/*
* Copyright 2016 IBM Corp.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
(function () {
var JavaWrapper = require(EclairJS_Globals.NAMESPACE + '/JavaWrapper');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
/**
* Evaluator for multilabel classification.
* @param predictionAndLabels an RDD of (predictions, labels) pairs,
* both are non-null Arrays, each with unique elements.
* @memberof module:eclairjs/mllib/evaluation
* @classdesc
* @param {module:eclairjs.RDD} predictionAndLabels
* @class
*/
var MultilabelMetrics = function(predictionAndLabels) {
this.logger = Logger.getLogger("MultilabelMetrics_js");
var jvmObject;
if (predictionAndLabels instanceof org.apache.spark.mllib.evaluation.MultilabelMetrics) {
jvmObject = predictionAndLabels;
} else {
jvmObject = new org.apache.spark.mllib.evaluation.MultilabelMetrics(Utils.unwrapObject(predictionAndLabels).rdd());
}
JavaWrapper.call(this, jvmObject);
};
MultilabelMetrics.prototype = Object.create(JavaWrapper.prototype);
MultilabelMetrics.prototype.constructor = MultilabelMetrics;
/**
* Returns accuracy
* @returns {float}
*/
MultilabelMetrics.prototype.accuracy = function() {
return this.getJavaObject().accuracy();
};
/**
* Returns subset accuracy (for equal sets of labels)
* @returns {float}
*/
MultilabelMetrics.prototype.subsetAccuracy = function() {
return this.getJavaObject().subsetAccuracy();
};
/**
* Returns Hamming-loss
* @returns {float}
*/
MultilabelMetrics.prototype.hammingLoss = function() {
return this.getJavaObject().hammingLoss();
};
/**
* Returns document-based precision averaged by the number of documents
* @param {float} [label] Returns precision for a given label (category)
* @returns {float}
*/
MultilabelMetrics.prototype.precision = function(label) {
if (label) {
return this.getJavaObject().precision(label);
} else {
return this.getJavaObject().precision();
}
};
/**
* Returns document-based recall averaged by the number of documents
* @param {float} [label] Returns recall for a given label (category)
* @returns {float}
*/
MultilabelMetrics.prototype.recall = function(label) {
if (label) {
return this.getJavaObject().recall(label);
} else {
return this.getJavaObject().recall();
}
};
/**
* Returns document-based f1-measure averaged by the number of documents
* @param {float} [label] Returns f1-measure for a given label (category)
* @returns {float}
*/
MultilabelMetrics.prototype.f1Measure = function(label) {
if (label) {
return this.getJavaObject().f1Measure(label);
} else {
return this.getJavaObject().f1Measure();
}
};
/**
* Returns micro-averaged label-based precision (equals to micro-averaged document-based precision)
* @returns {float}
*/
MultilabelMetrics.prototype.microPrecision = function() {
return this.getJavaObject().microPrecision();
};
/**
* Returns micro-averaged label-based recall (equals to micro-averaged document-based recall)
* @returns {float}
*/
MultilabelMetrics.prototype.microRecall = function() {
return this.getJavaObject().microRecall();
};
/**
* Returns micro-averaged label-based f1-measure (equals to micro-averaged document-based f1-measure)
* @returns {float}
*/
MultilabelMetrics.prototype.microF1Measure = function() {
return this.getJavaObject().microF1Measure();
};
/**
* Returns the sequence of labels in ascending order
* @returns {float[]}
*/
MultilabelMetrics.prototype.labels = function() {
return Java.from(this.getJavaObject().labels());
};
module.exports = MultilabelMetrics;
})();