/datarobot-batch-predictions | Type: Application | PCID required: Yes
Tools
datarobot_batch_predictions_batch_predictions_create
Creates a new Batch Prediction job Parameters:| Parameter | Type | Required | Default | Description | |
|---|---|---|---|---|---|
abortOnError | boolean | Yes | — | Should this job abort if too many errors are encountered | |
batchJobType | string | No | — | Batch job type. | |
chunkSize | object | No | — | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | |
columnNamesRemapping | object | No | — | Remap (rename or remove columns from) the output from this job | |
csvSettings | object | Yes | — | The CSV settings used for this job | |
deploymentId | string | No | — | ID of deployment which is used in job for processing predictions dataset | |
disableRowLevelErrorHandling | boolean | Yes | — | Skip row by row error handling | |
explanationAlgorithm | string | No | — | Which algorithm will be used to calculate prediction explanations | |
explanationClassNames | string[] | No | — | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | |
explanationNumTopClasses | integer | No | — | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | |
includePredictionStatus | boolean | Yes | — | Include prediction status column in the output | |
includeProbabilities | boolean | Yes | — | Include probabilities for all classes | |
includeProbabilitiesClasses | string[] | Yes | — | Include only probabilities for these specific class names. | |
intakeSettings | object | Yes | — | The intake option configured for this job | |
maxExplanations | integer | Yes | — | Number of explanations requested. Will be ordered by strength. | |
modelId | string | No | — | ID of leaderboard model which is used in job for processing predictions dataset | |
modelPackageId | string | No | — | ID of model package from registry is used in job for processing predictions dataset | |
monitoringBatchPrefix | string | null | No | — | Name of the batch to create with this job |
numConcurrent | integer | No | — | Number of simultaneous requests to run against the prediction instance | |
outputSettings | object | No | — | The output option configured for this job | |
passthroughColumns | string[] | No | — | Pass through columns from the original dataset | |
passthroughColumnsSet | string | No | — | Pass through all columns from the original dataset | |
pinnedModelId | string | No | — | Specify a model ID used for scoring | |
predictionInstance | object | No | — | Override the default prediction instance from the deployment when scoring this job. | |
predictionThreshold | number | No | — | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | |
predictionWarningEnabled | boolean | null | No | — | Enable prediction warnings. |
secondaryDatasetsConfigId | string | No | — | Configuration id for secondary datasets to use when making a prediction. | |
skipDriftTracking | boolean | Yes | — | Skip drift tracking for this job. | |
thresholdHigh | number | No | — | Compute explanations for predictions above this threshold | |
thresholdLow | number | No | — | Compute explanations for predictions below this threshold | |
timeseriesSettings | object | No | — | Time Series settings included of this job is a Time Series job. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"abortOnError": {
"type": "boolean",
"description": "Should this job abort if too many errors are encountered"
},
"batchJobType": {
"type": "string",
"description": "Batch job type.",
"enum": [
"monitoring",
"prediction"
]
},
"chunkSize": {
"description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes."
},
"columnNamesRemapping": {
"description": "Remap (rename or remove columns from) the output from this job"
},
"csvSettings": {
"type": "object",
"description": "The CSV settings used for this job",
"properties": {
"delimiter": {
"description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values)."
},
"encoding": {
"type": "string",
"description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\"."
},
"quotechar": {
"type": "string",
"description": "Fields containing the delimiter or newlines must be quoted using this character."
}
},
"required": [
"delimiter",
"encoding",
"quotechar"
]
},
"deploymentId": {
"type": "string",
"description": "ID of deployment which is used in job for processing predictions dataset"
},
"disableRowLevelErrorHandling": {
"type": "boolean",
"description": "Skip row by row error handling"
},
"explanationAlgorithm": {
"type": "string",
"description": "Which algorithm will be used to calculate prediction explanations",
"enum": [
"shap",
"xemp"
]
},
"explanationClassNames": {
"type": "array",
"items": {
"type": "string"
},
"description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1."
},
"explanationNumTopClasses": {
"type": "integer",
"description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1."
},
"includePredictionStatus": {
"type": "boolean",
"description": "Include prediction status column in the output"
},
"includeProbabilities": {
"type": "boolean",
"description": "Include probabilities for all classes"
},
"includeProbabilitiesClasses": {
"type": "array",
"items": {
"type": "string"
},
"description": "Include only probabilities for these specific class names."
},
"intakeSettings": {
"description": "The intake option configured for this job"
},
"maxExplanations": {
"type": "integer",
"description": "Number of explanations requested. Will be ordered by strength."
},
"modelId": {
"type": "string",
"description": "ID of leaderboard model which is used in job for processing predictions dataset"
},
"modelPackageId": {
"type": "string",
"description": "ID of model package from registry is used in job for processing predictions dataset"
},
"monitoringBatchPrefix": {
"type": [
"string",
"null"
],
"description": "Name of the batch to create with this job"
},
"numConcurrent": {
"type": "integer",
"description": "Number of simultaneous requests to run against the prediction instance"
},
"outputSettings": {
"description": "The output option configured for this job"
},
"passthroughColumns": {
"type": "array",
"items": {
"type": "string"
},
"description": "Pass through columns from the original dataset"
},
"passthroughColumnsSet": {
"type": "string",
"description": "Pass through all columns from the original dataset",
"enum": [
"all"
]
},
"pinnedModelId": {
"type": "string",
"description": "Specify a model ID used for scoring"
},
"predictionInstance": {
"type": "object",
"description": "Override the default prediction instance from the deployment when scoring this job.",
"properties": {
"apiKey": {
"type": "string",
"description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users."
},
"datarobotKey": {
"type": "string",
"description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key."
},
"hostName": {
"type": "string",
"description": "Override the default host name of the deployment with this."
},
"sslEnabled": {
"type": "boolean",
"description": "Use SSL (HTTPS) when communicating with the overriden prediction server."
}
},
"required": [
"hostName",
"sslEnabled"
]
},
"predictionThreshold": {
"type": "number",
"description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0."
},
"predictionWarningEnabled": {
"type": [
"boolean",
"null"
],
"description": "Enable prediction warnings."
},
"secondaryDatasetsConfigId": {
"type": "string",
"description": "Configuration id for secondary datasets to use when making a prediction."
},
"skipDriftTracking": {
"type": "boolean",
"description": "Skip drift tracking for this job."
},
"thresholdHigh": {
"type": "number",
"description": "Compute explanations for predictions above this threshold"
},
"thresholdLow": {
"type": "number",
"description": "Compute explanations for predictions below this threshold"
},
"timeseriesSettings": {
"description": "Time Series settings included of this job is a Time Series job."
}
},
"required": [
"PCID",
"abortOnError",
"csvSettings",
"disableRowLevelErrorHandling",
"includePredictionStatus",
"includeProbabilities",
"includeProbabilitiesClasses",
"intakeSettings",
"maxExplanations",
"skipDriftTracking"
]
}
datarobot_batch_predictions_batch_predictions_delete
Cancel a Batch Prediction job Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
predictionJobId | string | Yes | — | ID of the Batch Prediction job |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"predictionJobId": {
"type": "string",
"description": "ID of the Batch Prediction job"
}
},
"required": [
"PCID",
"predictionJobId"
]
}
datarobot_batch_predictions_batch_predictions_list
List batch prediction jobs Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | Yes | — | This many results will be skipped |
limit | integer | Yes | — | At most this many results are returned |
status | string[] | No | — | Includes only jobs that have the status value that matches this flag. Repeat the parameter for filtering on multiple statuses. |
source | string[] | No | — | Includes only jobs that have the source value that matches this flag. Repeat the parameter for filtering on multiple statuses.Prefix values with a dash (-) to exclude those sources. |
deploymentId | string | No | — | Includes only jobs for this particular deployment |
modelId | string | No | — | ID of leaderboard model which is used in job for processing predictions dataset |
jobId | string | No | — | Includes only job by specific id |
orderBy | string | No | — | Sort order which will be applied to batch prediction list. Prefix the attribute name with a dash to sort in descending order, e.g. “-created”. |
allJobs | boolean | No | — | [DEPRECATED - replaced with RBAC permission model] - No effect |
cutoffHours | integer | No | — | Only list jobs created at most this amount of hours ago. |
startDateTime | string | No | — | ISO-formatted datetime of the earliest time the job was added (inclusive). For example “2008-08-24T12:00:00Z”. Will ignore cutoffHours if set. |
endDateTime | string | No | — | ISO-formatted datetime of the latest time the job was added (inclusive). For example “2008-08-24T12:00:00Z”. |
batchPredictionJobDefinitionId | string | No | — | Includes only jobs for this particular definition |
hostname | string[] | No | — | Includes only jobs for this particular prediction instance hostname |
intakeType | string[] | No | — | Includes only jobs for these particular intakes type |
outputType | string[] | No | — | Includes only jobs for these particular outputs type |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "This many results will be skipped"
},
"limit": {
"type": "integer",
"description": "At most this many results are returned"
},
"status": {
"type": "array",
"items": {
"type": "string",
"enum": [
"INITIALIZING",
"RUNNING",
"COMPLETED",
"ABORTED",
"FAILED"
]
},
"description": "Includes only jobs that have the status value that matches this flag. Repeat the parameter for filtering on multiple statuses."
