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From git-site-r...@apache.org
Subject [01/51] [abbrv] [partial] incubator-predictionio-site git commit: Clean up before apache/incubator-predictionio#d674b89c7c3a17437bd406a497a08773c24c8007
Date Thu, 09 Feb 2017 20:38:04 GMT
Repository: incubator-predictionio-site
Updated Branches:
  refs/heads/asf-site 7177903a4 -> 807cf6330


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class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div
id="page-title"><h1>Customizing Data Preparator (Recommendation)</h1></div></div><div
id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul>
<li> <a href="#the-data-preparator-component">The Data Preparator Component</a
 > </li> <li> <a href="#modify-the-preparator">Modify the Preparator</a>
</li> <li> <a href="#deploy-the-modified-engine">Deploy the Modified Engine</a>
</li> <li> <a href="#adding-preparator-parameters">Adding Preparator Parameters</a>
</li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/recommendation/customize-data-prep.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="page-title"><h1>Customizing
Data Preparator (Recommendation)</h1></div></div><div class="content"><p>Data
Preparator is where pre-processing actions occurs. For example, one may want to remove some
very popular items from the training data because she thinks that these items may not help
finding individual person&#39;s tastes or one may have a black list of item that she wants
to remove from the training data before feeding it to the al
 gorithm.</p><p>This section assumes that you have created a <em>MyRecommendation</em>
engine based on the <a href="/templates/recommendation/quickstart/">Recommendation Engine
Template: QuickStart</a>. We will demonstrate how to add a filtering logic to exclude
a list of items in the training data.</p><p>A sample black list file containing
the items to be excluded is provided in <code>./data/sample_not_train_data.txt</code>.</p><p>A
full end-to-end example can be found on <a href="https://github.com/apache/incubator-predictionio/tree/develop/examples/scala-parallel-recommendation/custom-prepartor">GitHub</a>.</p><h2
id='the-data-preparator-component' class='header-anchors'>The Data Preparator Component</h2><p>Recall
<a href="/start/engines/">the DASE Architecture</a>, data is prepared by 2 components
sequentially: <em>Data Source</em> and <em>Data Preparator</em>. <em>Data
Source</em> reads data from the data store of Event Server and then <em>Data Preparator</em>
prepares <code>RDD
 [Rating]</code> for the ALS algorithm.</p><p>You may modify any component
in an engine template to fit your needs. This example shows you how to add the filtering logics
in Data Preparator.</p><h2 id='modify-the-preparator' class='header-anchors'>Modify
the Preparator</h2><p>The Data Preparator component can be found in <code>src/main/scala/Preparator.scala</code>
in the &quot;MyRecommendation&quot; directory. The unmodified version looks like the
following:</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</pre></td><td class="code"><pre><span class="k">class</span>
<span class="nc">Preparator</span>
-  <span class="k">extends</span> <span class="nc">PPreparator</span><span
class="o">[</span><span class="kt">TrainingData</span>, <span class="kt">PreparedData</span><span
class="o">]</span> <span class="o">{</span>
-
-  <span class="k">def</span> <span class="n">prepare</span><span
class="o">(</span><span class="n">sc</span><span class="k">:</span>
<span class="kt">SparkContext</span><span class="o">,</span> <span
class="n">trainingData</span><span class="k">:</span> <span class="kt">TrainingData</span><span
class="o">)</span><span class="k">:</span> <span class="kt">PreparedData</span>
<span class="o">=</span> <span class="o">{</span>
-    <span class="k">new</span> <span class="nc">PreparedData</span><span
class="o">(</span><span class="n">ratings</span> <span class="k">=</span>
<span class="n">trainingData</span><span class="o">.</span><span
class="n">ratings</span><span class="o">)</span>
-  <span class="o">}</span>
-<span class="o">}</span>
-</pre></td></tr></tbody></table> </div> <p>The
<code>prepare</code> method simply passes the ratings from <code>TrainingData</code>
to <code>PreparedData</code>.</p><p>You can modify the <code>prepare</code>
method to read a black list of items from a file and remove them from <code>TrainingData</code>,
so it becomes:</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
-16</pre></td><td class="code"><pre><span class="k">import</span>
<span class="nn">scala.io.Source</span> <span class="c1">// ADDED
-</span>
-<span class="k">class</span> <span class="nc">Preparator</span>
-  <span class="k">extends</span> <span class="nc">PPreparator</span><span
class="o">[</span><span class="kt">TrainingData</span>, <span class="kt">PreparedData</span><span
class="o">]</span> <span class="o">{</span>
-
-  <span class="k">def</span> <span class="n">prepare</span><span
class="o">(</span><span class="n">sc</span><span class="k">:</span>
<span class="kt">SparkContext</span><span class="o">,</span> <span
