http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/9fe018b6/evaluation/index.html ---------------------------------------------------------------------- diff --git a/evaluation/index.html b/evaluation/index.html index aa12e3d..0135cd9 100644 --- a/evaluation/index.html +++ b/evaluation/index.html @@ -1,4 +1,4 @@ -<!DOCTYPE html><html><head><title>Tuning and Evaluation</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="Tuning and Evaluation"/><link rel="canonical" href="https://predictionio.apache.org/evaluation/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" rel="stylesheet"/><link 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href="/start/deploy/"><span>Deploying Your First Engine</span></a></li><li class="level-2"><a class="final" href="/start/customize/"><span>Customizing the Engine</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Integrating with Your App</span></a><ul><li class="level-2"><a class="final" href="/appintegration/ "><span>App Integration Overview</span></a></li><li class="level-2"><a class="expandible" href="/sdk/"><span>List of SDKs</span></a><ul><li class="level-3"><a class="final" href="/sdk/java/"><span>Java & Android SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/php/"><span>PHP SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/python/"><span>Python SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/ruby/"><span>Ruby SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/community/"><span>Community Powered SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Deploying an Engine</span></a><ul><li class="level-2"><a class="final" href="/deploy/"><span>Deploying as a Web Service</span></a></li><li class="level-2"><a class="final" href="/batchpredict/"><span>Batch Predictions</span></a></li><li class="level-2"><a class="final" href="/deploy/monitoring/"><span>Monitoring Engine</span>< /a></li><li class="level-2"><a class="final" href="/deploy/engineparams/"><span>Setting Engine Parameters</span></a></li><li class="level-2"><a class="final" href="/deploy/enginevariants/"><span>Deploying Multiple Engine Variants</span></a></li><li class="level-2"><a class="final" href="/deploy/plugin/"><span>Engine Server Plugin</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Customizing an Engine</span></a><ul><li class="level-2"><a class="final" href="/customize/"><span>Learning DASE</span></a></li><li class="level-2"><a class="final" href="/customize/dase/"><span>Implement DASE</span></a></li><li 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class="level-2"><a class="final" href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li class="level-2"><a class="final" href="/evaluation/evaluationdashboard/"><span>Evaluation Dashboard</span></a></li><li class="level-2"><a class="final" href="/evaluation/metricchoose/"><span>Choosing Evaluation Metrics</span></a></li><li c lass="level-2"><a class="final" href="/evaluation/metricbuild/"><span>Building Evaluation Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>System Architecture</span></a><ul><li class="level-2"><a class="final" href="/system/"><span>Architecture Overview</span></a></li><li class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using Another Data Store</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>PredictionIO® Official Templates</span></a><ul><li class="level-2"><a class="final" href="/templates/"><span>Intro</span></a></li><li class="level-2"><a 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Rate Event</span></a></li><li class="level-3"><a class="final" >href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust >Score</span></a></li></ul></li><li class="level-2"><a class="expandible" >href="#"><span>Similar Product</span></a><ul><li class="level-3"><a >class="final" href="/templates/similarproduct/quickstart/"><span>Quick >Start</span></a></li><li class="level-3"><a class="final" href="/te mplates/similarproduct/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple Events and Multiple Algorithms</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/return-item-properties/"><span>Returns Item Properties</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate Event</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events for Users</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/recommended-user/"><span>Recommend