Source: mllib/evaluation/BinaryClassificationMetrics.js

/*
 * Copyright 2015 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.
 */

var Utils = require('../../utils.js');

var RDD = require('../../rdd/RDD.js');

var gKernelP;

/**
 * Evaluator for binary classification.
 *
 * @param scoreAndLabels an RDD of (score, label) pairs.
 * @param numBins if greater than 0, then the curves (ROC curve, PR curve) computed internally
 *                will be down-sampled to this many "bins". If 0, no down-sampling will occur.
 *                This is useful because the curve contains a point for each distinct score
 *                in the input, and this could be as large as the input itself -- millions of
 *                points or more, when thousands may be entirely sufficient to summarize
 *                the curve. After down-sampling, the curves will instead be made of approximately
 *                `numBins` points instead. Points are made from bins of equal numbers of
 *                consecutive points. The size of each bin is
 *                `floor(scoreAndLabels.count() / numBins)`, which means the resulting number
 *                of bins may not exactly equal numBins. The last bin in each partition may
 *                be smaller as a result, meaning there may be an extra sample at
 *                partition boundaries.
 *
 * @classdesc
 */

/**
 * @param {module:eclairjs/rdd.RDD} scoreAndLabels
 * @param {number} numBins
 * @class
 * @memberof module:eclairjs/mllib/evaluation
 */
function BinaryClassificationMetrics() {
  Utils.handleConstructor(this, arguments, gKernelP);
}

/**
 * Computes the area under the receiver operating characteristic (ROC) curve.
 * @returns {number}
 */
BinaryClassificationMetrics.prototype.areaUnderROC = function() {
  var args = {
    target: this,
    method: 'areaUnderROC',
    returnType: Number
  };

  return Utils.generate(args);
};

BinaryClassificationMetrics.prototype.precisionByThreshold = function() {
  var args = {
    target: this,
    method: 'precisionByThreshold',
    returnType: RDD
  };

  return Utils.generate(args);
};

BinaryClassificationMetrics.prototype.recallByThreshold = function() {
  var args = {
    target: this,
    method: 'recallByThreshold',
    returnType: RDD
  };

  return Utils.generate(args);
};

BinaryClassificationMetrics.prototype.fMeasureByThreshold = function(beta) {
  var args = {
    target: this,
    method: 'fMeasureByThreshold',
    args: Utils.wrapArguments(arguments),
    returnType: RDD
  };

  return Utils.generate(args);
};

BinaryClassificationMetrics.prototype.pr = function() {
  var args = {
    target: this,
    method: 'pr',
    returnType: RDD
  };

  return Utils.generate(args);
};

BinaryClassificationMetrics.moduleLocation = '/mllib/evaluation/BinaryClassificationMetrics';

module.exports = function(kP) {
  if (kP) gKernelP = kP;

  return BinaryClassificationMetrics;
};