datarobot-projects - Pinkfish - AI Agents & Workflows for Getting Work Done

Server path: /datarobot-projects | Type: Application | PCID required: Yes

Tools

Tool	Description
`datarobot_projects_access_control_list`	Get the project access control list.
`datarobot_projects_access_control_patch_many`	Update the project’s access controls.
`datarobot_projects_autopilot_create`	Pause or unpause Autopilot
`datarobot_projects_autopilots_create`	Start autopilot
`datarobot_projects_configure_and_start_autopilot`	Start modeling.
`datarobot_projects_datetime_partitioning_create`	Preview the fully specified datetime partitioning generated by the requested configuration. DEPRECATED API.
`datarobot_projects_datetime_partitioning_list`	Retrieve datetime partitioning configuration.
`datarobot_projects_deleted_projects_count_list`	Count soft-deleted projects.
`datarobot_projects_deleted_projects_list`	Retrieve the list of soft-deleted projects.
`datarobot_projects_deleted_projects_patch`	Recover soft-deleted project.
`datarobot_projects_external_time_series_baseline_data_validation_jobs_create`	Validate baseline data.
`datarobot_projects_external_time_series_baseline_data_validation_jobs_retrieve`	Retrieve the baseline validation job.
`datarobot_projects_hdfs_projects_create`	Create a project from an HDFS file source.
`datarobot_projects_jobs_delete`	Cancel a job.
`datarobot_projects_jobs_list`	List project jobs.
`datarobot_projects_jobs_retrieve`	Get a job.
`datarobot_projects_multiseries_ids_cross_series_properties_list`	Retrieve eligible cross-series group-by columns.
`datarobot_projects_multiseries_names_list`	List the names of a multiseries project.
`datarobot_projects_multiseries_properties_create`	Detect multiseries properties.
`datarobot_projects_optimized_datetime_partitionings_create`	Create an optimized datetime partitioning configuration using the target.
`datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_input_list`	Retrieve the optimized datetime partitioning input.
`datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_log_file_list`	Retrieve a text file containing the datetime partitioning log
`datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_log_list`	Retrieve the datetime partitioning log and log length as JSON
`datarobot_projects_optimized_datetime_partitionings_list`	Lists all created optimized datetime partitioning configurations.
`datarobot_projects_optimized_datetime_partitionings_retrieve`	Retrieve the optimized datetime partitioning configuration.
`datarobot_projects_project_cleanup_jobs_create`	Schedule project permadelete job.
`datarobot_projects_project_cleanup_jobs_delete`	Cancel scheduled project permadelete job.
`datarobot_projects_project_cleanup_jobs_download_list`	Download a projects permadeletion report.
`datarobot_projects_project_cleanup_jobs_list`	Retrieve project permadelete job status.
`datarobot_projects_project_cleanup_jobs_retrieve`	Retrieve project permadelete job status.
`datarobot_projects_project_cleanup_jobs_summary_list`	Get a projects cleanup jobs summary.
`datarobot_projects_project_clones_create`	Clone a project.
`datarobot_projects_projects_create`	Create a project.
`datarobot_projects_projects_delete`	Delete a project.
`datarobot_projects_projects_list`	List projects.
`datarobot_projects_projects_retrieve`	Get project.
`datarobot_projects_projects_update`	Update a project.
`datarobot_projects_segmentation_task_job_results_retrieve`	Retrieve segmentation task statuses.
`datarobot_projects_segmentation_tasks_create`	Create segmentation tasks.
`datarobot_projects_segmentation_tasks_list`	List segmentation tasks.
`datarobot_projects_segmentation_tasks_mappings_list`	Retrieve series ID to segment ID mappings.
`datarobot_projects_segmentation_tasks_retrieve`	Retrieve segmentation task.
`datarobot_projects_segments_patch`	Update child segment project.
`datarobot_projects_status_list`	Check project status.

datarobot_projects_access_control_list

Get the project access control list. Parameters:

Parameter	Type	Required	Default	Description
`offset`	integer	Yes	—	This many results will be skipped
`limit`	integer	Yes	—	At most this many results are returned
`username`	string	No	—	Optional, only return the access control information for a user with this username.
`userId`	string	No	—	Optional, only return the access control information for a user with this user ID.
`projectId`	string	Yes	—	The project ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "offset": {
      "type": "integer",
      "description": "This many results will be skipped"
    },
    "limit": {
      "type": "integer",
      "description": "At most this many results are returned"
    },
    "username": {
      "type": "string",
      "description": "Optional, only return the access control information for a user with this username."
    },
    "userId": {
      "type": "string",
      "description": "Optional, only return the access control information for a user with this user ID."
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    }
  },
  "required": [
    "PCID",
    "offset",
    "limit",
    "projectId"
  ]
}

datarobot_projects_access_control_patch_many

Update the project’s access controls. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`data`	any[]	Yes	—	The role to set for the user.
`includeFeatureDiscoveryEntities`	boolean	No	—	Whether to share all the related entities.
`sendNotification`	boolean	No	—	Send an email notification.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "data": {
      "type": "array",
      "description": "The role to set for the user."
    },
    "includeFeatureDiscoveryEntities": {
      "type": "boolean",
      "description": "Whether to share all the related entities."
    },
    "sendNotification": {
      "type": "boolean",
      "description": "Send an email notification."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "data"
  ]
}

datarobot_projects_autopilot_create

Pause or unpause Autopilot Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`command`	string	Yes	—	If `start`, will unpause the autopilot and run queued jobs if workers are available. If `stop`, will pause the autopilot so no new jobs will be started.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "command": {
      "type": "string",
      "description": "If `start`, will unpause the autopilot and run queued jobs if workers are available.  If `stop`, will pause the autopilot so no new jobs will be started.",
      "enum": [
        "start",
        "stop"
      ]
    }
  },
  "required": [
    "PCID",
    "projectId",
    "command"
  ]
}

datarobot_projects_autopilots_create

Start autopilot Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`autopilotClusterList`	any[]	No	—	Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to ‘clustering’.
`blendBestModels`	boolean	No	—	Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.
`considerBlendersInRecommendation`	boolean	No	—	Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects.
`featurelistId`	string	Yes	—	The ID of a featurelist that should be used for autopilot.
`mode`	string	No	—	The Autopilot mode.
`prepareModelForDeployment`	boolean	No	—	Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning “RECOMMENDED FOR DEPLOYMENT” label.
`runLeakageRemovedFeatureList`	boolean	No	—	Run Autopilot on Leakage Removed feature list (if exists).
`scoringCodeOnly`	boolean	No	—	Keep only models that can be converted to scorable java code during Autopilot run.
`useGpu`	boolean	No	—	Use GPU workers for Autopilot run.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "autopilotClusterList": {
      "type": "array",
      "description": "Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'."
    },
    "blendBestModels": {
      "type": "boolean",
      "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects."
    },
    "considerBlendersInRecommendation": {
      "type": "boolean",
      "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects."
    },
    "featurelistId": {
      "type": "string",
      "description": "The ID of a featurelist that should be used for autopilot."
    },
    "mode": {
      "type": "string",
      "description": "The Autopilot mode.",
      "enum": [
        "auto",
        "comprehensive",
        "quick"
      ]
    },
    "prepareModelForDeployment": {
      "type": "boolean",
      "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning \"RECOMMENDED FOR DEPLOYMENT\" label."
    },
    "runLeakageRemovedFeatureList": {
      "type": "boolean",
      "description": "Run Autopilot on Leakage Removed feature list (if exists)."
    },
    "scoringCodeOnly": {
      "type": "boolean",
      "description": "Keep only models that can be converted to scorable java code during Autopilot run."
    },
    "useGpu": {
      "type": "boolean",
      "description": "Use GPU workers for Autopilot run."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "featurelistId"
  ]
}

