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+<!DOCTYPE html><html><head><title>Evaluation Dashboard</title><meta 
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 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="#">ML Tuning and Evaluation</a><span 
class="spacer">&gt;</span></li><li><span class="last">Evaluation 
Dashboard</span></li></ul></div><div id="page-title"><h1>Evaluation 
Dashboard</h1></div></div><div id="table-of-content-wrapper"><a 
id="edit-page-link" href="https:/
 
/github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/evaluation/evaluationdashboard.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="#">ML Tuning and 
Evaluation</a><span class="spacer">&gt;</span></li><li><span 
class="last">Evaluation Dashboard</span></li></ul></div><div 
id="page-title"><h1>Evaluation Dashboard</h1></div></div><div 
class="content"><div class="alert-message warning"><p>This is an experimental 
development tool, which exposes environment variables and other sensitive 
information about the PredictionIO application (e.g. storage configs, 
credentials etc.). It is not recommended to be run in 
production.</p></div><p>PredictionIO provides a web dashboard which allows you 
to see previous evaluation and a drill down page about each evaluation. It is 
particularly useful when we ran multiple <a href="/evaluati
 on/paramtuning/">hyperparameter tunings</a> as we may easily lose track of all 
the engine variants evaluated.</p><p>We can start the dashboard with the 
following command:</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ 
</span>pio dashboard
+</pre></td></tr></tbody></table> </div> <p>The dashboard lists out all 
completed evaluations in a reversed chronological order. A high level 
description of each evaluation can be seen directly from the dashboard. We can 
also click on the <em>HTML</em> button to see the evaluation drill down 
page.</p><p><em>Note:</em> The dashboard server has SSL enabled and is 
authenticated by a key passed as a query string param <code>accessKey</code>. 
The configuration is in 
<code>conf/server.conf</code></p></div></div></div></div><footer><div 
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href="/datacollection/eventapi/"><span>Collecting Data with 
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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">ML Tuning and Evaluation</a><span 
class="spacer">&gt;</span></li><li><span 
class="last">Overview</span></li></ul></div><div id="page-title"><h1>ML Tuning 
and Evaluation</h1></div></div><div id="table-of-content-wrapper"><a 
id="edit-page-link" href="https://github.com/apache/incubator-pre
 dictionio/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="#">ML Tuning and 
Evaluation</a><span class="spacer">&gt;</span></li><li><span 
class="last">Overview</span></li></ul></div><div id="page-title"><h1>ML Tuning 
and Evaluation</h1></div></div><div class="content"><p>PredictionIO&#39;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 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/">Bulding Evaluation Metrics</a> - we illustrate 
how to implement a custom metric with as few as one line of code (plus some 
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 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="#">ML Tuning and Evaluation</a><span 
class="spacer">&gt;</span></li><li><span class="last">Building Evaluation 
Metrics</span></li></ul></div><div id="page-title"><h1>Building Evaluation 
Metrics</h1></div></div><div id="table-of-content-wrapper"><h5>On th
 is page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#overview">Overview</a> </li> <li> <a 
href="#example-1-accuracy-metric">Example 1: Accuracy Metric</a> </li> <li> <a 
href="#example-2-precision-metric">Example 2: Precision Metric</a> </li> </ul> 
</aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/evaluation/metricbuild.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="#">ML Tuning and 
Evaluation</a><span class="spacer">&gt;</span></li><li><span 
class="last">Building Evaluation Metrics</span></li></ul></div><div 
id="page-title"><h1>Building Evaluation Metrics</h1></div></div><div 
class="content"><p>PredictionIO enables developer to implement evaluation 
custom evaluation metric with just a few lines of code. We illustrate it with 
<a href="/templates/classifi
 cation/quickstart/">the classification template</a>.</p><h2 id='overview' 
class='header-anchors'>Overview</h2><p>A simplistic form of metric is a 
function which takes a <code>(Query, PredictedResult, 
ActualResult)</code>-tuple (<em>QPA-tuple</em>) as input and return a score. 
