DateTimePartition

Date Time Partitioning use for a Time Series Project. This should not be constructed directly. See io.github.timsetsfire.datarobot.DateTimePartitioningMethod when setting up DateTime Parititioning for a project via UI.

projectId: – The ID of the project
datetimePartitionColumn: (string)–Thedatecolumnthatwillbeusedasadate-time partition column
dateFormat: (string) – The date format of the partition column
useTimeSeries: (boolean)–(Newinversionv2.8)Abooleanvalueindicatingwhether a time series project should be created instead of a regular project which uses datetime partitioning.
unsupervisedMode: (boolean) – (New in version v2.20) A boolean value indicating whether an unsupervised project should be created
defaultToAPriori: (boolean)–(Deprecatedinversionv2.11)RenamedtodefaultTo- KnownInAdvance. This parameter always has the same value as defaultToKnownInAdvance and will be removed in a future release.
defaultToKnownInAdvance: (boolean) – (New in version v2.11) Indicates whether all features in a time series project default to being treated as known in advance, unless overridden by featureSettings. Features marked as known in advance must be specified into the future when making predictions. See the Time Series Overview for more context.
defaultToDoNotDerive: (boolean) – (New in version v2.17) Indicates whether all features in a time series project default to being treated as do-not-derive features, which excludes them from feature derivation.
featureDerivationWindowStart: – (New in version v2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin. Will be a negative integer, if present.
featureDerivationWindowEnd: – (New in version 2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end. Will be a non- positive integer, if present.
forecastWindowStart: – (New in version v2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. Will be a non-negative integer, if present.
forecastWindowEnd: – (New in version v2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. Will be a non-negative integer, if present.
windowsBasisUnit: – (New in version v2.14) Will only be specified for projects using time series. Indicates which unit is basis for feature derivation window and forecast window. Will be either detected time unit or “ROW”.
validationDuration: – The default validation duration for all backtests. Will not be specified if the primary date/time feature in a time series project is irregular.
availableTrainingStartDate: –Thestartdateofavailabletrainingdataforscoring the holdout
availableTrainingDuration: – The duration of available training duration for scor- ing the holdout
availableTrainingEndDate: –Theenddateofavailabletrainingdataforscoringthe holdout
primaryTrainingStartDate: – The start date of the primary training data for scoring the holdout
primaryTrainingDuration: – The duration of the primary training data for scoring the holdout
primaryTrainingEndDate: – The end date of the primary training data for scoring the holdout
gapStartDate: – The start date of the gap between the training and holdout scoring data
gapDuration: – The duration of the gap between the training and holdout scoring data
gapEndDate: – The end date of gap between the training and holdout scoring data
holdoutStartDate: – The start date of the holdout scoring data
holdoutDuration: – The duration of the holdout scoring data
holdoutEndDate: – The end date of the holdout scoring data
numberOfBacktests: – The number of backtests used
autopilotDataSelectionMethod: – Whether models created via the autopilot will use “rowCount” or “duration” as their dataSelectionMethod.
backtests: – An array of the configured backtests
partitioningWarnings: – An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partition- ing may be successfully submitted
featureSettings: – An array of per feature settings
numberOfKnownInAdvanceFeatures: – (New in version v2.14) Number of features that are marked as known in advance.
numberOfDoNotDeriveFeatures: – (New in version v2.17) Number of features that are marked as “do not derive”.
useCrossSeriesFeatures: (boolean) – (New in version v2.14) Indicating whether to use cross-series features.
aggregationType: (string) – (New in version v2.14) The aggregation type to apply when creating cross-series features. Optional, must be one of “total” or “average”.
crossSeriesGroupByColumns: (array) – (New in version v2.15) List of columns (currently of length 1). Optional setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like “men’s clothing”, “sports equipment”, etc..
calendarId: (string) – (new in version v2.15) Optional, the id of a calendar to use with this project.

