DateTimePartitioningMethod

datetimePartitionColumn: –Thedatecolumnthatwillbeusedasadate- time partition column
useTimeSeries: (New in version v2.8) Optional, defaults to false. A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
unsupervisedMode: (New in version v2.20) Optional, defaults to false. A boolean value indicating whether an unsupervised project should be created.
multiseriesIdColumns: (New in version v2.11) Optional, may only be specified for projects using time series. An array of column names identifying the multi- series id column(s) to use to identify series within the data. Currently only one multiseries id column may be specified. See the multiseries section of the docs for more context.
defaultToAPriori: (Deprecated in version v2.11) Optional, renamed to defaultToKnownInAdvance, see below for more detail.
defaultToKnownInAdvance: (New in version v2.11) Optional, for time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the Time Series Overview for more context.
defaultToDoNotDerive: (New in version v2.17) Optional, for time se- ries projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter.
featureDerivationWindowStart: (New in version v2.8) Optional, may only be specified for projects using time series. How many timeUnits of the datetimeParti- tionColumn into the past relative to the forecast point the feature derivation window should begin. Must be a negative integer, if specified.
featureDerivationWindowEnd: (int)–(Newinversion2.8)Optional,mayonlybe specified for projects using time series. How many timeUnits of the datetimePartitionCol- umn into the past relative to the forecast point the feature derivation window should end. Must be a non-positive integer, if specified.
forecastWindowStart: (Newinversionv2.8)Optional,mayonlybespecified for projects using time series. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. Must be a non-negative integer, if specified.
forecastWindowEnd: (New in version v2.8) Optional, may 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. Must be a non- negative integer, if specified.
windowsBasisUnit: (New in version v2.14) Optional, may only be speci- fied for projects using time series. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or “ROW”. If omitted, the default value is detected time unit.
validationDuration: Optional. A duration string representing the de- fault validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.
disableHoldout: (New in version v2.8) Optional. A boolean value indi- cating whether date partitioning should skip allocating a holdout fold. If omitted, the default value is false. When specifying disableHoldout: true, holdoutStartDate and holdoutDura- tion must not be set.
holdoutStartDate: Optional. A datetime string representing the start date of the holdout fold. When specifying holdoutStartDate, one of holdoutEndDate or holdout- Duration must also be specified. This attribute cannot be specified when disableHoldout is true.
holdoutEndDate: Optional. A datetime string representing the end date of the holdout fold. When specifying holdoutEndDate, holdoutStartDate must also be speci- fied. This attribute cannot be specified when disableHoldout is true.
holdoutDuration: Optional. A duration string representing the duration of the holdout fold. When specifying holdoutDuration, holdoutStartDate must also be spec- ified. This attribute cannot be specified when disableHoldout is true.
gapDuration: Optional, a duration string representing the duration of the gap between the training and the holdout data for the holdout model. For time series projects, defaults to the duration of the gap between the end of the feature derivation win- dow and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).
numberOfBacktests: Optional, the number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
autopilotDataSelectionMethod: –Optional,either“duration”or“row- Count”. Defaults to “duration”. Whether models created via the autopilot will use “row- Count” or “duration” as their dataSelectionMethod.
treatAsExponential: (New in version v2.9) Optional, defaults to “auto”. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. Valid options are “always”, “never”, “auto”.
differencingMethod: (New in version v2.9) Optional, defaults to “auto” for timeseries projects. Used to specify which differencing method to apply if the data is stationary. Valid options are “auto”, “simple”, “none”, “seasonal”. Parameter “periodicities” must be specified if “seasonal” is chosen.
backtests: Optional. An array specifying individual backtests. The index of the backtests specified should range from 0 to numberOfBacktests - 1.
featureSettings: (New in version v2.9) Optional, an array specifying per feature settings. Features can be left unspecified.
periodicities: (Newinversionv2.9)Optional,alistofperiodicities.Ifthis is provided, parameter “differencing_method” will default to “seasonal” if not provided or “auto”.
useCrossSeriesFeatures: (New in version v2.14) Indicating whether to use cross-series features.
aggregationType: (New in version v2.14) The aggregation type to apply when creating cross-series features. Optional, must be one of “total” or “average”.
crossSeriesGroupByColumns: (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.. Must be used with multiseries and useCrossSeriesFeatures enabled.
calendarId: – (New in version v2.15) Optional, the ID of the calendar to use with this project.

