Name
sn_hr_sp.HR_MLPortalUtilsSNC
Description
No description available
Script
var HR_MLPortalUtilsSNC = Class.create();
HR_MLPortalUtilsSNC.prototype = {
initialize: function() {},
/* getRecommendedArticles : Fetches the topN similar Knowledge artciles for a task based on the Machine Learning predictions.The first time when task is loaded , a call is made to ML prediction server to fetch the similar articles, these results are stored in ml_predictor_results table.Hence when the task is loaded from second time onwards the results are fetched from ml_predictor_results table
Parameters: 1) taskId: SysId of the task
2) tableName: Name of the task table
3) knowledgeBases : List of all knowledge bases supported by the portal in which the recommended articles are shown
4) solution : Solution name of the ML Solution definition
5) topN : Number of articles to be displayed
*/
getRecommendedArticles: function(taskId, tableName, knowledgeBases, solution, topN) {
var versioningEnabled = GlidePluginManager.isActive('com.snc.knowledge_advanced') && gs.getProperty("glide.knowman.versioning.enabled", "true") === "true";
var knowledgeArticles = [];
var count = 0;
var UserProfileRecommendationUtil = new sn_hr_sp.UserRecommendationUtil();
var articleDetails = {};
var task = new GlideRecord(tableName);
var results = {};
var predictorResults = new GlideRecord("ml_predictor_results");
var kbArticle = [];
var predictedConfidenceTopNValue;
var predictedConfidenceTopNList = [];
var predictedOutputArray = [];
var solutionGr = new GlideRecord('ml_solution');
solutionGr.addQuery('solution_name', solution);
solutionGr.addActiveQuery();
solutionGr.setLimit(1);
solutionGr.query();
if (solutionGr.next()) {
var thresholdLimit = solutionGr.threshold;
if (task.get(taskId)) {
predictorResults.addQuery('source_sys_id', taskId);
predictorResults.addQuery('solution', solutionGr.getUniqueValue());
predictorResults.orderByDesc('sys_created_on');
predictorResults.setLimit(1);
predictorResults.query();
if (predictorResults.next()) {
if (predictorResults.predicted_confidence < thresholdLimit)
return null;
predictedConfidenceTopNValue = predictorResults.predicted_confidence_topn;
predictedConfidenceTopNList = predictedConfidenceTopNValue.split(',');
results = predictorResults.predicted_output_value;
predictedOutputArray = results.split(",");
for (var i = 0; i < predictedConfidenceTopNList.length; i++) {
if (predictedConfidenceTopNList[i] < thresholdLimit)
break;
kbArticle.push(predictedOutputArray[i]);
}
} else if (tableName == "sn_ca_campaign_item") {
try {
var mlSolution = sn_ml.MLSolutionFactory.getSolution(solution);
var inputs = [{"short_description": String(task.content.title)}];
var options = {};
options.top_n = topN;
options.apply_threshold = true;
results = mlSolution.predict(inputs, options);
if (gs.nil(results))
return null;
var resultsJson = JSON.parse(results);
} catch (e) {
gs.error(e);
return null;
}
if (resultsJson) {
for(var o in resultsJson) {
for(var i in resultsJson[o]) {
kbArticle.push(resultsJson[o][i].predictedValue);
}
}
}
}
else {
try {
var mlSolution = sn_ml.SimilaritySolutionStore.get(solution);
var options = {};
options.top_n = topN;
options.mluc = 'MLUC-HR-00005';
options.apply_threshold = true;
results = mlSolution.getActiveVersion().predict(task, options);
if (gs.nil(results))
return null;
var resultsJson = JSON.parse(results);
} catch (e) {
gs.error(e);
return null;
}
predictedOutputArray = resultsJson[taskId];
if(!gs.nil(predictedOutputArray)) {
for (var j = 0; j < predictedOutputArray.length; j++)
kbArticle.push(predictedOutputArray[j].predictedValue);
}
}
if (gs.nil(kbArticle))
return null;
for (var j = 0; j < kbArticle.length; j++) {
articleDetails = {};
articleDetails = UserProfileRecommendationUtil.checkAndReturnAccessibleArticle(kbArticle[j], knowledgeBases, versioningEnabled);
if (!gs.nil(articleDetails)) {
knowledgeArticles.push(articleDetails);
++count;
if (count == topN)
break;
}
}
return knowledgeArticles;
} else {
gs.error(" Specified Task does not exist ");
return null;
}
} else {
gs.error("Solution GlideRecord does not exist, please train the solution definition.");
return null;
}
},
/* getSimilarUsers : To get the list of similar users for the logged in user based on similar HR profiles
Parameters: 1) userId: userId of the logged in user
2) topN: gets the topN similar users
*/
getSimilarUsers: function(userId, topN) {
var similarUsers = [];
var profiles = this.getSimilarProfiles(userId, topN);
if (!gs.nil(profiles)) {
var userProfile = new GlideRecord('sn_hr_core_profile');
for (var i = 0; i < profiles.length; i++) {
if (userProfile.get(profiles[i]))
similarUsers.push(userProfile.getValue('user'));
}
}
return similarUsers;
},
