Nothing
namedEnumList <- function(...) {
list <- as.list(...)
names(list) <- make.names(list)
list
}
#' Autopilot modes
#'
#' This is a list that contains the valid values for autopilot mode. If you wish, you can
#' specify autopilot modes using the list values, e.g. AutopilotMode$FullAuto instead of typing
#' the string "auto". This way you can benefit from autocomplete and not have to remember the valid
#' options.
#'
#' \code{FullAuto} represents running the entire autopilot. \code{Quick} runs a quicker, abridged
#' version of the autopilot that focuses on the most important models. \code{Manual} does not run
#' the autopilot and instead leaves it to the user to select the algorithms to be run.
#' \code{Comprehensive} runs all blueprints in the repository, and may be extremely slow.
#' @export
AutopilotMode <- list(
FullAuto = "auto",
Manual = "manual",
Quick = "quick",
Comprehensive = "comprehensive"
)
#' Job statuses
#'
#' This is a list that contains the valid values for job status when querying the list of jobs mode.
#' If you wish, you can specify job status modes using the list values, e.g. JobStatus$InProgress
#' instead of typing the string "inprogress". This way you can benefit from autocomplete and not
#' have to remember the valid options.
#' @export
JobStatus <- list(
Queue = "queue",
InProgress = "inprogress",
Error = "error",
Aborted = "ABORTED",
Completed = "COMPLETED")
JobFailureStatuses <- c(JobStatus$Error, JobStatus$Aborted)
#' Job type
#'
#' This is a list that contains the valid values for job type when querying the list of jobs.
#' @export
JobType <- list(
FeatureImpact = "featureImpact",
Predict = "predict",
Model = "model",
PrimeRulesets = "primeRulesets",
PrimeDownloadValidation = "primeDownloadValidation",
PrimeModel = "primeModel",
ModelExport = "modelExport",
PredictionExplanationsInitialization = "predictionExplanationsInitialization",
PredictionExplanations = "predictionExplanations",
PredictionIntervals = "calculatePredictionIntervals"
)
#' Prime Language
#'
#' This is a list that contains the valid values for downloadable code programming languages.
#' @export
PrimeLanguage <- list(
Python = "Python",
Java = "Java")
#' PostgreSQL drivers
#'
#' This is a list that contains the valid values for PostgreSQL drivers.
#' @export
PostgreSQLdrivers <- list(
Unicode = "PostgreSQL Unicode",
ANSI = "PostgreSQL ANSI")
#' Blend methods
#'
#' This is a list that contains the valid values for Blend methods
#' @export
BlendMethods <- list(
PLS = "PLS",
GLM = "GLM",
ENET = "ENET",
MED = "MED",
AVERAGE = "AVG",
MAE = "MAE",
MAEL1 = "MAEL1",
RANDOM_FOREST = "RF",
LIGHT_GBM = "LGBM",
TENSORFLOW = "TF",
FORECAST_DISTANCE = "FORECAST_DISTANCE",
FORECAST_DISTANCE_ENET = "FORECAST_DISTANCE_ENET",
FORECAST_DISTANCE_AVG = "FORECAST_DISTANCE_AVG"
)
#' CV methods
#'
#' This is a list that contains the valid values for CV methods
#' @export
cvMethods <- list(
RANDOM = "random",
STRATIFIED = "stratified",
USER = "user",
GROUP = "group",
DATETIME = "datetime"
)
#' Data Partition methods
#'
#' This is a list that contains the valid values for data partitions
#' @export
DataPartition <- list(
VALIDATION = "validation",
CROSSVALIDATION = "crossValidation",
HOLDOUT = "holdout"
)
#' Source types
#'
#' This is a list that contains the valid values for source type
#' @export
SourceType <- list(
Validation = "validation",
Training = "training"
)
#' Target Type modes
#'
#' This is a list that contains the valid values for the Target Types
#' @export
TargetType <- list(
Binary = "Binary",
Multiclass = "Multiclass",
Regression = "Regression"
)
#' Data subset for training predictions
#'
#' This is a list that contains the valid values for the \code{dataSubset} parameter
#' found in \code{RequestTrainingPredictions}. If you wish, you can specify
#' \code{dataSubset} using the list values here.
