Name
global.CMDBDynamicIREMLInterface
Description
No description available
Script
var CMDBDynamicIREMLInterface = Class.create();
CMDBDynamicIREMLInterface.prototype = {
_CMDB_DYNAMIC_IRE_MATCH: "cmdb_dynamic_ire_match",
_LOG_PROPERTY: "glide.identification_engine.ire_dynamic.log.level",
_ML_SOLUTION: "ml_solution",
_SOLUTION_NAME: "solution_name",
_ML_MODEL_ARTIFACT: "ml_model_artifact",
_TABLE_REF_SOLUTION: "solution",
_MODEL_ID: "model_id",
_DOT: ".",
_TEXTNODE: '#text',
_VERSION: "version",
_ACTIVE: "active",
_SYS_CREATED_ON: 'sys_created_on',
_XML_TAG_SOLUTION_NAME: "solutionName",
_XML_TAG_ALIAS_NAMES: "actualColumnNameToAliasColumnName",
_XML_TAG_CLASSIFIER: "m__Classifier",
_XML_TAG_NOMINAL_TO_BINARY: "m__NominalToBinary",
_XML_TAG_OUTPUT_FORMAT: "m__OutputFormat",
_XML_TAG_ATTRIBTUES: "m__Attributes",
_XML_TAG_TYPE: "m__Type",
_XML_TAG_NAME: "m__Name",
_XML_TAG_INDEX: "m__Index",
_XML_TAG_MODEL_WIGHTS: "w",
_XML_TAG_ALIAS_CLASS_VALUE_TO_ACTUAL_VALUE: "aliasClassValueToActualClassValue",
_XML_TAG_LABEL: "label",
_XML_TAG_ATTRIBUTE_INFO: "m__AttributeInfo",
_XML_TAG_HASH_TABLE: "m__Hashtable",
_XML_TAG_STRING: "string",
_XML_TAG_INT: "int",
_XML_TAG_BIAS_CLASS_AND_PROBABILITY: "biasClassAndProbability",
_BIAS: "bias",
_NO_MATCH: "0",
_MATCH: "1",
initialize: function(logProperty) {
if (JSUtil.nil(logProperty))
logProperty = this._LOG_PROPERTY;
this.log = new GSLog(logProperty, this.type);
},
/*
* Return the model metadate of the trained model
* @param mlSolutionName the soution for which the weights are required
* @param mlSolutionVersion the solution version for which the weights are required
* @return weights {status: "SUCCESS", weights: "{["feature_name":"weight"]}"}
*/
getModelMetaData: function(mlSolutionName, mlSolutionVersion) {
var result = {};
this._invalid.mlSolutionName = mlSolutionName;
this._invalid.result = result;
this._invalid.message = 'There was an issue while loading the model file from the ML platform tables for solution {0} and soutionVersion {1}';
this._invalid.log = this.log;
result.status = "SUCCESS";
try {
var gr = new GlideRecord(this._ML_SOLUTION);
gr.addQuery(this._SOLUTION_NAME, mlSolutionName);
gr.query();
if (!gr.next()) {
result.status = "FAILURE";
result.statusCode = "solution_unavailable";
return JSON.stringify(result);
}
this.log.logInfo('CMDBDynamicIREMLInterface::getModelMetaData The solution: ' + mlSolutionName + ' is available, successfully found.');
var solutionSysId = gr.getUniqueValue();
var modelFeatureNamesXML = this._getArtifactStream(solutionSysId, '0.gz', mlSolutionVersion);
if (this._invalid.node(modelFeatureNamesXML, 'model_features_xml_was_null'))
return JSON.stringify(result);
this.log.logInfo('CMDBDynamicIREMLInterface::getModelMetaData The modelFeatures XML was successfully loaded: ' + modelFeatureNamesXML);
var weightsXML = this._getArtifactStream(solutionSysId, mlSolutionName + '.gz', mlSolutionVersion);
if (this._invalid.node(weightsXML, 'model_features_weights_xml_was_null'))
return JSON.stringify(result);
this.log.logInfo('CMDBDynamicIREMLInterface::getModelMetaData The modelFeatureWeights XML was successfully loaded: ' + weightsXML);
var weightsResponse = this._extractWeightsFromModelFiles(mlSolutionName, modelFeatureNamesXML, weightsXML);
if (this._invalid.node(weightsResponse, 'model_features_weights_extraction_failed'))
return JSON.stringify(result);
//check if the status is Failure and update the main result response object
