AustraliaREFERENCE GUIDE

ClassificationSolutionVersion - Global

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ClassificationSolutionVersion- Global

The ClassificationSolutionVersion 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.

This API is used for working with solution versions based on ClassificationSolution API objects in the ClassificationSolution store.

The system creates a solution version each time you train a solution definition. Most versions are created during scheduled solution training.

Methods in this API are accessible using the following ClassificationSolution - Global methods:

Parent Topic:Server API reference

ClassificationSolutionVersion - getPredictionPerformance(Object options)

Calculates solution precision and coverage.

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

Name

</th><th>

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

options

</td><td>

Object

</td><td>

Selects a metric to retrieve within a span of dates. For more information, see Configuring target metrics. { "fromDate": "String", "metricName": "String", "toDate": "String" }

</td></tr><tr><td>

options.fromDate

</td><td>

String

</td><td>

Optional. Start date from which to retrieve metrics in system date time format.

</td></tr><tr><td>

options.metricName

</td><td>

String

</td><td>

Name of the metric.Valid values:

precision
coverage

</td></tr><tr><td>

options.toDate

</td><td>

String

</td><td>

Optional. End date from which to retrieve metrics.Formnt: System date time format.

</td></tr></tbody> </table><table id=“table_hby_x2w_plb” class=“returns”><thead><tr><th>

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

Object

</td><td>

Parseable JavaScript object containing the results for the input metric. { "metricName" : "String", "metricValue" : "String", "numberOfPredictionsConsidered" : "String" }

</td></tr><tr><td>

Object.metricName

</td><td>

Name of metric selected as input.Data type: String

</td></tr><tr><td>

Object.metricValue

</td><td>

Value of the selected metric.Data type: String

</td></tr><tr><td>

Object.numberOfRecordsConsidered

</td><td>

Number of records in the Predictor Results [ml_predictor_results] table considered for calculations.Data type: String

</td></tr></tbody> </table>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.

Name Type Description None

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

Object

</td><td>

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} }

</td></tr><tr><td>

</td><td>

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"
}

</td></tr><tr><td>

<Object>.<class name>.precision

</td><td>

Estimated solution precision metric for this class.Data type: String (numerical value)

</td></tr><tr><td>

<Object>.<class name>.coverage

</td><td>

Estimated solution coverage metric for this class.Data type: String (numerical value)

</td></tr><tr><td>

<Object>.<class name>.recall

</td><td>

Estimated solution recall metric for this class.Data type: String (numerical value)

</td></tr></tbody> </table>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.

Name Type Description None

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

Object

</td><td>

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" }

</td></tr><tr><td>

<Object>.algorithmConfig

</td><td>

Method for encoding the solution. Data type: Object

</td></tr><tr><td>

<Object>.algorithmConfig.algorithm

</td><td>

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

</td></tr><tr><td>

<Object>.algorithmConfig.targetClassRecall

</td><td>

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

</td></tr><tr><td>

<Object>.datasetProperties

</td><td>

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

Data type: Object

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

</td></tr><tr><td>

<Object>.datasetProperties.tableName

</td><td>

Name of the table for the dataset. For example, "tableName" : "Incident". Data type: String

</td></tr><tr><td>

<Object>.datasetProperties.fieldNames

</td><td>

List of field names from the specified table as strings. For example, "fieldNames" : ["short_description", "priority"]. Data type: Array

</td></tr><tr><td>

<Object>.datasetProperties.fieldNames.fieldDetails

</td><td>

List of JavaScript objects that specify field properties. Data type: Array of Objects

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

</td></tr><tr><td>

<Object>.datasetProperties.fieldNames.fieldDetails.<object>.name

</td><td>

Name of the field defining the type of information to restrict this dataset to. Data type: String

</td></tr><tr><td>

<Object>.datasetProperties.fieldDetails.<object>.type

</td><td>

Machine-learning field type. Data type: String

</td></tr><tr><td>

<Object>.datasetProperties.fieldDetails.encodedQuery

</td><td>

Encoded query string in the standard platform format. See Encoded query strings.Data type: String.

</td></tr><tr><td>

<Object>.domainName

</td><td>

Domain name associated with this dataset. See Domain separation and Predictive Intelligence.Type: String

</td></tr><tr><td>

<Object>.encoder

</td><td>

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

</td></tr><tr><td>

<Object>.groupByFieldName

</td><td>

Field name by which the system groups records to build classification solutions. Data type: String

</td></tr><tr><td>

<Object>.inputFieldNames

</td><td>

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

</td></tr><tr><td>

<Object>.isActive

</td><td>

Flag that indicates whether this version is active.Valid values:

true: Version is active.
false: Version is not active.

Data type: String

</td></tr><tr><td>

<Object>.label

</td><td>

Identifies the prediction task. { "label": "my first prediction" }

Data type: String

</td></tr><tr><td>

<Object>.name

</td><td>

System-assigned name. Data type: String

</td></tr><tr><td>

<Object>.predictedFieldName

</td><td>

Identifies a field to be trained for predictability. Data type: String

</td></tr><tr><td>

<Object>.processingLanguage

</td><td>

Processing language in two-letter ISO 639-1 language code format. Data type: String

</td></tr><tr><td>

<Object>.scope

</td><td>

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

</td></tr><tr><td>

<Object>.stopwords

</td><td>

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

</td></tr><tr><td>

<Object>.trainingFrequency

</td><td>

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

</td></tr><tr><td>

<Object>.versionNumber

</td><td>

Version number of the ClassificationSolution object.Data type: String

</td></tr></tbody> </table>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.