#'
#' For \code{All}, all available data is used.
#'
#' For \code{ValidationAndHoldout}, only data outside the training set is used.
#'
#' For \code{Holdout}, only holdout data is used.
#'
#' For \code{AllBacktests}, data is used from all backtest validation folds. This requires
#' the model to have successfully scored all backtests. Backtests are available on datetime
#' partitioned projects only.
#' @export
DataSubset <- list(
All = "all",
ValidationAndHoldout = "validationAndHoldout",
Holdout = "holdout",
AllBacktests = "allBacktests")
#' Treat as exponential
#' @export
TreatAsExponential <- list(
Always = "always",
Never = "never",
Auto = "auto"
)
#' Differencing method
#' @export
DifferencingMethod <- list(
Auto = "auto",
Simple = "simple",
None = "none",
Seasonal = "seasonal"
)
#' Time units
#' @export
TimeUnits <- list(
Second = "SECOND",
Minute = "MINUTE",
Hour = "HOUR",
Day = "DAY",
Week = "WEEK",
Month = "MONTH",
Quarter = "QUARTER",
Year = "YEAR")
#' Datetime trend plots resolutions
DatetimeTrendPlotsResolutions <- list(
Milliseconds = "milliseconds",
Seconds = "seconds",
Minutes = "minutes",
Hours = "hours",
Days = "days",
Weeks = "weeks",
Months = "months",
Quarters = "quarters",
Years = "years")
#' Datetime trend plots statuses
DatetimeTrendPlotsStatuses <- list(
Completed = "completed",
NotCompleted = "notCompleted",
InProgress = "inProgress",
Errored = "errored",
NotSupported = "notSupported",
InsufficientData = "insufficientData")
#' Periodicity time units
#'
#' Same as time units, but kept for backwards compatibility.
#' @export
PeriodicityTimeUnits <- TimeUnits
#' Periodicity max time step
#' @export
PeriodicityMaxTimeStep <- 9223372036854775807
#' Target leakage report values
#' @export
TargetLeakageType <- list(
SkippedDetection = "SKIPPED_DETECTION",
False = "FALSE",
ModerateRisk = "MODERATE_RISK",
HighRisk = "HIGH_RISK"
)
#' Recommended model type values
#'
#' \code{MostAccurate} retrieves the most accurate model based on validation or
#' cross-validation results. In most cases, this will be a blender model.
#'
#' \code{FastAccurate} retrieves the most accurate individual model (not blender) that passes
#' set guidelines for prediction speed. If no models meet the prediction speed guideline, this
#' will not retrieve anything.
#'
#' \code{RecommendedForDeployment} retrieves the most accurate individual model. This model
#' will have undergone specific pre-preparations to be deployment ready. See
#' \code{GetModelRecommendation} for details.
#' @export
RecommendedModelType <- list(
MostAccurate = "Most Accurate",
FastAccurate = "Fast & Accurate",
RecommendedForDeployment = "Recommended for Deployment"
)
#' Project stage
#' @export
ProjectStage <- list(
AIM = "aim",
EDA = "eda",
EMPTY = "empty",
MODELING = "modeling"
)
#' Sharing role
#'
#' This is a list that contains the valid values for granting access to other users (see
#' \code{Share}). If you wish, you can specify access roles using the list values, e.g.,
#' \code{SharingRole$ReadWrite} instead of typing the string "READ_WRITE". This way you can
#' benefit from autocomplete and not have to remember the valid options.
#'
#' \code{Owner} allows any action including deletion.
#'
#' \code{ReadWrite} or \code{Editor} allows modifications to the state, e.g., renaming
#' and creating data sources from a data store, but *not* deleting the entity.
#'
#' \code{ReadOnly} or \code{Consumer} - for data sources, enables creating projects and predictions;
#' for data stores, allows viewing them only.
#' @export
SharingRole <- list(
Owner = "OWNER",
ReadWrite = "READ_WRITE",
User = "USER",
Editor = "EDITOR",
ReadOnly = "READ_ONLY",
Consumer = "CONSUMER"
)
#' Series aggregation type
#'
#' For details, see "Calculating features across series" in the time series section of the
#' DataRobot user guide.
