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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Deploying an Engine</a><span class="spacer">&gt;</span></li><li><span 
class="last">Batch Predictions</span></li></ul></div><div 
id="page-title"><h1>Batch Predictions</h1></div></div><div id
 ="table-of-content-wrapper"><h5>On this page</h5><aside 
id="table-of-contents"><ul> <li> <a href="#overview">Overview</a> </li> <li> <a 
href="#compatibility">Compatibility</a> </li> <li> <a href="#usage">Usage</a> 
</li> <li> <a href="#example">Example</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/batchpredict/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="#">Deploying an Engine</a><span 
class="spacer">&gt;</span></li><li><span class="last">Batch 
Predictions</span></li></ul></div><div id="page-title"><h1>Batch 
Predictions</h1></div></div><div class="content"> <h2 id='overview' 
class='header-anchors'>Overview</h2><p>Process predictions for many queries 
using efficient parallelization through Spark. Useful for mass auditing of 
predictions 
 and for generating predictions to push into other systems.</p><p>Batch predict 
reads and writes multi-object JSON files similar to the <a 
href="/datacollection/batchimport/">batch import</a> format. JSON objects are 
separated by newlines and cannot themselves contain unencoded newlines.</p><h2 
id='compatibility' class='header-anchors'>Compatibility</h2><p><code>pio 
batchpredict</code> loads the engine and processes queries exactly like 
<code>pio deploy</code>. There is only one additional requirement for engines 
to utilize batch predict:</p><div class="alert-message warning"><p>All 
algorithm classes used in the engine must be <a 
href="https://www.scala-lang.org/api/2.11.8/index.html#scala.Serializable";>serializable</a>.
 <strong>This is already true for PredictionIO&#39;s base algorithm 
classes</strong>, but may be broken by including non-serializable fields in 
their constructor. Using the <a 
href="http://fdahms.com/2015/10/14/scala-and-the-transient-lazy-val-pattern/";><code>@transie
 nt</code> annotation</a> may help in these cases.</p></div><p>This requirement 
is due to processing the input queries as a <a 
href="https://spark.apache.org/docs/latest/rdd-programming-guide.html#resilient-distributed-datasets-rdds";>Spark
 RDD</a> which enables high-performance parallelization, even on a single 
machine.</p><h2 id='usage' class='header-anchors'>Usage</h2><h3 
id='<code>pio-batchpredict</code>' class='header-anchors' ><code>pio 
batchpredict</code></h3><p>Command to process bulk predictions. Takes the same 
options as <code>pio deploy</code> plus:</p><h3 
id='<code>--input-&lt;value&gt;</code>' class='header-anchors' ><code>--input 
&lt;value&gt;</code></h3><p>Path to file containing queries; a multi-object 
JSON file with one query object per line. Accepts any valid Hadoop file 
URL.</p><p>Default: <code>batchpredict-input.json</code></p><h3 
id='<code>--output-&lt;value&gt;</code>' class='header-anchors' ><code>--output 
&lt;value&gt;</code></h3><p>Path to file to receive res
 ults; a multi-object JSON file with one object per line, the prediction + 
original query. Accepts any valid Hadoop file URL. Actual output will be 
written as Hadoop partition files in a directory with the output 
name.</p><p>Default: <code>batchpredict-output.json</code></p><h3 
id='<code>--query-partitions-&lt;value&gt;</code>' class='header-anchors' 
><code>--query-partitions &lt;value&gt;</code></h3><p>Configure the concurrency 
of predictions by setting the number of partitions used internally for the RDD 
of queries. This will directly effect the number of resulting 
<code>part-*</code> output files. While setting to <code>1</code> may seem 
appealing to get a single output file, this will remove parallelization for the 
batch process, reducing performance and possibly exhausting 
memory.</p><p>Default: number created by Spark context&#39;s 
<code>textFile</code> (probably the number of cores available on the local 
machine)</p><h3 id='<code>--engine-instance-id-&lt;value&gt;</code>' clas
 s='header-anchors' ><code>--engine-instance-id 
&lt;value&gt;</code></h3><p>Identifier for the trained instance to use for 
batch predict.</p><p>Default: the latest trained instance.</p><h2 id='example' 
class='header-anchors'>Example</h2><h3 id='input' 
class='header-anchors'>Input</h3><p>A multi-object JSON file of queries as they 
would be sent to the engine&#39;s HTTP Queries API.</p><div 
class="alert-message note"><p>Read via <a 
href="https://spark.apache.org/docs/latest/rdd-programming-guide.html#external-datasets";>SparkContext&#39;s
 <code>textFile</code></a> and so may be a single file or any supported Hadoop 
format.</p></div><p>File: <code>batchpredict-input.json</code></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</pre></td><td class="code"><pre><span class="p">{</span><span 
