], linkFunction: Option[String], modelType: String, supportsMonotonicConstraints: Option[Boolean], blueprintId: String, isStarred: Boolean, id: String, projectId: String, isFrozen: Boolean, featurelistId: String, trainingRowCount: Option[Int], trainingEndDate: Option[String], samplePct: Option[Double], modelCategory: String, trainingStartDate: Option[String], metrics: Map[String, Metric], monotonicIncreasingFeaturelistId: Option[String], holdoutScore: Option[Double], predictionThreshold: Option[Double], processes: Array[String], featurelistName: String, predictionThresholdReadOnly: Option[Boolean], monotonicDecreasingFeaturelistId: Option[String])

    class used to represent DateTime models.

    class used to represent DateTime models. These should not be created directly.

    effectiveFeatureDerivationWindowStart

    How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the featureDerivationWindowStart set on the project due to the differencing method and period selected, or if the model is a time series native model such as ARIMA. Will be a negative integer.

    backtests

    an array of information on each backtesting fold of the model

    trainingDuration

    – the duration spanned by the dates in the partition column for the data used to train the model

    dataSelectionMethod

    – either “duration”, “rowCount”, or “selectedDateRange”. Identifies which of trainingDuration, trainingRowCount, or trainingStartDate and train- ingEndDate define the training size of the model when making predictions and scoring.

    parentModelId

    This is the ID of the parent model. Otherwise Null.

    holdoutStatus

    – the status of the holdout score. Either “COMPLETED”, “INSUFFI- CIENT_DATA” or “HOLDOUT_BOUNDARIES_EXCEEDED”

    windowsBasisUnit

    Indicates which unit is the basis for the feature derivation window and the forecast window. Will be either detected time unit or “ROW”.

    forecastWindowStart

    How many timeUnits into the future relative to the forecast point the forecast window should start. Will be a non-negative integer.

    timeWindowSamplePct

    – an integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod (random uniform by default). Will be null if no sampling was specified.

    samplingMethod

    (string) – string (New in version 2.20). Either ‘random’ or ‘latest’, indicates sampling method used to select training data. For row-based project this is the way how requested number of rows are selected. For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.

    effectiveFeatureDerivationWindowEnd

    How many timeUnits into the past relative to the forecast point the feature derivation window should end. Will be a non-positive integer.

    trainingInfo

    – json object describing the holdout and prediction training data as de- scribed below

    forecastWindowEnd

    How many timeUnits into the future relative to the forecast point the forecast window should end. Will be a non-negative integer.

    modelType

    - identifies the model, e.g. Nystroem Kernel SVM Regressor blueprintId – the blueprint used to construct the model - note this is not an ObjectId

    supportsMonotonicConstraints

    –boolean,whether this model supports enforcing montonic constraints

    id

    –the ID of the model

    projectId

    – the ID of the project to which the model belongs

    isFrozen

    – boolean, indicating whether the model is frozen, i.e. uses tuning parameters from a parent model

    featurelistId

    – the ID of the featurelist used by the model

    trainingRowCount

    – the number of rows used to train the model

    trainingEndDate

    – the end date of the dates in the partition column for the data used to train the model

    samplePct

    – always null for datetime models

    modelCategory

    –indicateswhatkindofmodelitis-willbeprimeforDataRobotPrime models, blend for blender models, and model for all other models

    trainingStartDate

    – the start date of the dates in the partition column for the data used to train the model

    metrics

    – the performance of the model according to ous metrics, see below modelType –

    monotonicIncreasingFeaturelistId

    the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.

    holdoutScore

    – the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked

    predictionThreshold

    threshold used for binary classification in predictions.

    processes

    – a json list of processes used by the model

    featurelistName

    – the name of the featurelist used by the model

    predictionThresholdReadOnly

    indicates whether modification of a predictions threshold is forbidden. Threshold modification is forbidden once a model has had a deployment created or predictions made via the dedicated prediction API.