},
"source": {
"type": "array",
"items": {
"type": "string",
"enum": [
"other",
"api_client_r",
"api_client_python",
"integration_snippet_requests",
"integration_snippet_api_client",
"integration_snippet_standalone_python",
"integration_snippet_standalone_powershell",
"ui_make_prediction",
"ui_leaderboard_make_prediction",
"ui_uxr_make_prediction",
"integration_job",
"scheduled_from_job_definition",
"manual_run_from_job_definition",
"model_package_insights",
"portable_batch_predictions",
"data_pipelines",
"challenger_replay",
"-other",
"-api_client_r",
"-api_client_python",
"-integration_snippet_requests",
"-integration_snippet_api_client",
"-integration_snippet_standalone_python",
"-integration_snippet_standalone_powershell",
"-ui_make_prediction",
"-ui_leaderboard_make_prediction",
"-ui_uxr_make_prediction",
"-integration_job",
"-scheduled_from_job_definition",
"-manual_run_from_job_definition",
"-model_package_insights",
"-portable_batch_predictions",
"-data_pipelines",
"-challenger_replay"
]
},
"description": "Includes only jobs that have the source value that matches this flag. Repeat the parameter for filtering on multiple statuses.Prefix values with a dash (`-`) to exclude those sources."
},
"deploymentId": {
"type": "string",
"description": "Includes only jobs for this particular deployment"
},
"modelId": {
"type": "string",
"description": "ID of leaderboard model which is used in job for processing predictions dataset"
},
"jobId": {
"type": "string",
"description": "Includes only job by specific id"
},
"orderBy": {
"type": "string",
"description": "Sort order which will be applied to batch prediction list. Prefix the attribute name with a dash to sort in descending order, e.g. \"-created\".",
"enum": [
"created",
"-created",
"status",
"-status"
]
},
"allJobs": {
"type": "boolean",
"description": "[DEPRECATED - replaced with RBAC permission model] - No effect"
},
"cutoffHours": {
"type": "integer",
"description": "Only list jobs created at most this amount of hours ago."
},
"startDateTime": {
"type": "string",
"description": "ISO-formatted datetime of the earliest time the job was added (inclusive). For example \"2008-08-24T12:00:00Z\". Will ignore cutoffHours if set."
},
"endDateTime": {
"type": "string",
"description": "ISO-formatted datetime of the latest time the job was added (inclusive). For example \"2008-08-24T12:00:00Z\"."
},
"batchPredictionJobDefinitionId": {
"type": "string",
"description": "Includes only jobs for this particular definition"
},
"hostname": {
"type": "array",
"items": {
"type": "string"
},
"description": "Includes only jobs for this particular prediction instance hostname"
},
"intakeType": {
"type": "array",
"items": {
"type": "string",
"enum": [
"local_file",
"azure",
"bigquery",
"dataStage",
"databricks",
"dataset",
"datasphere",
"dss",
"gcp",
"http",
"jdbc",
"localFile",
"recipe",
"s3",
"snowflake",
"synapse",
"trino"
]
},
"description": "Includes only jobs for these particular intakes type"
},
"outputType": {
"type": "array",
"items": {
"type": "string",
"enum": [
"local_file",
"azure",
"bigquery",
"databricks",
"datasphere",
"gcp",
"http",
"jdbc",
"localFile",
"s3",
"snowflake",
"synapse",
"trino"
]
},
"description": "Includes only jobs for these particular outputs type"
}
},
"required": [
"PCID",
"offset",
"limit"
]
}
datarobot_batch_predictions_batch_predictions_retrieve
Retrieve Batch Prediction job Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
predictionJobId | string | Yes | — | ID of the Batch Prediction job |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"predictionJobId": {
"type": "string",
"description": "ID of the Batch Prediction job"
}
},
"required": [
"PCID",
"predictionJobId"
]
}
datarobot_batch_predictions_batch_predictions_update
Update a Batch Prediction job Parameters:| Parameter | Type | Required | Default | Description | |
|---|---|---|---|---|---|
predictionJobId | string | Yes | — | ID of the Batch Prediction job | |
aborted | string | null | No | — | Time when job abortion happened |
completed | string | null | No | — | Time when job completed scoring |
failedRows | integer | No | — | Number of rows that have failed scoring | |
hidden | boolean | No | — | Hides or unhides the job from the job list | |
jobIntakeSize | integer | null | No | — | Number of bytes in the intake dataset for this job |
jobOutputSize | integer | null | No | — | Number of bytes in the output dataset for this job |
logs | string[] | No | — | The job log. | |
scoredRows | integer | No | — | Number of rows that have been used in prediction computation | |
skippedRows | integer | No | — | Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows. | |
started | string | null | No | — | Time when job scoring begin |
status | string | No | — | The current job status |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"predictionJobId": {
"type": "string",
"description": "ID of the Batch Prediction job"
},
"aborted": {
"type": [
"string",
"null"
],
"description": "Time when job abortion happened"
},
"completed": {
"type": [
"string",
"null"
],
"description": "Time when job completed scoring"
},
"failedRows": {
"type": "integer",
"description": "Number of rows that have failed scoring"
},
"hidden": {
"type": "boolean",
"description": "Hides or unhides the job from the job list"
},
"jobIntakeSize": {
"type": [
"integer",
"null"
],
"description": "Number of bytes in the intake dataset for this job"
},
"jobOutputSize": {
"type": [
"integer",
"null"
],
"description": "Number of bytes in the output dataset for this job"
},
"logs": {
"type": "array",
"items": {
"type": "string"
},
"description": "The job log."
},
"scoredRows": {
"type": "integer",
"description": "Number of rows that have been used in prediction computation"
},
"skippedRows": {
"type": "integer",
"description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows."
},
"started": {
"type": [
"string",
"null"
],
"description": "Time when job scoring begin"
},
"status": {
"type": "string",
"description": "The current job status",
"enum": [
"INITIALIZING",
"RUNNING",
"COMPLETED",
"ABORTED",
"FAILED"
]
}
},
"required": [
"PCID",
"predictionJobId"
]
}
datarobot_batch_predictions_computed_training_predictions_list
Retrieve training predictions Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | Yes | — | This many results will be skipped |
limit | integer | Yes | — | At most this many results are returned |
projectId | string | Yes | — | Project ID to retrieve training predictions for |
predictionId | string | Yes | — | Prediction ID to retrieve training predictions for |
Accept | string | No | — | Requested MIME type for the returned data |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "This many results will be skipped"
},
"limit": {
"type": "integer",
"description": "At most this many results are returned"
},
"projectId": {
"type": "string",
"description": "Project ID to retrieve training predictions for"
},
"predictionId": {
"type": "string",
"description": "Prediction ID to retrieve training predictions for"
},
"Accept": {
"type": "string",
"description": "Requested MIME type for the returned data",
"enum": [
"application/json",
"text/csv"
]
}
},
"required": [
"PCID",
"offset",
"limit",
"projectId",
"predictionId"
]
}
datarobot_batch_predictions_download_list
Download the scored data set of a batch prediction job Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
predictionJobId | string | Yes | — | ID of the Batch Prediction job |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"predictionJobId": {
"type": "string",
"description": "ID of the Batch Prediction job"
}
},
"required": [
"PCID",
"predictionJobId"
]
}
datarobot_batch_predictions_from_existing_create
Create a new a Batch Prediction job based on an existing Batch Prediction job. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
predictionJobId | string | Yes | — | ID of the Batch Prediction job to clone. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"predictionJobId": {
"type": "string",
"description": "ID of the Batch Prediction job to clone."