Linear Supertypes

Serializable, Serializable, Product, Equals, PartitioningMethod, AnyRef, Any

Instance Constructors

new DateTimePartitioningMethod(validationType: Option[enums.ValidationType.Value] = None, datetimePartitionColumn: Option[String] = None, useTimeSeries: Option[Boolean] = None, unsupervisedMode: Option[Boolean] = None, multiseriesIdColumns: Option[Array[String]] = None, defaultToAPriori: Option[Boolean] = None, defaultToKnownInAdvance: Option[Boolean] = None, defaultToDoNotDerive: Option[Boolean] = None, featureDerivationWindowStart: Option[Int] = None, featureDerivationWindowEnd: Option[Int] = None, forecastWindowStart: Option[Int] = None, forecastWindowEnd: Option[Int] = None, windowsBasisUnit: Option[String] = None, validationDuration: Option[String] = None, disableHoldout: Option[Boolean] = None, holdoutStartDate: Option[String] = None, holdoutEndDate: Option[String] = None, holdoutDuration: Option[String] = None, gapDuration: Option[String] = None, numberOfBacktests: Option[Int] = None, autopilotDataSelectionMethod: Option[String] = None, treatAsExponential: Option[String] = None, differencingMethod: Option[String] = None, backtests: Option[Seq[DateTimeBackTestsSetup]] = None, featureSettings: Option[Seq[FeatureSetting]] = None, periodicities: Option[Seq[Periodicity]] = None, useCrossSeriesFeatures: Option[Boolean] = None, aggregationType: Option[String] = None, crossSeriesGroupByColumns: Option[Array[String]] = None, calendarId: Option[String] = None)

datetimePartitionColumn
–Thedatecolumnthatwillbeusedasadate- time partition column
useTimeSeries
(New in version v2.8) Optional, defaults to false. A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
unsupervisedMode
(New in version v2.20) Optional, defaults to false. A boolean value indicating whether an unsupervised project should be created.
multiseriesIdColumns
(New in version v2.11) Optional, may only be specified for projects using time series. An array of column names identifying the multi- series id column(s) to use to identify series within the data. Currently only one multiseries id column may be specified. See the multiseries section of the docs for more context.
defaultToAPriori
(Deprecated in version v2.11) Optional, renamed to defaultToKnownInAdvance, see below for more detail.
defaultToKnownInAdvance
(New in version v2.11) Optional, for time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the Time Series Overview for more context.
defaultToDoNotDerive
(New in version v2.17) Optional, for time se- ries projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter.
featureDerivationWindowStart
(New in version v2.8) Optional, may only be specified for projects using time series. How many timeUnits of the datetimeParti- tionColumn into the past relative to the forecast point the feature derivation window should begin. Must be a negative integer, if specified.
featureDerivationWindowEnd
(int)–(Newinversion2.8)Optional,mayonlybe specified for projects using time series. How many timeUnits of the datetimePartitionCol- umn into the past relative to the forecast point the feature derivation window should end. Must be a non-positive integer, if specified.
forecastWindowStart
(Newinversionv2.8)Optional,mayonlybespecified for projects using time series. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. Must be a non-negative integer, if specified.
forecastWindowEnd
(New in version v2.8) Optional, may 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. Must be a non- negative integer, if specified.
windowsBasisUnit
(New in version v2.14) Optional, may only be speci- fied for projects using time series. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or “ROW”. If omitted, the default value is detected time unit.
validationDuration
Optional. A duration string representing the de- fault validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.
disableHoldout
(New in version v2.8) Optional. A boolean value indi- cating whether date partitioning should skip allocating a holdout fold. If omitted, the default value is false. When specifying disableHoldout: true, holdoutStartDate and holdoutDura- tion must not be set.
holdoutStartDate
Optional. A datetime string representing the start date of the holdout fold. When specifying holdoutStartDate, one of holdoutEndDate or holdout- Duration must also be specified. This attribute cannot be specified when disableHoldout is true.
holdoutEndDate
Optional. A datetime string representing the end date of the holdout fold. When specifying holdoutEndDate, holdoutStartDate must also be speci- fied. This attribute cannot be specified when disableHoldout is true.
holdoutDuration
Optional. A duration string representing the duration of the holdout fold. When specifying holdoutDuration, holdoutStartDate must also be spec- ified. This attribute cannot be specified when disableHoldout is true.
gapDuration
Optional, a duration string representing the duration of the gap between the training and the holdout data for the holdout model. For time series projects, defaults to the duration of the gap between the end of the feature derivation win- dow and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).
numberOfBacktests
Optional, the number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
autopilotDataSelectionMethod
–Optional,either“duration”or“row- Count”. Defaults to “duration”. Whether models created via the autopilot will use “row- Count” or “duration” as their dataSelectionMethod.
treatAsExponential
(New in version v2.9) Optional, defaults to “auto”. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. Valid options are “always”, “never”, “auto”.
differencingMethod
(New in version v2.9) Optional, defaults to “auto” for timeseries projects. Used to specify which differencing method to apply if the data is stationary. Valid options are “auto”, “simple”, “none”, “seasonal”. Parameter “periodicities” must be specified if “seasonal” is chosen.
backtests
Optional. An array specifying individual backtests. The index of the backtests specified should range from 0 to numberOfBacktests - 1.
featureSettings
(New in version v2.9) Optional, an array specifying per feature settings. Features can be left unspecified.
periodicities
(Newinversionv2.9)Optional,alistofperiodicities.Ifthis is provided, parameter “differencing_method” will default to “seasonal” if not provided or “auto”.
useCrossSeriesFeatures
(New in version v2.14) Indicating whether to use cross-series features.
aggregationType
(New in version v2.14) The aggregation type to apply when creating cross-series features. Optional, must be one of “total” or “average”.
crossSeriesGroupByColumns
(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.. Must be used with multiseries and useCrossSeriesFeatures enabled.
calendarId
– (New in version v2.15) Optional, the ID of the 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]

