case class 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]) extends Product with Serializable
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.
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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
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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==(arg0: Any): Boolean
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- val aggregationType: Option[String]
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def
asInstanceOf[T0]: T0
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- 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]
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- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
- val crossSeriesGroupByColumns: Option[String]
- val dateFormat: Option[String]
- val datetimePartitionColumn: Option[String]
- val defaultToAPriori: Option[Boolean]
- val defaultToDoNotDerive: Option[Boolean]
- val defaultToKnownInAdvance: Option[Boolean]
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final
def
eq(arg0: AnyRef): Boolean
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- 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]
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final
def
getClass(): Class[_]
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- @native() @HotSpotIntrinsicCandidate()
- val holdoutDuration: Option[String]
- val holdoutEndDate: Option[String]
- val holdoutStartDate: Option[String]
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- 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]
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
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- val unsupervisedMode: Option[Boolean]
- val useCrossSeriesFeatures: Option[Boolean]
- val useTimeSeries: Option[Boolean]
- val validationDuration: Option[String]
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- val windowsBasisUnit: Option[String]
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