The RegressionSolution API is a scriptable object used in Predictive Intelligence stores.

This API requires the Predictive Intelligence plugin (com.glide.platform_ml) and is provided within the sn_ml namespace.

The solution setup-to-training flow is as follows:
  1. Create a dataset using the DatasetDefinition API.
  2. Optional. Build an encoder using the Encoder API.
  3. Use the constructor to create a regression solution object.
  4. Add the solution object to the regression solution store using the RegressionSolutionStore - add() method.
  5. Train the solution using the submitTrainingJob() method. This creates a version of the object that you can manage using the RegressionSolutionVersion API.
  6. Get predictions using the RegressionSolutionVersion – predict() method.
Note: This API runs with full privileges before the Vancouver Patch 7 Hotfix 2b and Washington DC Patch 7 releases. With later releases, grant access using ACLs. For more information see Query ACLs.

For usage guidelines, refer to Using ML APIs.

RegressionSolution - RegressionSolution(Object config)

Creates a regression solution.

Table 1. Parameters
NameTypeDescription
config Object JavaScript object containing configuration properties of the solution.
{
  "algorithmConfig": {Object},
  "dataset": {Object},
  "domainName": "String",
  "encoder": {Object},
  "inputFieldNames": [Array],
  "label": "String",
  "minRowCount": "String",
  "predictedFieldName": "String",
  "predictedInterval": [Array],
  "processingLanguage": "String",
  "stopwords": [Array],
  "trainingFrequency": "String"
}
config.dataset Object DatasetDefinition name.
config.domainName String Optional. Domain name associated with this dataset. See Domain separation and Predictive Intelligence.

Default: Current domain, for example, "global".
config.encoder Object Optional. Trained encoder object to assign to this solution. See Encoder - Encoder(Object config).
config.inputFieldNames Array List of input field names as strings. The model uses these fields used to make predictions.
config.label String Identifies the prediction task.
config.minRowCount String Optional. Minimum number of records required in the dataset for training.

Default: 10000
config.predictedFieldName String Mandatory unless setting predictedInterval. Identifies a field to be trained for predictability.
config.predictedInterval Array Mandatory unless setting predictedFieldName. Sets a range of fields to train your solution for confidence. Supports providing 2 non-numeric date fields. For example, 'predictedInterval': ['sys_updated_on', 'sys_created_on'].
config.processingLanguage String Optional. Processing language in two-letter ISO 639-1 language code format.

Default: "en"
config.stopwords Array Optional. Preset list of strings that the system automatically generates based on the language property setting. For details, see Create a custom stopwords list.

Default: English Stopwords
config.trainingFrequency String The frequency to retrain the model.
Possible values:
  • every_30_days
  • every_60_days
  • every_90_days
  • every_120_days
  • every_180_days
  • run_once
Default: run_once

Example

The following example shows how to create an object and add it to the RegressionSolution store.

var myNewData = new sn_ml.DatasetDefinition(
  { 
     'tableName' : 'incident', 
     'fieldNames' : ['category', 'short_description', 'priority'],
     'fieldDetails' : [
       {
         'name' : 'category',
         'type' : 'nominal'
       },
       {
         'name' : 'short_description',
         'type' : 'text'
       }], 
     'encodedQuery' : 'activeANYTHING'
  });

var mySimSolution = new sn_ml.SimilaritySolution({
  'label': "my solution definition",
  'dataset' : myNewData,
  'predictedFieldName' : 'category',
  'inputFieldNames': ['short_description']
});

var mySimilarityName = sn_ml.SimilaritySolutionStore.add(mySimSolution);

Example

The following example shows how to create an object to train using the predictedInterval property.

var myIncidentData = new sn_ml.DatasetDefinition({
'tableName' : 'incident',
'fieldNames' : ['short_description', 'sys_updated_on','sys_created_on'],
'encodedQuery' : 'activeANYTHING'
});
 
var mySolution = new sn_ml.RegressionSolution({
'label': 'reg assinGroup',
'dataset' : myIncidentData,
'predictedInterval': ['sys_updated_on', 'sys_created_on'],
'inputFieldNames': ['short_description']
});
 
var my_unique_name = sn_ml.RegressionSolutionStore.add(mySolution)

RegressionSolution - cancelTrainingJob()

Cancels a job for a solution object that has been submitted for training.

Table 2. Parameters
Name Type Description
None
Table 3. Returns
Type Description
None

Example

The following example shows how to cancel an existing training job.

var mySolution = sn_ml.RegressionSolutionStore.get('ml_sn_global_global_regression');

mySolution.cancelTrainingJob();

RegressionSolution - getActiveVersion()

Gets the active RegressionSolutionVersion object.