Linear Supertypes

Serializable, Serializable, Product, Equals, AnyRef, Any

Instance Constructors

new DateTimePartition(projectId: Option[String], datetimePartitionColumn: Option[String], dateFormat: Option[String], useTimeSeries: Option[Boolean], unsupervisedMode: Option[Boolean], defaultToAPriori: Option[Boolean], defaultToKnownInAdvance: Option[Boolean], defaultToDoNotDerive: Option[Boolean], featureDerivationWindowStart: Option[Int], featureDerivationWindowEnd: Option[Int], forecastWindowStart: Option[Int], forecastWindowEnd: Option[Int], windowsBasisUnit: Option[String], validationDuration: Option[String], availableTrainingStartDate: Option[String], availableTrainingDuration: Option[String], availableTrainingEndDate: Option[String], primaryTrainingStartDate: Option[String], primaryTrainingDuration: Option[String], primaryTrainingEndDate: Option[String], gapStartDate: Option[String], gapDuration: Option[String], gapEndDate: Option[String], holdoutStartDate: Option[String], holdoutDuration: Option[String], holdoutEndDate: Option[String], numberOfBacktests: Option[Int], autopilotDataSelectionMethod: Option[String], backtests: Option[Seq[DateTimeBacktests]], partitioningWarnings: Option[Seq[PartitionWarning]], featureSettings: Option[Seq[FeatureSetting]], numberOfKnownInAdvanceFeatures: Option[Int], numberOfDoNotDeriveFeatures: Option[Int], useCrossSeriesFeatures: Option[Boolean], aggregationType: Option[String], crossSeriesGroupByColumns: Option[String], calendarId: Option[String])

projectId
– The ID of the project
datetimePartitionColumn
(string)–Thedatecolumnthatwillbeusedasadate-time partition column
dateFormat
(string) – The date format of the partition column
useTimeSeries
(boolean)–(Newinversionv2.8)Abooleanvalueindicatingwhether a time series project should be created instead of a regular project which uses datetime partitioning.
unsupervisedMode
(boolean) – (New in version v2.20) A boolean value indicating whether an unsupervised project should be created
defaultToAPriori
(boolean)–(Deprecatedinversionv2.11)RenamedtodefaultTo- KnownInAdvance. This parameter always has the same value as defaultToKnownInAdvance and will be removed in a future release.
defaultToKnownInAdvance
(boolean) – (New in version v2.11) Indicates whether all features in a time series project default to being treated as known in advance, unless overridden by featureSettings. Features marked as known in advance must be specified into the future when making predictions. See the Time Series Overview for more context.
defaultToDoNotDerive
(boolean) – (New in version v2.17) Indicates whether all features in a time series project default to being treated as do-not-derive features, which excludes them from feature derivation.
featureDerivationWindowStart
– (New in version v2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin. Will be a negative integer, if present.
featureDerivationWindowEnd
– (New in version 2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end. Will be a non- positive integer, if present.
forecastWindowStart
– (New in version v2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. Will be a non-negative integer, if present.
forecastWindowEnd
– (New in version v2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. Will be a non-negative integer, if present.
windowsBasisUnit
– (New in version v2.14) Will only be specified for projects using time series. Indicates which unit is basis for feature derivation window and forecast window. Will be either detected time unit or “ROW”.
validationDuration
– The default validation duration for all backtests. Will not be specified if the primary date/time feature in a time series project is irregular.
availableTrainingStartDate
–Thestartdateofavailabletrainingdataforscoring the holdout
availableTrainingDuration
– The duration of available training duration for scor- ing the holdout
availableTrainingEndDate
–Theenddateofavailabletrainingdataforscoringthe holdout
primaryTrainingStartDate
– The start date of the primary training data for scoring the holdout
primaryTrainingDuration
– The duration of the primary training data for scoring the holdout
primaryTrainingEndDate
– The end date of the primary training data for scoring the holdout
gapStartDate
– The start date of the gap between the training and holdout scoring data
gapDuration
– The duration of the gap between the training and holdout scoring data
gapEndDate
– The end date of gap between the training and holdout scoring data
holdoutStartDate
– The start date of the holdout scoring data
holdoutDuration
– The duration of the holdout scoring data
holdoutEndDate
– The end date of the holdout scoring data
numberOfBacktests
– The number of backtests used
autopilotDataSelectionMethod
– Whether models created via the autopilot will use “rowCount” or “duration” as their dataSelectionMethod.
backtests
– An array of the configured backtests
partitioningWarnings
– An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partition- ing may be successfully submitted
featureSettings
– An array of per feature settings
numberOfKnownInAdvanceFeatures
– (New in version v2.14) Number of features that are marked as known in advance.
numberOfDoNotDeriveFeatures
– (New in version v2.17) Number of features that are marked as “do not derive”.
useCrossSeriesFeatures
(boolean) – (New in version v2.14) Indicating whether to use cross-series features.
aggregationType
(string) – (New in version v2.14) The aggregation type to apply when creating cross-series features. Optional, must be one of “total” or “average”.
crossSeriesGroupByColumns
(array) – (New in version v2.15) List of columns (currently of length 1). Optional setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like “men’s clothing”, “sports equipment”, etc..
calendarId
(string) – (new in version v2.15) Optional, the id of a calendar to use with this project.