datarobot_projects_configure_and_start_autopilot

Start modeling. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`accuracyOptimizedMb`	boolean	No	—	Include additional, longer-running models that will be run by the autopilot and available to run manually.
`aggregationType`	string	No	—	For multiseries projects only. The aggregation type to apply when creating cross-series features.
`allowedPairwiseInteractionGroups`	any[]	No	—	For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [[‘A’, ‘B’, ‘C’], [‘C’, ‘D’]] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.
`allowedPairwiseInteractionGroupsFilename`	string	No	—	Filename that was used to upload allowed_pairwise_interaction_groups. Necessary for persistence of UI/UX when you specify that parameter via file.
`allowPartialHistoryTimeSeriesPredictions`	boolean	No	—	Specifies whether the time series predictions can use partial historical data.
`autopilotClusterList`	any[]	No	—	A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.
`autopilotDataSamplingMethod`	string	No	—	Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to ‘latest’ for ‘rowCount’ dataSelectionMethod and to ‘random’ for ‘duration’.
`autopilotDataSelectionMethod`	string	Yes	—	The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.
`autopilotWithFeatureDiscovery`	boolean	No	—	If true, autopilot will run on a feature list that includes features found via search for interactions.
`backtests`	any[]	No	—	An array specifying the format of the backtests.
`biasMitigationFeatureName`	string	No	—	The name of the protected feature used to mitigate bias on models.
`biasMitigationTechnique`	string	No	—	Method applied to perform bias mitigation.
`blendBestModels`	boolean	No	—	Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.
`blueprintThreshold`	integer	No	—	The runtime (in hours) which if exceeded will exclude a model from autopilot runs.
`calendarId`	string	No	—	The ID of the calendar to be used in this project.
`chunkDefinitionId`	string	No	—	Chunk definition id for incremental learning using chunking service
`classMappingAggregationSettings`	object	No	—	Class mapping aggregation settings.
`considerBlendersInRecommendation`	boolean	No	—	Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects.
`credentials`	any[]	No	—	List of credentials for the secondary datasets used in feature discovery project.
`crossSeriesGroupByColumns`	any[]	No	—	For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). 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.
`cvHoldoutLevel`	object	No	—	The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as `null`, then no holdout will be used in the project. The rest of the levels indicate which cross validation fold each row should fall into.
`cvMethod`	string	No	—	The partitioning method to be applied to the training data.
`dateRemoval`	boolean	No	—	If true, enable creating additional feature lists without dates (does not apply to time-aware projects).
`datetimePartitionColumn`	string	No	—	The date column that will be used as a datetime partition column.
`datetimePartitioningId`	string	No	—	The ID of a datetime partitioning to use for the project.When datetime_partitioning_id is specified, no other datetime partitioning related field is allowed to be specified, as these fields get loaded from the already created partitioning.
`defaultToAPriori`	boolean	No	—	Renamed to `defaultToKnownInAdvance`.
`defaultToDoNotDerive`	boolean	No	—	For time series 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.
`defaultToKnownInAdvance`	boolean	No	—	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 :ref:`Time Series Overview <time_series_overview>` for more context.
`differencingMethod`	string	No	—	For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.
`disableHoldout`	boolean	No	—	Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.
`eventsCount`	string	No	—	The name of a column specifying events count. The data in this column must be pure numeric and non negative without missing values
`exponentiallyWeightedMovingAlpha`	number	No	—	Discount factor (alpha) used for exponentially weighted moving features
`exposure`	string	No	—	The name of a column specifying row exposure.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values
`externalPredictions`	any[]	No	—	List of external prediction columns from the dataset.
`externalTimeSeriesBaselineDatasetId`	string	No	—	Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.
`externalTimeSeriesBaselineDatasetName`	string	No	—	The name of the time series baseline dataset for the project.
`fairnessMetricsSet`	string	No	—	Metric to use for calculating fairness. Can be one of `proportionalParity`, `equalParity`, `predictionBalance`, `trueFavorableAndUnfavorableRateParity` or `FavorableAndUnfavorablePredictiveValueParity`. Used and required only if Bias & Fairness in AutoML feature is enabled.
`fairnessThreshold`	number	No	—	The threshold value of the fairness metric. The valid range is [0:1]; the default fairness metric value is 0.8. This metric is only applicable if the Bias & Fairness in AutoML feature is enabled.
`featureDerivationWindowEnd`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.
`featureDerivationWindowStart`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.
`featureDiscoverySupervisedFeatureReduction`	boolean	No	—	Run supervised feature reduction for feature discovery projects.
`featureEngineeringPredictionPoint`	string	No	—	The date column to be used as the prediction point for time-based feature engineering.
`featurelistId`	string	No	—	The ID of a featurelist to use for autopilot.
`featureSettings`	any[]	No	—	An array specifying per feature settings. Features can be left unspecified.
`forecastDistance`	string	No	—	The name of a column specifying the forecast distance to which each row of the dataset belongs. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as forecast distance.
`forecastOffsets`	any[]	No	—	An array of strings with names of a columns specifying row offsets. Columns values are used as offset or predictions to boost for models. The data in this column must be pure numeric (e.g. not currency, date, length, etc.).
`forecastWindowEnd`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.
`forecastWindowStart`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.
`gapDuration`	string	No	—	The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).
`holdoutDuration`	string	No	—	The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.
`holdoutEndDate`	string	No	—	The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.
`holdoutLevel`	object	No	—	The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as `null`, then no holdout will be used in the project. However, the column must have exactly 2 values in order for this option to be valid
`holdoutPct`	number	No	—	The percentage of the dataset to assign to the holdout set
`holdoutStartDate`	string	No	—	The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.
`includeBiasMitigationFeatureAsPredictorVariable`	boolean	No	—	Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.
`incrementalLearningEarlyStoppingRounds`	integer	No	—	Early stopping rounds for the auto incremental learning service
`incrementalLearningOnBestModel`	boolean	No	—	Automatically run incremental learning on the best model during Autopilot run.
`incrementalLearningOnlyMode`	boolean	No	—	Keep only models that support incremental learning during Autopilot run.
`isHoldoutModified`	boolean	No	—	A boolean value indicating whether holdout settings (start/end dates) have been modified by user.
`majorityDownsamplingRate`	number	No	—	The percentage between 0 and 100 of the majority rows that should be kept. Must be specified only if using smart downsampling. If not specified, a default will be selected based on the dataset distribution. The chosen rate may not cause the majority class to become smaller than the minority class.
`metric`	string	No	—	The metric to use to select the best models. See `/api/v2/projects/(projectId)/features/metrics/` for the metrics that may be valid for a potential target. Note that weighted metrics must be used with a weights column.
`minSecondaryValidationModelCount`	integer	No	—	Compute ‘All backtest’ scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.
`mode`	string	No	—	The autopilot mode to use. Either ‘quick’, ‘auto’, ‘manual’, or ‘comprehensive’.
`modelSplits`	integer	No	—	Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data.
`monotonicDecreasingFeaturelistId`	string	No	—	The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired.
`monotonicIncreasingFeaturelistId`	string	No	—	The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired.
`multiseriesIdColumns`	any[]	No	—	May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries <multiseries>` section of the time series documentation for more context.
`numberOfBacktests`	integer	No	—	The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
`numberOfIncrementalLearningIterationsBeforeBestModelSelection`	integer	No	—	Number of incremental_learning iterations before best model selection.
`offset`	any[]	No	—	An array of strings with names of a columns specifying row offsets.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values
`onlyIncludeMonotonicBlueprints`	boolean	Yes	—	When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot.
`partitionKeyCols`	any[]	No	—	An array containing a single string - the name of the group partition column
`periodicities`	any[]	No	—	A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter ‘differencing_method’ will default to ‘seasonal’ if not provided or ‘auto’.
`positiveClass`	object	No	—	A value from the target column to use for the positive class. May only be specified for projects doing binary classification.If not specified, a positive class is selected automatically.
`preferableTargetValue`	object	No	—	A target value that should be treated as a positive outcome for the prediction. For example if we want to check gender discrimination for giving a loan and our target named `is_bad`, then the positive outcome for the prediction would be `No`, which means that the loan is good and that’s what we treat as a preferable result for the loaner. Used and required only if Bias & Fairness in AutoML feature is enabled.
`prepareModelForDeployment`	boolean	No	—	Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning ‘RECOMMENDED FOR DEPLOYMENT’ label.
`primaryLocationColumn`	string	No	—	Primary geospatial location column.
`protectedFeatures`	any[]	No	—	A list of project feature to mark as protected for Bias metric calculation and Fairness correction. Used and required only if Bias & Fairness in AutoML feature is enabled.
`quantileLevel`	number	No	—	The quantile level between 0.01 and 0.99 for specifying the Quantile metric.
`quickrun`	boolean	No	—	(Deprecated): ‘quick’ should be used in the `mode` parameter instead of using this parameter. If set to `true`, autopilot mode will be set to ‘quick’.Cannot be set to `true` when `mode` is set to ‘comprehensive’ or ‘manual’.
`rateTopPctThreshold`	number	No	—	The percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric.
`relationshipsConfigurationId`	string	No	—	Relationships configuration id to be used for Feature Discovery projects.
`reps`	integer	No	—	The number of cross validation folds to use.
`responseCap`	number	No	—	Used to cap the maximum response of a model
`runLeakageRemovedFeatureList`	boolean	No	—	Run Autopilot on Leakage Removed feature list (if exists).
`sampleStepPct`	number	No	—	A float between 0 and 100 indicating the desired percentage of data to sample when training models in comprehensive Autopilot. Note: this only supported for comprehensive Autopilot and the specified value may be lowered in order to be compatible with the project’s dataset and partition settings.
`scoringCodeOnly`	boolean	No	—	Keep only models that can be converted to scorable java code during Autopilot run.
`seed`	integer	No	—	A seed to use for randomization.
`segmentationTaskId`	string	No	—	Specifies the SegmentationTask that will be used for dividing the project up into multiple segmented projects.
`seriesId`	string	No	—	The name of a column specifying the series ID to which each row of the dataset belongs. Typically the series was used to derive the additional features, that are independent from each other. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as multi-series ID column.
`shapOnlyMode`	boolean	No	—	Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible.
`smartDownsampled`	boolean	No	—	Whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects.
`stopWords`	any[]	No	—	A list of stop words to be used for text blueprints. Note: `stop_words=True` must be set in the blueprint preprocessing parameters for this list of stop words to actually be used during preprocessing.
`target`	string	No	—	The name of the target feature.
`targetType`	string	No	—	Used to specify the targetType to use for a project when it is ambiguous, i.e. a numeric target with a few unique values that could be used for either regression or multiclass.
`trainingLevel`	object	No	—	The value of the partition column indicating a row is part of the training set.
`treatAsExponential`	string	No	—	For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed.
`unsupervisedMode`	boolean	No	—	If True, unsupervised project (without target) will be created. `target` cannot be specified if `unsupervisedMode` is True.
`unsupervisedType`	string	No	—	The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.
`useCrossSeriesFeatures`	boolean	No	—	Indicating if user wants to use cross-series features.
`useGpu`	boolean	No	—	Indicates whether project should use GPU workers
`useProjectSettings`	boolean	No	—	Specifies whether datetime-partitioned project should use project settings (i.e. backtests configuration has been modified by the user).
`userPartitionCol`	string	No	—	The name of the column containing the partition assignments.
`useSupervisedFeatureReduction`	boolean	No	—	When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.
`useTimeSeries`	boolean	No	—	A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
`validationDuration`	string	No	—	The default 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. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.
`validationLevel`	object	No	—	The value of the partition column indicating a row is part of the validation set.
`validationPct`	number	No	—	The percentage of the dataset to assign to the validation set
`validationType`	string	No	—	The validation method to be used. CV for cross validation or TVH for train-validation-holdout split.
`weights`	string	No	—	The name of a column specifying row weights. The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values
`windowsBasisUnit`	string	No	—	For time series projects only. 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.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "accuracyOptimizedMb": {
      "type": "boolean",
      "description": "Include additional, longer-running models that will be run by the autopilot and available to run manually."
    },
    "aggregationType": {
      "type": "string",
      "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
      "enum": [
        "total",
        "average"
      ]
    },
    "allowedPairwiseInteractionGroups": {
      "type": "array",
      "description": "For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [['A', 'B', 'C'], ['C', 'D']] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model."
    },
    "allowedPairwiseInteractionGroupsFilename": {
      "type": "string",
      "description": "Filename that was used to upload allowed_pairwise_interaction_groups. Necessary for persistence of UI/UX when you specify that parameter via file."
    },
    "allowPartialHistoryTimeSeriesPredictions": {
      "type": "boolean",
      "description": "Specifies whether the time series predictions can use partial historical data."
    },
    "autopilotClusterList": {
      "type": "array",
      "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`."
    },
    "autopilotDataSamplingMethod": {
      "type": "string",
      "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.",
      "enum": [
        "random",
        "latest"
      ]
    },
    "autopilotDataSelectionMethod": {
      "type": "string",
      "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
      "enum": [
        "duration",
        "rowCount"
      ]
    },
    "autopilotWithFeatureDiscovery": {
      "type": "boolean",
      "description": "If true, autopilot will run on a feature list that includes features found via search for interactions."
    },
    "backtests": {
      "type": "array",
      "description": "An array specifying the format of the backtests."
    },
    "biasMitigationFeatureName": {
      "type": "string",
      "description": "The name of the protected feature used to mitigate bias on models."
    },
    "biasMitigationTechnique": {
      "type": "string",
      "description": "Method applied to perform bias mitigation.",
      "enum": [
        "preprocessingReweighing",
        "postProcessingRejectionOptionBasedClassification"
      ]
    },
    "blendBestModels": {
      "type": "boolean",
      "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects."
    },
    "blueprintThreshold": {
      "type": "integer",
      "description": "The runtime (in hours) which if exceeded will exclude a model from autopilot runs."
    },
    "calendarId": {
      "type": "string",
      "description": "The ID of the calendar to be used in this project."
    },
    "chunkDefinitionId": {
      "type": "string",
      "description": "Chunk definition id for incremental learning using chunking service"
    },
    "classMappingAggregationSettings": {
      "type": "object",
      "description": "Class mapping aggregation settings.",
      "properties": {
        "aggregationClassName": {
          "type": "string",
          "description": "The name of the class that will be assigned to all rows with aggregated classes. Should not match any excluded_from_aggregation or we will have 2 classes with the same name and no way to distinguish between them. This option is only available formulticlass projects. By default 'DR_RARE_TARGET_VALUES' is used."
        },
        "excludedFromAggregation": {
          "type": "array",
          "description": "List of target values that should be guaranteed to kept as is, regardless of other settings."
        },
        "maxUnaggregatedClassValues": {