}
},
"required": [
"PCID",
"predictionJobId"
]
}
datarobot_batch_predictions_from_job_definition_create
Launch a Batch Prediction job for scoring Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
jobDefinitionId | string | Yes | — | ID of the Batch Prediction job definition |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"jobDefinitionId": {
"type": "string",
"description": "ID of the Batch Prediction job definition"
}
},
"required": [
"PCID",
"jobDefinitionId"
]
}
datarobot_batch_predictions_jobs_delete
Cancel a Batch job Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
batchJobId | string | Yes | — | ID of the Batch job |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"batchJobId": {
"type": "string",
"description": "ID of the Batch job"
}
},
"required": [
"PCID",
"batchJobId"
]
}
datarobot_batch_predictions_jobs_download_list
Download the scored data set of a batch job Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
batchJobId | string | Yes | — | ID of the Batch job |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"batchJobId": {
"type": "string",
"description": "ID of the Batch job"
}
},
"required": [
"PCID",
"batchJobId"
]
}
datarobot_batch_predictions_jobs_from_job_definition_create
Launch a Batch job for scoring Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
jobDefinitionId | string | Yes | — | ID of the Batch Prediction job definition |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"jobDefinitionId": {
"type": "string",
"description": "ID of the Batch Prediction job definition"
}
},
"required": [
"PCID",
"jobDefinitionId"
]
}
datarobot_batch_predictions_jobs_list
List batch jobs Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | Yes | — | This many results will be skipped |
limit | integer | Yes | — | At most this many results are returned |
status | string[] | No | — | Includes only jobs that have the status value that matches this flag. Repeat the parameter for filtering on multiple statuses. |
source | string[] | No | — | Includes only jobs that have the source value that matches this flag. Repeat the parameter for filtering on multiple statuses.Prefix values with a dash (-) to exclude those sources. |
deploymentId | string | No | — | Includes only jobs for this particular deployment |
modelId | string | No | — | ID of leaderboard model which is used in job for processing predictions dataset |
jobId | string | No | — | Includes only job by specific id |
orderBy | string | No | — | Sort order which will be applied to batch prediction list. Prefix the attribute name with a dash to sort in descending order, e.g. “-created”. |
allJobs | boolean | No | — | [DEPRECATED - replaced with RBAC permission model] - No effect |
cutoffHours | integer | No | — | Only list jobs created at most this amount of hours ago. |
startDateTime | string | No | — | ISO-formatted datetime of the earliest time the job was added (inclusive). For example “2008-08-24T12:00:00Z”. Will ignore cutoffHours if set. |
endDateTime | string | No | — | ISO-formatted datetime of the latest time the job was added (inclusive). For example “2008-08-24T12:00:00Z”. |
batchPredictionJobDefinitionId | string | No | — | Includes only jobs for this particular definition |
hostname | string[] | No | — | Includes only jobs for this particular prediction instance hostname |
batchJobType | string[] | No | — | Includes only jobs that have the batch job type that matches this flag. Repeat the parameter for filtering on multiple types. |
intakeType | string[] | No | — | Includes only jobs for these particular intakes type |
outputType | string[] | No | — | Includes only jobs for these particular outputs type |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "This many results will be skipped"
},
"limit": {
"type": "integer",
"description": "At most this many results are returned"
},
"status": {
"type": "array",
"items": {
"type": "string",
"enum": [
"INITIALIZING",
"RUNNING",
"COMPLETED",
"ABORTED",
"FAILED"
]
},
"description": "Includes only jobs that have the status value that matches this flag. Repeat the parameter for filtering on multiple statuses."
},
"source": {
"type": "array",
"items": {
"type": "string",
"enum": [
"other",
"api_client_r",
"api_client_python",
"integration_snippet_requests",
"integration_snippet_api_client",
"integration_snippet_standalone_python",
"integration_snippet_standalone_powershell",
"ui_make_prediction",
"ui_leaderboard_make_prediction",
"ui_uxr_make_prediction",
"integration_job",
"scheduled_from_job_definition",
"manual_run_from_job_definition",
"model_package_insights",
"portable_batch_predictions",
"data_pipelines",
"challenger_replay",
"-other",
"-api_client_r",
"-api_client_python",
"-integration_snippet_requests",
"-integration_snippet_api_client",
"-integration_snippet_standalone_python",
"-integration_snippet_standalone_powershell",
"-ui_make_prediction",
"-ui_leaderboard_make_prediction",
"-ui_uxr_make_prediction",
"-integration_job",
"-scheduled_from_job_definition",
"-manual_run_from_job_definition",
"-model_package_insights",
"-portable_batch_predictions",
"-data_pipelines",
"-challenger_replay"
]
},
"description": "Includes only jobs that have the source value that matches this flag. Repeat the parameter for filtering on multiple statuses.Prefix values with a dash (`-`) to exclude those sources."
},
"deploymentId": {
"type": "string",
"description": "Includes only jobs for this particular deployment"
},
"modelId": {
"type": "string",
"description": "ID of leaderboard model which is used in job for processing predictions dataset"
},
"jobId": {
"type": "string",
"description": "Includes only job by specific id"
},
"orderBy": {
"type": "string",
"description": "Sort order which will be applied to batch prediction list. Prefix the attribute name with a dash to sort in descending order, e.g. \"-created\".",
"enum": [
"created",
"-created",
"status",
"-status"
]
},
"allJobs": {
"type": "boolean",
"description": "[DEPRECATED - replaced with RBAC permission model] - No effect"
},
"cutoffHours": {
"type": "integer",
"description": "Only list jobs created at most this amount of hours ago."
},
"startDateTime": {
"type": "string",
"description": "ISO-formatted datetime of the earliest time the job was added (inclusive). For example \"2008-08-24T12:00:00Z\". Will ignore cutoffHours if set."
},
"endDateTime": {
"type": "string",
"description": "ISO-formatted datetime of the latest time the job was added (inclusive). For example \"2008-08-24T12:00:00Z\"."
},
"batchPredictionJobDefinitionId": {
"type": "string",
"description": "Includes only jobs for this particular definition"
},
"hostname": {
"type": "array",
"items": {
"type": "string"
},
"description": "Includes only jobs for this particular prediction instance hostname"
},
"batchJobType": {
"type": "array",
"items": {
"type": "string",
"enum": [
"monitoring",
"prediction"
]
},
"description": "Includes only jobs that have the batch job type that matches this flag. Repeat the parameter for filtering on multiple types."
},
"intakeType": {
"type": "array",
"items": {
"type": "string",
"enum": [
"local_file",
"azure",
"bigquery",
"dataStage",
"databricks",
"dataset",
"datasphere",
"dss",
"gcp",
"http",
"jdbc",
"localFile",
"recipe",
"s3",
"snowflake",
"synapse",
"trino"
]
},
"description": "Includes only jobs for these particular intakes type"
},
"outputType": {
"type": "array",
"items": {
"type": "string",
"enum": [
"local_file",
"azure",
"bigquery",
"databricks",
"datasphere",
"gcp",
"http",
"jdbc",
"localFile",
"s3",
"snowflake",
"synapse",
"trino"
]
},
"description": "Includes only jobs for these particular outputs type"
}
},
"required": [
"PCID",
"offset",
"limit"
]
}
datarobot_batch_predictions_jobs_retrieve
Retrieve Batch job Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
batchJobId | string | Yes | — | ID of the Batch job |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"batchJobId": {
"type": "string",
"description": "ID of the Batch job"
}
},
"required": [
"PCID",
"batchJobId"
]
}
datarobot_batch_predictions_prediction_job_definitions_create
Creates a new Batch Prediction job definition Parameters:| Parameter | Type | Required | Default | Description | |
|---|---|---|---|---|---|
abortOnError | boolean | Yes | — | Should this job abort if too many errors are encountered | |
batchJobType | string | No | — | Batch job type. | |
chunkSize | object | No | — | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | |
columnNamesRemapping | object | No | — | Remap (rename or remove columns from) the output from this job | |
csvSettings | object | Yes | — | The CSV settings used for this job | |
deploymentId | string | Yes | — | ID of deployment which is used in job for processing predictions dataset | |
disableRowLevelErrorHandling | boolean | Yes | — | Skip row by row error handling | |
enabled | boolean | No | — | If this job definition is enabled as a scheduled job. Optional if no schedule is supplied. | |
explanationAlgorithm | string | No | — | Which algorithm will be used to calculate prediction explanations | |
explanationClassNames | string[] | No | — | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | |
explanationNumTopClasses | integer | No | — | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | |
includePredictionStatus | boolean | Yes | — | Include prediction status column in the output | |
includeProbabilities | boolean | Yes | — | Include probabilities for all classes | |
includeProbabilitiesClasses | string[] | Yes | — | Include only probabilities for these specific class names. | |
intakeSettings | object | Yes | — | The intake option configured for this job | |
maxExplanations | integer | Yes | — | Number of explanations requested. Will be ordered by strength. | |
modelId | string | No | — | ID of leaderboard model which is used in job for processing predictions dataset | |
modelPackageId | string | No | — | ID of model package from registry is used in job for processing predictions dataset | |
monitoringBatchPrefix | string | null | No | — | Name of the batch to create with this job |
name | string | No | — | A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you. | |
numConcurrent | integer | No | — | Number of simultaneous requests to run against the prediction instance | |
outputSettings | object | No | — | The output option configured for this job | |
passthroughColumns | string[] | No | — | Pass through columns from the original dataset | |
passthroughColumnsSet | string | No | — | Pass through all columns from the original dataset | |
pinnedModelId | string | No | — | Specify a model ID used for scoring | |
predictionInstance | object | No | — | Override the default prediction instance from the deployment when scoring this job. | |
predictionThreshold | number | No | — | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | |
predictionWarningEnabled | boolean | null | No | — | Enable prediction warnings. |
schedule | object | No | — | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | |
secondaryDatasetsConfigId | string | No | — | Configuration id for secondary datasets to use when making a prediction. | |
skipDriftTracking | boolean | Yes | — | Skip drift tracking for this job. | |
thresholdHigh | number | No | — | Compute explanations for predictions above this threshold | |
thresholdLow | number | No | — | Compute explanations for predictions below this threshold | |
timeseriesSettings | object | No | — | Time Series settings included of this job is a Time Series job. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"abortOnError": {
"type": "boolean",
"description": "Should this job abort if too many errors are encountered"
},
"batchJobType": {
"type": "string",
"description": "Batch job type.",
"enum": [
"monitoring",
"prediction"
]
},
"chunkSize": {
"description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes."
},
"columnNamesRemapping": {
"description": "Remap (rename or remove columns from) the output from this job"
},
"csvSettings": {
"type": "object",
"description": "The CSV settings used for this job",
"properties": {
"delimiter": {
"description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values)."
},
"encoding": {
"type": "string",
"description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\"."
},
"quotechar": {
"type": "string",
"description": "Fields containing the delimiter or newlines must be quoted using this character."