class="n">trainingData</span><span class="k">:</span> <span class="kt">TrainingData</span><span
class="o">)</span><span class="k">:</span> <span class="kt">PreparedData</span>
<span class="o">=</span> <span class="o">{</span>
-    <span class="c1">// MODIFIED HERE
-</span>    <span class="k">val</span> <span class="n">noTrainItems</span>
<span class="k">=</span> <span class="nc">Source</span><span class="o">.</span><span
class="n">fromFile</span><span class="o">(</span><span class="s">"./data/sample_not_train_data.txt"</span><span
class="o">)</span>
-      <span class="o">.</span><span class="n">getLines</span><span
class="o">.</span><span class="n">toSet</span>
-    <span class="c1">// exclude noTrainItems from original trainingData
-</span>    <span class="k">val</span> <span class="n">ratings</span>
<span class="k">=</span> <span class="n">trainingData</span><span
class="o">.</span><span class="n">ratings</span><span class="o">.</span><span
class="n">filter</span><span class="o">(</span> <span class="n">r</span>
<span class="k">=&gt;</span>
-      <span class="o">!</span><span class="n">noTrainItems</span><span
class="o">.</span><span class="n">contains</span><span class="o">(</span><span
class="n">r</span><span class="o">.</span><span class="n">item</span><span
class="o">)</span>
-    <span class="o">)</span>
-    <span class="k">new</span> <span class="nc">PreparedData</span><span
class="o">(</span><span class="n">ratings</span><span class="o">)</span>
-  <span class="o">}</span>
-<span class="o">}</span>
-</pre></td></tr></tbody></table> </div> <blockquote>
<p>We will show you how not to hardcode the path <code>./data/sample_not_train_data.txt</code>
soon.</p></blockquote> <h2 id='deploy-the-modified-engine' class='header-anchors'>Deploy
the Modified Engine</h2><p>Now you can deploy the modified engine as described
in <a href="/templates/recommendation/quickstart/">Quick Start</a>.</p><p>Make
sure the <code>appName</code> defined in the file <code>engine.json</code>
matches your <em>App Name</em>:</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</pre></td><td class="code"><pre>...
-<span class="s2">"datasource"</span>: <span class="o">{</span>
-  <span class="s2">"params"</span> : <span class="o">{</span>
-    <span class="s2">"appName"</span>: <span class="s2">"YourAppName"</span>
-  <span class="o">}</span>
-<span class="o">}</span>,
-...
-</pre></td></tr></tbody></table> </div> <p>To build
<em>MyRecommendation</em> and deploy it as a service:</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><span class="gp">$ </span>pio
build
-<span class="gp">$ </span>pio train
-<span class="gp">$ </span>pio deploy
-</pre></td></tr></tbody></table> </div> <p>This
will deploy an engine that binds to <a href="http://localhost:8000">http://localhost:8000</a>.
You can visit that page in your web browser to check its status.</p><p>Now, You
can try to retrieve predicted results. To recommend 4 movies to user whose ID is 1, send this
JSON <code>{ &quot;user&quot;: &quot;1&quot;, &quot;num&quot;:
4 }</code> to the deployed engine</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>curl -H <span class="s2">"Content-Type: application/json"</span>
-d <span class="s1">'{ "user": "1", "num": 4 }'</span> http://localhost:8000/queries.json
-</pre></td></tr></tbody></table> </div> <p>and
it will return a JSON of recommended movies.</p><div class="highlight json"><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</pre></td><td class="code"><pre><span class="p">{</span><span
class="w">
-  </span><span class="s2">"itemScores"</span><span class="p">:</span><span
class="w"> </span><span class="p">[</span><span class="w">
-    </span><span class="p">{</span><span class="s2">"item"</span><span
class="p">:</span><span class="w"> </span><span class="s2">"22"</span><span
class="p">,</span><span class="w"> </span><span class="s2">"score"</span><span
class="p">:</span><span class="w"> </span><span class="mf">4.072304374729956</span><span
class="p">},</span><span class="w">
-    </span><span class="p">{</span><span class="s2">"item"</span><span
class="p">:</span><span class="w"> </span><span class="s2">"62"</span><span
class="p">,</span><span class="w"> </span><span class="s2">"score"</span><span
class="p">:</span><span class="w"> </span><span class="mf">4.058482414005789</span><span
class="p">},</span><span class="w">
-    </span><span class="p">{</span><span class="s2">"item"</span><span
class="p">:</span><span class="w"> </span><span class="s2">"75"</span><span
class="p">,</span><span class="w"> </span><span class="s2">"score"</span><span
class="p">:</span><span class="w"> </span><span class="mf">4.046063009943821</span><span
class="p">},</span><span class="w">
-    </span><span class="p">{</span><span class="s2">"item"</span><span
class="p">:</span><span class="w"> </span><span class="s2">"68"</span><span
class="p">,</span><span class="w"> </span><span class="s2">"score"</span><span
class="p">:</span><span class="w"> </span><span class="mf">3.8153661512945325</span><span
class="p">}</span><span class="w">
-  </span><span class="p">]</span><span class="w">
