/*
* 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');
/**
* ::Experimental::
* Evaluator for multiclass classification.
*
* @memberof module:eclairjs/mllib/evaluation
* @classdesc
* @param {module:eclairjs.RDD} predictionAndLabels an RDD of (prediction, label) pairs.
* @class
*/
var MulticlassMetrics = function (predictionAndLabels) {
var jvmObject;
this.logger = Logger.getLogger("MulticlassMetrics_js");
if (predictionAndLabels instanceof org.apache.spark.mllib.evaluation.MulticlassMetrics) {
jvmObject = predictionAndLabels;
} else {
jvmObject = new org.apache.spark.mllib.evaluation.MulticlassMetrics(Utils.unwrapObject(predictionAndLabels).rdd());
}
JavaWrapper.call(this, jvmObject);
};
MulticlassMetrics.prototype = Object.create(JavaWrapper.prototype);
MulticlassMetrics.prototype.constructor = MulticlassMetrics;
/**
* Returns confusion matrix:
* predicted classes are in columns,
* they are ordered by class label ascending,
* as in "labels"
* @returns {module:eclairjs/mllib/linalg.Matrix}
*/
MulticlassMetrics.prototype.confusionMatrix = function () {
var javaObject = this.getJavaObject().confusionMatrix();
return Utils.javaToJs(javaObject);
};
/**
* Returns true positive rate for a given label (category)
* @param {float} label the label.
* @returns {float}
*/
MulticlassMetrics.prototype.truePositiveRate = function (label) {
return this.getJavaObject().truePositiveRate(label);
};
/**
* Returns weighted true positive rate (equals to precision, recall and f-measure)
* @returns {float}
*/
MulticlassMetrics.prototype.weightedTruePositiveRate = function () {
return this.getJavaObject().weightedTruePositiveRate();
};
/**
* Returns false positive rate for a given label (category)
* @param {float} label the label.
* @returns {float}
*/
MulticlassMetrics.prototype.falsePositiveRate = function (label) {
return this.getJavaObject().falsePositiveRate(label);
};
/**
* Returns weighted false positive rate
* @returns {float}
*/
MulticlassMetrics.prototype.weightedFalsePositiveRate = function () {
return this.getJavaObject().weightedFalsePositiveRate();
};
/**
* Returns precision
* @param {float} [label] Returns precision for a given label (category)
* @returns {float}
*/
MulticlassMetrics.prototype.precision = function (label) {
if (label) {
return this.getJavaObject().precision(label);
} else {
return this.getJavaObject().precision();
}
};
/**
* Returns weighted averaged precision
* @returns {float}
*/
MulticlassMetrics.prototype.weightedPrecision = function () {
return this.getJavaObject().weightedPrecision();
};
/**
* Returns recall (equals to precision for multiclass classifier because sum of all false positives is equal to sum of all false negatives)
* @param {float} [label] Returns recall for a given label (category)
* @returns {float}
*/
MulticlassMetrics.prototype.recall = function (label) {
if (label) {
return this.getJavaObject().recall(label);
} else {
return this.getJavaObject().recall();
}
};
/**
* Returns weighted averaged recall (equals to precision, recall and f-measure)
* @returns {float}
*/
MulticlassMetrics.prototype.weightedRecall = function () {
return this.getJavaObject().weightedRecall();
};
/**
* Returns f-measure (equals to precision and recall because precision equals recall)
* @param {float} [label] Returns f1-measure for a given label (category)
* @param {float} [beta]
* @returns {float}
*/
MulticlassMetrics.prototype.fMeasure = function (label, beta) {
if (label && beta) {
return this.getJavaObject().fMeasure(label, beta);
} if (label) {
return this.getJavaObject().fMeasure(label);
} else {
return this.getJavaObject().fMeasure();
}
};
/**
* Returns weighted averaged f-measure
* @param {number} [beta] the beta parameter.
* @returns {number}
*/
MulticlassMetrics.prototype.weightedFMeasure = function (beta) {
if (beta) {
return this.getJavaObject().weightedFMeasure(beta);
} else {
return this.getJavaObject().weightedFMeasure();
}
};
/**
* Returns the sequence of labels in ascending order
* @returns {float[]}
*/
MulticlassMetrics.prototype.labels = function () {
return Java.from(this.getJavaObject().labels());
};
module.exports = MulticlassMetrics;
})();