Projects¶
Use the endpoints described below to manage projects and their properties.
GET /api/v2/deletedProjects/¶
Retrieve a list of soft-deleted projects matching search criteria
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| searchFor | query | string | false | Project or dataset name to filter by |
| creator | query | string | false | Creator ID to filter projects by |
| organization | query | string | false | 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 | query | string(date-time) | false | ISO-8601 formatted date projects were deleted before |
| deletedAfter | query | string(date-time) | false | ISO-8601 formatted date projects were deleted after |
| projectId | query | string | false | Project ID to search |
| limit | query | integer | false | At most this many results are returned. |
| offset | query | integer | false | This many results will be skipped. |
| orderBy | query | string | false | Order deleted projects by |
Enumerated Values¶
| Parameter | Value |
|---|---|
| orderBy | [projectId, projectName, datasetName, deletedOn, deletedBy, creator, -projectId, -projectName, -datasetName, -deletedOn, -deletedBy, -creator] |
Example responses¶
200 Response
{
"properties": {
"count": {
"description": "Number of items returned on this page.",
"type": "integer"
},
"data": {
"description": "List of deleted projects",
"items": {
"properties": {
"createdBy": {
"description": "The user who created the project",
"properties": {
"email": {
"description": "Email of the user",
"type": "string"
},
"id": {
"description": "ID of the user",
"type": "string"
}
},
"required": [
"email",
"id"
],
"type": "object"
},
"deletedBy": {
"description": "The user who created the project",
"properties": {
"email": {
"description": "Email of the user",
"type": "string"
},
"id": {
"description": "ID of the user",
"type": "string"
}
},
"required": [
"email",
"id"
],
"type": "object"
},
"deletionTime": {
"description": "ISO-8601 formatted date when project was deleted",
"format": "date-time",
"type": [
"string",
"null"
]
},
"fileName": {
"description": "The name of the file uploaded for the project dataset",
"type": [
"string",
"null"
]
},
"id": {
"description": "The ID of the project",
"type": "string"
},
"organization": {
"description": "The organization the project belongs to",
"properties": {
"id": {
"description": "ID of the organization the project belongs to",
"type": "string"
},
"name": {
"description": "Name of the organization the project belongs to",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
},
"projectName": {
"default": "Untitled Project",
"description": "The name of the project",
"type": "string"
},
"scheduledForDeletion": {
"description": "Whether project permanent deletion has already been scheduled",
"type": "boolean"
}
},
"required": [
"createdBy",
"deletedBy",
"deletionTime",
"fileName",
"id",
"organization",
"projectName",
"scheduledForDeletion"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
}
},
"required": [
"data",
"next",
"previous"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | List of soft-deleted projects | DeletedProjectListResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
PATCH /api/v2/deletedProjects/{projectId}/¶
Recover (undelete) soft-deleted project
Body parameter¶
{
"properties": {
"action": {
"description": "Action to perform on a project",
"enum": [
"undelete"
],
"type": "string"
}
},
"required": [
"action"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
| body | body | ProjectRecover | false | none |
Example responses¶
200 Response
{
"properties": {
"message": {
"description": "Operation result description",
"type": "string"
}
},
"required": [
"message"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Recovery operation result description | ProjectRecoverResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/deletedProjectsCount/¶
Get current number of deleted projects matching search criteria. Value is limited by DELETED_PROJECTS_BATCH_LIMIT system setting. That means that the actual amount of deleted projects can be greater than the limit, but counting will stop when reaching it.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| searchFor | query | string | false | Project or dataset name to filter by |
| creator | query | string | false | Creator ID to filter projects by |
| organization | query | string | false | 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 | query | string(date-time) | false | ISO-8601 formatted date projects were deleted before |
| deletedAfter | query | string(date-time) | false | ISO-8601 formatted date projects were deleted after |
| projectId | query | string | false | Project ID to search |
| limit | query | integer | false | Count deleted projects until specified value reached. |
Example responses¶
200 Response
{
"properties": {
"deletedProjectsCount": {
"description": "Amount of soft-deleted projects. The value is limited by projectCountLimit",
"minimum": 0,
"type": "integer"
},
"projectCountLimit": {
"description": "Deleted projects counting limit value. Stop counting above this limit",
"minimum": 0,
"type": "integer"
},
"valueExceedsLimit": {
"description": "If an actual number of soft-deleted projects exceeds counting limit",
"type": "boolean"
}
},
"required": [
"deletedProjectsCount",
"projectCountLimit",
"valueExceedsLimit"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Soft-deleted projects amount, current counting limit value and boolean flag to notify if an actual amount of soft-deleted projects in the system exceeds the limit value. | DeletedProjectCountResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/hdfsProjects/¶
Create a project from an HDFS file via WebHDFS API. Represent the file using URL, optionally, port, and optionally, user/password credentials. For example, {"url": "hdfs://<ip>/path/to/file.csv", "port": "50070"}.
Body parameter¶
{
"properties": {
"password": {
"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.",
"type": "string"
},
"port": {
"description": "Port of the WebHDFS Namenode server. If not specified, defaults to HDFS default port 50070.",
"type": "integer"
},
"projectName": {
"description": "Name of the project to be created. If not specified, project name will be based on the file name.",
"type": "string"
},
"url": {
"description": "URL of the WebHDFS resource. Represent the file using the `hdfs://` protocol marker (for example, `hdfs:///tmp/somedataset.csv`).",
"format": "uri",
"type": "string"
},
"user": {
"description": "Username for authenticating to HDFS using Kerberos",
"type": "string"
}
},
"required": [
"url"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| body | body | HdfsProjectCreate | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projectCleanupJobs/¶
Get async status of the project permadelete job
Example responses¶
200 Response
{
"properties": {
"jobs": {
"description": "List of active permadelete jobs with their statuses.",
"items": {
"properties": {
"created": {
"description": "The time the status record was created.",
"format": "date-time",
"type": "string"
},
"data": {
"description": "List of projects and associated statuses.",
"items": {
"properties": {
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "Project ID",
"type": "string"
},
"status": {
"description": "The processing state of project cleanup task.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
}
},
"required": [
"message",
"projectId",
"status"
],
"type": "object"
},
"maxItems": 1000,
"minItems": 1,
"type": "array"
},
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"status": {
"description": "The processing state of the cleanup job.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
},
"statusId": {
"description": "The ID of the status object.",
"type": "string"
}
},
"required": [
"created",
"data",
"message",
"status",
"statusId"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"jobs"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Permadelete Job Status with details per project | ProjectNukeJobListStatus |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projectCleanupJobs/¶
Add list of projects to permadelete and returns async status
Body parameter¶
{
"properties": {
"creator": {
"description": "Creator ID to filter projects by",
"type": "string"
},
"deletedAfter": {
"description": "ISO-8601 formatted date projects were deleted after",
"format": "date-time",
"type": "string"
},
"deletedBefore": {
"description": "ISO-8601 formatted date projects were deleted before",
"format": "date-time",
"type": "string"
},
"limit": {
"default": 1000,
"description": "At most this many projects are deleted.",
"maximum": 1000,
"minimum": 1,
"type": "integer"
},
"offset": {
"default": 0,
"description": "This many projects will be skipped.",
"minimum": 0,
"type": "integer"
},
"organization": {
"description": "ID of organization that projects should belong to",
"type": "string"
},
"projectIds": {
"description": "List of project IDs to delete permanently.",
"items": {
"type": "string"
},
"maxItems": 1000,
"minItems": 1,
"type": "array"
},
"searchFor": {
"description": "Project or dataset name to filter by",
"type": "string"
}
},
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| body | body | ProjectNuke | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 202 | Accepted | Location URL to check permadelete status per project | None |
Response Headers¶
| Status | Header | Type | Format | Description |
|---|---|---|---|---|
| 202 | Location | string | A url that can be polled to check the status. |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
DELETE /api/v2/projectCleanupJobs/{statusId}/¶
Stop permadelete job, if possible
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| statusId | path | string | true | The ID of the status object. |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projectCleanupJobs/{statusId}/¶
Get async status of the project permadelete job
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| statusId | path | string | true | The ID of the status object. |
Example responses¶
200 Response
{
"properties": {
"created": {
"description": "The time the status record was created.",
"format": "date-time",
"type": "string"
},
"data": {
"description": "List of projects and associated statuses.",
"items": {
"properties": {
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "Project ID",
"type": "string"
},
"status": {
"description": "The processing state of project cleanup task.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
}
},
"required": [
"message",
"projectId",
"status"
],
"type": "object"
},
"maxItems": 1000,
"minItems": 1,
"type": "array"
},
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"status": {
"description": "The processing state of the cleanup job.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
},
"statusId": {
"description": "The ID of the status object.",
"type": "string"
}
},
"required": [
"created",
"data",
"message",
"status",
"statusId"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Permadelete Job Status with details per project | ProjectNukeJobStatus |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projectCleanupJobs/{statusId}/download/¶
Get a file containing a per-project report of permanent deletion.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| statusId | path | string | true | The ID of the status object. |
Example responses¶
200 Response
{
"properties": {
"created": {
"description": "The time the status record was created.",
"format": "date-time",
"type": "string"
},
"data": {
"description": "List of projects and associated statuses.",
"items": {
"properties": {
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "Project ID",
"type": "string"
},
"status": {
"description": "The processing state of project cleanup task.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
}
},
"required": [
"message",
"projectId",
"status"
],
"type": "object"
},
"maxItems": 1000,
"minItems": 1,
"type": "array"
},
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"status": {
"description": "The processing state of the cleanup job.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
},
"statusId": {
"description": "The ID of the status object.",
"type": "string"
}
},
"required": [
"created",
"data",
"message",
"status",
"statusId"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | JSON-formatted project permadeletion report. | ProjectNukeJobStatus |
Response Headers¶
| Status | Header | Type | Format | Description |
|---|---|---|---|---|
| 200 | Content-Disposition | string | Contains an auto generated filename for this download ('attachment;filename="project_permadeletion_ |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projectCleanupJobs/{statusId}/summary/¶
Get number of projects whose deletion finished in particular state
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| statusId | path | string | true | The ID of the status object. |
Example responses¶
200 Response
{
"properties": {
"jobId": {
"description": "The ID of the permadeletion multi-job.",
"type": "string"
},
"summary": {
"description": "Project permanent deletion status to count mapping.",
"properties": {
"aborted": {
"description": "Number of project permadelete jobs with Aborted status.",
"minimum": 0,
"type": "integer"
},
"completed": {
"description": "Number of project permadelete jobs with Completed status.",
"minimum": 0,
"type": "integer"
},
"error": {
"description": "Number of project permadelete jobs with Error status.",
"minimum": 0,
"type": "integer"
},
"expired": {
"description": "Number of project permadelete jobs with Expired status.",
"minimum": 0,
"type": "integer"
}
},
"required": [
"aborted",
"completed",
"error",
"expired"
],
"type": "object"
}
},
"required": [
"jobId",
"summary"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Project permanent deletion job status to occurrence count | ProjectNukeJobStatusSummary |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projectClones/¶
Create a clone of an existing project.
The resultant project will begin the initial exploratory
data analysis and will be ready to set the target of the new project shortly.
Body parameter¶
{
"properties": {
"copyOptions": {
"default": false,
"description": "Whether all project options should be copied to the cloned project.",
"type": "boolean"
},
"projectId": {
"description": "The ID of the project to clone.",
"type": "string"
},
"projectName": {
"description": "The name of the project to be created.",
"type": "string"
}
},
"required": [
"projectId"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| body | body | ProjectClone | false | none |
Example responses¶
200 Response
{
"properties": {
"pid": {
"description": "The project ID.",
"type": "string"
}
},
"required": [
"pid"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Project cloning has successfully started. See the Location header. | ProjectCreateResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/¶
List all available projects.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectName | query | string | false | if provided will filter returned projects for projects with matching names |
| projectId | query | any | false | if provided will filter returned projects with matching project IDs |
| orderBy | query | string | false | if provided will order the results by this field |
| featureDiscovery | query | string | false | Return only feature discovery projects |
| offset | query | integer | false | This many results will be skipped. |
| limit | query | integer | false | At most this many results are returned. |
Enumerated Values¶
| Parameter | Value |
|---|---|
| orderBy | [projectName, -projectName] |
| featureDiscovery | [false, False, true, True] |
Example responses¶
200 Response
{
"items": {
"properties": {
"advancedOptions": {
"description": "Information related to the current model of the deployment.",
"properties": {
"allowedPairwiseInteractionGroups": {
"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.",
"items": {
"items": {
"type": "string"
},
"type": "array"
},
"type": "array",
"x-versionadded": "v2.19"
},
"blendBestModels": {
"description": "blend best models during Autopilot run [DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.19",
"x-versiondeprecated": "2.30.0"
},
"blueprintThreshold": {
"description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs",
"type": [
"integer",
"null"
]
},
"considerBlendersInRecommendation": {
"description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.21",
"x-versiondeprecated": "2.30.0"
},
"defaultMonotonicDecreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"defaultMonotonicIncreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"downsampledMajorityRows": {
"description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"downsampledMinorityRows": {
"description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"eventsCount": {
"description": "the name of the event count column, if specified, otherwise null.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.8"
},
"exposure": {
"description": "the name of the exposure column, if specified.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.6"
},
"majorityDownsamplingRate": {
"description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling",
"type": [
"number",
"null"
],
"x-versionadded": "v2.5"
},
"minSecondaryValidationModelCount": {
"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.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"offset": {
"description": "the list of names of the offset columns, if specified, otherwise null.",
"items": {
"type": "string"
},
"type": "array",
"x-versionadded": "v2.6"
},
"onlyIncludeMonotonicBlueprints": {
"default": false,
"description": "whether the project only includes blueprints support enforcing monotonic constraints",
"type": "boolean",
"x-versionadded": "v2.11"
},
"prepareModelForDeployment": {
"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.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.19"
},
"responseCap": {
"description": "defaults to False, if specified used to cap the maximum response of a model",
"oneOf": [
{
"type": "boolean"
},
{
"maximum": 1,
"minimum": 0.5,
"type": "number"
}
]
},
"runLeakageRemovedFeatureList": {
"description": "Run Autopilot on Leakage Removed feature list (if exists).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"scoringCodeOnly": {
"description": "Keep only models that can be converted to scorable java code during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"seed": {
"description": "defaults to null, the random seed to be used if specified",
"type": [
"string",
"null"
]
},
"shapOnlyMode": {
"description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.21"
},
"smartDownsampled": {
"description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).",
"type": "boolean",
"x-versionadded": "v2.5"
},
"weights": {
"description": "the name of the weight column, if specified, otherwise null.",
"type": [
"string",
"null"
]
}
},
"required": [
"blendBestModels",
"blueprintThreshold",
"defaultMonotonicDecreasingFeaturelistId",
"defaultMonotonicIncreasingFeaturelistId",
"downsampledMajorityRows",
"downsampledMinorityRows",
"majorityDownsamplingRate",
"onlyIncludeMonotonicBlueprints",
"prepareModelForDeployment",
"responseCap",
"seed",
"shapOnlyMode",
"smartDownsampled",
"weights"
],
"type": "object"
},
"autopilotClusterList": {
"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'.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotMode": {
"description": "The current autopilot mode, 0 for full autopilot, 2 for manual mode, 3 for quick mode, 4 for comprehensive mode",
"type": "integer"
},
"created": {
"description": "The time of project creation.",
"format": "date-time",
"type": "string"
},
"featureEngineeringPredictionPoint": {
"description": "The date column to be used as the prediction point for time-based feature engineering.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.22"
},
"fileName": {
"description": "The name of the dataset used to create the project.",
"type": "string"
},
"holdoutUnlocked": {
"description": "whether the holdout has been unlocked",
"type": "boolean"
},
"id": {
"description": "The ID of a project.",
"type": "string"
},
"maxClusters": {
"description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `minClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100.",
"maximum": 100,
"minimum": 2,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.23"
},
"maxTrainPct": {
"description": "the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data.",
"type": "number"
},
"maxTrainRows": {
"description": "the maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data",
"type": "integer"
},
"metric": {
"description": "the metric used to select the best-performing models.",
"type": "string"
},
"minClusters": {
"description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `maxClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2.",
"maximum": 100,
"minimum": 2,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.23"
},
"partition": {
"description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose `cvMethod` is `\"datetime\"`, full specification of the partitioning method can be found at [GET /api/v2/projects/{projectId}/datetimePartitioning/][get-apiv2projectsprojectiddatetimepartitioning].",
"properties": {
"cvHoldoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with cross validation, the value assigned to the holdout set"
},
"cvMethod": {
"description": "the partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.",
"enum": [
"random",
"user",
"stratified",
"group",
"datetime"
],
"type": "string"
},
"datetimeCol": {
"description": "if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "if a datetime partition column was used, the name of the column",
"type": "string"
},
"holdoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set"
},
"holdoutPct": {
"description": "the percentage of the dataset reserved for the holdout set",
"type": "number"
},
"partitionKeyCols": {
"description": "An array containing a single string - the name of the group partition column",
"items": {
"type": "string"
},
"type": "array"
},
"reps": {
"description": "if cross validation was used, the number of folds to use",
"type": [
"number",
"null"
]
},
"trainingLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with train-validation-holdout split, the value assigned to the training set"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.9"
},
"userPartitionCol": {
"description": "if a user partition column was used, the name of the column",
"type": [
"string",
"null"
]
},
"validationLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with train-validation-holdout split, the value assigned to the validation set"
},
"validationPct": {
"description": "if train-validation-holdout split was used, the percentage of the dataset used for the validation set",
"type": [
"number",
"null"
]
},
"validationType": {
"description": "either CV for cross-validation or TVH for train-validation-holdout split",
"enum": [
"CV",
"TVH"
],
"type": "string"
}
},
"required": [
"cvHoldoutLevel",
"cvMethod",
"datetimeCol",
"holdoutLevel",
"holdoutPct",
"partitionKeyCols",
"reps",
"trainingLevel",
"useTimeSeries",
"userPartitionCol",
"validationLevel",
"validationPct",
"validationType"
],
"type": "object"
},
"positiveClass": {
"description": "if the project uses binary classification, the class designated to be the positive class. Otherwise, null.",
"type": [
"number",
"null"
]
},
"projectName": {
"description": "The name of a project.",
"type": "string"
},
"stage": {
"description": "the stage of the project - if modeling, then the target is successfully set, and modeling or predictions can proceed.",
"type": "string"
},
"target": {
"description": "the target of the project, null if project is unsupervised.",
"type": "string"
},
"targetType": {
"description": "The target type of the project.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"minInflated",
"Multilabel",
"TextGeneration",
"GeoPoint",
"VectorDatabase"
],
"type": [
"string",
"null"
]
},
"unsupervisedMode": {
"description": "indicates whether a project is unsupervised.",
"type": "boolean",
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCase": {
"description": "The information of the use case associated with the project.",
"properties": {
"id": {
"description": "The ID of the use case.",
"type": [
"string",
"null"
]
},
"name": {
"description": "The name of the use case.",
"type": [
"string",
"null"
]
}
},
"type": "object",
"x-versionadded": "v2.36"
},
"useFeatureDiscovery": {
"description": "A boolean value indicating whether a feature discovery project was created as opposed to a regular project.",
"type": "boolean",
"x-versionadded": "v2.21"
}
},
"required": [
"advancedOptions",
"autopilotMode",
"created",
"fileName",
"holdoutUnlocked",
"id",
"maxTrainPct",
"maxTrainRows",
"metric",
"partition",
"positiveClass",
"projectName",
"stage",
"target",
"targetType",
"unsupervisedMode",
"useFeatureDiscovery"
],
"type": "object"
},
"type": "array"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The list of projects | Inline |
Response Schema¶
Status Code 200
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| anonymous | [ProjectDetailsResponse] | false | none | |
| » advancedOptions | ProjectAdvancedOptionsResponse | true | Information related to the current model of the deployment. | |
| »» allowedPairwiseInteractionGroups | [array] | false | 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. | |
| »» blendBestModels | boolean | true | blend best models during Autopilot run [DEPRECATED] | |
| »» blueprintThreshold | integer,null | true | an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs | |
| »» considerBlendersInRecommendation | boolean | false | Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED] | |
| »» defaultMonotonicDecreasingFeaturelistId | string,null | true | null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time. | |
| »» defaultMonotonicIncreasingFeaturelistId | string,null | true | null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time. | |
| »» downsampledMajorityRows | integer,null | true | the total number of the majority rows available for modeling, or null for projects without smart downsampling | |
| »» downsampledMinorityRows | integer,null | true | the total number of the minority rows available for modeling, or null for projects without smart downsampling | |
| »» eventsCount | string,null | false | the name of the event count column, if specified, otherwise null. | |
| »» exposure | string,null | false | the name of the exposure column, if specified. | |
| »» majorityDownsamplingRate | number,null | true | the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling | |
| »» minSecondaryValidationModelCount | boolean | false | 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. | |
| »» offset | [string] | false | the list of names of the offset columns, if specified, otherwise null. | |
| »» onlyIncludeMonotonicBlueprints | boolean | true | whether the project only includes blueprints support enforcing monotonic constraints | |
| »» prepareModelForDeployment | boolean,null | true | 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. | |
| »» responseCap | any | true | defaults to False, if specified used to cap the maximum response of a model |
oneOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | boolean | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | number | false | maximum: 1 minimum: 0.5 |
none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »» runLeakageRemovedFeatureList | boolean | false | Run Autopilot on Leakage Removed feature list (if exists). | |
| »» scoringCodeOnly | boolean | false | Keep only models that can be converted to scorable java code during Autopilot run. | |
| »» seed | string,null | true | defaults to null, the random seed to be used if specified | |
| »» shapOnlyMode | boolean,null | true | Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always null. |
|
| »» smartDownsampled | boolean | true | whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows). | |
| »» weights | string,null | true | the name of the weight column, if specified, otherwise null. | |
| » autopilotClusterList | [integer] | false | maxItems: 10 |
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'. |
| » autopilotMode | integer | true | The current autopilot mode, 0 for full autopilot, 2 for manual mode, 3 for quick mode, 4 for comprehensive mode | |
| » created | string(date-time) | true | The time of project creation. | |
| » featureEngineeringPredictionPoint | string,null | false | The date column to be used as the prediction point for time-based feature engineering. | |
| » fileName | string | true | The name of the dataset used to create the project. | |
| » holdoutUnlocked | boolean | true | whether the holdout has been unlocked | |
| » id | string | true | The ID of a project. | |
| » maxClusters | integer,null | false | maximum: 100 minimum: 2 |
Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to minClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100. |
| » maxTrainPct | number | true | the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data. | |
| » maxTrainRows | integer | true | the maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data | |
| » metric | string | true | the metric used to select the best-performing models. | |
| » minClusters | integer,null | false | maximum: 100 minimum: 2 |
Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to maxClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2. |
| » partition | ProjectPartitionResponse | true | The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose cvMethod is "datetime", full specification of the partitioning method can be found at GET /api/v2/projects/{projectId}/datetimePartitioning/. |
|
| »» cvHoldoutLevel | any | true | if a user partition column was used with cross validation, the value assigned to the holdout set |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | number | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | integer | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »» cvMethod | string | true | the partitioning method used. Note that "date" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0. | |
| »» datetimeCol | string,null | true | if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0. | |
| »» datetimePartitionColumn | string | false | if a datetime partition column was used, the name of the column | |
| »» holdoutLevel | any | true | if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | number | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | integer | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »» holdoutPct | number | true | the percentage of the dataset reserved for the holdout set | |
| »» partitionKeyCols | [string] | true | An array containing a single string - the name of the group partition column | |
| »» reps | number,null | true | if cross validation was used, the number of folds to use | |
| »» trainingLevel | any | true | if a user partition column was used with train-validation-holdout split, the value assigned to the training set |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | number | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | integer | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »» useTimeSeries | boolean,null | true | A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning. | |
| »» userPartitionCol | string,null | true | if a user partition column was used, the name of the column | |
| »» validationLevel | any | true | if a user partition column was used with train-validation-holdout split, the value assigned to the validation set |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | number | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »»» anonymous | integer | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| »» validationPct | number,null | true | if train-validation-holdout split was used, the percentage of the dataset used for the validation set | |
| »» validationType | string | true | either CV for cross-validation or TVH for train-validation-holdout split | |
| » positiveClass | number,null | true | if the project uses binary classification, the class designated to be the positive class. Otherwise, null. | |
| » projectName | string | true | The name of a project. | |
| » stage | string | true | the stage of the project - if modeling, then the target is successfully set, and modeling or predictions can proceed. | |
| » target | string | true | the target of the project, null if project is unsupervised. | |
| » targetType | string,null | true | The target type of the project. | |
| » unsupervisedMode | boolean | true | indicates whether a project is unsupervised. | |
| » unsupervisedType | string,null | false | Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'. | |
| » useCase | UseCaseIdName | false | The information of the use case associated with the project. | |
| »» id | string,null | false | The ID of the use case. | |
| »» name | string,null | false | The name of the use case. | |
| » useFeatureDiscovery | boolean | true | A boolean value indicating whether a feature discovery project was created as opposed to a regular project. |
Enumerated Values¶
| Property | Value |
|---|---|
| cvMethod | [random, user, stratified, group, datetime] |
| validationType | [CV, TVH] |
| targetType | [Binary, Regression, Multiclass, minInflated, Multilabel, TextGeneration, GeoPoint, VectorDatabase] |
| unsupervisedType | [anomaly, clustering] |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projects/¶
Create a new project.
Body parameter¶
{
"properties": {
"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.",
"oneOf": [
{
"properties": {
"credentialType": {
"description": "The type of these credentials, 'basic' here.",
"enum": [
"basic"
],
"type": "string"
},
"password": {
"description": "The password for database authentication. The password is encrypted at rest and never saved / stored.",
"type": "string"
},
"user": {
"description": "The username for database authentication.",
"type": "string"
}
},
"required": [
"credentialType",
"password",
"user"
],
"type": "object"
},
{
"properties": {
"awsAccessKeyId": {
"description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.",
"type": "string"
},
"awsSecretAccessKey": {
"description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.",
"type": "string"
},
"awsSessionToken": {
"default": null,
"description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.",
"type": [
"string",
"null"
]
},
"configId": {
"description": "ID of Secure configurations of credentials shared by admin.If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 's3' here.",
"enum": [
"s3"
],
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
},
{
"properties": {
"credentialType": {
"description": "The type of these credentials, 'oauth' here.",
"enum": [
"oauth"
],
"type": "string"
},
"oauthAccessToken": {
"default": null,
"description": "The oauth access token.",
"type": [
"string",
"null"
]
},
"oauthClientId": {
"default": null,
"description": "The oauth client ID.",
"type": [
"string",
"null"
]
},
"oauthClientSecret": {
"default": null,
"description": "The oauth client secret.",
"type": [
"string",
"null"
]
},
"oauthRefreshToken": {
"description": "The oauth refresh token.",
"type": "string"
}
},
"required": [
"credentialType",
"oauthRefreshToken"
],
"type": "object"
},
{
"properties": {
"configId": {
"description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr or passphrase.",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 'snowflake_key_pair_user_account' here.",
"enum": [
"snowflake_key_pair_user_account"
],
"type": "string"
},
"passphrase": {
"description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.",
"type": "string"
},
"privateKeyStr": {
"description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.",
"type": "string"
},
"user": {
"description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.",
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
},
{
"properties": {
"configId": {
"description": "ID of Secure configurations shared by admin.Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 'gcp' here.",
"enum": [
"gcp"
],
"type": "string"
},
"gcpKey": {
"description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.",
"properties": {
"authProviderX509CertUrl": {
"description": "Auth provider X509 certificate URL.",
"format": "uri",
"type": "string"
},
"authUri": {
"description": "Auth URI.",
"format": "uri",
"type": "string"
},
"clientEmail": {
"description": "Client email address.",
"type": "string"
},
"clientId": {
"description": "Client ID.",
"type": "string"
},
"clientX509CertUrl": {
"description": "Client X509 certificate URL.",
"format": "uri",
"type": "string"
},
"privateKey": {
"description": "Private key.",
"type": "string"
},
"privateKeyId": {
"description": "Private key ID",
"type": "string"
},
"projectId": {
"description": "Project ID.",
"type": "string"
},
"tokenUri": {
"description": "Token URI.",
"format": "uri",
"type": "string"
},
"type": {
"description": "GCP account type.",
"enum": [
"service_account"
],
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
},
"googleConfigId": {
"description": "ID of Secure configurations shared by admin. This is deprecated.Please use configId instead. If specified, cannot include gcpKey.",
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
},
{
"properties": {
"credentialType": {
"description": "The type of these credentials, 'databricks_access_token_account' here.",
"enum": [
"databricks_access_token_account"
],
"type": "string"
},
"databricksAccessToken": {
"description": "Databricks personal access token.",
"minLength": 1,
"type": "string"
}
},
"required": [
"credentialType",
"databricksAccessToken"
],
"type": "object"
},
{
"properties": {
"clientId": {
"description": "Client ID for Databricks service principal.",
"minLength": 1,
"type": "string"
},
"clientSecret": {
"description": "Client secret for Databricks service principal.",
"minLength": 1,
"type": "string"
},
"configId": {
"description": "The ID of the saved shared credentials. If specified, cannot include clientIdand clientSecret.",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 'databricks_service_principal_account' here.",
"enum": [
"databricks_service_principal_account"
],
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
},
{
"properties": {
"azureTenantId": {
"description": "Tenant ID of the Azure AD service principal.",
"type": "string"
},
"clientId": {
"description": "Client ID of the Azure AD service principal.",
"type": "string"
},
"clientSecret": {
"description": "Client Secret of the Azure AD service principal.",
"type": "string"
},
"configId": {
"description": "ID of secure configurations of credentials shared by admin.",
"type": "string",
"x-versionadded": "v2.35"
},
"credentialType": {
"description": "The type of these credentials, 'azure_service_principal' here.",
"enum": [
"azure_service_principal"
],
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
}
],
"x-versionadded": "v2.23"
},
"credentialId": {
"description": "The ID of the set of credentials to authenticate with the database. Can only be used along with datasetId or dataSourceId.",
"type": "string",
"x-versionadded": "v2.19"
},
"dataSourceId": {
"description": "Identifier for the data source to retrieve.",
"type": "string"
},
"datasetId": {
"description": "The ID of the dataset entry to use for the project dataset.",
"type": "string"
},
"datasetVersionId": {
"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.",
"type": "string"
},
"password": {
"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.",
"type": "string",
"x-versiondeprecated": "v2.23"
},
"projectName": {
"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.",
"type": "string"
},
"recipeId": {
"description": "The ID of the wrangling recipe that will be used for project creation.",
"type": "string"
},
"url": {
"description": "The URL to download the dataset used to create the project.",
"format": "url",
"type": "string"
},
"useKerberos": {
"description": "If true, use Kerberos authentication for database authentication. Default is false. Can only be used along with datasetId or dataSourceId.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"user": {
"description": "The username for database authentication. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead.",
"type": "string",
"x-versiondeprecated": "v2.23"
}
},
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| body | body | ProjectCreate | false | none |
Example responses¶
202 Response
{
"properties": {
"pid": {
"description": "The project ID.",
"type": "string"
}
},
"required": [
"pid"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 202 | Accepted | Creation has successfully started. See the Location header. | ProjectCreateResponse |
| 403 | Forbidden | User does not have permission to use specified dataset item for project. | None |
| 404 | Not Found | The dataset item with the given ID or version ID is not found. | None |
| 422 | Unprocessable Entity | Ingest not yet completed. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
DELETE /api/v2/projects/{projectId}/¶
Delete a project
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 204 | No Content | The project has been successfully deleted. | None |
| 409 | Conflict | The project is in use and cannot be deleted. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/¶
Look up a particular project
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
Example responses¶
200 Response
{
"properties": {
"advancedOptions": {
"description": "Information related to the current model of the deployment.",
"properties": {
"allowedPairwiseInteractionGroups": {
"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.",
"items": {
"items": {
"type": "string"
},
"type": "array"
},
"type": "array",
"x-versionadded": "v2.19"
},
"blendBestModels": {
"description": "blend best models during Autopilot run [DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.19",
"x-versiondeprecated": "2.30.0"
},
"blueprintThreshold": {
"description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs",
"type": [
"integer",
"null"
]
},
"considerBlendersInRecommendation": {
"description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.21",
"x-versiondeprecated": "2.30.0"
},
"defaultMonotonicDecreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"defaultMonotonicIncreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"downsampledMajorityRows": {
"description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"downsampledMinorityRows": {
"description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"eventsCount": {
"description": "the name of the event count column, if specified, otherwise null.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.8"
},
"exposure": {
"description": "the name of the exposure column, if specified.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.6"
},
"majorityDownsamplingRate": {
"description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling",
"type": [
"number",
"null"
],
"x-versionadded": "v2.5"
},
"minSecondaryValidationModelCount": {
"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.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"offset": {
"description": "the list of names of the offset columns, if specified, otherwise null.",
"items": {
"type": "string"
},
"type": "array",
"x-versionadded": "v2.6"
},
"onlyIncludeMonotonicBlueprints": {
"default": false,
"description": "whether the project only includes blueprints support enforcing monotonic constraints",
"type": "boolean",
"x-versionadded": "v2.11"
},
"prepareModelForDeployment": {
"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.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.19"
},
"responseCap": {
"description": "defaults to False, if specified used to cap the maximum response of a model",
"oneOf": [
{
"type": "boolean"
},
{
"maximum": 1,
"minimum": 0.5,
"type": "number"
}
]
},
"runLeakageRemovedFeatureList": {
"description": "Run Autopilot on Leakage Removed feature list (if exists).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"scoringCodeOnly": {
"description": "Keep only models that can be converted to scorable java code during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"seed": {
"description": "defaults to null, the random seed to be used if specified",
"type": [
"string",
"null"
]
},
"shapOnlyMode": {
"description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.21"
},
"smartDownsampled": {
"description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).",
"type": "boolean",
"x-versionadded": "v2.5"
},
"weights": {
"description": "the name of the weight column, if specified, otherwise null.",
"type": [
"string",
"null"
]
}
},
"required": [
"blendBestModels",
"blueprintThreshold",
"defaultMonotonicDecreasingFeaturelistId",
"defaultMonotonicIncreasingFeaturelistId",
"downsampledMajorityRows",
"downsampledMinorityRows",
"majorityDownsamplingRate",
"onlyIncludeMonotonicBlueprints",
"prepareModelForDeployment",
"responseCap",
"seed",
"shapOnlyMode",
"smartDownsampled",
"weights"
],
"type": "object"
},
"autopilotClusterList": {
"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'.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotMode": {
"description": "The current autopilot mode. 0: Full Autopilot. 2: Manual Mode. 3: Quick Mode. 4: Comprehensive Autopilot. null: Mode not set.",
"enum": [
"0",
"2",
"3",
"4"
],
"type": [
"string",
"null"
]
},
"catalogId": {
"description": "The ID of the AI catalog entry used to create the project, or null if not created from the AI catalog.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.21"
},
"catalogVersionId": {
"description": "The ID of the AI catalog version used to create the project, or null if not created from the AI catalog.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.21"
},
"created": {
"description": "The time of project creation.",
"format": "date-time",
"type": "string"
},
"externalTimeSeriesBaselineDatasetMetadata": {
"description": "The id of the catalog item that is being used as the external baseline data",
"properties": {
"datasetId": {
"description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.",
"type": [
"string",
"null"
]
},
"datasetName": {
"description": "The name of the timeseries baseline dataset for the project",
"type": [
"string",
"null"
]
}
},
"required": [
"datasetId",
"datasetName"
],
"type": "object"
},
"featureEngineeringPredictionPoint": {
"description": "The date column to be used as the prediction point for time-based feature engineering.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.22"
},
"fileName": {
"description": "The name of the dataset used to create the project.",
"type": "string"
},
"holdoutUnlocked": {
"description": "Whether the holdout has been unlocked.",
"type": "boolean"
},
"id": {
"description": "The ID of the project.",
"type": "string"
},
"isScoringAvailableForModelsTrainedIntoValidationHoldout": {
"description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.",
"type": "boolean",
"x-versionadded": "v2.31"
},
"maxClusters": {
"description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `minClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100.",
"maximum": 100,
"minimum": 2,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.23"
},
"maxTrainPct": {
"description": "The maximum percentage of the dataset that can be used to successfully train a model without going into the validation data.",
"type": "number"
},
"maxTrainRows": {
"description": "The maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data.",
"type": "integer"
},
"metric": {
"description": "The metric used to select the best-performing models.",
"type": "string"
},
"minClusters": {
"description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `maxClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2.",
"maximum": 100,
"minimum": 2,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.23"
},
"partition": {
"description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null.",
"properties": {
"cvHoldoutLevel": {
"description": "If a user partition column was used with cross validation, the value assigned to the holdout set",
"type": [
"string",
"null"
]
},
"cvMethod": {
"description": "The partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.",
"enum": [
"random",
"stratified",
"datetime",
"user",
"group",
"date"
],
"type": "string"
},
"datetimeCol": {
"description": "If a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "If a datetime partition column was used, the name of the column.",
"type": [
"string",
"null"
]
},
"holdoutLevel": {
"description": "If a user partition column was used with train-validation-holdout split, the value assigned to the holdout set.",
"type": [
"string",
"null"
]
},
"holdoutPct": {
"description": "The percentage of the dataset reserved for the holdout set.",
"type": [
"number",
"null"
]
},
"partitionKeyCols": {
"description": "An array containing a single string - the name of the group partition column",
"items": {
"type": "string"
},
"type": "array"
},
"reps": {
"description": "If cross validation was used, the number of folds to use.",
"type": [
"integer",
"null"
]
},
"trainingLevel": {
"description": "If a user partition column was used with train-validation-holdout split, the value assigned to the training set.",
"type": [
"string",
"null"
]
},
"useTimeSeries": {
"description": "Indicates whether a time series project was created as opposed to a regular project using datetime partitioning.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.9"
},
"userPartitionCol": {
"description": "If a user partition column was used, the name of the column.",
"type": [
"string",
"null"
]
},
"validationLevel": {
"description": "If a user partition column was used with train-validation-holdout split, the value assigned to the validation set.",
"type": [
"string",
"null"
]
},
"validationPct": {
"description": "If train-validation-holdout split was used, the percentage of the dataset used for the validation set.",
"type": [
"number",
"null"
]
},
"validationType": {
"description": "The type of validation used. Either CV (cross validation) or TVH (train-validation-holdout split).",
"enum": [
"CV",
"TVH"
],
"type": "string"
}
},
"required": [
"cvHoldoutLevel",
"cvMethod",
"datetimeCol",
"holdoutLevel",
"holdoutPct",
"partitionKeyCols",
"reps",
"trainingLevel",
"userPartitionCol",
"validationLevel",
"validationPct",
"validationType"
],
"type": "object"
},
"positiveClass": {
"description": "If the project uses binary classification, the class designated to be the positive class.",
"oneOf": [
{
"type": "string"
},
{
"type": "integer"
}
]
},
"primaryLocationColumn": {
"description": "Primary location column name",
"type": "string",
"x-versionadded": "v2.27"
},
"projectName": {
"description": "The name of the project.",
"type": "string"
},
"queryGeneratorId": {
"description": "The ID of the time series data prep query generator associated with the project, or null if there is no associated query generator.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.27"
},
"quickrun": {
"description": "If the Autopilot mode is set to quick. DEPRECATED: look at autopilot_mode instead.",
"type": "boolean",
"x-versionadded": "v2.22",
"x-versiondeprecated": "v2.30"
},
"relationshipsConfigurationId": {
"description": "Relationships configuration id to be used for Feature Discovery projects.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.27"
},
"segmentation": {
"description": "Segmentation info for the project.",
"properties": {
"parentProjectId": {
"description": "ID of the original project.",
"type": [
"string",
"null"
]
},
"segment": {
"description": "Segment value.",
"type": [
"string",
"null"
]
},
"segmentationTaskId": {
"description": "ID of the Segmentation Task.",
"type": "string"
}
},
"required": [
"segmentationTaskId"
],
"type": "object"
},
"stage": {
"description": "The stage of the project. If modeling, then the target is successfully set and modeling or predictions can proceed.",
"enum": [
"modeling",
"aim",
"fasteda",
"eda",
"eda2",
"empty"
],
"type": "string"
},
"target": {
"description": "The target of the project, null if project is unsupervised.",
"type": "string"
},
"targetType": {
"description": "The type of the selected target. Null if the project is unsupervised.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel"
],
"type": [
"string",
"null"
]
},
"unsupervisedMode": {
"description": "Indicates whether a project is unsupervised.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCase": {
"description": "The information of the use case associated with the project.",
"properties": {
"id": {
"description": "The ID of the use case.",
"type": [
"string",
"null"
]
},
"name": {
"description": "The name of the use case.",
"type": [
"string",
"null"
]
}
},
"type": "object",
"x-versionadded": "v2.36"
},
"useFeatureDiscovery": {
"description": "Indicates whether a feature discovery project was created as opposed to a regular project",
"type": "boolean",
"x-versionadded": "v2.21"
},
"useGpu": {
"description": "Indicates whether project should use GPU workers",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
}
},
"required": [
"advancedOptions",
"autopilotMode",
"catalogId",
"catalogVersionId",
"created",
"fileName",
"holdoutUnlocked",
"id",
"isScoringAvailableForModelsTrainedIntoValidationHoldout",
"maxTrainPct",
"maxTrainRows",
"metric",
"partition",
"positiveClass",
"projectName",
"queryGeneratorId",
"quickrun",
"stage",
"target",
"targetType",
"unsupervisedMode",
"useFeatureDiscovery"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The project. | ProjectRetrieveResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
PATCH /api/v2/projects/{projectId}/¶
Change project name, worker count, or unlock the holdout. If any of the optional json arguments are not provided, that aspect of the project will not be altered.
