DateTimeModel

Instance Constructors

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

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def advancedTuning(description: String)(implicit client: DataRobotClient): AdvancedTuningSession

Definition Classes
Model
final def asInstanceOf[T0]: T0

Definition Classes
Any
val blueprintId: String

the blueprint used to construct the model - note this is not an ObjectId
the blueprint used to construct the model - note this is not an ObjectId

Definition Classes
Model
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@HotSpotIntrinsicCandidate() @throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
val featurelistId: String

the ID of the featurelist used by the model
the ID of the featurelist used by the model

Definition Classes
Model
val featurelistName: String

the name of the featurelist used by the model
the name of the featurelist used by the model

Definition Classes
Model
def getCapabilities()(implicit client: DataRobotClient): (Map[String, Boolean], Map[String, String])

Definition Classes
Model
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@HotSpotIntrinsicCandidate()
def getCrossValidationScores()(implicit client: DataRobotClient): Map[String, Map[String, Map[String, Double]]]

Definition Classes
Model
def getFeatureEffects(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureEffects

Definition Classes
DateTimeModel → Model
def getFeatureEffectsMetaData()(implicit client: DataRobotClient): String

Definition Classes
DateTimeModel → Model
def getFeatureFit(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureFits

Definition Classes
DateTimeModel → Model
def getFeatureFitMetaData()(implicit client: DataRobotClient): String

Definition Classes
DateTimeModel → Model
def getFeatureImpact()(implicit client: DataRobotClient): FeatureImpacts

Definition Classes
Model
def getHyperParameters()(implicit client: DataRobotClient): Map[String, List[Map[String, Any]]]

Definition Classes
Model
def getLiftChart(source: enums.Source.Value)(implicit client: DataRobotClient): LiftChart

Definition Classes
Model
def getLiftCharts()(implicit client: DataRobotClient): Map[String, List[LiftChart]]

Definition Classes
Model
def getMissingValueReport()(implicit client: DataRobotClient): List[Map[_ <: String, Any]]

Definition Classes
Model
def getModelBlueprintChart()(implicit client: DataRobotClient): Graph[BlueprintNode, LDiEdge]

Definition Classes
Model
def getModelCoefficients()(implicit client: DataRobotClient): ModelCoefficients

Definition Classes
Model
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]
def getMultiseriesScoresAsCsv(): Nothing
def getResiduals()(implicit client: DataRobotClient): Map[String, Map[String, ResidualData]]

Definition Classes
Model
def getRocCurve(source: enums.Source.Value)(implicit client: DataRobotClient): RocCurve

Definition Classes
Model
def getRocCurves()(implicit client: DataRobotClient): Map[String, List[RocCurve]]

Definition Classes
Model
def getScoringCode(destination: Option[String] = None, sourceCode: Boolean = false)(implicit client: DataRobotClient): Unit

Definition Classes
Model
def getWordCloud()(implicit client: DataRobotClient): WordCloud

Definition Classes
Model
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@HotSpotIntrinsicCandidate()
val id: String

Model ID
Model ID

Definition Classes
Model
val isFrozen: Boolean

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

Definition Classes
Model
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
var isStarred: Boolean

(bool) (New in version v2.13) whether the model has been starred
(bool) (New in version v2.13) whether the model has been starred

Definition Classes
Model
val metrics: Map[String, Metric]

the performance of the model according to various metrics, see below
the performance of the model according to various metrics, see below

Definition Classes
Model
val modelCategory: String

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

Definition Classes
Model
val modelType: String

identifies the model, e.g.
identifies the model, e.g. Nystroem Kernel SVM Regressor

Definition Classes
Model
val monotonicDecreasingFeaturelistId: Option[String]

(new in v2.11) null or str, the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target.
(new in v2.11) null or str, 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.

Definition Classes
Model
val monotonicIncreasingFeaturelistId: Option[String]

(new in v2.11) null or str, the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target.
(new in v2.11) null or str, 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.

Definition Classes
Model
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@HotSpotIntrinsicCandidate()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@HotSpotIntrinsicCandidate()
var predictionThreshold: Option[Double]

(float) (New in version v2.13) threshold used for binary classification in predictions.
(float) (New in version v2.13) threshold used for binary classification in predictions.

Definition Classes
Model
var predictionThresholdReadOnly: Option[Boolean]

(boolean) (New in version v2.13) indicates whether modification of a predictions threshold is forbidden.
(boolean) (New in version v2.13) 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.

Definition Classes
Model
val processes: Array[String]

a json list of processes used by the model
a json list of processes used by the model

Definition Classes
Model
val projectId: String

the ID of the project to which the model belongs
the ID of the project to which the model belongs

Definition Classes
Model
def requestAndGetFeatureEffects(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureEffects

Definition Classes
DateTimeModel → Model
def requestAndGetFeatureFit(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureFits

Definition Classes
DateTimeModel → Model
def requestAndGetFeatureImpact(maxWait: Int = 600000)(implicit client: DataRobotClient): AnyRef

Definition Classes
Model
def requestFeatureEffects(backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): Job

Definition Classes
DateTimeModel → Model
def requestFeatureFit(backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): Job

Definition Classes
DateTimeModel → Model
def requestFeatureImpact()(implicit client: DataRobotClient): Job

Definition Classes
Model
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
def requestFrozenModel(samplePct: Option[Float] = None, trainingRowCount: Option[Int] = None)(implicit client: DataRobotClient): ModelJob

