ClassificationSolutionVersion

ClassificationSolutionVersion - getPredictionPerformance(Object options)

Calculates solution precision and coverage.

To use this method, you must first create settings using the setPredictionSettings() method.

표 1. Parameters
Name	Type	Description
options	Object	Selects a metric to retrieve within a span of dates. For more information, see Configuring target metrics. `{ "fromDate": "String", "metricName": "String", "toDate": "String" }`
options.fromDate	String	Optional. Start date from which to retrieve metrics in system date time format.
options.metricName	String	Name of the metric. Valid values: precision coverage
options.toDate	String	Optional. End date from which to retrieve metrics. Formnt: System date time format.

표 2. Returns
Type	Description
Object	Parseable JavaScript object containing the results for the input metric. `{ "metricName" : "String", "metricValue" : "String", "numberOfPredictionsConsidered" : "String" }`
Object.metricName	Name of metric selected as input. Data type: String
Object.metricValue	Value of the selected metric. Data type: String
Object.numberOfRecordsConsidered	Number of records in the Predictor Results [ml_predictor_results] table considered for calculations. Data type: String

The following example shows how to get a precision value from the active version of a solution.

// Get precision value
var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_incident_categorization');

gs.print(JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getPredictionPerformance({"metricName": "precision"})), null, 2));

Output:

{
  "metricName": "precision",
  "metricValue": "70.10",
  "numberOfPredictionsConsidered": "10"
}

ClassificationSolutionVersion - getPredictionSettings()

Gets estimated precision, estimated coverage, estimated recall values from the ML Solutions [ml_solution] table and records from the Class Confidence [ml_class] table.

표 3. Parameters
Name	Type	Description
None

표 4. Returns
Type	Description
Object	Returns prediction settings for each class. Data includes precision, coverage, recall, and distribution values of all classes included in the trained model from the ML Solutions [ml_solution] table. For details, see Predictive model components. `{ "<class name>": {Object} }`
<Object>.<class name>	Training statistics data from the Class Confidence [ml_class] table. For more information, see Configuring target metrics. Data type: Object `{ "precision": "String", "coverage": "String", "recall": "String" }`
<Object>.<class name>.precision	Estimated solution precision metric for this class. Data type: String (numerical value)
<Object>.<class name>.coverage	Estimated solution coverage metric for this class. Data type: String (numerical value)
<Object>.<class name>.recall	Estimated solution recall metric for this class. Data type: String (numerical value)

The following example shows how to get prediction settings of an active solution version.

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

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

Output:

{
  "solutionSettings": {
    "precision": "61.69",
    "coverage": "66.75",
    "recall": "57.54"
  },
  "classSettings": {
    "Approvals": {
      "precision": "100",
      "coverage": "0",
      "recall": "0"
    },
    "Authentication": {
      "precision": "38.71",
      "coverage": "98.44",
      "recall": "56.25"
    },
    "Automation": {
      "precision": "100",
      "coverage": "0",
      "recall": "0"
    }, ...

ClassificationSolutionVersion - getProperties()

Gets solution object properties and version number.

표 5. Parameters
Name	Type	Description
None

표 6. Returns
Type	Description
Object	Contents of the Dataset and ClassificationSolution version details. Results vary by object property setup. `{ "algorithmConfig": { "algorithm": "String", "targetClassRecall": "String" }, "datasetProperties": {Object}, "domainName": "String", "encoder": {Object}, "groupByFieldName": "String", "inputFieldNames": [Array], "isActive": Boolean, "label": "String", "name": "String", "predictedFieldName": "String", "processingLanguage": "String", "scope": "String", "stopwords": [Array], "stopwords": [Array], "trainingFrequency": "String", "versionNumber": "Number" }`
<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. Data type: Object `{ "encodedQuery": "String", "fieldDetails": [Array], "fieldNames": [Array], "tableName": "String" }`
<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. Data type: Array of Objects `[ { "name": "String", "type": "String" } ]`
<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>.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>.isActive	Flag that indicates whether this version is active. Valid values: true: Version is active. false: Version is not active. 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
<Object>.versionNumber	Version number of the ClassificationSolution object. Data type: String

The following example gets properties of the active object version in the store.