Users</span></a></li></ul></li><li class="level-2"><a class="expandible" href="#"><span>Classification</span></a><ul><li class="level-3"><a class="final" href=" /templates/classification/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/classification/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/classification/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/classification/add-algorithm/"><span>Use Alternative Algorithm</span></a></li><li class="level-3"><a class="final" href="/templates/classification/reading-custom-properties/"><span>Read Custom 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class="level-2"><a class="final" href="https://www.apache.org/security/";><span>Security</span></a></li></ul></li></ul></nav></div><div class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">Tuning and Evaluation</a><span class="spacer">></span></li><li><span class="last">Overview</span></li></ul></div><div id="page-title"><h1>Tuning and Evaluation</h1></div></div><div id="table-of-content-wrapper"><a id="edit-page-link" href="https://github. com/apache/predictionio/tree/livedoc/docs/manual/source/evaluation/index.html.md"><img src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">Tuning and Evaluation</a><span class="spacer">></span></li><li><span class="last">Overview</span></li></ul></div><div id="page-title"><h1>Tuning and Evaluation</h1></div></div><div class="content"> <p>PredictionIO's evaluation module allows you to streamline the process of testing lots of knobs in engine parameters and deploy the best one out of it using statisically sound cross-validation methods.</p><p>There are two key components:</p><h3 id='engine' class='header-anchors'>Engine</h3><p>It is our evaluation target. During evaluation, in addition to the <em>train</em> and <em>deploy</em> mode we describe in earlier sections, the engine also generates a list of testing data points. These data points are a sequence of <em>Query</em> and <em>Actual Result</em> tuples. <em>Queries</em> are sent to the engine and the engine responds with a <em>Predicted Result</em>, in the same way as how the engine serves a query.</p><h3 id='evaluator' class='header-anchors'>Evaluator</h3><p>The evaluator joins the sequence of <em>Query</em>, <em>Predicted Result</em>, and <em>Actual Result</em> together and evaluates the quality of the engine. PredictionIO enables you to implement any metric with just a few lines of code.</p><p><img alt="PredictionIO Evaluation Overview" src="/images/engine-evaluation-4ab1bfeb.png"/></p><p>We will discuss various aspects of evaluation with PredictionIO.</p> <ul> <li><a href="/evaluation/paramtuning/">Hyperparameter Tuning</a> - it is an end-to-end example of using PredictionIO evaluation module to select and deploy the best engine parameter.</li> <li><a href="/evaluation/evaluationdashboard/">Evaluation Dashboard</a> - it is the dashboard where you can see a detailed b reakdown of all previous evaluations.</li> <li><a href="/evaluation/metricchoose/">Choosing Evaluation Metrics</a> - we cover some basic machine learning metrics</li> <li><a href="/evaluation/metricbuild/">Bulding Evaluation Metrics</a> - we illustrate how to implement a custom metric with as few as one line of code (plus some boilerplates).</li> </ul> </div></div></div></div><footer><div class="container"><div 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com/apache/predictionio/tree/livedoc/docs/manual/source/evaluation/index.html.md"><img src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a href="#">Tuning and Evaluation</a><span class="spacer">></span></li><li><span class="last">Overview</span></li></ul></div><div id="page-title"><h1>Tuning and Evaluation</h1></div></div><div class="content"> <p>PredictionIO's evaluation module allows you to streamline the process of testing lots of knobs in engine parameters and deploy the best one out of it using statistically sound cross-validation methods.</p><p>There are two key components:</p><h3 id='engine' class='header-anchors'>Engine</h3><p>It is our evaluation target. During evaluation, in addition to the <em>train</em> and <em>deploy</em> mode we describe in earlier sections, the engine also generates a list of testing data points. These data points are a sequence of <em>Query</em> and <em>Actual Result</em> tuples. <em>Queries</em> are sent to the engine and the engine responds with a <em>Predicted Result</em>, in the same way as how the engine serves a query.