if (this._invalid_condition(weightsResponse.status == 'FAILURE', weightsResponse.statusCode))
return JSON.stringify(result);
if (weightsResponse.status == 'SUCCESS')
result.weights = weightsResponse;
} catch (ex) {
this.log.logErr('CMDBDynamicIREMLInterface::getModelMetaData msg: ' + ex);
}
return JSON.stringify(result);
},
_invalid: {
mlSolutionName: '',
result: {},
message: '',
log: {},
_log: function(code) {
this.result.status = 'FAILURE';
this.result.statusCode = code;
this.log.logErr(this.formatMessage(this.message, [this.mlSolutionName, code]));
},
iterator: function(iterator, code) {
return this.condition(gs.nil(iterator) || !iterator.hasNext(), code);
},
node: function(node, code) {
return this.condition(gs.nil(node), code);
},
condition: function(condition, code) {
if (condition) {
this._log(code);
return true;
}
return false;
},
formatMessage: function(str, args) {
this._replaceExpr = this._replaceExpr || [];
for (var i = 0; i < args.length; i++) {
if (i >= this._replaceExpr.length)
this._replaceExpr[i] = new RegExp('\\{' + i + '\\}', 'gi');
str = str.replace(this._replaceExpr[i], args[i]);
}
return str;
},
},
/*
@param mlSolutionName : The solution Name of the model
@param modelFeatureNamesXML : featureNames XML
@param weightsXML: the weights XML
*/
_extractWeightsFromModelFiles: function(mlSolutionName, modelFeatureNamesStream, weightsStream) {
var weights = {};
this._invalid.mlSolutionName = mlSolutionName;
this._invalid.result = weights;
this._invalid.message = 'There was an issue while loading the model weights from the ML platform tables. {0}, error {1}';
this._invalid.log = this.log;
try {
var featuresDoc = sn_cmdb.DynamicIREScriptableAPI.getScopedXMLDocument2(modelFeatureNamesStream);
var root = featuresDoc.getDocumentElement().getNodeName();
var solutionNameTag = featuresDoc.getFirstNode('/' + root + '/' + this._XML_TAG_SOLUTION_NAME);
if (this._invalid.node(solutionNameTag, 'solution_name_tag_not_present_check_with_ml_platform_team_if_version_has_changed'))
return weights;
var solutionName = solutionNameTag.getTextContent();
if (this._invalid.condition(solutionName != mlSolutionName, 'solution_name_passed_does_not_match_with_model_meta_data'))
return weights;
var aliasTag = featuresDoc.getFirstNode('/' + root + '/' + this._XML_TAG_ALIAS_NAMES);
if (this._invalid.node(aliasTag, 'alias_name_tag_not_present_check_with_ml_platform'))
return weights;
var aliasTagChildren = aliasTag.getChildNodeIterator();
if (this._invalid.iterator(aliasTagChildren, 'alias_name_tag_does_not_have_children_check_with_ml_platform'))
return weights;
var columnMap = {};
var aliasTagChildNode = null;
var stringNodes = null;
var i = 0;
while (aliasTagChildren.hasNext() && (aliasTagChildNode = aliasTagChildren.next()) != null) {
if (this._invalid.node(aliasTagChildNode, 'entry_tag_does_not_exists_for_item ' + (i + 1) + '_check_with_ml_platform'))
return weights;
if (aliasTagChildNode.getNodeName() == this._TEXTNODE)
continue;
i++;
stringNodes = aliasTagChildNode.getChildNodeIterator();
if (this._invalid.iterator(stringNodes, 'entry_tag_does_not_have_children_for_item' + i + '_check_with_ml_platform'))
return weights;
var item0, item1;
while (stringNodes.hasNext() && (item0 = stringNodes.next()) != null && item0.getNodeName() == this._TEXTNODE);
while (stringNodes.hasNext() && (item1 = stringNodes.next()) != null && item1.getNodeName() == this._TEXTNODE);