}
},
"required": [
"delimiter",
"encoding",
"quotechar"
]
},
"deploymentId": {
"type": "string",
"description": "ID of deployment which is used in job for processing predictions dataset"
},
"disableRowLevelErrorHandling": {
"type": "boolean",
"description": "Skip row by row error handling"
},
"enabled": {
"type": "boolean",
"description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied."
},
"explanationAlgorithm": {
"type": "string",
"description": "Which algorithm will be used to calculate prediction explanations",
"enum": [
"shap",
"xemp"
]
},
"explanationClassNames": {
"type": "array",
"items": {
"type": "string"
},
"description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1."
},
"explanationNumTopClasses": {
"type": "integer",
"description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1."
},
"includePredictionStatus": {
"type": "boolean",
"description": "Include prediction status column in the output"
},
"includeProbabilities": {
"type": "boolean",
"description": "Include probabilities for all classes"
},
"includeProbabilitiesClasses": {
"type": "array",
"items": {
"type": "string"
},
"description": "Include only probabilities for these specific class names."
},
"intakeSettings": {
"description": "The intake option configured for this job"
},
"maxExplanations": {
"type": "integer",
"description": "Number of explanations requested. Will be ordered by strength."
},
"modelId": {
"type": "string",
"description": "ID of leaderboard model which is used in job for processing predictions dataset"
},
"modelPackageId": {
"type": "string",
"description": "ID of model package from registry is used in job for processing predictions dataset"
},
"monitoringBatchPrefix": {
"type": [
"string",
"null"
],
"description": "Name of the batch to create with this job"
},
"name": {
"type": "string",
"description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you."
},
"numConcurrent": {
"type": "integer",
"description": "Number of simultaneous requests to run against the prediction instance"
},
"outputSettings": {
"description": "The output option configured for this job"
},
"passthroughColumns": {
"type": "array",
"items": {
"type": "string"
},
"description": "Pass through columns from the original dataset"
},
"passthroughColumnsSet": {
"type": "string",
"description": "Pass through all columns from the original dataset",
"enum": [
"all"
]
},
"pinnedModelId": {
"type": "string",
"description": "Specify a model ID used for scoring"
},
"predictionInstance": {
"type": "object",
"description": "Override the default prediction instance from the deployment when scoring this job.",
"properties": {
"apiKey": {
"type": "string",
"description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users."
},
"datarobotKey": {
"type": "string",
"description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key."
},
"hostName": {
"type": "string",
"description": "Override the default host name of the deployment with this."
},
"sslEnabled": {
"type": "boolean",
"description": "Use SSL (HTTPS) when communicating with the overriden prediction server."
}
},
"required": [
"hostName",
"sslEnabled"
]
},
"predictionThreshold": {
"type": "number",
"description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0."
},
"predictionWarningEnabled": {
"type": [
"boolean",
"null"
],
"description": "Enable prediction warnings."
},
"schedule": {
"type": "object",
"description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.",
"properties": {
"dayOfMonth": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31
]
},
"description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored."
},
"dayOfWeek": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
"sunday",
"SUNDAY",
"Sunday",
"monday",
"MONDAY",
"Monday",
"tuesday",
"TUESDAY",
"Tuesday",
"wednesday",
"WEDNESDAY",
"Wednesday",
"thursday",
"THURSDAY",
"Thursday",
"friday",
"FRIDAY",
"Friday",
"saturday",
"SATURDAY",
"Saturday",
"sun",
"SUN",
"Sun",
"mon",
"MON",
"Mon",
"tue",
"TUE",
"Tue",
"wed",
"WED",
"Wed",
"thu",
"THU",
"Thu",
"fri",
"FRI",
"Fri",
"sat",
"SAT",
"Sat"
]
},
"description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field."
},
"hour": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
]
},
"description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``."
},
"minute": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59
]
},
"description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``."
},
"month": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
"january",
"JANUARY",
"January",
"february",
"FEBRUARY",
"February",
"march",
"MARCH",
"March",
"april",
"APRIL",
"April",
"may",
"MAY",
"May",
"june",
"JUNE",
"June",
"july",
"JULY",
"July",
"august",
"AUGUST",
"August",
"september",
"SEPTEMBER",
"September",
"october",
"OCTOBER",
"October",
"november",
"NOVEMBER",
"November",
"december",
"DECEMBER",
"December",
"jan",
"JAN",
"Jan",
"feb",
"FEB",
"Feb",
"mar",
"MAR",
"Mar",
"apr",
"APR",
"Apr",
"jun",
"JUN",
"Jun",
"jul",
"JUL",
"Jul",
"aug",
"AUG",
"Aug",
"sep",
"SEP",
"Sep",
"oct",
"OCT",
"Oct",
"nov",
"NOV",
"Nov",
"dec",
"DEC",
"Dec"
]
},
"description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``."
}
},
"required": [
"dayOfMonth",
"dayOfWeek",
"hour",
"minute",
"month"
]
},
"secondaryDatasetsConfigId": {
"type": "string",
"description": "Configuration id for secondary datasets to use when making a prediction."
},
"skipDriftTracking": {
"type": "boolean",
"description": "Skip drift tracking for this job."
},
"thresholdHigh": {
"type": "number",
"description": "Compute explanations for predictions above this threshold"
},
"thresholdLow": {
"type": "number",
"description": "Compute explanations for predictions below this threshold"
},
"timeseriesSettings": {
"description": "Time Series settings included of this job is a Time Series job."
}
},
"required": [
"PCID",
"abortOnError",
"csvSettings",
"deploymentId",
"disableRowLevelErrorHandling",
"includePredictionStatus",
"includeProbabilities",
"includeProbabilitiesClasses",
"intakeSettings",
"maxExplanations",
"skipDriftTracking"
]
}
datarobot_batch_predictions_prediction_job_definitions_delete
Delete Batch Prediction job definition Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
jobDefinitionId | string | Yes | — | ID of the Batch Prediction job definition |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"jobDefinitionId": {
"type": "string",
"description": "ID of the Batch Prediction job definition"
}
},
"required": [
"PCID",
"jobDefinitionId"
]
}
datarobot_batch_predictions_prediction_job_definitions_list
List Batch Prediction job definitions Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | Yes | — | This many results will be skipped |
limit | integer | Yes | — | At most this many results are returned |
searchName | string | No | — | A human-readable name for the definition, must be unique across organisations. |
deploymentId | string | No | — | Includes only definitions for this particular deployment |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "This many results will be skipped"
},
"limit": {
"type": "integer",
"description": "At most this many results are returned"
},
"searchName": {
"type": "string",
"description": "A human-readable name for the definition, must be unique across organisations."
},
"deploymentId": {
"type": "string",
"description": "Includes only definitions for this particular deployment"
}
},
"required": [
"PCID",
"offset",
"limit"
]
}
datarobot_batch_predictions_prediction_job_definitions_patch
Update Batch Prediction job definition Parameters:| Parameter | Type | Required | Default | Description | |
|---|---|---|---|---|---|
jobDefinitionId | string | Yes | — | ID of the Batch Prediction job definition | |
abortOnError | boolean | No | — | Should this job abort if too many errors are encountered | |
batchJobType | string | No | — | Batch job type. | |
chunkSize | object | No | — | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | |
columnNamesRemapping | object | No | — | Remap (rename or remove columns from) the output from this job | |
csvSettings | object | No | — | The CSV settings used for this job | |
deploymentId | string | No | — | ID of deployment which is used in job for processing predictions dataset | |
disableRowLevelErrorHandling | boolean | No | — | Skip row by row error handling | |
enabled | boolean | No | — | If this job definition is enabled as a scheduled job. Optional if no schedule is supplied. | |
explanationAlgorithm | string | No | — | Which algorithm will be used to calculate prediction explanations | |
explanationClassNames | string[] | No | — | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | |
explanationNumTopClasses | integer | No | — | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | |
includePredictionStatus | boolean | No | — | Include prediction status column in the output | |
includeProbabilities | boolean | No | — | Include probabilities for all classes | |
includeProbabilitiesClasses | string[] | No | — | Include only probabilities for these specific class names. | |
intakeSettings | object | No | — | The intake option configured for this job | |
maxExplanations | integer | No | — | Number of explanations requested. Will be ordered by strength. | |
modelId | string | No | — | ID of leaderboard model which is used in job for processing predictions dataset | |
modelPackageId | string | No | — | ID of model package from registry is used in job for processing predictions dataset | |
monitoringBatchPrefix | string | null | No | — | Name of the batch to create with this job |
name | string | No | — | A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you. | |
numConcurrent | integer | No | — | Number of simultaneous requests to run against the prediction instance | |
outputSettings | object | No | — | The output option configured for this job | |
passthroughColumns | string[] | No | — | Pass through columns from the original dataset | |
passthroughColumnsSet | string | No | — | Pass through all columns from the original dataset | |
pinnedModelId | string | No | — | Specify a model ID used for scoring | |
predictionInstance | object | No | — | Override the default prediction instance from the deployment when scoring this job. | |
predictionThreshold | number | No | — | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | |
predictionWarningEnabled | boolean | null | No | — | Enable prediction warnings. |
schedule | object | No | — | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | |
secondaryDatasetsConfigId | string | No | — | Configuration id for secondary datasets to use when making a prediction. | |
skipDriftTracking | boolean | No | — | Skip drift tracking for this job. | |
thresholdHigh | number | No | — | Compute explanations for predictions above this threshold | |
thresholdLow | number | No | — | Compute explanations for predictions below this threshold | |
timeseriesSettings | object | No | — | Time Series settings included of this job is a Time Series job. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"jobDefinitionId": {
"type": "string",
"description": "ID of the Batch Prediction job definition"
},
"abortOnError": {
"type": "boolean",
"description": "Should this job abort if too many errors are encountered"
},
"batchJobType": {
"type": "string",
"description": "Batch job type.",
"enum": [
"monitoring",
"prediction"
]
},
"chunkSize": {
"description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes."