class="s2">"user"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">}</span><span class="w">
+</span><span class="p">{</span><span class="s2">"user"</span><span 
class="p">:</span><span class="s2">"2"</span><span class="p">}</span><span 
class="w">
+</span><span class="p">{</span><span class="s2">"user"</span><span 
class="p">:</span><span class="s2">"3"</span><span class="p">}</span><span 
class="w">
+</span><span class="p">{</span><span class="s2">"user"</span><span 
class="p">:</span><span class="s2">"4"</span><span class="p">}</span><span 
class="w">
+</span><span class="p">{</span><span class="s2">"user"</span><span 
class="p">:</span><span class="s2">"5"</span><span class="p">}</span><span 
class="w">
+</span></pre></td></tr></tbody></table> </div> <h3 id='execute' 
class='header-anchors'>Execute</h3><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>pio batchpredict <span class="se">\</span>
+  --input batchpredict-input.json <span class="se">\</span>
+  --output batchpredict-output.json
+</pre></td></tr></tbody></table> </div> <p>This command will run to 
completion, aborting if any errors are encountered.</p><h3 id='output' 
class='header-anchors'>Output</h3><p>A multi-object JSON file of predictions + 
original queries. The predictions are JSON objects as they would be returned 
from the engine&#39;s HTTP Queries API.</p><div class="alert-message 
note"><p>Results are written via Spark RDD&#39;s <code>saveAsTextFile</code> so 
each partition will be written to its own <code>part-*</code> file. See <a 
href="#post-processing-results">post-processing results</a>.</p></div><p>File 
1: <code>batchpredict-output.json/part-00000</code></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</pre></td><td class="code"><pre><span class="p">{</span><span 
class="s2">"query"</span><span class="p">:{</span><span 
class="s2">"user"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">33</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"2"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">32</span><span class="p">}]}}</span><span class="w">
+</span><span class="p">{</span><span class="s2">"query"</span><span 
class="p">:{</span><span class="s2">"user"</span><span class="p">:</span><span 
class="s2">"3"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"2"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">16</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"3"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">12</span><span class="p">}]}}</span><span class="w">
+</span><span class="p">{</span><span class="s2">"query"</span><span 
class="p">:{</span><span class="s2">"user"</span><span class="p">:</span><span 
class="s2">"4"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"3"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">19</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">18</span><span class="p">}]}}</span><span class="w">
+</span></pre></td></tr></tbody></table> </div> <p>File 2: 
<code>batchpredict-output.json/part-00001</code></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</pre></td><td class="code"><pre><span class="p">{</span><span 
class="s2">"query"</span><span class="p">:{</span><span 
class="s2">"user"</span><span class="p">:</span><span 
class="s2">"2"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"5"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">55</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"3"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">28</span><span class="p">}]}}</span><span class="w">
+</span><span class="p">{</span><span class="s2">"query"</span><span 
class="p">:{</span><span class="s2">"user"</span><span class="p">:</span><span 
class="s2">"5"</span><span class="p">},</span><span 
class="s2">"prediction"</span><span class="p">:{</span><span 
class="s2">"itemScores"</span><span class="p">:[{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"1"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">24</span><span class="p">},{</span><span 
class="s2">"item"</span><span class="p">:</span><span 
class="s2">"4"</span><span class="p">,</span><span 
class="s2">"score"</span><span class="p">:</span><span 
class="mi">14</span><span class="p">}]}}</span><span class="w">
+</span></pre></td></tr></tbody></table> </div> <h3 
id='post-processing-results' class='header-anchors'>Post-processing 
Results</h3><p>After the process exits successfully, the parts may be 
concatenated into a single output file using a command like:</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>cat batchpredict-output.json/part-<span class="k">*</span> 
&gt; batchpredict-output-all.json
+</pre></td></tr></tbody></table> </div> </div></div></div></div><footer><div 
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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="/community/">Getting Involved</a><span 