Table 4. Parameters
Name Type Description
None
Table 5. Returns
Type Description
Object Active RegressionSolutionVersion object.

Example

The following example shows how to get an active RegressionSolution version from the store and return its training status.

var mlSolution = sn_ml.RegressionSolutionStore.get('ml_x_snc_global_global_regression');

gs.print(JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getStatus()), null, 2));

Output:

{
  "state": "solution_complete",
  "percentComplete": "100",
  "hasJobEnded": "true"
}

RegressionSolution - getAllVersions()

Gets all versions of a RegressionSolution object.

Table 6. Parameters
Name Type Description
None
Table 7. Returns
Type Description
Array Existing versions of a solution object. See also RegressionSolutionVersion API.

Example

The following example shows how to get all RegressionSolution version objects and call the getVersionNumber() and getStatus() solution version methods on them.

var mlSolution = sn_ml.RegressionSolutionStore.get('ml_x_snc_global_global_regression');

var mlSolutionVersions = mlSolution.getAllVersions();

for (i = 0; i < mlSolutionVersions.length; i++) {
gs.print("Version " + mlSolutionVersions[i].getVersionNumber() + " Status: " + mlSolutionVersions[i].getStatus() +"\n");
};

Output:

Version 3 Status: {"state":"solution_complete","percentComplete":"100","hasJobEnded":"true"}

Version 2 Status: {"state":"solution_complete","percentComplete":"100","hasJobEnded":"true"}

Version 1 Status: {"state":"solution_cancelled","percentComplete":"0","hasJobEnded":"true"}

RegressionSolution - getLatestVersion()

Gets the latest version of a solution.

Table 8. Parameters
Name Type Description
None
Table 9. Returns
Type Description
Object RegressionSolutionVersion object corresponding to the latest version of a RegressionSolution().

Example

The following example shows how to get the latest version of a solution and return its training status.

var mlSolution = sn_ml.RegressionSolutionStore.get('ml_x_snc_global_global_regression');

gs.print(JSON.stringify(JSON.parse(mlSolution.getLatestVersion().getStatus()), null, 2));

Output:

{
  "state": "solution_complete",
  "percentComplete": "100",
  "hasJobEnded": "true"
}

RegressionSolution - getName()

Gets the name of the object to use for interaction with the store.

Table 10. Parameters
Name Type Description
None
Table 11. Returns
Type Description
String Name of the solution object.

Example

The following example shows how to update RegressionSolution dataset information and print the name of the object.

// Update solution
var myIncidentData = new sn_ml.DatasetDefinition({
   'tableName' : 'incident',
   'fieldNames' : ['category', 'short_description', 'priority'],
   'encodedQuery' : 'activeANYTHING'
});

var eligibleFields = JSON.parse(myIncidentData.getEligibleFields('regression'));

var myRegression = new sn_ml.RegressionSolution({
   'label': "my regression solution",
   'dataset' : myIncidentData,
   'inputFieldNames': eligibleFields['eligibleInputFieldNames'],
   'predictedFieldName': 'category'
});

// update solution
sn_ml.RegressionSolutionStore.update('ml_x_snc_global_global_my_solution_definition_4', myRegression);

// print solution name
gs.print('Solution Name: '+myRegression.getName());

Output:

Solution Name: ml_x_snc_global_global_my_solution_definition_4

RegressionSolution - getProperties()

Gets solution object properties.

Table 12. Parameters
Name Type Description
None
Table 13. Returns
TypeDescription
Object Contents of the Dataset and RegressionSolution() object details in the RegressionSolutionStore.
{
  "datasetProperties": {Object},
  "domainName": "String",
  "encoder": {Object},
  "inputFieldNames": [Array],
  "label": "String",
  "name": "String",
  "predictedFieldName": "String",
  "predictedInterval": [Array],
  "processingLanguage": "String",
  "scope": "String",
  "stopwords": [Array],
  "trainingFrequency": "String"
}
<Object>.datasetProperties

Lists the properties of the DatasetDefinition() object associated with the solution.

{
  "encodedQuery": "String",
  "fieldDetails": [Array],
  "fieldNames": [Array],
  "tableName": "String"
}

Data type: Object.
<Object>.datasetProperties.tableName Name of the table for the dataset. For example, "tableName" : "Incident".

Data type: String.
<Object>.datasetProperties.fieldNames List of field names from the specified table as strings. For example, "fieldNames" : ["short_description", "priority"].

Data type: Array.
<Object>.datasetProperties.fieldNames.fieldDetails List of JavaScript objects that specify field properties. 
[
  {
    "name": "String",
    "type": "String"
  }
]

Data type: Array.
<Object>.datasetProperties.fieldNames.fieldDetails.<object>.name Name of the field defining the type of information to restrict this dataset to.