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

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

Output:

*** Script: {
  "datasetProperties": {
    "encodedQuery": "activeANYTHING^EQ",
    "fieldNames": [
      "short_description",
      "category"
    ],
    "tableName": "incident"
  },
  "domainName": "global",
  "inputFieldNames": [
    "short_description"
  ],
  "isActive": "true",
  "label": "Incident Categorization_Trainer",
  "name": "ml_incident_categorization",
  "predictedFieldName": "category",
  "processingLanguage": "en",
  "stopwords": [
    "Default English Stopwords"
  ],
  "versionNumber": "1"
}

ClassificationSolutionVersion - getStatus(Boolean includeDetails)

Gets training completion status.

표 7. Parameters
Name	Type	Description
includeDetails	Boolean	Flag that indicates whether to return status details. Valid values: true: Return additional details. false: Don't return additional details. Default: False

표 8. Returns
Type	Description
Object	JavaScript object containing training status information for a ClassificationSolution object. `{ "state": "String", "percentComplete": "Number as a String", "hasJobEnded": "Boolean value as a String", "details": {Object} }`
<Object>.state	Training completion state. If the training job reaches a terminal state, the job does not leave that state. If the state is terminal, the hasJobEnded property is set to `true`. Possible values: `fetching_files_for_training` `preparing_data` `retry` `solution_cancelled` (terminal) `solution_complete` (terminal) `solution_error` (terminal) `solution_incomplete` `training_request_received` `training_request_timed_out` (terminal) `training_solution` `uploading_solution` `waiting_for_training` Data type: String
<Object>.hasJobEnded	Flag that indicates whether training is complete. Valid values: true: Training is complete. false: Training is incomplete. Data type: Boolean value as a String
<Object>.percentComplete	Training percent complete. If the completion percentage is less than 100, the job might be in a terminal state. For example, if training times out. Data type: Number as a String Range: 0 thru 100
<Object>.details	Object containing a list of additional training details. Data type: Object

The following example shows a successful result with training complete.

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

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

Output:

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

The following example shows an unsuccessful result with training complete.

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

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

Output:

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

ClassificationSolutionVersion - getTrainingStatistics()

Gets all the training statistics for a given solution.

표 9. Parameters
Name	Type	Description
None

표 10. Returns
Type	Description
Object	JavaScript object with statistics for each class included in training and values for each class that was excluded from training. For details, see Predictive model components. `{ "includedClasses": {Object}, "excludedClasses": {Object} }`
<Object>.includedClasses	Object containing precision, coverage, recall, and distribution values of each class in the trained model. Data type: Object `"includedClasses": { "<class name>": { "distribution": "String", "statistics": [Array] },`
<Object>.includedClasses.<class name>.distribution	Value of the outcome probability distribution for this class. Data type: String
<Object>.includedClasses.<class name>.statistics	List of objects providing training statistics data. For more information, see Configuring target metrics. Data type: Array of Objects `[ { "coverage": "String", "precision": "String", "recall": "String", "selected": "String" } ]`
<Object>.includedClasses.<class name>.statistics.coverage	Estimated solution coverage metric for this class. Data type: String (numerical value)
<Object>.includedClasses.<class name>.statistics.precision	Estimated solution precision metric for this class. Data type: String (numerical value)
<Object>.includedClasses.<class name>.statistics.recall	Estimated solution recall metric for this class. Data type: String (numerical value)
<Object>.includedClasses.<class name>.statistics.selected	Flag that indicates whether this class contains records included in solution training. Valid values: true: This class contains records selected for training the solution. false: This class is excluded from training. Data type: String (Boolean value) Default: false
<Object>.excludedClasses	Distribution and row count of all the classes excluded from training. Data type: Object `"excludedClasses": { "<class name>": { "distribution": "0.11", "rowCount": "9" }`
<Object>.excludedClasses.<class name>.distribution	Value of the outcome probability distribution for this class. Data type: String
<Object>.excludedClasses.<class name>.rowCount	Number of rows excluded from training. Data type: String (numerical value)

The following example shows how to get training statistics from the active solution.