Body parameter¶
{
"properties": {
"gpuWorkerCount": {
"description": "The desired new number of gpu workers if the\n number of gpu workers should be changed.\n Must not exceed the number of gpu workers available to the user. `0` is allowed.\n `-1` requests the maximum number available to the user.",
"type": "integer"
},
"holdoutUnlocked": {
"description": "If specified, the holdout will be unlocked;\n note that the holdout cannot be relocked after unlocking",
"enum": [
"True"
],
"type": "string"
},
"projectDescription": {
"description": "The new description of the project, if the description should be updated.",
"maxLength": 500,
"type": "string",
"x-versionadded": "v2.20"
},
"projectName": {
"description": "The new name of the project, if it should be renamed.",
"maxLength": 100,
"type": "string"
},
"workerCount": {
"description": "The desired new number of workers if the\n number of workers should be changed.\n Must not exceed the number of workers available to the user. `0` is allowed.\n (New in version v2.14) `-1` requests the maximum number available to the user.",
"type": "integer"
}
},
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
| body | body | ProjectUpdate | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The project was successfully updated | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/accessControl/¶
Get a list of users who have access to this project and their roles on the project.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| offset | query | integer | true | This many results will be skipped |
| limit | query | integer | true | At most this many results are returned |
| username | query | string | false | Optional, only return the access control information for a user with this username. |
| userId | query | string | false | Optional, only return the access control information for a user with this user ID. |
| projectId | path | string | true | The project ID |
Example responses¶
200 Response
{
"properties": {
"count": {
"description": "The number of items returned.",
"type": "integer"
},
"data": {
"description": "The access control list.",
"items": {
"properties": {
"canShare": {
"description": "Whether the recipient can share the role further.",
"type": "boolean"
},
"role": {
"description": "The role of the user on this entity.",
"type": "string"
},
"userId": {
"description": "The identifier of the user that has access to this entity.",
"type": "string"
},
"username": {
"description": "The username of the user that has access to the entity.",
"type": "string"
}
},
"required": [
"canShare",
"role",
"userId",
"username"
],
"type": "object"
},
"maxItems": 1000,
"type": "array"
},
"next": {
"description": "URL pointing to the next page.",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page.",
"type": [
"string",
"null"
]
}
},
"required": [
"count",
"data",
"next",
"previous"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The project's access control list. | SharingListResponse |
| 404 | Not Found | Either the project does not exist or the user does not have permissions to view the project. | None |
| 422 | Unprocessable Entity | Both username and userId were specified | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
PATCH /api/v2/projects/{projectId}/accessControl/¶
Set roles for users on this project.
Body parameter¶
{
"properties": {
"data": {
"description": "The role to set for the user.",
"items": {
"properties": {
"role": {
"description": "The role to set for the user.",
"enum": [
"ADMIN",
"CONSUMER",
"DATA_SCIENTIST",
"EDITOR",
"OBSERVER",
"OWNER",
"READ_ONLY",
"READ_WRITE",
"USER"
],
"type": [
"string",
"null"
]
},
"username": {
"description": "The username of the user to set the role for.",
"type": "string"
}
},
"required": [
"role",
"username"
],
"type": "object"
},
"maxItems": 100,
"type": "array"
},
"includeFeatureDiscoveryEntities": {
"default": false,
"description": "Whether to share all the related entities.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"sendNotification": {
"default": true,
"description": "Send an email notification.",
"type": "boolean",
"x-versionadded": "v2.20"
}
},
"required": [
"data"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| body | body | SharingUpdateOrRemove | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 204 | No Content | Roles updated successfully. | None |
| 409 | Conflict | The request would leave the project without an owner. | None |
| 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
PATCH /api/v2/projects/{projectId}/aim/¶
Start the data modeling process.
Body parameter¶
{
"properties": {
"accuracyOptimizedMb": {
"description": "Include additional, longer-running models that will be run by the autopilot and available to run manually.",
"type": "boolean"
},
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": "string",
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": "boolean",
"x-versionadded": "v2.24"
},
"allowedPairwiseInteractionGroups": {
"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.",
"items": {
"items": {
"type": "string"
},
"maxItems": 10,
"minItems": 2,
"type": "array"
},
"maxItems": 100,
"type": "array",
"x-versionadded": "v2.20"
},
"allowedPairwiseInteractionGroupsFilename": {
"description": "Filename that was used to upload allowed_pairwise_interaction_groups. Necessary for persistence of UI/UX when you specify that parameter via file.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.20"
},
"autopilotClusterList": {
"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`.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotDataSamplingMethod": {
"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"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"default": "duration",
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"autopilotWithFeatureDiscovery": {
"description": "If true, autopilot will run on a feature list that includes features found via search for interactions.",
"type": "boolean"
},
"backtests": {
"description": "An array specifying the format of the backtests.",
"items": {
"properties": {
"gapDuration": {
"description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"index": {
"description": "The index from zero of the backtest specified by this object.",
"type": "integer"
},
"primaryTrainingEndDate": {
"description": "A datetime string representing the end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"primaryTrainingStartDate": {
"description": "A datetime string representing the start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationDuration": {
"description": "A duration string representing the duration of the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"validationEndDate": {
"description": "A datetime string representing the end date of the validation data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"index",
"validationStartDate"
],
"type": "object"
},
"maxItems": 50,
"minItems": 1,
"type": "array"
},
"biasMitigationFeatureName": {
"description": "The name of the protected feature used to mitigate bias on models.",
"minLength": 1,
"type": "string",
"x-versionadded": "v2.27"
},
"biasMitigationTechnique": {
"description": "Method applied to perform bias mitigation.",
"enum": [
"preprocessingReweighing",
"postProcessingRejectionOptionBasedClassification"
],
"type": "string",
"x-versionadded": "v2.27"
},
"blendBestModels": {
"description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"blueprintThreshold": {
"description": "The runtime (in hours) which if exceeded will exclude a model from autopilot runs.",
"maximum": 1440,
"minimum": 1,
"type": [
"integer",
"null"
]
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": "string",
"x-versionadded": "v2.15"
},
"chunkDefinitionId": {
"description": "Chunk definition id for incremental learning using chunking service",
"type": "string",
"x-versionadded": "v2.33"
},
"classMappingAggregationSettings": {
"description": "Class mapping aggregation settings.",
"properties": {
"aggregationClassName": {
"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.",
"type": "string"
},
"excludedFromAggregation": {
"default": [],
"description": "List of target values that should be guaranteed to kept as is, regardless of other settings.",
"items": {
"type": "string"
},
"type": "array"
},
"maxUnaggregatedClassValues": {
"default": 1000,
"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.",
"maximum": 1000,
"minimum": 3,
"type": "integer"
},
"minClassSupport": {
"default": 1,
"description": "Minimum number of instances necessary for each target value in the dataset. All values with fewer instances than this value will be aggregated",
"type": "integer"
}
},
"type": "object"
},
"considerBlendersInRecommendation": {
"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.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"credentials": {
"description": "List of credentials for the secondary datasets used in feature discovery project.",
"items": {
"oneOf": [
{
"properties": {
"catalogVersionId": {
"description": "The ID of the latest version of the catalog entry.",
"type": "string"
},
"password": {
"description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.",
"type": "string"
},
"url": {
"description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.",
"type": "string"
},
"user": {
"description": "The username for database authentication.",
"type": "string"
}
},
"required": [
"password",
"user"
],
"type": "object"
},
{
"properties": {
"catalogVersionId": {
"description": "The ID of the latest version of the catalog entry.",
"type": "string"
},
"credentialId": {
"description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.",
"type": "string"
},
"url": {
"description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.",
"type": "string"
}
},
"required": [
"credentialId"
],
"type": "object"
}
]
},
"maxItems": 30,
"type": "array",
"x-versionadded": "v2.19"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"cvHoldoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"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": {
"description": "The partitioning method to be applied to the training data.",
"enum": [
"random",
"user",
"stratified",
"group",
"datetime"
],
"type": "string"
},
"dateRemoval": {
"description": "If true, enable creating additional feature lists without dates (does not apply to time-aware projects).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"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.",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"disableHoldout": {
"default": false,
"description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"eventsCount": {
"description": "The name of a column specifying events count. The data in this column must be pure numeric and non negative without missing values",
"type": "string"
},
"exponentiallyWeightedMovingAlpha": {
"description": "Discount factor (alpha) used for exponentially weighted moving features",
"maximum": 1,
"minimum": 0,
"type": "number"
},
"exposure": {
"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",
"type": "string"
},
"externalPredictions": {
"description": "List of external prediction columns from the dataset.",
"items": {
"type": "string"
},
"maxItems": 100,
"minItems": 1,
"type": "array",
"x-versionadded": "v2.26"
},
"externalTimeSeriesBaselineDatasetId": {
"description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.",
"type": "string",
"x-versionadded": "v2.24"
},
"externalTimeSeriesBaselineDatasetName": {
"description": "The name of the time series baseline dataset for the project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.24"
},
"fairnessMetricsSet": {
"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"
],
"type": "string",
"x-versionadded": "v2.24"
},
"fairnessThreshold": {
"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.",
"maximum": 1,
"minimum": 0,
"type": "number",
"x-versionadded": "v2.24"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureDiscoverySupervisedFeatureReduction": {
"description": "Run supervised feature reduction for feature discovery projects.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"featureEngineeringPredictionPoint": {
"description": "The date column to be used as the prediction point for time-based feature engineering.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.20"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array"
},
"featurelistId": {
"description": "The ID of a featurelist to use for autopilot.",
"type": "string"
},
"forecastDistance": {
"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.",
"type": "string",
"x-versionadded": "v2.35"
},
"forecastOffsets": {
"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.).",
"items": {
"type": "string"
},
"maxItems": 100,
"minItems": 1,
"type": "array",
"x-versionadded": "v2.35"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"gapDuration": {
"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).",
"format": "duration",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"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.",
"format": "date-time",
"type": "string"
},
"holdoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"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": {
"description": "The percentage of the dataset to assign to the holdout set",
"maximum": 98,
"minimum": 0,
"type": "number"
},
"holdoutStartDate": {
"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.",
"format": "date-time",
"type": "string"
},
"includeBiasMitigationFeatureAsPredictorVariable": {
"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.",
"type": "boolean",
"x-versionadded": "v2.27"
},
"incrementalLearningEarlyStoppingRounds": {
"description": "Early stopping rounds for the auto incremental learning service",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.33"
},
"incrementalLearningOnBestModel": {
"description": "Automatically run incremental learning on the best model during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.33"
},
"incrementalLearningOnlyMode": {
"description": "Keep only models that support incremental learning during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.33"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"majorityDownsamplingRate": {
"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.",
"type": "number"
},
"metric": {
"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.",
"type": "string"
},
"minSecondaryValidationModelCount": {
"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.",
"maximum": 10,
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.19"
},
"mode": {
"description": "The autopilot mode to use. Either 'quick', 'auto', 'manual' or 'comprehensive'",
"enum": [
"0",
"2",
"4",
"3",
"auto",
"manual",
"comprehensive",
"quick"
],
"type": "string"
},
"modelSplits": {
"default": 5,
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"monotonicDecreasingFeaturelistId": {
"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.",
"type": [
"string",
"null"
]
},
"monotonicIncreasingFeaturelistId": {
"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.",
"type": [
"string",
"null"
]
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"type": "integer"
},
"numberOfIncrementalLearningIterationsBeforeBestModelSelection": {
"description": "Number of incremental_learning iterations before best model selection.",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.35"
},
"offset": {
"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",
"items": {
"type": "string"
},
"type": "array"
},
"onlyIncludeMonotonicBlueprints": {
"default": false,
"description": "When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot.",
"type": "boolean"
},
"partitionKeyCols": {
"description": "An array containing a single string - the name of the group partition column",
"items": {
"type": "string"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"positiveClass": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"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": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"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.",
"x-versionadded": "v2.24"
},
"prepareModelForDeployment": {
"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.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"primaryLocationColumn": {
"description": "Primary geospatial location column.",
"type": [
"string",
"null"
]
},
"protectedFeatures": {
"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.",
"items": {
"type": "string"
},
"maxItems": 10,
"minItems": 1,
"type": "array",
"x-versionadded": "v2.24"
},
"quantileLevel": {
"description": "The quantile level between 0.01 and 0.99 for specifying the Quantile metric.",
"exclusiveMaximum": 1,
"exclusiveMinimum": 0,
"type": "number",
"x-versionadded": "v2.22"
},
"quickrun": {
"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'.",
"type": "boolean",
"x-versiondeprecated": "v2.21"
},
"rateTopPctThreshold": {
"description": "The percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric.",
"maximum": 100,
"minimum": 0,
"type": "number"
},
"relationshipsConfigurationId": {
"description": "Relationships configuration id to be used for Feature Discovery projects.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.21"
},
"reps": {
"description": "The number of cross validation folds to use.",
"maximum": 999999,
"minimum": 2,
"type": "integer"
},
"responseCap": {
"description": "Used to cap the maximum response of a model",
"maximum": 1,
"minimum": 0.5,
"type": "number"
},
"runLeakageRemovedFeatureList": {
"description": "Run Autopilot on Leakage Removed feature list (if exists).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"sampleStepPct": {
"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.",
"exclusiveMinimum": 0,
"maximum": 100,
"type": "number",
"x-versionadded": "v2.20"
},
"scoringCodeOnly": {
"description": "Keep only models that can be converted to scorable java code during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"seed": {
"description": "A seed to use for randomization.",
"maximum": 999999999,
"minimum": 0,
"type": "integer"
},
"segmentationTaskId": {
"description": "Specifies the SegmentationTask that will be used for dividing the project up into multiple segmented projects.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.24"
},
"seriesId": {
"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.",
"type": "string",
"x-versionadded": "v2.35"
},
"shapOnlyMode": {
"description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"smartDownsampled": {
"description": "Whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects.",
"type": "boolean"
},
"stopWords": {
"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.",
"items": {
"maxLength": 100,
"type": "string"
},
"maxItems": 1000,
"type": "array",
"x-versionadded": "v2.21"
},
"target": {
"description": "The name of the target feature.",
"type": "string"
},
"targetType": {
"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"
],
"type": "string"
},
"trainingLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"description": "The value of the partition column indicating a row is part of the training set."
},
"treatAsExponential": {
"default": "auto",
"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"
],
"type": "string"
},
"unsupervisedMode": {
"default": false,
"description": "If True, unsupervised project (without target) will be created. ``target`` cannot be specified if ``unsupervisedMode`` is True.",
"type": "boolean",
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCrossSeriesFeatures": {
"description": "Indicating if user wants to use cross-series features.",
"type": "boolean"
},
"useGpu": {
"description": "Indicates whether project should use GPU workers",
"type": "boolean",
"x-versionadded": "v2.31"
},
"useProjectSettings": {
"description": "Specifies whether datetime-partitioned project should use project settings (i.e. backtests configuration has been modified by the user).",
"type": "boolean",
"x-versionadded": "v2.25"
},
"useSupervisedFeatureReduction": {
"default": true,
"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.",
"type": "boolean"
},
"useTimeSeries": {
"default": false,
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"userPartitionCol": {
"description": "The name of the column containing the partition assignments.",
"type": "string"
},
"validationDuration": {
"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.",
"format": "duration",
"type": "string"
},
"validationLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"description": "The value of the partition column indicating a row is part of the validation set."
},
"validationPct": {
"description": "The percentage of the dataset to assign to the validation set",
"maximum": 99,
"minimum": 0,
"type": "number"
},
"validationType": {
"description": "The validation method to be used. CV for cross validation or TVH for train-validation-holdout split.",
"enum": [
"CV",
"TVH"
],
"type": "string"
},
"weights": {
"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",
"type": "string"
},
"windowsBasisUnit": {
"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"
],
"type": "string",
"x-versionadded": "v2.14"
}
},
"required": [
"autopilotDataSelectionMethod",
"onlyIncludeMonotonicBlueprints"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
| body | body | Aim | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 202 | Accepted | Autopilot has successfully started. See the Location header. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projects/{projectId}/autopilot/¶
Pause or unpause the autopilot for a project.
Body parameter¶
{
"properties": {
"command": {
"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"
],
"type": "string"
}
},
"required": [
"command"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
| body | body | Autopilot | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 202 | Accepted | Request received | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projects/{projectId}/autopilots/¶
Start autopilot on provided featurelist.
Body parameter¶
{
"properties": {
"autopilotClusterList": {
"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'.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"blendBestModels": {
"description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"considerBlendersInRecommendation": {
"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.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"featurelistId": {
"description": "The ID of a featurelist that should be used for autopilot.",
"type": "string"
},
"mode": {
"default": "auto",
"description": "The autopilot mode.",
"enum": [
"auto",
"comprehensive",
"quick"
],
"type": "string"
},
"prepareModelForDeployment": {
"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.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"runLeakageRemovedFeatureList": {
"description": "Run Autopilot on Leakage Removed feature list (if exists).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"scoringCodeOnly": {
"description": "Keep only models that can be converted to scorable java code during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"useGpu": {
"description": "Use GPU workers for Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.33"
}
},
"required": [
"featurelistId"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
| body | body | AutopilotStart | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 201 | Created | Successfully started | None |
| 422 | Unprocessable Entity | Autopilot on this featurelist has already completed or is already in progress. This status code is also returned if target was not selected for specified project. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/datetimePartitioning/¶
Retrieve datetime partitioning configuration
The datetime partition object in the response describes the full partitioning parameters. Since it becomes available after the target has been fully specified and the project is ready for modeling, there are some additional fields available compared to the response from POST /api/v2/projects/{projectId}/datetimePartitioning/.
The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available.
All durations and datetimes will be specified in accordance with the :ref:timestamp and duration formatting rules<time_format>.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
Example responses¶
200 Response
{
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"autopilotDataSamplingMethod": {
"description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"random",
"latest"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"availableHoldoutEndDate": {
"description": "The maximum valid date of holdout scoring data.",
"format": "date-time",
"type": [
"string",
"null"
]
},
"availableTrainingDuration": {
"description": "The duration of available training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"availableTrainingRowCount": {
"description": "The number of rows in the available training data for scoring the holdout partition",
"type": "integer"
},
"availableTrainingStartDate": {
"description": "The start date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingRowCount": {
"description": "The number of rows in the available training data for this backtest.",
"type": "integer"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapRowCount": {
"description": "The number of rows in the gap between the training and the validation scoring data for this backtest.",
"type": "integer"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingRowCount": {
"description": "The number of rows in the primary training data for this backtest.",
"type": "integer"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"totalRowCount": {
"description": "The total number of rows in this backtest",
"type": "integer"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationRowCount": {
"description": "The number of rows in the validation scoring data for this backtest.",
"type": "integer"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingRowCount",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapRowCount",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingRowCount",
"primaryTrainingStartDate",
"totalRowCount",
"validationDuration",
"validationEndDate",
"validationRowCount",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.15"
},
"calendarName": {
"description": "The name of the calendar used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.17"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"dateFormat": {
"description": "The date format of the partition column.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"description": "The ID of the current optimized datetime partitioning",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"disableHoldout": {
"description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array",
"x-versionadded": "v2.9"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"gapDuration": {
"description": "The duration of the gap between the training and holdout scoring data.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"gapRowCount": {
"description": "The number of rows in the gap between the training and holdout scoring data",
"type": "integer"
},
"gapStartDate": {
"description": "The start date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of the holdout scoring data.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutRowCount": {
"description": "The number of rows in the holdout scoring data",
"type": "integer"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"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. ",
"maximum": 10,
"minimum": 1,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"numberOfDoNotDeriveFeatures": {
"description": "Number of features that are marked as \"do not derive\".",
"type": "integer",
"x-versionadded": "v2.17"
},
"numberOfKnownInAdvanceFeatures": {
"description": "Number of features that are marked as \"known in advance\".",
"type": "integer",
"x-versionadded": "v2.14"
},
"partitioningExtendedWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"partitioningWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"primaryTrainingDuration": {
"description": "The duration of primary training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"primaryTrainingRowCount": {
"description": "The number of rows in the primary training data for scoring the holdout",
"type": "integer"
},
"primaryTrainingStartDate": {
"description": "The start date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"projectId": {
"description": "The ID of the project.",
"type": "string"
},
"totalRowCount": {
"description": "The total number of rows in the project dataset",
"type": "integer"
},
"treatAsExponential": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"unsupervisedType": {
"description": "The type of unsupervised project.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.31"
},
"useCrossSeriesFeatures": {
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.14"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": [
"string",
"null"
]
},
"windowsBasisUnit": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
}
},
"required": [
"aggregationType",
"autopilotDataSamplingMethod",
"autopilotDataSelectionMethod",
"availableHoldoutEndDate",
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingRowCount",
"availableTrainingStartDate",
"backtests",
"calendarId",
"calendarName",
"crossSeriesGroupByColumns",
"dateFormat",
"datetimePartitionColumn",
"defaultToAPriori",
"defaultToDoNotDerive",
"defaultToKnownInAdvance",
"differencingMethod",
"disableHoldout",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"featureSettings",
"forecastWindowEnd",
"forecastWindowStart",
"gapDuration",
"gapEndDate",
"gapRowCount",
"gapStartDate",
"holdoutDuration",
"holdoutEndDate",
"holdoutRowCount",
"holdoutStartDate",
"modelSplits",
"multiseriesIdColumns",
"numberOfBacktests",
"numberOfDoNotDeriveFeatures",
"numberOfKnownInAdvanceFeatures",
"partitioningWarnings",
"periodicities",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingRowCount",
"primaryTrainingStartDate",
"projectId",
"totalRowCount",
"treatAsExponential",
"useCrossSeriesFeatures",
"useTimeSeries",
"validationDuration",
"windowsBasisUnit"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Generated datetime partitioning. | FinalDatetimePartitioningResponse |
| 422 | Unprocessable Entity | Partitioning has not been set. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projects/{projectId}/datetimePartitioning/¶
Preview the fully specified datetime partitioning generated by the requested configuration. DEPRECATED: Please use the optimized partitioning route instead: POST /api/v2/projects/{projectId}/optimizedDatetimePartitionings/
Populates the full datetime partitioning that would be used if the same arguments were passed to PATCH /api/v2/projects/{projectId}/aim/ based on the requested configuration, generating defaults for all non-specified values, so that potential configurations can be tested prior to setting the target and applying a configuration.
useTimeSeries controls whether a time series project should be created or a normal project that uses datetime partitioning. See :ref:Time-Series Projects<time_series_overview> for more detail on the differences between time series projects and datetime partitioned projects. Time-series projects are only available to some users and use the additional settings of featureDerivationWindowStart and featureDerivationWindowEnd to establish feature derivation window and forecastWindowStart and forecastWindowEnd to establish a forecast window. The overview referenced above provides more information about using feature derivation and forecast windows.
When specifying a feature derivation window of a forecast window, the number of units it spans (end - start) must be an integer multiple of the timeStep of the datetimePartitionColumn.
All durations and datetimes should be specified in accordance with the :ref:timestamp and duration formatting rules<time_format>.
Body parameter¶
{
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": "string",
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"default": false,
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": "boolean",
"x-versionadded": "v2.24"
},
"autopilotClusterList": {
"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`.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotDataSamplingMethod": {
"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"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"backtests": {
"description": "An array specifying individual backtests.",
"items": {
"oneOf": [
{
"description": "Method 1 - pass validation and gap durations",
"properties": {
"gapDuration": {
"description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"validationDuration": {
"description": "A duration string representing the duration of the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"gapDuration",
"index",
"validationDuration",
"validationStartDate"
],
"type": "object"
},
{
"description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.",
"properties": {
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"primaryTrainingEndDate": {
"description": "A datetime string representing the end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"primaryTrainingStartDate": {
"description": "A datetime string representing the start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationEndDate": {
"description": "A datetime string representing the end date of the validation data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"index",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationEndDate",
"validationStartDate"
],
"type": "object"
}
]
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": "string",
"x-versionadded": "v2.15"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"disableHoldout": {
"default": false,
"description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"gapDuration": {
"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).",
"format": "duration",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"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.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"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.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"default": 5,
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"treatAsExponential": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"default": false,
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": "boolean",
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCrossSeriesFeatures": {
"default": false,
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": "boolean",
"x-versionadded": "v2.14"
},
"useSupervisedFeatureReduction": {
"default": true,
"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.",
"type": "boolean"
},
"useTimeSeries": {
"default": false,
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": "string"
},
"windowsBasisUnit": {
"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"
],
"type": "string",
"x-versionadded": "v2.14"
}
},
"required": [
"datetimePartitionColumn"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| body | body | DatetimePartitioningDataForOpenApi | false | none |
Example responses¶
200 Response
{
"description": "Partitioning information for a datetime project.",
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"autopilotDataSamplingMethod": {
"description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"random",
"latest"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"availableHoldoutEndDate": {
"description": "The maximum valid date of holdout scoring data.",
"format": "date-time",
"type": [
"string",
"null"
]
},
"availableTrainingDuration": {
"description": "The duration of available training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationDuration",
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.15"
},
"calendarName": {
"description": "The name of the calendar used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.17"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"dateFormat": {
"description": "The date format of the partition column.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"description": "The ID of the current optimized datetime partitioning",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"disableHoldout": {
"description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array",
"x-versionadded": "v2.9"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"gapDuration": {
"description": "The duration of the gap between the training and holdout scoring data.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of the holdout scoring data.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"numberOfDoNotDeriveFeatures": {
"description": "Number of features that are marked as \"do not derive\".",
"type": "integer",
"x-versionadded": "v2.17"
},
"numberOfKnownInAdvanceFeatures": {
"description": "Number of features that are marked as \"known in advance\".",
"type": "integer",
"x-versionadded": "v2.14"
},
"partitioningExtendedWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"partitioningWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"primaryTrainingDuration": {
"description": "The duration of primary training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"projectId": {
"description": "The ID of the project.",
"type": "string"
},
"treatAsExponential": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"unsupervisedType": {
"description": "The type of unsupervised project.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.31"
},
"useCrossSeriesFeatures": {
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.14"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": [
"string",
"null"
]
},
"windowsBasisUnit": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
}
},
"required": [
"autopilotDataSamplingMethod",
"autopilotDataSelectionMethod",
"availableHoldoutEndDate",
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"backtests",
"dateFormat",
"datetimePartitionColumn",
"defaultToAPriori",
"defaultToDoNotDerive",
"defaultToKnownInAdvance",
"differencingMethod",
"disableHoldout",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"featureSettings",
"forecastWindowEnd",
"forecastWindowStart",
"gapDuration",
"gapEndDate",
"gapStartDate",
"holdoutDuration",
"holdoutEndDate",
"holdoutStartDate",
"multiseriesIdColumns",
"numberOfBacktests",
"numberOfDoNotDeriveFeatures",
"numberOfKnownInAdvanceFeatures",
"partitioningWarnings",
"periodicities",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"projectId",
"treatAsExponential",
"useTimeSeries",
"validationDuration",
"windowsBasisUnit"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Generated datetime partitioning. | DatetimePartitioningResponse |
| 404 | Not Found | Requested feature was not found. | None |
| 422 | Unprocessable Entity | Partitioning generation failed. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projects/{projectId}/externalTimeSeriesBaselineDataValidationJobs/¶
This route validates if a provided catalog version id can be used as baseline for calculating metrics. This functionality is available only for time series projects.For a baseline dataset to be valid, the number of unique date amd multiseries_id columnrows must match the unique number of date and multiseries_id column rows in the uploadedtraining dataset. This functionality is limited to one forecast distance. Additionally, the catalog must be a snapshot.
