Scoring rules REST API

You use the Scoring rules API to define and edit the scoring definition that is used by an analytical model.

To change the scoring rules or scoring definition, IBM® Maximo® Production Optimization SaaS provides the following APIs:

GET /api/v1/scoring/scoringrule to get all existing scoring rules for the tenant.
DELETE /api/v1/scoring/scoringrule/{name} to delete a scoring rule.
GET /api/v1/scoring/scoringrule/{name} to get an existing scoring rule.
POST /api/v1/scoring/scoringrule/{name} to define a new scoring rule.
PUT /api/v1/scoring/scoringrule/{name} to update an existing scoring rule.

Define a new scoring rule

To define a new scoring rule, use the operation and API path that are outlined in the following table:

Table 1. Define a new scoring rule API

Operation	Path	Description
POST	/api/v1/scoring/scoringrule/{name}	Defines a new scoring rule or scoring definition.

Table 2. The parameters to define a new scoring rule

Parameters	Location	Required	Description
tenantId	header	true	The Maximo Production Optimization SaaS tenant ID.
userId	header	true	The Maximo Production Optimization SaaS user ID.
apiKey	header	true	The Maximo Production Optimization SaaS API key.
name	path	true	The scoring rule name.
payload	body	true	The scoring rule definitions.

For example, to create a scoring rule for the failure probability prediction model that has the name failureprobability_scoring_demo, you must supply the following information:

Model
Data source mapping for the model
Data source location, the object name, and bucket name
The IBM Watson™ Machine Learning scoring endpoints that are used
The Cloud Object Storage API keys
IBM® Maximo® Production Optimization SaaS API keys
IBM Watson™ Machine Learning API keys

The following sample payload creates a new scoring rule for a failure probability prediction model:

  {
    "model": "failureprobability",
    "data_config": {
      "entity_datetime_column_name": "dateofsensordata",
      "entity_failure_type_enum": {
        "0": "NoFault",
        "1": "Fault"
      },
      "entity_id_column_name": "asset_id",
      "entity_ok_status": [
        0
      ],
      "entity_predict_period": "7d"
    },
    "scoring_config": {
      "bucket": "po-datasets",
      "delete": false,
      "feature_endpoint": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-b2fb936b440e/online",
      "object": "sensor_data_demo1.csv",
      "scoring_endpoint": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-ca30cf20c22c/online"
    },
    "cos_config": {
      "apikey": "AkGexxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxJGikd",
      "endpoint_url": "https://s3-api.us-geo.objectstorage.softlayer.net",
      "ibm_auth_endpoint": "https://iam.ng.bluemix.net/oidc/token"
    },
    "po_config": {
      "apiKey": "27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a",
      "tenantId": "xxx",
      "url": "https://prod.productionoptimization.ibm.com",
      "userId": "xxx"
    },
    "wml_config": {
      "apikey": "5wm9nxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxPQ19V",
      "iam_apikey_description": "Auto generated apikey during resource-key operation for Instance - crn:v1:bluemix:public:pm-20:us-south:a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f:d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135::",
      "iam_apikey_name": "auto-generated-apikey-10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e",
      "iam_role_crn": "crn:v1:bluemix:public:iam::::serviceRole:Writer",
      "iam_serviceid_crn": "crn:v1:bluemix:public:iam-identity::a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f::serviceid:ServiceId-4d4712af-2641-493f-ad54-d792c96ec445",
      "instance_id": "d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135",
      "password": "4c890xxx-xxxx-xxxx-xxxx-xxxxxxx5e237",
      "url": "https://us-south.ml.cloud.ibm.com",
      "username": "10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e"
    }
  }

The sample includes the following sessions:

model: Defines the model that is used for the scoring. The model must be one of the following values:
- processoptimization: Process variability prediction and optimization
- anomalydetection: Anomaly detection analytics
- failureprobability: Failure probability prediction
- timetoevent: Time-to-event prediction
- customize: Customized models
data_config: Defines the model data parameters that are used before and after the scoring of the data model. The data_config session uses different parameters for each of the models:
- processoptimization
```
  "data_config": {
      "metrics_name": "PA_demo_metrics_name", 
      "setpoints": [
          "setpointA", 
          "setpointB",
          ...
      ], 
      "targets": [
          "TargetA", 
          "TargetB",
          ...
      ],
      "others":[
          "Other Variable A",
          "Other Variable B",
          ...
      ], 
      "entity_id": "sample_entity_id"
  }
```
  where:
  - The "metrics_name" field is the predefined record type metric's name.
  - The "setpoints" field is a list of the setpoints or control variables that should match the column names for each setpoint for your scoring data. All the setpoints must be predefined as the setpoint metrics type.
  - The "targets" field is a list of the targets or optimized values that must match the column names for each target for your scoring data. All the targets must be predefined as the optimized metrics type.
  - The "others" field is a list of the other variables or observed variables that should match the column names for each non-manipulated variable for your scoring data. All the others must be predefined as the other metrics type.
  - The "entity_id" field is the entity ID in the plant hierarchy.
  Note: Refer to Recommendation view configuration to predefine the metrics that are used here.
- anomalydetection
```
  "data_config": {
    "entity_id_column_name": "asset_id",
    "entity_datetime_column_name": "timestamp",
    "entity_anomaly_threshold": 50
  }
```
  where:
  - The entity_datetime_column_name is the timestamp column from your raw data. If not specified, the code generates a time.
  - The entity_anomaly_threshold is the anomaly threshold from the model.
- failureprobability
```
  "data_config": {
    "entity_id_column_name": "asset_id",
    "entity_datetime_column_name": "dateofsensordata",
    "entity_predict_period": "7d",
    "entity_ok_status": [0],
    "entity_failure_type_enum": {
      "0" : "NoFault",
      "1" : "Fault"
    }        
  }
```
  where:
  - The entity_id_column_name is the asset id column from your raw data. Note: Do not specify this column with the asset_id.
  - The entity_datetime_column_name is the timestamp column from the raw data.
  - The entity_predict_period is the prediction period. The format is d,M,w,y for the day, month, week, year.
  - The entity_failure_type_enum is the failure type enumeration.
  - The entity_ok_status is the list of states that Maximo Production Optimization SaaS shows as normal and green.
- timetoevent
```
  "data_config": {
    "entity_id_column_name": "asset_id",
    "entity_datetime_column_name": "datetime",
    "entity_variable_column_names": ["p_episode","t_episode","f_episode","c_episode"],
    "predict_unit": "h"
  }
```
  where:
  - The entity_id_column_name is the asset id column from your raw data.
  - The entity_datetime_column_name is the timestamp column from your raw data.
  - The predict_unit is the prediction unit. The format d,M,w,y for the day, month, week, year.
  - The entity_variable_column_names are the list of variables that are used for the model.
- customize
```
  "data_config": {
  }
```
  Note: When the source data does not include the entity ID information, you can specify the entity_id instead of entity_id_column_name in the data_config session, for example:
```
  data_config: {
    "entity_id": "equipment_a",
    "entity_datetime_column_name": "timestamp",
    "entity_anomaly_threshold": 50
  }
```
scoring_config: Includes the Machine Learning endpoints and source data information, for example, object and bucket name.
```
  "scoring_config": {
    "bucket": "po-datasets",
    "object": "sensor_data_demo1.csv",
    "delete": false,
    "feature_endpoint": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-b2fb936b440e/online",
    "scoring_endpoint": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-ca30cf20c22c/online"
  }
```
where:
- The object is the object name that stored the data in Cloud Object Storage.
- The bucket is the Cloud Object Storage bucket name.
- The delete option indicates whether to delete the data file after the scoring of the model. The default is set to false.
- The scoring_endpoint is the Watson Machine Learning scoring endpoint that is created during the model training.
- The feature_endpoint is optional to the scoring endpoint. The option is used for the feature engineering, such as the failure probability. If the feature_endpoint is configured, the feature engineering is done before the scoring.

cos_config: includes the API keys of the data source Cloud Object Storage.

  "cos_config": {
    "apikey": "AkGexxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxJGikd",
    "endpoint_url": "https://s3-api.us-geo.objectstorage.softlayer.net",
    "ibm_auth_endpoint": "https://iam.ng.bluemix.net/oidc/token"
  },

po_config: includes the API keys for Maximo Production Optimization SaaS.
```
  "po_config": {
    "apiKey": "27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a",
    "tenantId": "xxx",
    "url": "http://ts-svc:9080",
    "userId": "xxx"
  }
```
Note: The URL can be the following internal service name URL:

http://ts-svc:9080

Note, the link to access the internal service name URL works only when you have a valid deployment of Maximo Production Optimization SaaS.

wml_config: includes the API keys for Machine Learning.

