Based on the project dataset and the partitioning specification, inspect the full partitioning that would be used if the same specification were passed into SetTarget
GenerateDatetimePartition(project, spec)character. Either (1) a character string giving the unique alphanumeric identifier for the project, or (2) a list containing the element projectId with this identifier.
list. Datetime partition specification returned by
CreateDatetimePartitionSpecification
list describing datetime partition with following components
projectId. Character string the id of the project this partitioning applies to.
datetimePartitionColumn. Character string the name of the column whose values as dates are used to assign a row to a particular partition.
dateFormat. Character string the format (e.g. " partition column was interpreted (compatible with strftime [https://docs.python.org/2/library/time.html#time.strftime]).
autopilotDataSelectionMethod. Character string Whether models created by the autopilot use "rowCount" or "duration" as their dataSelectionMethod.
validationDuration. Character string the validation duration specified when initializing the partitioning - not directly significant if the backtests have been modified, but used as the default validationDuration for the backtests.
availableTrainingStartDate. Character string The start date of the available training data for scoring the holdout.
availableTrainingDuration. Character string The duration of the available training data for scoring the holdout.
availableTrainingRowCount. integer The number of rows in the available training data for scoring the holdout. Only available when retrieving the partitioning after setting the target.
availableTrainingEndDate. Character string The end date of the available training data for scoring the holdout.
primaryTrainingStartDate. Character string The start date of primary training data for scoring the holdout.
primaryTrainingDuration. Character string The duration of the primary training data for scoring the holdout.
primaryTrainingRowCount. integer The number of rows in the primary training data for scoring the holdout. Only available when retrieving the partitioning after setting the target.
primaryTrainingEndDate. Character string The end date of the primary training data for scoring the holdout.
gapStartDate. Character string The start date of the gap between training and holdout scoring data.
gapDuration. Character string The duration of the gap between training and holdout scoring data.
gapRowCount. integer The number of rows in the gap between training and holdout scoring data. Only available when retrieving the partitioning after setting the target.
gapEndDate. Character string The end date of the gap between training and holdout scoring data.
holdoutStartDate. Character string The start date of holdout scoring data.
holdoutDuration. Character string The duration of the holdout scoring data.
holdoutRowCount. integer The number of rows in the holdout scoring data. Only available when retrieving the partitioning after setting the target.
holdoutEndDate. Character string The end date of the holdout scoring data.
numberOfBacktests. integer the number of backtests used.
backtests. data.frame of partition backtest. Each elemnet represent one backtest and has following components: index, availableTrainingStartDate, availableTrainingDuration, availableTrainingRowCount, availableTrainingEndDate, primaryTrainingStartDate, primaryTrainingDuration, primaryTrainingRowCount, primaryTrainingEndDate, gapStartDate, gapDuration, gapRowCount, gapEndDate, validationStartDate, validationDuration, validationRowCount, validationEndDate, totalRowCount.
useTimeSeries logical. Whether the project is a time series project (if TRUE) or an OTV project which uses datetime partitioning (if FALSE).
defaultToAPriori logical. Whether the project defaults to treating features as a priori. A priori features are time series features that are expected to be known for dates in the future when making predictions (e.g., "is this a holiday").
featureDerivationWindowStart integer. Offset into the past to define how far
back relative to the forecast point the feature derivation window should start. Only used for
time series projects. Expressed in terms of the timeUnit of the
datetimePartitionColumn.
featureDerivationWindowEnd integer. Offset into the past to define how far back relative
to the forecast point the feature derivation window should end. Only used for
time series projects. Expressed in terms of the timeUnit of the
datetimePartitionColumn.
forecastWindowStart integer. Offset into the future to define how far forward relative
to the forceast point the forecaset window should start. Only used for time series
projects. Expressed in terms of the timeUnit of the datetimePartitionColumn.
forecastWindowEnd integer. Offset into the future to define how far forward relative to
the forceast point the forecaset window should end. Only used for time series
projects. Expressed in terms of the timeUnit of the datetimePartitionColumn.
totalRowCount. integer the number of rows in the project dataset. Only available when retrieving the partitioning after setting the target. Thus it will be null for GenerateDatetimePartition and populated for GetDatetimePartition.
# NOT RUN {
projectId <- "59a5af20c80891534e3c2bde"
partitionSpec <- CreateDatetimePartitionSpecification("date_col")
GenerateDatetimePartition(projectId, partitionSpec)
# }
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