Body parameter¶
{
"properties": {
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"catalogVersionId": {
"description": "The version id of the external baseline data item in the AI catalog.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as the datetime partition column for the specified project.",
"type": "string"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"minItems": 1,
"type": "array"
},
"target": {
"description": "The selected target of the specified project.",
"type": "string"
}
},
"required": [
"catalogVersionId",
"datetimePartitionColumn",
"forecastWindowEnd",
"forecastWindowStart",
"target"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| body | body | ExternalTSBaselinePayload | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 202 | Accepted | Validate baseline data that is provided in the form of a catalog version id. We willconfirm that the dataset contains the proper date, target column, and multiseries ID column. If the provided dataset meets the criteria, the job will be successful. | None |
| 403 | Forbidden | User does not have access to this functionality. | None |
| 422 | Unprocessable Entity | Unable to process external time sereis baseline validation job. | None |
Response Headers¶
| Status | Header | Type | Format | Description |
|---|---|---|---|---|
| 202 | Location | string | A url that can be polled to check the status. |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/externalTimeSeriesBaselineDataValidationJobs/{baselineValidationJobId}/¶
Retrieve information to confirm if the validation job triggered via /api/v2/projects/(projectId)/externalTimeSeriesBaselineDataValidationJobs/ is valid.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project to retrieve the validation job information from. |
| baselineValidationJobId | path | string | true | The id for the validation job |
Example responses¶
200 Response
{
"properties": {
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"baselineValidationJobId": {
"description": "The id of the validation job.",
"type": "string"
},
"catalogVersionId": {
"description": "The version id of the external baseline data item in the AI catalog.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as the datetime partition column for the specified project.",
"type": "string"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isExternalBaselineDatasetValid": {
"description": "Indicates whether the external dataset has pass the validation check or not.",
"type": "boolean"
},
"message": {
"description": "A message providing mode detail on the validation result.",
"type": [
"string",
"null"
]
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"minItems": 1,
"type": "array"
},
"projectId": {
"description": "The project id of the external baseline data item.",
"type": "string"
},
"target": {
"description": "The selected target of the specified project.",
"type": "string"
}
},
"required": [
"baselineValidationJobId",
"catalogVersionId",
"datetimePartitionColumn",
"isExternalBaselineDatasetValid",
"message",
"projectId",
"target"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | ExternalTSBaselineResponse |
| 403 | Forbidden | User does not have access to this functionality. | None |
| 404 | Not Found | External time series validation job not found. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/jobs/¶
List the project's jobs.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| status | query | string | false | 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 | path | string | true | The project ID. |
Enumerated Values¶
| Parameter | Value |
|---|---|
| status | [queue, inprogress, error] |
Example responses¶
200 Response
{
"properties": {
"count": {
"description": "the number of jobs returned.",
"type": "integer"
},
"jobs": {
"description": "A json array of jobs",
"items": {
"properties": {
"id": {
"description": "The job ID.",
"type": "string"
},
"isBlocked": {
"description": "True if the job is waiting for its dependencies to be resolved first.",
"type": "boolean"
},
"jobType": {
"description": "The job type.",
"enum": [
"model",
"predict",
"trainingPredictions",
"featureImpact",
"featureEffects",
"shapImpact",
"anomalyAssessment",
"shapExplanations",
"shapMatrix",
"reasonCodesInitialization",
"reasonCodes",
"predictionExplanations",
"predictionExplanationsInitialization",
"primeDownloadValidation",
"ruleFitDownloadValidation",
"primeRulesets",
"primeModel",
"modelExport",
"usageData",
"modelXRay",
"accuracyOverTime",
"seriesAccuracy",
"validateRatingTable",
"generateComplianceDocumentation",
"automatedDocumentation",
"eda",
"pipeline",
"calculatePredictionIntervals",
"calculatePredictionIntervalBoundUsingOnlineConformal",
"batchVarTypeTransform",
"computeImageActivationMaps",
"computeImageAugmentations",
"computeImageEmbeddings",
"computeDocumentTextExtractionSamples",
"externalDatasetInsights",
"startDatetimePartitioning",
"runSegmentationTasks",
"piiDetection",
"computeBiasAndFairness",
"sensitivityTesting",
"clusterInsights",
"onnxExport",
"scoringCodeSegmentedModeling",
"insights",
"distributionPredictionModel",
"batchScoringAvailableForecastPoints",
"notebooksScheduling",
"uncategorized"
],
"type": "string"
},
"message": {
"description": "Error message in case of failure.",
"type": "string"
},
"modelId": {
"description": "The model this job is associated with.",
"type": "string"
},
"projectId": {
"description": "The project the job belongs to.",
"type": "string"
},
"status": {
"description": "The job status.",
"enum": [
"queue",
"inprogress",
"error",
"ABORTED",
"COMPLETED"
],
"type": "string"
},
"url": {
"description": "A URL that can be used to request details about the job.",
"type": "string"
}
},
"required": [
"id",
"isBlocked",
"jobType",
"message",
"modelId",
"projectId",
"status",
"url"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"type": [
"string",
"null"
]
}
},
"required": [
"count",
"jobs",
"next",
"previous"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The project's jobs | JobListResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
DELETE /api/v2/projects/{projectId}/jobs/{jobId}/¶
Cancel a pending job.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
| jobId | path | string | true | The job ID |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 204 | No Content | The job has been canceled. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/jobs/{jobId}/¶
Retrieve details for a job that has been started but has not yet completed.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
| jobId | path | string | true | The job ID |
Example responses¶
200 Response
{
"properties": {
"id": {
"description": "The job ID.",
"type": "string"
},
"isBlocked": {
"description": "True if the job is waiting for its dependencies to be resolved first.",
"type": "boolean"
},
"jobType": {
"description": "The job type.",
"enum": [
"model",
"predict",
"trainingPredictions",
"featureImpact",
"featureEffects",
"shapImpact",
"anomalyAssessment",
"shapExplanations",
"shapMatrix",
"reasonCodesInitialization",
"reasonCodes",
"predictionExplanations",
"predictionExplanationsInitialization",
"primeDownloadValidation",
"ruleFitDownloadValidation",
"primeRulesets",
"primeModel",
"modelExport",
"usageData",
"modelXRay",
"accuracyOverTime",
"seriesAccuracy",
"validateRatingTable",
"generateComplianceDocumentation",
"automatedDocumentation",
"eda",
"pipeline",
"calculatePredictionIntervals",
"calculatePredictionIntervalBoundUsingOnlineConformal",
"batchVarTypeTransform",
"computeImageActivationMaps",
"computeImageAugmentations",
"computeImageEmbeddings",
"computeDocumentTextExtractionSamples",
"externalDatasetInsights",
"startDatetimePartitioning",
"runSegmentationTasks",
"piiDetection",
"computeBiasAndFairness",
"sensitivityTesting",
"clusterInsights",
"onnxExport",
"scoringCodeSegmentedModeling",
"insights",
"distributionPredictionModel",
"batchScoringAvailableForecastPoints",
"notebooksScheduling",
"uncategorized"
],
"type": "string"
},
"message": {
"description": "Error message in case of failure.",
"type": "string"
},
"modelId": {
"description": "The model this job is associated with.",
"type": "string"
},
"projectId": {
"description": "The project the job belongs to.",
"type": "string"
},
"status": {
"description": "The job status.",
"enum": [
"queue",
"inprogress",
"error",
"ABORTED",
"COMPLETED"
],
"type": "string"
},
"url": {
"description": "A URL that can be used to request details about the job.",
"type": "string"
}
},
"required": [
"id",
"isBlocked",
"jobType",
"message",
"modelId",
"projectId",
"status",
"url"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The job details | JobDetailsResponse |
| 303 | See Other | The requested job has already finished. See the Location header for the job details. | None |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/multiseriesIds/{multiseriesId}/crossSeriesProperties/¶
Retrieve eligible cross-series group-by columns.
Note that validation will have to have been triggered via POST /api/v2/projects/{projectId}/crossSeriesProperties/ in order for results to appear here.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| crossSeriesGroupByColumns | query | any | false | The names of the columns to retrieve the validation status for. If not specified, all eligible columns will be returned. |
| projectId | path | string | true | The project to retrieve cross-series group-by columns for. |
| multiseriesId | path | string | true | The name of the column to be used as the multiseries ID column. |
Example responses¶
200 Response
{
"properties": {
"crossSeriesGroupByColumns": {
"description": "A list of columns with information about each column's eligibility as a cross-series group-by column.",
"items": {
"properties": {
"eligibility": {
"description": "Information about the column's eligibility. If the column is not eligible, this will include the reason why.",
"type": "string"
},
"isEligible": {
"description": "Indicates whether this column can be used as a group-by column.",
"type": "boolean"
},
"name": {
"description": "The name of the column.",
"type": "string"
}
},
"required": [
"eligibility",
"isEligible",
"name"
],
"type": "object"
},
"type": "array"
},
"multiseriesId": {
"description": "The name of the multiseries ID column.",
"type": "string"
}
},
"required": [
"crossSeriesGroupByColumns",
"multiseriesId"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Request was successful. | CrossSeriesGroupByColumnRetrieveResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/multiseriesNames/¶
List the individual series names of a multiseries project
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| offset | query | integer | false | Number of results to skip. |
| limit | query | integer | false | At most this many results are returned. The default may change without notice. |
| projectId | path | string | true | The project ID |
Example responses¶
200 Response
{
"properties": {
"count": {
"description": "Total number of series items in the response",
"type": "integer"
},
"data": {
"description": "Data fields of the multi series names",
"properties": {
"items": {
"description": "List of series names",
"items": {
"description": "Name of one series item",
"type": "string"
},
"type": "array"
}
},
"required": [
"items"
],
"type": "object"
},
"next": {
"description": "A URL pointing to the next page (if `null`, there is no next page).",
"type": [
"string",
"null"
]
},
"previous": {
"description": "A URL pointing to the previous page (if `null`, there is no previous page).",
"type": [
"string",
"null"
]
},
"totalSeriesCount": {
"description": "Total number of series items",
"type": "integer"
}
},
"required": [
"count",
"data",
"next",
"previous",
"totalSeriesCount"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | MultiseriesNamesControllerResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projects/{projectId}/multiseriesProperties/¶
Analyze relationships between potential partition and multiseries ID columns. Time series projects require that each timestamp have at most one row corresponding to it. However, multiple series of data can be handled within a single project by designating a multiseries ID column that assigns each row to a particular series. See the :ref:multiseries <multiseries> docs on time series projects for more information. A detection job analyzing the relationship between the multiseries ID column and the datetime partition column must be ran before it can be used. If the desired multiseries ID column(s) are known, it can be specified to limit the analysis to only those columns.
Body parameter¶
{
"properties": {
"datetimePartitionColumn": {
"description": "The date column that will be used to perform detection and validation for.",
"type": "string"
},
"multiseriesIdColumns": {
"description": "List of one or more names of potential multiseries id columns. If not provided, all numerical and categorical columns are used.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array"
}
},
"required": [
"datetimePartitionColumn"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| body | body | MultiseriesPayload | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 202 | Accepted | Request to analyze relationships between potential partition and multiseries ID columns was submitted. See Location header. | None |
Response Headers¶
| Status | Header | Type | Format | Description |
|---|---|---|---|---|
| 202 | Location | string | A url that can be polled to check the status. |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/¶
List all created optimized datetime partitioning configurations
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| offset | query | integer | false | This many results will be skipped. |
| limit | query | integer | true | At most this many results are returned. |
| projectId | path | string | true | The project ID |
Example responses¶
200 Response
{
"properties": {
"count": {
"description": "Number of items returned on this page.",
"type": "integer"
},
"data": {
"description": "A list of datetime partitionings returned in order of creation.",
"items": {
"description": "Datetime partition information.",
"properties": {
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"id": {
"description": "The ID of the datetime partitioning.",
"type": "string"
},
"partitionData": {
"description": "Partitioning information for a datetime project.",
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"autopilotDataSamplingMethod": {
"description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"random",
"latest"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"availableHoldoutEndDate": {
"description": "The maximum valid date of holdout scoring data.",
"format": "date-time",
"type": [
"string",
"null"
]
},
"availableTrainingDuration": {
"description": "The duration of available training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationDuration",
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.15"
},
"calendarName": {
"description": "The name of the calendar used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.17"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"dateFormat": {
"description": "The date format of the partition column.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"description": "The ID of the current optimized datetime partitioning",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"disableHoldout": {
"description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array",
"x-versionadded": "v2.9"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"gapDuration": {
"description": "The duration of the gap between the training and holdout scoring data.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of the holdout scoring data.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"numberOfDoNotDeriveFeatures": {
"description": "Number of features that are marked as \"do not derive\".",
"type": "integer",
"x-versionadded": "v2.17"
},
"numberOfKnownInAdvanceFeatures": {
"description": "Number of features that are marked as \"known in advance\".",
"type": "integer",
"x-versionadded": "v2.14"
},
"partitioningExtendedWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"partitioningWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"primaryTrainingDuration": {
"description": "The duration of primary training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"projectId": {
"description": "The ID of the project.",
"type": "string"
},
"treatAsExponential": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"unsupervisedType": {
"description": "The type of unsupervised project.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.31"
},
"useCrossSeriesFeatures": {
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.14"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": [
"string",
"null"
]
},
"windowsBasisUnit": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
}
},
"required": [
"autopilotDataSamplingMethod",
"autopilotDataSelectionMethod",
"availableHoldoutEndDate",
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"backtests",
"dateFormat",
"datetimePartitionColumn",
"defaultToAPriori",
"defaultToDoNotDerive",
"defaultToKnownInAdvance",
"differencingMethod",
"disableHoldout",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"featureSettings",
"forecastWindowEnd",
"forecastWindowStart",
"gapDuration",
"gapEndDate",
"gapStartDate",
"holdoutDuration",
"holdoutEndDate",
"holdoutStartDate",
"multiseriesIdColumns",
"numberOfBacktests",
"numberOfDoNotDeriveFeatures",
"numberOfKnownInAdvanceFeatures",
"partitioningWarnings",
"periodicities",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"projectId",
"treatAsExponential",
"useTimeSeries",
"validationDuration",
"windowsBasisUnit"
],
"type": "object"
},
"target": {
"description": "The name of the target column.",
"type": "string"
}
},
"required": [
"datetimePartitionColumn",
"id",
"partitionData",
"target"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
}
},
"required": [
"data",
"next",
"previous"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | List of optimized datetime partitionings for projectId | OptimizedDatetimePartitioningListResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projects/{projectId}/optimizedDatetimePartitionings/¶
Create an optimized datetime partitioning configuration using the target.
Initializes a job to construct an optimized datetime partitioning using the date and target information to ensure that backtests sufficiently cover regions of interest in the target. This is an asynchronous job. The results of the asynchronous job (backtests and other parameters can be used in the synchronous version.
useTimeSeries controls whether a time series project should be created or a normal project that uses datetime partitioning. See :ref:Time-Series Projects<time_series_overview> for more detail on the differences between time series projects and datetime partitioned projects. Time-series projects are only available to some users and use the additional settings of featureDerivationWindowStart and featureDerivationWindowEnd to establish feature derivation window and forecastWindowStart and forecastWindowEnd to establish a forecast window. The overview referenced above provides more information about using feature derivation and forecast windows.
When specifying a feature derivation window of a forecast window, the number of units it spans (end - start) must be an integer multiple of the timeStep of the datetimePartitionColumn.
All durations and datetimes should be specified in accordance with the :ref:timestamp and duration formatting rules<time_format>.
Body parameter¶
{
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": "string",
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"default": false,
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": "boolean",
"x-versionadded": "v2.24"
},
"autopilotClusterList": {
"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`.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotDataSamplingMethod": {
"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"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"backtests": {
"description": "An array specifying individual backtests.",
"items": {
"oneOf": [
{
"description": "Method 1 - pass validation and gap durations",
"properties": {
"gapDuration": {
"description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"validationDuration": {
"description": "A duration string representing the duration of the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"gapDuration",
"index",
"validationDuration",
"validationStartDate"
],
"type": "object"
},
{
"description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.",
"properties": {
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"primaryTrainingEndDate": {
"description": "A datetime string representing the end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"primaryTrainingStartDate": {
"description": "A datetime string representing the start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationEndDate": {
"description": "A datetime string representing the end date of the validation data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"index",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationEndDate",
"validationStartDate"
],
"type": "object"
}
]
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": "string",
"x-versionadded": "v2.15"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"disableHoldout": {
"default": false,
"description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"gapDuration": {
"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).",
"format": "duration",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"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.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"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.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"default": 5,
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"target": {
"description": "Name of the target column.",
"type": "string",
"x-versionadded": "v2.22"
},
"treatAsExponential": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"default": false,
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": "boolean",
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCrossSeriesFeatures": {
"default": false,
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": "boolean",
"x-versionadded": "v2.14"
},
"useSupervisedFeatureReduction": {
"default": true,
"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.",
"type": "boolean"
},
"useTimeSeries": {
"default": false,
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": "string"
},
"windowsBasisUnit": {
"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"
],
"type": "string",
"x-versionadded": "v2.14"
}
},
"required": [
"datetimePartitionColumn"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| body | body | OptimizedDatetimePartitioningData | false | none |
Example responses¶
200 Response
{
"description": "Partitioning information for a datetime project.",
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"autopilotDataSamplingMethod": {
"description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"random",
"latest"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"availableHoldoutEndDate": {
"description": "The maximum valid date of holdout scoring data.",
"format": "date-time",
"type": [
"string",
"null"
]
},
"availableTrainingDuration": {
"description": "The duration of available training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationDuration",
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.15"
},
"calendarName": {
"description": "The name of the calendar used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.17"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"dateFormat": {
"description": "The date format of the partition column.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"description": "The ID of the current optimized datetime partitioning",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"disableHoldout": {
"description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array",
"x-versionadded": "v2.9"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"gapDuration": {
"description": "The duration of the gap between the training and holdout scoring data.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of the holdout scoring data.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"numberOfDoNotDeriveFeatures": {
"description": "Number of features that are marked as \"do not derive\".",
"type": "integer",
"x-versionadded": "v2.17"
},
"numberOfKnownInAdvanceFeatures": {
"description": "Number of features that are marked as \"known in advance\".",
"type": "integer",
"x-versionadded": "v2.14"
},
"partitioningExtendedWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"partitioningWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"primaryTrainingDuration": {
"description": "The duration of primary training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"projectId": {
"description": "The ID of the project.",
"type": "string"
},
"treatAsExponential": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"unsupervisedType": {
"description": "The type of unsupervised project.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.31"
},
"useCrossSeriesFeatures": {
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.14"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": [
"string",
"null"
]
},
"windowsBasisUnit": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
}
},
"required": [
"autopilotDataSamplingMethod",
"autopilotDataSelectionMethod",
"availableHoldoutEndDate",
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"backtests",
"dateFormat",
"datetimePartitionColumn",
"defaultToAPriori",
"defaultToDoNotDerive",
"defaultToKnownInAdvance",
"differencingMethod",
"disableHoldout",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"featureSettings",
"forecastWindowEnd",
"forecastWindowStart",
"gapDuration",
"gapEndDate",
"gapStartDate",
"holdoutDuration",
"holdoutEndDate",
"holdoutStartDate",
"multiseriesIdColumns",
"numberOfBacktests",
"numberOfDoNotDeriveFeatures",
"numberOfKnownInAdvanceFeatures",
"partitioningWarnings",
"periodicities",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"projectId",
"treatAsExponential",
"useTimeSeries",
"validationDuration",
"windowsBasisUnit"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Generated datetime partitioning. | DatetimePartitioningResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/¶
Retrieve optimized datetime partitioning configuration
The optimized datetime partition objects are structurally identical to the original datetime partition objects, however they are retrieved from a mongo database after creation as opposed to being calculated synchronously. The datetime partition object in the response describes the full partitioning parameters.
The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available.
.. note:: All durations and datetimes should be specified in accordance with the :ref:timestamp and duration formatting rules <time_format>.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| datetimePartitioningId | path | string | true | The ID of the datetime partitioning to retrieve. |
Example responses¶
200 Response
{
"description": "Partitioning information for a datetime project.",
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"autopilotDataSamplingMethod": {
"description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"random",
"latest"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"availableHoldoutEndDate": {
"description": "The maximum valid date of holdout scoring data.",
"format": "date-time",
"type": [
"string",
"null"
]
},
"availableTrainingDuration": {
"description": "The duration of available training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationDuration",
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.15"
},
"calendarName": {
"description": "The name of the calendar used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.17"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"dateFormat": {
"description": "The date format of the partition column.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"description": "The ID of the current optimized datetime partitioning",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"disableHoldout": {
"description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array",
"x-versionadded": "v2.9"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"gapDuration": {
"description": "The duration of the gap between the training and holdout scoring data.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of the holdout scoring data.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"numberOfDoNotDeriveFeatures": {
"description": "Number of features that are marked as \"do not derive\".",
"type": "integer",
"x-versionadded": "v2.17"
},
"numberOfKnownInAdvanceFeatures": {
"description": "Number of features that are marked as \"known in advance\".",
"type": "integer",
"x-versionadded": "v2.14"
},
"partitioningExtendedWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"partitioningWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"primaryTrainingDuration": {
"description": "The duration of primary training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"projectId": {
"description": "The ID of the project.",
"type": "string"
},
"treatAsExponential": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"unsupervisedType": {
"description": "The type of unsupervised project.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.31"
},
"useCrossSeriesFeatures": {
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.14"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": [
"string",
"null"
]
},
"windowsBasisUnit": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
}
},
"required": [
"autopilotDataSamplingMethod",
"autopilotDataSelectionMethod",
"availableHoldoutEndDate",
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"backtests",
"dateFormat",
"datetimePartitionColumn",
"defaultToAPriori",
"defaultToDoNotDerive",
"defaultToKnownInAdvance",
"differencingMethod",
"disableHoldout",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"featureSettings",
"forecastWindowEnd",
"forecastWindowStart",
"gapDuration",
"gapEndDate",
"gapStartDate",
"holdoutDuration",
"holdoutEndDate",
"holdoutStartDate",
"multiseriesIdColumns",
"numberOfBacktests",
"numberOfDoNotDeriveFeatures",
"numberOfKnownInAdvanceFeatures",
"partitioningWarnings",
"periodicities",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"projectId",
"treatAsExponential",
"useTimeSeries",
"validationDuration",
"windowsBasisUnit"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Optimized datetime partitioning configuration | DatetimePartitioningResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/datetimePartitioningInput/¶
Retrieve optimized datetime partitioning input
The datetime partition object in the response describes the inputs used to create the full partitioning object.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| datetimePartitioningId | path | string | true | The ID of the datetime partitioning to retrieve. |
Example responses¶
200 Response
{
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": "string",
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"default": false,
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": "boolean",
"x-versionadded": "v2.24"
},
"autopilotClusterList": {
"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`.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotDataSamplingMethod": {
"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"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"backtests": {
"description": "An array specifying individual backtests.",
"items": {
"oneOf": [
{
"description": "Method 1 - pass validation and gap durations",
"properties": {
"gapDuration": {
"description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"validationDuration": {
"description": "A duration string representing the duration of the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"gapDuration",
"index",
"validationDuration",
"validationStartDate"
],
"type": "object"
},
{
"description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.",
"properties": {
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"primaryTrainingEndDate": {
"description": "A datetime string representing the end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"primaryTrainingStartDate": {
"description": "A datetime string representing the start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationEndDate": {
"description": "A datetime string representing the end date of the validation data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"index",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationEndDate",
"validationStartDate"
],
"type": "object"
}
]
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": "string",
"x-versionadded": "v2.15"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"disableHoldout": {
"default": false,
"description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"gapDuration": {
"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).",
"format": "duration",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"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.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"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.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"default": 5,
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"target": {
"description": "Name of the target column.",
"type": "string",
"x-versionadded": "v2.22"
},
"treatAsExponential": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"default": false,
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": "boolean",
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCrossSeriesFeatures": {
"default": false,
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": "boolean",
"x-versionadded": "v2.14"
},
"useSupervisedFeatureReduction": {
"default": true,
"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.",
"type": "boolean"
},
"useTimeSeries": {
"default": false,
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": "string"
},
"windowsBasisUnit": {
"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"
],
"type": "string",
"x-versionadded": "v2.14"
}
},
"required": [
"datetimePartitionColumn"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | Optimized datetime partitioning input | OptimizedDatetimePartitioningData |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/datetimePartitioningLog/¶
Retrieve the datetime partitioning log content and log length for an optimized datetime partitioning as JSON.
The Date/Time Partitioning Log provides details about the partitioning process for an OTV or Time Series project.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| offset | query | integer | false | Number of results to skip. |
| limit | query | integer | false | At most this many results are returned. The default may change without notice. |
| projectId | path | string | true | The project ID |
| datetimePartitioningId | path | string | true | The ID of the datetime partitioning to retrieve. |
Example responses¶
200 Response
{
"properties": {
"count": {
"description": "The number of items returned on this page.",
"type": "integer"
},
"datetimePartitioningLog": {
"description": "The content of the date/time partitioning log.",
"type": "string"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"totalLogLines": {
"description": "The total number of lines in feature derivation log.",
"type": "integer"
}
},
"required": [
"count",
"datetimePartitioningLog",
"next",
"previous",
"totalLogLines"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | DatetimePartitioningLogListControllerResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/datetimePartitioningLog/file/¶
Retrieve a text file containing the datetime partitioning log.
The Date/Time Partitioning Log provides details about the partitioning process for an OTV or Time Series project.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| datetimePartitioningId | path | string | true | The ID of the datetime partitioning to retrieve. |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | None |
Response Headers¶
| Status | Header | Type | Format | Description |
|---|---|---|---|---|
| 200 | Content-Disposition | string | attachment;filename=<filename>.txt The suggested filename is dynamically generated |
|
| 200 | Content-Type | string | MIME type of the returned data |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/segmentationTaskJobResults/{segmentationTaskId}/¶
Retrieve the statuses of segmentation task jobs associated with the ID.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| segmentationTaskId | path | string | true | The ID of the segmentation task to check the status of. |
Example responses¶
200 Response
{
"properties": {
"completedJobs": {
"description": "List of completed segmentation tasks",
"items": {
"properties": {
"name": {
"description": "Name of the segmentation task job",
"type": "string"
},
"segmentationTaskId": {
"description": "Id of the completed segmentation task",
"type": "string"
},
"segmentsCount": {
"description": "Number of segments produced by the task",
"type": "integer"
},
"segmentsEda": {
"description": "Array of segments EDA information.",
"items": {
"properties": {
"maxDate": {
"description": "Latest date in segment.",
"format": "date-time",
"type": "string"
},
"minDate": {
"description": "Earliest date in segment.",
"format": "date-time",
"type": "string"
},
"name": {
"description": "Name of the segment.",
"type": "string"
},
"numberOfRows": {
"description": "Number of rows in segment.",
"exclusiveMinimum": 0,
"type": "integer"
},
"sizeInBytes": {
"description": "Size of segment in bytes.",
"exclusiveMinimum": 0,
"type": "integer"
}
},
"required": [
"maxDate",
"minDate",
"name",
"numberOfRows",
"sizeInBytes"
],
"type": "object"
},
"type": "array"
},
"url": {
"description": "Url to retrieve detailed information about the segmentation task",
"type": "string"
}
},
"required": [
"name",
"segmentationTaskId",
"segmentsCount",
"segmentsEda",
"url"
],
"type": "object"
},
"type": "array"
},
"failedJobs": {
"description": "List of failed segmentation tasks",
"items": {
"properties": {
"message": {
"description": "Response containing the error message from the segmentation task",
"type": "string"
},
"name": {
"description": "Name of the segmentation task job",
"type": "string"
},
"parameters": {
"description": "Parameters submitted by the user to the failed job",
"type": "object"
}
},
"required": [
"message",
"name",
"parameters"
],
"type": "object"
},
"type": "array"
},
"numberOfJobs": {
"description": "Total number of completed and failed jobs processed",
"type": "integer"
}
},
"required": [
"completedJobs",
"failedJobs",
"numberOfJobs"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | SegmentationResultsResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/segmentationTasks/¶
List all segmentation tasks created for the project.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| offset | query | integer | false | This many results will be skipped. |
| limit | query | integer | false | At most this many results are returned. |
| projectId | path | string | true | The project ID |
Example responses¶
200 Response
{
"properties": {
"count": {
"description": "Number of items returned on this page.",
"type": "integer"
},
"data": {
"description": "List of the segmentation tasks that are associated with the project Id.",
"items": {
"properties": {
"created": {
"description": "Date and time when the segmentation task was originally created",
"format": "date-time",
"type": "string"
},
"data": {
"description": "Data for the segmentation task",
"properties": {
"clusteringModelId": {
"description": "Id of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringModelName": {
"description": "Name of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringProjectId": {
"description": "Id of the project used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "Name of the datetime partitioning column used by the segmentation task",
"type": "string"
},
"modelPackageId": {
"description": "External model package id used by the segmentation task for automated segmentation",
"type": "string"
},
"multiseriesIdColumns": {
"description": "The multiseries id columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
},
"userDefinedSegmentIdColumns": {
"description": "The user defined segmentation columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"metadata": {
"description": "Metadata for the segmentation task",
"properties": {
"useAutomatedSegmentation": {
"description": "Segmentation task uses automated segmentation",
"type": "boolean"
},
"useMultiseriesIdColumns": {
"description": "Segmentation task uses a multiseries column",
"type": "boolean"
},
"useTimeSeries": {
"description": "Segmentation task is a time series task",
"type": "boolean"
}
},
"required": [
"useAutomatedSegmentation",
"useMultiseriesIdColumns",
"useTimeSeries"
],
"type": "object"
},
"name": {
"description": "Name of the segmentation task",
"type": "string"
},
"projectId": {
"description": "Id of the parent project associated with the segmentation task",
"type": "string"
},
"segmentationTaskId": {
"description": "Id of the segmentation task",
"type": "string"
},
"segments": {
"description": "Names of the unique segments generated by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
},
"segmentsCount": {
"description": "Number of segments generated by the segmentation task",
"type": "integer"
},
"segmentsEda": {
"description": "Array of segments EDA information.",
"items": {
"properties": {
"maxDate": {
"description": "Latest date in segment.",
"format": "date-time",
"type": "string"
},
"minDate": {
"description": "Earliest date in segment.",
"format": "date-time",
"type": "string"
},
"name": {
"description": "Name of the segment.",
"type": "string"
},
"numberOfRows": {
"description": "Number of rows in segment.",
"exclusiveMinimum": 0,
"type": "integer"
},
"sizeInBytes": {
"description": "Size of segment in bytes.",
"exclusiveMinimum": 0,
"type": "integer"
}
},
"required": [
"maxDate",
"minDate",
"name",
"numberOfRows",
"sizeInBytes"
],
"type": "object"
},
"type": "array"
},
"type": {
"description": "Type of segmentation task (e.g. AutoML, AutoTS)",
"type": "string"
}
},
"required": [
"created",
"data",
"metadata",
"name",
"projectId",
"segmentationTaskId",
"segments",
"segmentsCount",
"segmentsEda",
"type"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
}
},
"required": [
"data",
"next",
"previous"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | SegmentationTaskListResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
POST /api/v2/projects/{projectId}/segmentationTasks/¶
Create segmentation tasks for the dataset used in the project.
Body parameter¶
{
"properties": {
"datetimePartitionColumn": {
"description": "The date column that will be used to identify the date in time series segmentation.",
"type": "string"
},
"modelPackageId": {
"description": "Model package id for using an external model registry package.",
"type": "string"
},
"multiseriesIdColumns": {
"description": "List of one or more names of multiseries id columns.",
"items": {
"type": "string"
},
"maxItems": 1,
"minItems": 1,
"type": "array"
},
"target": {
"description": "The target for the dataset",
"type": "string"
},
"useAutomatedSegmentation": {
"default": false,
"description": "Enable the use of automated segmentation tasks",
"type": "boolean"
},
"useTimeSeries": {
"default": false,
"description": "Enable time series based segmentation tasks",
"type": "boolean"
},
"userDefinedSegmentIdColumns": {
"description": "List of one or more names of columns to be used for user-defined business rule segmentations.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array"
}
},
"required": [
"target"
],
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| body | body | SegmentationTaskCreate | false | none |
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 202 | Accepted | Job submitted. See Location header. | None |
Response Headers¶
| Status | Header | Type | Format | Description |
|---|---|---|---|---|
| 202 | Location | string | A url that can be polled to check the status. |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/segmentationTasks/{segmentationTaskId}/¶
Retrieve information about a segmentation task.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| segmentationTaskId | path | string | true | The ID of the segmentation task. |
Example responses¶
200 Response
{
"properties": {
"created": {
"description": "Date and time when the segmentation task was originally created",
"format": "date-time",
"type": "string"
},
"data": {
"description": "Data for the segmentation task",
"properties": {
"clusteringModelId": {
"description": "Id of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringModelName": {
"description": "Name of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringProjectId": {
"description": "Id of the project used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "Name of the datetime partitioning column used by the segmentation task",
"type": "string"
},
"modelPackageId": {
"description": "External model package id used by the segmentation task for automated segmentation",
"type": "string"
},
"multiseriesIdColumns": {
"description": "The multiseries id columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
},
"userDefinedSegmentIdColumns": {
"description": "The user defined segmentation columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"metadata": {
"description": "Metadata for the segmentation task",
"properties": {
"useAutomatedSegmentation": {
"description": "Segmentation task uses automated segmentation",
"type": "boolean"
},
"useMultiseriesIdColumns": {
"description": "Segmentation task uses a multiseries column",
"type": "boolean"
},
"useTimeSeries": {
"description": "Segmentation task is a time series task",
"type": "boolean"
}
},
"required": [
"useAutomatedSegmentation",
"useMultiseriesIdColumns",
"useTimeSeries"
],
"type": "object"
},
"name": {
"description": "Name of the segmentation task",
"type": "string"
},
"projectId": {
"description": "Id of the parent project associated with the segmentation task",
"type": "string"
},
"segmentationTaskId": {
"description": "Id of the segmentation task",
"type": "string"
},
"segments": {
"description": "Names of the unique segments generated by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
},
"segmentsCount": {
"description": "Number of segments generated by the segmentation task",
"type": "integer"
},
"segmentsEda": {
"description": "Array of segments EDA information.",
"items": {
"properties": {
"maxDate": {
"description": "Latest date in segment.",
"format": "date-time",
"type": "string"
},
"minDate": {
"description": "Earliest date in segment.",
"format": "date-time",
"type": "string"
},
"name": {
"description": "Name of the segment.",
"type": "string"
},
"numberOfRows": {
"description": "Number of rows in segment.",
"exclusiveMinimum": 0,
"type": "integer"
},
"sizeInBytes": {
"description": "Size of segment in bytes.",
"exclusiveMinimum": 0,
"type": "integer"
}
},
"required": [
"maxDate",
"minDate",
"name",
"numberOfRows",
"sizeInBytes"
],
"type": "object"
},
"type": "array"
},
"type": {
"description": "Type of segmentation task (e.g. AutoML, AutoTS)",
"type": "string"
}
},
"required": [
"created",
"data",
"metadata",
"name",
"projectId",
"segmentationTaskId",
"segments",
"segmentsCount",
"segmentsEda",
"type"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | SegmentationTaskResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/segmentationTasks/{segmentationTaskId}/mappings/¶
Retrieve the seriesId to segmentId mappings for a Segmentation Task.
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| offset | query | integer | false | This many results will be skipped. |
| limit | query | integer | false | At most this many results are returned. |
| projectId | path | string | true | The project ID |
| segmentationTaskId | path | string | true | The ID of the segmentation task. |
Example responses¶
200 Response
{
"properties": {
"count": {
"description": "Number of items returned on this page.",
"type": "integer"
},
"data": {
"description": "An array of segmentation mappings.",
"items": {
"properties": {
"segment": {
"description": "The segment name associated with the multiseries ID column by the segmentation task",
"type": "string"
},
"seriesId": {
"description": "The multiseries ID column used to identify series for segmentation",
"type": "string"
}
},
"required": [
"segment",
"seriesId"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
}
},
"required": [
"data",
"next",
"previous"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | none | SegmentationTaskSegmentMappingsResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
PATCH /api/v2/projects/{projectId}/segments/{segmentId}/¶
The only supported operation right now is segment restart, which removes existing child segment project and starts another child project instead for the given segment. Should be only used for child segments which are stuck during project startup or upload.