Data type: String.
<Object>.datasetProperties.fieldDetails.<object>.type Machine-learning field type.

Data type: String.
<Object>.datasetProperties.fieldDetails.encodedQuery Encoded query string in standard Glide format. See Encoded query strings.

Data type: String.
<Object>.domainName Domain name associated with this dataset. See Domain separation and Predictive Intelligence.

Data type: String.
<Object>.encoder Encoder object assigned to this solution. See Encoder - Encoder(Object config).

Data type: Object.
<Object>.inputFieldNames List of input field names as strings. The model uses these fields used to make predictions.

Data type: String.
<Object>.label Identifies the prediction task. 
{
  "label": "my first prediction"
}

Data type: String.
<Object>.name System-assigned name.

Data type: String.
<Object>.predictedFieldName Identifies a field to be trained for predictability.

Data type: String.
<Object>.predictedInterval Range of values specifying the prediction confidence level.

Data type: Array
<Object>.processingLanguage Processing language in two-letter ISO 639-1 language code format.

Data type: String.
<Object>.scope Object scope. Currently the only valid value is global.

Data type: String
<Object>.stopwords Optional. Preset list of strings that the system automatically generates based on the language property setting. For details, see Create a custom stopwords list.

Data type: Array.
<Object>.trainingFrequency The frequency to retrain the model.
Possible values:
  • every_30_days
  • every_60_days
  • every_90_days
  • every_120_days
  • every_180_days
  • run_once
Default: run_once

Data type: String.

Example

The following example gets properties of a solution object in the store.

var mySolution = sn_ml.RegressionSolutionStore.get('ml_sn_global_global_regression_solution');

gs.print(JSON.stringify(JSON.parse(mySolution.getProperties()), null, 2));
Output:
*** Script: {
  "datasetProperties": {
    "tableName": "cloudinfratext",
    "fieldNames": [
      "short_description",
      "sourcedc",
      "targetdc",
      "dbsize",
      "duration"
    ]
  },
  "domainName": "global",
  "encoderProperties": {
    "datasetsProperties": [],
    "name": "wc_regression"
  },
  "inputFieldNames": [
    "short_description",
    "sourcedc",
    "targetdc",
    "dbsize"
  ],
  "label": "Regression Test for DB Restore",
  "name": "ml_x_snc_global_global_regression",
  "predictedFieldName": "duration",
  "processingLanguage": "en",
  "scope": "global",
  "stopwords": [
    "Default English Stopwords"
  ],
  "trainingFrequency": "every_30_days"
}

RegressionSolution - getVersion(String version)

Gets a solution by provided version number.

Table 14. Parameters
Name Type Description
version String Existing version number of a solution.
Table 15. Returns
Type Description
Object Specified version of the RegressionSolution() object on which you can call RegressionSolutionVersion API methods.

Example

The following example shows how to get the training status of a solution by version number.

var mlSolution = sn_ml.RegressionSolutionStore.get('ml_x_snc_global_global_regression');

gs.print(JSON.stringify(JSON.parse(mlSolution.getVersion('1').getStatus()), null, 2));

Output:

{
  "state": "solution_complete",
  "percentComplete": "100",
  "hasJobEnded": "true"
}

RegressionSolution - setActiveVersion(String version)

Activates a specified version of a solution in the store.

Table 16. Parameters
Name Type Description
version String Name of the RegressionSolution() object version to activate.

Activating this version deactivates any other version.
Table 17. Returns
Type Description
None

Example

The following example shows how to activate a solution version in the store.

sn_ml.RegressionSolution.setActiveVersion("ml_incident_categorization");

RegressionSolution - submitTrainingJob()

Submits a training job.

Note: Before running this method, you must first add a solution to the store using the RegressionSolutionStore - add() method.
Table 18. Parameters
Name Type Description
None
Table 19. Returns
Type Description
Object RegressionSolutionVersion object corresponding to the RegressionSolution being trained.

Example

The following example shows how to create a dataset, apply it to a solution, add the solution to a store, and submit the training job.

// Create a dataset 
var myData = new sn_ml.DatasetDefinition({

  'tableName' : 'incident',
  'fieldNames' : ['assignment_group', 'short_description', 'description'],
  'encodedQuery' : 'activeANYTHING'

});

// Create a solution 
var mySolution = new sn_ml.RegressionSolution({

  'label': "my solution definition",
  'dataset' : myData,
  'predictedFieldName' : 'assignment_group',
  'inputFieldNames':['short_description']

});

// Add the solution to the store to later be able to retrieve it.
var my_unique_name = sn_ml.RegressionSolutionStore.add(mySolution);

// Train the solution - this is a long running job 
var myRegressionVersion = mySolution.submitTrainingJob();