},
"columnNamesRemapping": {
"description": "Remap (rename or remove columns from) the output from this job"
},
"csvSettings": {
"type": "object",
"description": "The CSV settings used for this job",
"properties": {
"delimiter": {
"description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values)."
},
"encoding": {
"type": "string",
"description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\"."
},
"quotechar": {
"type": "string",
"description": "Fields containing the delimiter or newlines must be quoted using this character."
}
},
"required": [
"delimiter",
"encoding",
"quotechar"
]
},
"deploymentId": {
"type": "string",
"description": "ID of deployment which is used in job for processing predictions dataset"
},
"disableRowLevelErrorHandling": {
"type": "boolean",
"description": "Skip row by row error handling"
},
"enabled": {
"type": "boolean",
"description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied."
},
"explanationAlgorithm": {
"type": "string",
"description": "Which algorithm will be used to calculate prediction explanations",
"enum": [
"shap",
"xemp"
]
},
"explanationClassNames": {
"type": "array",
"items": {
"type": "string"
},
"description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1."
},
"explanationNumTopClasses": {
"type": "integer",
"description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1."
},
"includePredictionStatus": {
"type": "boolean",
"description": "Include prediction status column in the output"
},
"includeProbabilities": {
"type": "boolean",
"description": "Include probabilities for all classes"
},
"includeProbabilitiesClasses": {
"type": "array",
"items": {
"type": "string"
},
"description": "Include only probabilities for these specific class names."
},
"intakeSettings": {
"description": "The intake option configured for this job"
},
"maxExplanations": {
"type": "integer",
"description": "Number of explanations requested. Will be ordered by strength."
},
"modelId": {
"type": "string",
"description": "ID of leaderboard model which is used in job for processing predictions dataset"
},
"modelPackageId": {
"type": "string",
"description": "ID of model package from registry is used in job for processing predictions dataset"
},
"monitoringBatchPrefix": {
"type": [
"string",
"null"
],
"description": "Name of the batch to create with this job"
},
"name": {
"type": "string",
"description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you."
},
"numConcurrent": {
"type": "integer",
"description": "Number of simultaneous requests to run against the prediction instance"
},
"outputSettings": {
"description": "The output option configured for this job"
},
"passthroughColumns": {
"type": "array",
"items": {
"type": "string"
},
"description": "Pass through columns from the original dataset"
},
"passthroughColumnsSet": {
"type": "string",
"description": "Pass through all columns from the original dataset",
"enum": [
"all"
]
},
"pinnedModelId": {
"type": "string",
"description": "Specify a model ID used for scoring"
},
"predictionInstance": {
"type": "object",
"description": "Override the default prediction instance from the deployment when scoring this job.",
"properties": {
"apiKey": {
"type": "string",
"description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users."
},
"datarobotKey": {
"type": "string",
"description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key."
},
"hostName": {
"type": "string",
"description": "Override the default host name of the deployment with this."
},
"sslEnabled": {
"type": "boolean",
"description": "Use SSL (HTTPS) when communicating with the overriden prediction server."
}
},
"required": [
"hostName",
"sslEnabled"
]
},
"predictionThreshold": {
"type": "number",
"description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0."
},
"predictionWarningEnabled": {
"type": [
"boolean",
"null"
],
"description": "Enable prediction warnings."
},
"schedule": {
"type": "object",
"description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.",
"properties": {
"dayOfMonth": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31
]
},
"description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored."
},
"dayOfWeek": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
"sunday",
"SUNDAY",
"Sunday",
"monday",
"MONDAY",
"Monday",
"tuesday",
"TUESDAY",
"Tuesday",
"wednesday",
"WEDNESDAY",
"Wednesday",
"thursday",
"THURSDAY",
"Thursday",
"friday",
"FRIDAY",
"Friday",
"saturday",
"SATURDAY",
"Saturday",
"sun",
"SUN",
"Sun",
"mon",
"MON",
"Mon",
"tue",
"TUE",
"Tue",
"wed",
"WED",
"Wed",
"thu",
"THU",
"Thu",
"fri",
"FRI",
"Fri",
"sat",
"SAT",
"Sat"
]
},
"description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field."
},
"hour": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23
]
},
"description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``."
},
"minute": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59
]
},
"description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``."
},
"month": {
"type": "array",
"items": {
"type": "number",
"enum": [
"*",
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
"january",
"JANUARY",
"January",
"february",
"FEBRUARY",
"February",
"march",
"MARCH",
"March",
"april",
"APRIL",
"April",
"may",
"MAY",
"May",
"june",
"JUNE",
"June",
"july",
"JULY",
"July",
"august",
"AUGUST",
"August",
"september",
"SEPTEMBER",
"September",
"october",
"OCTOBER",
"October",
"november",
"NOVEMBER",
"November",
"december",
"DECEMBER",
"December",
"jan",
"JAN",
"Jan",
"feb",
"FEB",
"Feb",
"mar",
"MAR",
"Mar",
"apr",
"APR",
"Apr",
"jun",
"JUN",
"Jun",
"jul",
"JUL",
"Jul",
"aug",
"AUG",
"Aug",
"sep",
"SEP",
"Sep",
"oct",
"OCT",
"Oct",
"nov",
"NOV",
"Nov",
"dec",
"DEC",
"Dec"
]
},
"description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``."
}
},
"required": [
"dayOfMonth",
"dayOfWeek",
"hour",
"minute",
"month"
]
},
"secondaryDatasetsConfigId": {
"type": "string",
"description": "Configuration id for secondary datasets to use when making a prediction."
},
"skipDriftTracking": {
"type": "boolean",
"description": "Skip drift tracking for this job."
},
"thresholdHigh": {
"type": "number",
"description": "Compute explanations for predictions above this threshold"
},
"thresholdLow": {
"type": "number",
"description": "Compute explanations for predictions below this threshold"
},
"timeseriesSettings": {
"description": "Time Series settings included of this job is a Time Series job."
}
},
"required": [
"PCID",
"jobDefinitionId"
]
}
datarobot_batch_predictions_prediction_job_definitions_portable_list
Retrieve job definition snippet for PBP Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
jobDefinitionId | string | Yes | — | ID of the Batch Prediction job definition |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"jobDefinitionId": {
"type": "string",
"description": "ID of the Batch Prediction job definition"
}
},
"required": [
"PCID",
"jobDefinitionId"
]
}
datarobot_batch_predictions_prediction_job_definitions_retrieve
Retrieve Batch Prediction job definition Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
jobDefinitionId | string | Yes | — | ID of the Batch Prediction job definition |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"jobDefinitionId": {
"type": "string",
"description": "ID of the Batch Prediction job definition"
}
},
"required": [
"PCID",
"jobDefinitionId"
]
}
datarobot_batch_predictions_projects_models_prediction_explanations_initialization_create
Create a new prediction explanations initialization. Parameters:| Parameter | Type | Required | Default | Description | |
|---|---|---|---|---|---|
projectId | string | Yes | — | The project ID. | |
modelId | string | Yes | — | The model ID. | |
maxExplanations | integer | No | — | The maximum number of prediction explanations to supply per row of the dataset. | |
thresholdHigh | number | null | No | — | The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. |
thresholdLow | number | null | No | — | The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"modelId": {
"type": "string",
"description": "The model ID."
},
"maxExplanations": {
"type": "integer",
"description": "The maximum number of prediction explanations to supply per row of the dataset."
},
"thresholdHigh": {
"type": [
"number",
"null"
],
"description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows."
},
"thresholdLow": {
"type": [
"number",
"null"
],
"description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows."
}
},
"required": [
"PCID",
"projectId",
"modelId"
]
}
datarobot_batch_predictions_projects_models_prediction_explanations_initialization_delete_many
Delete an existing PredictionExplanationsInitialization. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID. |
modelId | string | Yes | — | The model ID. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"modelId": {
"type": "string",
"description": "The model ID."
}
},
"required": [
"PCID",
"projectId",
"modelId"
]
}
datarobot_batch_predictions_projects_models_prediction_explanations_initialization_list
Retrieve the current PredictionExplanationsInitialization. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
excludeAdjustedPredictions | string | No | — | Whether to include adjusted predictions in the PredictionExplanationsSample response. |
projectId | string | Yes | — | The project ID. |
modelId | string | Yes | — | The model ID. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"excludeAdjustedPredictions": {
"type": "string",
"description": "Whether to include adjusted predictions in the PredictionExplanationsSample response.",
"enum": [
"false",
"False",
"true",
"True"
]
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"modelId": {
"type": "string",
"description": "The model ID."