Body parameter¶
{
"properties": {
"operation": {
"default": "restart",
"description": "Name of operation to do on project segment",
"enum": [
"restart"
],
"type": "string"
}
},
"type": "object"
}
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID |
| segmentId | path | string | true | The name of the segment |
| body | body | ProjectSegmentUpdate | false | none |
Example responses¶
200 Response
{
"properties": {
"projectId": {
"description": "New project id of the restarted segment",
"type": "string"
},
"segmentId": {
"description": "The name of the restarted segment",
"type": "string"
}
},
"required": [
"projectId",
"segmentId"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The segment is updated. | ProjectSegmentUpdateResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
GET /api/v2/projects/{projectId}/status/¶
Check the status of a project
Parameters
| Name | In | Type | Required | Description |
|---|---|---|---|---|
| projectId | path | string | true | The project ID. |
Example responses¶
200 Response
{
"properties": {
"autopilotDone": {
"description": "whether the current autopilot run has finished",
"type": "boolean"
},
"stage": {
"description": "the current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed",
"enum": [
"modeling",
"aim",
"fasteda",
"eda",
"eda2",
"empty"
],
"type": "string"
},
"stageDescription": {
"description": "a description of the current stage of the project",
"type": "string"
}
},
"required": [
"autopilotDone",
"stage",
"stageDescription"
],
"type": "object"
}
Responses¶
| Status | Meaning | Description | Schema |
|---|---|---|---|
| 200 | OK | The project status | ProjectStatusResponse |
To perform this operation, you must be authenticated by means of one of the following methods:
BearerAuth
Schemas¶
AccessControl
{
"properties": {
"canShare": {
"description": "Whether the recipient can share the role further.",
"type": "boolean"
},
"role": {
"description": "The role of the user on this entity.",
"type": "string"
},
"userId": {
"description": "The identifier of the user that has access to this entity.",
"type": "string"
},
"username": {
"description": "The username of the user that has access to the entity.",
"type": "string"
}
},
"required": [
"canShare",
"role",
"userId",
"username"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| canShare | boolean | true | Whether the recipient can share the role further. | |
| role | string | true | The role of the user on this entity. | |
| userId | string | true | The identifier of the user that has access to this entity. | |
| username | string | true | The username of the user that has access to the entity. |
Aim
{
"properties": {
"accuracyOptimizedMb": {
"description": "Include additional, longer-running models that will be run by the autopilot and available to run manually.",
"type": "boolean"
},
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": "string",
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": "boolean",
"x-versionadded": "v2.24"
},
"allowedPairwiseInteractionGroups": {
"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.",
"items": {
"items": {
"type": "string"
},
"maxItems": 10,
"minItems": 2,
"type": "array"
},
"maxItems": 100,
"type": "array",
"x-versionadded": "v2.20"
},
"allowedPairwiseInteractionGroupsFilename": {
"description": "Filename that was used to upload allowed_pairwise_interaction_groups. Necessary for persistence of UI/UX when you specify that parameter via file.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.20"
},
"autopilotClusterList": {
"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`.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotDataSamplingMethod": {
"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"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"default": "duration",
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"autopilotWithFeatureDiscovery": {
"description": "If true, autopilot will run on a feature list that includes features found via search for interactions.",
"type": "boolean"
},
"backtests": {
"description": "An array specifying the format of the backtests.",
"items": {
"properties": {
"gapDuration": {
"description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"index": {
"description": "The index from zero of the backtest specified by this object.",
"type": "integer"
},
"primaryTrainingEndDate": {
"description": "A datetime string representing the end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"primaryTrainingStartDate": {
"description": "A datetime string representing the start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationDuration": {
"description": "A duration string representing the duration of the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"validationEndDate": {
"description": "A datetime string representing the end date of the validation data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"index",
"validationStartDate"
],
"type": "object"
},
"maxItems": 50,
"minItems": 1,
"type": "array"
},
"biasMitigationFeatureName": {
"description": "The name of the protected feature used to mitigate bias on models.",
"minLength": 1,
"type": "string",
"x-versionadded": "v2.27"
},
"biasMitigationTechnique": {
"description": "Method applied to perform bias mitigation.",
"enum": [
"preprocessingReweighing",
"postProcessingRejectionOptionBasedClassification"
],
"type": "string",
"x-versionadded": "v2.27"
},
"blendBestModels": {
"description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"blueprintThreshold": {
"description": "The runtime (in hours) which if exceeded will exclude a model from autopilot runs.",
"maximum": 1440,
"minimum": 1,
"type": [
"integer",
"null"
]
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": "string",
"x-versionadded": "v2.15"
},
"chunkDefinitionId": {
"description": "Chunk definition id for incremental learning using chunking service",
"type": "string",
"x-versionadded": "v2.33"
},
"classMappingAggregationSettings": {
"description": "Class mapping aggregation settings.",
"properties": {
"aggregationClassName": {
"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.",
"type": "string"
},
"excludedFromAggregation": {
"default": [],
"description": "List of target values that should be guaranteed to kept as is, regardless of other settings.",
"items": {
"type": "string"
},
"type": "array"
},
"maxUnaggregatedClassValues": {
"default": 1000,
"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.",
"maximum": 1000,
"minimum": 3,
"type": "integer"
},
"minClassSupport": {
"default": 1,
"description": "Minimum number of instances necessary for each target value in the dataset. All values with fewer instances than this value will be aggregated",
"type": "integer"
}
},
"type": "object"
},
"considerBlendersInRecommendation": {
"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.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"credentials": {
"description": "List of credentials for the secondary datasets used in feature discovery project.",
"items": {
"oneOf": [
{
"properties": {
"catalogVersionId": {
"description": "The ID of the latest version of the catalog entry.",
"type": "string"
},
"password": {
"description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.",
"type": "string"
},
"url": {
"description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.",
"type": "string"
},
"user": {
"description": "The username for database authentication.",
"type": "string"
}
},
"required": [
"password",
"user"
],
"type": "object"
},
{
"properties": {
"catalogVersionId": {
"description": "The ID of the latest version of the catalog entry.",
"type": "string"
},
"credentialId": {
"description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.",
"type": "string"
},
"url": {
"description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.",
"type": "string"
}
},
"required": [
"credentialId"
],
"type": "object"
}
]
},
"maxItems": 30,
"type": "array",
"x-versionadded": "v2.19"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"cvHoldoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"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": {
"description": "The partitioning method to be applied to the training data.",
"enum": [
"random",
"user",
"stratified",
"group",
"datetime"
],
"type": "string"
},
"dateRemoval": {
"description": "If true, enable creating additional feature lists without dates (does not apply to time-aware projects).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"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.",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"disableHoldout": {
"default": false,
"description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"eventsCount": {
"description": "The name of a column specifying events count. The data in this column must be pure numeric and non negative without missing values",
"type": "string"
},
"exponentiallyWeightedMovingAlpha": {
"description": "Discount factor (alpha) used for exponentially weighted moving features",
"maximum": 1,
"minimum": 0,
"type": "number"
},
"exposure": {
"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",
"type": "string"
},
"externalPredictions": {
"description": "List of external prediction columns from the dataset.",
"items": {
"type": "string"
},
"maxItems": 100,
"minItems": 1,
"type": "array",
"x-versionadded": "v2.26"
},
"externalTimeSeriesBaselineDatasetId": {
"description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.",
"type": "string",
"x-versionadded": "v2.24"
},
"externalTimeSeriesBaselineDatasetName": {
"description": "The name of the time series baseline dataset for the project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.24"
},
"fairnessMetricsSet": {
"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"
],
"type": "string",
"x-versionadded": "v2.24"
},
"fairnessThreshold": {
"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.",
"maximum": 1,
"minimum": 0,
"type": "number",
"x-versionadded": "v2.24"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureDiscoverySupervisedFeatureReduction": {
"description": "Run supervised feature reduction for feature discovery projects.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"featureEngineeringPredictionPoint": {
"description": "The date column to be used as the prediction point for time-based feature engineering.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.20"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array"
},
"featurelistId": {
"description": "The ID of a featurelist to use for autopilot.",
"type": "string"
},
"forecastDistance": {
"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.",
"type": "string",
"x-versionadded": "v2.35"
},
"forecastOffsets": {
"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.).",
"items": {
"type": "string"
},
"maxItems": 100,
"minItems": 1,
"type": "array",
"x-versionadded": "v2.35"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"gapDuration": {
"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).",
"format": "duration",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"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.",
"format": "date-time",
"type": "string"
},
"holdoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"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": {
"description": "The percentage of the dataset to assign to the holdout set",
"maximum": 98,
"minimum": 0,
"type": "number"
},
"holdoutStartDate": {
"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.",
"format": "date-time",
"type": "string"
},
"includeBiasMitigationFeatureAsPredictorVariable": {
"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.",
"type": "boolean",
"x-versionadded": "v2.27"
},
"incrementalLearningEarlyStoppingRounds": {
"description": "Early stopping rounds for the auto incremental learning service",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.33"
},
"incrementalLearningOnBestModel": {
"description": "Automatically run incremental learning on the best model during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.33"
},
"incrementalLearningOnlyMode": {
"description": "Keep only models that support incremental learning during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.33"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"majorityDownsamplingRate": {
"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.",
"type": "number"
},
"metric": {
"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.",
"type": "string"
},
"minSecondaryValidationModelCount": {
"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.",
"maximum": 10,
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.19"
},
"mode": {
"description": "The autopilot mode to use. Either 'quick', 'auto', 'manual' or 'comprehensive'",
"enum": [
"0",
"2",
"4",
"3",
"auto",
"manual",
"comprehensive",
"quick"
],
"type": "string"
},
"modelSplits": {
"default": 5,
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"monotonicDecreasingFeaturelistId": {
"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.",
"type": [
"string",
"null"
]
},
"monotonicIncreasingFeaturelistId": {
"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.",
"type": [
"string",
"null"
]
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"type": "integer"
},
"numberOfIncrementalLearningIterationsBeforeBestModelSelection": {
"description": "Number of incremental_learning iterations before best model selection.",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.35"
},
"offset": {
"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",
"items": {
"type": "string"
},
"type": "array"
},
"onlyIncludeMonotonicBlueprints": {
"default": false,
"description": "When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot.",
"type": "boolean"
},
"partitionKeyCols": {
"description": "An array containing a single string - the name of the group partition column",
"items": {
"type": "string"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"positiveClass": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"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": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"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.",
"x-versionadded": "v2.24"
},
"prepareModelForDeployment": {
"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.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"primaryLocationColumn": {
"description": "Primary geospatial location column.",
"type": [
"string",
"null"
]
},
"protectedFeatures": {
"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.",
"items": {
"type": "string"
},
"maxItems": 10,
"minItems": 1,
"type": "array",
"x-versionadded": "v2.24"
},
"quantileLevel": {
"description": "The quantile level between 0.01 and 0.99 for specifying the Quantile metric.",
"exclusiveMaximum": 1,
"exclusiveMinimum": 0,
"type": "number",
"x-versionadded": "v2.22"
},
"quickrun": {
"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'.",
"type": "boolean",
"x-versiondeprecated": "v2.21"
},
"rateTopPctThreshold": {
"description": "The percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric.",
"maximum": 100,
"minimum": 0,
"type": "number"
},
"relationshipsConfigurationId": {
"description": "Relationships configuration id to be used for Feature Discovery projects.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.21"
},
"reps": {
"description": "The number of cross validation folds to use.",
"maximum": 999999,
"minimum": 2,
"type": "integer"
},
"responseCap": {
"description": "Used to cap the maximum response of a model",
"maximum": 1,
"minimum": 0.5,
"type": "number"
},
"runLeakageRemovedFeatureList": {
"description": "Run Autopilot on Leakage Removed feature list (if exists).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"sampleStepPct": {
"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.",
"exclusiveMinimum": 0,
"maximum": 100,
"type": "number",
"x-versionadded": "v2.20"
},
"scoringCodeOnly": {
"description": "Keep only models that can be converted to scorable java code during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"seed": {
"description": "A seed to use for randomization.",
"maximum": 999999999,
"minimum": 0,
"type": "integer"
},
"segmentationTaskId": {
"description": "Specifies the SegmentationTask that will be used for dividing the project up into multiple segmented projects.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.24"
},
"seriesId": {
"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.",
"type": "string",
"x-versionadded": "v2.35"
},
"shapOnlyMode": {
"description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"smartDownsampled": {
"description": "Whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects.",
"type": "boolean"
},
"stopWords": {
"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.",
"items": {
"maxLength": 100,
"type": "string"
},
"maxItems": 1000,
"type": "array",
"x-versionadded": "v2.21"
},
"target": {
"description": "The name of the target feature.",
"type": "string"
},
"targetType": {
"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"
],
"type": "string"
},
"trainingLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"description": "The value of the partition column indicating a row is part of the training set."
},
"treatAsExponential": {
"default": "auto",
"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"
],
"type": "string"
},
"unsupervisedMode": {
"default": false,
"description": "If True, unsupervised project (without target) will be created. ``target`` cannot be specified if ``unsupervisedMode`` is True.",
"type": "boolean",
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCrossSeriesFeatures": {
"description": "Indicating if user wants to use cross-series features.",
"type": "boolean"
},
"useGpu": {
"description": "Indicates whether project should use GPU workers",
"type": "boolean",
"x-versionadded": "v2.31"
},
"useProjectSettings": {
"description": "Specifies whether datetime-partitioned project should use project settings (i.e. backtests configuration has been modified by the user).",
"type": "boolean",
"x-versionadded": "v2.25"
},
"useSupervisedFeatureReduction": {
"default": true,
"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.",
"type": "boolean"
},
"useTimeSeries": {
"default": false,
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"userPartitionCol": {
"description": "The name of the column containing the partition assignments.",
"type": "string"
},
"validationDuration": {
"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.",
"format": "duration",
"type": "string"
},
"validationLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "integer"
},
{
"type": "number"
}
],
"description": "The value of the partition column indicating a row is part of the validation set."
},
"validationPct": {
"description": "The percentage of the dataset to assign to the validation set",
"maximum": 99,
"minimum": 0,
"type": "number"
},
"validationType": {
"description": "The validation method to be used. CV for cross validation or TVH for train-validation-holdout split.",
"enum": [
"CV",
"TVH"
],
"type": "string"
},
"weights": {
"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",
"type": "string"
},
"windowsBasisUnit": {
"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"
],
"type": "string",
"x-versionadded": "v2.14"
}
},
"required": [
"autopilotDataSelectionMethod",
"onlyIncludeMonotonicBlueprints"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| accuracyOptimizedMb | boolean | false | Include additional, longer-running models that will be run by the autopilot and available to run manually. | |
| aggregationType | string | false | For multiseries projects only. The aggregation type to apply when creating cross-series features. | |
| allowPartialHistoryTimeSeriesPredictions | boolean | false | Specifies whether the time series predictions can use partial historical data. | |
| allowedPairwiseInteractionGroups | [array] | false | maxItems: 100 |
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,null | false | Filename that was used to upload allowed_pairwise_interaction_groups. Necessary for persistence of UI/UX when you specify that parameter via file. | |
| autopilotClusterList | [integer] | false | maxItems: 10 |
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 | false | 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 | true | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | |
| autopilotWithFeatureDiscovery | boolean | false | If true, autopilot will run on a feature list that includes features found via search for interactions. | |
| backtests | [Backtest] | false | maxItems: 50 minItems: 1 |
An array specifying the format of the backtests. |
| biasMitigationFeatureName | string | false | minLength: 1 minLength: 1 |
The name of the protected feature used to mitigate bias on models. |
| biasMitigationTechnique | string | false | Method applied to perform bias mitigation. | |
| blendBestModels | boolean | false | Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects. | |
| blueprintThreshold | integer,null | false | maximum: 1440 minimum: 1 |
The runtime (in hours) which if exceeded will exclude a model from autopilot runs. |
| calendarId | string | false | The ID of the calendar to be used in this project. | |
| chunkDefinitionId | string | false | Chunk definition id for incremental learning using chunking service | |
| classMappingAggregationSettings | ClassMappingAggregationSettings | false | Class mapping aggregation settings. | |
| considerBlendersInRecommendation | boolean | false | 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 | [oneOf] | false | maxItems: 30 |
List of credentials for the secondary datasets used in feature discovery project. |
oneOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | PasswordCredentials | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | CredentialId | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| crossSeriesGroupByColumns | [string] | false | maxItems: 1 |
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 | any | false | 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. |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| cvMethod | string | false | The partitioning method to be applied to the training data. | |
| dateRemoval | boolean | false | If true, enable creating additional feature lists without dates (does not apply to time-aware projects). | |
| datetimePartitionColumn | string | false | The date column that will be used as a datetime partition column. | |
| datetimePartitioningId | string | false | 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 | false | Renamed to defaultToKnownInAdvance. |
|
| defaultToDoNotDerive | boolean | false | 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 | false | 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 | false | 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 | false | Whether to suppress allocating a holdout fold. If disableHoldout is set to true, holdoutStartDate and holdoutDuration must not be set. |
|
| eventsCount | string | false | 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 | false | maximum: 1 minimum: 0 |
Discount factor (alpha) used for exponentially weighted moving features |
| exposure | string | false | 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 | [string] | false | maxItems: 100 minItems: 1 |
List of external prediction columns from the dataset. |
| externalTimeSeriesBaselineDatasetId | string | false | Catalog version id for external prediction data that can be used as a baseline to calculate new metrics. | |
| externalTimeSeriesBaselineDatasetName | string,null | false | The name of the time series baseline dataset for the project. | |
| fairnessMetricsSet | string | false | 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 | false | maximum: 1 minimum: 0 |
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 | false | maximum: 0 |
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 | false | maximum: 0 |
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 | false | Run supervised feature reduction for feature discovery projects. | |
| featureEngineeringPredictionPoint | string,null | false | The date column to be used as the prediction point for time-based feature engineering. | |
| featureSettings | [FeatureSetting] | false | An array specifying per feature settings. Features can be left unspecified. | |
| featurelistId | string | false | The ID of a featurelist to use for autopilot. | |
| forecastDistance | string | false | 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 | [string] | false | maxItems: 100 minItems: 1 |
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 | false | minimum: 0 |
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 | false | minimum: 0 |
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(duration) | false | 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(duration) | false | 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(date-time) | false | 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 | any | false | 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 |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| holdoutPct | number | false | maximum: 98 minimum: 0 |
The percentage of the dataset to assign to the holdout set |
| holdoutStartDate | string(date-time) | false | 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 | false | 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 | false | minimum: 0 |
Early stopping rounds for the auto incremental learning service |
| incrementalLearningOnBestModel | boolean | false | Automatically run incremental learning on the best model during Autopilot run. | |
| incrementalLearningOnlyMode | boolean | false | Keep only models that support incremental learning during Autopilot run. | |
| isHoldoutModified | boolean | false | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | |
| majorityDownsamplingRate | number | false | 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 | false | 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 | false | maximum: 10 minimum: 0 |
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 | false | The autopilot mode to use. Either 'quick', 'auto', 'manual' or 'comprehensive' | |
| modelSplits | integer | false | maximum: 10 minimum: 1 |
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,null | false | 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,null | false | 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 | [string] | false | minItems: 1 |
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 | false | 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 | false | maximum: 10 minimum: 1 |
Number of incremental_learning iterations before best model selection. |
| offset | [string] | false | 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 | true | When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot. | |
| partitionKeyCols | [string] | false | An array containing a single string - the name of the group partition column | |
| periodicities | [Periodicity] | false | 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 | any | false | 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. |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| preferableTargetValue | any | false | 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. |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| prepareModelForDeployment | boolean | false | 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,null | false | Primary geospatial location column. | |
| protectedFeatures | [string] | false | maxItems: 10 minItems: 1 |
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 | false | The quantile level between 0.01 and 0.99 for specifying the Quantile metric. | |
| quickrun | boolean | false | (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 | false | maximum: 100 minimum: 0 |
The percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric. |
| relationshipsConfigurationId | string,null | false | Relationships configuration id to be used for Feature Discovery projects. | |
| reps | integer | false | maximum: 999999 minimum: 2 |
The number of cross validation folds to use. |
| responseCap | number | false | maximum: 1 minimum: 0.5 |
Used to cap the maximum response of a model |
| runLeakageRemovedFeatureList | boolean | false | Run Autopilot on Leakage Removed feature list (if exists). | |
| sampleStepPct | number | false | maximum: 100 |
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 | false | Keep only models that can be converted to scorable java code during Autopilot run. | |
| seed | integer | false | maximum: 999999999 minimum: 0 |
A seed to use for randomization. |
| segmentationTaskId | string,null | false | Specifies the SegmentationTask that will be used for dividing the project up into multiple segmented projects. | |
| seriesId | string | false | 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 | false | Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. | |
| smartDownsampled | boolean | false | Whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects. | |
| stopWords | [string] | false | maxItems: 1000 |
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 | false | The name of the target feature. | |
| targetType | string | false | 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 | any | false | The value of the partition column indicating a row is part of the training set. |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| treatAsExponential | string | false | 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 | false | If True, unsupervised project (without target) will be created. target cannot be specified if unsupervisedMode is True. |
|
| unsupervisedType | string,null | false | The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly. |
|
| useCrossSeriesFeatures | boolean | false | Indicating if user wants to use cross-series features. | |
| useGpu | boolean | false | Indicates whether project should use GPU workers | |
| useProjectSettings | boolean | false | Specifies whether datetime-partitioned project should use project settings (i.e. backtests configuration has been modified by the user). | |
| useSupervisedFeatureReduction | boolean | false | 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 | false | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | |
| userPartitionCol | string | false | The name of the column containing the partition assignments. | |
| validationDuration | string(duration) | false | 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 | any | false | The value of the partition column indicating a row is part of the validation set. |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| validationPct | number | false | maximum: 99 minimum: 0 |
The percentage of the dataset to assign to the validation set |
| validationType | string | false | The validation method to be used. CV for cross validation or TVH for train-validation-holdout split. | |
| weights | string | false | 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 | false | 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. |
Enumerated Values¶
| Property | Value |
|---|---|
| aggregationType | [total, average] |
| autopilotDataSamplingMethod | [random, latest] |
| autopilotDataSelectionMethod | [duration, rowCount] |
| biasMitigationTechnique | [preprocessingReweighing, postProcessingRejectionOptionBasedClassification] |
| cvMethod | [random, user, stratified, group, datetime] |
| differencingMethod | [auto, none, simple, seasonal] |
| fairnessMetricsSet | [proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity, favorableAndUnfavorablePredictiveValueParity] |
| mode | [0, 2, 4, 3, auto, manual, comprehensive, quick] |
| targetType | [Binary, Regression, Multiclass, Multilabel] |
| treatAsExponential | [auto, never, always] |
| unsupervisedType | [anomaly, clustering] |
| validationType | [CV, TVH] |
| windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] |
AllowExtra
{
"description": "Parameters submitted by the user to the failed job",
"type": "object"
}
Parameters submitted by the user to the failed job
Properties¶
None
Autopilot
{
"properties": {
"command": {
"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"
],
"type": "string"
}
},
"required": [
"command"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| command | string | true | 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. |
Enumerated Values¶
| Property | Value |
|---|---|
| command | [start, stop] |
AutopilotStart
{
"properties": {
"autopilotClusterList": {
"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'.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"blendBestModels": {
"description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"considerBlendersInRecommendation": {
"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.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"featurelistId": {
"description": "The ID of a featurelist that should be used for autopilot.",
"type": "string"
},
"mode": {
"default": "auto",
"description": "The autopilot mode.",
"enum": [
"auto",
"comprehensive",
"quick"
],
"type": "string"
},
"prepareModelForDeployment": {
"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.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"runLeakageRemovedFeatureList": {
"description": "Run Autopilot on Leakage Removed feature list (if exists).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"scoringCodeOnly": {
"description": "Keep only models that can be converted to scorable java code during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"useGpu": {
"description": "Use GPU workers for Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.33"
}
},
"required": [
"featurelistId"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| autopilotClusterList | [integer] | false | maxItems: 10 |
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 | false | Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects. | |
| considerBlendersInRecommendation | boolean | false | 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 | true | The ID of a featurelist that should be used for autopilot. | |
| mode | string | false | The autopilot mode. | |
| prepareModelForDeployment | boolean | false | 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 | false | Run Autopilot on Leakage Removed feature list (if exists). | |
| scoringCodeOnly | boolean | false | Keep only models that can be converted to scorable java code during Autopilot run. | |
| useGpu | boolean | false | Use GPU workers for Autopilot run. |
Enumerated Values¶
| Property | Value |
|---|---|
| mode | [auto, comprehensive, quick] |
AzureServicePrincipalCredentials
{
"properties": {
"azureTenantId": {
"description": "Tenant ID of the Azure AD service principal.",
"type": "string"
},
"clientId": {
"description": "Client ID of the Azure AD service principal.",
"type": "string"
},
"clientSecret": {
"description": "Client Secret of the Azure AD service principal.",
"type": "string"
},
"configId": {
"description": "ID of secure configurations of credentials shared by admin.",
"type": "string",
"x-versionadded": "v2.35"
},
"credentialType": {
"description": "The type of these credentials, 'azure_service_principal' here.",
"enum": [
"azure_service_principal"
],
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| azureTenantId | string | false | Tenant ID of the Azure AD service principal. | |
| clientId | string | false | Client ID of the Azure AD service principal. | |
| clientSecret | string | false | Client Secret of the Azure AD service principal. | |
| configId | string | false | ID of secure configurations of credentials shared by admin. | |
| credentialType | string | true | The type of these credentials, 'azure_service_principal' here. |
Enumerated Values¶
| Property | Value |
|---|---|
| credentialType | azure_service_principal |
Backtest
{
"properties": {
"gapDuration": {
"description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"index": {
"description": "The index from zero of the backtest specified by this object.",
"type": "integer"
},
"primaryTrainingEndDate": {
"description": "A datetime string representing the end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"primaryTrainingStartDate": {
"description": "A datetime string representing the start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationDuration": {
"description": "A duration string representing the duration of the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"validationEndDate": {
"description": "A datetime string representing the end date of the validation data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"index",
"validationStartDate"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| gapDuration | string(duration) | false | A duration string representing the duration of the gap between the training and the validation data for this backtest. | |
| index | integer | true | The index from zero of the backtest specified by this object. | |
| primaryTrainingEndDate | string(date-time) | false | A datetime string representing the end date of the primary training data for this backtest. | |
| primaryTrainingStartDate | string(date-time) | false | A datetime string representing the start date of the primary training data for this backtest. | |
| validationDuration | string(duration) | false | A duration string representing the duration of the validation data for this backtest. | |
| validationEndDate | string(date-time) | false | A datetime string representing the end date of the validation data for this backtest. | |
| validationStartDate | string(date-time) | true | A datetime string representing the start date of the validation data for this backtest. |
BacktestNewMethodForOpenApi
{
"description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.",
"properties": {
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"primaryTrainingEndDate": {
"description": "A datetime string representing the end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"primaryTrainingStartDate": {
"description": "A datetime string representing the start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationEndDate": {
"description": "A datetime string representing the end date of the validation data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"index",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationEndDate",
"validationStartDate"
],
"type": "object"
}
Method 2 - directly configure the start and end dates of each partition, including the training partition.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| index | integer | true | The index from zero of the backtest. | |
| primaryTrainingEndDate | string(date-time) | true | A datetime string representing the end date of the primary training data for this backtest. | |
| primaryTrainingStartDate | string(date-time) | true | A datetime string representing the start date of the primary training data for this backtest. | |
| validationEndDate | string(date-time) | true | A datetime string representing the end date of the validation data for this backtest. | |
| validationStartDate | string(date-time) | true | A datetime string representing the start date of the validation data for this backtest. |
BacktestOldMethodForOpenApi
{
"description": "Method 1 - pass validation and gap durations",
"properties": {
"gapDuration": {
"description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"validationDuration": {
"description": "A duration string representing the duration of the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"gapDuration",
"index",
"validationDuration",
"validationStartDate"
],
"type": "object"
}
Method 1 - pass validation and gap durations
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| gapDuration | string(duration) | true | A duration string representing the duration of the gap between the training and the validation data for this backtest. | |
| index | integer | true | The index from zero of the backtest. | |
| validationDuration | string(duration) | true | A duration string representing the duration of the validation data for this backtest. | |
| validationStartDate | string(date-time) | true | A datetime string representing the start date of the validation data for this backtest. |
BacktestResponse
{
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationDuration",
"validationEndDate",
"validationStartDate"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| availableTrainingDuration | string(duration) | true | The duration of the available training data for this backtest. | |
| availableTrainingEndDate | string(date-time) | true | The end date of the available training data for this backtest. | |
| availableTrainingStartDate | string(date-time) | true | The start date of the available training data for this backtest. | |
| gapDuration | string(duration) | true | The duration of the gap between the training and the validation scoring data for this backtest. | |
| gapEndDate | string(date-time) | true | The end date of the gap between the training and validation scoring data for this backtest. | |
| gapStartDate | string(date-time) | true | The start date of the gap between the training and validation scoring data for this backtest. | |
| index | integer | true | The index from zero of this backtest. | |
| primaryTrainingDuration | string(duration) | true | The duration of the primary training data for this backtest. | |
| primaryTrainingEndDate | string(date-time) | true | The end date of the primary training data for this backtest. | |
| primaryTrainingStartDate | string(date-time) | true | The start date of the primary training data for this backtest. | |
| validationDuration | string | true | The duration of the validation scoring data for this backtest. | |
| validationEndDate | string(date-time) | true | The end date of the validation scoring data for this backtest. | |
| validationStartDate | string(date-time) | true | The start date of the validation scoring data for this backtest. |
Backtests
{
"properties": {
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"validationEndDate",
"validationStartDate"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| validationEndDate | string(date-time) | true | The end date of the validation scoring data for this backtest. | |
| validationStartDate | string(date-time) | true | The start date of the validation scoring data for this backtest. |
BasicCredentials
{
"properties": {
"credentialType": {
"description": "The type of these credentials, 'basic' here.",
"enum": [
"basic"
],
"type": "string"
},
"password": {
"description": "The password for database authentication. The password is encrypted at rest and never saved / stored.",
"type": "string"
},
"user": {
"description": "The username for database authentication.",
"type": "string"
}
},
"required": [
"credentialType",
"password",
"user"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| credentialType | string | true | The type of these credentials, 'basic' here. | |
| password | string | true | The password for database authentication. The password is encrypted at rest and never saved / stored. | |
| user | string | true | The username for database authentication. |
Enumerated Values¶
| Property | Value |
|---|---|
| credentialType | basic |
ClassMappingAggregationSettings
{
"description": "Class mapping aggregation settings.",
"properties": {
"aggregationClassName": {
"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.",
"type": "string"
},
"excludedFromAggregation": {
"default": [],
"description": "List of target values that should be guaranteed to kept as is, regardless of other settings.",
"items": {
"type": "string"
},
"type": "array"
},
"maxUnaggregatedClassValues": {
"default": 1000,
"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.",
"maximum": 1000,
"minimum": 3,
"type": "integer"
},
"minClassSupport": {
"default": 1,
"description": "Minimum number of instances necessary for each target value in the dataset. All values with fewer instances than this value will be aggregated",
"type": "integer"
}
},
"type": "object"
}
Class mapping aggregation settings.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| aggregationClassName | string | false | 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 | [string] | false | List of target values that should be guaranteed to kept as is, regardless of other settings. | |
| maxUnaggregatedClassValues | integer | false | maximum: 1000 minimum: 3 |
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 | integer | false | Minimum number of instances necessary for each target value in the dataset. All values with fewer instances than this value will be aggregated |
CredentialId
{
"properties": {
"catalogVersionId": {
"description": "The ID of the latest version of the catalog entry.",
"type": "string"
},
"credentialId": {
"description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.",
"type": "string"
},
"url": {
"description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.",
"type": "string"
}
},
"required": [
"credentialId"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| catalogVersionId | string | false | The ID of the latest version of the catalog entry. | |
| credentialId | string | true | The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional. | |
| url | string | false | The link to retrieve more detailed information about the entity that uses this catalog dataset. |
CrossSeriesGroupByColumnRetrieveResponse
{
"properties": {
"crossSeriesGroupByColumns": {
"description": "A list of columns with information about each column's eligibility as a cross-series group-by column.",
"items": {
"properties": {
"eligibility": {
"description": "Information about the column's eligibility. If the column is not eligible, this will include the reason why.",
"type": "string"
},
"isEligible": {
"description": "Indicates whether this column can be used as a group-by column.",