class="spacer">&gt;</span></li><li><span class="last">Contribute 
Code</span></li></ul></div><div id="page-title"><h1>Contribute 
Code</h1></div></d
 iv><div id="table-of-content-wrapper"><h5>On this page</h5><aside 
id="table-of-contents"><ul> <li> <a href="#areas-in-need-of-help">Areas in Need 
of Help</a> </li> <li> <a href="#how-to-report-an-issue">How to Report an 
Issue</a> </li> <li> <a href="#how-to-help-resolve-existing-issues">How to Help 
Resolve Existing Issues</a> </li> <li> <a 
href="#how-to-add-propose-a-new-feature">How to Add / Propose a New Feature</a> 
</li> <li> <a href="#how-to-issue-a-pull-request">How to Issue a Pull 
Request</a> </li> <li> <a href="#getting-started">Getting Started</a> </li> 
<li> <a href="#anatomy-of-apache-predictionio-incubating-code-tree">Anatomy of 
Apache PredictionIO (incubating) Code Tree</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/community/contribute-code.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="b
 readcrumbs" class="hidden-sm hidden xs"><ul><li><a href="/community/">Getting 
Involved</a><span class="spacer">&gt;</span></li><li><span 
class="last">Contribute Code</span></li></ul></div><div 
id="page-title"><h1>Contribute Code</h1></div></div><div class="content"> 
<p>Thank you for your interest in contributing to Apache PredictionIO 
(incubating). Our mission is to enable developers to build scalable machine 
learning applications easily. Here is how you can help with the project 
development. If you have any question regarding development at anytime, please 
free to <a 
href="mailto:[email protected]";>subscribe</a> and 
post to the <a 
href="mailto:[email protected]";>Development 
Mailing List</a>.</p><h2 id='areas-in-need-of-help' 
class='header-anchors'>Areas in Need of Help</h2><p>We accept contributions of 
all kinds at any time. We are compiling this list to show features that are 
highly sought after by the community.</p> <ul>
  <li>Tests and CI</li> <li>Engine template, tutorials, and samples</li> 
<li>Client SDKs</li> <li>Building engines in Java (updating the Java controller 
API)</li> <li>Code clean up and refactoring</li> <li>Code and data pipeline 
optimization</li> <li>Developer experience (UX) improvement</li> </ul> <h2 
id='how-to-report-an-issue' class='header-anchors'>How to Report an 
Issue</h2><p>If you wish to report an issue you found, you can do so on <a 
href="https://issues.apache.org/jira/browse/PIO";>Apache PredictionIO 
(incubating) JIRA</a>.</p><h2 id='how-to-help-resolve-existing-issues' 
class='header-anchors'>How to Help Resolve Existing Issues</h2><p>In general, 
bug fixes should be done the same way as new features, but critical bug fixes 
will follow a different path.</p><h2 id='how-to-add-/-propose-a-new-feature' 
class='header-anchors'>How to Add / Propose a New Feature</h2><p>Before adding 
new features into JIRA, please check that the feature does not currently exist 
in JIRA.</p> <ol> <l
 i>To propose a new feature, simply <a 
href="mailto:[email protected]";>subscribe</a> and 
post your proposal to <a 
href="mailto:[email protected]";>Apache PredictionIO 
(incubating) Development Mailing List</a>.</li> <li>Discuss with the community 
and the core development team on what needs to be done, and lay down concrete 
plans on deliverables.</li> <li>Once solid plans are made, start creating 
tickets in the <a href="https://issues.apache.org/jira/browse/PIO";>issue 
tracker</a>.</li> <li>Work side by side with other developers using Apache 
PredictionIO (incubating) Development Mailing List as primary mode of 
communication. You never know if someone else has a better idea. ;)</li> </ol> 
<h3 id='adding-ticket-to-jira' class='header-anchors'>Adding ticket to 
JIRA</h3> <ol> <li>Add a descriptive Summary and a detailed description</li> 
<li>Set Issue Type to Bug, Improvement, New Feature, Test or Wish</li> <li>Set 
Priority to Blocker, Critic
 al, Major, Minor or Trivial</li> <li>Fill out Affects Version with the version 
of PredictionIO you are currently using</li> <li>Fill out Environment if needed 
for description of your bug / feature</li> <li>Please leave other fields 
blank</li> </ol> <h3 id='triaging-jira' class='header-anchors'>Triaging 
JIRA</h3><p>Tickets will be triaged by PredictionIO committers.</p> <ul> 
<li><p><strong>Target Version</strong>: Either a particular version or 
<code>Future</code> if to be done later</p> <ul> <li>Once a fix has been 
committed, the Fix Version will filled in with the appropriate release</li> 
</ul></li> <li><p><strong>Component</strong>: Each ticket will be annotated 
with one or more of the following Components</p> <ul> 
<li><strong>Core</strong>: affects the main code branch / will be part of a 
release</li> <li><strong>Documentation</strong>: affects the documents / will 