          "type": "integer",
          "description": "The maximum number of unique labels before aggregation kicks in. Should be at least len(excludedFromAggregation) + 1 for multiclass and at least len(excludedFromAggregation) for multilabel."
        },
        "minClassSupport": {
          "type": "integer",
          "description": "Minimum number of instances necessary for each target value in the dataset. All values with fewer instances than this value will be aggregated"
        }
      }
    },
    "considerBlendersInRecommendation": {
      "type": "boolean",
      "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects."
    },
    "credentials": {
      "type": "array",
      "description": "List of credentials for the secondary datasets used in feature discovery project."
    },
    "crossSeriesGroupByColumns": {
      "type": "array",
      "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). 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."
    },
    "cvHoldoutLevel": {
      "description": "The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as ``null``, then no holdout will be used in the project. The rest of the levels indicate which cross validation fold each row should fall into."
    },
    "cvMethod": {
      "type": "string",
      "description": "The partitioning method to be applied to the training data.",
      "enum": [
        "random",
        "user",
        "stratified",
        "group",
        "datetime"
      ]
    },
    "dateRemoval": {
      "type": "boolean",
      "description": "If true, enable creating additional feature lists without dates (does not apply to time-aware projects)."
    },
    "datetimePartitionColumn": {
      "type": "string",
      "description": "The date column that will be used as a datetime partition column."
    },
    "datetimePartitioningId": {
      "type": "string",
      "description": "The ID of a datetime partitioning to use for the project.When datetime_partitioning_id is specified, no other datetime partitioning related field is allowed to be specified, as these fields get loaded from the already created partitioning."
    },
    "defaultToAPriori": {
      "type": "boolean",
      "description": "Renamed to `defaultToKnownInAdvance`."
    },
    "defaultToDoNotDerive": {
      "type": "boolean",
      "description": "For time series 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."
    },
    "defaultToKnownInAdvance": {
      "type": "boolean",
      "description": "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 :ref:`Time Series Overview <time_series_overview>` for more context."
    },
    "differencingMethod": {
      "type": "string",
      "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.",
      "enum": [
        "auto",
        "none",
        "simple",
        "seasonal"
      ]
    },
    "disableHoldout": {
      "type": "boolean",
      "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set."
    },
    "eventsCount": {
      "type": "string",
      "description": "The name of a column specifying events count. The data in this column must be pure numeric and non negative without missing values"
    },
    "exponentiallyWeightedMovingAlpha": {
      "type": "number",
      "description": "Discount factor (alpha) used for exponentially weighted moving features"
    },
    "exposure": {
      "type": "string",
      "description": "The name of a column specifying row exposure.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values"
    },
    "externalPredictions": {
      "type": "array",
      "description": "List of external prediction columns from the dataset."
    },
    "externalTimeSeriesBaselineDatasetId": {
      "type": "string",
      "description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics."
    },
    "externalTimeSeriesBaselineDatasetName": {
      "type": "string",
      "description": "The name of the time series baseline dataset for the project."
    },
    "fairnessMetricsSet": {
      "type": "string",
      "description": "Metric to use for calculating fairness. Can be one of ``proportionalParity``, ``equalParity``, ``predictionBalance``, ``trueFavorableAndUnfavorableRateParity`` or ``FavorableAndUnfavorablePredictiveValueParity``. Used and required only if *Bias & Fairness in AutoML* feature is enabled.",
      "enum": [
        "proportionalParity",
        "equalParity",
        "predictionBalance",
        "trueFavorableAndUnfavorableRateParity",
        "favorableAndUnfavorablePredictiveValueParity"
      ]
    },
    "fairnessThreshold": {
      "type": "number",
      "description": "The threshold value of the fairness metric. The valid range is [0:1]; the default fairness metric value is 0.8. This metric is only applicable if the *Bias & Fairness in AutoML* feature is enabled."
    },
    "featureDerivationWindowEnd": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end."
    },
    "featureDerivationWindowStart": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin."
    },
    "featureDiscoverySupervisedFeatureReduction": {
      "type": "boolean",
      "description": "Run supervised feature reduction for feature discovery projects."
    },
    "featureEngineeringPredictionPoint": {
      "type": "string",
      "description": "The date column to be used as the prediction point for time-based feature engineering."
    },
    "featurelistId": {
      "type": "string",
      "description": "The ID of a featurelist to use for autopilot."
    },
    "featureSettings": {
      "type": "array",
      "description": "An array specifying per feature settings. Features can be left unspecified."
    },
    "forecastDistance": {
      "type": "string",
      "description": "The name of a column specifying the forecast distance to which each row of the dataset belongs. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as forecast distance."
    },
    "forecastOffsets": {
      "type": "array",
      "description": "An array of strings with names of a columns specifying row offsets. Columns values are used as offset or predictions to boost for models.  The data in this column must be pure numeric (e.g. not currency, date, length, etc.)."
    },
    "forecastWindowEnd": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end."
    },
    "forecastWindowStart": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start."
    },
    "gapDuration": {
      "type": "string",
      "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D)."
    },
    "holdoutDuration": {
      "type": "string",
      "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true."
    },
    "holdoutEndDate": {
      "type": "string",
      "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true."
    },
    "holdoutLevel": {
      "description": "The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as ``null``, then no holdout will be used in the project. However, the column must have exactly 2 values in order for this option to be valid"
    },
    "holdoutPct": {
      "type": "number",
      "description": "The percentage of the dataset to assign to the holdout set"
    },
    "holdoutStartDate": {
      "type": "string",
      "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true."
    },
    "includeBiasMitigationFeatureAsPredictorVariable": {
      "type": "boolean",
      "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task."
    },
    "incrementalLearningEarlyStoppingRounds": {
      "type": "integer",
      "description": "Early stopping rounds for the auto incremental learning service"
    },
    "incrementalLearningOnBestModel": {
      "type": "boolean",
      "description": "Automatically run incremental learning on the best model during Autopilot run."
    },
    "incrementalLearningOnlyMode": {
      "type": "boolean",
      "description": "Keep only models that support incremental learning during Autopilot run."
    },
    "isHoldoutModified": {
      "type": "boolean",
      "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user."
    },
    "majorityDownsamplingRate": {
      "type": "number",
      "description": "The percentage between 0 and 100 of the majority rows that should be kept. Must be specified only if using smart downsampling. If not specified, a default will be selected based on the dataset distribution. The chosen rate may not cause the majority class to become smaller than the minority class."
    },
    "metric": {
      "type": "string",
      "description": "The metric to use to select the best models. See `/api/v2/projects/(projectId)/features/metrics/` for the metrics that may be valid for a potential target.  Note that weighted metrics must be used with a weights column."
    },
    "minSecondaryValidationModelCount": {
      "type": "integer",
      "description": "Compute 'All backtest' scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default."
    },
    "mode": {
      "type": "string",
      "description": "The autopilot mode to use. Either 'quick', 'auto', 'manual', or 'comprehensive'.",
      "enum": [
        "0",
        "2",
        "4",
        "3",
        "auto",
        "manual",
        "comprehensive",
        "quick"
      ]
    },
    "modelSplits": {
      "type": "integer",
      "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data."
    },
    "monotonicDecreasingFeaturelistId": {
      "type": "string",
      "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target.  If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired."
    },
    "monotonicIncreasingFeaturelistId": {
      "type": "string",
      "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired."
    },
    "multiseriesIdColumns": {
      "type": "array",
      "description": "May be used only with time series projects. An array of the column names identifying  the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries <multiseries>` section of the time series documentation for more context."
    },
    "numberOfBacktests": {
      "type": "integer",
      "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations."
    },
    "numberOfIncrementalLearningIterationsBeforeBestModelSelection": {
      "type": "integer",
      "description": "Number of incremental_learning iterations before best model selection."
    },
    "offset": {
      "type": "array",
      "description": "An array of strings with names of a columns specifying row offsets.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values"
    },
    "onlyIncludeMonotonicBlueprints": {
      "type": "boolean",
      "description": "When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot."
    },
    "partitionKeyCols": {
      "type": "array",
      "description": "An array containing a single string - the name of the group partition column"
    },
    "periodicities": {
      "type": "array",
      "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'."
    },
    "positiveClass": {
      "description": "A value from the target column to use for the positive class. May only be specified for projects doing binary classification.If not specified, a positive class is selected automatically."
    },
    "preferableTargetValue": {
      "description": "A target value that should be treated as a positive outcome for the prediction. For example if we want to check gender discrimination for giving a loan and our target named ``is_bad``, then the positive outcome for the prediction would be ``No``, which means that the loan is good and that's what we treat as a preferable result for the loaner. Used and required only if *Bias & Fairness in AutoML* feature is enabled."
    },
    "prepareModelForDeployment": {
      "type": "boolean",
      "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning 'RECOMMENDED FOR DEPLOYMENT' label."
    },
    "primaryLocationColumn": {
      "type": "string",
      "description": "Primary geospatial location column."
    },
    "protectedFeatures": {
      "type": "array",
      "description": "A list of project feature to mark as protected for Bias metric calculation and Fairness correction. Used and required only if *Bias & Fairness in AutoML* feature is enabled."
    },
    "quantileLevel": {
      "type": "number",
      "description": "The quantile level between 0.01 and 0.99 for specifying the Quantile metric."
    },
    "quickrun": {
      "type": "boolean",
      "description": "(Deprecated): 'quick' should be used in the `mode` parameter instead of using this parameter. If set to `true`, autopilot mode will be set to 'quick'.Cannot be set to `true` when `mode` is set to 'comprehensive' or 'manual'."
    },
    "rateTopPctThreshold": {
      "type": "number",
      "description": "The percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric."
    },
    "relationshipsConfigurationId": {
      "type": "string",
      "description": "Relationships configuration id to be used for Feature Discovery projects."
    },
    "reps": {
      "type": "integer",
      "description": "The number of cross validation folds to use."
    },
    "responseCap": {
      "type": "number",
      "description": "Used to cap the maximum response of a model"
    },
    "runLeakageRemovedFeatureList": {
      "type": "boolean",
      "description": "Run Autopilot on Leakage Removed feature list (if exists)."
    },
    "sampleStepPct": {
      "type": "number",
      "description": "A float between 0 and 100 indicating the desired percentage of data to sample when training models in comprehensive Autopilot. Note: this only supported for comprehensive Autopilot and the specified value may be lowered in order to be compatible with the project's dataset and partition settings."
    },
    "scoringCodeOnly": {
      "type": "boolean",
      "description": "Keep only models that can be converted to scorable java code during Autopilot run."
    },
    "seed": {
      "type": "integer",
      "description": "A seed to use for randomization."
    },
    "segmentationTaskId": {
      "type": "string",
      "description": "Specifies the SegmentationTask that will be used for dividing the project up into multiple segmented projects."
    },
    "seriesId": {
      "type": "string",
      "description": "The name of a column specifying the series ID to which each row of the dataset belongs. Typically the series was used to derive the additional features, that are independent from each other. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as multi-series ID column."
    },
    "shapOnlyMode": {
      "type": "boolean",
      "description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible."
    },
    "smartDownsampled": {
      "type": "boolean",
      "description": "Whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects."
    },
    "stopWords": {
      "type": "array",
      "description": "A list of stop words to be used for text blueprints. Note: ``stop_words=True`` must be set in the blueprint preprocessing parameters for this list of stop words to actually be used during preprocessing."
    },
    "target": {
      "type": "string",
      "description": "The name of the target feature."
    },
    "targetType": {
      "type": "string",
      "description": "Used to specify the targetType to use for a project when it is ambiguous, i.e. a numeric target with a few unique values that could be used for either regression or multiclass.",
      "enum": [
        "Binary",
        "Regression",
        "Multiclass",
        "Multilabel"
      ]
    },
    "trainingLevel": {
      "description": "The value of the partition column indicating a row is part of the training set."
    },
    "treatAsExponential": {
      "type": "string",
      "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed.",
      "enum": [
        "auto",
        "never",
        "always"
      ]
    },
    "unsupervisedMode": {
      "type": "boolean",
      "description": "If True, unsupervised project (without target) will be created. ``target`` cannot be specified if ``unsupervisedMode`` is True."
    },
    "unsupervisedType": {
      "type": "string",
      "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
      "enum": [
        "anomaly",
        "clustering"
      ]
    },
    "useCrossSeriesFeatures": {
      "type": "boolean",
      "description": "Indicating if user wants to use cross-series features."
    },
    "useGpu": {
      "type": "boolean",
      "description": "Indicates whether project should use GPU workers"
    },
    "useProjectSettings": {
      "type": "boolean",
      "description": "Specifies whether datetime-partitioned project should use project settings (i.e. backtests configuration has been modified by the user)."
    },
    "userPartitionCol": {
      "type": "string",
      "description": "The name of the column containing the partition assignments."
    },
    "useSupervisedFeatureReduction": {
      "type": "boolean",
      "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features."
    },
    "useTimeSeries": {
      "type": "boolean",
      "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning."
    },
    "validationDuration": {
      "type": "string",
      "description": "The default 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. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular."
    },
    "validationLevel": {
      "description": "The value of the partition column indicating a row is part of the validation set."
    },
    "validationPct": {
      "type": "number",
      "description": "The percentage of the dataset to assign to the validation set"
    },
    "validationType": {
      "type": "string",
      "description": "The validation method to be used.  CV for cross validation or TVH for train-validation-holdout split.",
      "enum": [
        "CV",
        "TVH"
      ]
    },
    "weights": {
      "type": "string",
      "description": "The name of a column specifying row weights. The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values"
    },
    "windowsBasisUnit": {
      "type": "string",
      "description": "For time series projects only. 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.",
      "enum": [
        "MILLISECOND",
        "SECOND",
        "MINUTE",
        "HOUR",
        "DAY",
        "WEEK",
        "MONTH",
        "QUARTER",
        "YEAR",
        "ROW"
      ]
    }
  },
  "required": [
    "PCID",
    "projectId",
    "autopilotDataSelectionMethod",
    "onlyIncludeMonotonicBlueprints"
  ]
}