"type": "boolean"
},
"name": {
"description": "The name of the column.",
"type": "string"
}
},
"required": [
"eligibility",
"isEligible",
"name"
],
"type": "object"
},
"type": "array"
},
"multiseriesId": {
"description": "The name of the multiseries ID column.",
"type": "string"
}
},
"required": [
"crossSeriesGroupByColumns",
"multiseriesId"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| crossSeriesGroupByColumns | [CrossSeriesGroupByColumnsListItem] | true | A list of columns with information about each column's eligibility as a cross-series group-by column. | |
| multiseriesId | string | true | The name of the multiseries ID column. |
CrossSeriesGroupByColumnsListItem
{
"properties": {
"eligibility": {
"description": "Information about the column's eligibility. If the column is not eligible, this will include the reason why.",
"type": "string"
},
"isEligible": {
"description": "Indicates whether this column can be used as a group-by column.",
"type": "boolean"
},
"name": {
"description": "The name of the column.",
"type": "string"
}
},
"required": [
"eligibility",
"isEligible",
"name"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| eligibility | string | true | Information about the column's eligibility. If the column is not eligible, this will include the reason why. | |
| isEligible | boolean | true | Indicates whether this column can be used as a group-by column. | |
| name | string | true | The name of the column. |
DatabricksAccessTokenCredentials
{
"properties": {
"credentialType": {
"description": "The type of these credentials, 'databricks_access_token_account' here.",
"enum": [
"databricks_access_token_account"
],
"type": "string"
},
"databricksAccessToken": {
"description": "Databricks personal access token.",
"minLength": 1,
"type": "string"
}
},
"required": [
"credentialType",
"databricksAccessToken"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| credentialType | string | true | The type of these credentials, 'databricks_access_token_account' here. | |
| databricksAccessToken | string | true | minLength: 1 minLength: 1 |
Databricks personal access token. |
Enumerated Values¶
| Property | Value |
|---|---|
| credentialType | databricks_access_token_account |
DatabricksServicePrincipalCredentials
{
"properties": {
"clientId": {
"description": "Client ID for Databricks service principal.",
"minLength": 1,
"type": "string"
},
"clientSecret": {
"description": "Client secret for Databricks service principal.",
"minLength": 1,
"type": "string"
},
"configId": {
"description": "The ID of the saved shared credentials. If specified, cannot include clientIdand clientSecret.",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 'databricks_service_principal_account' here.",
"enum": [
"databricks_service_principal_account"
],
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| clientId | string | false | minLength: 1 minLength: 1 |
Client ID for Databricks service principal. |
| clientSecret | string | false | minLength: 1 minLength: 1 |
Client secret for Databricks service principal. |
| configId | string | false | The ID of the saved shared credentials. If specified, cannot include clientIdand clientSecret. | |
| credentialType | string | true | The type of these credentials, 'databricks_service_principal_account' here. |
Enumerated Values¶
| Property | Value |
|---|---|
| credentialType | databricks_service_principal_account |
DatetimePartitioningDataForOpenApi
{
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": "string",
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"default": false,
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": "boolean",
"x-versionadded": "v2.24"
},
"autopilotClusterList": {
"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`.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotDataSamplingMethod": {
"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"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"backtests": {
"description": "An array specifying individual backtests.",
"items": {
"oneOf": [
{
"description": "Method 1 - pass validation and gap durations",
"properties": {
"gapDuration": {
"description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"validationDuration": {
"description": "A duration string representing the duration of the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"gapDuration",
"index",
"validationDuration",
"validationStartDate"
],
"type": "object"
},
{
"description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.",
"properties": {
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"primaryTrainingEndDate": {
"description": "A datetime string representing the end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"primaryTrainingStartDate": {
"description": "A datetime string representing the start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationEndDate": {
"description": "A datetime string representing the end date of the validation data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"index",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationEndDate",
"validationStartDate"
],
"type": "object"
}
]
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": "string",
"x-versionadded": "v2.15"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"disableHoldout": {
"default": false,
"description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"gapDuration": {
"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).",
"format": "duration",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"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.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"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.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"default": 5,
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"treatAsExponential": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"default": false,
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": "boolean",
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCrossSeriesFeatures": {
"default": false,
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": "boolean",
"x-versionadded": "v2.14"
},
"useSupervisedFeatureReduction": {
"default": true,
"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.",
"type": "boolean"
},
"useTimeSeries": {
"default": false,
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": "string"
},
"windowsBasisUnit": {
"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"
],
"type": "string",
"x-versionadded": "v2.14"
}
},
"required": [
"datetimePartitionColumn"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| aggregationType | string | false | For multiseries projects only. The aggregation type to apply when creating cross-series features. | |
| allowPartialHistoryTimeSeriesPredictions | boolean | false | Specifies whether the time series predictions can use partial historical data. | |
| autopilotClusterList | [integer] | false | maxItems: 10 |
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 | false | 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 | false | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | |
| backtests | [oneOf] | false | maxItems: 20 minItems: 1 |
An array specifying individual backtests. |
oneOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | BacktestOldMethodForOpenApi | false | Method 1 - pass validation and gap durations |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | BacktestNewMethodForOpenApi | false | Method 2 - directly configure the start and end dates of each partition, including the training partition. |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| calendarId | string | false | The ID of the calendar to be used in this project. | |
| clusteringBufferDisabled | boolean | false | A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project. | |
| crossSeriesGroupByColumns | [string] | false | maxItems: 1 |
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 | true | The date column that will be used as a datetime partition column. | |
| defaultToAPriori | boolean | false | Renamed to defaultToKnownInAdvance. |
|
| defaultToDoNotDerive | boolean | false | 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 | false | 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 | false | 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 | false | Whether to suppress allocating a holdout fold. If disableHoldout is set to true, holdoutStartDate and holdoutDuration must not be set. |
|
| featureDerivationWindowEnd | integer | false | maximum: 0 |
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 | false | maximum: 0 |
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 | [FeatureSetting] | false | An array specifying per feature settings. Features can be left unspecified. | |
| forecastWindowEnd | integer | false | minimum: 0 |
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 | false | minimum: 0 |
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(duration) | false | 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(duration) | false | 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(date-time) | false | 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(date-time) | false | 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 | false | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | |
| modelSplits | integer | false | maximum: 10 minimum: 1 |
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 | [string] | false | minItems: 1 |
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 | false | maximum: 20 minimum: 1 |
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 | [Periodicity] | false | 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 | false | 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 | false | A boolean value indicating whether an unsupervised project should be created. | |
| unsupervisedType | string,null | false | The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly. |
|
| useCrossSeriesFeatures | boolean | false | For multiseries projects only. Indicating whether to use cross-series features. | |
| useSupervisedFeatureReduction | boolean | false | 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 | false | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | |
| validationDuration | string(duration) | false | 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 | false | 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. |
Enumerated Values¶
| Property | Value |
|---|---|
| aggregationType | [total, average] |
| autopilotDataSamplingMethod | [random, latest] |
| autopilotDataSelectionMethod | [duration, rowCount] |
| differencingMethod | [auto, none, simple, seasonal] |
| treatAsExponential | [auto, never, always] |
| unsupervisedType | [anomaly, clustering] |
| windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] |
DatetimePartitioningLogListControllerResponse
{
"properties": {
"count": {
"description": "The number of items returned on this page.",
"type": "integer"
},
"datetimePartitioningLog": {
"description": "The content of the date/time partitioning log.",
"type": "string"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"totalLogLines": {
"description": "The total number of lines in feature derivation log.",
"type": "integer"
}
},
"required": [
"count",
"datetimePartitioningLog",
"next",
"previous",
"totalLogLines"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| count | integer | true | The number of items returned on this page. | |
| datetimePartitioningLog | string | true | The content of the date/time partitioning log. | |
| next | string,null(uri) | true | URL pointing to the next page (if null, there is no next page). | |
| previous | string,null(uri) | true | URL pointing to the previous page (if null, there is no previous page). | |
| totalLogLines | integer | true | The total number of lines in feature derivation log. |
DatetimePartitioningResponse
{
"description": "Partitioning information for a datetime project.",
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"autopilotDataSamplingMethod": {
"description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"random",
"latest"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"availableHoldoutEndDate": {
"description": "The maximum valid date of holdout scoring data.",
"format": "date-time",
"type": [
"string",
"null"
]
},
"availableTrainingDuration": {
"description": "The duration of available training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationDuration",
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.15"
},
"calendarName": {
"description": "The name of the calendar used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.17"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"dateFormat": {
"description": "The date format of the partition column.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"description": "The ID of the current optimized datetime partitioning",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"disableHoldout": {
"description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array",
"x-versionadded": "v2.9"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"gapDuration": {
"description": "The duration of the gap between the training and holdout scoring data.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of the holdout scoring data.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"numberOfDoNotDeriveFeatures": {
"description": "Number of features that are marked as \"do not derive\".",
"type": "integer",
"x-versionadded": "v2.17"
},
"numberOfKnownInAdvanceFeatures": {
"description": "Number of features that are marked as \"known in advance\".",
"type": "integer",
"x-versionadded": "v2.14"
},
"partitioningExtendedWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"partitioningWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"primaryTrainingDuration": {
"description": "The duration of primary training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"projectId": {
"description": "The ID of the project.",
"type": "string"
},
"treatAsExponential": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"unsupervisedType": {
"description": "The type of unsupervised project.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.31"
},
"useCrossSeriesFeatures": {
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.14"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": [
"string",
"null"
]
},
"windowsBasisUnit": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
}
},
"required": [
"autopilotDataSamplingMethod",
"autopilotDataSelectionMethod",
"availableHoldoutEndDate",
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"backtests",
"dateFormat",
"datetimePartitionColumn",
"defaultToAPriori",
"defaultToDoNotDerive",
"defaultToKnownInAdvance",
"differencingMethod",
"disableHoldout",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"featureSettings",
"forecastWindowEnd",
"forecastWindowStart",
"gapDuration",
"gapEndDate",
"gapStartDate",
"holdoutDuration",
"holdoutEndDate",
"holdoutStartDate",
"multiseriesIdColumns",
"numberOfBacktests",
"numberOfDoNotDeriveFeatures",
"numberOfKnownInAdvanceFeatures",
"partitioningWarnings",
"periodicities",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"projectId",
"treatAsExponential",
"useTimeSeries",
"validationDuration",
"windowsBasisUnit"
],
"type": "object"
}
Partitioning information for a datetime project.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| aggregationType | string,null | false | For multiseries projects only. The aggregation type to apply when creating cross-series features. | |
| allowPartialHistoryTimeSeriesPredictions | boolean,null | false | Specifies whether the time series predictions can use partial historical data. | |
| autopilotDataSamplingMethod | string | true | The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets. | |
| autopilotDataSelectionMethod | string | true | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | |
| availableHoldoutEndDate | string,null(date-time) | true | The maximum valid date of holdout scoring data. | |
| availableTrainingDuration | string(duration) | true | The duration of available training duration for scoring the holdout. | |
| availableTrainingEndDate | string(date-time) | true | The end date of available training data for scoring the holdout. | |
| availableTrainingStartDate | string(date-time) | true | The start date of available training data for scoring the holdout. | |
| backtests | [BacktestResponse] | true | maxItems: 20 minItems: 1 |
An array of the configured backtests. |
| calendarId | string,null | false | The ID of the calendar to be used in this project. | |
| calendarName | string,null | false | The name of the calendar used in this project. | |
| clusteringBufferDisabled | boolean | false | A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project. | |
| crossSeriesGroupByColumns | [string] | false | maxItems: 1 |
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. |
| dateFormat | string | true | The date format of the partition column. | |
| datetimePartitionColumn | string | true | The date column that will be used as a datetime partition column. | |
| datetimePartitioningId | string | false | The ID of the current optimized datetime partitioning | |
| defaultToAPriori | boolean | true | Renamed to defaultToKnownInAdvance. |
|
| defaultToDoNotDerive | boolean | true | 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 | true | 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,null | true | 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 | true | A boolean value indicating whether date partitioning skipped allocating a holdout fold. | |
| featureDerivationWindowEnd | integer,null | true | maximum: 0 |
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,null | true | maximum: 0 |
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 | [FeatureSetting] | true | An array specifying per feature settings. Features can be left unspecified. | |
| forecastWindowEnd | integer,null | true | minimum: 0 |
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,null | true | minimum: 0 |
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(duration) | true | The duration of the gap between the training and holdout scoring data. | |
| gapEndDate | string(date-time) | true | The end date of the gap between the training and holdout scoring data. | |
| gapStartDate | string(date-time) | true | The start date of the gap between the training and holdout scoring data. | |
| holdoutDuration | string(duration) | true | The duration of the holdout scoring data. | |
| holdoutEndDate | string(date-time) | true | The end date of holdout scoring data. | |
| holdoutStartDate | string(date-time) | true | The start date of holdout scoring data. | |
| isHoldoutModified | boolean | false | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | |
| modelSplits | integer | false | maximum: 10 minimum: 1 |
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 | [string] | true | minItems: 1 |
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 | true | maximum: 20 minimum: 1 |
The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations. |
| numberOfDoNotDeriveFeatures | integer | true | Number of features that are marked as "do not derive". | |
| numberOfKnownInAdvanceFeatures | integer | true | Number of features that are marked as "known in advance". | |
| partitioningExtendedWarnings | [PartitioningExtendedWarning] | false | An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted. | |
| partitioningWarnings | [PartitioningWarning] | true | An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted. | |
| periodicities | [Periodicity] | true | 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'. | |
| primaryTrainingDuration | string(duration) | true | The duration of primary training duration for scoring the holdout. | |
| primaryTrainingEndDate | string(date-time) | true | The end date of primary training data for scoring the holdout. | |
| primaryTrainingStartDate | string(date-time) | true | The start date of primary training data for scoring the holdout. | |
| projectId | string | true | The ID of the project. | |
| treatAsExponential | string,null | true | 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,null | false | A boolean value indicating whether an unsupervised project should be created. | |
| unsupervisedType | string,null | false | The type of unsupervised project. | |
| useCrossSeriesFeatures | boolean,null | false | For multiseries projects only. Indicating whether to use cross-series features. | |
| useTimeSeries | boolean | true | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | |
| validationDuration | string,null(duration) | true | 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. | |
| windowsBasisUnit | string,null | true | 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. |
Enumerated Values¶
| Property | Value |
|---|---|
| aggregationType | [total, average] |
| autopilotDataSamplingMethod | [random, latest] |
| autopilotDataSelectionMethod | [duration, rowCount] |
| differencingMethod | [auto, none, simple, seasonal] |
| treatAsExponential | [auto, never, always] |
| unsupervisedType | [anomaly, clustering] |
| windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] |
DeletedProjectCountResponse
{
"properties": {
"deletedProjectsCount": {
"description": "Amount of soft-deleted projects. The value is limited by projectCountLimit",
"minimum": 0,
"type": "integer"
},
"projectCountLimit": {
"description": "Deleted projects counting limit value. Stop counting above this limit",
"minimum": 0,
"type": "integer"
},
"valueExceedsLimit": {
"description": "If an actual number of soft-deleted projects exceeds counting limit",
"type": "boolean"
}
},
"required": [
"deletedProjectsCount",
"projectCountLimit",
"valueExceedsLimit"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| deletedProjectsCount | integer | true | minimum: 0 |
Amount of soft-deleted projects. The value is limited by projectCountLimit |
| projectCountLimit | integer | true | minimum: 0 |
Deleted projects counting limit value. Stop counting above this limit |
| valueExceedsLimit | boolean | true | If an actual number of soft-deleted projects exceeds counting limit |
DeletedProjectListResponse
{
"properties": {
"count": {
"description": "Number of items returned on this page.",
"type": "integer"
},
"data": {
"description": "List of deleted projects",
"items": {
"properties": {
"createdBy": {
"description": "The user who created the project",
"properties": {
"email": {
"description": "Email of the user",
"type": "string"
},
"id": {
"description": "ID of the user",
"type": "string"
}
},
"required": [
"email",
"id"
],
"type": "object"
},
"deletedBy": {
"description": "The user who created the project",
"properties": {
"email": {
"description": "Email of the user",
"type": "string"
},
"id": {
"description": "ID of the user",
"type": "string"
}
},
"required": [
"email",
"id"
],
"type": "object"
},
"deletionTime": {
"description": "ISO-8601 formatted date when project was deleted",
"format": "date-time",
"type": [
"string",
"null"
]
},
"fileName": {
"description": "The name of the file uploaded for the project dataset",
"type": [
"string",
"null"
]
},
"id": {
"description": "The ID of the project",
"type": "string"
},
"organization": {
"description": "The organization the project belongs to",
"properties": {
"id": {
"description": "ID of the organization the project belongs to",
"type": "string"
},
"name": {
"description": "Name of the organization the project belongs to",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
},
"projectName": {
"default": "Untitled Project",
"description": "The name of the project",
"type": "string"
},
"scheduledForDeletion": {
"description": "Whether project permanent deletion has already been scheduled",
"type": "boolean"
}
},
"required": [
"createdBy",
"deletedBy",
"deletionTime",
"fileName",
"id",
"organization",
"projectName",
"scheduledForDeletion"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
}
},
"required": [
"data",
"next",
"previous"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| count | integer | false | Number of items returned on this page. | |
| data | [DeletedProjectResponse] | true | List of deleted projects | |
| next | string,null(uri) | true | URL pointing to the next page (if null, there is no next page). | |
| previous | string,null(uri) | true | URL pointing to the previous page (if null, there is no previous page). |
DeletedProjectOrganization
{
"description": "The organization the project belongs to",
"properties": {
"id": {
"description": "ID of the organization the project belongs to",
"type": "string"
},
"name": {
"description": "Name of the organization the project belongs to",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
}
The organization the project belongs to
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| id | string | true | ID of the organization the project belongs to | |
| name | string | true | Name of the organization the project belongs to |
DeletedProjectResponse
{
"properties": {
"createdBy": {
"description": "The user who created the project",
"properties": {
"email": {
"description": "Email of the user",
"type": "string"
},
"id": {
"description": "ID of the user",
"type": "string"
}
},
"required": [
"email",
"id"
],
"type": "object"
},
"deletedBy": {
"description": "The user who created the project",
"properties": {
"email": {
"description": "Email of the user",
"type": "string"
},
"id": {
"description": "ID of the user",
"type": "string"
}
},
"required": [
"email",
"id"
],
"type": "object"
},
"deletionTime": {
"description": "ISO-8601 formatted date when project was deleted",
"format": "date-time",
"type": [
"string",
"null"
]
},
"fileName": {
"description": "The name of the file uploaded for the project dataset",
"type": [
"string",
"null"
]
},
"id": {
"description": "The ID of the project",
"type": "string"
},
"organization": {
"description": "The organization the project belongs to",
"properties": {
"id": {
"description": "ID of the organization the project belongs to",
"type": "string"
},
"name": {
"description": "Name of the organization the project belongs to",
"type": "string"
}
},
"required": [
"id",
"name"
],
"type": "object"
},
"projectName": {
"default": "Untitled Project",
"description": "The name of the project",
"type": "string"
},
"scheduledForDeletion": {
"description": "Whether project permanent deletion has already been scheduled",
"type": "boolean"
}
},
"required": [
"createdBy",
"deletedBy",
"deletionTime",
"fileName",
"id",
"organization",
"projectName",
"scheduledForDeletion"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| createdBy | DeletedProjectUser | true | The user who created the project | |
| deletedBy | DeletedProjectUser | true | The user who created the project | |
| deletionTime | string,null(date-time) | true | ISO-8601 formatted date when project was deleted | |
| fileName | string,null | true | The name of the file uploaded for the project dataset | |
| id | string | true | The ID of the project | |
| organization | DeletedProjectOrganization | true | The organization the project belongs to | |
| projectName | string | true | The name of the project | |
| scheduledForDeletion | boolean | true | Whether project permanent deletion has already been scheduled |
DeletedProjectUser
{
"description": "The user who created the project",
"properties": {
"email": {
"description": "Email of the user",
"type": "string"
},
"id": {
"description": "ID of the user",
"type": "string"
}
},
"required": [
"email",
"id"
],
"type": "object"
}
The user who created the project
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| string | true | Email of the user | ||
| id | string | true | ID of the user |
ExternalTSBaselineMetadata
{
"description": "The id of the catalog item that is being used as the external baseline data",
"properties": {
"datasetId": {
"description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.",
"type": [
"string",
"null"
]
},
"datasetName": {
"description": "The name of the timeseries baseline dataset for the project",
"type": [
"string",
"null"
]
}
},
"required": [
"datasetId",
"datasetName"
],
"type": "object"
}
The id of the catalog item that is being used as the external baseline data
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| datasetId | string,null | true | Catalog version id for external prediction data that can be used as a baseline to calculate new metrics. | |
| datasetName | string,null | true | The name of the timeseries baseline dataset for the project |
ExternalTSBaselinePayload
{
"properties": {
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"catalogVersionId": {
"description": "The version id of the external baseline data item in the AI catalog.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as the datetime partition column for the specified project.",
"type": "string"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"minItems": 1,
"type": "array"
},
"target": {
"description": "The selected target of the specified project.",
"type": "string"
}
},
"required": [
"catalogVersionId",
"datetimePartitionColumn",
"forecastWindowEnd",
"forecastWindowStart",
"target"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| backtests | [Backtests] | false | maxItems: 20 minItems: 1 |
An array of the configured backtests. |
| catalogVersionId | string | true | The version id of the external baseline data item in the AI catalog. | |
| datetimePartitionColumn | string | true | The date column that will be used as the datetime partition column for the specified project. | |
| forecastWindowEnd | integer | true | minimum: 0 |
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 | true | minimum: 0 |
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(date-time) | false | The end date of holdout scoring data. | |
| holdoutStartDate | string(date-time) | false | The start date of holdout scoring data. | |
| multiseriesIdColumns | [string] | false | maxItems: 1 minItems: 1 |
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 | true | The selected target of the specified project. |
ExternalTSBaselineResponse
{
"properties": {
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"baselineValidationJobId": {
"description": "The id of the validation job.",
"type": "string"
},
"catalogVersionId": {
"description": "The version id of the external baseline data item in the AI catalog.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as the datetime partition column for the specified project.",
"type": "string"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isExternalBaselineDatasetValid": {
"description": "Indicates whether the external dataset has pass the validation check or not.",
"type": "boolean"
},
"message": {
"description": "A message providing mode detail on the validation result.",
"type": [
"string",
"null"
]
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"minItems": 1,
"type": "array"
},
"projectId": {
"description": "The project id of the external baseline data item.",
"type": "string"
},
"target": {
"description": "The selected target of the specified project.",
"type": "string"
}
},
"required": [
"baselineValidationJobId",
"catalogVersionId",
"datetimePartitionColumn",
"isExternalBaselineDatasetValid",
"message",
"projectId",
"target"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| backtests | [Backtests] | false | maxItems: 20 minItems: 1 |
An array of the configured backtests. |
| baselineValidationJobId | string | true | The id of the validation job. | |
| catalogVersionId | string | true | The version id of the external baseline data item in the AI catalog. | |
| datetimePartitionColumn | string | true | The date column that will be used as the datetime partition column for the specified project. | |
| forecastWindowEnd | integer | false | minimum: 0 |
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 | false | minimum: 0 |
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(date-time) | false | The end date of holdout scoring data. | |
| holdoutStartDate | string(date-time) | false | The start date of holdout scoring data. | |
| isExternalBaselineDatasetValid | boolean | true | Indicates whether the external dataset has pass the validation check or not. | |
| message | string,null | true | A message providing mode detail on the validation result. | |
| multiseriesIdColumns | [string] | false | maxItems: 1 minItems: 1 |
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. |
| projectId | string | true | The project id of the external baseline data item. | |
| target | string | true | The selected target of the specified project. |
FeatureSetting
{
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| aPriori | boolean | false | Renamed to knownInAdvance. |
|
| doNotDerive | boolean | false | For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the defaultToDoNotDerive flag. |
|
| featureName | string | true | The name of the feature being specified. | |
| knownInAdvance | boolean | false | For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the defaultToKnownInAdvance flag. |
FinalBacktestResponse
{
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingRowCount": {
"description": "The number of rows in the available training data for this backtest.",
"type": "integer"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapRowCount": {
"description": "The number of rows in the gap between the training and the validation scoring data for this backtest.",
"type": "integer"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingRowCount": {
"description": "The number of rows in the primary training data for this backtest.",
"type": "integer"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"totalRowCount": {
"description": "The total number of rows in this backtest",
"type": "integer"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationRowCount": {
"description": "The number of rows in the validation scoring data for this backtest.",
"type": "integer"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingRowCount",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapRowCount",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingRowCount",
"primaryTrainingStartDate",
"totalRowCount",
"validationDuration",
"validationEndDate",
"validationRowCount",
"validationStartDate"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| availableTrainingDuration | string(duration) | true | The duration of the available training data for this backtest. | |
| availableTrainingEndDate | string(date-time) | true | The end date of the available training data for this backtest. | |
| availableTrainingRowCount | integer | true | The number of rows in the available training data for this backtest. | |
| availableTrainingStartDate | string(date-time) | true | The start date of the available training data for this backtest. | |
| gapDuration | string(duration) | true | The duration of the gap between the training and the validation scoring data for this backtest. | |
| gapEndDate | string(date-time) | true | The end date of the gap between the training and validation scoring data for this backtest. | |
| gapRowCount | integer | true | The number of rows in the gap between the training and the validation scoring data for this backtest. | |
| gapStartDate | string(date-time) | true | The start date of the gap between the training and validation scoring data for this backtest. | |
| index | integer | true | The index from zero of this backtest. | |
| primaryTrainingDuration | string(duration) | true | The duration of the primary training data for this backtest. | |
| primaryTrainingEndDate | string(date-time) | true | The end date of the primary training data for this backtest. | |
| primaryTrainingRowCount | integer | true | The number of rows in the primary training data for this backtest. | |
| primaryTrainingStartDate | string(date-time) | true | The start date of the primary training data for this backtest. | |
| totalRowCount | integer | true | The total number of rows in this backtest | |
| validationDuration | string | true | The duration of the validation scoring data for this backtest. | |
| validationEndDate | string(date-time) | true | The end date of the validation scoring data for this backtest. | |
| validationRowCount | integer | true | The number of rows in the validation scoring data for this backtest. | |
| validationStartDate | string(date-time) | true | The start date of the validation scoring data for this backtest. |
FinalDatetimePartitioningResponse
{
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"autopilotDataSamplingMethod": {
"description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"random",
"latest"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"availableHoldoutEndDate": {
"description": "The maximum valid date of holdout scoring data.",
"format": "date-time",
"type": [
"string",
"null"
]
},
"availableTrainingDuration": {
"description": "The duration of available training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"availableTrainingRowCount": {
"description": "The number of rows in the available training data for scoring the holdout partition",
"type": "integer"
},
"availableTrainingStartDate": {
"description": "The start date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingRowCount": {
"description": "The number of rows in the available training data for this backtest.",
"type": "integer"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapRowCount": {
"description": "The number of rows in the gap between the training and the validation scoring data for this backtest.",
"type": "integer"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingRowCount": {
"description": "The number of rows in the primary training data for this backtest.",
"type": "integer"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"totalRowCount": {
"description": "The total number of rows in this backtest",
"type": "integer"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationRowCount": {
"description": "The number of rows in the validation scoring data for this backtest.",
"type": "integer"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingRowCount",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapRowCount",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingRowCount",
"primaryTrainingStartDate",
"totalRowCount",
"validationDuration",
"validationEndDate",
"validationRowCount",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.15"
},
"calendarName": {
"description": "The name of the calendar used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.17"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"dateFormat": {
"description": "The date format of the partition column.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"description": "The ID of the current optimized datetime partitioning",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"disableHoldout": {
"description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array",
"x-versionadded": "v2.9"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"gapDuration": {
"description": "The duration of the gap between the training and holdout scoring data.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"gapRowCount": {
"description": "The number of rows in the gap between the training and holdout scoring data",
"type": "integer"
},
"gapStartDate": {
"description": "The start date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of the holdout scoring data.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutRowCount": {
"description": "The number of rows in the holdout scoring data",
"type": "integer"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"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. ",
"maximum": 10,
"minimum": 1,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"numberOfDoNotDeriveFeatures": {
"description": "Number of features that are marked as \"do not derive\".",
"type": "integer",
"x-versionadded": "v2.17"
},
"numberOfKnownInAdvanceFeatures": {
"description": "Number of features that are marked as \"known in advance\".",
"type": "integer",
"x-versionadded": "v2.14"
},
"partitioningExtendedWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"partitioningWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"primaryTrainingDuration": {
"description": "The duration of primary training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"primaryTrainingRowCount": {
"description": "The number of rows in the primary training data for scoring the holdout",
"type": "integer"
},
"primaryTrainingStartDate": {
"description": "The start date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"projectId": {
"description": "The ID of the project.",
"type": "string"
},
"totalRowCount": {
"description": "The total number of rows in the project dataset",
"type": "integer"
},
"treatAsExponential": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"unsupervisedType": {
"description": "The type of unsupervised project.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.31"
},
"useCrossSeriesFeatures": {
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.14"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": [
"string",
"null"
]
},
"windowsBasisUnit": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
}
},
"required": [
"aggregationType",
"autopilotDataSamplingMethod",
"autopilotDataSelectionMethod",
"availableHoldoutEndDate",
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingRowCount",
"availableTrainingStartDate",
"backtests",
"calendarId",
"calendarName",
"crossSeriesGroupByColumns",
"dateFormat",
"datetimePartitionColumn",
"defaultToAPriori",
"defaultToDoNotDerive",
"defaultToKnownInAdvance",
"differencingMethod",
"disableHoldout",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"featureSettings",
"forecastWindowEnd",
"forecastWindowStart",
"gapDuration",
"gapEndDate",
"gapRowCount",
"gapStartDate",
"holdoutDuration",
"holdoutEndDate",
"holdoutRowCount",
"holdoutStartDate",
"modelSplits",
"multiseriesIdColumns",
"numberOfBacktests",
"numberOfDoNotDeriveFeatures",
"numberOfKnownInAdvanceFeatures",
"partitioningWarnings",
"periodicities",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingRowCount",
"primaryTrainingStartDate",
"projectId",
"totalRowCount",
"treatAsExponential",
"useCrossSeriesFeatures",
"useTimeSeries",
"validationDuration",
"windowsBasisUnit"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| aggregationType | string,null | true | For multiseries projects only. The aggregation type to apply when creating cross-series features. | |
| allowPartialHistoryTimeSeriesPredictions | boolean,null | false | Specifies whether the time series predictions can use partial historical data. | |
| autopilotDataSamplingMethod | string | true | The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets. | |
| autopilotDataSelectionMethod | string | true | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | |
| availableHoldoutEndDate | string,null(date-time) | true | The maximum valid date of holdout scoring data. | |
| availableTrainingDuration | string(duration) | true | The duration of available training duration for scoring the holdout. | |
| availableTrainingEndDate | string(date-time) | true | The end date of available training data for scoring the holdout. | |
| availableTrainingRowCount | integer | true | The number of rows in the available training data for scoring the holdout partition | |
| availableTrainingStartDate | string(date-time) | true | The start date of available training data for scoring the holdout. | |
| backtests | [FinalBacktestResponse] | true | maxItems: 20 minItems: 1 |
An array of the configured backtests. |
| calendarId | string,null | true | The ID of the calendar to be used in this project. | |
| calendarName | string,null | true | The name of the calendar used in this project. | |
| crossSeriesGroupByColumns | [string] | true | maxItems: 1 |
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. |
| dateFormat | string | true | The date format of the partition column. | |
| datetimePartitionColumn | string | true | The date column that will be used as a datetime partition column. | |
| datetimePartitioningId | string | false | The ID of the current optimized datetime partitioning | |
| defaultToAPriori | boolean | true | Renamed to defaultToKnownInAdvance. |
|
| defaultToDoNotDerive | boolean | true | 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 | true | 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,null | true | 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 | true | A boolean value indicating whether date partitioning skipped allocating a holdout fold. | |
| featureDerivationWindowEnd | integer,null | true | maximum: 0 |
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,null | true | maximum: 0 |
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 | [FeatureSetting] | true | An array specifying per feature settings. Features can be left unspecified. | |
| forecastWindowEnd | integer,null | true | minimum: 0 |
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,null | true | minimum: 0 |
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(duration) | true | The duration of the gap between the training and holdout scoring data. | |
| gapEndDate | string(date-time) | true | The end date of the gap between the training and holdout scoring data. | |
| gapRowCount | integer | true | The number of rows in the gap between the training and holdout scoring data | |
| gapStartDate | string(date-time) | true | The start date of the gap between the training and holdout scoring data. | |
| holdoutDuration | string(duration) | true | The duration of the holdout scoring data. | |
| holdoutEndDate | string(date-time) | true | The end date of holdout scoring data. | |
| holdoutRowCount | integer | true | The number of rows in the holdout scoring data | |
