This function returns a DataRobot S3 object of class dataRobotDatetimeModel for the model defined by project and modelId.
GetDatetimeModel(project, modelId)An S3 object of class `dataRobotDatetimeModel`, which is a list with the following components:
featurelistId character. Unique alphanumeric identifier for the featurelist on which the model is based.
processes character. Vector with components describing preprocessing; may include `modelType`.
featurelistName character. The name of the featurelist on which the model is based.
projectId character. The unique alphanumeric identifier for the project.
samplePct numeric. Percentage of the dataset used to form the training dataset for model fitting.
isFrozen logical. Is model created with frozen tuning parameters?
modelType character. A description of the model.
metrics list. List with one element for each valid metric associated with the model. Each element is a list with elements for each possible evaluation type (holdout, validation, and crossValidation).
modelCategory character. The model category (e.g., blend, model).
blueprintId character. The unique DataRobot blueprint identifier on which the model is based.
modelId character. The unique alphanumeric model identifier.
modelNumber. integer. The assigned model number.
projectName character. Optional description of project defined by projectId.
projectTarget character. The target variable predicted by all models in the project.
projectMetric character. The fitting metric optimized by all project models.
trainingRowCount integer. The number of rows of the project dataset used in training
the model. In a datetime partitioned project, if specified, defines the number of
rows used to train the model and evaluate backtest scores; if unspecified, either
trainingDuration or trainingStartDate and trainingEndDate was used to
determine that instead.
trainingDuration character. Only present for models in datetime partitioned projects. If specified, a duration string specifying the duration spanned by the data used to train the model and evaluate backtest scores.
trainingStartDate character. Only present for frozen models in datetime partitioned projects. If specified, the start date of the data used to train the model.
trainingEndDate character. Only present for frozen models in datetime partitioned projects. If specified, the end date of the data used to train the model.
backtests list. What data was used to fit each backtest, the score for the project metric, and why the backtest score is unavailable if it is not provided.
dataSelectionMethod character. Which of trainingRowCount, trainingDuration, or trainingStartDate and trainingEndDate were used to determine the data used to fit the model. One of "rowCount", "duration", or "selectedDateRange".
trainingInfo list. Which data was used to train on when scoring the holdout and making predictions. trainingInfo will have the following keys: `holdoutTrainingStartDate`, `holdoutTrainingDuration`, `holdoutTrainingRowCount`, `holdoutTrainingEndDate`, `predictionTrainingStartDate`, `predictionTrainingDuration`, `predictionTrainingRowCount`, `predictionTrainingEndDate`. Start and end dates will be datetime string, durations will be duration strings, and rows will be integers.
holdoutScore numeric. The score against the holdout, if available and the holdout is unlocked, according to the project metric.
holdoutStatus character. The status of the holdout score, e.g. "COMPLETED", "HOLDOUT_BOUNDARIES_EXCEEDED".
effectiveFeatureDerivationWindowStart integer. Only available for time series projects. 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 in time series projects and `NULL` otherwise.
effectiveFeatureDerivationWindowEnd integer. Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end. Will be a non-positive integer in time series projects and `NULL` otherwise.
forecastWindowStart integer. Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start. Note that this field will be the same as what is shown in the project settings. Will be a non-negative integer in time series projects and `NULL` otherwise.
forecastWindowEnd integer. Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end. Note that this field will be the same as what is shown in the project settings. Will be a non-negative integer in time series projects and `NULL` otherwise.
windowsBasisUnit character. Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window. Note that this field will be the same as what is shown in the project settings. In time series projects, will be either the detected time unit or "ROW", and `NULL` otherwise.
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.
character. Unique alphanumeric identifier for the model of interest.
If the project does not use datetime partitioning an error will occur.
if (FALSE) {
projectId <- "59a5af20c80891534e3c2bde"
modelId <- "5996f820af07fc605e81ead4"
GetDatetimeModel(projectId, modelId)
}
Run the code above in your browser using DataLab