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
val aggregationType: Option[String]

(string) – (New in version v2.14) The aggregation type to apply when creating cross-series features.
(string) – (New in version v2.14) The aggregation type to apply when creating cross-series features. Optional, must be one of “total” or “average”.
final def asInstanceOf[T0]: T0

Definition Classes
Any
val autopilotDataSelectionMethod: Option[String]

– Whether models created via the autopilot will use “rowCount” or “duration” as their dataSelectionMethod.
val availableTrainingDuration: Option[String]

– The duration of available training duration for scor- ing the holdout
val availableTrainingEndDate: Option[String]

–Theenddateofavailabletrainingdataforscoringthe holdout
val availableTrainingStartDate: Option[String]

–Thestartdateofavailabletrainingdataforscoring the holdout
val backtests: Option[Seq[DateTimeBacktests]]

– An array of the configured backtests
val calendarId: Option[String]

(string) – (new in version v2.15) Optional, the id of a calendar to use with this project.
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@HotSpotIntrinsicCandidate() @throws( ... )
val crossSeriesGroupByColumns: Option[String]

(array) – (New in version v2.15) List of columns (currently of length 1).
(array) – (New in version v2.15) List of columns (currently of length 1). Optional setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like “men’s clothing”, “sports equipment”, etc..
val dateFormat: Option[String]

(string) – The date format of the partition column
val datetimePartitionColumn: Option[String]

(string)–Thedatecolumnthatwillbeusedasadate-time partition column
val defaultToAPriori: Option[Boolean]

(boolean)–(Deprecatedinversionv2.11)RenamedtodefaultTo- KnownInAdvance.
(boolean)–(Deprecatedinversionv2.11)RenamedtodefaultTo- KnownInAdvance. This parameter always has the same value as defaultToKnownInAdvance and will be removed in a future release.
val defaultToDoNotDerive: Option[Boolean]

(boolean) – (New in version v2.17) Indicates whether all features in a time series project default to being treated as do-not-derive features, which excludes them from feature derivation.
val defaultToKnownInAdvance: Option[Boolean]

(boolean) – (New in version v2.11) Indicates whether all features in a time series project default to being treated as known in advance, unless overridden by featureSettings.
(boolean) – (New in version v2.11) Indicates whether all features in a time series project default to being treated as known in advance, unless overridden by featureSettings. Features marked as known in advance must be specified into the future when making predictions. See the Time Series Overview for more context.
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val featureDerivationWindowEnd: Option[Int]

– (New in version 2.8) Will only be specified for projects using time series.
– (New in version 2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end. Will be a non- positive integer, if present.
val featureDerivationWindowStart: Option[Int]

– (New in version v2.8) Will only be specified for projects using time series.
– (New in version v2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin. Will be a negative integer, if present.
val featureSettings: Option[Seq[FeatureSetting]]

– An array of per feature settings
val forecastWindowEnd: Option[Int]

– (New in version v2.8) Will only be specified for projects using time series.
– (New in version v2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. Will be a non-negative integer, if present.
val forecastWindowStart: Option[Int]

– (New in version v2.8) Will only be specified for projects using time series.
– (New in version v2.8) Will only be specified for projects using time series. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. Will be a non-negative integer, if present.
val gapDuration: Option[String]

– The duration of the gap between the training and holdout scoring data
val gapEndDate: Option[String]