}
},
"required": [
"PCID",
"projectId",
"modelId"
]
}
datarobot_batch_predictions_projects_predict_jobs_delete
Cancel a queued prediction job. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID. |
jobId | string | Yes | — | The job ID. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"jobId": {
"type": "string",
"description": "The job ID."
}
},
"required": [
"PCID",
"projectId",
"jobId"
]
}
datarobot_batch_predictions_projects_predict_jobs_list
List all prediction jobs for a project. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
status | string | No | — | If provided, only jobs with the same status will be included in the results; otherwise, queued and inprogress jobs (but not errored jobs) will be returned. |
projectId | string | Yes | — | The project ID. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"status": {
"type": "string",
"description": "If provided, only jobs with the same status will be included in the results; otherwise, queued and inprogress jobs (but not errored jobs) will be returned.",
"enum": [
"queue",
"inprogress",
"error"
]
},
"projectId": {
"type": "string",
"description": "The project ID."
}
},
"required": [
"PCID",
"projectId"
]
}
datarobot_batch_predictions_projects_predict_jobs_retrieve
Look up a particular prediction job. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID. |
jobId | string | Yes | — | The job ID. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"jobId": {
"type": "string",
"description": "The job ID."
}
},
"required": [
"PCID",
"projectId",
"jobId"
]
}
datarobot_batch_predictions_projects_prediction_datasets_data_source_uploads_create
Upload a dataset for predictions from aDataSource.
Parameters:
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID to which the data source will be uploaded to. |
actualValueColumn | string | No | — | The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. |
credentialData | object | No | — | The credentials to authenticate with the database, to use instead of user/password or credential ID. |
credentialId | string | No | — | The credential ID to use for database authentication. |
credentials | any[] | No | — | A list of credentials for the secondary datasets used in feature discovery project. |
dataSourceId | string | Yes | — | The ID of DataSource. |
forecastPoint | string | No | — | For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. |
password | string | No | — | 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. DEPRECATED: please use credentialId or credentialData instead. |
predictionsEndDate | string | No | — | The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. |
predictionsStartDate | string | No | — | The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. |
relaxKnownInAdvanceFeaturesCheck | boolean | No | — | For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed. |
secondaryDatasetsConfigId | string | No | — | For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction. |
useKerberos | boolean | No | — | If true, use kerberos authentication for database authentication. Default is false. |
user | string | No | — | The username for database authentication. DEPRECATED: please use credentialId or credentialData instead. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID to which the data source will be uploaded to."
},
"actualValueColumn": {
"type": "string",
"description": "The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset."
},
"credentialData": {
"description": "The credentials to authenticate with the database, to use instead of user/password or credential ID."
},
"credentialId": {
"type": "string",
"description": "The credential ID to use for database authentication."
},
"credentials": {
"type": "array",
"description": "A list of credentials for the secondary datasets used in feature discovery project."
},
"dataSourceId": {
"type": "string",
"description": "The ID of ``DataSource``."
},
"forecastPoint": {
"type": "string",
"description": "For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error."
},
"password": {
"type": "string",
"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. DEPRECATED: please use ``credentialId`` or ``credentialData`` instead."
},
"predictionsEndDate": {
"type": "string",
"description": "The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"predictionsStartDate": {
"type": "string",
"description": "The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"relaxKnownInAdvanceFeaturesCheck": {
"type": "boolean",
"description": "For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed."
},
"secondaryDatasetsConfigId": {
"type": "string",
"description": "For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction."
},
"useKerberos": {
"type": "boolean",
"description": "If true, use kerberos authentication for database authentication. Default is false."
},
"user": {
"type": "string",
"description": "The username for database authentication. DEPRECATED: please use ``credentialId`` or ``credentialData`` instead."
}
},
"required": [
"PCID",
"projectId",
"dataSourceId"
]
}
datarobot_batch_predictions_projects_prediction_datasets_dataset_uploads_create
Create prediction dataset Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID. |
actualValueColumn | string | No | — | Actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. |
credentialData | object | No | — | The credentials to authenticate with the database, to be used instead of credential ID. |
credentialId | string | No | — | The ID of the set of credentials to authenticate with the database. |
credentials | any[] | No | — | List of credentials for the secondary datasets used in feature discovery project. |
datasetId | string | Yes | — | The ID of the dataset entry to use for prediction dataset. |
datasetVersionId | string | No | — | The ID of the dataset version to use for the prediction dataset. If not specified - uses latest version associated with datasetId. |
forecastPoint | string | No | — | For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. |
password | string | No | — | 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.DEPRECATED: please use credentialId or credentialData instead. |
predictionsEndDate | string | No | — | The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. |
predictionsStartDate | string | No | — | The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. |
relaxKnownInAdvanceFeaturesCheck | boolean | No | — | For time series projects only. If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed. |
secondaryDatasetsConfigId | string | No | — | For feature discovery projects only. The Id of the alternative secondary dataset config to use during prediction. |
useKerberos | boolean | No | — | If true, use kerberos authentication for database authentication. Default is false. |
user | string | No | — | The username for database authentication. DEPRECATED: please use credentialId or credentialData instead. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"actualValueColumn": {
"type": "string",
"description": "Actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset."
},
"credentialData": {
"description": "The credentials to authenticate with the database, to be used instead of credential ID."
},
"credentialId": {
"type": "string",
"description": "The ID of the set of credentials to authenticate with the database."
},
"credentials": {
"type": "array",
"description": "List of credentials for the secondary datasets used in feature discovery project."
},
"datasetId": {
"type": "string",
"description": "The ID of the dataset entry to use for prediction dataset."
},
"datasetVersionId": {
"type": "string",
"description": "The ID of the dataset version to use for the prediction dataset. If not specified - uses latest version associated with datasetId."
},
"forecastPoint": {
"type": "string",
"description": "For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error."
},
"password": {
"type": "string",
"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.DEPRECATED: please use credentialId or credentialData instead."
},
"predictionsEndDate": {
"type": "string",
"description": "The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"predictionsStartDate": {
"type": "string",
"description": "The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"relaxKnownInAdvanceFeaturesCheck": {
"type": "boolean",
"description": "For time series projects only. If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed."
},
"secondaryDatasetsConfigId": {
"type": "string",
"description": "For feature discovery projects only. The Id of the alternative secondary dataset config to use during prediction."
},
"useKerberos": {
"type": "boolean",
"description": "If true, use kerberos authentication for database authentication. Default is false."
},
"user": {
"type": "string",
"description": "The username for database authentication. DEPRECATED: please use credentialId or credentialData instead."
}
},
"required": [
"PCID",
"projectId",
"datasetId"
]
}
datarobot_batch_predictions_projects_prediction_datasets_delete
Delete a dataset that was uploaded for prediction. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID that owns the data. |
datasetId | string | Yes | — | The dataset ID to delete. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID that owns the data."
},
"datasetId": {
"type": "string",
"description": "The dataset ID to delete."
}
},
"required": [
"PCID",
"projectId",
"datasetId"
]
}
datarobot_batch_predictions_projects_prediction_datasets_file_uploads_create
Upload a file for predictions from an attached file. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID to which the data will be uploaded for prediction. |
actualValueColumn | string | No | — | The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. |
credentials | string | No | — | The list of credentials for the secondary datasets used in feature discovery project. |
file | string | Yes | — | The dataset file to upload for prediction. |
forecastPoint | string | No | — | For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. |
predictionsEndDate | string | No | — | Used for time series projects only. The end date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. |
predictionsStartDate | string | No | — | Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. |
relaxKnownInAdvanceFeaturesCheck | string | No | — | A boolean flag. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or false, missing values are not allowed. For time series projects only. |
secondaryDatasetsConfigId | string | No | — | Optional, for feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID to which the data will be uploaded for prediction."
},
"actualValueColumn": {
"type": "string",
"description": "The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset."
},
"credentials": {
"type": "string",
"description": "The list of credentials for the secondary datasets used in feature discovery project."
},
"file": {
"type": "string",
"description": "The dataset file to upload for prediction."
},
"forecastPoint": {
"type": "string",
"description": "For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error."
},
"predictionsEndDate": {
"type": "string",
"description": "Used for time series projects only. The end date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"predictionsStartDate": {
"type": "string",
"description": "Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"relaxKnownInAdvanceFeaturesCheck": {
"type": "string",
"description": "A boolean flag. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or false, missing values are not allowed. For time series projects only.",
"enum": [
"false",
"False",
"true",
"True"
]
},
"secondaryDatasetsConfigId": {
"type": "string",
"description": "Optional, for feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction."
}
},
"required": [
"PCID",
"projectId",
"file"
]
}
datarobot_batch_predictions_projects_prediction_datasets_list
List prediction datasets uploaded to a project. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | Yes | — | This many results will be skipped. |
limit | integer | Yes | — | At most this many results are returned. If 0, all results. |
projectId | string | Yes | — | The project ID to query. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "This many results will be skipped."
},
"limit": {
"type": "integer",
"description": "At most this many results are returned. If 0, all results."
},
"projectId": {
"type": "string",
"description": "The project ID to query."
