MLSolutionUtil- Global

The MLSolutionUtil script include provides methods for getting Predictive Intelligence predictions.

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

For more information, see Using ML APIs.

Parent Topic:Server API reference

Related topics

MLSolution

MLSolutionFactory

MLSolutionUtil - MLSolutionUtil()

Instantiates a new MLSolutionUtil object.

Name	Type	Description
None

var mlSolutionUtil = new MLSolutionUtil();

MLSolutionUtil - getPredictions(Object input, Array solutionNames, Object options)

Gets predictions for one or more specified solutions.

Name	Type	Description
input	Object	GlideRecord or array of JSON objects as key-value pairs.
solutionNames	Array	Array of solution names to retrieve predictions from.
options	Object	Optional. JSON object key-value pair with the following properties:- top\_n: Number. If provided, returns the top results, up to the specified number of predictions. - apply\_threshold: Boolean. Checks the threshold value for the solution and applies it to the result set. The threshold value is solution threshold for similarity or class-level threshold for classification. Default value is true. - custom\_results\_filter: String. Similarity solutions only. Specifies the allowed set from which results are returned using an encoded query.

Type

Description

Array

JSON key-value pair containing the prediction result grouped by solution name and sorted by sys\_id or record\_number.- predictedValue: String. Value representing the prediction result. - predictedSysId: String. The sys\_id of the predicted value. Results can be from any table on which information is being predicted. - confidence: Number. Value of the confidence associated with the prediction. For example, 53.84. - threshold: Number. Value of the configured threshold associated with the prediction.

var solutionNames = ['soluton1', 'solution2'];

var input = new GlideRecord("incident");
input.get("0ef47232db801300864adfea5e961912");

// configure optional parameters
var options = {};
options.top_n = 3;
options.apply_threshold = false;

var mlSolutionUtil = new MLSolutionUtil();
var results = mlSolutionUtil.getPredictions(input, solutionNames, options);

// pretty print JSON results
gs.print(JSON.stringify(JSON.parse(results), null, 2));

Output:

{
  solution1:  {
    input_gr_sys_id1: [
                {
                    predictedValue : xxx,
                    predictedSysId : xx0,
                    confidence : xxx,
                    threshold : xxx

                }, 
                {
                    predictedValue : yyy,
                    predictedSysId : xx1,
                    confidence : xxx,
                    threshold : xxx
                }
        ],
    input_gr_sys_id2 : [
                {
                    predictedValue : xxx,
                    predictedSysId : xx0,
                    confidence : xxx,
                    threshold : xxx

                }, 
            ...
        ]
  }

  solution2:  {
      ...
}