/* getSimilarProfiles:Gets the similar HR profiles of the logged in user.The first time when user is logged in , a call is made to ML prediction server to fetch the similar profiles, these results are stored in ml_predictor_results table.Hence when the user is logged in from second time onwards results are fetched from ml_predictor_results table
Parameters: 1) userId: userId of the logged in user
2) topN: gets the topN similar HR Profiles
*/
getSimilarProfiles: function(userId, topN) {
var predictedConfidenceTopNValue;
var predictedConfidenceTopNList = [];
var predictedOutputArray = [];
var similarProfiles = [];
var results = {};
var currentDomain;
var user = new GlideRecord('sys_user');
if (user.get(userId))
currentDomain = user.sys_domain;
var profile = new GlideRecord('sn_hr_core_profile');
profile.addQuery('user', userId);
profile.setLimit(1);
profile.query();
if (profile.next()) {
/*Get the solution definition from HR AI Configuration */
var config = this.fetchHRAiConfig('similar_users', currentDomain);
if (!config.next()) {
config = this.fetchHRAiConfig('similar_users', 'global');
if (!config.next())
return;
}
if (config.solution_capability_definition.active) {
var solution = config.solution_capability_definition.solution_name;
var solutionGr = new GlideRecord('ml_solution');
solutionGr.addQuery('solution_name', solution);
solutionGr.addActiveQuery();
solutionGr.setLimit(1);
solutionGr.query();
if (solutionGr.next()) {
var thresholdLimit = solutionGr.threshold;
var predictorResults = new GlideRecord('ml_predictor_results');
predictorResults.addQuery('source_sys_id', profile.getUniqueValue());
predictorResults.addQuery('solution', solutionGr.getUniqueValue());
predictorResults.orderByDesc('sys_created_on');
predictorResults.setLimit(1);
predictorResults.query();
if (predictorResults.next()) {
/* If the predicted confidence of the ML result is less than the threshold limit set for the solution return null*/
if (predictorResults.predicted_confidence < thresholdLimit)
return null;
/*If ML Predictor table has the latest solution only then query the ML predictor else repredict the value for the latest results */
if (predictorResults.solution.version == config.solution_capability_definition.current_solution_version) {
predictedConfidenceTopNValue = predictorResults.predicted_confidence_topn;
predictedConfidenceTopNList = predictedConfidenceTopNValue.split(',');
results = predictorResults.predicted_output_value;
predictedOutputArray = results.split(",");
for (var i = 0; i < predictedConfidenceTopNList.length; i++) {
if (predictedConfidenceTopNList[i] < thresholdLimit)
break;
similarProfiles.push(predictedOutputArray[i]);
}
return similarProfiles;
} else {
return this.predictSimilarProfiles(profile, solution, topN);
}
} else {
return this.predictSimilarProfiles(profile, solution, topN);
}
} else {
gs.error("Solution GlideRecord does not exist, please train the solution definition.");
return null;
}
} else {
gs.error("Please provide a solution defintion for similar users in the HR AI Configuration");
return null;
}
} else
return null;
},
/* predictSimilarProfiles: Returns the similar profiles of a HR Profile based on ML prediction
Parameters: 1) profile: HR Profile Record for which the similar records need to be fetched
2) solution : Name of the solution of the corresponding ML solution definition defined for fetching similar HR Profiles
2) topN: gets the topN similar profiles
*/
predictSimilarProfiles: function(profile, solution, topN) {
var mlSolution = sn_ml.SimilaritySolutionStore.get(solution);
var similarProfiles = [];
var results = {};
try {
var options = {};
options.mluc = "MLUC-HR-00007";
if (gs.nil(topN))
options.top_n = 25;
else
options.top_n = topN;
options.apply_threshold = true;
results = mlSolution.getActiveVersion().predict(profile, options);
var resultsJson = JSON.parse(results);
} catch (e) {
gs.error(e);
return null;
}
var pedictedResults = resultsJson[profile.getUniqueValue()];
if(!gs.nil(pedictedResults)) {
for (var i = 0; i < pedictedResults.length; i++)
similarProfiles.push(pedictedResults[i].predictedValue);
}
return similarProfiles;
},
/* fetchHRAiConfig : Returns the HR AI Configuration Record for the use case specified based on domain
Parmenters: 1) useCase : Use Case specified in HR AI Confoguration table
2) domain : Current domain of the user
*/
fetchHRAiConfig: function(useCase, domain) {
try {
var hrAIConfiguration = new GlideRecord('sn_hr_core_ai_configuration');
hrAIConfiguration.addQuery('use_case', useCase);
hrAIConfiguration.addQuery('sys_domain', domain);
hrAIConfiguration.setLimit(1);
hrAIConfiguration.query();
return hrAIConfiguration;
} catch (err) {
gs.error('Error in fetching HR AI configuration: ' + err.toString());
}
},
type: 'HR_MLPortalUtilsSNC'
};Sys ID
c2affdd2db1b00101e2ef9741d9619e4