if (this._invalid.condition((gs.nil(item0) || gs.nil(item1) ||
gs.nil(item0.getTextContent()) || gs.nil(item1.getTextContent()) ||
item0.getTextContent().trim() == '' || item1.getTextContent().trim() == ''), 'string_tag_does_not_have_value_for_item_check_with_ml_platform'))
return weights;
columnMap[item1.getTextContent()] = item0.getTextContent();
}
var weightsDoc = new sn_cmdb.DynamicIREScriptableAPI.getScopedXMLDocument2(weightsStream);
root = weightsDoc.getDocumentElement().getNodeName();
var xpath = '/' + root + '/wekaClassifier/' + this._XML_TAG_CLASSIFIER + '/' + this._XML_TAG_CLASSIFIER + '/' + this._XML_TAG_CLASSIFIER;
var labelTag = weightsDoc.getFirstNode(xpath + '/m__Model/' + this._XML_TAG_LABEL);
if (this._invalid.node(labelTag, 'label_tag_not_present_check_with_ml_platform_team_if_version_has_changed'))
return weights;
var intNodesOflabelTag = labelTag.getChildNodeIterator();
if (this._invalid.iterator(intNodesOflabelTag, 'label_tag_does_not_have_children_check_with_ml_platform'))
return weights;
var labelTagIntNode = null;
var count = 0;
while (intNodesOflabelTag.hasNext() && (labelTagIntNode = intNodesOflabelTag.next()) != null) {
if (labelTagIntNode.getNodeName() == this._TEXTNODE)
continue;
columnMap['label_index_' + count] = labelTagIntNode.getTextContent();
count++;
}
var attributesTag = weightsDoc.getFirstNode(xpath + '/' + this._XML_TAG_NOMINAL_TO_BINARY + '/' + this._XML_TAG_OUTPUT_FORMAT + '/' + this._XML_TAG_ATTRIBTUES);
if (this._invalid.node(attributesTag, 'm__Attributes_tag_not_present_check_with_ml_platform_team_if_version_has_changed'))
return weights;
var wekaAttributeList = attributesTag.getChildNodeIterator();
if (this._invalid.iterator(wekaAttributeList, 'm__Attributes_tag_does_not_have_children_weka.core.Attribute_missing_check_with_ml_platform'))
return weights;
var indexToFeatureNameMap = {},
wekaAttribute;
while (wekaAttributeList.hasNext() && (wekaAttribute = wekaAttributeList.next()) != null) {
if (wekaAttribute.getNodeName() == this._TEXTNODE)
continue;
var wekaAttributeChildren = wekaAttribute.getChildNodeIterator();
if (gs.nil(wekaAttributeChildren) || !wekaAttributeChildren.hasNext())
continue;
var wekaAttributeChild, childData = {};
while (wekaAttributeChildren.hasNext() && (wekaAttributeChild = wekaAttributeChildren.next()) != null) {
if (wekaAttributeChild.getNodeName() == this._TEXTNODE)
continue;
if (wekaAttributeChild.getNodeName() == this._XML_TAG_ATTRIBUTE_INFO) {
childData[this._XML_TAG_ATTRIBUTE_INFO] = true;
var attributeInfoChildren = wekaAttributeChild.getChildNodeIterator();
if (this._invalid.iterator(attributeInfoChildren, 'm__AttributeInfo_tag_does_not_have_children_check_with_ml_platform'))
return weights;
var hashtableTag;
while (attributeInfoChildren.hasNext() && (hashtableTag = attributeInfoChildren.next()).getNodeName() != this._XML_TAG_HASH_TABLE);
if (this._invalid.condition(hashtableTag.getNodeName() != this._XML_TAG_HASH_TABLE, 'm__Hashtable_tag_not_present_check_with_ml_platform_team_if_version_has_changed'))
return weights;
var hashtableEntries = hashtableTag.getChildNodeIterator();
if (this._invalid.iterator(hashtableEntries, 'm__Hashtable_tag_does_not_have_children_check_with_ml_platform'))
return weights;
var entryNode, entry = {};
while (hashtableEntries.hasNext() && (entryNode = hashtableEntries.next()) != null) {
if (entryNode.getNodeName() == this._TEXTNODE)
continue;
var entryChildren = entryNode.getChildNodeIterator(),
entryChild;