Exploiting this properties allows us to implement custom metric with a single 
line of code (plus some boilerplates). We demonstate this with two metrics: 
accuracy and precision.</p> <h2 id='example-1:-accuracy-metric' 
class='header-anchors'>Example 1: Accuracy Metric</h2><p>Accuracy is a metric 
capturing the portion of correct prediction among all test data points. A way 
to model this is for each correct QPA-tuple, we give a score of 1.0 and 
otherwise 0.0, then we take an average of all tuple scores.</p><p>PredictionIO 
has a [[AverageMetric]] helper class which provides this feature. This class 
takes 4 type parameters, [[EvalInfo]], [[Query]], [[PredictedResult]], and 
[[ActualResult]], these types can be found 
 from the engine&#39;s signature. Line 5 below is the custom 
calculation.</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
+5
+6</pre></td><td class="code"><pre><span class="k">case</span> <span 
class="k">class</span> <span class="nc">Accuracy</span>
+  <span class="k">extends</span> <span class="nc">AverageMetric</span><span 
class="o">[</span><span class="kt">EmptyEvaluationInfo</span>, <span 
class="kt">Query</span>, <span class="kt">PredictedResult</span>, <span 
class="kt">ActualResult</span><span class="o">]</span> <span class="o">{</span>
+  <span class="k">def</span> <span class="n">calculate</span><span 
class="o">(</span><span class="n">query</span><span class="k">:</span> <span 
class="kt">Query</span><span class="o">,</span> <span 
class="n">predicted</span><span class="k">:</span> <span 
class="kt">PredictedResult</span><span class="o">,</span> <span 
class="n">actual</span><span class="k">:</span> <span 
class="kt">ActualResult</span><span class="o">)</span>
+  <span class="k">:</span> <span class="kt">Double</span> <span 
class="o">=</span> 
+    <span class="o">(</span><span class="k">if</span> <span 
class="o">(</span><span class="n">predicted</span><span class="o">.</span><span 
class="n">label</span> <span class="o">==</span> <span 
class="n">actual</span><span class="o">.</span><span 
class="n">label</span><span class="o">)</span> <span class="mf">1.0</span> 
<span class="k">else</span> <span class="mf">0.0</span><span class="o">)</span>
+<span class="o">}</span>
+</pre></td></tr></tbody></table> </div> <p>Once we define a metric, we tell 
PredictionIO we are using it in the <code>Evaluation</code> object. We can run 
the following command to kick start the evaluation.</p><div class="highlight 
shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" 
style="text-align: right"><pre class="lineno">1
+2
+3
+4
+5</pre></td><td class="code"><pre><span class="gp">$ </span>pio build
+...
+<span class="gp">$ </span>pio <span class="nb">eval 
</span>org.template.classification.AccuracyEvaluation <span class="se">\</span>
+    org.template.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&#39;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&#39;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&#39;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&#39;t care</em> negative cases.</p><p>PredictionIO provides 
a helper class <code>OptionAverageMetric</code> allows user to specify 
<em>don&#39;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 simliar to the accuracy 
metric. The negatively predicted case are the <em>don&#39;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
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+15</pre></td><td class="code"><pre><span class="k">case</span> <span 
class="k">class</span> <span class="nc">Precision</span><span 
class="o">(</span><span class="n">label</span><span class="k">:</span> <span 
class="kt">Double</span><span class="o">)</span>
+  <span class="k">extends</span> <span 
class="nc">OptionAverageMetric</span><span class="o">[</span><span 
class="kt">EmptyEvaluationInfo</span>, <span class="kt">Query</span>, <span 
class="kt">PredictedResult</span>, <span class="kt">ActualResult</span><span 
