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<!doctype html> <html> <head> <meta name="generator" content="JSDoc 3"> <meta charset="utf-8"> <title>Class: PredictionCurve</title> <link rel="stylesheet" href="https://brick.a.ssl.fastly.net/Karla:400,400i,700,700i" type="text/css"> <link rel="stylesheet" href="https://brick.a.ssl.fastly.net/Noto+Serif:400,400i,700,700i" type="text/css"> <link rel="stylesheet" href="https://brick.a.ssl.fastly.net/Inconsolata:500" type="text/css"> <link href="css/baseline.css" rel="stylesheet"> </head> <body onload="prettyPrint()"> <nav id="jsdoc-navbar" role="navigation" class="jsdoc-navbar"> <div id="jsdoc-navbar-container"> <div id="jsdoc-navbar-content"> <a href="index.html" class="jsdoc-navbar-package-name">QMiner JavaScript API v9.4.0</a> </div> </div> </nav> <div id="jsdoc-body-container"> <div id="jsdoc-content"> <div id="jsdoc-content-container"> <div id="jsdoc-main" role="main"> <header class="page-header"> <div class="symbol-detail-labels"><span class="label label-kind">class</span> <span class="label label-static">static</span></div> <h1><small><a href="module-analytics.html">analytics</a>~<wbr><a href="module-analytics-metrics.html">metrics</a>.<wbr></small><span class="symbol-name">PredictionCurve</span></h1> <p class="source-link">Source: <a href="analyticsdoc.js.html#source-line-3578">analyticsdoc.<wbr>js:3578</a></p> <div class="symbol-classdesc"> <p>Used for computing ROC curve and other related measures such as AUC.</p> </div> <dl class="dl-compact"> </dl> </header> <section id="summary"> <div class="summary-callout"> <h2 class="summary-callout-heading">Properties</h2> <div class="summary-content"> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics-metrics.PredictionCurve.html#allNegatives">allNegatives</a></dt> <dd> </dd> <dt><a href="module-analytics-metrics.PredictionCurve.html#allPositives">allPositives</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics-metrics.PredictionCurve.html#grounds">grounds</a></dt> <dd> </dd> <dt><a href="module-analytics-metrics.PredictionCurve.html#length">length</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics-metrics.PredictionCurve.html#predictions">predictions</a></dt> <dd> </dd> </dl> </div> </div> </div> <div class="summary-callout"> <h2 class="summary-callout-heading">Methods</h2> <div class="summary-content"> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics-metrics.PredictionCurve.html#auc">auc([sample])</a></dt> <dd> </dd> <dt><a href="module-analytics-metrics.PredictionCurve.html#bestF1">bestF1()</a></dt> <dd> </dd> <dt><a href="module-analytics-metrics.PredictionCurve.html#breakEvenPoint">breakEvenPoint()</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics-metrics.PredictionCurve.html#desiredPrecision">desiredPrecision(desiredPrecision)</a></dt> <dd> </dd> <dt><a href="module-analytics-metrics.PredictionCurve.html#desiredRecall">desiredRecall(desiredRecall)</a></dt> <dd> </dd> <dt><a href="module-analytics-metrics.PredictionCurve.html#precisionRecallCurve">precisionRecallCurve([sample])</a></dt> <dd> </dd> </dl> </div> <div class="summary-column"> <dl class="dl-summary-callout"> <dt><a href="module-analytics-metrics.PredictionCurve.html#push">push(ground, predicted)</a></dt> <dd> </dd> <dt><a href="module-analytics-metrics.PredictionCurve.html#roc">roc([sample])</a></dt> <dd> </dd> </dl> </div> </div> </div> </section> <section> <h2 id="PredictionCurve">new <span class="symbol-name">PredictionCurve</span><span class="signature"><span class="signature-params">(yTrue, yPred)</span></span></h2> <p>Class implements several prediction curve measures (ROC, AOC, Precision-Recall, ...).