(New in version v2.14) The aggregation type to apply when creating cross-series features.
(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]

–Optional,either“duration”or“row- Count”.
–Optional,either“duration”or“row- Count”. Defaults to “duration”. Whether models created via the autopilot will use “row- Count” or “duration” as their dataSelectionMethod.
val backtests: Option[Seq[DateTimeBackTestsSetup]]

Optional.
Optional. An array specifying individual backtests. The index of the backtests specified should range from 0 to numberOfBacktests - 1.
val calendarId: Option[String]

– (New in version v2.15) Optional, the ID of the calendar to use with this project.
def clone(): AnyRef

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

(New in version v2.15) List of columns (currently of length 1).
(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.. Must be used with multiseries and useCrossSeriesFeatures enabled.
val cvMethod: enums.CVMethod.Value
val datetimePartitionColumn: Option[String]

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

(Deprecated in version v2.11) Optional, renamed to defaultToKnownInAdvance, see below for more detail.
val defaultToDoNotDerive: Option[Boolean]

(New in version v2.17) Optional, for time se- ries projects only.
(New in version v2.17) Optional, for time se- ries projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter.
val defaultToKnownInAdvance: Option[Boolean]

(New in version v2.11) Optional, for time series projects only.
(New in version v2.11) Optional, for time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the Time Series Overview for more context.
val differencingMethod: Option[String]

(New in version v2.9) Optional, defaults to “auto” for timeseries projects.
(New in version v2.9) Optional, defaults to “auto” for timeseries projects. Used to specify which differencing method to apply if the data is stationary. Valid options are “auto”, “simple”, “none”, “seasonal”. Parameter “periodicities” must be specified if “seasonal” is chosen.
val disableHoldout: Option[Boolean]

(New in version v2.8) Optional.
(New in version v2.8) Optional. A boolean value indi- cating whether date partitioning should skip allocating a holdout fold. If omitted, the default value is false. When specifying disableHoldout: true, holdoutStartDate and holdoutDura- tion must not be set.
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val featureDerivationWindowEnd: Option[Int]

(int)–(Newinversion2.8)Optional,mayonlybe specified for projects using time series.
(int)–(Newinversion2.8)Optional,mayonlybe specified for projects using time series. How many timeUnits of the datetimePartitionCol- umn into the past relative to the forecast point the feature derivation window should end. Must be a non-positive integer, if specified.
val featureDerivationWindowStart: Option[Int]