while (!gs.nil(entryChildren) && entryChildren.hasNext() && (entryChild = entryChildren.next()) != null) {
if (entryChild.getNodeName() == this._TEXTNODE)
continue;
entry[entryChild.getNodeName()] = entryChild.getTextContent();
}
if (this._invalid.condition(Object.keys(entry).length != 2, 'm__Hashtable_tag_does_not_have_2_children_check_with_ml_platform'))
return weights;
columnMap[entry[this._XML_TAG_STRING]] = columnMap["label_index_" + entry[this._XML_TAG_INT]];
}
} else
childData[wekaAttributeChild.getNodeName()] = wekaAttributeChild.getTextContent();
}
if (!childData[this._XML_TAG_ATTRIBUTE_INFO] && childData[this._XML_TAG_TYPE] == '0')
indexToFeatureNameMap[childData[this._XML_TAG_INDEX]] = columnMap[childData[this._XML_TAG_NAME]];
else {
if (this._invalid.condition(!childData[this._XML_TAG_ATTRIBUTE_INFO], 'm__AttributeInfo_tag_not_present_check_with_ml_platform_team_if_version_has_changed'))
return weights;
indexToFeatureNameMap[childData[this._XML_TAG_INDEX]] = 'outputLabel';
}
}
var model = weightsDoc.getFirstNode(xpath + '/m__Model/' + this._XML_TAG_MODEL_WIGHTS);
if (this._invalid.node(model, 'w_tag_not_present_check_with_ml_platform_team_if_version_has_changed'))
return weights;
var weightsList = model.getChildNodeIterator();
if (this._invalid.iterator(weightsList, 'w_tag_does_not_have_children_check_with_ml_platform_if_version_has_changed'))
return weights;
var weight, featurecount = 0,
lastWeight;
while (weightsList.hasNext() && (weight = weightsList.next()) != null) {
if (weight.getNodeName() == this._TEXTNODE)
continue;
var feature = indexToFeatureNameMap[featurecount++];
if (!gs.nil(feature) && feature != 'outputLabel')
weights[feature] = weight.getTextContent();
lastWeight = weight.getTextContent();
}
weights.feature_count = featurecount;
var biasClassAndProbabilityTag = weightsDoc.getFirstNode('/' + root + '/trainerResult/' + this._XML_TAG_BIAS_CLASS_AND_PROBABILITY);
if (this._invalid.node(biasClassAndProbabilityTag, 'biasClassAndProbabiity_not_present_check_with_ml_platform_team_if_version_has_changed'))
return weights;
var biasClassAndProbabilityTagChildren = biasClassAndProbabilityTag.getChildNodeIterator();
if (this._invalid.iterator(biasClassAndProbabilityTagChildren, 'biasClassAndProbabiity_tag_does_not_have_children_check_with_ml_platform_if_version_has_changed'))
return weights;
var biasClassAndProbability;
while (biasClassAndProbabilityTagChildren.hasNext() && (biasClassAndProbability = biasClassAndProbabilityTagChildren.next()) != null) {
if (biasClassAndProbability.getNodeName() != this._TEXTNODE)
break;
}
if (this._invalid.condition(gs.nil(biasClassAndProbability) || biasClassAndProbability.getNodeName() != 'entry', 'biasClassAndProbabiity_tag_first_child_null_check_with_ml_platform_if_version_has_changed'))
return weights;
var biasClassAndProbabilityEntries = biasClassAndProbability.getChildNodeIterator();
if (this._invalid.iterator(biasClassAndProbabilityEntries, 'biasClassAndProbabiity_tag_first_child_null_check_with_ml_platform_if_version_has_changed'))
return weights;
var biasClass;
while (biasClassAndProbabilityEntries.hasNext() && (biasClass = biasClassAndProbabilityEntries.next()) != null) {
if (biasClass.getNodeName() != this._TEXTNODE)
break;
}
var noMatchRecords = this._getRecordCount(this._NO_MATCH);
var matchRecords = this._getRecordCount(this._MATCH);
var biasClassLableFromData = matchRecords > noMatchRecords ? this._MATCH : this._NO_MATCH;