  "wml_config": {
    "apikey": "5wm9nxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxPQ19V",
    "iam_apikey_description": "Auto generated apikey during resource-key operation for Instance - crn:v1:bluemix:public:pm-20:us-south:a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f:d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135::",
    "iam_apikey_name": "auto-generated-apikey-10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e",
    "iam_role_crn": "crn:v1:bluemix:public:iam::::serviceRole:Writer",
    "iam_serviceid_crn": "crn:v1:bluemix:public:iam-identity::a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f::serviceid:ServiceId-4d4712af-2641-493f-ad54-d792c96ec445",
    "instance_id": "d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135",
    "password": "4c890xxx-xxxx-xxxx-xxxx-xxxxxxx5e237",
    "url": "https://us-south.ml.cloud.ibm.com",
    "username": "10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e"
  }

When you have the information, you can use the openapi UI or the curl command to create the scoring rule, for example:

    curl -X POST "https://prod.productionoptimization.ibm.com/api/v1/scoring/scoringrule/failureprobability_scoring_demo"  
     -H  "accept: application/json"  
     -H  "userId: xxx"  
     -H  "tenantId: xxx"  
     -H  "apiKey: 27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a"  
     -H  "Content-Type: application/json"  
     -d "{\"cos_config\":{\"apikey\":\"AkGexxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxJGikd\",\"endpoint_url\":\"https://s3-api.us-geo.objectstorage.softlayer.net\",\"ibm_auth_endpoint\":\"https://iam.ng.bluemix.net/oidc/token\"},\"data_config\":{\"entity_datetime_column_name\":\"dateofsensordata\",\"entity_failure_type_enum\":{\"0\":\"NoFault\",\"1\":\"Fault\"},\"entity_id_column_name\":\"asset_id\",\"entity_ok_status\":[0],\"entity_predict_period\":\"7d\"},\"model\":\"failureprobability\",\"po_config\":{\"apiKey\":\"27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a\",\"tenantId\":\"xxx\",\"url\":\"http://ts-svc:9080\",\"userId\":\"xxx\"},\"scoring_config\":{\"bucket\":\"po-datasets\",\"delete\":false,\"feature_endpoint\":\"https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-b2fb936b440e/online\",\"object\":\"sensor_data_demo1.csv\",\"scoring_endpoint\":\"https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-ca30cf20c22c/online\"},\"wml_config\":{\"apikey\":\"5wm9nxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxPQ19V\",\"iam_apikey_description\":\"Auto generated apikey during resource-key operation for Instance - crn:v1:bluemix:public:pm-20:us-south:a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f:d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135::\",\"iam_apikey_name\":\"auto-generated-apikey-10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e\",\"iam_role_crn\":\"crn:v1:bluemix:public:iam::::serviceRole:Writer\",\"iam_serviceid_crn\":\"crn:v1:bluemix:public:iam-identity::a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f::serviceid:ServiceId-4d4712af-2641-493f-ad54-d792c96ec445\",\"instance_id\":\"d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135\",\"password\":\"4c890xxx-xxxx-xxxx-xxxx-xxxxxxx5e237\",\"url\":\"https://us-south.ml.cloud.ibm.com\",\"username\":\"10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e\"}}"

The response is 201 with the scoring rule information that is created:

    {
      "data": {
        "_id": "783a9a6d8f608b85efe5592ca206c4a0",
        "_rev": "1-35cb45610c0e9dc4598e2061ea15b3f4",
        "cos_config": {
          "apikey": "AkGexxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxJGikd",
          "endpoint_url": "https://s3-api.us-geo.objectstorage.softlayer.net",
          "ibm_auth_endpoint": "https://iam.ng.bluemix.net/oidc/token"
        },
        "data_config": {
          "entity_datetime_column_name": "dateofsensordata",
          "entity_failure_type_enum": {
            "0": "NoFault",
            "1": "Fault"
          },
          "entity_id_column_name": "asset_id",
          "entity_ok_status": [
            0
          ],
          "entity_predict_period": "7d"
        },
        "model": "failureprobability",
        "name": "failureprobability_scoring_demo",
        "po_config": {
          "apiKey": "27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a",
          "tenantId": "xxx",
          "url": "http://ts-svc:9080",
          "userId": "xxx"
        },
        "scoring_config": {
          "bucket": "po-datasets",
          "delete": false,
          "feature_endpoint": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-b2fb936b440e/online",
          "object": "sensor_data_demo1.csv",
          "scoring_endpoint": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-ca30cf20c22c/online"
        },
        "wml_config": {
          "apikey": "5wm9nxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxPQ19V",
          "iam_apikey_description": "Auto generated apikey during resource-key operation for Instance - crn:v1:bluemix:public:pm-20:us-south:a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f:d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135::",
          "iam_apikey_name": "auto-generated-apikey-10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e",
          "iam_role_crn": "crn:v1:bluemix:public:iam::::serviceRole:Writer",
          "iam_serviceid_crn": "crn:v1:bluemix:public:iam-identity::a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f::serviceid:ServiceId-4d4712af-2641-493f-ad54-d792c96ec445",
          "instance_id": "d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135",
          "password": "4c890xxx-xxxx-xxxx-xxxx-xxxxxxx5e237",
          "url": "https://us-south.ml.cloud.ibm.com",
          "username": "10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e"
        }
      },
      "msg": "created rule failureprobability_scoring_demo for xxx...",
      "status": 201
    }

Get an existing rule based on the name of the tenant

To get an existing rule based on the name of the tenant, use the operation and API path that are outlined in the following table:

Table 3. Get an existing rule based on the name of the tenant API

Operation	Path	Description
GET	/api/v1/scoring/scoringrule/{name}	Get an existing scoring rule or scoring definition

Table 4. The parameters for getting an existing rule based on the name of the tenant

Parameters	Location	Required	Description
tenantId	header	true	Maximo Production Optimization SaaS tenant ID
userId	header	true	Maximo Production Optimization SaaS user ID
apiKey	header	true	Maximo Production Optimization SaaS API key
name	path	true	The scoring rule name

  curl -X GET "https://prod.productionoptimization.ibm.com/api/v1/scoring/scoringrule/failureprobability_scoring_demo"  
  -H  "accept: application/json"  
  -H  "userId: xxx" -H  "tenantId: xxx"  
  -H  "apiKey: 27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a"

Get all the existing scoring rules of a tenant

To get all existing scoring rules that are used by a tenant, use the following API:

Table 5. Get all existing scoring rules of a tenant API

Operation	Path	Description
GET	/api/v1/scoring/scoringrule	Get all existing scoring rules ir scoring definitions for the tenant

Table 6. The parameters for getting all existing scoring rules of a tenant API

Parameters	Location	Required	Description
tenantId	header	true	Maximo Production Optimization SaaS tenant ID
userId	header	true	Maximo Production Optimization SaaS user ID
apiKey	header	true	Maximo Production Optimization SaaS API key
skip	query	false	Skip n records when querying
limit	query	false	Limit n records that are returned

  curl -X GET "https://prod.productionoptimization.ibm.com/api/v1/scoring/scoringrule"  
  -H  "accept: application/json"  
  -H  "tenantId: xxx" -H  "userId: xxx"  
  -H  "apiKey: 27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a"

Update an existing scoring rule

To update an existing scoring rule, use the operation and API path that are outlined in the following table:

Table 7. Update an existing scoring rule API

Operation	Path	Description
PUT	/api/v1/scoring/scoringrule/{name}	Update an existing scoring rule or scoring definition

Table 8. The parameters for updating an existing scoring rule API

Parameters	Location	Required	Description
tenantId	header	true	PO tenant ID
userId	header	true	PO user ID
apiKey	header	true	PO API key
name	path	true	The scoring rule name
payload	body	true	The updated scoring rule definitions

You need to input the updated part with the model that is used in your payload, for example:

  {
    "model": "failureprobability",
    "data_config": {
      "entity_datetime_column_name": "dateofsensordata",
      "entity_failure_type_enum": {
        "0": "NoFault",
        "1": "Fault"
      },
      "entity_id_column_name": "asset_id",
      "entity_ok_status": [
        0
      ],
      "entity_predict_period": "1w"
    }
  }

The example updates the scoring rule 'failureprobability_scoring_demo'.

