{
  "datasetProperties": {Object},
  "domainName": "String",
  "encoder": {Object},  
  "inputFieldNames": [Array],
  "isActive": Boolean,
  "label": "String",
  "name": "String",
  "predictedFieldName": "String",
  "predictedInterval": [Array],
  "processingLanguage": "String",
  "scope": "String",
  "stopwords": [Array],
  "versionNumber": "Number"
}

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

[
  {
    "name": "String",
    "type": "String"
  }
]

{
  "label": "my first prediction"
}

次の例では、ストア内のアクティブなオブジェクトバージョンのプロパティを取得します。

// Get properties
var mlSolution = sn_ml.RegressionSolutionStore.get('ml_incident_categorization');

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

出力：

{
  "datasetProperties": {
    "encodedQuery": "",
    "fieldNames": [
      "short_description",
      "sourcedc",
      "targetdc",
      "dbsize",
      "duration"
    ],
    "tableName": "cloudinfratext"
  },
  "domainName": "global",
  "encoderProperties": {
    "datasetsProperties": [],
    "name": "wc_regression"
  },
  "inputFieldNames": [
    "short_description",
    "sourcedc",
    "targetdc",
    "dbsize"
  ],
  "isActive": "true",
  "label": "Regression Test for DB Restore",
  "name": "ml_x_snc_global_global_regression",
  "predictedFieldName": "duration",
  "processingLanguage": "en",
  "stopwords": [
    "Default English Stopwords"
  ],
  "versionNumber": "1"
}

{
  "state": "String",
  "percentComplete": "Number as a String",
  "hasJobEnded": "Boolean value as a String",
  "details": {Object}
}

次の例は、トレーニングが完了した正常な結果を示しています。

// Get status
var mlSolution = sn_ml.RegressionSolutionStore.get('ml_incident_categorization');

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

出力：

{
 "state":"solution_complete",
 "percentComplete":"100",
 "hasJobEnded":"true",
 "details":{"stepLabel":"Solution Complete"} // This information is only returned if getStatus(true);
}

次の例は、トレーニング完了の失敗の結果を示しています。

// Get status
var solutionName = 'ml_x_snc_global_global_regression_solution';
var mlSolution = sn_ml.RegressionSolutionStore.get(solutionName);
var trainingStatus = mlSolution.getLatestVersion().getStatus();

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

出力：

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

次の例は、バージョン番号を取得する方法を示しています。

// Get version number
var mlSolution = sn_ml.RegressionSolutionStore.get('ml_incident_categorization');

gs.print("Version number: "+JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getVersionNumber()), null, 2));

出力：

Version number: 1

{
  "apply_threshold": Boolean,
  "top_n": Number
}

{
    <identifier>: [Array]
}

<identifier>: [
  {
    "confidence": Number,
    "predictedSysId": "String",
    "predictedValue": "String", 
    "threshold": Number
  }
]

次の例は、入力に GlideRecord をsys_id別に取得し、上位 3 件の結果に制限し、しきい値を除外するオプションのパラメーターを含む predict() メソッドの予測結果を表示する方法を示しています。

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

// single GlideRecord input
var input = new GlideRecord("incident");
input.get("<sys_id>");

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

var results = mlSolution.getVersion(1).predict(input, options);
// pretty print JSON results
gs.print(JSON.stringify(JSON.parse(results), null, 2));

{
        "<sys_id/now_GR>": [
    {
      "confidence": 62.10782320780268,
      "threshold": 20.36,
      "predictedValue": "Clone Issues",
      "predictedSysId": ""
    },
    {
      "confidence": 6.945237375770391,
      "threshold": 16.63,
      "predictedValue": "Instance Administration",
      "predictedSysId": ""
    },
    {
      "confidence": 5.321061076300759,
      "threshold": 23.7,
      "predictedValue": "Administration",
      "predictedSysId": ""
    }
  ]
}

次の例は、フィールド名のアレイを入力のキーと値のペアとして受け取り、上位 3 つの結果に制限し、しきい値を除外するオプションのパラメーターを含む predict() メソッドの予測結果を表示する方法を示しています。

var mlSolution = sn_ml.RegressionSolutionStore.get('ml_incident_categorization');
// key-value pairs input
var input = [{"short_description":"my email is not working"}, {short_description:"need help with password"}];
// configure optional parameters
var options = {};
options.top_n = 3;
options.apply_threshold = false;
var results = mlSolution.predict(input, options);
// pretty print JSON results
gs.print(JSON.stringify(JSON.parse(results), null, 2));

{
  "1": [
    {
      "confidence": 37.5023032262591,
      "threshold": 10.72,
      "predictedValue": "Authentication",
      "predictedSysId": ""
    },
    {
      "confidence": 24.439964862166583,
      "threshold": 23.7,
      "predictedValue": "Administration",
      "predictedSysId": ""
    },
    {
      "confidence": 11.736320486031047,
      "threshold": 100,
      "predictedValue": "Security",
      "predictedSysId": ""
    }
  ],
  "2": [
    {
      "confidence": 99,
      "threshold": 17.77,
      "predictedValue": "Email",
      "predictedSysId": ""
    },
    {
      "confidence": 3.182137005157543,
      "threshold": 10.72,
      "predictedValue": "Authentication",
      "predictedSysId": ""
    },
    {
      "confidence": 2.8773826570713514,
      "threshold": -1,
      "predictedValue": "Email (I/f)",
      "predictedSysId": ""
    }
  ]
}