</p> <section> <h3> Example </h3> <div> <pre class="prettyprint"><code>// import metrics module var metrics = require('qminer').analytics.metrics; // true and predicted lables var true_lables = [0, 1, 0, 0, 1]; var pred_prob = [0.3, 0.5, 0.2, 0.5, 0.8]; // create predictionCurve instance var predictionCurve = new metrics.PredictionCurve(); // simulate data flow for (var i in true_lables) { // push new value predictionCurve.push(true_lables[i], pred_prob[i]); } var roc = predictionCurve.roc(); // get ROC var auc = predictionCurve.auc(); // get AUC var pr = predictionCurve.precisionRecallCurve() // get precision-recall curve</code></pre> </div> </section> <section> <h3>Parameters</h3> <table class="jsdoc-details-table"> <thead> <tr> <th>Name</th> <th>Type</th> <th>Optional</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td> <p>yTrue</p> </td> <td> <p>(Array of number or <a href="module-la.Vector.html">module:la.Vector</a>)</p> </td> <td> <p> </p> </td> <td> <p>Ground truth (correct) lable(s) of binary classification in range {-1, 1} or {0, 1}.</p> </td> </tr> <tr> <td> <p>yPred</p> </td> <td> <p>(Array of number or <a href="module-la.Vector.html">module:la.Vector</a>)</p> </td> <td> <p> </p> </td> <td> <p>Estimated probabilities.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> </dl> </section> <section> <h2>Properties</h2> <section> <h3 id="allNegatives"><span class="symbol-name">allNegatives</span><small class="property-type">  number</small></h3> <p>Count of all negative examples.</p> <dl class="dl-compact"> </dl> <h3 id="allPositives"><span class="symbol-name">allPositives</span><small class="property-type">  number</small></h3> <p>Count of all positive examples.</p> <dl class="dl-compact"> </dl> <h3 id="grounds"><span class="symbol-name">grounds</span><small class="property-type">  <a href="module-la.Vector.html">module:la.Vector</a></small></h3> <p>Store of ground truths.</p> <dl class="dl-compact"> </dl> <h3 id="length"><span class="symbol-name">length</span><small class="property-type">  number</small></h3> <p>Count of all examples.</p> <dl class="dl-compact"> </dl> <h3 id="predictions"><span class="symbol-name">predictions</span><small class="property-type">  <a href="module-la.Vector.html">module:la.Vector</a></small></h3> <p>Store of predictions.</p> <dl class="dl-compact"> </dl> </section> <h2>Methods</h2> <section> <h3 id="auc"><span class="symbol-name">auc</span><span class="signature"><span class="signature-params">([sample])</span> → <span class="signature-returns"> number</span></span></h3> <p>Get Area Under the Curve (AUC) of the current curve.</p> <section> <h4>Parameter</h4> <table class="jsdoc-details-table"> <thead> <tr> <th>Name</th> <th>Type</th> <th>Optional</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td> <p>sample</p> </td> <td> <p>number</p> </td> <td> <p>Yes</p> </td> <td> <p>Desired number of samples in output.</p> <p>Defaults to <code>10</code>.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B Area under ROC curve.</p> </dd> </dl> <h3 id="bestF1"><span class="symbol-name">bestF1</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> number</span></span></h3> <p>Gets threshold for prediction score, which results in the highest F1.</p> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B Threshold with highest F1 score.</p> </dd> </dl> <h3 id="breakEvenPoint"><span class="symbol-name">breakEvenPoint</span><span class="signature"><span class="signature-params">()</span> → <span class="signature-returns"> number</span></span></h3> <p>Get break-even point, the value where precision and recall intersect.</p> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B Break-even point.</p> </dd> </dl> <h3 id="desiredPrecision"><span class="symbol-name">desiredPrecision</span><span class="signature"><span class="signature-params">(desiredPrecision)</span> → <span class="signature-returns"> number</span></span></h3> <p>Gets threshold for prediction score, nearest to specified precision.</p> <section> <h4>Parameter</h4> <table class="jsdoc-details-table"> <thead> <tr> <th>Name</th> <th>Type</th> <th>Optional</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td> <p>desiredPrecision</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Desired precision score.