// Get training stats
var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_incident_categorization');

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

Output:

{
  "includedClasses": {
    "Approvals": {
      "distribution": "0.43",
      "statistics": [
        {
          "precision": "100",
          "coverage": "0",
          "recall": "0",
          "selected": "true"
        }
      ]
    }, 
    "Authentication": {
      "distribution": "4.3",
      "statistics": [
        {
          "precision": "39.33",
          "coverage": "95.31",
          "recall": "54.69",
          "selected": "false"
        },
        {
          "precision": "100",
          "coverage": "0",
          "recall": "0",
          "selected": "false"
        },
     ...
    }
    ...
  "excludedClasses": {
    "ACE": {
      "distribution": "0.11",
      "rowCount": "9"
    },
    "AHA": {
      "distribution": "0.01",
      "rowCount": "1"
    }, 
    ...
}

ClassificationSolutionVersion - getVersionNumber()

Gets the version number of a solution object.

표 11. Parameters
Name	Type	Description
None

표 12. Returns
Type	Description
String	Version number.

The following example shows how to get a version number.

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

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

Output:

Version number: 1

ClassificationSolutionVersion - predict(Object input, Object options)

Gets the input data for a prediction.

표 13. Parameters
Name	Type	Description
input	Object	GlideRecord or array of JSON objects containing field names and values as key-value pairs.
options	Object	Optional values for filtering prediction results. `{ "apply_threshold": Boolean, "top_n": Number }`
options.apply_threshold	Boolean	Flag that indicates whether to check the threshold value for the solution and apply it to the result set. Valid values: true: Return results in which confidence is greater than threshold. false: Return all results. Default: True
options.top_n	Number	If provided, returns the top results, up to the specified number of predictions.

표 14. Returns
Type	Description
Object	JSON object containing the prediction results sorted by sys_id or record_number. `{ <identifier>: [Array] }`
<Object>.<identifier>	List of objects with details for each prediction result. Data type: Array of Objects `<identifier>: [ { "confidence": Number, "predictedSysId": "String", "predictedValue": "String", "threshold": Number } ]`
<Object>.<identifier>.<object>.confidence	Value of the confidence associated with the prediction. For example, 53.84. Data type: Number
<Object>.<identifier>.<object>.predictedSysId	The sys_id of the predicted value. Results can be from any table on which information is being predicted. Data type: String
<Object>.<identifier>.<object>.predictedValue	Value representing the prediction result. Data type: String
<Object>.<identifier>.<object>.threshold	Value of the configured threshold associated with the prediction. Data type: Number

The following example shows how to display prediction results for a predict() method that takes a GlideRecord by sys_id for input and includes optional parameters to restrict to top three results and exclude the threshold value.

var mlSolution = sn_ml.ClassificationSolutionStore.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));

Output:

{
  "<sys_id/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": ""
    }
  ]
}

The following example shows how to display prediction results for a predict() method that takes an array of field names as key-value pairs for input and includes optional parameters to restrict to top three results and exclude the threshold value.

var mlSolution = sn_ml.ClassificationSolutionStore.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));

Output:

{
  "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": ""
    }
  ]
}

ClassificationSolutionVersion - setPredictionSettings(Object options)

Sets precision, coverage, or recall values at solution level or class level.

표 15. Parameters
Name	Type	Description
options	Object	Metric configuration values. For information about these features, see Configuring target metrics. `{ "metricName" : "String", "metricValue" : "String", "className" : "String" }`
options.metricName	String	Name of the metric to set. Valid values: coverage precision recall
options.metricValue	String	Numeric value to assign to the metric.
options.className	String	Optional. Name of the class to restrict results to. Use the getTrainingStatistics() method to retrieve a complete list of classes for a solution.

표 16. Returns
Type	Description
None

The following example shows how to set a precision metric to 89.5 for a class named Clone Issues.

var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');
var input = {"metricName" : "precision", "metricValue" : "89.5", "className" : "Clone Issues"};

mlSolution.getActiveVersion().setTrainingStatistics(input);

ClassificationSolutionVersion - Global

Australia API Reference

ClassificationSolutionVersion - Global

ClassificationSolutionVersion - getPredictionPerformance(Object options)

ClassificationSolutionVersion - getPredictionSettings()

ClassificationSolutionVersion - getProperties()

ClassificationSolutionVersion - getStatus(Boolean includeDetails)

ClassificationSolutionVersion - getTrainingStatistics()

ClassificationSolutionVersion - getVersionNumber()

ClassificationSolutionVersion - predict(Object input, Object options)

ClassificationSolutionVersion - setPredictionSettings(Object options)