| holdoutStartDate | string(date-time) | true | The start date of holdout scoring data. | |
| isHoldoutModified | boolean | false | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | |
| modelSplits | integer,null | true | maximum: 10 minimum: 1 |
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 | [string] | true | minItems: 1 |
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 | true | maximum: 20 minimum: 1 |
The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations. |
| numberOfDoNotDeriveFeatures | integer | true | Number of features that are marked as "do not derive". | |
| numberOfKnownInAdvanceFeatures | integer | true | Number of features that are marked as "known in advance". | |
| partitioningExtendedWarnings | [PartitioningExtendedWarning] | false | An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted. | |
| partitioningWarnings | [PartitioningWarning] | true | An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted. | |
| periodicities | [Periodicity] | true | 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'. | |
| primaryTrainingDuration | string(duration) | true | The duration of primary training duration for scoring the holdout. | |
| primaryTrainingEndDate | string(date-time) | true | The end date of primary training data for scoring the holdout. | |
| primaryTrainingRowCount | integer | true | The number of rows in the primary training data for scoring the holdout | |
| primaryTrainingStartDate | string(date-time) | true | The start date of primary training data for scoring the holdout. | |
| projectId | string | true | The ID of the project. | |
| totalRowCount | integer | true | The total number of rows in the project dataset | |
| treatAsExponential | string,null | true | 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,null | false | A boolean value indicating whether an unsupervised project should be created. | |
| unsupervisedType | string,null | false | The type of unsupervised project. | |
| useCrossSeriesFeatures | boolean,null | true | For multiseries projects only. Indicating whether to use cross-series features. | |
| useTimeSeries | boolean | true | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | |
| validationDuration | string,null(duration) | true | 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. | |
| windowsBasisUnit | string,null | true | 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. |
Enumerated Values¶
| Property | Value |
|---|---|
| aggregationType | [total, average] |
| autopilotDataSamplingMethod | [random, latest] |
| autopilotDataSelectionMethod | [duration, rowCount] |
| differencingMethod | [auto, none, simple, seasonal] |
| treatAsExponential | [auto, never, always] |
| unsupervisedType | [anomaly, clustering] |
| windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] |
GCPKey
{
"description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.",
"properties": {
"authProviderX509CertUrl": {
"description": "Auth provider X509 certificate URL.",
"format": "uri",
"type": "string"
},
"authUri": {
"description": "Auth URI.",
"format": "uri",
"type": "string"
},
"clientEmail": {
"description": "Client email address.",
"type": "string"
},
"clientId": {
"description": "Client ID.",
"type": "string"
},
"clientX509CertUrl": {
"description": "Client X509 certificate URL.",
"format": "uri",
"type": "string"
},
"privateKey": {
"description": "Private key.",
"type": "string"
},
"privateKeyId": {
"description": "Private key ID",
"type": "string"
},
"projectId": {
"description": "Project ID.",
"type": "string"
},
"tokenUri": {
"description": "Token URI.",
"format": "uri",
"type": "string"
},
"type": {
"description": "GCP account type.",
"enum": [
"service_account"
],
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}
The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| authProviderX509CertUrl | string(uri) | false | Auth provider X509 certificate URL. | |
| authUri | string(uri) | false | Auth URI. | |
| clientEmail | string | false | Client email address. | |
| clientId | string | false | Client ID. | |
| clientX509CertUrl | string(uri) | false | Client X509 certificate URL. | |
| privateKey | string | false | Private key. | |
| privateKeyId | string | false | Private key ID | |
| projectId | string | false | Project ID. | |
| tokenUri | string(uri) | false | Token URI. | |
| type | string | true | GCP account type. |
Enumerated Values¶
| Property | Value |
|---|---|
| type | service_account |
GoogleServiceAccountCredentials
{
"properties": {
"configId": {
"description": "ID of Secure configurations shared by admin.Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 'gcp' here.",
"enum": [
"gcp"
],
"type": "string"
},
"gcpKey": {
"description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.",
"properties": {
"authProviderX509CertUrl": {
"description": "Auth provider X509 certificate URL.",
"format": "uri",
"type": "string"
},
"authUri": {
"description": "Auth URI.",
"format": "uri",
"type": "string"
},
"clientEmail": {
"description": "Client email address.",
"type": "string"
},
"clientId": {
"description": "Client ID.",
"type": "string"
},
"clientX509CertUrl": {
"description": "Client X509 certificate URL.",
"format": "uri",
"type": "string"
},
"privateKey": {
"description": "Private key.",
"type": "string"
},
"privateKeyId": {
"description": "Private key ID",
"type": "string"
},
"projectId": {
"description": "Project ID.",
"type": "string"
},
"tokenUri": {
"description": "Token URI.",
"format": "uri",
"type": "string"
},
"type": {
"description": "GCP account type.",
"enum": [
"service_account"
],
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
},
"googleConfigId": {
"description": "ID of Secure configurations shared by admin. This is deprecated.Please use configId instead. If specified, cannot include gcpKey.",
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| configId | string | false | ID of Secure configurations shared by admin.Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey. | |
| credentialType | string | true | The type of these credentials, 'gcp' here. | |
| gcpKey | GCPKey | false | The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. | |
| googleConfigId | string | false | ID of Secure configurations shared by admin. This is deprecated.Please use configId instead. If specified, cannot include gcpKey. |
Enumerated Values¶
| Property | Value |
|---|---|
| credentialType | gcp |
HdfsProjectCreate
{
"properties": {
"password": {
"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.",
"type": "string"
},
"port": {
"description": "Port of the WebHDFS Namenode server. If not specified, defaults to HDFS default port 50070.",
"type": "integer"
},
"projectName": {
"description": "Name of the project to be created. If not specified, project name will be based on the file name.",
"type": "string"
},
"url": {
"description": "URL of the WebHDFS resource. Represent the file using the `hdfs://` protocol marker (for example, `hdfs:///tmp/somedataset.csv`).",
"format": "uri",
"type": "string"
},
"user": {
"description": "Username for authenticating to HDFS using Kerberos",
"type": "string"
}
},
"required": [
"url"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| password | string | false | 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 | false | Port of the WebHDFS Namenode server. If not specified, defaults to HDFS default port 50070. | |
| projectName | string | false | Name of the project to be created. If not specified, project name will be based on the file name. | |
| url | string(uri) | true | URL of the WebHDFS resource. Represent the file using the hdfs:// protocol marker (for example, hdfs:///tmp/somedataset.csv). |
|
| user | string | false | Username for authenticating to HDFS using Kerberos |
JobDetailsResponse
{
"properties": {
"id": {
"description": "The job ID.",
"type": "string"
},
"isBlocked": {
"description": "True if the job is waiting for its dependencies to be resolved first.",
"type": "boolean"
},
"jobType": {
"description": "The job type.",
"enum": [
"model",
"predict",
"trainingPredictions",
"featureImpact",
"featureEffects",
"shapImpact",
"anomalyAssessment",
"shapExplanations",
"shapMatrix",
"reasonCodesInitialization",
"reasonCodes",
"predictionExplanations",
"predictionExplanationsInitialization",
"primeDownloadValidation",
"ruleFitDownloadValidation",
"primeRulesets",
"primeModel",
"modelExport",
"usageData",
"modelXRay",
"accuracyOverTime",
"seriesAccuracy",
"validateRatingTable",
"generateComplianceDocumentation",
"automatedDocumentation",
"eda",
"pipeline",
"calculatePredictionIntervals",
"calculatePredictionIntervalBoundUsingOnlineConformal",
"batchVarTypeTransform",
"computeImageActivationMaps",
"computeImageAugmentations",
"computeImageEmbeddings",
"computeDocumentTextExtractionSamples",
"externalDatasetInsights",
"startDatetimePartitioning",
"runSegmentationTasks",
"piiDetection",
"computeBiasAndFairness",
"sensitivityTesting",
"clusterInsights",
"onnxExport",
"scoringCodeSegmentedModeling",
"insights",
"distributionPredictionModel",
"batchScoringAvailableForecastPoints",
"notebooksScheduling",
"uncategorized"
],
"type": "string"
},
"message": {
"description": "Error message in case of failure.",
"type": "string"
},
"modelId": {
"description": "The model this job is associated with.",
"type": "string"
},
"projectId": {
"description": "The project the job belongs to.",
"type": "string"
},
"status": {
"description": "The job status.",
"enum": [
"queue",
"inprogress",
"error",
"ABORTED",
"COMPLETED"
],
"type": "string"
},
"url": {
"description": "A URL that can be used to request details about the job.",
"type": "string"
}
},
"required": [
"id",
"isBlocked",
"jobType",
"message",
"modelId",
"projectId",
"status",
"url"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| id | string | true | The job ID. | |
| isBlocked | boolean | true | True if the job is waiting for its dependencies to be resolved first. | |
| jobType | string | true | The job type. | |
| message | string | true | Error message in case of failure. | |
| modelId | string | true | The model this job is associated with. | |
| projectId | string | true | The project the job belongs to. | |
| status | string | true | The job status. | |
| url | string | true | A URL that can be used to request details about the job. |
Enumerated Values¶
| Property | Value |
|---|---|
| jobType | [model, predict, trainingPredictions, featureImpact, featureEffects, shapImpact, anomalyAssessment, shapExplanations, shapMatrix, reasonCodesInitialization, reasonCodes, predictionExplanations, predictionExplanationsInitialization, primeDownloadValidation, ruleFitDownloadValidation, primeRulesets, primeModel, modelExport, usageData, modelXRay, accuracyOverTime, seriesAccuracy, validateRatingTable, generateComplianceDocumentation, automatedDocumentation, eda, pipeline, calculatePredictionIntervals, calculatePredictionIntervalBoundUsingOnlineConformal, batchVarTypeTransform, computeImageActivationMaps, computeImageAugmentations, computeImageEmbeddings, computeDocumentTextExtractionSamples, externalDatasetInsights, startDatetimePartitioning, runSegmentationTasks, piiDetection, computeBiasAndFairness, sensitivityTesting, clusterInsights, onnxExport, scoringCodeSegmentedModeling, insights, distributionPredictionModel, batchScoringAvailableForecastPoints, notebooksScheduling, uncategorized] |
| status | [queue, inprogress, error, ABORTED, COMPLETED] |
JobListResponse
{
"properties": {
"count": {
"description": "the number of jobs returned.",
"type": "integer"
},
"jobs": {
"description": "A json array of jobs",
"items": {
"properties": {
"id": {
"description": "The job ID.",
"type": "string"
},
"isBlocked": {
"description": "True if the job is waiting for its dependencies to be resolved first.",
"type": "boolean"
},
"jobType": {
"description": "The job type.",
"enum": [
"model",
"predict",
"trainingPredictions",
"featureImpact",
"featureEffects",
"shapImpact",
"anomalyAssessment",
"shapExplanations",
"shapMatrix",
"reasonCodesInitialization",
"reasonCodes",
"predictionExplanations",
"predictionExplanationsInitialization",
"primeDownloadValidation",
"ruleFitDownloadValidation",
"primeRulesets",
"primeModel",
"modelExport",
"usageData",
"modelXRay",
"accuracyOverTime",
"seriesAccuracy",
"validateRatingTable",
"generateComplianceDocumentation",
"automatedDocumentation",
"eda",
"pipeline",
"calculatePredictionIntervals",
"calculatePredictionIntervalBoundUsingOnlineConformal",
"batchVarTypeTransform",
"computeImageActivationMaps",
"computeImageAugmentations",
"computeImageEmbeddings",
"computeDocumentTextExtractionSamples",
"externalDatasetInsights",
"startDatetimePartitioning",
"runSegmentationTasks",
"piiDetection",
"computeBiasAndFairness",
"sensitivityTesting",
"clusterInsights",
"onnxExport",
"scoringCodeSegmentedModeling",
"insights",
"distributionPredictionModel",
"batchScoringAvailableForecastPoints",
"notebooksScheduling",
"uncategorized"
],
"type": "string"
},
"message": {
"description": "Error message in case of failure.",
"type": "string"
},
"modelId": {
"description": "The model this job is associated with.",
"type": "string"
},
"projectId": {
"description": "The project the job belongs to.",
"type": "string"
},
"status": {
"description": "The job status.",
"enum": [
"queue",
"inprogress",
"error",
"ABORTED",
"COMPLETED"
],
"type": "string"
},
"url": {
"description": "A URL that can be used to request details about the job.",
"type": "string"
}
},
"required": [
"id",
"isBlocked",
"jobType",
"message",
"modelId",
"projectId",
"status",
"url"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"type": [
"string",
"null"
]
}
},
"required": [
"count",
"jobs",
"next",
"previous"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| count | integer | true | the number of jobs returned. | |
| jobs | [JobDetailsResponse] | true | A json array of jobs | |
| next | string,null | true | URL pointing to the next page (if null, there is no next page). | |
| previous | string,null | true | URL pointing to the previous page (if null, there is no previous page). |
MultiseriesNamesControllerDataRecord
{
"description": "Data fields of the multi series names",
"properties": {
"items": {
"description": "List of series names",
"items": {
"description": "Name of one series item",
"type": "string"
},
"type": "array"
}
},
"required": [
"items"
],
"type": "object"
}
Data fields of the multi series names
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| items | [string] | true | List of series names |
MultiseriesNamesControllerResponse
{
"properties": {
"count": {
"description": "Total number of series items in the response",
"type": "integer"
},
"data": {
"description": "Data fields of the multi series names",
"properties": {
"items": {
"description": "List of series names",
"items": {
"description": "Name of one series item",
"type": "string"
},
"type": "array"
}
},
"required": [
"items"
],
"type": "object"
},
"next": {
"description": "A URL pointing to the next page (if `null`, there is no next page).",
"type": [
"string",
"null"
]
},
"previous": {
"description": "A URL pointing to the previous page (if `null`, there is no previous page).",
"type": [
"string",
"null"
]
},
"totalSeriesCount": {
"description": "Total number of series items",
"type": "integer"
}
},
"required": [
"count",
"data",
"next",
"previous",
"totalSeriesCount"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| count | integer | true | Total number of series items in the response | |
| data | MultiseriesNamesControllerDataRecord | true | Data fields of the multi series names | |
| next | string,null | true | A URL pointing to the next page (if null, there is no next page). |
|
| previous | string,null | true | A URL pointing to the previous page (if null, there is no previous page). |
|
| totalSeriesCount | integer | true | Total number of series items |
MultiseriesPayload
{
"properties": {
"datetimePartitionColumn": {
"description": "The date column that will be used to perform detection and validation for.",
"type": "string"
},
"multiseriesIdColumns": {
"description": "List of one or more names of potential multiseries id columns. If not provided, all numerical and categorical columns are used.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array"
}
},
"required": [
"datetimePartitionColumn"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| datetimePartitionColumn | string | true | The date column that will be used to perform detection and validation for. | |
| multiseriesIdColumns | [string] | false | minItems: 1 |
List of one or more names of potential multiseries id columns. If not provided, all numerical and categorical columns are used. |
OAuthCredentials
{
"properties": {
"credentialType": {
"description": "The type of these credentials, 'oauth' here.",
"enum": [
"oauth"
],
"type": "string"
},
"oauthAccessToken": {
"default": null,
"description": "The oauth access token.",
"type": [
"string",
"null"
]
},
"oauthClientId": {
"default": null,
"description": "The oauth client ID.",
"type": [
"string",
"null"
]
},
"oauthClientSecret": {
"default": null,
"description": "The oauth client secret.",
"type": [
"string",
"null"
]
},
"oauthRefreshToken": {
"description": "The oauth refresh token.",
"type": "string"
}
},
"required": [
"credentialType",
"oauthRefreshToken"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| credentialType | string | true | The type of these credentials, 'oauth' here. | |
| oauthAccessToken | string,null | false | The oauth access token. | |
| oauthClientId | string,null | false | The oauth client ID. | |
| oauthClientSecret | string,null | false | The oauth client secret. | |
| oauthRefreshToken | string | true | The oauth refresh token. |
Enumerated Values¶
| Property | Value |
|---|---|
| credentialType | oauth |
OptimizedDatetimePartitioningData
{
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": "string",
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"default": false,
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": "boolean",
"x-versionadded": "v2.24"
},
"autopilotClusterList": {
"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`.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotDataSamplingMethod": {
"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"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"backtests": {
"description": "An array specifying individual backtests.",
"items": {
"oneOf": [
{
"description": "Method 1 - pass validation and gap durations",
"properties": {
"gapDuration": {
"description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"validationDuration": {
"description": "A duration string representing the duration of the validation data for this backtest.",
"format": "duration",
"type": "string"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"gapDuration",
"index",
"validationDuration",
"validationStartDate"
],
"type": "object"
},
{
"description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.",
"properties": {
"index": {
"description": "The index from zero of the backtest.",
"type": "integer"
},
"primaryTrainingEndDate": {
"description": "A datetime string representing the end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"primaryTrainingStartDate": {
"description": "A datetime string representing the start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationEndDate": {
"description": "A datetime string representing the end date of the validation data for this backtest.",
"format": "date-time",
"type": "string",
"x-versionadded": "v2.19"
},
"validationStartDate": {
"description": "A datetime string representing the start date of the validation data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"index",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationEndDate",
"validationStartDate"
],
"type": "object"
}
]
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": "string",
"x-versionadded": "v2.15"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"disableHoldout": {
"default": false,
"description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": "integer",
"x-versionadded": "v2.8"
},
"gapDuration": {
"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).",
"format": "duration",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"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.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"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.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"default": 5,
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"target": {
"description": "Name of the target column.",
"type": "string",
"x-versionadded": "v2.22"
},
"treatAsExponential": {
"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"
],
"type": "string",
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"default": false,
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": "boolean",
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCrossSeriesFeatures": {
"default": false,
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": "boolean",
"x-versionadded": "v2.14"
},
"useSupervisedFeatureReduction": {
"default": true,
"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.",
"type": "boolean"
},
"useTimeSeries": {
"default": false,
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": "string"
},
"windowsBasisUnit": {
"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"
],
"type": "string",
"x-versionadded": "v2.14"
}
},
"required": [
"datetimePartitionColumn"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| aggregationType | string | false | For multiseries projects only. The aggregation type to apply when creating cross-series features. | |
| allowPartialHistoryTimeSeriesPredictions | boolean | false | Specifies whether the time series predictions can use partial historical data. | |
| autopilotClusterList | [integer] | false | maxItems: 10 |
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 | false | 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 | false | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | |
| backtests | [oneOf] | false | maxItems: 20 minItems: 1 |
An array specifying individual backtests. |
oneOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | BacktestOldMethodForOpenApi | false | Method 1 - pass validation and gap durations |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | BacktestNewMethodForOpenApi | false | Method 2 - directly configure the start and end dates of each partition, including the training partition. |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| calendarId | string | false | The ID of the calendar to be used in this project. | |
| clusteringBufferDisabled | boolean | false | A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project. | |
| crossSeriesGroupByColumns | [string] | false | maxItems: 1 |
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 | true | The date column that will be used as a datetime partition column. | |
| defaultToAPriori | boolean | false | Renamed to defaultToKnownInAdvance. |
|
| defaultToDoNotDerive | boolean | false | 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 | false | 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 | false | 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 | false | Whether to suppress allocating a holdout fold. If disableHoldout is set to true, holdoutStartDate and holdoutDuration must not be set. |
|
| featureDerivationWindowEnd | integer | false | maximum: 0 |
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 | false | maximum: 0 |
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 | [FeatureSetting] | false | An array specifying per feature settings. Features can be left unspecified. | |
| forecastWindowEnd | integer | false | minimum: 0 |
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 | false | minimum: 0 |
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(duration) | false | 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(duration) | false | 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(date-time) | false | 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(date-time) | false | 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 | false | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | |
| modelSplits | integer | false | maximum: 10 minimum: 1 |
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 | [string] | false | minItems: 1 |
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 | false | maximum: 20 minimum: 1 |
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 | [Periodicity] | false | 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 | false | Name of the target column. | |
| treatAsExponential | string | false | 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 | false | A boolean value indicating whether an unsupervised project should be created. | |
| unsupervisedType | string,null | false | The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly. |
|
| useCrossSeriesFeatures | boolean | false | For multiseries projects only. Indicating whether to use cross-series features. | |
| useSupervisedFeatureReduction | boolean | false | 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 | false | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | |
| validationDuration | string(duration) | false | 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 | false | 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. |
Enumerated Values¶
| Property | Value |
|---|---|
| aggregationType | [total, average] |
| autopilotDataSamplingMethod | [random, latest] |
| autopilotDataSelectionMethod | [duration, rowCount] |
| differencingMethod | [auto, none, simple, seasonal] |
| treatAsExponential | [auto, never, always] |
| unsupervisedType | [anomaly, clustering] |
| windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] |
OptimizedDatetimePartitioningList
{
"description": "Datetime partition information.",
"properties": {
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"id": {
"description": "The ID of the datetime partitioning.",
"type": "string"
},
"partitionData": {
"description": "Partitioning information for a datetime project.",
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"autopilotDataSamplingMethod": {
"description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"random",
"latest"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"availableHoldoutEndDate": {
"description": "The maximum valid date of holdout scoring data.",
"format": "date-time",
"type": [
"string",
"null"
]
},
"availableTrainingDuration": {
"description": "The duration of available training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationDuration",
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.15"
},
"calendarName": {
"description": "The name of the calendar used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.17"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"dateFormat": {
"description": "The date format of the partition column.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"description": "The ID of the current optimized datetime partitioning",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"disableHoldout": {
"description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array",
"x-versionadded": "v2.9"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"gapDuration": {
"description": "The duration of the gap between the training and holdout scoring data.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of the holdout scoring data.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"numberOfDoNotDeriveFeatures": {
"description": "Number of features that are marked as \"do not derive\".",
"type": "integer",
"x-versionadded": "v2.17"
},
"numberOfKnownInAdvanceFeatures": {
"description": "Number of features that are marked as \"known in advance\".",
"type": "integer",
"x-versionadded": "v2.14"
},
"partitioningExtendedWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"partitioningWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"primaryTrainingDuration": {
"description": "The duration of primary training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"projectId": {
"description": "The ID of the project.",
"type": "string"
},
"treatAsExponential": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"unsupervisedType": {
"description": "The type of unsupervised project.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.31"
},
"useCrossSeriesFeatures": {
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.14"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": [
"string",
"null"
]
},
"windowsBasisUnit": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
}
},
"required": [
"autopilotDataSamplingMethod",
"autopilotDataSelectionMethod",
"availableHoldoutEndDate",
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"backtests",
"dateFormat",
"datetimePartitionColumn",
"defaultToAPriori",
"defaultToDoNotDerive",
"defaultToKnownInAdvance",
"differencingMethod",
"disableHoldout",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"featureSettings",
"forecastWindowEnd",
"forecastWindowStart",
"gapDuration",
"gapEndDate",
"gapStartDate",
"holdoutDuration",
"holdoutEndDate",
"holdoutStartDate",
"multiseriesIdColumns",
"numberOfBacktests",
"numberOfDoNotDeriveFeatures",
"numberOfKnownInAdvanceFeatures",
"partitioningWarnings",
"periodicities",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"projectId",
"treatAsExponential",
"useTimeSeries",
"validationDuration",
"windowsBasisUnit"
],
"type": "object"
},
"target": {
"description": "The name of the target column.",
"type": "string"
}
},
"required": [
"datetimePartitionColumn",
"id",
"partitionData",
"target"
],
"type": "object"
}
Datetime partition information.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| datetimePartitionColumn | string | true | The date column that will be used as a datetime partition column. | |
| id | string | true | The ID of the datetime partitioning. | |
| partitionData | DatetimePartitioningResponse | true | Partitioning information for a datetime project. | |
| target | string | true | The name of the target column. |
OptimizedDatetimePartitioningListResponse
{
"properties": {
"count": {
"description": "Number of items returned on this page.",
"type": "integer"
},
"data": {
"description": "A list of datetime partitionings returned in order of creation.",
"items": {
"description": "Datetime partition information.",
"properties": {
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"id": {
"description": "The ID of the datetime partitioning.",
"type": "string"
},
"partitionData": {
"description": "Partitioning information for a datetime project.",
"properties": {
"aggregationType": {
"description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.",
"enum": [
"total",
"average"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
},
"allowPartialHistoryTimeSeriesPredictions": {
"description": "Specifies whether the time series predictions can use partial historical data.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"autopilotDataSamplingMethod": {
"description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"random",
"latest"
],
"type": "string"
},
"autopilotDataSelectionMethod": {
"description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.",
"enum": [
"duration",
"rowCount"
],
"type": "string"
},
"availableHoldoutEndDate": {
"description": "The maximum valid date of holdout scoring data.",
"format": "date-time",
"type": [
"string",
"null"
]
},
"availableTrainingDuration": {
"description": "The duration of available training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of available training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"backtests": {
"description": "An array of the configured backtests.",
"items": {
"properties": {
"availableTrainingDuration": {
"description": "The duration of the available training data for this backtest.",
"format": "duration",
"type": "string"
},
"availableTrainingEndDate": {
"description": "The end date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"availableTrainingStartDate": {
"description": "The start date of the available training data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapDuration": {
"description": "The duration of the gap between the training and the validation scoring data for this backtest.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"index": {
"description": "The index from zero of this backtest.",
"type": "integer"
},
"primaryTrainingDuration": {
"description": "The duration of the primary training data for this backtest.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of the primary training data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationDuration": {
"description": "The duration of the validation scoring data for this backtest.",
"type": "string"
},
"validationEndDate": {
"description": "The end date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
},
"validationStartDate": {
"description": "The start date of the validation scoring data for this backtest.",
"format": "date-time",
"type": "string"
}
},
"required": [
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"gapDuration",
"gapEndDate",
"gapStartDate",
"index",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"validationDuration",
"validationEndDate",
"validationStartDate"
],
"type": "object"
},
"maxItems": 20,
"minItems": 1,
"type": "array"
},
"calendarId": {
"description": "The ID of the calendar to be used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.15"
},
"calendarName": {
"description": "The name of the calendar used in this project.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.17"
},
"clusteringBufferDisabled": {
"default": false,
"description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.",
"type": "boolean",
"x-versionadded": "v2.30"
},
"crossSeriesGroupByColumns": {
"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.",
"items": {
"type": "string"
},
"maxItems": 1,
"type": "array",
"x-versionadded": "v2.15"
},
"dateFormat": {
"description": "The date format of the partition column.",
"type": "string"
},
"datetimePartitionColumn": {
"description": "The date column that will be used as a datetime partition column.",
"type": "string"
},
"datetimePartitioningId": {
"description": "The ID of the current optimized datetime partitioning",
"type": "string",
"x-versionadded": "v2.30"
},
"defaultToAPriori": {
"description": "Renamed to `defaultToKnownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"defaultToDoNotDerive": {
"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.",
"type": "boolean",
"x-versionadded": "v2.17"
},
"defaultToKnownInAdvance": {
"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.",
"type": "boolean",
"x-versionadded": "v2.11"
},
"differencingMethod": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"disableHoldout": {
"description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"featureDerivationWindowEnd": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureDerivationWindowStart": {
"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.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"featureSettings": {
"description": "An array specifying per feature settings. Features can be left unspecified.",
"items": {
"properties": {
"aPriori": {
"description": "Renamed to `knownInAdvance`.",
"type": "boolean",
"x-versiondeprecated": "v2.11"
},
"doNotDerive": {
"description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.",
"type": "boolean"
},
"featureName": {
"description": "The name of the feature being specified.",
"type": "string"
},
"knownInAdvance": {
"description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.",
"type": "boolean"
}
},
"required": [
"featureName"
],
"type": "object"
},
"type": "array",
"x-versionadded": "v2.9"
},
"forecastWindowEnd": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"forecastWindowStart": {
"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.",
"minimum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.8"
},
"gapDuration": {
"description": "The duration of the gap between the training and holdout scoring data.",
"format": "duration",
"type": "string"
},
"gapEndDate": {
"description": "The end date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"gapStartDate": {
"description": "The start date of the gap between the training and holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutDuration": {
"description": "The duration of the holdout scoring data.",
"format": "duration",
"type": "string"
},
"holdoutEndDate": {
"description": "The end date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"holdoutStartDate": {
"description": "The start date of holdout scoring data.",
"format": "date-time",
"type": "string"
},
"isHoldoutModified": {
"description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.",
"type": "boolean"
},
"modelSplits": {
"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. ",
"maximum": 10,
"minimum": 1,
"type": "integer",
"x-versionadded": "v2.21"
},
"multiseriesIdColumns": {
"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.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array",
"x-versionadded": "v2.11"
},
"numberOfBacktests": {
"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.",
"maximum": 20,
"minimum": 1,
"type": "integer"
},
"numberOfDoNotDeriveFeatures": {
"description": "Number of features that are marked as \"do not derive\".",
"type": "integer",
"x-versionadded": "v2.17"
},
"numberOfKnownInAdvanceFeatures": {
"description": "Number of features that are marked as \"known in advance\".",
"type": "integer",
"x-versionadded": "v2.14"
},
"partitioningExtendedWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"partitioningWarnings": {
"description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.",
"items": {
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
},
"type": "array"
},
"periodicities": {
"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'.",
"items": {
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
},
"type": "array"
},
"primaryTrainingDuration": {
"description": "The duration of primary training duration for scoring the holdout.",
"format": "duration",
"type": "string"
},
"primaryTrainingEndDate": {
"description": "The end date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"primaryTrainingStartDate": {
"description": "The start date of primary training data for scoring the holdout.",
"format": "date-time",
"type": "string"
},
"projectId": {
"description": "The ID of the project.",
"type": "string"
},
"treatAsExponential": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.9"
},
"unsupervisedMode": {
"description": "A boolean value indicating whether an unsupervised project should be created.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
},
"unsupervisedType": {
"description": "The type of unsupervised project.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.31"
},
"useCrossSeriesFeatures": {
"description": "For multiseries projects only. Indicating whether to use cross-series features.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.14"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.",
"type": "boolean",
"x-versionadded": "v2.8"
},
"validationDuration": {
"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.",
"format": "duration",
"type": [
"string",
"null"
]
},
"windowsBasisUnit": {
"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"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.14"
}
},
"required": [
"autopilotDataSamplingMethod",
"autopilotDataSelectionMethod",
"availableHoldoutEndDate",
"availableTrainingDuration",
"availableTrainingEndDate",
"availableTrainingStartDate",
"backtests",
"dateFormat",
"datetimePartitionColumn",
"defaultToAPriori",
"defaultToDoNotDerive",
"defaultToKnownInAdvance",
"differencingMethod",
"disableHoldout",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"featureSettings",
"forecastWindowEnd",
"forecastWindowStart",
"gapDuration",
"gapEndDate",
"gapStartDate",
"holdoutDuration",
"holdoutEndDate",
"holdoutStartDate",
"multiseriesIdColumns",
"numberOfBacktests",
"numberOfDoNotDeriveFeatures",
"numberOfKnownInAdvanceFeatures",
"partitioningWarnings",
"periodicities",
"primaryTrainingDuration",
"primaryTrainingEndDate",
"primaryTrainingStartDate",
"projectId",
"treatAsExponential",
"useTimeSeries",
"validationDuration",
"windowsBasisUnit"
],
"type": "object"
},
"target": {
"description": "The name of the target column.",
"type": "string"
}
},
"required": [
"datetimePartitionColumn",
"id",
"partitionData",
"target"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
}
},
"required": [
"data",
"next",
"previous"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| count | integer | false | Number of items returned on this page. | |
| data | [OptimizedDatetimePartitioningList] | true | A list of datetime partitionings returned in order of creation. | |
| next | string,null(uri) | true | URL pointing to the next page (if null, there is no next page). | |
| previous | string,null(uri) | true | URL pointing to the previous page (if null, there is no previous page). |
Partition
{
"description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null.",
"properties": {
"cvHoldoutLevel": {
"description": "If a user partition column was used with cross validation, the value assigned to the holdout set",
"type": [
"string",
"null"
]
},
"cvMethod": {
"description": "The partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.",
"enum": [
"random",
"stratified",
"datetime",
"user",
"group",
"date"
],
"type": "string"
},
"datetimeCol": {
"description": "If a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "If a datetime partition column was used, the name of the column.",
"type": [
"string",
"null"
]
},
"holdoutLevel": {
"description": "If a user partition column was used with train-validation-holdout split, the value assigned to the holdout set.",
"type": [
"string",
"null"
]
},
"holdoutPct": {
"description": "The percentage of the dataset reserved for the holdout set.",
"type": [
"number",
"null"
]
},
"partitionKeyCols": {
"description": "An array containing a single string - the name of the group partition column",
"items": {
"type": "string"
},
"type": "array"
},
"reps": {
"description": "If cross validation was used, the number of folds to use.",
"type": [
"integer",
"null"
]
},
"trainingLevel": {
"description": "If a user partition column was used with train-validation-holdout split, the value assigned to the training set.",
"type": [
"string",
"null"
]
},
"useTimeSeries": {
"description": "Indicates whether a time series project was created as opposed to a regular project using datetime partitioning.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.9"
},
"userPartitionCol": {
"description": "If a user partition column was used, the name of the column.",
"type": [
"string",
"null"
]
},
"validationLevel": {
"description": "If a user partition column was used with train-validation-holdout split, the value assigned to the validation set.",
"type": [
"string",
"null"
]
},
"validationPct": {
"description": "If train-validation-holdout split was used, the percentage of the dataset used for the validation set.",
"type": [
"number",
"null"
]
},
"validationType": {
"description": "The type of validation used. Either CV (cross validation) or TVH (train-validation-holdout split).",
"enum": [
"CV",
"TVH"
],
"type": "string"
}
},
"required": [
"cvHoldoutLevel",
"cvMethod",
"datetimeCol",
"holdoutLevel",
"holdoutPct",
"partitionKeyCols",
"reps",
"trainingLevel",
"userPartitionCol",
"validationLevel",
"validationPct",
"validationType"
],
"type": "object"
}
The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| cvHoldoutLevel | string,null | true | If a user partition column was used with cross validation, the value assigned to the holdout set | |
| cvMethod | string | true | The partitioning method used. Note that "date" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0. | |
| datetimeCol | string,null | true | If a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0. | |
| datetimePartitionColumn | string,null | false | If a datetime partition column was used, the name of the column. | |
| holdoutLevel | string,null | true | If a user partition column was used with train-validation-holdout split, the value assigned to the holdout set. | |
| holdoutPct | number,null | true | The percentage of the dataset reserved for the holdout set. | |
| partitionKeyCols | [string] | true | An array containing a single string - the name of the group partition column | |
| reps | integer,null | true | If cross validation was used, the number of folds to use. | |
| trainingLevel | string,null | true | If a user partition column was used with train-validation-holdout split, the value assigned to the training set. | |
| useTimeSeries | boolean,null | false | Indicates whether a time series project was created as opposed to a regular project using datetime partitioning. | |
| userPartitionCol | string,null | true | If a user partition column was used, the name of the column. | |
| validationLevel | string,null | true | If a user partition column was used with train-validation-holdout split, the value assigned to the validation set. | |
| validationPct | number,null | true | If train-validation-holdout split was used, the percentage of the dataset used for the validation set. | |
| validationType | string | true | The type of validation used. Either CV (cross validation) or TVH (train-validation-holdout split). |
Enumerated Values¶
| Property | Value |
|---|---|
| cvMethod | [random, stratified, datetime, user, group, date] |
| validationType | [CV, TVH] |
PartitioningExtendedWarning
{
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| backtestIndex | integer,null | true | Backtest index. Null if the warning does not correspond to a single backtest. | |
| partition | string | true | Partition name. | |