</p><h3 id='evaluator' class='header-anchors'>Evaluator</h3><p>The evaluator joins the sequence of <em>Query</em>, <em>Predicted Result</em>, and <em>Actual Result</em> together and evaluates the quality of the engine. PredictionIO enables you to implement any metric with just a few lines of code.</p><p><img alt="PredictionIO Evaluation Overview" src="/images/engine-evaluation-4ab1bfeb.png"/></p><p>We will discuss various aspects of evaluation with PredictionIO.</p> <ul> <li><a href="/evaluation/paramtuning/">Hyperparameter Tuning</a> - it is an end-to-end example of using PredictionIO evaluation module to select and deploy the best engine parameter.</li> <li><a href="/evaluation/evaluationdashboard/">Evaluation Dashboard</a> - it is the dashboard where you can see a detailed breakdown of all previous evaluations.</li> <li><a href="/evaluation/metricchoose/">Choosing Evaluation Metrics</a> - we cover some basic machine learning metrics</li> <li><a href="/evaluation/metricbuild/">Building Evaluation Metrics</a> - we illustrate how to implement a custom metric with as few as one line of code (plus some boilerplates).</li> </ul> </div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//predictionio.apache.org/install/" target="blank">Download</a></li><li><a href="//predictionio.apache.org/" target="blank">Docs</a></li><li><a href="//github.com/apache/predictionio" target="blank">GitHub</a></li><li><a href="mailto:user-subscr...@predictionio.apache.org"; target="blank">Subscribe to User Mailing List</a></li><li><a href="//stackoverflow.com/questions/tagged/predictionio" target="blank">Stackoverflow</a></l i></ul></div></div><div class="col-md-6 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/predictionio" target="blank">Source Code</a></li><li><a href="//issues.apache.org/jira/browse/PIO" target="blank">Bug Tracker</a></li><li><a href="mailto:dev-subscr...@predictionio.apache.org"; target="blank">Subscribe to Development Mailing List</a></li></ul></div></div></div><div class="row"><div class="col-md-12 footer-link-column"><p>Apache PredictionIO, PredictionIO, Apache, the Apache feather logo, and the Apache PredictionIO project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</p><p>All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p></div></div></div><div id="footer-bottom"><div class="container"><d iv class="row"><div class="col-md-12"><div id="footer-logo-wrapper"><img alt="PredictionIO" src="/images/logos/logo-white-d1e9c6e6.png"/><span>®</span></div><div id="social-icons-wrapper"><a class="github-button" href="https://github.com/apache/predictionio"; data-icon="octicon-star" data-show-count="true" aria-label="Star apache/predictionio on GitHub">Star</a> <a class="github-button" href="https://github.com/apache/predictionio/fork"; data-icon="octicon-repo-forked" data-show-count="true" aria-label="Fork apache/predictionio on GitHub">Fork</a> <script id="github-bjs" async="" defer="" src="https://buttons.github.io/buttons.js";></script><a href="https://twitter.com/predictionio"; target="blank"><img alt="PredictionIO on Twitter" src="/images/icons/twitter-ea9dc152.png"/></a> <a href="https://www.facebook.com/predictionio"; target="blank"><img alt="PredictionIO on Facebook" src="/images/icons/facebook-5c57939c.png"/></a> </div></div></div></div></div></footer></div><script>(function( w,d,t,u,n,s,e){w['SwiftypeObject']=n;w[n]=w[n]||function(){ (w[n].q=w[n].q||[]).push(arguments);};s=d.createElement(t); e=d.getElementsByTagName(t)[0];s.async=1;s.src=u;e.parentNode.insertBefore(s,e); })(window,document,'script','//s.swiftypecdn.com/install/v1/st.js','_st');