    monotonicDecreasingFeaturelistId

    the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def advancedTuning(description: String)(implicit client: DataRobotClient): AdvancedTuningSession
    Definition Classes
    Model
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. val blueprintId: String
    Definition Classes
    Model
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  10. val featurelistId: String
    Definition Classes
    Model
  11. val featurelistName: String
    Definition Classes
    Model
  12. def getCapabilities()(implicit client: DataRobotClient): (Map[String, Boolean], Map[String, String])
    Definition Classes
    Model
  13. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  14. def getCrossValidationScores()(implicit client: DataRobotClient): Map[String, Map[String, Map[String, Double]]]
    Definition Classes
    Model
  15. def getFeatureEffects(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureEffects
    Definition Classes
    DateTimeModel → Model
  16. def getFeatureEffectsMetaData()(implicit client: DataRobotClient): String
    Definition Classes
    DateTimeModel → Model
  17. def getFeatureFit(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureFits
    Definition Classes
    DateTimeModel → Model
  18. def getFeatureFitMetaData()(implicit client: DataRobotClient): String
    Definition Classes
    DateTimeModel → Model
  19. def getFeatureImpact()(implicit client: DataRobotClient): FeatureImpacts
    Definition Classes
    Model
  20. def getHyperParameters()(implicit client: DataRobotClient): Map[String, List[Map[String, Any]]]
    Definition Classes
    Model
  21. def getLiftChart(source: enums.Source.Value)(implicit client: DataRobotClient): LiftChart
    Definition Classes
    Model
  22. def getLiftCharts()(implicit client: DataRobotClient): Map[String, List[LiftChart]]
    Definition Classes
    Model
  23. def getMissingValueReport()(implicit client: DataRobotClient): List[Map[_ <: String, Any]]
    Definition Classes
    Model
  24. def getModelBlueprintChart()(implicit client: DataRobotClient): Graph[BlueprintNode, LDiEdge]
    Definition Classes
    Model
  25. def getModelCoefficients()(implicit client: DataRobotClient): ModelCoefficients
    Definition Classes
    Model
  26. def getMultiseriesScores(orderBy: Option[String] = None, offset: Option[Int] = None, limit: Option[Int] = None, metric: Option[String] = None, multiseriesValue: Option[String] = None)(implicit client: DataRobotClient): List[MultiseriesMetrics]
  27. def getMultiseriesScoresAsCsv(): Nothing
  28. def getResiduals()(implicit client: DataRobotClient): Map[String, Map[String, ResidualData]]
    Definition Classes
    Model
  29. def getRocCurve(source: enums.Source.Value)(implicit client: DataRobotClient): RocCurve
    Definition Classes
    Model
  30. def getRocCurves()(implicit client: DataRobotClient): Map[String, List[RocCurve]]
    Definition Classes
    Model
  31. def getScoringCode(destination: Option[String] = None, sourceCode: Boolean = false)(implicit client: DataRobotClient): Unit
    Definition Classes
    Model
  32. def getWordCloud()(implicit client: DataRobotClient): WordCloud
    Definition Classes
    Model
  33. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  34. val id: String
    Definition Classes
    Model
  35. val isFrozen: Boolean
    Definition Classes
    Model
  36. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  37. var isStarred: Boolean
    Definition Classes
    Model
  38. val metrics: Map[String, Metric]
    Definition Classes
    Model
  39. val modelCategory: String
    Definition Classes
    Model
  40. val modelType: String
    Definition Classes
    Model
  41. val monotonicDecreasingFeaturelistId: Option[String]
    Definition Classes
    Model
  42. val monotonicIncreasingFeaturelistId: Option[String]
    Definition Classes
    Model
  43. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  44. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  45. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  46. var predictionThreshold: Option[Double]
    Definition Classes
    Model
  47. var predictionThresholdReadOnly: Option[Boolean]
    Definition Classes
    Model
  48. val processes: Array[String]
    Definition Classes
    Model
  49. val projectId: String
    Definition Classes
    Model
  50. def requestAndGetFeatureEffects(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureEffects
    Definition Classes
    DateTimeModel → Model
  51. def requestAndGetFeatureFit(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureFits
    Definition Classes
    DateTimeModel → Model
  52. def requestAndGetFeatureImpact(maxWait: Int = 600000)(implicit client: DataRobotClient): AnyRef
    Definition Classes
    Model
  53. def requestFeatureEffects(backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): Job
    Definition Classes
    DateTimeModel → Model
  54. def requestFeatureFit(backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): Job
    Definition Classes
    DateTimeModel → Model
  55. def requestFeatureImpact()(implicit client: DataRobotClient): Job
    Definition Classes
    Model
  56. def requestFrozenDateTimeModel(featurelistId: Option[String] = None, trainingDuration: Option[String] = None, trainingRowCount: Option[Int] = None, trainingStartDate: Option[String] = None, trainingEndDate: Option[String] = None, timeWindowSamplePct: Option[Int] = None, samplingMethod: Option[String] = None)(implicit client: DataRobotClient): ModelJob
  57. def requestFrozenModel(samplePct: Option[Float] = None, trainingRowCount: Option[Int] = None)(implicit client: DataRobotClient): ModelJob
    Definition Classes
    Model
  58. def requestMultiseriesScores()(implicit client: DataRobotClient): Job
  59. def requestPredictions(datasetId: String, includePredictionIntervals: Option[Boolean] = None, predictionIntervalSize: Option[Int] = None, forecastPoint: Option[String] = None, predictionsStartDate: Option[String] = None, predictionsEndDate: Option[String] = None)(implicit client: DataRobotClient): PredictJob
    Definition Classes
    Model
  60. def runCrossValidation()(implicit client: DataRobotClient): Job
    Definition Classes
    Model
  61. val samplePct: Option[Double]
    Definition Classes
    Model
  62. def scoreBacktests()(implicit client: DataRobotClient): ModelJob
  63. def starModel()(implicit client: DataRobotClient): Model
    Definition Classes
    Model
  64. val supportsMonotonicConstraints: Option[Boolean]
    Definition Classes
    Model
  65. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  66. def toString(): String
    Definition Classes
    DateTimeModel → Model → AnyRef → Any
  67. def toggleStar(starred: Boolean)(implicit client: DataRobotClient): Model
    Definition Classes
    Model
  68. val trainingDuration: Option[String]
    Definition Classes
    Model
  69. val trainingEndDate: Option[String]
    Definition Classes
    Model
  70. val trainingRowCount: Option[Int]
    Definition Classes
    Model
  71. val trainingStartDate: Option[String]
    Definition Classes
    Model
  72. def unstarModel()(implicit client: DataRobotClient): Model
    Definition Classes
    Model
  73. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  74. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  75. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

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