| warnings | [PartitioningSingleExtendedWarning] | true | A list of strings representing warnings for the specified partition |
PartitioningSingleExtendedWarning
{
"properties": {
"message": {
"description": "Warning message.",
"type": "string"
},
"title": {
"description": "Warning short title.",
"type": "string"
},
"type": {
"description": "Warning severity type.",
"type": "string"
}
},
"required": [
"message",
"title",
"type"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| message | string | true | Warning message. | |
| title | string | true | Warning short title. | |
| type | string | true | Warning severity type. |
PartitioningWarning
{
"properties": {
"backtestIndex": {
"description": "Backtest index. Null if the warning does not correspond to a single backtest.",
"type": [
"integer",
"null"
]
},
"partition": {
"description": "Partition name.",
"type": "string"
},
"warnings": {
"description": "A list of strings representing warnings for the specified partition",
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"backtestIndex",
"partition",
"warnings"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| backtestIndex | integer,null | true | Backtest index. Null if the warning does not correspond to a single backtest. | |
| partition | string | true | Partition name. | |
| warnings | [string] | true | A list of strings representing warnings for the specified partition |
PasswordCredentials
{
"properties": {
"catalogVersionId": {
"description": "The ID of the latest version of the catalog entry.",
"type": "string"
},
"password": {
"description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.",
"type": "string"
},
"url": {
"description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.",
"type": "string"
},
"user": {
"description": "The username for database authentication.",
"type": "string"
}
},
"required": [
"password",
"user"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| catalogVersionId | string | false | The ID of the latest version of the catalog entry. | |
| password | string | true | The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. | |
| url | string | false | The link to retrieve more detailed information about the entity that uses this catalog dataset. | |
| user | string | true | The username for database authentication. |
Periodicity
{
"properties": {
"timeSteps": {
"description": "The number of time steps.",
"minimum": 0,
"type": "integer"
},
"timeUnit": {
"description": "The time unit or `ROW` if windowsBasisUnit is `ROW`",
"enum": [
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR",
"ROW"
],
"type": "string"
}
},
"required": [
"timeSteps",
"timeUnit"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| timeSteps | integer | true | minimum: 0 |
The number of time steps. |
| timeUnit | string | true | The time unit or ROW if windowsBasisUnit is ROW |
Enumerated Values¶
| Property | Value |
|---|---|
| timeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] |
ProjectAdvancedOptionsResponse
{
"description": "Information related to the current model of the deployment.",
"properties": {
"allowedPairwiseInteractionGroups": {
"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.",
"items": {
"items": {
"type": "string"
},
"type": "array"
},
"type": "array",
"x-versionadded": "v2.19"
},
"blendBestModels": {
"description": "blend best models during Autopilot run [DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.19",
"x-versiondeprecated": "2.30.0"
},
"blueprintThreshold": {
"description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs",
"type": [
"integer",
"null"
]
},
"considerBlendersInRecommendation": {
"description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.21",
"x-versiondeprecated": "2.30.0"
},
"defaultMonotonicDecreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"defaultMonotonicIncreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"downsampledMajorityRows": {
"description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"downsampledMinorityRows": {
"description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"eventsCount": {
"description": "the name of the event count column, if specified, otherwise null.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.8"
},
"exposure": {
"description": "the name of the exposure column, if specified.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.6"
},
"majorityDownsamplingRate": {
"description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling",
"type": [
"number",
"null"
],
"x-versionadded": "v2.5"
},
"minSecondaryValidationModelCount": {
"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.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"offset": {
"description": "the list of names of the offset columns, if specified, otherwise null.",
"items": {
"type": "string"
},
"type": "array",
"x-versionadded": "v2.6"
},
"onlyIncludeMonotonicBlueprints": {
"default": false,
"description": "whether the project only includes blueprints support enforcing monotonic constraints",
"type": "boolean",
"x-versionadded": "v2.11"
},
"prepareModelForDeployment": {
"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.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.19"
},
"responseCap": {
"description": "defaults to False, if specified used to cap the maximum response of a model",
"oneOf": [
{
"type": "boolean"
},
{
"maximum": 1,
"minimum": 0.5,
"type": "number"
}
]
},
"runLeakageRemovedFeatureList": {
"description": "Run Autopilot on Leakage Removed feature list (if exists).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"scoringCodeOnly": {
"description": "Keep only models that can be converted to scorable java code during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"seed": {
"description": "defaults to null, the random seed to be used if specified",
"type": [
"string",
"null"
]
},
"shapOnlyMode": {
"description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.21"
},
"smartDownsampled": {
"description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).",
"type": "boolean",
"x-versionadded": "v2.5"
},
"weights": {
"description": "the name of the weight column, if specified, otherwise null.",
"type": [
"string",
"null"
]
}
},
"required": [
"blendBestModels",
"blueprintThreshold",
"defaultMonotonicDecreasingFeaturelistId",
"defaultMonotonicIncreasingFeaturelistId",
"downsampledMajorityRows",
"downsampledMinorityRows",
"majorityDownsamplingRate",
"onlyIncludeMonotonicBlueprints",
"prepareModelForDeployment",
"responseCap",
"seed",
"shapOnlyMode",
"smartDownsampled",
"weights"
],
"type": "object"
}
Information related to the current model of the deployment.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| allowedPairwiseInteractionGroups | [array] | false | 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. | |
| blendBestModels | boolean | true | blend best models during Autopilot run [DEPRECATED] | |
| blueprintThreshold | integer,null | true | an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs | |
| considerBlendersInRecommendation | boolean | false | Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED] | |
| defaultMonotonicDecreasingFeaturelistId | string,null | true | null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time. | |
| defaultMonotonicIncreasingFeaturelistId | string,null | true | null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time. | |
| downsampledMajorityRows | integer,null | true | the total number of the majority rows available for modeling, or null for projects without smart downsampling | |
| downsampledMinorityRows | integer,null | true | the total number of the minority rows available for modeling, or null for projects without smart downsampling | |
| eventsCount | string,null | false | the name of the event count column, if specified, otherwise null. | |
| exposure | string,null | false | the name of the exposure column, if specified. | |
| majorityDownsamplingRate | number,null | true | the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling | |
| minSecondaryValidationModelCount | boolean | false | 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. | |
| offset | [string] | false | the list of names of the offset columns, if specified, otherwise null. | |
| onlyIncludeMonotonicBlueprints | boolean | true | whether the project only includes blueprints support enforcing monotonic constraints | |
| prepareModelForDeployment | boolean,null | true | 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. | |
| responseCap | any | true | defaults to False, if specified used to cap the maximum response of a model |
oneOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | boolean | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | maximum: 1 minimum: 0.5 |
none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| runLeakageRemovedFeatureList | boolean | false | Run Autopilot on Leakage Removed feature list (if exists). | |
| scoringCodeOnly | boolean | false | Keep only models that can be converted to scorable java code during Autopilot run. | |
| seed | string,null | true | defaults to null, the random seed to be used if specified | |
| shapOnlyMode | boolean,null | true | Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always null. |
|
| smartDownsampled | boolean | true | whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows). | |
| weights | string,null | true | the name of the weight column, if specified, otherwise null. |
ProjectClone
{
"properties": {
"copyOptions": {
"default": false,
"description": "Whether all project options should be copied to the cloned project.",
"type": "boolean"
},
"projectId": {
"description": "The ID of the project to clone.",
"type": "string"
},
"projectName": {
"description": "The name of the project to be created.",
"type": "string"
}
},
"required": [
"projectId"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| copyOptions | boolean | false | Whether all project options should be copied to the cloned project. | |
| projectId | string | true | The ID of the project to clone. | |
| projectName | string | false | The name of the project to be created. |
ProjectCreate
{
"properties": {
"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.",
"oneOf": [
{
"properties": {
"credentialType": {
"description": "The type of these credentials, 'basic' here.",
"enum": [
"basic"
],
"type": "string"
},
"password": {
"description": "The password for database authentication. The password is encrypted at rest and never saved / stored.",
"type": "string"
},
"user": {
"description": "The username for database authentication.",
"type": "string"
}
},
"required": [
"credentialType",
"password",
"user"
],
"type": "object"
},
{
"properties": {
"awsAccessKeyId": {
"description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.",
"type": "string"
},
"awsSecretAccessKey": {
"description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.",
"type": "string"
},
"awsSessionToken": {
"default": null,
"description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.",
"type": [
"string",
"null"
]
},
"configId": {
"description": "ID of Secure configurations of credentials shared by admin.If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 's3' here.",
"enum": [
"s3"
],
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
},
{
"properties": {
"credentialType": {
"description": "The type of these credentials, 'oauth' here.",
"enum": [
"oauth"
],
"type": "string"
},
"oauthAccessToken": {
"default": null,
"description": "The oauth access token.",
"type": [
"string",
"null"
]
},
"oauthClientId": {
"default": null,
"description": "The oauth client ID.",
"type": [
"string",
"null"
]
},
"oauthClientSecret": {
"default": null,
"description": "The oauth client secret.",
"type": [
"string",
"null"
]
},
"oauthRefreshToken": {
"description": "The oauth refresh token.",
"type": "string"
}
},
"required": [
"credentialType",
"oauthRefreshToken"
],
"type": "object"
},
{
"properties": {
"configId": {
"description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr or passphrase.",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 'snowflake_key_pair_user_account' here.",
"enum": [
"snowflake_key_pair_user_account"
],
"type": "string"
},
"passphrase": {
"description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.",
"type": "string"
},
"privateKeyStr": {
"description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.",
"type": "string"
},
"user": {
"description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.",
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
},
{
"properties": {
"configId": {
"description": "ID of Secure configurations shared by admin.Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 'gcp' here.",
"enum": [
"gcp"
],
"type": "string"
},
"gcpKey": {
"description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.",
"properties": {
"authProviderX509CertUrl": {
"description": "Auth provider X509 certificate URL.",
"format": "uri",
"type": "string"
},
"authUri": {
"description": "Auth URI.",
"format": "uri",
"type": "string"
},
"clientEmail": {
"description": "Client email address.",
"type": "string"
},
"clientId": {
"description": "Client ID.",
"type": "string"
},
"clientX509CertUrl": {
"description": "Client X509 certificate URL.",
"format": "uri",
"type": "string"
},
"privateKey": {
"description": "Private key.",
"type": "string"
},
"privateKeyId": {
"description": "Private key ID",
"type": "string"
},
"projectId": {
"description": "Project ID.",
"type": "string"
},
"tokenUri": {
"description": "Token URI.",
"format": "uri",
"type": "string"
},
"type": {
"description": "GCP account type.",
"enum": [
"service_account"
],
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
},
"googleConfigId": {
"description": "ID of Secure configurations shared by admin. This is deprecated.Please use configId instead. If specified, cannot include gcpKey.",
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
},
{
"properties": {
"credentialType": {
"description": "The type of these credentials, 'databricks_access_token_account' here.",
"enum": [
"databricks_access_token_account"
],
"type": "string"
},
"databricksAccessToken": {
"description": "Databricks personal access token.",
"minLength": 1,
"type": "string"
}
},
"required": [
"credentialType",
"databricksAccessToken"
],
"type": "object"
},
{
"properties": {
"clientId": {
"description": "Client ID for Databricks service principal.",
"minLength": 1,
"type": "string"
},
"clientSecret": {
"description": "Client secret for Databricks service principal.",
"minLength": 1,
"type": "string"
},
"configId": {
"description": "The ID of the saved shared credentials. If specified, cannot include clientIdand clientSecret.",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 'databricks_service_principal_account' here.",
"enum": [
"databricks_service_principal_account"
],
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
},
{
"properties": {
"azureTenantId": {
"description": "Tenant ID of the Azure AD service principal.",
"type": "string"
},
"clientId": {
"description": "Client ID of the Azure AD service principal.",
"type": "string"
},
"clientSecret": {
"description": "Client Secret of the Azure AD service principal.",
"type": "string"
},
"configId": {
"description": "ID of secure configurations of credentials shared by admin.",
"type": "string",
"x-versionadded": "v2.35"
},
"credentialType": {
"description": "The type of these credentials, 'azure_service_principal' here.",
"enum": [
"azure_service_principal"
],
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
}
],
"x-versionadded": "v2.23"
},
"credentialId": {
"description": "The ID of the set of credentials to authenticate with the database. Can only be used along with datasetId or dataSourceId.",
"type": "string",
"x-versionadded": "v2.19"
},
"dataSourceId": {
"description": "Identifier for the data source to retrieve.",
"type": "string"
},
"datasetId": {
"description": "The ID of the dataset entry to use for the project dataset.",
"type": "string"
},
"datasetVersionId": {
"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.",
"type": "string"
},
"password": {
"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.",
"type": "string",
"x-versiondeprecated": "v2.23"
},
"projectName": {
"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.",
"type": "string"
},
"recipeId": {
"description": "The ID of the wrangling recipe that will be used for project creation.",
"type": "string"
},
"url": {
"description": "The URL to download the dataset used to create the project.",
"format": "url",
"type": "string"
},
"useKerberos": {
"description": "If true, use Kerberos authentication for database authentication. Default is false. Can only be used along with datasetId or dataSourceId.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"user": {
"description": "The username for database authentication. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead.",
"type": "string",
"x-versiondeprecated": "v2.23"
}
},
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| credentialData | any | false | The credentials to authenticate with the database, to be used instead of credential ID. Can only be used along with datasetId or dataSourceId. |
oneOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | BasicCredentials | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | S3Credentials | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | OAuthCredentials | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | SnowflakeKeyPairCredentials | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | GoogleServiceAccountCredentials | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | DatabricksAccessTokenCredentials | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | DatabricksServicePrincipalCredentials | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | AzureServicePrincipalCredentials | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| credentialId | string | false | The ID of the set of credentials to authenticate with the database. Can only be used along with datasetId or dataSourceId. | |
| dataSourceId | string | false | Identifier for the data source to retrieve. | |
| datasetId | string | false | The ID of the dataset entry to use for the project dataset. | |
| datasetVersionId | string | false | 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. | |
| password | string | false | 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 | false | 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 | false | The ID of the wrangling recipe that will be used for project creation. | |
| url | string(url) | false | The URL to download the dataset used to create the project. | |
| useKerberos | boolean | false | If true, use Kerberos authentication for database authentication. Default is false. Can only be used along with datasetId or dataSourceId. | |
| user | string | false | The username for database authentication. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead. |
ProjectCreateResponse
{
"properties": {
"pid": {
"description": "The project ID.",
"type": "string"
}
},
"required": [
"pid"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| pid | string | true | The project ID. |
ProjectDetailsResponse
{
"properties": {
"advancedOptions": {
"description": "Information related to the current model of the deployment.",
"properties": {
"allowedPairwiseInteractionGroups": {
"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.",
"items": {
"items": {
"type": "string"
},
"type": "array"
},
"type": "array",
"x-versionadded": "v2.19"
},
"blendBestModels": {
"description": "blend best models during Autopilot run [DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.19",
"x-versiondeprecated": "2.30.0"
},
"blueprintThreshold": {
"description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs",
"type": [
"integer",
"null"
]
},
"considerBlendersInRecommendation": {
"description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.21",
"x-versiondeprecated": "2.30.0"
},
"defaultMonotonicDecreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"defaultMonotonicIncreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"downsampledMajorityRows": {
"description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"downsampledMinorityRows": {
"description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"eventsCount": {
"description": "the name of the event count column, if specified, otherwise null.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.8"
},
"exposure": {
"description": "the name of the exposure column, if specified.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.6"
},
"majorityDownsamplingRate": {
"description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling",
"type": [
"number",
"null"
],
"x-versionadded": "v2.5"
},
"minSecondaryValidationModelCount": {
"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.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"offset": {
"description": "the list of names of the offset columns, if specified, otherwise null.",
"items": {
"type": "string"
},
"type": "array",
"x-versionadded": "v2.6"
},
"onlyIncludeMonotonicBlueprints": {
"default": false,
"description": "whether the project only includes blueprints support enforcing monotonic constraints",
"type": "boolean",
"x-versionadded": "v2.11"
},
"prepareModelForDeployment": {
"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.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.19"
},
"responseCap": {
"description": "defaults to False, if specified used to cap the maximum response of a model",
"oneOf": [
{
"type": "boolean"
},
{
"maximum": 1,
"minimum": 0.5,
"type": "number"
}
]
},
"runLeakageRemovedFeatureList": {
"description": "Run Autopilot on Leakage Removed feature list (if exists).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"scoringCodeOnly": {
"description": "Keep only models that can be converted to scorable java code during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"seed": {
"description": "defaults to null, the random seed to be used if specified",
"type": [
"string",
"null"
]
},
"shapOnlyMode": {
"description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.21"
},
"smartDownsampled": {
"description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).",
"type": "boolean",
"x-versionadded": "v2.5"
},
"weights": {
"description": "the name of the weight column, if specified, otherwise null.",
"type": [
"string",
"null"
]
}
},
"required": [
"blendBestModels",
"blueprintThreshold",
"defaultMonotonicDecreasingFeaturelistId",
"defaultMonotonicIncreasingFeaturelistId",
"downsampledMajorityRows",
"downsampledMinorityRows",
"majorityDownsamplingRate",
"onlyIncludeMonotonicBlueprints",
"prepareModelForDeployment",
"responseCap",
"seed",
"shapOnlyMode",
"smartDownsampled",
"weights"
],
"type": "object"
},
"autopilotClusterList": {
"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'.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotMode": {
"description": "The current autopilot mode, 0 for full autopilot, 2 for manual mode, 3 for quick mode, 4 for comprehensive mode",
"type": "integer"
},
"created": {
"description": "The time of project creation.",
"format": "date-time",
"type": "string"
},
"featureEngineeringPredictionPoint": {
"description": "The date column to be used as the prediction point for time-based feature engineering.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.22"
},
"fileName": {
"description": "The name of the dataset used to create the project.",
"type": "string"
},
"holdoutUnlocked": {
"description": "whether the holdout has been unlocked",
"type": "boolean"
},
"id": {
"description": "The ID of a project.",
"type": "string"
},
"maxClusters": {
"description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `minClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100.",
"maximum": 100,
"minimum": 2,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.23"
},
"maxTrainPct": {
"description": "the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data.",
"type": "number"
},
"maxTrainRows": {
"description": "the maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data",
"type": "integer"
},
"metric": {
"description": "the metric used to select the best-performing models.",
"type": "string"
},
"minClusters": {
"description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `maxClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2.",
"maximum": 100,
"minimum": 2,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.23"
},
"partition": {
"description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose `cvMethod` is `\"datetime\"`, full specification of the partitioning method can be found at [GET /api/v2/projects/{projectId}/datetimePartitioning/][get-apiv2projectsprojectiddatetimepartitioning].",
"properties": {
"cvHoldoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with cross validation, the value assigned to the holdout set"
},
"cvMethod": {
"description": "the partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.",
"enum": [
"random",
"user",
"stratified",
"group",
"datetime"
],
"type": "string"
},
"datetimeCol": {
"description": "if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "if a datetime partition column was used, the name of the column",
"type": "string"
},
"holdoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set"
},
"holdoutPct": {
"description": "the percentage of the dataset reserved for the holdout set",
"type": "number"
},
"partitionKeyCols": {
"description": "An array containing a single string - the name of the group partition column",
"items": {
"type": "string"
},
"type": "array"
},
"reps": {
"description": "if cross validation was used, the number of folds to use",
"type": [
"number",
"null"
]
},
"trainingLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with train-validation-holdout split, the value assigned to the training set"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.9"
},
"userPartitionCol": {
"description": "if a user partition column was used, the name of the column",
"type": [
"string",
"null"
]
},
"validationLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with train-validation-holdout split, the value assigned to the validation set"
},
"validationPct": {
"description": "if train-validation-holdout split was used, the percentage of the dataset used for the validation set",
"type": [
"number",
"null"
]
},
"validationType": {
"description": "either CV for cross-validation or TVH for train-validation-holdout split",
"enum": [
"CV",
"TVH"
],
"type": "string"
}
},
"required": [
"cvHoldoutLevel",
"cvMethod",
"datetimeCol",
"holdoutLevel",
"holdoutPct",
"partitionKeyCols",
"reps",
"trainingLevel",
"useTimeSeries",
"userPartitionCol",
"validationLevel",
"validationPct",
"validationType"
],
"type": "object"
},
"positiveClass": {
"description": "if the project uses binary classification, the class designated to be the positive class. Otherwise, null.",
"type": [
"number",
"null"
]
},
"projectName": {
"description": "The name of a project.",
"type": "string"
},
"stage": {
"description": "the stage of the project - if modeling, then the target is successfully set, and modeling or predictions can proceed.",
"type": "string"
},
"target": {
"description": "the target of the project, null if project is unsupervised.",
"type": "string"
},
"targetType": {
"description": "The target type of the project.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"minInflated",
"Multilabel",
"TextGeneration",
"GeoPoint",
"VectorDatabase"
],
"type": [
"string",
"null"
]
},
"unsupervisedMode": {
"description": "indicates whether a project is unsupervised.",
"type": "boolean",
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCase": {
"description": "The information of the use case associated with the project.",
"properties": {
"id": {
"description": "The ID of the use case.",
"type": [
"string",
"null"
]
},
"name": {
"description": "The name of the use case.",
"type": [
"string",
"null"
]
}
},
"type": "object",
"x-versionadded": "v2.36"
},
"useFeatureDiscovery": {
"description": "A boolean value indicating whether a feature discovery project was created as opposed to a regular project.",
"type": "boolean",
"x-versionadded": "v2.21"
}
},
"required": [
"advancedOptions",
"autopilotMode",
"created",
"fileName",
"holdoutUnlocked",
"id",
"maxTrainPct",
"maxTrainRows",
"metric",
"partition",
"positiveClass",
"projectName",
"stage",
"target",
"targetType",
"unsupervisedMode",
"useFeatureDiscovery"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| advancedOptions | ProjectAdvancedOptionsResponse | true | Information related to the current model of the deployment. | |
| autopilotClusterList | [integer] | false | maxItems: 10 |
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'. |
| autopilotMode | integer | true | The current autopilot mode, 0 for full autopilot, 2 for manual mode, 3 for quick mode, 4 for comprehensive mode | |
| created | string(date-time) | true | The time of project creation. | |
| featureEngineeringPredictionPoint | string,null | false | The date column to be used as the prediction point for time-based feature engineering. | |
| fileName | string | true | The name of the dataset used to create the project. | |
| holdoutUnlocked | boolean | true | whether the holdout has been unlocked | |
| id | string | true | The ID of a project. | |
| maxClusters | integer,null | false | maximum: 100 minimum: 2 |
Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to minClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100. |
| maxTrainPct | number | true | the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data. | |
| maxTrainRows | integer | true | the maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data | |
| metric | string | true | the metric used to select the best-performing models. | |
| minClusters | integer,null | false | maximum: 100 minimum: 2 |
Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to maxClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2. |
| partition | ProjectPartitionResponse | true | The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose cvMethod is "datetime", full specification of the partitioning method can be found at GET /api/v2/projects/{projectId}/datetimePartitioning/. |
|
| positiveClass | number,null | true | if the project uses binary classification, the class designated to be the positive class. Otherwise, null. | |
| projectName | string | true | The name of a project. | |
| stage | string | true | the stage of the project - if modeling, then the target is successfully set, and modeling or predictions can proceed. | |
| target | string | true | the target of the project, null if project is unsupervised. | |
| targetType | string,null | true | The target type of the project. | |
| unsupervisedMode | boolean | true | indicates whether a project is unsupervised. | |
| unsupervisedType | string,null | false | Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'. | |
| useCase | UseCaseIdName | false | The information of the use case associated with the project. | |
| useFeatureDiscovery | boolean | true | A boolean value indicating whether a feature discovery project was created as opposed to a regular project. |
Enumerated Values¶
| Property | Value |
|---|---|
| targetType | [Binary, Regression, Multiclass, minInflated, Multilabel, TextGeneration, GeoPoint, VectorDatabase] |
| unsupervisedType | [anomaly, clustering] |
ProjectNuke
{
"properties": {
"creator": {
"description": "Creator ID to filter projects by",
"type": "string"
},
"deletedAfter": {
"description": "ISO-8601 formatted date projects were deleted after",
"format": "date-time",
"type": "string"
},
"deletedBefore": {
"description": "ISO-8601 formatted date projects were deleted before",
"format": "date-time",
"type": "string"
},
"limit": {
"default": 1000,
"description": "At most this many projects are deleted.",
"maximum": 1000,
"minimum": 1,
"type": "integer"
},
"offset": {
"default": 0,
"description": "This many projects will be skipped.",
"minimum": 0,
"type": "integer"
},
"organization": {
"description": "ID of organization that projects should belong to",
"type": "string"
},
"projectIds": {
"description": "List of project IDs to delete permanently.",
"items": {
"type": "string"
},
"maxItems": 1000,
"minItems": 1,
"type": "array"
},
"searchFor": {
"description": "Project or dataset name to filter by",
"type": "string"
}
},
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| creator | string | false | Creator ID to filter projects by | |
| deletedAfter | string(date-time) | false | ISO-8601 formatted date projects were deleted after | |
| deletedBefore | string(date-time) | false | ISO-8601 formatted date projects were deleted before | |
| limit | integer | false | maximum: 1000 minimum: 1 |
At most this many projects are deleted. |
| offset | integer | false | minimum: 0 |
This many projects will be skipped. |
| organization | string | false | ID of organization that projects should belong to | |
| projectIds | [string] | false | maxItems: 1000 minItems: 1 |
List of project IDs to delete permanently. |
| searchFor | string | false | Project or dataset name to filter by |
ProjectNukeJobListStatus
{
"properties": {
"jobs": {
"description": "List of active permadelete jobs with their statuses.",
"items": {
"properties": {
"created": {
"description": "The time the status record was created.",
"format": "date-time",
"type": "string"
},
"data": {
"description": "List of projects and associated statuses.",
"items": {
"properties": {
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "Project ID",
"type": "string"
},
"status": {
"description": "The processing state of project cleanup task.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
}
},
"required": [
"message",
"projectId",
"status"
],
"type": "object"
},
"maxItems": 1000,
"minItems": 1,
"type": "array"
},
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"status": {
"description": "The processing state of the cleanup job.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
},
"statusId": {
"description": "The ID of the status object.",
"type": "string"
}
},
"required": [
"created",
"data",
"message",
"status",
"statusId"
],
"type": "object"
},
"type": "array"
}
},
"required": [
"jobs"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| jobs | [ProjectNukeJobStatus] | true | List of active permadelete jobs with their statuses. |
ProjectNukeJobStatus
{
"properties": {
"created": {
"description": "The time the status record was created.",
"format": "date-time",
"type": "string"
},
"data": {
"description": "List of projects and associated statuses.",
"items": {
"properties": {
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "Project ID",
"type": "string"
},
"status": {
"description": "The processing state of project cleanup task.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
}
},
"required": [
"message",
"projectId",
"status"
],
"type": "object"
},
"maxItems": 1000,
"minItems": 1,
"type": "array"
},
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"status": {
"description": "The processing state of the cleanup job.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
},
"statusId": {
"description": "The ID of the status object.",
"type": "string"
}
},
"required": [
"created",
"data",
"message",
"status",
"statusId"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| created | string(date-time) | true | The time the status record was created. | |
| data | [ProjectPermadeleteStatus] | true | maxItems: 1000 minItems: 1 |
List of projects and associated statuses. |
| message | string,null | true | May contain further information about the status. | |
| status | string | true | The processing state of the cleanup job. | |
| statusId | string | true | The ID of the status object. |
Enumerated Values¶
| Property | Value |
|---|---|
| status | [ABORTED, BLOCKED, COMPLETED, CREATED, ERROR, EXPIRED, INCOMPLETE, INITIALIZED, PAUSED, RUNNING] |
ProjectNukeJobStatusSummary
{
"properties": {
"jobId": {
"description": "The ID of the permadeletion multi-job.",
"type": "string"
},
"summary": {
"description": "Project permanent deletion status to count mapping.",
"properties": {
"aborted": {
"description": "Number of project permadelete jobs with Aborted status.",
"minimum": 0,
"type": "integer"
},
"completed": {
"description": "Number of project permadelete jobs with Completed status.",
"minimum": 0,
"type": "integer"
},
"error": {
"description": "Number of project permadelete jobs with Error status.",
"minimum": 0,
"type": "integer"
},
"expired": {
"description": "Number of project permadelete jobs with Expired status.",
"minimum": 0,
"type": "integer"
}
},
"required": [
"aborted",
"completed",
"error",
"expired"
],
"type": "object"
}
},
"required": [
"jobId",
"summary"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| jobId | string | true | The ID of the permadeletion multi-job. | |
| summary | ProjectNukeJobStatusSummaryObject | true | Project permanent deletion status to count mapping. |
ProjectNukeJobStatusSummaryObject
{
"description": "Project permanent deletion status to count mapping.",
"properties": {
"aborted": {
"description": "Number of project permadelete jobs with Aborted status.",
"minimum": 0,
"type": "integer"
},
"completed": {
"description": "Number of project permadelete jobs with Completed status.",
"minimum": 0,
"type": "integer"
},
"error": {
"description": "Number of project permadelete jobs with Error status.",
"minimum": 0,
"type": "integer"
},
"expired": {
"description": "Number of project permadelete jobs with Expired status.",
"minimum": 0,
"type": "integer"
}
},
"required": [
"aborted",
"completed",
"error",
"expired"
],
"type": "object"
}
Project permanent deletion status to count mapping.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| aborted | integer | true | minimum: 0 |
Number of project permadelete jobs with Aborted status. |
| completed | integer | true | minimum: 0 |
Number of project permadelete jobs with Completed status. |
| error | integer | true | minimum: 0 |
Number of project permadelete jobs with Error status. |
| expired | integer | true | minimum: 0 |
Number of project permadelete jobs with Expired status. |
ProjectPartitionResponse
{
"description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose `cvMethod` is `\"datetime\"`, full specification of the partitioning method can be found at [GET /api/v2/projects/{projectId}/datetimePartitioning/][get-apiv2projectsprojectiddatetimepartitioning].",
"properties": {
"cvHoldoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with cross validation, the value assigned to the holdout set"
},
"cvMethod": {
"description": "the partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.",
"enum": [
"random",
"user",
"stratified",
"group",
"datetime"
],
"type": "string"
},
"datetimeCol": {
"description": "if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "if a datetime partition column was used, the name of the column",
"type": "string"
},
"holdoutLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set"
},
"holdoutPct": {
"description": "the percentage of the dataset reserved for the holdout set",
"type": "number"
},
"partitionKeyCols": {
"description": "An array containing a single string - the name of the group partition column",
"items": {
"type": "string"
},
"type": "array"
},
"reps": {
"description": "if cross validation was used, the number of folds to use",
"type": [
"number",
"null"
]
},
"trainingLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with train-validation-holdout split, the value assigned to the training set"
},
"useTimeSeries": {
"description": "A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.9"
},
"userPartitionCol": {
"description": "if a user partition column was used, the name of the column",
"type": [
"string",
"null"
]
},
"validationLevel": {
"anyOf": [
{
"type": "string"
},
{
"type": "number"
},
{
"type": "integer"
}
],
"description": "if a user partition column was used with train-validation-holdout split, the value assigned to the validation set"
},
"validationPct": {
"description": "if train-validation-holdout split was used, the percentage of the dataset used for the validation set",
"type": [
"number",
"null"
]
},
"validationType": {
"description": "either CV for cross-validation or TVH for train-validation-holdout split",
"enum": [
"CV",
"TVH"
],
"type": "string"
}
},
"required": [
"cvHoldoutLevel",
"cvMethod",
"datetimeCol",
"holdoutLevel",
"holdoutPct",
"partitionKeyCols",
"reps",
"trainingLevel",
"useTimeSeries",
"userPartitionCol",
"validationLevel",
"validationPct",
"validationType"
],
"type": "object"
}
The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose cvMethod is "datetime", full specification of the partitioning method can be found at GET /api/v2/projects/{projectId}/datetimePartitioning/.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| cvHoldoutLevel | any | true | if a user partition column was used with cross validation, the value assigned to the holdout set |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| cvMethod | string | true | the partitioning method used. Note that "date" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0. | |
| datetimeCol | string,null | true | if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0. | |
| datetimePartitionColumn | string | false | if a datetime partition column was used, the name of the column | |
| holdoutLevel | any | true | if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| holdoutPct | number | true | the percentage of the dataset reserved for the holdout set | |
| partitionKeyCols | [string] | true | An array containing a single string - the name of the group partition column | |
| reps | number,null | true | if cross validation was used, the number of folds to use | |
| trainingLevel | any | true | if a user partition column was used with train-validation-holdout split, the value assigned to the training set |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| useTimeSeries | boolean,null | true | A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning. | |
| userPartitionCol | string,null | true | if a user partition column was used, the name of the column | |
| validationLevel | any | true | if a user partition column was used with train-validation-holdout split, the value assigned to the validation set |
anyOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | number | false | none |
or
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| validationPct | number,null | true | if train-validation-holdout split was used, the percentage of the dataset used for the validation set | |
| validationType | string | true | either CV for cross-validation or TVH for train-validation-holdout split |
Enumerated Values¶
| Property | Value |
|---|---|
| cvMethod | [random, user, stratified, group, datetime] |
| validationType | [CV, TVH] |
ProjectPermadeleteStatus
{
"properties": {
"message": {
"description": "May contain further information about the status.",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "Project ID",
"type": "string"
},
"status": {
"description": "The processing state of project cleanup task.",
"enum": [
"ABORTED",
"BLOCKED",
"COMPLETED",
"CREATED",
"ERROR",
"EXPIRED",
"INCOMPLETE",
"INITIALIZED",
"PAUSED",
"RUNNING"
],
"type": "string"
}
},
"required": [
"message",
"projectId",
"status"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| message | string,null | true | May contain further information about the status. | |
| projectId | string | true | Project ID | |
| status | string | true | The processing state of project cleanup task. |
Enumerated Values¶
| Property | Value |
|---|---|
| status | [ABORTED, BLOCKED, COMPLETED, CREATED, ERROR, EXPIRED, INCOMPLETE, INITIALIZED, PAUSED, RUNNING] |
ProjectRecover
{
"properties": {
"action": {
"description": "Action to perform on a project",
"enum": [
"undelete"
],
"type": "string"
}
},
"required": [
"action"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| action | string | true | Action to perform on a project |
Enumerated Values¶
| Property | Value |
|---|---|
| action | undelete |
ProjectRecoverResponse
{
"properties": {
"message": {
"description": "Operation result description",
"type": "string"
}
},
"required": [
"message"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| message | string | true | Operation result description |
ProjectRetrieveResponse
{
"properties": {
"advancedOptions": {
"description": "Information related to the current model of the deployment.",
"properties": {
"allowedPairwiseInteractionGroups": {
"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.",
"items": {
"items": {
"type": "string"
},
"type": "array"
},
"type": "array",
"x-versionadded": "v2.19"
},
"blendBestModels": {
"description": "blend best models during Autopilot run [DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.19",
"x-versiondeprecated": "2.30.0"
},
"blueprintThreshold": {
"description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs",
"type": [
"integer",
"null"
]
},
"considerBlendersInRecommendation": {
"description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]",
"type": "boolean",
"x-versionadded": "v2.21",
"x-versiondeprecated": "2.30.0"
},
"defaultMonotonicDecreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"defaultMonotonicIncreasingFeaturelistId": {
"description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.11"
},
"downsampledMajorityRows": {
"description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"downsampledMinorityRows": {
"description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling",
"type": [
"integer",
"null"
],
"x-versionadded": "v2.5"
},
"eventsCount": {
"description": "the name of the event count column, if specified, otherwise null.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.8"
},
"exposure": {
"description": "the name of the exposure column, if specified.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.6"
},