http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/9fe018b6/evaluation/metricbuild/index.html ---------------------------------------------------------------------- diff --git a/evaluation/metricbuild/index.html b/evaluation/metricbuild/index.html index 01ca251..48e6ada 100644 --- a/evaluation/metricbuild/index.html +++ b/evaluation/metricbuild/index.html @@ -16,7 +16,7 @@ ... <span class="gp">$ </span>pio <span class="nb">eval </span>org.example.classification.AccuracyEvaluation org.example.classification.EngineParamsList ... -</pre></td></tr></tbody></table> </div> <p>(See MyClassification/src/main/scala/<strong><em>Evaluation.scala</em></strong> for full usage.)</p><h2 id='example-2:-precision-metric' class='header-anchors'>Example 2: Precision Metric</h2><p>Precision is a metric for binary classifier capturing the portion of correction prediction among all <em>positive</em> predictions. We don't care about the cases where the QPA-tuple gives a negative prediction. (Recall that a binary classifier only provide two output values: <em>positive</em> and <em>negative</em>.) The following table illustrates all four cases:</p> <table><thead> <tr> <th style="text-align: center">PredictedResult</th> <th style="text-align: center">ActualResult</th> <th style="text-align: center">Value</th> </tr> </thead><tbody> <tr> <td style="text-align: center">Positive</td> <td style="text-align: center">Positive</td> <td style="text-align: center">1.0</td> </tr> <tr> <td style="text-align: center">Positive</td> <td style ="text-align: center">Negative</td> <td style="text-align: center">0.0</td> </tr> <tr> <td style="text-align: center">Negative</td> <td style="text-align: center">Positive</td> <td style="text-align: center">Don't care</td> </tr> <tr> <td style="text-align: center">Negative</td> <td style="text-align: center">Negative</td> <td style="text-align: center">Don't care</td> </tr> </tbody></table> <p>Calculating the precision metric is a slightly more involved procedure than calculating the accuracy metric as we have to specially handle the <em>don't care</em> negative cases.</p><p>PredictionIO provides a helper class <code>OptionAverageMetric</code> allows user to specify <em>don't care</em> values as <code>None</code>. It only aggregates the non-None values. Lines 3 to 4 is the method signature of <code>calcuate</code> method. The key difference is that the return value is a <code>Option[Double]</code>, in contrast to <code>Double</code> for <code>AverageMetric</code>. T his class only computes the average of <code>Some(.)</code> results. Lines 5 to 13 are the actual logic. The first <code>if</code> factors out the positively predicted case, and the computation is similiar to the accuracy metric. The negatively predicted case are the <em>don't cares</em>, which we return <code>None</code>.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +</pre></td></tr></tbody></table> </div> <p>(See MyClassification/src/main/scala/<strong><em>Evaluation.scala</em></strong> for full usage.)</p><h2 id='example-2:-precision-metric' class='header-anchors'>Example 2: Precision Metric</h2><p>Precision is a metric for binary classifier capturing the portion of correction prediction among all <em>positive</em> predictions. We don't care about the cases where the QPA-tuple gives a negative prediction. (Recall that a binary classifier only provide two output values: <em>positive</em> and <em>negative</em>.) The following table illustrates all four cases:</p> <table><thead> <tr> <th style="text-align: center">PredictedResult</th> <th style="text-align: center">ActualResult</th> <th style="text-align: center">Value</th> </tr> </thead><tbody> <tr> <td style="text-align: center">Positive</td> <td style="text-align: center">Positive</td> <td style="text-align: center">1.0</td> </tr> <tr> <td style="text-align: center">Positive</td> <td style ="text-align: center">Negative</td> <td style="text-align: center">0.0</td> </tr> <tr> <td style="text-align: center">Negative</td> <td style="text-align: center">Positive</td> <td style="text-align: center">Don't care</td> </tr> <tr> <td style="text-align: center">Negative</td> <td style="text-align: center">Negative</td> <td style="text-align: center">Don't care</td> </tr> </tbody></table> <p>Calculating the precision metric is a slightly more involved procedure than calculating the accuracy metric as we have to specially handle the <em>don't care</em> negative cases.</p><p>PredictionIO provides a helper class <code>OptionAverageMetric</code> allows user to specify <em>don't care</em> values as <code>None</code>. It only aggregates the non-None values. Lines 3 to 4 is the method signature of <code>calculate</code> method. The key difference is that the return value is a <code>Option[Double]</code>, in contrast to <code>Double</code> for <code>AverageMetric</code>. This class only computes the average of <code>Some(.)