}
},
"required": [
"PCID",
"offset",
"limit",
"projectId"
]
}
datarobot_batch_predictions_projects_prediction_datasets_retrieve
Get the metadata of a specific dataset. This only works for datasets uploaded to an existing project for prediction. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID that owns the data. |
datasetId | string | Yes | — | The dataset ID to query for. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID that owns the data."
},
"datasetId": {
"type": "string",
"description": "The dataset ID to query for."
}
},
"required": [
"PCID",
"projectId",
"datasetId"
]
}
datarobot_batch_predictions_projects_prediction_datasets_url_uploads_create
Upload a file for predictions from a URL. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID to which the data will be uploaded for prediction. |
actualValueColumn | string | No | — | The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional. |
credentials | any[] | No | — | The list of credentials for the secondary datasets used in feature discovery project. |
forecastPoint | string | No | — | For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. |
predictionsEndDate | string | No | — | Used for time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. |
predictionsStartDate | string | No | — | Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. |
relaxKnownInAdvanceFeaturesCheck | boolean | No | — | For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed. |
secondaryDatasetsConfigId | string | No | — | For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction. |
url | string | Yes | — | The URL to download the dataset from. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID to which the data will be uploaded for prediction."
},
"actualValueColumn": {
"type": "string",
"description": "The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional."
},
"credentials": {
"type": "array",
"description": "The list of credentials for the secondary datasets used in feature discovery project."
},
"forecastPoint": {
"type": "string",
"description": "For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error."
},
"predictionsEndDate": {
"type": "string",
"description": "Used for time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"predictionsStartDate": {
"type": "string",
"description": "Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"relaxKnownInAdvanceFeaturesCheck": {
"type": "boolean",
"description": "For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed."
},
"secondaryDatasetsConfigId": {
"type": "string",
"description": "For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction."
},
"url": {
"type": "string",
"description": "The URL to download the dataset from."
}
},
"required": [
"PCID",
"projectId",
"url"
]
}
datarobot_batch_predictions_projects_prediction_explanations_create
Create a new PredictionExplanations object (and its accompanying PredictionExplanationsRecord). Parameters:| Parameter | Type | Required | Default | Description | |
|---|---|---|---|---|---|
projectId | string | Yes | — | The project ID. | |
classNames | string[] | No | — | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | |
datasetId | string | Yes | — | The dataset ID. | |
maxExplanations | integer | No | — | The maximum number of prediction explanations to supply per row of the dataset. | |
modelId | string | Yes | — | The model ID. | |
numTopClasses | integer | No | — | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | |
thresholdHigh | number | null | No | — | The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. |
thresholdLow | number | null | No | — | The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"classNames": {
"type": "array",
"items": {
"type": "string"
},
"description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1."
},
"datasetId": {
"type": "string",
"description": "The dataset ID."
},
"maxExplanations": {
"type": "integer",
"description": "The maximum number of prediction explanations to supply per row of the dataset."
},
"modelId": {
"type": "string",
"description": "The model ID."
},
"numTopClasses": {
"type": "integer",
"description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1."
},
"thresholdHigh": {
"type": [
"number",
"null"
],
"description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows."
},
"thresholdLow": {
"type": [
"number",
"null"
],
"description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows."
}
},
"required": [
"PCID",
"projectId",
"datasetId",
"modelId"
]
}
datarobot_batch_predictions_projects_prediction_explanations_list
Retrieve stored Prediction Explanations. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | No | — | This many results will be skipped. |
limit | integer | Yes | — | At most this many results are returned. The default may change and a new maximum limit may be imposed without notice. |
excludeAdjustedPredictions | string | No | — | Whether to include adjusted predictions in the PredictionExplanationsRow response. |
projectId | string | Yes | — | The project ID. |
predictionExplanationsId | string | Yes | — | The ID of the PredictionExplanationsRecord to retrieve. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "This many results will be skipped."
},
"limit": {
"type": "integer",
"description": "At most this many results are returned. The default may change and a new maximum limit may be imposed without notice."
},
"excludeAdjustedPredictions": {
"type": "string",
"description": "Whether to include adjusted predictions in the PredictionExplanationsRow response.",
"enum": [
"false",
"False",
"true",
"True"
]
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"predictionExplanationsId": {
"type": "string",
"description": "The ID of the PredictionExplanationsRecord to retrieve."
}
},
"required": [
"PCID",
"limit",
"projectId",
"predictionExplanationsId"
]
}
datarobot_batch_predictions_projects_prediction_explanations_records_delete
Delete saved Prediction Explanations. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID. |
predictionExplanationsId | string | Yes | — | The ID of the PredictionExplanationsRecord to retrieve. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"predictionExplanationsId": {
"type": "string",
"description": "The ID of the PredictionExplanationsRecord to retrieve."
}
},
"required": [
"PCID",
"projectId",
"predictionExplanationsId"
]
}
datarobot_batch_predictions_projects_prediction_explanations_records_list
List PredictionExplanationsRecord objects for a project. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | No | — | This many results will be skipped. |
limit | integer | No | — | At most this many results are returned. |
modelId | string | No | — | If specified, only prediction explanations records computed for this model will be returned. |
projectId | string | Yes | — | The project ID. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "This many results will be skipped."
},
"limit": {
"type": "integer",
"description": "At most this many results are returned."
},
"modelId": {
"type": "string",
"description": "If specified, only prediction explanations records computed for this model will be returned."
},
"projectId": {
"type": "string",
"description": "The project ID."
}
},
"required": [
"PCID",
"projectId"
]
}
datarobot_batch_predictions_projects_prediction_explanations_records_retrieve
Retrieve a PredictionExplanationsRecord object. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project ID. |
predictionExplanationsId | string | Yes | — | The ID of the PredictionExplanationsRecord to retrieve. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project ID."
},
"predictionExplanationsId": {
"type": "string",
"description": "The ID of the PredictionExplanationsRecord to retrieve."
}
},
"required": [
"PCID",
"projectId",
"predictionExplanationsId"
]
}
datarobot_batch_predictions_projects_predictions_create
Make new predictions. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | The project to make predictions within. |
Content-Type | string | Yes | — | Content types available for making request. multipart/form-data is the legacy deprecated method to send the small file with the prediction request. |
actualValueColumn | string | No | — | For time series projects only. The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional. |
datasetId | string | Yes | — | The dataset to compute predictions for - must have previously been uploaded. |
explanationAlgorithm | string | No | — | If set to shap, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations). |
forecastPoint | string | No | — | For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. |
includeFdwCounts | boolean | No | — | For time series projects with partial history only. Indicates if feature derivation window counts featureDerivationWindowCounts will be part of the response. |
includePredictionIntervals | boolean | No | — | Specifies whether prediction intervals should be calculated for this request. Defaults to True if predictionIntervalsSize is specified, otherwise defaults to False. |
maxExplanations | integer | No | — | Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of ‘shap’: If not set, explanations are returned for all features. If the number of features is greater than the ‘maxExplanations’, the sum of remaining values will also be returned as ‘shapRemainingTotal’. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if ‘explanationAlgorithm’ is omitted. |
modelId | string | Yes | — | The model to make predictions on. |
predictionIntervalsSize | integer | No | — | Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if includePredictionIntervals is True. |
predictionsEndDate | string | No | — | The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. |
predictionsStartDate | string | No | — | The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. |
predictionThreshold | number | No | — | Threshold used for binary classification in predictions. Accepts values from 0.0 to 1.0. If not specified, model default prediction threshold will be used. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "The project to make predictions within."
},
"Content-Type": {
"type": "string",
"description": "Content types available for making request. `multipart/form-data` is the legacy deprecated method to send the small file with the prediction request.",
"enum": [
"application/json",
"multipart/form-data"
]
},
"actualValueColumn": {
"type": "string",
"description": "For time series projects only. The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional."
},
"datasetId": {
"type": "string",
"description": "The dataset to compute predictions for - must have previously been uploaded."
},
"explanationAlgorithm": {
"type": "string",
"description": "If set to `shap`, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).",
"enum": [
"shap"
]
},
"forecastPoint": {
"type": "string",
"description": "For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error."
},
"includeFdwCounts": {
"type": "boolean",
"description": "For time series projects with partial history only. Indicates if feature derivation window counts `featureDerivationWindowCounts` will be part of the response."
},
"includePredictionIntervals": {
"type": "boolean",
"description": "Specifies whether prediction intervals should be calculated for this request. Defaults to True if `predictionIntervalsSize` is specified, otherwise defaults to False."
},
"maxExplanations": {
"type": "integer",
"description": "Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of 'shap': If not set, explanations are returned for all features. If the number of features is greater than the 'maxExplanations', the sum of remaining values will also be returned as 'shapRemainingTotal'. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if 'explanationAlgorithm' is omitted."
},
"modelId": {
"type": "string",
"description": "The model to make predictions on."
},
"predictionIntervalsSize": {
"type": "integer",
"description": "Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if `includePredictionIntervals` is True."
},
"predictionsEndDate": {
"type": "string",
"description": "The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"predictionsStartDate": {
"type": "string",
"description": "The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter."
},
"predictionThreshold": {
"type": "number",
"description": "Threshold used for binary classification in predictions. Accepts values from 0.0 to 1.0. If not specified, model default prediction threshold will be used."