Definition Classes
Model
def requestMultiseriesScores()(implicit client: DataRobotClient): Job
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
def runCrossValidation()(implicit client: DataRobotClient): Job

Definition Classes
Model
val samplePct: Option[Double]

the percentage of the dataset used in training the model
the percentage of the dataset used in training the model

Definition Classes
Model
def scoreBacktests()(implicit client: DataRobotClient): ModelJob
def starModel()(implicit client: DataRobotClient): Model

Definition Classes
Model
val supportsMonotonicConstraints: Option[Boolean]

(new in v2.11) boolean, whether this model supports enforcing montonic constraints
(new in v2.11) boolean, whether this model supports enforcing montonic constraints

Definition Classes
Model
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
DateTimeModel → Model → AnyRef → Any
def toggleStar(starred: Boolean)(implicit client: DataRobotClient): Model

Definition Classes
Model
val trainingDuration: Option[String]

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

Definition Classes
Model
val trainingEndDate: Option[String]

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

Definition Classes
Model
val trainingRowCount: Option[Int]

the number of rows used to train the model
the number of rows used to train the model

Definition Classes
Model
val trainingStartDate: Option[String]

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

Definition Classes
Model
def unstarModel()(implicit client: DataRobotClient): Model

Definition Classes
Model
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Deprecated Value Members

def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@Deprecated @deprecated @throws( classOf[java.lang.Throwable] )
Deprecated
(Since version ) see corresponding Javadoc for more information.

Related Docs: object DateTimeModel | package datarobot

class DateTimeModel extends Model

Instance Constructors

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def advancedTuning(description: String)(implicit client: DataRobotClient): AdvancedTuningSession

final def asInstanceOf[T0]: T0

val blueprintId: String

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val featurelistId: String

val featurelistName: String

def getCapabilities()(implicit client: DataRobotClient): (Map[String, Boolean], Map[String, String])

final def getClass(): Class[_]

def getCrossValidationScores()(implicit client: DataRobotClient): Map[String, Map[String, Map[String, Double]]]

def getFeatureEffects(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureEffects

def getFeatureEffectsMetaData()(implicit client: DataRobotClient): String

def getFeatureFit(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureFits

def getFeatureFitMetaData()(implicit client: DataRobotClient): String

def getFeatureImpact()(implicit client: DataRobotClient): FeatureImpacts

def getHyperParameters()(implicit client: DataRobotClient): Map[String, List[Map[String, Any]]]

def getLiftChart(source: enums.Source.Value)(implicit client: DataRobotClient): LiftChart

def getLiftCharts()(implicit client: DataRobotClient): Map[String, List[LiftChart]]

def getMissingValueReport()(implicit client: DataRobotClient): List[Map[_ <: String, Any]]

def getModelBlueprintChart()(implicit client: DataRobotClient): Graph[BlueprintNode, LDiEdge]

def getModelCoefficients()(implicit client: DataRobotClient): ModelCoefficients

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]

def getMultiseriesScoresAsCsv(): Nothing

def getResiduals()(implicit client: DataRobotClient): Map[String, Map[String, ResidualData]]

def getRocCurve(source: enums.Source.Value)(implicit client: DataRobotClient): RocCurve

def getRocCurves()(implicit client: DataRobotClient): Map[String, List[RocCurve]]

def getScoringCode(destination: Option[String] = None, sourceCode: Boolean = false)(implicit client: DataRobotClient): Unit

def getWordCloud()(implicit client: DataRobotClient): WordCloud

def hashCode(): Int

val id: String

val isFrozen: Boolean

final def isInstanceOf[T0]: Boolean

var isStarred: Boolean

val metrics: Map[String, Metric]

val modelCategory: String

val modelType: String

val monotonicDecreasingFeaturelistId: Option[String]

val monotonicIncreasingFeaturelistId: Option[String]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var predictionThreshold: Option[Double]

var predictionThresholdReadOnly: Option[Boolean]

val processes: Array[String]

val projectId: String

def requestAndGetFeatureEffects(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureEffects

def requestAndGetFeatureFit(source: String = "validation", backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): FeatureFits

def requestAndGetFeatureImpact(maxWait: Int = 600000)(implicit client: DataRobotClient): AnyRef

def requestFeatureEffects(backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): Job

def requestFeatureFit(backtestIndex: Option[String] = Some("0"))(implicit client: DataRobotClient): Job

def requestFeatureImpact()(implicit client: DataRobotClient): Job

def requestFrozenModel(samplePct: Option[Float] = None, trainingRowCount: Option[Int] = None)(implicit client: DataRobotClient): ModelJob

def requestMultiseriesScores()(implicit client: DataRobotClient): Job

def runCrossValidation()(implicit client: DataRobotClient): Job

val samplePct: Option[Double]

def scoreBacktests()(implicit client: DataRobotClient): ModelJob

def starModel()(implicit client: DataRobotClient): Model

val supportsMonotonicConstraints: Option[Boolean]

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def toggleStar(starred: Boolean)(implicit client: DataRobotClient): Model

val trainingDuration: Option[String]

val trainingEndDate: Option[String]

val trainingRowCount: Option[Int]

val trainingStartDate: Option[String]

def unstarModel()(implicit client: DataRobotClient): Model

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

final def wait(): Unit

Deprecated Value Members

def finalize(): Unit