if ("class0".equalsIgnoreCase(biasClass.getTextContent())) {
//bias class is class0
if (this._MATCH == biasClassLableFromData) {
//class0 = 1 from labels
if (this._MATCH == columnMap["class0"]) {
//weka class0 is 1 in ARFF format
//class0 = 1 from labels
weights["rev"] = "false";
} else if (this._NO_MATCH == columnMap["class0"]) {
//class = 0 from weka
weights["rev"] = "true";
} else if (this._invalid.condition(true, 'There_cannot_be_more_than_2_classes'))
return weights;
} else if (this._NO_MATCH == biasClassLableFromData) {
//class0 = 0 from labels
if (this._MATCH == columnMap["class0"]) {
//weka class0 is 1 in ARFF format
//class0 = 0 from labels
weights["rev"] = "true";
} else if (this._NO_MATCH == columnMap["class0"]) {
//class = 0 from weka
weights["rev"] = "false";
} else if (this._invalid.condition(true, 'There_cannot_be_more_than_2_classes'))
return weights;
} else if (this._invalid.condition(true, 'There_cannot_be_more_than_2_classes'))
return weights;
} else if ("class1" == biasClass.getTextContent()) {
//bias class is class1
if (this._MATCH == biasClassLableFromData) {
//class1 = 1 from labels
if (this._MATCH == columnMap["class1"]) {
//class1 = 1 from weka
weights["rev"] = "false";
} else if (this._NO_MATCH == columnMap["class1"]) {
//class1=0 from weka
weights["rev"] = "true";
} else if (this._invalid.condition(true, 'There_cannot_be_more_than_2_classes'))
return weights;
} else if (this._NO_MATCH == biasClassLableFromData) {
//class1 = 0 from labels
if (this._MATCH == columnMap["class1"]) {
//class1 = 1 from weka
weights["rev"] = "true";
} else if (this._NO_MATCH == columnMap["class1"]) {
//class1=0 from weka
weights["rev"] = "false";
} else if (this._invalid.condition(true, 'There_cannot_be_more_than_2_classes'))
return weights;
} else if (this._invalid.condition(true, 'There_cannot_be_more_than_2_classes'))
return weights;
} else if (this._invalid.condition(true, 'There_cannot_be_more_than_2_classes'))
return weights;
weights[this._BIAS] = lastWeight;
weights.status = "SUCCESS";
} catch (ex) {
this._invalid.condition(true, 'UNHANDLED EXCEPTION');
}
return weights;
},
_getRecordCount: function(label) {
var gr = new GlideAggregate(this._CMDB_DYNAMIC_IRE_MATCH);
gr.addQuery('expected_match', label);
gr.addAggregate('COUNT');
gr.query();
var records = 0;
if (gr.next()) {
records = gr.getAggregate('COUNT');
}
return records;
},
/**
* Helper method to fetch the modelXML from the
* ml platform tables
* @param the sysId of the solution.
* @param the version of the solution.
* @return XML string of the model.
*/
_getArtifactStream: function(solutionSysId, id, mlSolutionVersion) {
var model = new GlideRecord(this._ML_MODEL_ARTIFACT);
model.addQuery(this._TABLE_REF_SOLUTION, solutionSysId);
model.addQuery(this._MODEL_ID, id);
if (!gs.nil(mlSolutionVersion))
model.addQuery(this._TABLE_REF_SOLUTION + this._DOT + this._VERSION, mlSolutionVersion);
else {
model.addQuery(this._TABLE_REF_SOLUTION + this._DOT + this._ACTIVE, 'true');
model.orderByDesc(this._TABLE_REF_SOLUTION + this._DOT + this._SYS_CREATED_ON);
}
model.query();
if (!model.next())
return null;
var attachment = new GlideSysAttachment();
var agr = attachment.getAttachments(this._ML_MODEL_ARTIFACT, model.getUniqueValue());
if (!agr.next())
return null;
return sn_cmdb.DynamicIREScriptableAPI.getDecompressedAttachmentStream(agr.getUniqueValue());
},
type: 'CMDBDynamicIREMLInterface'
};Sys ID
20137fa0c33220104ef3c31b7940ddc0