-</span><span class="p">}</span><span class="w">
-</span></pre></td></tr></tbody></table> </div>
<p>Congratulations! You have learned how to add customized logic to your Data Preparator!</p><h2
id='adding-preparator-parameters' class='header-anchors'>Adding Preparator Parameters</h2><p>Optionally,
you may want to take the hardcoded path (<code>./data/sample_not_train_data.txt</code>)
away from the source code.</p><p>PredictionIO offers <code>PreparatorParams</code>
so you can read variable values from <code>engine.json</code> instead.</p><p>Modify
<code>src/main/scala/Preparator.scala</code> again in the <em>MyRecommendation</em>
directory to:</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
-16
-17
-18
-19</pre></td><td class="code"><pre><span class="k">import</span>
<span class="nn">org.apache.predictionio.controller.Params</span> <span class="c1">//
ADDED
-</span>
- <span class="c1">// ADDED CustomPreparatorParams case class
-</span><span class="k">case</span> <span class="k">class</span>
<span class="nc">CustomPreparatorParams</span><span class="o">(</span>
-  <span class="n">filepath</span><span class="k">:</span> <span
class="kt">String</span>
-<span class="o">)</span> <span class="k">extends</span> <span
class="nc">Params</span>
-
-<span class="k">class</span> <span class="nc">Preparator</span><span
class="o">(</span><span class="n">pp</span><span class="k">:</span>
<span class="kt">CustomPreparatorParams</span><span class="o">)</span>
<span class="c1">// ADDED CustomPreparatorParams
-</span>  <span class="k">extends</span> <span class="nc">PPreparator</span><span
class="o">[</span><span class="kt">TrainingData</span>, <span class="kt">PreparedData</span><span
class="o">]</span> <span class="o">{</span>
-
-  <span class="k">def</span> <span class="n">prepare</span><span
class="o">(</span><span class="n">sc</span><span class="k">:</span>
<span class="kt">SparkContext</span><span class="o">,</span> <span
class="n">trainingData</span><span class="k">:</span> <span class="kt">TrainingData</span><span
class="o">)</span><span class="k">:</span> <span class="kt">PreparedData</span>
<span class="o">=</span> <span class="o">{</span>
-    <span class="k">val</span> <span class="n">noTrainItems</span>
<span class="k">=</span> <span class="nc">Source</span><span class="o">.</span><span
class="n">fromFile</span><span class="o">(</span><span class="n">pp</span><span
class="o">.</span><span class="n">filepath</span><span class="o">).</span><span
class="n">getLines</span><span class="o">.</span><span class="n">toSet</span>
<span class="c1">//CHANGED
-</span>    <span class="k">val</span> <span class="n">ratings</span>
<span class="k">=</span> <span class="n">trainingData</span><span
class="o">.</span><span class="n">ratings</span><span class="o">.</span><span
class="n">filter</span><span class="o">(</span> <span class="n">r</span>
<span class="k">=&gt;</span>
-      <span class="o">!</span><span class="n">noTrainItems</span><span
class="o">.</span><span class="n">contains</span><span class="o">(</span><span
class="n">r</span><span class="o">.</span><span class="n">item</span><span
class="o">)</span>
-    <span class="o">)</span>
-    <span class="k">new</span> <span class="nc">PreparedData</span><span
class="o">(</span><span class="n">ratings</span><span class="o">)</span>
-  <span class="o">}</span>
-<span class="o">}</span>
-
-</pre></td></tr></tbody></table> </div> <p>In <code>engine.json</code>,
you define the parameters <code>filepath</code> for the Data Preparator:</p><div
class="highlight json"><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</pre></td><td class="code"><pre><span class="p">{</span><span
class="w">
-  </span><span class="err">...</span><span class="w">
-  </span><span class="s2">"preparator"</span><span class="p">:</span><span
class="w"> </span><span class="p">{</span><span class="w">
-    </span><span class="s2">"params"</span><span class="p">:</span><span
class="w"> </span><span class="p">{</span><span class="w">
-      </span><span class="s2">"filepath"</span><span class="p">:</span><span
class="w"> </span><span class="s2">"./data/sample_not_train_data.txt"</span><span
class="w">
-    </span><span class="p">}</span><span class="w">
-  </span><span class="p">},</span><span class="w">
-  </span><span class="err">...</span><span class="w">
-</span><span class="err">}</span><span class="w">
-</span></pre></td></tr></tbody></table> </div>
<p>Try to build <em>MyRecommendation</em> and deploy it again:</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><span class="gp">$ </span>pio
build
-<span class="gp">$ </span>pio train
-<span class="gp">$ </span>pio deploy
-</pre></td></tr></tbody></table> </div> <p>You
can change the <code>filepath</code> value without re-building the code next time.</p><h4
id='<a-href="/templates/recommendation/customize-serving/">next:-customizing-serving</a>'
class='header-anchors' ><a href="/templates/recommendation/customize-serving/">Next:
Customizing Serving</a></h4></div></div></div></div><footer><div
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