– The end date of gap between the training and holdout scoring data
val gapStartDate: Option[String]

– The start date of the gap between the training and holdout scoring data
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@HotSpotIntrinsicCandidate()
val holdoutDuration: Option[String]

– The duration of the holdout scoring data
val holdoutEndDate: Option[String]

– The end date of the holdout scoring data
val holdoutStartDate: Option[String]

– The start date of the holdout scoring data
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
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()
val numberOfBacktests: Option[Int]

– The number of backtests used
val numberOfDoNotDeriveFeatures: Option[Int]

– (New in version v2.17) Number of features that are marked as “do not derive”.
val numberOfKnownInAdvanceFeatures: Option[Int]

– (New in version v2.14) Number of features that are marked as known in advance.
val partitioningWarnings: Option[Seq[PartitionWarning]]

– An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partition- ing may be successfully submitted
val primaryTrainingDuration: Option[String]

– The duration of the primary training data for scoring the holdout
val primaryTrainingEndDate: Option[String]

– The end date of the primary training data for scoring the holdout
val primaryTrainingStartDate: Option[String]

– The start date of the primary training data for scoring the holdout
val projectId: Option[String]

– The ID of the project
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val unsupervisedMode: Option[Boolean]

(boolean) – (New in version v2.20) A boolean value indicating whether an unsupervised project should be created
val useCrossSeriesFeatures: Option[Boolean]

(boolean) – (New in version v2.14) Indicating whether to use cross-series features.
val useTimeSeries: Option[Boolean]

(boolean)–(Newinversionv2.8)Abooleanvalueindicatingwhether a time series project should be created instead of a regular project which uses datetime partitioning.
val validationDuration: Option[String]

– The default validation duration for all backtests.
– The default validation duration for all backtests. Will not be specified if the primary date/time feature in a time series project is irregular.
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( ... )
val windowsBasisUnit: Option[String]

– (New in version v2.14) Will only be specified for projects using time series.
– (New in version v2.14) Will only be specified for projects using time series. Indicates which unit is basis for feature derivation window and forecast window. Will be either detected time unit or “ROW”.

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 Doc: package datarobot

Instance Constructors

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val aggregationType: Option[String]

final def asInstanceOf[T0]: T0

val autopilotDataSelectionMethod: Option[String]

val availableTrainingDuration: Option[String]

val availableTrainingEndDate: Option[String]

val availableTrainingStartDate: Option[String]

val backtests: Option[Seq[DateTimeBacktests]]

val calendarId: Option[String]

def clone(): AnyRef

val crossSeriesGroupByColumns: Option[String]

val dateFormat: Option[String]

val datetimePartitionColumn: Option[String]

val defaultToAPriori: Option[Boolean]

val defaultToDoNotDerive: Option[Boolean]

val defaultToKnownInAdvance: Option[Boolean]

final def eq(arg0: AnyRef): Boolean

val featureDerivationWindowEnd: Option[Int]

val featureDerivationWindowStart: Option[Int]

val featureSettings: Option[Seq[FeatureSetting]]

val forecastWindowEnd: Option[Int]

val forecastWindowStart: Option[Int]

val gapDuration: Option[String]

val gapEndDate: Option[String]

val gapStartDate: Option[String]

final def getClass(): Class[_]

val holdoutDuration: Option[String]

val holdoutEndDate: Option[String]

val holdoutStartDate: Option[String]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numberOfBacktests: Option[Int]

val numberOfDoNotDeriveFeatures: Option[Int]

val numberOfKnownInAdvanceFeatures: Option[Int]

val partitioningWarnings: Option[Seq[PartitionWarning]]

val primaryTrainingDuration: Option[String]

val primaryTrainingEndDate: Option[String]

val primaryTrainingStartDate: Option[String]

val projectId: Option[String]

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

val unsupervisedMode: Option[Boolean]

val useCrossSeriesFeatures: Option[Boolean]

val useTimeSeries: Option[Boolean]

val validationDuration: Option[String]

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

final def wait(arg0: Long): Unit

final def wait(): Unit

val windowsBasisUnit: Option[String]

Deprecated Value Members

def finalize(): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

Ungrouped