  curl -X PUT "https://prod.productionoptimization.ibm.com/api/v1/scoring/scoringrule/failureprobability_scoring_demo"  
  -H  "accept: application/json"  
  -H  "tenantId: xxx" -H   
  "userId: xxx"  
  -H  "apiKey: 27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a"  
  -H  "Content-Type: application/json"  
  -d "{\"model\":\"failureprobability\",\"data_config\":{\"entity_datetime_column_name\":\"dateofsensordata\",\"entity_failure_type_enum\":{\"0\":\"NoFault\",\"1\":\"Fault\"},\"entity_id_column_name\":\"asset_id\",\"entity_ok_status\":[0],\"entity_predict_period\":\"1w\"}}"

When the operation is successful, it returns the following updated information:

  {
    "data": {
      "_id": "783a9a6d8f608b85efe5592ca206c4a0",
      "_rev": "3-c4b1798e13912354288c84e81b69deee",
      "cos_config": {
        "apikey": "AkGexxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxJGikd",
        "endpoint_url": "https://s3-api.us-geo.objectstorage.softlayer.net",
        "ibm_auth_endpoint": "https://iam.ng.bluemix.net/oidc/token"
      },
      "data_config": {
        "entity_datetime_column_name": "dateofsensordata",
        "entity_failure_type_enum": {
          "0": "NoFault",
          "1": "Fault"
        },
        "entity_id_column_name": "asset_id",
        "entity_ok_status": [
          0
        ],
        "entity_predict_period": "1w"
      },
      "model": "failureprobability",
      "name": "failureprobability_scoring_demo",
      "po_config": {
        "apiKey": "27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a",
        "tenantId": "xxx",
        "url": "http://ts-svc:9080",
        "userId": "xxx"
      },
      "scoring_config": {
        "bucket": "po-datasets",
        "delete": false,
        "feature_endpoint": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-b2fb936b440e/online",
        "object": "sensor_data_demo1.csv",
        "scoring_endpoint": "https://us-south.ml.cloud.ibm.com/v3/wml_instances/d739718a-xxxx-xxxx-xxxx-xxxxxxxxxxxx/deployments/52f96e1b-xxxx-xxxx-xxxx-ca30cf20c22c/online"
      },
      "wml_config": {
        "apikey": "5wm9nxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxPQ19V",
        "iam_apikey_description": "Auto generated apikey during resource-key operation for Instance - crn:v1:bluemix:public:pm-20:us-south:a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f:d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135::",
        "iam_apikey_name": "auto-generated-apikey-10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e",
        "iam_role_crn": "crn:v1:bluemix:public:iam::::serviceRole:Writer",
        "iam_serviceid_crn": "crn:v1:bluemix:public:iam-identity::a/b1ed0xxxxxxxxxxxxxxxxxxxxxx3034f::serviceid:ServiceId-4d4712af-2641-493f-ad54-d792c96ec445",
        "instance_id": "d7397xxx-xxxx-xxxx-xxxx-xxxxxxx7a135",
        "password": "4c890xxx-xxxx-xxxx-xxxx-xxxxxxx5e237",
        "url": "https://us-south.ml.cloud.ibm.com",
        "username": "10864xxx-xxxx-xxxx-xxxx-xxxxxxx86c6e"
      }
    },
    "msg": "scoring rule failureprobability_scoring_demo for xxx updated",
    "status": 200
  }

Delete an existing scoring rule

To delete an existing scoring rule, use the following API:

Table 9. Delete an existing scoring rule API

Operation	Path	Description
DELETE	/api/v1/scoring/scoringrule/{name}	Delete a scoring rule or scoring definition

Table 10. The parameters required to delete an existing scoring rule

Parameters	In	Required	Description
tenantId	header	true	Maximo Production Optimization SaaS tenant ID
userId	header	true	Maximo Production Optimization SaaS user ID
apiKey	header	true	Maximo Production Optimization SaaS API key
name	path	true	The scoring rule name

For example:

  curl -X DELETE "https://prod.productionoptimization.ibm.com/api/v1/scoring/scoringrule/test_abc"  
  -H  "accept: application/json"  
  -H  "tenantId: xxx"  
  -H  "userId: xxx"  
  -H  "apiKey: 27a06148xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx88b866bac8a"

The following response is returned:

  {
    "data": {
      "_id": "4f54a07dbce386365937b128a513a6ba"
    },
    "msg": "scoring rule test_abc for T1 deleted",
    "status": 200
  }