</code> results. Lines 5 to 13 are the actual logic. The first <code>if</code> factors out the positively predicted case, and the computation is similar to the accuracy metric. The negatively predicted case are the <em>don't cares</em>, which we return <code>None</code>.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 2 3 4 http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/9fe018b6/evaluation/paramtuning/index.html ---------------------------------------------------------------------- diff --git a/evaluation/paramtuning/index.html b/evaluation/paramtuning/index.html index 3009ff8..1b3dfdd 100644 --- a/evaluation/paramtuning/index.html +++ b/evaluation/paramtuning/index.html @@ -240,7 +240,7 @@ The best variant params can be found <span class="k">in </span>best.json <span class="o">}</span> <span class="o">}</span> <span class="o">}</span> -</pre></td></tr></tbody></table> </div> <p>The <code>readEval</code> method returns a sequence of (<code>TrainingData</code>, <code>EvaluationInfo</code>, <code>RDD[(Query, ActualResult)]</code>. <code>TrainingData</code> is the same class we use for deploy, <code>RDD[(Query, ActualResult)]</code> is the validation set, <code>EvaluationInfo</code> can be used to hold some global evaluation data ; it is not used in the current example.</p><p>Lines 11 to 41 is the logic of reading and transforming data from the datastore; it is equvialent to the existing <code>readTraining</code> method. After line 41, the variable <code>labeledPoints</code> contains the complete dataset with which we use to generate the (training, validation) sequence.</p><p>Lines 43 to 57 is the <em>k-fold</em> logic. Line 45 gives each data point a unique id, and we decide whether the point belongs to the training or validation set depends on the <em>mod</em> of the id (lines 48 to 49). For each point in the valida tion set, we construct the <code>Query</code> and <code>ActualResult</code> (line 55) which is used validate the engine.</p><h2 id='evaluation-metrics' class='header-anchors'>Evaluation Metrics</h2><p>We define a <code>Metric</code> which gives a <em>score</em> to engine params. The higher the score, the better the engine params are. In this template, we use accuracy score which measures the portion of correct prediction among all data points.</p><p>In MyClassification/src/main/scala/<strong>Evaluation.scala</strong>, the class <code>Accuracy</code> implements the <em>accuracy</em> score. It extends a base helper class <code>AverageMetric</code> which calculates the average score overall <em>(Query, PredictionResult, ActualResult)</em> tuple.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +</pre></td></tr></tbody></table> </div> <p>The <code>readEval</code> method returns a sequence of (<code>TrainingData</code>, <code>EvaluationInfo</code>, <code>RDD[(Query, ActualResult)]</code>. <code>TrainingData</code> is the same class we use for deploy, <code>RDD[(Query, ActualResult)]</code> is the validation set, <code>EvaluationInfo</code> can be used to hold some global evaluation data ; it is not used in the current example.</p><p>Lines 11 to 41 is the logic of reading and transforming data from the datastore; it is equivalent to the existing <code>readTraining</code> method. After line 41, the variable <code>labeledPoints</code> contains the complete dataset with which we use to generate the (training, validation) sequence.</p><p>Lines 43 to 57 is the <em>k-fold</em> logic. Line 45 gives each data point a unique id, and we decide whether the point belongs to the training or validation set depends on the <em>mod</em> of the id (lines 48 to 49). For each point in the valida tion set, we construct the <code>Query</code> and <code>ActualResult</code> (line 55) which is used validate the engine.</p><h2 id='evaluation-metrics' class='header-anchors'>Evaluation Metrics</h2><p>We define a <code>Metric</code> which gives a <em>score</em> to engine params. The higher the score, the better the engine params are. In this template, we use accuracy score which measures the portion of correct prediction among all data points.</p><p>In MyClassification/src/main/scala/<strong>Evaluation.scala</strong>, the class <code>Accuracy</code> implements the <em>accuracy</em> score. It extends a base helper class <code>AverageMetric</code> which calculates the average score overall <em>(Query, PredictionResult, ActualResult)</em> tuple.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 2 3 4