}
},
"required": [
"PCID",
"projectId",
"Content-Type",
"datasetId",
"modelId"
]
}
datarobot_batch_predictions_projects_predictions_list
Get the list of prediction records. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | Yes | — | The number of results to skip. |
limit | integer | Yes | — | At most this many results are returned. To specify no limit, use 0. The default may change and a maximum limit may be imposed without notice. |
datasetId | string | No | — | The dataset ID used to create the predictions. |
modelId | string | No | — | The model ID. |
projectId | string | Yes | — | The project of the predictions. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "The number of results to skip."
},
"limit": {
"type": "integer",
"description": "At most this many results are returned. To specify no limit, use 0. The default may change and a maximum limit may be imposed without notice."
},
"datasetId": {
"type": "string",
"description": "The dataset ID used to create the predictions."
},
"modelId": {
"type": "string",
"description": "The model ID."
},
"projectId": {
"type": "string",
"description": "The project of the predictions."
}
},
"required": [
"PCID",
"offset",
"limit",
"projectId"
]
}
datarobot_batch_predictions_projects_predictions_metadata_list
Get the list of prediction metadata records. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | Yes | — | The number of results to skip. |
limit | integer | Yes | — | At most this many results are returned. To specify no limit, use 0. The default may change and a maximum limit may be imposed without notice. |
predictionDatasetId | string | No | — | The dataset ID used to create the predictions. |
modelId | string | No | — | The model ID. |
projectId | string | Yes | — | The project of the predictions. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "The number of results to skip."
},
"limit": {
"type": "integer",
"description": "At most this many results are returned. To specify no limit, use 0. The default may change and a maximum limit may be imposed without notice."
},
"predictionDatasetId": {
"type": "string",
"description": "The dataset ID used to create the predictions."
},
"modelId": {
"type": "string",
"description": "The model ID."
},
"projectId": {
"type": "string",
"description": "The project of the predictions."
}
},
"required": [
"PCID",
"offset",
"limit",
"projectId"
]
}
datarobot_batch_predictions_projects_predictions_metadata_retrieve
Retrieve metadata for a set of predictions. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
predictionId | string | Yes | — | The ID of the prediction record to retrieve. If you have the jobId, you can retrieve the predictionId using :http:get:/api/v2/projects/(projectId)/predictJobs/(jobId)/. |
projectId | string | Yes | — | The ID of the project the prediction belongs to. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"predictionId": {
"type": "string",
"description": "The ID of the prediction record to retrieve. If you have the jobId, you can retrieve the predictionId using :http:get:`/api/v2/projects/(projectId)/predictJobs/(jobId)/`."
},
"projectId": {
"type": "string",
"description": "The ID of the project the prediction belongs to."
}
},
"required": [
"PCID",
"predictionId",
"projectId"
]
}
datarobot_batch_predictions_projects_predictions_retrieve
Get a completed set of predictions. Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
predictionId | string | Yes | — | The ID of the prediction record to retrieve. If you have the jobId, you can retrieve the predictionId using :http:get:/api/v2/projects/(projectId)/predictJobs/(jobId)/. |
projectId | string | Yes | — | The ID of the project the prediction belongs to. |
Accept | string | No | — | Requested MIME type for the returned data |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"predictionId": {
"type": "string",
"description": "The ID of the prediction record to retrieve. If you have the jobId, you can retrieve the predictionId using :http:get:`/api/v2/projects/(projectId)/predictJobs/(jobId)/`."
},
"projectId": {
"type": "string",
"description": "The ID of the project the prediction belongs to."
},
"Accept": {
"type": "string",
"description": "Requested MIME type for the returned data",
"enum": [
"application/json",
"text/csv"
]
}
},
"required": [
"PCID",
"predictionId",
"projectId"
]
}
datarobot_batch_predictions_projects_training_predictions_create
Submits a job to compute predictions for training data Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
projectId | string | Yes | — | Project ID to compute training predictions for |
dataSubset | string | Yes | — | Subset of data predicted on: The value “all” returns predictions for all rows in the dataset including data used for training, validation, holdout and any rows discarded. This is not available for large datasets or projects created with Date/Time partitioning. The value “validationAndHoldout” returns predictions for the rows used to calculate the validation score and the holdout score. Not available for large projects or Date/Time projects for models trained into the validation set. The value “holdout” returns predictions for the rows used to calculate the holdout score. Not available for projects created without a holdout or for models trained into holdout for large datasets or created with Date/Time partitioning. The value “allBacktests” returns predictions for the rows used to calculate the backtesting scores for Date/Time projects. The value “validation” returns predictions for the rows used to calculate the validation score. |
explanationAlgorithm | string | No | — | If set to “shap”, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations) |
maxExplanations | integer | No | — | Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of “shap”: If not set, explanations are returned for all features. If the number of features is greater than the “maxExplanations”, the sum of remaining values will also be returned as “shapRemainingTotal”. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if “explanationAlgorithm” is omitted. |
modelId | string | Yes | — | The model to make predictions on |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"projectId": {
"type": "string",
"description": "Project ID to compute training predictions for"
},
"dataSubset": {
"type": "string",
"description": "Subset of data predicted on: The value \"all\" returns predictions for all rows in the dataset including data used for training, validation, holdout and any rows discarded. This is not available for large datasets or projects created with Date/Time partitioning. The value \"validationAndHoldout\" returns predictions for the rows used to calculate the validation score and the holdout score. Not available for large projects or Date/Time projects for models trained into the validation set. The value \"holdout\" returns predictions for the rows used to calculate the holdout score. Not available for projects created without a holdout or for models trained into holdout for large datasets or created with Date/Time partitioning. The value \"allBacktests\" returns predictions for the rows used to calculate the backtesting scores for Date/Time projects. The value \"validation\" returns predictions for the rows used to calculate the validation score.",
"enum": [
"all",
"validationAndHoldout",
"holdout",
"allBacktests",
"validation",
"crossValidation"
]
},
"explanationAlgorithm": {
"type": "string",
"description": "If set to \"shap\", the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations)"
},
"maxExplanations": {
"type": "integer",
"description": "Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of \"shap\": If not set, explanations are returned for all features. If the number of features is greater than the \"maxExplanations\", the sum of remaining values will also be returned as \"shapRemainingTotal\". Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if \"explanationAlgorithm\" is omitted."
},
"modelId": {
"type": "string",
"description": "The model to make predictions on"
}
},
"required": [
"PCID",
"projectId",
"dataSubset",
"modelId"
]
}
datarobot_batch_predictions_scheduled_jobs_list
List scheduled deployment batch prediction jobs a user can view Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | Yes | — | The number of scheduled jobs to skip. Defaults to 0. |
limit | integer | Yes | — | The number of scheduled jobs (max 100) to return. Defaults to 20 |
orderBy | string | No | — | The order to sort the scheduled jobs. Defaults to order by last successful run timestamp in descending order. |
search | string | No | — | Case insensitive search against scheduled jobs name or type name. |
deploymentId | string | No | — | Filter by the prediction integration deployment ID. Ignored for non prediction integration type ID. |
typeId | string | No | — | filter by scheduled job type ID. |
queryByUser | string | No | — | Which user field to filter with. |
filterEnabled | string | No | — | Filter jobs using the enabled field. If true, only enabled jobs are returned, otherwise if false, only disabled jobs are returned. The default returns both enabled and disabled jobs. |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "The number of scheduled jobs to skip. Defaults to 0."
},
"limit": {
"type": "integer",
"description": "The number of scheduled jobs (max 100) to return. Defaults to 20"
},
"orderBy": {
"type": "string",
"description": "The order to sort the scheduled jobs. Defaults to order by last successful run timestamp in descending order."
},
"search": {
"type": "string",
"description": "Case insensitive search against scheduled jobs name or type name."
},
"deploymentId": {
"type": "string",
"description": "Filter by the prediction integration deployment ID. Ignored for non prediction integration type ID."
},
"typeId": {
"type": "string",
"description": "filter by scheduled job type ID.",
"enum": [
"datasetRefresh"
]
},
"queryByUser": {
"type": "string",
"description": "Which user field to filter with.",
"enum": [
"createdBy",
"updatedBy"
]
},
"filterEnabled": {
"type": "string",
"description": "Filter jobs using the enabled field. If `true`, only enabled jobs are returned, otherwise if `false`, only disabled jobs are returned. The default returns both enabled and disabled jobs.",
"enum": [
"false",
"False",
"true",
"True"
]
}
},
"required": [
"PCID",
"offset",
"limit"
]
}
datarobot_batch_predictions_training_predictions_list
List training prediction jobs Parameters:| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
offset | integer | Yes | — | This many results will be skipped |
limit | integer | Yes | — | At most this many results are returned |
projectId | string | Yes | — | Project ID to retrieve training predictions for |
Show inputSchema
Show inputSchema
{
"type": "object",
"properties": {
"PCID": {
"type": "string",
"description": "Pink Connect ID for the authenticated connection"
},
"offset": {
"type": "integer",
"description": "This many results will be skipped"
},
"limit": {
"type": "integer",
"description": "At most this many results are returned"
},
"projectId": {
"type": "string",
"description": "Project ID to retrieve training predictions for"
}
},
"required": [
"PCID",
"offset",
"limit",
"projectId"
]
}