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

(New in version v2.9) Optional, an array specifying per feature settings.
(New in version v2.9) Optional, an array specifying per feature settings. Features can be left unspecified.
val forecastWindowEnd: Option[Int]

(New in version v2.8) Optional, may only be specified for projects using time series.
(New in version v2.8) Optional, may 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. Must be a non- negative integer, if specified.
val forecastWindowStart: Option[Int]

(Newinversionv2.8)Optional,mayonlybespecified for projects using time series.
(Newinversionv2.8)Optional,mayonlybespecified for projects using time series. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. Must be a non-negative integer, if specified.
val gapDuration: Option[String]

Optional, a duration string representing the duration of the gap between the training and the holdout data for the holdout model.
Optional, a duration string representing the duration of the gap between the training and the holdout data for the holdout model. For time series projects, defaults to the duration of the gap between the end of the feature derivation win- dow and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).
final def getClass(): Class[_]

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

Optional.
Optional. A duration string representing the duration of the holdout fold. When specifying holdoutDuration, holdoutStartDate must also be spec- ified. This attribute cannot be specified when disableHoldout is true.
val holdoutEndDate: Option[String]

Optional.
Optional. A datetime string representing the end date of the holdout fold. When specifying holdoutEndDate, holdoutStartDate must also be speci- fied. This attribute cannot be specified when disableHoldout is true.
val holdoutStartDate: Option[String]

Optional.
Optional. A datetime string representing the start date of the holdout fold. When specifying holdoutStartDate, one of holdoutEndDate or holdout- Duration must also be specified. This attribute cannot be specified when disableHoldout is true.
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val multiseriesIdColumns: Option[Array[String]]

(New in version v2.11) Optional, may only be specified for projects using time series.
(New in version v2.11) Optional, may only be specified for projects using time series. An array of column names identifying the multi- series id column(s) to use to identify series within the data. Currently only one multiseries id column may be specified. See the multiseries section of the docs for more context.
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]

Optional, the number of backtests to use.
Optional, the number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
val periodicities: Option[Seq[Periodicity]]

(Newinversionv2.9)Optional,alistofperiodicities.Ifthis is provided, parameter “differencing_method” will default to “seasonal” if not provided or “auto”.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val treatAsExponential: Option[String]

(New in version v2.9) Optional, defaults to “auto”.
(New in version v2.9) Optional, defaults to “auto”. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. Valid options are “always”, “never”, “auto”.
val unsupervisedMode: Option[Boolean]

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

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

(New in version v2.8) Optional, defaults to false.
(New in version v2.8) Optional, defaults to false. A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
val validationDuration: Option[String]

Optional.
Optional. A duration string representing the de- fault validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.
val validationType: Option[enums.ValidationType.Value]
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) Optional, may only be speci- fied for projects using time series.
(New in version v2.14) Optional, may only be speci- fied for projects using time series. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or “ROW”. If omitted, the default value is detected time unit.

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 backtests: Option[Seq[DateTimeBackTestsSetup]]

val calendarId: Option[String]

def clone(): AnyRef

val crossSeriesGroupByColumns: Option[Array[String]]

val cvMethod: enums.CVMethod.Value

val datetimePartitionColumn: Option[String]

val defaultToAPriori: Option[Boolean]

val defaultToDoNotDerive: Option[Boolean]

val defaultToKnownInAdvance: Option[Boolean]

val differencingMethod: Option[String]

val disableHoldout: 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]

final def getClass(): Class[_]

val holdoutDuration: Option[String]

val holdoutEndDate: Option[String]

val holdoutStartDate: Option[String]

final def isInstanceOf[T0]: Boolean

val multiseriesIdColumns: Option[Array[String]]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numberOfBacktests: Option[Int]

val periodicities: Option[Seq[Periodicity]]

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

val treatAsExponential: Option[String]

val unsupervisedMode: Option[Boolean]

val useCrossSeriesFeatures: Option[Boolean]

val useTimeSeries: Option[Boolean]

val validationDuration: Option[String]

val validationType: Option[enums.ValidationType.Value]

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 PartitioningMethod

Inherited from AnyRef

Inherited from Any

Ungrouped