Name

</th><th>

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

includeDetails

</td><td>

Boolean

</td><td>

Flag that indicates whether to return status details.Valid values:

true: Return additional details.
false: Don’t return additional details.

Default: False

</td></tr></tbody> </table><table id=“table_yfh_vbw_plb” class=“returns”><thead><tr><th>

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

Object

</td><td>

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} }

</td></tr><tr><td>

<Object>.state

</td><td>

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

</td></tr><tr><td>

<Object>.hasJobEnded

</td><td>

Flag that indicates whether training is complete.Valid values:

true: Training is complete.
false: Training is incomplete.

Data type: Boolean value as a String

</td></tr><tr><td>

<Object>.percentComplete

</td><td>

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

</td></tr><tr><td>

<Object>.details

</td><td>

Object containing a list of additional training details.Data type: Object

</td></tr></tbody> </table>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.

Name Type Description None

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

Object

</td><td>

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} }

</td></tr><tr><td>

<Object>.includedClasses

</td><td>

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]
    },

</td></tr><tr><td>

<Object>.includedClasses.<class name>.distribution

</td><td>

Value of the outcome probability distribution for this class.Data type: String

</td></tr><tr><td>

<Object>.includedClasses.<class name>.statistics

</td><td>

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"
  }
]

</td></tr><tr><td>

<Object>.includedClasses.<class name>.statistics.coverage

</td><td>

Estimated solution coverage metric for this class.Data type: String (numerical value)

</td></tr><tr><td>

<Object>.includedClasses.<class name>.statistics.precision

</td><td>

Estimated solution precision metric for this class.Data type: String (numerical value)

</td></tr><tr><td>

<Object>.includedClasses.<class name>.statistics.recall

</td><td>

Estimated solution recall metric for this class.Data type: String (numerical value)

</td></tr><tr><td>

<Object>.includedClasses.<class name>.statistics.selected

</td><td>

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

</td></tr><tr><td>

<Object>.excludedClasses

</td><td>

Distribution and row count of all the classes excluded from training.Data type: Object

  "excludedClasses": {
    "<class name>": {
      "distribution": "0.11",
      "rowCount": "9"
    }

</td></tr><tr><td>

<Object>.excludedClasses.<class name>.distribution

</td><td>

Value of the outcome probability distribution for this class.Data type: String

</td></tr><tr><td>

<Object>.excludedClasses.<class name>.rowCount

</td><td>

Number of rows excluded from training.Data type: String (numerical value)

</td></tr></tbody> </table>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.

Name Type Description None 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.

Name

</th><th>

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

input

</td><td>

Object

</td><td>

GlideRecord or array of JSON objects containing field names and values as key-value pairs.

</td></tr><tr><td>

options

</td><td>

Object

</td><td>

Optional values for filtering prediction results. { "apply_threshold": Boolean, "top_n": Number }

</td></tr><tr><td>

options.apply_threshold

</td><td>

Boolean

</td><td>

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

</td></tr><tr><td>

options.top_n

</td><td>

Number

</td><td>

If provided, returns the top results, up to the specified number of predictions.

</td></tr></tbody> </table><table id=“table_xwz_pbw_plb” class=“returns”><thead><tr><th>

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

Object

</td><td>

JSON object containing the prediction results sorted by sys_id or record_number. { <identifier>: [Array] }

</td></tr><tr><td>

</td><td>

List of objects with details for each prediction result.Data type: Array of Objects

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

</td></tr><tr><td>

<Object>.<identifier>.<object>.confidence

</td><td>

Value of the confidence associated with the prediction. For example, 53.84. Data type: Number

</td></tr><tr><td>

<Object>.<identifier>.<object>.predictedSysId

</td><td>

The sys_id of the predicted value. Results can be from any table on which information is being predicted. Data type: String

</td></tr><tr><td>

<Object>.<identifier>.<object>.predictedValue

</td><td>

Value representing the prediction result. Data type: String

</td></tr><tr><td>

<Object>.<identifier>.<object>.threshold

</td><td>

Value of the configured threshold associated with the prediction. Data type: Number

</td></tr></tbody> </table>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.

Name

</th><th>

Type

</th><th>

Description

</th></tr></thead><tbody><tr><td>

options

</td><td>

Object

</td><td>

Metric configuration values. For information about these features, see Configuring target metrics. { "metricName" : "String", "metricValue" : "String", "className" : "String" }

</td></tr><tr><td>

options.metricName

</td><td>

String

</td><td>

Name of the metric to set.Valid values:

coverage
precision
recall

</td></tr><tr><td>

options.metricValue

</td><td>

String

</td><td>

Numeric value to assign to the metric.

</td></tr><tr><td>

options.className

</td><td>

String

</td><td>

Optional. Name of the class to restrict results to. Use the getTrainingStatistics() method to retrieve a complete list of classes for a solution.

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);

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