class="o">]</span> <span class="o">{</span>
+  <span class="k">def</span> <span class="n">calculate</span><span 
class="o">(</span><span class="n">query</span><span class="k">:</span> <span 
class="kt">Query</span><span class="o">,</span> <span 
class="n">predicted</span><span class="k">:</span> <span 
class="kt">PredictedResult</span><span class="o">,</span> <span 
class="n">actual</span><span class="k">:</span> <span 
class="kt">ActualResult</span><span class="o">)</span>
+  <span class="k">:</span> <span class="kt">Option</span><span 
class="o">[</span><span class="kt">Double</span><span class="o">]</span> <span 
class="k">=</span> <span class="o">{</span>
+    <span class="k">if</span> <span class="o">(</span><span 
class="n">predicted</span><span class="o">.</span><span class="n">label</span> 
<span class="o">==</span> <span class="n">label</span><span class="o">)</span> 
<span class="o">{</span>
+      <span class="k">if</span> <span class="o">(</span><span 
class="n">predicted</span><span class="o">.</span><span class="n">label</span> 
<span class="o">==</span> <span class="n">actual</span><span 
class="o">.</span><span class="n">label</span><span class="o">)</span> <span 
class="o">{</span>
+        <span class="nc">Some</span><span class="o">(</span><span 
class="mf">1.0</span><span class="o">)</span>  <span class="c1">// True positive
+</span>      <span class="o">}</span> <span class="k">else</span> <span 
class="o">{</span>
+        <span class="nc">Some</span><span class="o">(</span><span 
class="mf">0.0</span><span class="o">)</span>  <span class="c1">// False 
positive
+</span>      <span class="o">}</span>
+    <span class="o">}</span> <span class="k">else</span> <span 
class="o">{</span>
+      <span class="nc">None</span>  <span class="c1">// Unrelated case for 
calcuating precision
+</span>    <span class="o">}</span>
+  <span class="o">}</span>
+<span class="o">}</span>
+</pre></td></tr></tbody></table> </div> <p>We define a new 
<code>Evaluation</code> object to tell PredictionIO how to use this new 
precision metric.</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1
+2
+3</pre></td><td class="code"><pre>object PrecisionEvaluation extends 
Evaluation <span class="o">{</span>
+  engineMetric <span class="o">=</span> <span 
class="o">(</span>ClassificationEngine<span class="o">()</span>, new 
Precision<span class="o">(</span>label <span class="o">=</span> 1.0<span 
class="o">))</span>
+<span class="o">}</span>
+</pre></td></tr></tbody></table> </div> <p>We can kickstarts the evaluation 
with the following command, notice that we are reusing the same engine params 
list as before. This address the separation of concern when we conduct 
hyperparameter tuning.</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26
+27
+28
+29
+30
+31
+32
+33
+34
+35
+36
+37
+38
+39
+40
+41
+42
+43
+44
+45
+46</pre></td><td class="code"><pre><span class="gp">$ </span>pio build
+...
+<span class="gp">$ </span>pio <span class="nb">eval 
</span>org.template.classification.PrecisionEvaluation <span class="se">\</span>
+    org.template.classification.EngineParamsList 
+...
+<span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span 
class="nv">$]</span> Starting evaluation instance ID: SMhzYbJ9QgKkD0fQzTA7MA
+...
+<span class="o">[</span>INFO] <span class="o">[</span>MetricEvaluator] 
Iteration 0
+<span class="o">[</span>INFO] <span class="o">[</span>MetricEvaluator] 
EngineParams: <span class="o">{</span><span 
class="s2">"dataSourceParams"</span>:<span class="o">{</span><span 
class="s2">""</span>:<span class="o">{</span><span 
class="s2">"appId"</span>:19,<span class="s2">"evalK"</span>:5<span 
class="o">}}</span>,<span class="s2">"preparatorParams"</span>:<span 
class="o">{</span><span class="s2">""</span>:<span class="o">{}}</span>,<span 
class="s2">"algorithmParamsList"</span>:[<span class="o">{</span><span 
class="s2">"naive"</span>:<span class="o">{</span><span 