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B Threshold for prediction score, nearest to specified <code>precision</code>.</p> </dd> </dl> <h3 id="desiredRecall"><span class="symbol-name">desiredRecall</span><span class="signature"><span class="signature-params">(desiredRecall)</span> → <span class="signature-returns"> number</span></span></h3> <p>Gets threshold for prediction score, nearest to specified recall.</p> <section> <h4>Parameter</h4> <table class="jsdoc-details-table"> <thead> <tr> <th>Name</th> <th>Type</th> <th>Optional</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td> <p>desiredRecall</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Desired recall score.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code>number</code>B Recal Score Threshold. Threshold for recall score, nearest to specified <code>recall</code>.</p> </dd> </dl> <h3 id="precisionRecallCurve"><span class="symbol-name">precisionRecallCurve</span><span class="signature"><span class="signature-params">([sample])</span> → <span class="signature-returns"> <a href="module-la.Matrix.html">module:la.Matrix</a></span></span></h3> <p>Get precision recall curve sampled on <code>sample</code> points.</p> <section> <h4>Parameter</h4> <table class="jsdoc-details-table"> <thead> <tr> <th>Name</th> <th>Type</th> <th>Optional</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td> <p>sample</p> </td> <td> <p>number</p> </td> <td> <p>Yes</p> </td> <td> <p>Desired number of samples in output.</p> <p>Defaults to <code>10</code>.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.Matrix.html">module:la.Matrix</a></code>B Precision-recall pairs.</p> </dd> </dl> <h3 id="push"><span class="symbol-name">push</span><span class="signature"><span class="signature-params">(ground, predicted)</span></span></h3> <p>Add new measurement with ground score (1 or -1) and predicted value or integer array (when there are zero or more then one lables).</p> <section> <h4>Parameters</h4> <table class="jsdoc-details-table"> <thead> <tr> <th>Name</th> <th>Type</th> <th>Optional</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td> <p>ground</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Correct lable.</p> </td> </tr> <tr> <td> <p>predicted</p> </td> <td> <p>number</p> </td> <td> <p> </p> </td> <td> <p>Estimated probabilities.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> </dl> <h3 id="roc"><span class="symbol-name">roc</span><span class="signature"><span class="signature-params">([sample])</span> → <span class="signature-returns"> <a href="module-la.Matrix.html">module:la.Matrix</a></span></span></h3> <p>Get Receiver Operating Characteristic (ROC) parametrization sampled on <code>sample</code> points.</p> <section> <h4>Parameter</h4> <table class="jsdoc-details-table"> <thead> <tr> <th>Name</th> <th>Type</th> <th>Optional</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td> <p>sample</p> </td> <td> <p>number</p> </td> <td> <p>Yes</p> </td> <td> <p>Desired number of samples in output.</p> <p>Defaults to <code>10</code>.</p> </td> </tr> </tbody> </table> </section> <dl class="dl-compact"> <dt>Returns</dt> <dd> <p><code><a href="module-la.Matrix.html">module:la.Matrix</a></code>B A matrix with increasing false and true positive rates.</p> </dd> </dl> </section> </section> </div> </div> <nav id="jsdoc-toc-nav" role="navigation"></nav> </div> </div> <footer id="jsdoc-footer" class="jsdoc-footer"> <div id="jsdoc-footer-container"> <p> </p> </div> </footer> <script src="scripts/jquery.min.js"></script> <script src="scripts/tree.jquery.js"></script> <script src="scripts/prettify.js"></script> <script src="scripts/jsdoc-toc.js"></script> <script src="scripts/linenumber.js"></script> <script src="scripts/scrollanchor.js"></script> </body> </html>