datarobot_projects_datetime_partitioning_create

Preview the fully specified datetime partitioning generated by the requested configuration. DEPRECATED API. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`aggregationType`	string	No	—	For multiseries projects only. The aggregation type to apply when creating cross-series features.
`allowPartialHistoryTimeSeriesPredictions`	boolean	No	—	Specifies whether the time series predictions can use partial historical data.
`autopilotClusterList`	any[]	No	—	A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.
`autopilotDataSamplingMethod`	string	No	—	Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to ‘latest’ for ‘rowCount’ dataSelectionMethod and to ‘random’ for ‘duration’.
`autopilotDataSelectionMethod`	string	No	—	The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.
`backtests`	any[]	No	—	An array specifying individual backtests.
`calendarId`	string	No	—	The ID of the calendar to be used in this project.
`clusteringBufferDisabled`	boolean	No	—	A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.
`crossSeriesGroupByColumns`	any[]	No	—	For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). 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.
`datetimePartitionColumn`	string	Yes	—	The date column that will be used as a datetime partition column.
`defaultToAPriori`	boolean	No	—	Renamed to `defaultToKnownInAdvance`.
`defaultToDoNotDerive`	boolean	No	—	For time series 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.
`defaultToKnownInAdvance`	boolean	No	—	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 :ref:`Time Series Overview <time_series_overview>` for more context.
`differencingMethod`	string	No	—	For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.
`disableHoldout`	boolean	No	—	Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.
`featureDerivationWindowEnd`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.
`featureDerivationWindowStart`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.
`featureSettings`	any[]	No	—	An array specifying per feature settings. Features can be left unspecified.
`forecastWindowEnd`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.
`forecastWindowStart`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.
`gapDuration`	string	No	—	The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).
`holdoutDuration`	string	No	—	The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.
`holdoutEndDate`	string	No	—	The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.
`holdoutStartDate`	string	No	—	The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.
`isHoldoutModified`	boolean	No	—	A boolean value indicating whether holdout settings (start/end dates) have been modified by user.
`modelSplits`	integer	No	—	Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data.
`multiseriesIdColumns`	any[]	No	—	May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries <multiseries>` section of the time series documentation for more context.
`numberOfBacktests`	integer	No	—	The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
`periodicities`	any[]	No	—	A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter ‘differencing_method’ will default to ‘seasonal’ if not provided or ‘auto’.
`treatAsExponential`	string	No	—	For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.
`unsupervisedMode`	boolean	No	—	A boolean value indicating whether an unsupervised project should be created.
`unsupervisedType`	string	No	—	The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.
`useCrossSeriesFeatures`	boolean	No	—	For multiseries projects only. Indicating whether to use cross-series features.
`useSupervisedFeatureReduction`	boolean	No	—	When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.
`useTimeSeries`	boolean	No	—	A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
`validationDuration`	string	No	—	The default 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. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.
`windowsBasisUnit`	string	No	—	For time series projects only. 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.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "aggregationType": {
      "type": "string",
      "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
      "enum": [
        "total",
        "average"
      ]
    },
    "allowPartialHistoryTimeSeriesPredictions": {
      "type": "boolean",
      "description": "Specifies whether the time series predictions can use partial historical data."
    },
    "autopilotClusterList": {
      "type": "array",
      "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`."
    },
    "autopilotDataSamplingMethod": {
      "type": "string",
      "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.",
      "enum": [
        "random",
        "latest"
      ]
    },
    "autopilotDataSelectionMethod": {
      "type": "string",
      "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
      "enum": [
        "duration",
        "rowCount"
      ]
    },
    "backtests": {
      "type": "array",
      "description": "An array specifying individual backtests."
    },
    "calendarId": {
      "type": "string",
      "description": "The ID of the calendar to be used in this project."
    },
    "clusteringBufferDisabled": {
      "type": "boolean",
      "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project."
    },
    "crossSeriesGroupByColumns": {
      "type": "array",
      "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). 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."
    },
    "datetimePartitionColumn": {
      "type": "string",
      "description": "The date column that will be used as a datetime partition column."
    },
    "defaultToAPriori": {
      "type": "boolean",
      "description": "Renamed to `defaultToKnownInAdvance`."
    },
    "defaultToDoNotDerive": {
      "type": "boolean",
      "description": "For time series 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."
    },
    "defaultToKnownInAdvance": {
      "type": "boolean",
      "description": "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 :ref:`Time Series Overview <time_series_overview>` for more context."
    },
    "differencingMethod": {
      "type": "string",
      "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.",
      "enum": [
        "auto",
        "none",
        "simple",
        "seasonal"
      ]
    },
    "disableHoldout": {
      "type": "boolean",
      "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set."
    },
    "featureDerivationWindowEnd": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end."
    },
    "featureDerivationWindowStart": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin."
    },
    "featureSettings": {
      "type": "array",
      "description": "An array specifying per feature settings. Features can be left unspecified."
    },
    "forecastWindowEnd": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end."
    },
    "forecastWindowStart": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start."
    },
    "gapDuration": {
      "type": "string",
      "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D)."
    },
    "holdoutDuration": {
      "type": "string",
      "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true."
    },
    "holdoutEndDate": {
      "type": "string",
      "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true."
    },
    "holdoutStartDate": {
      "type": "string",
      "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true."
    },
    "isHoldoutModified": {
      "type": "boolean",
      "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user."
    },
    "modelSplits": {
      "type": "integer",
      "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data."
    },
    "multiseriesIdColumns": {
      "type": "array",
      "description": "May be used only with time series projects. An array of the column names identifying  the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries <multiseries>` section of the time series documentation for more context."
    },
    "numberOfBacktests": {
      "type": "integer",
      "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations."
    },
    "periodicities": {
      "type": "array",
      "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'."
    },
    "treatAsExponential": {
      "type": "string",
      "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.",
      "enum": [
        "auto",
        "never",
        "always"
      ]
    },
    "unsupervisedMode": {
      "type": "boolean",
      "description": "A boolean value indicating whether an unsupervised project should be created."
    },
    "unsupervisedType": {
      "type": "string",
      "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
      "enum": [
        "anomaly",
        "clustering"
      ]
    },
    "useCrossSeriesFeatures": {
      "type": "boolean",
      "description": "For multiseries projects only. Indicating whether to use cross-series features."
    },
    "useSupervisedFeatureReduction": {
      "type": "boolean",
      "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features."
    },
    "useTimeSeries": {
      "type": "boolean",
      "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning."
    },
    "validationDuration": {
      "type": "string",
      "description": "The default 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. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular."
    },
    "windowsBasisUnit": {
      "type": "string",
      "description": "For time series projects only. 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.",
      "enum": [
        "MILLISECOND",
        "SECOND",
        "MINUTE",
        "HOUR",
        "DAY",
        "WEEK",
        "MONTH",
        "QUARTER",
        "YEAR",
        "ROW"
      ]
    }
  },
  "required": [
    "PCID",
    "projectId",
    "datetimePartitionColumn"
  ]
}