#' @export
SeriesAggregationType <- list(
Average = "average",
Total = "total"
)
#' Model replacement reason
#'
#' @export
ModelReplacementReason <- list(
Accuracy = "ACCURACY",
DataDrift = "DATA_DRIFT",
Errors = "ERRORS",
ScheduledRefresh = "SCHEDULED_REFRESH",
ScoringSpeed = "SCORING_SPEED",
Other = "OTHER"
)
#' Types of variable transformations
#'
#' @export
VariableTransformTypes <- list(
Categorical = "categorical",
CategoricalInt = "categoricalInt",
Numeric = "numeric",
Text = "text"
)
#' Deployment service health metrics
#'
#' Added in DataRobot API 2.18.
#'
#' For usage, see \code{GetDeploymentServiceStats}.
#' @export
DeploymentServiceHealthMetric <- list(
TotalPredictions = "totalPredictions",
TotalRequests = "totalRequests",
SlowRequests = "slowRequests",
ExecutionTime = "executionTime",
ResponseTime = "responseTime",
UserErrorRate = "userErrorRate",
ServerErrorRate = "serverErrorRate",
NumConsumers = "numConsumers",
CacheHitRatio = "cacheHitRatio",
MedianLoad = "medianLoad",
PeakLoad = "peakLoad"
)
#' Segment analysis attributes
#'
#' Added in DataRobot API 2.20.
#'
#' For usage, see \code{GetDeploymentServiceStats}.
#' @export
SegmentAnalysisAttribute <- list(
DataRobotConsumer = "DataRobot-Consumer",
DataRobotRemoteIP = "DataRobot-Remote-IP",
DataRobotHostName = "DataRobot-Host-Name"
)
#' Accuracy metrics for regression deployments
#'
#' Added in DataRobot API 2.18.
#' @export
RegressionDeploymentAccuracyMetric <- namedEnumList(c(
"Gamma Deviance",
"FVE Gamma",
"FVE Poisson",
"FVE Tweedie",
"MAD",
"MAE",
"MAPE",
"Poisson Deviance",
"R Squared",
"RMSE",
"RMSLE",
"Tweedie Deviance"
))
#' Accuracy metrics for classification deployments
#'
#' Added in DataRobot API 2.18.
#' @export
ClassificationDeploymentAccuracyMetric <- namedEnumList(c(
"Accuracy",
"AUC",
"Balanced Accuracy",
"FVE Binomial",
"Gini Norm",
"Kolmogorov-Smirnov",
"LogLoss",
"Rate@Top5%",
"Rate@Top10%",
"TPR",
"FPR",
"TNR",
"PPV",
"F1"
))
#' Accuracy metrics for multiclass deployments
#'
#' Added in DataRobot API 2.23.
#' @export
#'
MulticlassDeploymentAccuracyMetric <- namedEnumList(c(
"LogLoss",
"FVE Binomial",
"FVE Multinomial"
))
#' Deployment accuracy metrics
#'
#' All possible deployment accuracy metrics. Added in DataRobot API 2.18.
#'
#' For usage, see `DeploymentAccuracy` and `DeploymentAccuracyOverTime`.
#' @export
#' @md
DeploymentAccuracyMetric <- {
# Combine and dedupe the different metric types
DeploymentAccuracyMetric <- c(
RegressionDeploymentAccuracyMetric,
ClassificationDeploymentAccuracyMetric,
MulticlassDeploymentAccuracyMetric
)
DeploymentAccuracyMetric[!duplicated(DeploymentAccuracyMetric)]
}
#' Model capabilities
#'
#' For usage, see `\code{GetModelCapabilities}`.
#' @export
ModelCapability <- namedEnumList(c(
"supportsEarlyStopping",
"supportsImageEmbedding",
"supportsNNVisualizations",
"supportsImageActivationMaps",
"supportsMonotonicConstraints",
"supportsBlending",
"supportsShap",
"supportsCodeGeneration",
"supportsModelTrainingMetrics",
"hasParameters",
"eligibleForPrime",
"hasWordCloud"
))
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