ClassificationSolution - Global

The ClassificationSolution 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 classification solution object.
4. Add the solution object to the classification solution store using the ClassificationSolutionStore - add() method.
5. Train the solution using the submitTrainingJob() method. This creates a version of the object that you can manage using the ClassificationSolutionVersion API.
6. Get predictions using the ClassificationSolutionVersion – 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.

ClassificationSolution - ClassificationSolution(Object config)

Creates a classification solution.

Table 1. Parameters

Name	Type	Description
config	Object	JavaScript object containing configuration properties of the solution. { "algorithmConfig": {Object}, "dataset": {Object}, "domainName": "String", "encoder": {Object}, "groupByFieldName": "String", "inputFieldNames": [Array], "label": "String", "minRowCount": "String", "predictedFieldName": "String", "processingLanguage": "String", "stopwords": [Array], "trainingFrequency": "String" }
config.algorithmConfig	Object	JavaScript object containing algorithm configuration properties. 'algorithmConfig' : { "algorithm": "String", "targetClassRecall": "String" }
config.algorithmConfig.algorithm	String	Method for encoding your solution. Possible values: xgboost: XGBoost encoding to optimize the training. logisticRegression: Method using the logistic regression model for categorical targets such as nominal or ordinal.
config.algorithmConfig.targetClassRecall	String	Applies a class recall parameter to steer a solution's training to bias a specific class. Format is "<ClassName:RecallValue>" where the recall value is a number between 0 and 100 representing a percentage. For example, to set and apply this solution parameter to 90% accuracy for all records you train in the Email class, the value is set to "Email:90".
config.dataset	Object	DatasetDefinition name.
config.domainName	String	Optional. Domain name associated with this dataset. Default: Current domain, for example, "global".
config.encoder	Object	Optional. Trained encoder object to assign to this solution. See Encoder - Encoder(Object config).
config.explainability	Boolean	Flag that indicates whether to provide model explainability. Use model explainability to identify the importance of each input field to your model's predictions. Valid values: true: The Classification model includes explainability details. Information can be viewed on the Feature Importance tab of the model's solution form. false: The Classification model does not include explainability details. Default: false See also: Model Explainability
config.groupByFieldName	String	Optional. Field name by which the system groups records to build classification solutions. For usage information, see Using group by for classification.
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	Identifies a field to be trained for predictability.
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

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

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

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

var myClassificationName = sn_ml.ClassificationSolutionStore.add(mySolution);

ClassificationSolution - 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

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

var mySolution = sn_ml.ClassificationSolutionStore.get('ml_sn_global_global_classification');

mySolution.cancelTrainingJob();

ClassificationSolution - getActiveVersion()

Gets the active ClassificationSolutionVersion object.

Table 4. Parameters

Name	Type	Description
None

Table 5. Returns

Type	Description
Object	Active ClassificationSolutionVersion object.

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

var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');

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

Output:

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

ClassificationSolution - getAllVersions()

Gets all versions of a classification solution.

Table 6. Parameters

Name	Type	Description
None

Table 7. Returns

Type	Description
Array	Existing versions of a solution object. See also ClassificationSolutionVersion API.

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

var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');

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"}

ClassificationSolution - getLatestVersion()

Gets the latest version of a solution.

Table 8. Parameters

Name	Type	Description
None

Table 9. Returns

Type	Description
Object	ClassificationSolutionVersion object corresponding to the latest version of a ClassificationSolution().

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

var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');

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

Output:

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

ClassificationSolution - 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.

The following example shows how to update ClassificationSolution 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('classification'));

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

// update solution
sn_ml.ClassificationSolutionStore.update('ml_x_snc_global_global_my_solution_definition_4', myClassification);

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

Output:

Solution Name: ml_x_snc_global_global_my_solution_definition_4

ClassificationSolution - getProperties()

Gets solution object properties.

Table 12. Parameters

Name	Type	Description
None

Table 13. Returns

Type	Description
Object	Contents of the Dataset and ClassificationSolution() object details in the ClassificationSolutionStore. { "algorithmConfig": { "algorithm": "String", "targetClassRecall": "String" }, "datasetProperties": {Object}, "domainName": "String", "encoder": {Object}, "groupByFieldName": "String", "inputFieldNames": [Array], "label": "String", "name": "String", "predictedFieldName": "String", "processingLanguage": "String", "scope": "String", "stopwords": [Array], "trainingFrequency": "String" }
<Object>.algorithmConfig	Method for encoding the solution. Data type: Object.
<Object>.algorithmConfig.algorithm	Name of the encoding algorithm for training this solution. Possible values: xgboost: XGBoost encoding to optimize the training. logisticRegression: Method using the logistic regression model for categorical targets such as nominal or ordinal. Data type: String.
<Object>.algorithmConfig.targetClassRecall	Class recall parameter to steer a solution's training to bias a specific class. The recall value is a number between 0 and 100 representing a percentage. Data type: 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 the standard platform format. See Encoded query strings. Data type: String.
<Object>.domainName	Domain name associated with this dataset. See Domain separation and Predictive Intelligence. Type: String
<Object>.encoder	Encoder object assigned to this solution. See Encoder - Encoder(Object config). Data type: Object.
<Object>.groupByFieldName	Field name by which the system groups records to build classification solutions. Data type: String
<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>.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.

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

var mySolution = sn_ml.ClassificationSolutionStore.get('ml_sn_global_global_classification_solution');

gs.print(JSON.stringify(JSON.parse(mySolution.getProperties()), null, 2));

Output:

*** Script: {
  "datasetProperties": {
    "tableName": "incident",
    "fieldNames": [
      "category",
      "short_description",
      "priority",
      "assignment_group.name"
    ],
    "fieldDetails": [
      {
        "name": "category",
        "type": "nominal"
      },
      {
        "name": "short_description",
        "type": "text"
      }
    ]
  },
  "domainName": "global",
  "inputFieldNames": [
    "short_description"
  ],
  "label": "my solution definition",
  "name": "ml_x_snc_global_global_my_solution_definition_26",
  "predictedFieldName": "category",
  "processingLanguage": "en",
  "scope": "global",
  "stopwords": [
    "Default English Stopwords"
  ],
  "trainingFrequency": "run_once"
}

ClassificationSolution - 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 ClassificationSolution() object on which you can call ClassificationSolutionVersion API methods.

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

var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');

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

Output:

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

ClassificationSolution - setActiveVersion(String version)

Activates a specified version of a solution in the store.

Table 16. Parameters

Name	Type	Description
version	String	Name of the ClassificationSolution() object version to activate. Activating this version deactivates any other version.

Table 17. Returns

Type	Description
None

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

sn_ml.ClassificationSolution.setActiveVersion("ml_incident_categorization");

ClassificationSolution - submitTrainingJob()

Submits a training job.

Note:

Before running this method, you must first add a solution to the store using the ClassificationSolutionStore - add() method.

Table 18. Parameters

Name	Type	Description
None

Table 19. Returns

Type	Description
Object	ClassificationSolutionVersion object corresponding to the ClassificationSolution being trained.

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.ClassificationSolution({

  '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.ClassificationSolutionStore.add(mySolution);

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