datarobot_projects_datetime_partitioning_list

Retrieve datetime partitioning configuration. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    }
  },
  "required": [
    "PCID",
    "projectId"
  ]
}

datarobot_projects_deleted_projects_count_list

Count soft-deleted projects. Parameters:

Parameter	Type	Required	Default	Description
`searchFor`	string	No	—	Project or dataset name to filter by.
`creator`	string	No	—	Creator ID to filter projects by
`organization`	string	No	—	ID of organization that projects should belong to. Given project belongs to the organization the user who created the project is part of that organization.If there are no users in organization, then no projects will match the query.
`deletedBefore`	string	No	—	ISO-8601 formatted date projects were deleted before
`deletedAfter`	string	No	—	ISO-8601 formatted date projects were deleted after
`projectId`	string	No	—	Project ID to search
`limit`	integer	No	—	Count deleted projects until specified value reached.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "searchFor": {
      "type": "string",
      "description": "Project or dataset name to filter by."
    },
    "creator": {
      "type": "string",
      "description": "Creator ID to filter projects by"
    },
    "organization": {
      "type": "string",
      "description": "ID of organization that projects should belong to. Given project belongs to the organization the user who created the project is part of that organization.If there are no users in organization, then no projects will match the query."
    },
    "deletedBefore": {
      "type": "string",
      "description": "ISO-8601 formatted date projects were deleted before"
    },
    "deletedAfter": {
      "type": "string",
      "description": "ISO-8601 formatted date projects were deleted after"
    },
    "projectId": {
      "type": "string",
      "description": "Project ID to search"
    },
    "limit": {
      "type": "integer",
      "description": "Count deleted projects until specified value reached."
    }
  },
  "required": [
    "PCID"
  ]
}

datarobot_projects_deleted_projects_list

Retrieve the list of soft-deleted projects. Parameters:

Parameter	Type	Required	Default	Description
`searchFor`	string	No	—	Project or dataset name to filter by.
`creator`	string	No	—	Creator ID to filter projects by
`organization`	string	No	—	ID of organization that projects should belong to. Given project belongs to the organization the user who created the project is part of that organization.If there are no users in organization, then no projects will match the query.
`deletedBefore`	string	No	—	ISO-8601 formatted date projects were deleted before
`deletedAfter`	string	No	—	ISO-8601 formatted date projects were deleted after
`projectId`	string	No	—	Project ID to search
`limit`	integer	No	—	At most this many results are returned.
`offset`	integer	No	—	This many results will be skipped.
`orderBy`	string	No	—	Order deleted projects by

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "searchFor": {
      "type": "string",
      "description": "Project or dataset name to filter by."
    },
    "creator": {
      "type": "string",
      "description": "Creator ID to filter projects by"
    },
    "organization": {
      "type": "string",
      "description": "ID of organization that projects should belong to. Given project belongs to the organization the user who created the project is part of that organization.If there are no users in organization, then no projects will match the query."
    },
    "deletedBefore": {
      "type": "string",
      "description": "ISO-8601 formatted date projects were deleted before"
    },
    "deletedAfter": {
      "type": "string",
      "description": "ISO-8601 formatted date projects were deleted after"
    },
    "projectId": {
      "type": "string",
      "description": "Project ID to search"
    },
    "limit": {
      "type": "integer",
      "description": "At most this many results are returned."
    },
    "offset": {
      "type": "integer",
      "description": "This many results will be skipped."
    },
    "orderBy": {
      "type": "string",
      "description": "Order deleted projects by",
      "enum": [
        "projectId",
        "projectName",
        "datasetName",
        "deletedOn",
        "deletedBy",
        "creator",
        "-projectId",
        "-projectName",
        "-datasetName",
        "-deletedOn",
        "-deletedBy",
        "-creator"
      ]
    }
  },
  "required": [
    "PCID"
  ]
}

datarobot_projects_deleted_projects_patch

Recover soft-deleted project. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`action`	string	Yes	—	Action to perform on a project

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "action": {
      "type": "string",
      "description": "Action to perform on a project",
      "enum": [
        "undelete"
      ]
    }
  },
  "required": [
    "PCID",
    "projectId",
    "action"
  ]
}

datarobot_projects_external_time_series_baseline_data_validation_jobs_create

Validate baseline data. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`backtests`	any[]	No	—	An array of the configured backtests.
`catalogVersionId`	string	Yes	—	The version ID of the external baseline data item in the AI catalog.
`datetimePartitionColumn`	string	Yes	—	The date column that will be used as the datetime partition column for the specified project.
`forecastWindowEnd`	integer	Yes	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.
`forecastWindowStart`	integer	Yes	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.
`holdoutEndDate`	string	No	—	The end date of holdout scoring data.
`holdoutStartDate`	string	No	—	The start date of holdout scoring data.
`multiseriesIdColumns`	any[]	No	—	An array of column names identifying the multiseries ID column(s)to use to identify series within the data. Must match the multiseries ID column(s) for the specified project. Currently, only one multiseries ID column may be specified.
`target`	string	Yes	—	The selected target of the specified project.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "backtests": {
      "type": "array",
      "description": "An array of the configured backtests."
    },
    "catalogVersionId": {
      "type": "string",
      "description": "The version ID of the external baseline data item in the AI catalog."
    },
    "datetimePartitionColumn": {
      "type": "string",
      "description": "The date column that will be used as the datetime partition column for the specified project."
    },
    "forecastWindowEnd": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end."
    },
    "forecastWindowStart": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start."
    },
    "holdoutEndDate": {
      "type": "string",
      "description": "The end date of holdout scoring data."
    },
    "holdoutStartDate": {
      "type": "string",
      "description": "The start date of holdout scoring data."
    },
    "multiseriesIdColumns": {
      "type": "array",
      "description": "An array of column names identifying the multiseries ID column(s)to use to identify series within the data. Must match the multiseries ID column(s) for the specified project. Currently, only one multiseries ID column may be specified."
    },
    "target": {
      "type": "string",
      "description": "The selected target of the specified project."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "catalogVersionId",
    "datetimePartitionColumn",
    "forecastWindowEnd",
    "forecastWindowStart",
    "target"
  ]
}

datarobot_projects_external_time_series_baseline_data_validation_jobs_retrieve

Retrieve the baseline validation job. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project to retrieve the validation job information from.
`baselineValidationJobId`	string	Yes	—	The ID for the validation job.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project to retrieve the validation job information from."
    },
    "baselineValidationJobId": {
      "type": "string",
      "description": "The ID for the validation job."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "baselineValidationJobId"
  ]
}

datarobot_projects_hdfs_projects_create

Create a project from an HDFS file source. Parameters:

Parameter	Type	Required	Default	Description
`password`	string	No	—	Password for authenticating to HDFS using Kerberos. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.
`port`	integer	No	—	Port of the WebHDFS Namenode server. If not specified, defaults to HDFS default port 50070.
`projectName`	string	No	—	Name of the project to be created. If not specified, project name will be based on the file name.
`url`	string	Yes	—	URL of the WebHDFS resource. Represent the file using the `hdfs://` protocol marker (for example, `hdfs:///tmp/somedataset.csv`).
`user`	string	No	—	Username for authenticating to HDFS using Kerberos

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "password": {
      "type": "string",
      "description": "Password for authenticating to HDFS using Kerberos. The password will be encrypted on the server side in scope of HTTP request and never saved or stored."
    },
    "port": {
      "type": "integer",
      "description": "Port of the WebHDFS Namenode server. If not specified, defaults to HDFS default port 50070."
    },
    "projectName": {
      "type": "string",
      "description": "Name of the project to be created. If not specified, project name will be based on the file name."
    },
    "url": {
      "type": "string",
      "description": "URL of the WebHDFS resource. Represent the file using the `hdfs://` protocol marker (for example,  `hdfs:///tmp/somedataset.csv`)."
    },
    "user": {
      "type": "string",
      "description": "Username for authenticating to HDFS using Kerberos"
    }
  },
  "required": [
    "PCID",
    "url"
  ]
}

datarobot_projects_jobs_delete

Cancel a job. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`jobId`	string	Yes	—	The job ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "jobId": {
      "type": "string",
      "description": "The job ID."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "jobId"
  ]
}

datarobot_projects_jobs_list

List project jobs. Parameters:

Parameter	Type	Required	Default	Description
`status`	string	No	—	If provided, only jobs with the same status will be included in the results; otherwise, queued and inprogress jobs (but not errored jobs) will be returned.
`projectId`	string	Yes	—	The project ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "status": {
      "type": "string",
      "description": "If provided, only jobs with the same status will be included in the results; otherwise, queued and inprogress jobs (but not errored jobs) will be returned.",
      "enum": [
        "queue",
        "inprogress",
        "error"
      ]
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    }
  },
  "required": [
    "PCID",
    "projectId"
  ]
}

datarobot_projects_jobs_retrieve

Get a job. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`jobId`	string	Yes	—	The job ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "jobId": {
      "type": "string",
      "description": "The job ID."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "jobId"
  ]
}

datarobot_projects_multiseries_ids_cross_series_properties_list

Retrieve eligible cross-series group-by columns. Parameters:

Parameter	Type	Required	Default	Description
`crossSeriesGroupByColumns`	any[]	No	—	The names of the columns to retrieve the validation status for. If not specified, all eligible columns will be returned.
`projectId`	string	Yes	—	The project to retrieve cross-series group-by columns for.
`multiseriesId`	string	Yes	—	The name of the column to be used as the multiseries ID column.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "crossSeriesGroupByColumns": {
      "type": "array",
      "description": "The names of the columns to retrieve the validation status for. If not specified, all eligible columns will be returned."
    },
    "projectId": {
      "type": "string",
      "description": "The project to retrieve cross-series group-by columns for."
    },
    "multiseriesId": {
      "type": "string",
      "description": "The name of the column to be used as the multiseries ID column."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "multiseriesId"
  ]
}

datarobot_projects_multiseries_names_list

List the names of a multiseries project. Parameters:

Parameter	Type	Required	Default	Description
`offset`	integer	No	—	The number of results to skip.
`limit`	integer	No	—	At most this many results are returned. The default may change without notice.
`projectId`	string	Yes	—	The project ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "offset": {
      "type": "integer",
      "description": "The number of results to skip."
    },
    "limit": {
      "type": "integer",
      "description": "At most this many results are returned. The default may change without notice."
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    }
  },
  "required": [
    "PCID",
    "projectId"
  ]
}

datarobot_projects_multiseries_properties_create

Detect multiseries properties. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`datetimePartitionColumn`	string	Yes	—	The date column that will be used to perform detection and validation for.
`multiseriesIdColumns`	any[]	No	—	The list of one or more names of potential multiseries ID columns. If not provided, all numerical and categorical columns are used.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "datetimePartitionColumn": {
      "type": "string",
      "description": "The date column that will be used to perform detection and validation for."
    },
    "multiseriesIdColumns": {
      "type": "array",
      "description": "The list of one or more names of potential multiseries ID columns. If not provided, all numerical and categorical columns are used."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "datetimePartitionColumn"
  ]
}

datarobot_projects_optimized_datetime_partitionings_create

Create an optimized datetime partitioning configuration using the target. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`aggregationType`	string	No	—	For multiseries projects only. The aggregation type to apply when creating cross-series features.
`allowPartialHistoryTimeSeriesPredictions`	boolean	No	—	Specifies whether the time series predictions can use partial historical data.
`autopilotClusterList`	any[]	No	—	A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.
`autopilotDataSamplingMethod`	string	No	—	Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to ‘latest’ for ‘rowCount’ dataSelectionMethod and to ‘random’ for ‘duration’.
`autopilotDataSelectionMethod`	string	No	—	The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.
`backtests`	any[]	No	—	An array specifying individual backtests.
`calendarId`	string	No	—	The ID of the calendar to be used in this project.
`clusteringBufferDisabled`	boolean	No	—	A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.
`crossSeriesGroupByColumns`	any[]	No	—	For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). 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.
`datetimePartitionColumn`	string	Yes	—	The date column that will be used as a datetime partition column.
`defaultToAPriori`	boolean	No	—	Renamed to `defaultToKnownInAdvance`.
`defaultToDoNotDerive`	boolean	No	—	For time series 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.
`defaultToKnownInAdvance`	boolean	No	—	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 :ref:`Time Series Overview <time_series_overview>` for more context.
`differencingMethod`	string	No	—	For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.
`disableHoldout`	boolean	No	—	Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.
`featureDerivationWindowEnd`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.
`featureDerivationWindowStart`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.
`featureSettings`	any[]	No	—	An array specifying per feature settings. Features can be left unspecified.
`forecastWindowEnd`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.
`forecastWindowStart`	integer	No	—	For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.
`gapDuration`	string	No	—	The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).
`holdoutDuration`	string	No	—	The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.
`holdoutEndDate`	string	No	—	The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.
`holdoutStartDate`	string	No	—	The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.
`isHoldoutModified`	boolean	No	—	A boolean value indicating whether holdout settings (start/end dates) have been modified by user.
`modelSplits`	integer	No	—	Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data.
`multiseriesIdColumns`	any[]	No	—	May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries <multiseries>` section of the time series documentation for more context.
`numberOfBacktests`	integer	No	—	The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.
`periodicities`	any[]	No	—	A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter ‘differencing_method’ will default to ‘seasonal’ if not provided or ‘auto’.
`target`	string	No	—	The name of the target column.
`treatAsExponential`	string	No	—	For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.
`unsupervisedMode`	boolean	No	—	A boolean value indicating whether an unsupervised project should be created.
`unsupervisedType`	string	No	—	The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.
`useCrossSeriesFeatures`	boolean	No	—	For multiseries projects only. Indicating whether to use cross-series features.
`useSupervisedFeatureReduction`	boolean	No	—	When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.
`useTimeSeries`	boolean	No	—	A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.
`validationDuration`	string	No	—	The default 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. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.
`windowsBasisUnit`	string	No	—	For time series projects only. 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.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "aggregationType": {
      "type": "string",
      "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
      "enum": [
        "total",
        "average"
      ]
    },
    "allowPartialHistoryTimeSeriesPredictions": {
      "type": "boolean",
      "description": "Specifies whether the time series predictions can use partial historical data."
    },
    "autopilotClusterList": {
      "type": "array",
      "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`."
    },
    "autopilotDataSamplingMethod": {
      "type": "string",
      "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.",
      "enum": [
        "random",
        "latest"
      ]
    },
    "autopilotDataSelectionMethod": {
      "type": "string",
      "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
      "enum": [
        "duration",
        "rowCount"
      ]
    },
    "backtests": {
      "type": "array",
      "description": "An array specifying individual backtests."
    },
    "calendarId": {
      "type": "string",
      "description": "The ID of the calendar to be used in this project."
    },
    "clusteringBufferDisabled": {
      "type": "boolean",
      "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project."
    },
    "crossSeriesGroupByColumns": {
      "type": "array",
      "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). 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."
    },
    "datetimePartitionColumn": {
      "type": "string",
      "description": "The date column that will be used as a datetime partition column."
    },
    "defaultToAPriori": {
      "type": "boolean",
      "description": "Renamed to `defaultToKnownInAdvance`."
    },
    "defaultToDoNotDerive": {
      "type": "boolean",
      "description": "For time series 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."
    },
    "defaultToKnownInAdvance": {
      "type": "boolean",
      "description": "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 :ref:`Time Series Overview <time_series_overview>` for more context."
    },
    "differencingMethod": {
      "type": "string",
      "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.",
      "enum": [
        "auto",
        "none",
        "simple",
        "seasonal"
      ]
    },
    "disableHoldout": {
      "type": "boolean",
      "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set."
    },
    "featureDerivationWindowEnd": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end."
    },
    "featureDerivationWindowStart": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin."
    },
    "featureSettings": {
      "type": "array",
      "description": "An array specifying per feature settings. Features can be left unspecified."
    },
    "forecastWindowEnd": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end."
    },
    "forecastWindowStart": {
      "type": "integer",
      "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start."
    },
    "gapDuration": {
      "type": "string",
      "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D)."
    },
    "holdoutDuration": {
      "type": "string",
      "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true."
    },
    "holdoutEndDate": {
      "type": "string",
      "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true."
    },
    "holdoutStartDate": {
      "type": "string",
      "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true."
    },
    "isHoldoutModified": {
      "type": "boolean",
      "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user."
    },
    "modelSplits": {
      "type": "integer",
      "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data."
    },
    "multiseriesIdColumns": {
      "type": "array",
      "description": "May be used only with time series projects. An array of the column names identifying  the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries <multiseries>` section of the time series documentation for more context."
    },
    "numberOfBacktests": {
      "type": "integer",
      "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations."
    },
    "periodicities": {
      "type": "array",
      "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'."
    },
    "target": {
      "type": "string",
      "description": "The name of the target column."
    },
    "treatAsExponential": {
      "type": "string",
      "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.",
      "enum": [
        "auto",
        "never",
        "always"
      ]
    },
    "unsupervisedMode": {
      "type": "boolean",
      "description": "A boolean value indicating whether an unsupervised project should be created."
    },
    "unsupervisedType": {
      "type": "string",
      "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
      "enum": [
        "anomaly",
        "clustering"
      ]
    },
    "useCrossSeriesFeatures": {
      "type": "boolean",
      "description": "For multiseries projects only. Indicating whether to use cross-series features."
    },
    "useSupervisedFeatureReduction": {
      "type": "boolean",
      "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features."
    },
    "useTimeSeries": {
      "type": "boolean",
      "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning."
    },
    "validationDuration": {
      "type": "string",
      "description": "The default 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. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular."
    },
    "windowsBasisUnit": {
      "type": "string",
      "description": "For time series projects only. 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.",
      "enum": [
        "MILLISECOND",
        "SECOND",
        "MINUTE",
        "HOUR",
        "DAY",
        "WEEK",
        "MONTH",
        "QUARTER",
        "YEAR",
        "ROW"
      ]
    }
  },
  "required": [
    "PCID",
    "projectId",
    "datetimePartitionColumn"
  ]
}

datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_input_list

Retrieve the optimized datetime partitioning input. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`datetimePartitioningId`	string	Yes	—	The ID of the datetime partitioning to retrieve.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "datetimePartitioningId": {
      "type": "string",
      "description": "The ID of the datetime partitioning to retrieve."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "datetimePartitioningId"
  ]
}

datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_log_file_list

Retrieve a text file containing the datetime partitioning log Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`datetimePartitioningId`	string	Yes	—	The ID of the datetime partitioning to retrieve.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "datetimePartitioningId": {
      "type": "string",
      "description": "The ID of the datetime partitioning to retrieve."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "datetimePartitioningId"
  ]
}

datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_log_list

Retrieve the datetime partitioning log and log length as JSON Parameters:

Parameter	Type	Required	Default	Description
`offset`	integer	No	—	The number of results to skip.
`limit`	integer	No	—	At most this many results are returned. The default may change without notice.
`projectId`	string	Yes	—	The project ID.
`datetimePartitioningId`	string	Yes	—	The ID of the datetime partitioning to retrieve.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "offset": {
      "type": "integer",
      "description": "The number of results to skip."
    },
    "limit": {
      "type": "integer",
      "description": "At most this many results are returned. The default may change without notice."
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "datetimePartitioningId": {
      "type": "string",
      "description": "The ID of the datetime partitioning to retrieve."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "datetimePartitioningId"
  ]
}

datarobot_projects_optimized_datetime_partitionings_list

Lists all created optimized datetime partitioning configurations. Parameters:

Parameter	Type	Required	Default	Description
`offset`	integer	No	—	This many results will be skipped.
`limit`	integer	Yes	—	At most this many results are returned.
`projectId`	string	Yes	—	The project ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "offset": {
      "type": "integer",
      "description": "This many results will be skipped."
    },
    "limit": {
      "type": "integer",
      "description": "At most this many results are returned."
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    }
  },
  "required": [
    "PCID",
    "limit",
    "projectId"
  ]
}

datarobot_projects_optimized_datetime_partitionings_retrieve

Retrieve the optimized datetime partitioning configuration. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`datetimePartitioningId`	string	Yes	—	The ID of the datetime partitioning to retrieve.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "datetimePartitioningId": {
      "type": "string",
      "description": "The ID of the datetime partitioning to retrieve."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "datetimePartitioningId"
  ]
}

datarobot_projects_project_cleanup_jobs_create

Schedule project permadelete job. Parameters:

Parameter	Type	Required	Default	Description
`creator`	string	No	—	Creator ID to filter projects by
`deletedAfter`	string	No	—	ISO-8601 formatted date projects were deleted after
`deletedBefore`	string	No	—	ISO-8601 formatted date projects were deleted before
`limit`	integer	No	—	At most this many projects are deleted.
`offset`	integer	No	—	This many projects will be skipped.
`organization`	string	No	—	ID of organization that projects should belong to
`projectIds`	any[]	No	—	The list of project IDs to delete permanently.
`searchFor`	string	No	—	Project or dataset name to filter by.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "creator": {
      "type": "string",
      "description": "Creator ID to filter projects by"
    },
    "deletedAfter": {
      "type": "string",
      "description": "ISO-8601 formatted date projects were deleted after"
    },
    "deletedBefore": {
      "type": "string",
      "description": "ISO-8601 formatted date projects were deleted before"
    },
    "limit": {
      "type": "integer",
      "description": "At most this many projects are deleted."
    },
    "offset": {
      "type": "integer",
      "description": "This many projects will be skipped."
    },
    "organization": {
      "type": "string",
      "description": "ID of organization that projects should belong to"
    },
    "projectIds": {
      "type": "array",
      "description": "The list of project IDs to delete permanently."
    },
    "searchFor": {
      "type": "string",
      "description": "Project or dataset name to filter by."
    }
  },
  "required": [
    "PCID"
  ]
}

datarobot_projects_project_cleanup_jobs_delete

Cancel scheduled project permadelete job. Parameters:

Parameter	Type	Required	Default	Description
`statusId`	string	Yes	—	The ID of the status object.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "statusId": {
      "type": "string",
      "description": "The ID of the status object."
    }
  },
  "required": [
    "PCID",
    "statusId"
  ]
}

datarobot_projects_project_cleanup_jobs_download_list

Download a projects permadeletion report. Parameters:

Parameter	Type	Required	Default	Description
`statusId`	string	Yes	—	The ID of the status object.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "statusId": {
      "type": "string",
      "description": "The ID of the status object."
    }
  },
  "required": [
    "PCID",
    "statusId"
  ]
}

datarobot_projects_project_cleanup_jobs_list

Retrieve project permadelete job status.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    }
  },
  "required": [
    "PCID"
  ]
}

datarobot_projects_project_cleanup_jobs_retrieve

Retrieve project permadelete job status. Parameters:

Parameter	Type	Required	Default	Description
`statusId`	string	Yes	—	The ID of the status object.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "statusId": {
      "type": "string",
      "description": "The ID of the status object."
    }
  },
  "required": [
    "PCID",
    "statusId"
  ]
}

datarobot_projects_project_cleanup_jobs_summary_list

Get a projects cleanup jobs summary. Parameters:

Parameter	Type	Required	Default	Description
`statusId`	string	Yes	—	The ID of the status object.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "statusId": {
      "type": "string",
      "description": "The ID of the status object."
    }
  },
  "required": [
    "PCID",
    "statusId"
  ]
}

datarobot_projects_project_clones_create

Clone a project. Parameters:

Parameter	Type	Required	Default	Description
`copyOptions`	boolean	No	—	Whether all project options should be copied to the cloned project.
`projectId`	string	Yes	—	The ID of the project to clone.
`projectName`	string	No	—	The name of the project to be created.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "copyOptions": {
      "type": "boolean",
      "description": "Whether all project options should be copied to the cloned project."
    },
    "projectId": {
      "type": "string",
      "description": "The ID of the project to clone."
    },
    "projectName": {
      "type": "string",
      "description": "The name of the project to be created."
    }
  },
  "required": [
    "PCID",
    "projectId"
  ]
}

datarobot_projects_projects_create

Create a project. Parameters:

Parameter	Type	Required	Default	Description
`credentialData`	object	No	—	The credentials to authenticate with the database, to be used instead of credential ID. Can only be used along with datasetId or dataSourceId.
`credentialId`	string	No	—	The ID of the set of credentials to authenticate with the database. Can only be used along with datasetId or dataSourceId.
`datasetId`	string	No	—	The ID of the dataset entry to use for the project dataset.
`datasetVersionId`	string	No	—	Only used when also providing a datasetId, and specifies the the ID of the dataset version to use for the project dataset. If not specified, the latest version associated with the dataset ID is used.
`dataSourceId`	string	No	—	Identifier for the data source to retrieve.
`password`	string	No	—	The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead.
`projectName`	string	No	—	The name of the project to be created. If not specified, ‘Untitled Project’ will be used for database connections and file name will be used as the project name.
`recipeId`	string	No	—	The ID of the wrangling recipe that will be used for project creation.
`url`	string	No	—	The URL to download the dataset used to create the project.
`useKerberos`	boolean	No	—	If true, use Kerberos authentication for database authentication. Default is false. Can only be used along with datasetId or dataSourceId.
`user`	string	No	—	The username for database authentication. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "credentialData": {
      "description": "The credentials to authenticate with the database, to be used instead of credential ID. Can only be used along with datasetId or dataSourceId."
    },
    "credentialId": {
      "type": "string",
      "description": "The ID of the set of credentials to authenticate with the database. Can only be used along with datasetId or dataSourceId."
    },
    "datasetId": {
      "type": "string",
      "description": "The ID of the dataset entry to use for the project dataset."
    },
    "datasetVersionId": {
      "type": "string",
      "description": "Only used when also providing a datasetId, and specifies the the ID of the dataset version to use for the project dataset. If not specified, the latest version associated with the dataset ID is used."
    },
    "dataSourceId": {
      "type": "string",
      "description": "Identifier for the data source to retrieve."
    },
    "password": {
      "type": "string",
      "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead."
    },
    "projectName": {
      "type": "string",
      "description": "The name of the project to be created. If not specified, 'Untitled Project' will be used for database connections and file name will be used as the project name."
    },
    "recipeId": {
      "type": "string",
      "description": "The ID of the wrangling recipe that will be used for project creation."
    },
    "url": {
      "type": "string",
      "description": "The URL to download the dataset used to create the project."
    },
    "useKerberos": {
      "type": "boolean",
      "description": "If true, use Kerberos authentication for database authentication. Default is false. Can only be used along with datasetId or dataSourceId."
    },
    "user": {
      "type": "string",
      "description": "The username for database authentication. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead."
    }
  },
  "required": [
    "PCID"
  ]
}

datarobot_projects_projects_delete

Delete a project. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    }
  },
  "required": [
    "PCID",
    "projectId"
  ]
}

datarobot_projects_projects_list

List projects. Parameters:

Parameter	Type	Required	Default	Description
`projectName`	string	No	—	if provided will filter returned projects for projects with matching names
`projectId`	any[]	No	—	if provided will filter returned projects with matching project IDs
`orderBy`	string	No	—	if provided will order the results by this field
`featureDiscovery`	string	No	—	Return only feature discovery projects
`offset`	integer	No	—	This many results will be skipped.
`limit`	integer	No	—	At most this many results are returned.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectName": {
      "type": "string",
      "description": "if provided will filter returned projects for projects with matching names"
    },
    "projectId": {
      "type": "array",
      "description": "if provided will filter returned projects with matching project IDs"
    },
    "orderBy": {
      "type": "string",
      "description": "if provided will order the results by this field",
      "enum": [
        "projectName",
        "-projectName"
      ]
    },
    "featureDiscovery": {
      "type": "string",
      "description": "Return only feature discovery projects",
      "enum": [
        "false",
        "False",
        "true",
        "True"
      ]
    },
    "offset": {
      "type": "integer",
      "description": "This many results will be skipped."
    },
    "limit": {
      "type": "integer",
      "description": "At most this many results are returned."
    }
  },
  "required": [
    "PCID"
  ]
}

datarobot_projects_projects_retrieve

Get project. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    }
  },
  "required": [
    "PCID",
    "projectId"
  ]
}

datarobot_projects_projects_update

Update a project. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`gpuWorkerCount`	integer	No	—	The desired new number of gpu workers if the number of gpu workers should be changed. Must not exceed the number of gpu workers available to the user. `0` is allowed. `-1` requests the maximum number available to the user.
`holdoutUnlocked`	string	No	—	If specified, the holdout will be unlocked; note that the holdout cannot be relocked after unlocking
`projectDescription`	string	No	—	The new description of the project, if the description should be updated.
`projectName`	string	No	—	The new name of the project, if it should be renamed.
`workerCount`	integer	No	—	The desired new number of workers if the number of workers should be changed. Must not exceed the number of workers available to the user. `0` is allowed. (New in version v2.14) `-1` requests the maximum number available to the user.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "gpuWorkerCount": {
      "type": "integer",
      "description": "The desired new number of gpu workers if the             number of gpu workers should be changed.             Must not exceed the number of gpu workers available to the user. `0` is allowed.             `-1` requests the maximum number available to the user."
    },
    "holdoutUnlocked": {
      "type": "string",
      "description": "If specified, the holdout will be unlocked;             note that the holdout cannot be relocked after unlocking",
      "enum": [
        "True"
      ]
    },
    "projectDescription": {
      "type": "string",
      "description": "The new description of the project, if the description should be updated."
    },
    "projectName": {
      "type": "string",
      "description": "The new name of the project, if it should be renamed."
    },
    "workerCount": {
      "type": "integer",
      "description": "The desired new number of workers if the             number of workers should be changed.             Must not exceed the number of workers available to the user. `0` is allowed.             (New in version v2.14) `-1` requests the maximum number available to the user."
    }
  },
  "required": [
    "PCID",
    "projectId"
  ]
}

datarobot_projects_segmentation_task_job_results_retrieve

Retrieve segmentation task statuses. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`segmentationTaskId`	string	Yes	—	The ID of the segmentation task to check the status of.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "segmentationTaskId": {
      "type": "string",
      "description": "The ID of the segmentation task to check the status of."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "segmentationTaskId"
  ]
}

datarobot_projects_segmentation_tasks_create

Create segmentation tasks. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`datetimePartitionColumn`	string	No	—	The date column that will be used to identify the date in time series segmentation.
`modelPackageId`	string	No	—	The model package ID for using an external model registry package.
`multiseriesIdColumns`	any[]	No	—	The list of one or more names of multiseries ID columns.
`target`	string	Yes	—	The target for the dataset.
`useAutomatedSegmentation`	boolean	No	—	Enable the use of automated segmentation tasks.
`userDefinedSegmentIdColumns`	any[]	No	—	The list of one or more names of columns to be used for user-defined business rule segmentations.
`useTimeSeries`	boolean	No	—	Enable time series-based segmentation tasks.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "datetimePartitionColumn": {
      "type": "string",
      "description": "The date column that will be used to identify the date in time series segmentation."
    },
    "modelPackageId": {
      "type": "string",
      "description": "The model package ID for using an external model registry package."
    },
    "multiseriesIdColumns": {
      "type": "array",
      "description": "The list of one or more names of multiseries ID columns."
    },
    "target": {
      "type": "string",
      "description": "The target for the dataset."
    },
    "useAutomatedSegmentation": {
      "type": "boolean",
      "description": "Enable the use of automated segmentation tasks."
    },
    "userDefinedSegmentIdColumns": {
      "type": "array",
      "description": "The list of one or more names of columns to be used for user-defined business rule segmentations."
    },
    "useTimeSeries": {
      "type": "boolean",
      "description": "Enable time series-based segmentation tasks."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "target"
  ]
}

datarobot_projects_segmentation_tasks_list

List segmentation tasks. Parameters:

Parameter	Type	Required	Default	Description
`offset`	integer	No	—	This many results will be skipped.
`limit`	integer	No	—	At most this many results are returned.
`projectId`	string	Yes	—	The project ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "offset": {
      "type": "integer",
      "description": "This many results will be skipped."
    },
    "limit": {
      "type": "integer",
      "description": "At most this many results are returned."
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    }
  },
  "required": [
    "PCID",
    "projectId"
  ]
}

datarobot_projects_segmentation_tasks_mappings_list

Retrieve series ID to segment ID mappings. Parameters:

Parameter	Type	Required	Default	Description
`offset`	integer	No	—	This many results will be skipped.
`limit`	integer	No	—	At most this many results are returned.
`projectId`	string	Yes	—	The project ID.
`segmentationTaskId`	string	Yes	—	The ID of the segmentation task.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "offset": {
      "type": "integer",
      "description": "This many results will be skipped."
    },
    "limit": {
      "type": "integer",
      "description": "At most this many results are returned."
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "segmentationTaskId": {
      "type": "string",
      "description": "The ID of the segmentation task."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "segmentationTaskId"
  ]
}

datarobot_projects_segmentation_tasks_retrieve

Retrieve segmentation task. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`segmentationTaskId`	string	Yes	—	The ID of the segmentation task.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "segmentationTaskId": {
      "type": "string",
      "description": "The ID of the segmentation task."
    }
  },
  "required": [
    "PCID",
    "projectId",
    "segmentationTaskId"
  ]
}

datarobot_projects_segments_patch

Update child segment project. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.
`segmentId`	string	Yes	—	The name of the segment.
`operation`	string	No	—	The name of the operation to perform on the project segment.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    },
    "segmentId": {
      "type": "string",
      "description": "The name of the segment."
    },
    "operation": {
      "type": "string",
      "description": "The name of the operation to perform on the project segment.",
      "enum": [
        "restart"
      ]
    }
  },
  "required": [
    "PCID",
    "projectId",
    "segmentId"
  ]
}

datarobot_projects_status_list

Check project status. Parameters:

Parameter	Type	Required	Default	Description
`projectId`	string	Yes	—	The project ID.

Show inputSchema

{
  "type": "object",
  "properties": {
    "PCID": {
      "type": "string",
      "description": "Pink Connect ID for the authenticated connection"
    },
    "projectId": {
      "type": "string",
      "description": "The project ID."
    }
  },
  "required": [
    "PCID",
    "projectId"
  ]
}

Triggers API

Platform API

Embedded MCP Servers

Application MCP Servers

​Tools

​datarobot_projects_access_control_list

​datarobot_projects_access_control_patch_many

​datarobot_projects_autopilot_create

​datarobot_projects_autopilots_create

​datarobot_projects_configure_and_start_autopilot

​datarobot_projects_datetime_partitioning_create

​datarobot_projects_datetime_partitioning_list

​datarobot_projects_deleted_projects_count_list

​datarobot_projects_deleted_projects_list

​datarobot_projects_deleted_projects_patch

​datarobot_projects_external_time_series_baseline_data_validation_jobs_create

​datarobot_projects_external_time_series_baseline_data_validation_jobs_retrieve

​datarobot_projects_hdfs_projects_create

​datarobot_projects_jobs_delete

​datarobot_projects_jobs_list

​datarobot_projects_jobs_retrieve

​datarobot_projects_multiseries_ids_cross_series_properties_list

​datarobot_projects_multiseries_names_list

​datarobot_projects_multiseries_properties_create

​datarobot_projects_optimized_datetime_partitionings_create

​datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_input_list

​datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_log_file_list

​datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_log_list

​datarobot_projects_optimized_datetime_partitionings_list

​datarobot_projects_optimized_datetime_partitionings_retrieve

​datarobot_projects_project_cleanup_jobs_create

​datarobot_projects_project_cleanup_jobs_delete

​datarobot_projects_project_cleanup_jobs_download_list

​datarobot_projects_project_cleanup_jobs_list

​datarobot_projects_project_cleanup_jobs_retrieve

​datarobot_projects_project_cleanup_jobs_summary_list

​datarobot_projects_project_clones_create

​datarobot_projects_projects_create

​datarobot_projects_projects_delete

​datarobot_projects_projects_list

​datarobot_projects_projects_retrieve

​datarobot_projects_projects_update

​datarobot_projects_segmentation_task_job_results_retrieve

​datarobot_projects_segmentation_tasks_create

​datarobot_projects_segmentation_tasks_list

​datarobot_projects_segmentation_tasks_mappings_list

​datarobot_projects_segmentation_tasks_retrieve

​datarobot_projects_segments_patch

​datarobot_projects_status_list

Tools

datarobot_projects_access_control_list

datarobot_projects_access_control_patch_many

datarobot_projects_autopilot_create

datarobot_projects_autopilots_create

datarobot_projects_configure_and_start_autopilot

datarobot_projects_datetime_partitioning_create

datarobot_projects_datetime_partitioning_list

datarobot_projects_deleted_projects_count_list

datarobot_projects_deleted_projects_list

datarobot_projects_deleted_projects_patch

datarobot_projects_external_time_series_baseline_data_validation_jobs_create

datarobot_projects_external_time_series_baseline_data_validation_jobs_retrieve

datarobot_projects_hdfs_projects_create

datarobot_projects_jobs_delete

datarobot_projects_jobs_list

datarobot_projects_jobs_retrieve

datarobot_projects_multiseries_ids_cross_series_properties_list

datarobot_projects_multiseries_names_list

datarobot_projects_multiseries_properties_create

datarobot_projects_optimized_datetime_partitionings_create

datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_input_list

datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_log_file_list

datarobot_projects_optimized_datetime_partitionings_datetime_partitioning_log_list

datarobot_projects_optimized_datetime_partitionings_list

datarobot_projects_optimized_datetime_partitionings_retrieve

datarobot_projects_project_cleanup_jobs_create

datarobot_projects_project_cleanup_jobs_delete

datarobot_projects_project_cleanup_jobs_download_list

datarobot_projects_project_cleanup_jobs_list

datarobot_projects_project_cleanup_jobs_retrieve

datarobot_projects_project_cleanup_jobs_summary_list

datarobot_projects_project_clones_create

datarobot_projects_projects_create

datarobot_projects_projects_delete

datarobot_projects_projects_list

datarobot_projects_projects_retrieve

datarobot_projects_projects_update

datarobot_projects_segmentation_task_job_results_retrieve

datarobot_projects_segmentation_tasks_create

datarobot_projects_segmentation_tasks_list

datarobot_projects_segmentation_tasks_mappings_list

datarobot_projects_segmentation_tasks_retrieve

datarobot_projects_segments_patch

datarobot_projects_status_list