"majorityDownsamplingRate": {
"description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling",
"type": [
"number",
"null"
],
"x-versionadded": "v2.5"
},
"minSecondaryValidationModelCount": {
"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.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"offset": {
"description": "the list of names of the offset columns, if specified, otherwise null.",
"items": {
"type": "string"
},
"type": "array",
"x-versionadded": "v2.6"
},
"onlyIncludeMonotonicBlueprints": {
"default": false,
"description": "whether the project only includes blueprints support enforcing monotonic constraints",
"type": "boolean",
"x-versionadded": "v2.11"
},
"prepareModelForDeployment": {
"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.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.19"
},
"responseCap": {
"description": "defaults to False, if specified used to cap the maximum response of a model",
"oneOf": [
{
"type": "boolean"
},
{
"maximum": 1,
"minimum": 0.5,
"type": "number"
}
]
},
"runLeakageRemovedFeatureList": {
"description": "Run Autopilot on Leakage Removed feature list (if exists).",
"type": "boolean",
"x-versionadded": "v2.21"
},
"scoringCodeOnly": {
"description": "Keep only models that can be converted to scorable java code during Autopilot run.",
"type": "boolean",
"x-versionadded": "v2.19"
},
"seed": {
"description": "defaults to null, the random seed to be used if specified",
"type": [
"string",
"null"
]
},
"shapOnlyMode": {
"description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.21"
},
"smartDownsampled": {
"description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).",
"type": "boolean",
"x-versionadded": "v2.5"
},
"weights": {
"description": "the name of the weight column, if specified, otherwise null.",
"type": [
"string",
"null"
]
}
},
"required": [
"blendBestModels",
"blueprintThreshold",
"defaultMonotonicDecreasingFeaturelistId",
"defaultMonotonicIncreasingFeaturelistId",
"downsampledMajorityRows",
"downsampledMinorityRows",
"majorityDownsamplingRate",
"onlyIncludeMonotonicBlueprints",
"prepareModelForDeployment",
"responseCap",
"seed",
"shapOnlyMode",
"smartDownsampled",
"weights"
],
"type": "object"
},
"autopilotClusterList": {
"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'.",
"items": {
"maximum": 100,
"minimum": 2,
"type": "integer"
},
"maxItems": 10,
"type": "array",
"x-versionadded": "v2.25"
},
"autopilotMode": {
"description": "The current autopilot mode. 0: Full Autopilot. 2: Manual Mode. 3: Quick Mode. 4: Comprehensive Autopilot. null: Mode not set.",
"enum": [
"0",
"2",
"3",
"4"
],
"type": [
"string",
"null"
]
},
"catalogId": {
"description": "The ID of the AI catalog entry used to create the project, or null if not created from the AI catalog.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.21"
},
"catalogVersionId": {
"description": "The ID of the AI catalog version used to create the project, or null if not created from the AI catalog.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.21"
},
"created": {
"description": "The time of project creation.",
"format": "date-time",
"type": "string"
},
"externalTimeSeriesBaselineDatasetMetadata": {
"description": "The id of the catalog item that is being used as the external baseline data",
"properties": {
"datasetId": {
"description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.",
"type": [
"string",
"null"
]
},
"datasetName": {
"description": "The name of the timeseries baseline dataset for the project",
"type": [
"string",
"null"
]
}
},
"required": [
"datasetId",
"datasetName"
],
"type": "object"
},
"featureEngineeringPredictionPoint": {
"description": "The date column to be used as the prediction point for time-based feature engineering.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.22"
},
"fileName": {
"description": "The name of the dataset used to create the project.",
"type": "string"
},
"holdoutUnlocked": {
"description": "Whether the holdout has been unlocked.",
"type": "boolean"
},
"id": {
"description": "The ID of the project.",
"type": "string"
},
"isScoringAvailableForModelsTrainedIntoValidationHoldout": {
"description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.",
"type": "boolean",
"x-versionadded": "v2.31"
},
"maxClusters": {
"description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `minClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100.",
"maximum": 100,
"minimum": 2,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.23"
},
"maxTrainPct": {
"description": "The maximum percentage of the dataset that can be used to successfully train a model without going into the validation data.",
"type": "number"
},
"maxTrainRows": {
"description": "The maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data.",
"type": "integer"
},
"metric": {
"description": "The metric used to select the best-performing models.",
"type": "string"
},
"minClusters": {
"description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `maxClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2.",
"maximum": 100,
"minimum": 2,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.23"
},
"partition": {
"description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null.",
"properties": {
"cvHoldoutLevel": {
"description": "If a user partition column was used with cross validation, the value assigned to the holdout set",
"type": [
"string",
"null"
]
},
"cvMethod": {
"description": "The partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.",
"enum": [
"random",
"stratified",
"datetime",
"user",
"group",
"date"
],
"type": "string"
},
"datetimeCol": {
"description": "If a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "If a datetime partition column was used, the name of the column.",
"type": [
"string",
"null"
]
},
"holdoutLevel": {
"description": "If a user partition column was used with train-validation-holdout split, the value assigned to the holdout set.",
"type": [
"string",
"null"
]
},
"holdoutPct": {
"description": "The percentage of the dataset reserved for the holdout set.",
"type": [
"number",
"null"
]
},
"partitionKeyCols": {
"description": "An array containing a single string - the name of the group partition column",
"items": {
"type": "string"
},
"type": "array"
},
"reps": {
"description": "If cross validation was used, the number of folds to use.",
"type": [
"integer",
"null"
]
},
"trainingLevel": {
"description": "If a user partition column was used with train-validation-holdout split, the value assigned to the training set.",
"type": [
"string",
"null"
]
},
"useTimeSeries": {
"description": "Indicates whether a time series project was created as opposed to a regular project using datetime partitioning.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.9"
},
"userPartitionCol": {
"description": "If a user partition column was used, the name of the column.",
"type": [
"string",
"null"
]
},
"validationLevel": {
"description": "If a user partition column was used with train-validation-holdout split, the value assigned to the validation set.",
"type": [
"string",
"null"
]
},
"validationPct": {
"description": "If train-validation-holdout split was used, the percentage of the dataset used for the validation set.",
"type": [
"number",
"null"
]
},
"validationType": {
"description": "The type of validation used. Either CV (cross validation) or TVH (train-validation-holdout split).",
"enum": [
"CV",
"TVH"
],
"type": "string"
}
},
"required": [
"cvHoldoutLevel",
"cvMethod",
"datetimeCol",
"holdoutLevel",
"holdoutPct",
"partitionKeyCols",
"reps",
"trainingLevel",
"userPartitionCol",
"validationLevel",
"validationPct",
"validationType"
],
"type": "object"
},
"positiveClass": {
"description": "If the project uses binary classification, the class designated to be the positive class.",
"oneOf": [
{
"type": "string"
},
{
"type": "integer"
}
]
},
"primaryLocationColumn": {
"description": "Primary location column name",
"type": "string",
"x-versionadded": "v2.27"
},
"projectName": {
"description": "The name of the project.",
"type": "string"
},
"queryGeneratorId": {
"description": "The ID of the time series data prep query generator associated with the project, or null if there is no associated query generator.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.27"
},
"quickrun": {
"description": "If the Autopilot mode is set to quick. DEPRECATED: look at autopilot_mode instead.",
"type": "boolean",
"x-versionadded": "v2.22",
"x-versiondeprecated": "v2.30"
},
"relationshipsConfigurationId": {
"description": "Relationships configuration id to be used for Feature Discovery projects.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.27"
},
"segmentation": {
"description": "Segmentation info for the project.",
"properties": {
"parentProjectId": {
"description": "ID of the original project.",
"type": [
"string",
"null"
]
},
"segment": {
"description": "Segment value.",
"type": [
"string",
"null"
]
},
"segmentationTaskId": {
"description": "ID of the Segmentation Task.",
"type": "string"
}
},
"required": [
"segmentationTaskId"
],
"type": "object"
},
"stage": {
"description": "The stage of the project. If modeling, then the target is successfully set and modeling or predictions can proceed.",
"enum": [
"modeling",
"aim",
"fasteda",
"eda",
"eda2",
"empty"
],
"type": "string"
},
"target": {
"description": "The target of the project, null if project is unsupervised.",
"type": "string"
},
"targetType": {
"description": "The type of the selected target. Null if the project is unsupervised.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel"
],
"type": [
"string",
"null"
]
},
"unsupervisedMode": {
"description": "Indicates whether a project is unsupervised.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.20"
},
"unsupervisedType": {
"description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.",
"enum": [
"anomaly",
"clustering"
],
"type": [
"string",
"null"
],
"x-versionadded": "v2.23"
},
"useCase": {
"description": "The information of the use case associated with the project.",
"properties": {
"id": {
"description": "The ID of the use case.",
"type": [
"string",
"null"
]
},
"name": {
"description": "The name of the use case.",
"type": [
"string",
"null"
]
}
},
"type": "object",
"x-versionadded": "v2.36"
},
"useFeatureDiscovery": {
"description": "Indicates whether a feature discovery project was created as opposed to a regular project",
"type": "boolean",
"x-versionadded": "v2.21"
},
"useGpu": {
"description": "Indicates whether project should use GPU workers",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
}
},
"required": [
"advancedOptions",
"autopilotMode",
"catalogId",
"catalogVersionId",
"created",
"fileName",
"holdoutUnlocked",
"id",
"isScoringAvailableForModelsTrainedIntoValidationHoldout",
"maxTrainPct",
"maxTrainRows",
"metric",
"partition",
"positiveClass",
"projectName",
"queryGeneratorId",
"quickrun",
"stage",
"target",
"targetType",
"unsupervisedMode",
"useFeatureDiscovery"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| advancedOptions | ProjectAdvancedOptionsResponse | true | Information related to the current model of the deployment. | |
| autopilotClusterList | [integer] | false | maxItems: 10 |
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'. |
| autopilotMode | string,null | true | The current autopilot mode. 0: Full Autopilot. 2: Manual Mode. 3: Quick Mode. 4: Comprehensive Autopilot. null: Mode not set. | |
| catalogId | string,null | true | The ID of the AI catalog entry used to create the project, or null if not created from the AI catalog. | |
| catalogVersionId | string,null | true | The ID of the AI catalog version used to create the project, or null if not created from the AI catalog. | |
| created | string(date-time) | true | The time of project creation. | |
| externalTimeSeriesBaselineDatasetMetadata | ExternalTSBaselineMetadata | false | The id of the catalog item that is being used as the external baseline data | |
| featureEngineeringPredictionPoint | string,null | false | The date column to be used as the prediction point for time-based feature engineering. | |
| fileName | string | true | The name of the dataset used to create the project. | |
| holdoutUnlocked | boolean | true | Whether the holdout has been unlocked. | |
| id | string | true | The ID of the project. | |
| isScoringAvailableForModelsTrainedIntoValidationHoldout | boolean | true | Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass. | |
| maxClusters | integer,null | false | maximum: 100 minimum: 2 |
Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to minClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100. |
| maxTrainPct | number | true | The maximum percentage of the dataset that can be used to successfully train a model without going into the validation data. | |
| maxTrainRows | integer | true | The maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data. | |
| metric | string | true | The metric used to select the best-performing models. | |
| minClusters | integer,null | false | maximum: 100 minimum: 2 |
Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to maxClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2. |
| partition | Partition | true | The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. | |
| positiveClass | any | true | If the project uses binary classification, the class designated to be the positive class. |
oneOf
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | string | false | none |
xor
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| » anonymous | integer | false | none |
continued
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| primaryLocationColumn | string | false | Primary location column name | |
| projectName | string | true | The name of the project. | |
| queryGeneratorId | string,null | true | The ID of the time series data prep query generator associated with the project, or null if there is no associated query generator. | |
| quickrun | boolean | true | If the Autopilot mode is set to quick. DEPRECATED: look at autopilot_mode instead. | |
| relationshipsConfigurationId | string,null | false | Relationships configuration id to be used for Feature Discovery projects. | |
| segmentation | ProjectSegmentationInfoResponse | false | Segmentation info for the project. | |
| stage | string | true | The stage of the project. If modeling, then the target is successfully set and modeling or predictions can proceed. | |
| target | string | true | The target of the project, null if project is unsupervised. | |
| targetType | string,null | true | The type of the selected target. Null if the project is unsupervised. | |
| unsupervisedMode | boolean,null | true | Indicates whether a project is unsupervised. | |
| unsupervisedType | string,null | false | Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'. | |
| useCase | UseCaseIdName | false | The information of the use case associated with the project. | |
| useFeatureDiscovery | boolean | true | Indicates whether a feature discovery project was created as opposed to a regular project | |
| useGpu | boolean,null | false | Indicates whether project should use GPU workers |
Enumerated Values¶
| Property | Value |
|---|---|
| autopilotMode | [0, 2, 3, 4] |
| stage | [modeling, aim, fasteda, eda, eda2, empty] |
| targetType | [Binary, Regression, Multiclass, Multilabel] |
| unsupervisedType | [anomaly, clustering] |
ProjectSegmentUpdate
{
"properties": {
"operation": {
"default": "restart",
"description": "Name of operation to do on project segment",
"enum": [
"restart"
],
"type": "string"
}
},
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| operation | string | false | Name of operation to do on project segment |
Enumerated Values¶
| Property | Value |
|---|---|
| operation | restart |
ProjectSegmentUpdateResponse
{
"properties": {
"projectId": {
"description": "New project id of the restarted segment",
"type": "string"
},
"segmentId": {
"description": "The name of the restarted segment",
"type": "string"
}
},
"required": [
"projectId",
"segmentId"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| projectId | string | true | New project id of the restarted segment | |
| segmentId | string | true | The name of the restarted segment |
ProjectSegmentationInfoResponse
{
"description": "Segmentation info for the project.",
"properties": {
"parentProjectId": {
"description": "ID of the original project.",
"type": [
"string",
"null"
]
},
"segment": {
"description": "Segment value.",
"type": [
"string",
"null"
]
},
"segmentationTaskId": {
"description": "ID of the Segmentation Task.",
"type": "string"
}
},
"required": [
"segmentationTaskId"
],
"type": "object"
}
Segmentation info for the project.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| parentProjectId | string,null | false | ID of the original project. | |
| segment | string,null | false | Segment value. | |
| segmentationTaskId | string | true | ID of the Segmentation Task. |
ProjectStatusResponse
{
"properties": {
"autopilotDone": {
"description": "whether the current autopilot run has finished",
"type": "boolean"
},
"stage": {
"description": "the current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed",
"enum": [
"modeling",
"aim",
"fasteda",
"eda",
"eda2",
"empty"
],
"type": "string"
},
"stageDescription": {
"description": "a description of the current stage of the project",
"type": "string"
}
},
"required": [
"autopilotDone",
"stage",
"stageDescription"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| autopilotDone | boolean | true | whether the current autopilot run has finished | |
| stage | string | true | the current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed | |
| stageDescription | string | true | a description of the current stage of the project |
Enumerated Values¶
| Property | Value |
|---|---|
| stage | [modeling, aim, fasteda, eda, eda2, empty] |
ProjectUpdate
{
"properties": {
"gpuWorkerCount": {
"description": "The desired new number of gpu workers if the\n number of gpu workers should be changed.\n Must not exceed the number of gpu workers available to the user. `0` is allowed.\n `-1` requests the maximum number available to the user.",
"type": "integer"
},
"holdoutUnlocked": {
"description": "If specified, the holdout will be unlocked;\n note that the holdout cannot be relocked after unlocking",
"enum": [
"True"
],
"type": "string"
},
"projectDescription": {
"description": "The new description of the project, if the description should be updated.",
"maxLength": 500,
"type": "string",
"x-versionadded": "v2.20"
},
"projectName": {
"description": "The new name of the project, if it should be renamed.",
"maxLength": 100,
"type": "string"
},
"workerCount": {
"description": "The desired new number of workers if the\n number of workers should be changed.\n Must not exceed the number of workers available to the user. `0` is allowed.\n (New in version v2.14) `-1` requests the maximum number available to the user.",
"type": "integer"
}
},
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| gpuWorkerCount | integer | false | 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 | false | If specified, the holdout will be unlocked; note that the holdout cannot be relocked after unlocking |
|
| projectDescription | string | false | maxLength: 500 |
The new description of the project, if the description should be updated. |
| projectName | string | false | maxLength: 100 |
The new name of the project, if it should be renamed. |
| workerCount | integer | false | 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. |
Enumerated Values¶
| Property | Value |
|---|---|
| holdoutUnlocked | True |
S3Credentials
{
"properties": {
"awsAccessKeyId": {
"description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.",
"type": "string"
},
"awsSecretAccessKey": {
"description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.",
"type": "string"
},
"awsSessionToken": {
"default": null,
"description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.",
"type": [
"string",
"null"
]
},
"configId": {
"description": "ID of Secure configurations of credentials shared by admin.If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 's3' here.",
"enum": [
"s3"
],
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| awsAccessKeyId | string | false | The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified. | |
| awsSecretAccessKey | string | false | The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified. | |
| awsSessionToken | string,null | false | The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified. | |
| configId | string | false | ID of Secure configurations of credentials shared by admin.If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken | |
| credentialType | string | true | The type of these credentials, 's3' here. |
Enumerated Values¶
| Property | Value |
|---|---|
| credentialType | s3 |
SegmentationDataMappingResponse
{
"properties": {
"segment": {
"description": "The segment name associated with the multiseries ID column by the segmentation task",
"type": "string"
},
"seriesId": {
"description": "The multiseries ID column used to identify series for segmentation",
"type": "string"
}
},
"required": [
"segment",
"seriesId"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| segment | string | true | The segment name associated with the multiseries ID column by the segmentation task | |
| seriesId | string | true | The multiseries ID column used to identify series for segmentation |
SegmentationEDACompletedResponse
{
"properties": {
"maxDate": {
"description": "Latest date in segment.",
"format": "date-time",
"type": "string"
},
"minDate": {
"description": "Earliest date in segment.",
"format": "date-time",
"type": "string"
},
"name": {
"description": "Name of the segment.",
"type": "string"
},
"numberOfRows": {
"description": "Number of rows in segment.",
"exclusiveMinimum": 0,
"type": "integer"
},
"sizeInBytes": {
"description": "Size of segment in bytes.",
"exclusiveMinimum": 0,
"type": "integer"
}
},
"required": [
"maxDate",
"minDate",
"name",
"numberOfRows",
"sizeInBytes"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| maxDate | string(date-time) | true | Latest date in segment. | |
| minDate | string(date-time) | true | Earliest date in segment. | |
| name | string | true | Name of the segment. | |
| numberOfRows | integer | true | Number of rows in segment. | |
| sizeInBytes | integer | true | Size of segment in bytes. |
SegmentationResultsCompletedResponse
{
"properties": {
"name": {
"description": "Name of the segmentation task job",
"type": "string"
},
"segmentationTaskId": {
"description": "Id of the completed segmentation task",
"type": "string"
},
"segmentsCount": {
"description": "Number of segments produced by the task",
"type": "integer"
},
"segmentsEda": {
"description": "Array of segments EDA information.",
"items": {
"properties": {
"maxDate": {
"description": "Latest date in segment.",
"format": "date-time",
"type": "string"
},
"minDate": {
"description": "Earliest date in segment.",
"format": "date-time",
"type": "string"
},
"name": {
"description": "Name of the segment.",
"type": "string"
},
"numberOfRows": {
"description": "Number of rows in segment.",
"exclusiveMinimum": 0,
"type": "integer"
},
"sizeInBytes": {
"description": "Size of segment in bytes.",
"exclusiveMinimum": 0,
"type": "integer"
}
},
"required": [
"maxDate",
"minDate",
"name",
"numberOfRows",
"sizeInBytes"
],
"type": "object"
},
"type": "array"
},
"url": {
"description": "Url to retrieve detailed information about the segmentation task",
"type": "string"
}
},
"required": [
"name",
"segmentationTaskId",
"segmentsCount",
"segmentsEda",
"url"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| name | string | true | Name of the segmentation task job | |
| segmentationTaskId | string | true | Id of the completed segmentation task | |
| segmentsCount | integer | true | Number of segments produced by the task | |
| segmentsEda | [SegmentationEDACompletedResponse] | true | Array of segments EDA information. | |
| url | string | true | Url to retrieve detailed information about the segmentation task |
SegmentationResultsFailedResponse
{
"properties": {
"message": {
"description": "Response containing the error message from the segmentation task",
"type": "string"
},
"name": {
"description": "Name of the segmentation task job",
"type": "string"
},
"parameters": {
"description": "Parameters submitted by the user to the failed job",
"type": "object"
}
},
"required": [
"message",
"name",
"parameters"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| message | string | true | Response containing the error message from the segmentation task | |
| name | string | true | Name of the segmentation task job | |
| parameters | AllowExtra | true | Parameters submitted by the user to the failed job |
SegmentationResultsResponse
{
"properties": {
"completedJobs": {
"description": "List of completed segmentation tasks",
"items": {
"properties": {
"name": {
"description": "Name of the segmentation task job",
"type": "string"
},
"segmentationTaskId": {
"description": "Id of the completed segmentation task",
"type": "string"
},
"segmentsCount": {
"description": "Number of segments produced by the task",
"type": "integer"
},
"segmentsEda": {
"description": "Array of segments EDA information.",
"items": {
"properties": {
"maxDate": {
"description": "Latest date in segment.",
"format": "date-time",
"type": "string"
},
"minDate": {
"description": "Earliest date in segment.",
"format": "date-time",
"type": "string"
},
"name": {
"description": "Name of the segment.",
"type": "string"
},
"numberOfRows": {
"description": "Number of rows in segment.",
"exclusiveMinimum": 0,
"type": "integer"
},
"sizeInBytes": {
"description": "Size of segment in bytes.",
"exclusiveMinimum": 0,
"type": "integer"
}
},
"required": [
"maxDate",
"minDate",
"name",
"numberOfRows",
"sizeInBytes"
],
"type": "object"
},
"type": "array"
},
"url": {
"description": "Url to retrieve detailed information about the segmentation task",
"type": "string"
}
},
"required": [
"name",
"segmentationTaskId",
"segmentsCount",
"segmentsEda",
"url"
],
"type": "object"
},
"type": "array"
},
"failedJobs": {
"description": "List of failed segmentation tasks",
"items": {
"properties": {
"message": {
"description": "Response containing the error message from the segmentation task",
"type": "string"
},
"name": {
"description": "Name of the segmentation task job",
"type": "string"
},
"parameters": {
"description": "Parameters submitted by the user to the failed job",
"type": "object"
}
},
"required": [
"message",
"name",
"parameters"
],
"type": "object"
},
"type": "array"
},
"numberOfJobs": {
"description": "Total number of completed and failed jobs processed",
"type": "integer"
}
},
"required": [
"completedJobs",
"failedJobs",
"numberOfJobs"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| completedJobs | [SegmentationResultsCompletedResponse] | true | List of completed segmentation tasks | |
| failedJobs | [SegmentationResultsFailedResponse] | true | List of failed segmentation tasks | |
| numberOfJobs | integer | true | Total number of completed and failed jobs processed |
SegmentationTaskCreate
{
"properties": {
"datetimePartitionColumn": {
"description": "The date column that will be used to identify the date in time series segmentation.",
"type": "string"
},
"modelPackageId": {
"description": "Model package id for using an external model registry package.",
"type": "string"
},
"multiseriesIdColumns": {
"description": "List of one or more names of multiseries id columns.",
"items": {
"type": "string"
},
"maxItems": 1,
"minItems": 1,
"type": "array"
},
"target": {
"description": "The target for the dataset",
"type": "string"
},
"useAutomatedSegmentation": {
"default": false,
"description": "Enable the use of automated segmentation tasks",
"type": "boolean"
},
"useTimeSeries": {
"default": false,
"description": "Enable time series based segmentation tasks",
"type": "boolean"
},
"userDefinedSegmentIdColumns": {
"description": "List of one or more names of columns to be used for user-defined business rule segmentations.",
"items": {
"type": "string"
},
"minItems": 1,
"type": "array"
}
},
"required": [
"target"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| datetimePartitionColumn | string | false | The date column that will be used to identify the date in time series segmentation. | |
| modelPackageId | string | false | Model package id for using an external model registry package. | |
| multiseriesIdColumns | [string] | false | maxItems: 1 minItems: 1 |
List of one or more names of multiseries id columns. |
| target | string | true | The target for the dataset | |
| useAutomatedSegmentation | boolean | false | Enable the use of automated segmentation tasks | |
| useTimeSeries | boolean | false | Enable time series based segmentation tasks | |
| userDefinedSegmentIdColumns | [string] | false | minItems: 1 |
List of one or more names of columns to be used for user-defined business rule segmentations. |
SegmentationTaskDataResponse
{
"description": "Data for the segmentation task",
"properties": {
"clusteringModelId": {
"description": "Id of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringModelName": {
"description": "Name of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringProjectId": {
"description": "Id of the project used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "Name of the datetime partitioning column used by the segmentation task",
"type": "string"
},
"modelPackageId": {
"description": "External model package id used by the segmentation task for automated segmentation",
"type": "string"
},
"multiseriesIdColumns": {
"description": "The multiseries id columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
},
"userDefinedSegmentIdColumns": {
"description": "The user defined segmentation columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
}
Data for the segmentation task
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| clusteringModelId | string,null | false | Id of the model used by the segmentation task for automated segmentation | |
| clusteringModelName | string,null | false | Name of the model used by the segmentation task for automated segmentation | |
| clusteringProjectId | string,null | false | Id of the project used by the segmentation task for automated segmentation | |
| datetimePartitionColumn | string | false | Name of the datetime partitioning column used by the segmentation task | |
| modelPackageId | string | false | External model package id used by the segmentation task for automated segmentation | |
| multiseriesIdColumns | [string] | false | The multiseries id columns used by the segmentation task | |
| userDefinedSegmentIdColumns | [string] | false | The user defined segmentation columns used by the segmentation task |
SegmentationTaskListResponse
{
"properties": {
"count": {
"description": "Number of items returned on this page.",
"type": "integer"
},
"data": {
"description": "List of the segmentation tasks that are associated with the project Id.",
"items": {
"properties": {
"created": {
"description": "Date and time when the segmentation task was originally created",
"format": "date-time",
"type": "string"
},
"data": {
"description": "Data for the segmentation task",
"properties": {
"clusteringModelId": {
"description": "Id of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringModelName": {
"description": "Name of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringProjectId": {
"description": "Id of the project used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "Name of the datetime partitioning column used by the segmentation task",
"type": "string"
},
"modelPackageId": {
"description": "External model package id used by the segmentation task for automated segmentation",
"type": "string"
},
"multiseriesIdColumns": {
"description": "The multiseries id columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
},
"userDefinedSegmentIdColumns": {
"description": "The user defined segmentation columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"metadata": {
"description": "Metadata for the segmentation task",
"properties": {
"useAutomatedSegmentation": {
"description": "Segmentation task uses automated segmentation",
"type": "boolean"
},
"useMultiseriesIdColumns": {
"description": "Segmentation task uses a multiseries column",
"type": "boolean"
},
"useTimeSeries": {
"description": "Segmentation task is a time series task",
"type": "boolean"
}
},
"required": [
"useAutomatedSegmentation",
"useMultiseriesIdColumns",
"useTimeSeries"
],
"type": "object"
},
"name": {
"description": "Name of the segmentation task",
"type": "string"
},
"projectId": {
"description": "Id of the parent project associated with the segmentation task",
"type": "string"
},
"segmentationTaskId": {
"description": "Id of the segmentation task",
"type": "string"
},
"segments": {
"description": "Names of the unique segments generated by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
},
"segmentsCount": {
"description": "Number of segments generated by the segmentation task",
"type": "integer"
},
"segmentsEda": {
"description": "Array of segments EDA information.",
"items": {
"properties": {
"maxDate": {
"description": "Latest date in segment.",
"format": "date-time",
"type": "string"
},
"minDate": {
"description": "Earliest date in segment.",
"format": "date-time",
"type": "string"
},
"name": {
"description": "Name of the segment.",
"type": "string"
},
"numberOfRows": {
"description": "Number of rows in segment.",
"exclusiveMinimum": 0,
"type": "integer"
},
"sizeInBytes": {
"description": "Size of segment in bytes.",
"exclusiveMinimum": 0,
"type": "integer"
}
},
"required": [
"maxDate",
"minDate",
"name",
"numberOfRows",
"sizeInBytes"
],
"type": "object"
},
"type": "array"
},
"type": {
"description": "Type of segmentation task (e.g. AutoML, AutoTS)",
"type": "string"
}
},
"required": [
"created",
"data",
"metadata",
"name",
"projectId",
"segmentationTaskId",
"segments",
"segmentsCount",
"segmentsEda",
"type"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
}
},
"required": [
"data",
"next",
"previous"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| count | integer | false | Number of items returned on this page. | |
| data | [SegmentationTaskResponse] | true | List of the segmentation tasks that are associated with the project Id. | |
| next | string,null(uri) | true | URL pointing to the next page (if null, there is no next page). | |
| previous | string,null(uri) | true | URL pointing to the previous page (if null, there is no previous page). |
SegmentationTaskMetadataResponse
{
"description": "Metadata for the segmentation task",
"properties": {
"useAutomatedSegmentation": {
"description": "Segmentation task uses automated segmentation",
"type": "boolean"
},
"useMultiseriesIdColumns": {
"description": "Segmentation task uses a multiseries column",
"type": "boolean"
},
"useTimeSeries": {
"description": "Segmentation task is a time series task",
"type": "boolean"
}
},
"required": [
"useAutomatedSegmentation",
"useMultiseriesIdColumns",
"useTimeSeries"
],
"type": "object"
}
Metadata for the segmentation task
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| useAutomatedSegmentation | boolean | true | Segmentation task uses automated segmentation | |
| useMultiseriesIdColumns | boolean | true | Segmentation task uses a multiseries column | |
| useTimeSeries | boolean | true | Segmentation task is a time series task |
SegmentationTaskResponse
{
"properties": {
"created": {
"description": "Date and time when the segmentation task was originally created",
"format": "date-time",
"type": "string"
},
"data": {
"description": "Data for the segmentation task",
"properties": {
"clusteringModelId": {
"description": "Id of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringModelName": {
"description": "Name of the model used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"clusteringProjectId": {
"description": "Id of the project used by the segmentation task for automated segmentation",
"type": [
"string",
"null"
]
},
"datetimePartitionColumn": {
"description": "Name of the datetime partitioning column used by the segmentation task",
"type": "string"
},
"modelPackageId": {
"description": "External model package id used by the segmentation task for automated segmentation",
"type": "string"
},
"multiseriesIdColumns": {
"description": "The multiseries id columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
},
"userDefinedSegmentIdColumns": {
"description": "The user defined segmentation columns used by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"metadata": {
"description": "Metadata for the segmentation task",
"properties": {
"useAutomatedSegmentation": {
"description": "Segmentation task uses automated segmentation",
"type": "boolean"
},
"useMultiseriesIdColumns": {
"description": "Segmentation task uses a multiseries column",
"type": "boolean"
},
"useTimeSeries": {
"description": "Segmentation task is a time series task",
"type": "boolean"
}
},
"required": [
"useAutomatedSegmentation",
"useMultiseriesIdColumns",
"useTimeSeries"
],
"type": "object"
},
"name": {
"description": "Name of the segmentation task",
"type": "string"
},
"projectId": {
"description": "Id of the parent project associated with the segmentation task",
"type": "string"
},
"segmentationTaskId": {
"description": "Id of the segmentation task",
"type": "string"
},
"segments": {
"description": "Names of the unique segments generated by the segmentation task",
"items": {
"type": "string"
},
"type": "array"
},
"segmentsCount": {
"description": "Number of segments generated by the segmentation task",
"type": "integer"
},
"segmentsEda": {
"description": "Array of segments EDA information.",
"items": {
"properties": {
"maxDate": {
"description": "Latest date in segment.",
"format": "date-time",
"type": "string"
},
"minDate": {
"description": "Earliest date in segment.",
"format": "date-time",
"type": "string"
},
"name": {
"description": "Name of the segment.",
"type": "string"
},
"numberOfRows": {
"description": "Number of rows in segment.",
"exclusiveMinimum": 0,
"type": "integer"
},
"sizeInBytes": {
"description": "Size of segment in bytes.",
"exclusiveMinimum": 0,
"type": "integer"
}
},
"required": [
"maxDate",
"minDate",
"name",
"numberOfRows",
"sizeInBytes"
],
"type": "object"
},
"type": "array"
},
"type": {
"description": "Type of segmentation task (e.g. AutoML, AutoTS)",
"type": "string"
}
},
"required": [
"created",
"data",
"metadata",
"name",
"projectId",
"segmentationTaskId",
"segments",
"segmentsCount",
"segmentsEda",
"type"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| created | string(date-time) | true | Date and time when the segmentation task was originally created | |
| data | SegmentationTaskDataResponse | true | Data for the segmentation task | |
| metadata | SegmentationTaskMetadataResponse | true | Metadata for the segmentation task | |
| name | string | true | Name of the segmentation task | |
| projectId | string | true | Id of the parent project associated with the segmentation task | |
| segmentationTaskId | string | true | Id of the segmentation task | |
| segments | [string] | true | Names of the unique segments generated by the segmentation task | |
| segmentsCount | integer | true | Number of segments generated by the segmentation task | |
| segmentsEda | [SegmentationEDACompletedResponse] | true | Array of segments EDA information. | |
| type | string | true | Type of segmentation task (e.g. AutoML, AutoTS) |
SegmentationTaskSegmentMappingsResponse
{
"properties": {
"count": {
"description": "Number of items returned on this page.",
"type": "integer"
},
"data": {
"description": "An array of segmentation mappings.",
"items": {
"properties": {
"segment": {
"description": "The segment name associated with the multiseries ID column by the segmentation task",
"type": "string"
},
"seriesId": {
"description": "The multiseries ID column used to identify series for segmentation",
"type": "string"
}
},
"required": [
"segment",
"seriesId"
],
"type": "object"
},
"type": "array"
},
"next": {
"description": "URL pointing to the next page (if null, there is no next page).",
"format": "uri",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page (if null, there is no previous page).",
"format": "uri",
"type": [
"string",
"null"
]
}
},
"required": [
"data",
"next",
"previous"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| count | integer | false | Number of items returned on this page. | |
| data | [SegmentationDataMappingResponse] | true | An array of segmentation mappings. | |
| next | string,null(uri) | true | URL pointing to the next page (if null, there is no next page). | |
| previous | string,null(uri) | true | URL pointing to the previous page (if null, there is no previous page). |
SharingListResponse
{
"properties": {
"count": {
"description": "The number of items returned.",
"type": "integer"
},
"data": {
"description": "The access control list.",
"items": {
"properties": {
"canShare": {
"description": "Whether the recipient can share the role further.",
"type": "boolean"
},
"role": {
"description": "The role of the user on this entity.",
"type": "string"
},
"userId": {
"description": "The identifier of the user that has access to this entity.",
"type": "string"
},
"username": {
"description": "The username of the user that has access to the entity.",
"type": "string"
}
},
"required": [
"canShare",
"role",
"userId",
"username"
],
"type": "object"
},
"maxItems": 1000,
"type": "array"
},
"next": {
"description": "URL pointing to the next page.",
"type": [
"string",
"null"
]
},
"previous": {
"description": "URL pointing to the previous page.",
"type": [
"string",
"null"
]
}
},
"required": [
"count",
"data",
"next",
"previous"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| count | integer | true | The number of items returned. | |
| data | [AccessControl] | true | maxItems: 1000 |
The access control list. |
| next | string,null | true | URL pointing to the next page. | |
| previous | string,null | true | URL pointing to the previous page. |
SharingUpdateOrRemove
{
"properties": {
"data": {
"description": "The role to set for the user.",
"items": {
"properties": {
"role": {
"description": "The role to set for the user.",
"enum": [
"ADMIN",
"CONSUMER",
"DATA_SCIENTIST",
"EDITOR",
"OBSERVER",
"OWNER",
"READ_ONLY",
"READ_WRITE",
"USER"
],
"type": [
"string",
"null"
]
},
"username": {
"description": "The username of the user to set the role for.",
"type": "string"
}
},
"required": [
"role",
"username"
],
"type": "object"
},
"maxItems": 100,
"type": "array"
},
"includeFeatureDiscoveryEntities": {
"default": false,
"description": "Whether to share all the related entities.",
"type": "boolean",
"x-versionadded": "v2.21"
},
"sendNotification": {
"default": true,
"description": "Send an email notification.",
"type": "boolean",
"x-versionadded": "v2.20"
}
},
"required": [
"data"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| data | [UpdateAccessControl] | true | maxItems: 100 |
The role to set for the user. |
| includeFeatureDiscoveryEntities | boolean | false | Whether to share all the related entities. | |
| sendNotification | boolean | false | Send an email notification. |
SnowflakeKeyPairCredentials
{
"properties": {
"configId": {
"description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr or passphrase.",
"type": "string"
},
"credentialType": {
"description": "The type of these credentials, 'snowflake_key_pair_user_account' here.",
"enum": [
"snowflake_key_pair_user_account"
],
"type": "string"
},
"passphrase": {
"description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.",
"type": "string"
},
"privateKeyStr": {
"description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.",
"type": "string"
},
"user": {
"description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.",
"type": "string"
}
},
"required": [
"credentialType"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| configId | string | false | The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr or passphrase. | |
| credentialType | string | true | The type of these credentials, 'snowflake_key_pair_user_account' here. | |
| passphrase | string | false | Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified. | |
| privateKeyStr | string | false | Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified. | |
| user | string | false | Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified. |
Enumerated Values¶
| Property | Value |
|---|---|
| credentialType | snowflake_key_pair_user_account |
UpdateAccessControl
{
"properties": {
"role": {
"description": "The role to set for the user.",
"enum": [
"ADMIN",
"CONSUMER",
"DATA_SCIENTIST",
"EDITOR",
"OBSERVER",
"OWNER",
"READ_ONLY",
"READ_WRITE",
"USER"
],
"type": [
"string",
"null"
]
},
"username": {
"description": "The username of the user to set the role for.",
"type": "string"
}
},
"required": [
"role",
"username"
],
"type": "object"
}
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| role | string,null | true | The role to set for the user. | |
| username | string | true | The username of the user to set the role for. |
Enumerated Values¶
| Property | Value |
|---|---|
| role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] |
UseCaseIdName
{
"description": "The information of the use case associated with the project.",
"properties": {
"id": {
"description": "The ID of the use case.",
"type": [
"string",
"null"
]
},
"name": {
"description": "The name of the use case.",
"type": [
"string",
"null"
]
}
},
"type": "object",
"x-versionadded": "v2.36"
}
The information of the use case associated with the project.
Properties¶
| Name | Type | Required | Restrictions | Description |
|---|---|---|---|---|
| id | string,null | false | The ID of the use case. | |
| name | string,null | false | The name of the use case. |