class="s2">"lambda"</span>:10.0<span class="o">}}]</span>,<span 
class="s2">"servingParams"</span>:<span class="o">{</span><span 
class="s2">""</span>:<span class="o">{}}}</span>
+<span class="o">[</span>INFO] <span class="o">[</span>MetricEvaluator] Result: 
MetricScores<span class="o">(</span>0.8846153846153846,List<span 
class="o">())</span>
+<span class="o">[</span>INFO] <span class="o">[</span>MetricEvaluator] 
Iteration 1
+<span class="o">[</span>INFO] <span class="o">[</span>MetricEvaluator] 
EngineParams: <span class="o">{</span><span 
class="s2">"dataSourceParams"</span>:<span class="o">{</span><span 
class="s2">""</span>:<span class="o">{</span><span 
class="s2">"appId"</span>:19,<span class="s2">"evalK"</span>:5<span 
class="o">}}</span>,<span class="s2">"preparatorParams"</span>:<span 
class="o">{</span><span class="s2">""</span>:<span class="o">{}}</span>,<span 
class="s2">"algorithmParamsList"</span>:[<span class="o">{</span><span 
class="s2">"naive"</span>:<span class="o">{</span><span 
class="s2">"lambda"</span>:100.0<span class="o">}}]</span>,<span 
class="s2">"servingParams"</span>:<span class="o">{</span><span 
class="s2">""</span>:<span class="o">{}}}</span>
+<span class="o">[</span>INFO] <span class="o">[</span>MetricEvaluator] Result: 
MetricScores<span class="o">(</span>0.7936507936507936,List<span 
class="o">())</span>
+<span class="o">[</span>INFO] <span class="o">[</span>MetricEvaluator] 
Iteration 2
+<span class="o">[</span>INFO] <span class="o">[</span>MetricEvaluator] 
EngineParams: <span class="o">{</span><span 
class="s2">"dataSourceParams"</span>:<span class="o">{</span><span 
class="s2">""</span>:<span class="o">{</span><span 
class="s2">"appId"</span>:19,<span class="s2">"evalK"</span>:5<span 
class="o">}}</span>,<span class="s2">"preparatorParams"</span>:<span 
class="o">{</span><span class="s2">""</span>:<span class="o">{}}</span>,<span 
class="s2">"algorithmParamsList"</span>:[<span class="o">{</span><span 
class="s2">"naive"</span>:<span class="o">{</span><span 
class="s2">"lambda"</span>:1000.0<span class="o">}}]</span>,<span 
class="s2">"servingParams"</span>:<span class="o">{</span><span 
class="s2">""</span>:<span class="o">{}}}</span>
+<span class="o">[</span>INFO] <span class="o">[</span>MetricEvaluator] Result: 
MetricScores<span class="o">(</span>0.37593984962406013,List<span 
class="o">())</span>
+<span class="o">[</span>INFO] <span class="o">[</span>CoreWorkflow<span 
class="nv">$]</span> Updating evaluation instance with result: 
MetricEvaluatorResult:
+  <span class="c"># engine params evaluated: 3</span>
+Optimal Engine Params:
+  <span class="o">{</span>
+  <span class="s2">"dataSourceParams"</span>:<span class="o">{</span>
+    <span class="s2">""</span>:<span class="o">{</span>
+      <span class="s2">"appId"</span>:19,
+      <span class="s2">"evalK"</span>:5
+    <span class="o">}</span>
+  <span class="o">}</span>,
+  <span class="s2">"preparatorParams"</span>:<span class="o">{</span>
+    <span class="s2">""</span>:<span class="o">{</span>
+
+    <span class="o">}</span>
+  <span class="o">}</span>,
+  <span class="s2">"algorithmParamsList"</span>:[
+    <span class="o">{</span>
+      <span class="s2">"naive"</span>:<span class="o">{</span>
+        <span class="s2">"lambda"</span>:10.0
+      <span class="o">}</span>
+    <span class="o">}</span>
+  <span class="o">]</span>,
+  <span class="s2">"servingParams"</span>:<span class="o">{</span>
+    <span class="s2">""</span>:<span class="o">{</span>
+
+    <span class="o">}</span>
+  <span class="o">}</span>
+<span class="o">}</span>
+Metrics:
+  org.template.classification.Precision: 0.8846153846153846
+</pre></td></tr></tbody></table> </div> <p>(See 
MyClassification/src/main/scala/<strong><em>PrecisionEvaluation.scala</em></strong>
 for the full usage.)</p></div></div></div></div><footer><div 
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