be pushed to livedoc branch</li> <li><strong>Templates</strong>: affects one of 
the separate github repositories for
  a template</li> </ul></li> </ul> <h2 id='how-to-issue-a-pull-request' 
class='header-anchors'>How to Issue a Pull Request</h2><p>When you have 
finished your code, you can <a 
href="https://help.github.com/articles/creating-a-pull-request/";>create a pull 
request</a> against the <strong>develop</strong> branch.</p> <ul> <li>The title 
must contain a tag associating with an existing JIRA ticket. You must create a 
ticket so that the infrastructure can correctly track issues across Apache JIRA 
and GitHub. If your ticket is <code>PIO-789</code>, your title must look 
something like <code>[PIO-789] Some short description</code>.</li> <li>Please 
also, in your commit message summary, include the JIRA ticket number similar to 
above.</li> <li>Make sure the title and description are clear and concise. For 
more details on writing a good commit message, check out <a 
href="http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html";>this
 guide</a>.</li> <li>If the change is visual, make sure
  to include a screenshot or GIF.</li> <li>Make sure it is being opened into 
the right branch.</li> <li>Make sure it has been rebased on top of that 
branch.</li> </ul> <div class="alert-message note"><p>When it is close to a 
release, and if there are major development ongoing, a release branch will be 
forked from the develop branch to stabilize the code for binary release. Please 
refer to the <em>git flow</em> methodology page for more 
information.</p></div><h2 id='getting-started' class='header-anchors'>Getting 
Started</h2><p>Apache PredictionIO (incubating) relies heavily on the <a 
href="http://nvie.com/posts/a-successful-git-branching-model/";>git flow 
methodology</a>. Please make sure you read and understand it before you start 
your development. By default, cloning Apache PredictionIO (incubating) will put 
you in the <em>develop</em> branch, which in most cases is where all the latest 
development go to.</p><div class="alert-message note"><p>For core development, 
please follow the 
 <a href="http://docs.scala-lang.org/style/";>Scala Style 
Guide</a>.</p></div><h3 
id='create-a-fork-of-the-apache-predictionio-(incubating)-repository' 
class='header-anchors'>Create a Fork of the Apache PredictionIO (incubating) 
Repository</h3> <ol> <li>Start by creating a GitHub account if you do not 
already have one.</li> <li>Go to <a 
href="https://github.com/PredictionIO/PredictionIO";>Apache PredictionIO 
(incubating)’s GitHub mirror</a> and fork it to your own account.</li> 
<li>Clone your fork to your local machine.</li> </ol> <p>If you need additional 
help, please refer to <a 
href="https://help.github.com/articles/fork-a-repo/";>https://help.github.com/articles/fork-a-repo/</a>.</p><h3
 id='building-apache-predictionio-(incubating)-from-source' 
class='header-anchors'>Building Apache PredictionIO (incubating) from 
Source</h3><p>After the previous section, you should have a copy of Apache 
PredictionIO (incubating) in your local machine ready to be built.</p> <ol> 
<li>Make sure you a
 re on the <em>develop</em> branch. You can double check by <code>git 
status</code> or simply <code>git checkout develop</code>.</li> <li>At the root 
of the repository, do <code>./make-distribution.sh</code> to build 
PredictionIO.</li> </ol> <h3 id='setting-up-the-environment' 
class='header-anchors'>Setting Up the Environment</h3><p>Apache PredictionIO 
(incubating) relies on 3rd party software to perform its tasks. To set them up, 
simply follow this <a 
href="http://predictionio.incubator.apache.org/install/install-sourcecode/#installing-dependencies";>documentation</a>.</p><h3
 id='start-hacking' class='header-anchors'>Start Hacking</h3><p>You should have 
a Apache PredictionIO (incubating) development environment by now. Happy 
hacking!</p><h2 id='anatomy-of-apache-predictionio-(incubating)-code-tree' 
class='header-anchors'>Anatomy of Apache PredictionIO (incubating) Code 
Tree</h2><p>The following describes each directory’s purpose.</p><h3 id='bin' 
class='header-anchors'>bin</h3><p>Sh
 ell scripts and any relevant components to go into the binary distribution. 
Utility shell scripts can also be included here.</p><h3 id='conf' 
class='header-anchors'>conf</h3><p>Configuration files that are used by both a 
source tree and binary distribution.</p><h3 id='core' 
class='header-anchors'>core</h3><p>Core Apache PredictionIO (incubating) code 
that provides the DASE controller API, core data structures, and workflow 
creation and management code.</p><h3 id='data' 
class='header-anchors'>data</h3><p>Apache PredictionIO (incubating) Event 
Server, and backend-agnostic storage layer for event store and metadata 
store.</p><h3 id='docs' class='header-anchors'>docs</h3><p>Source code for <a 
href="http://predictionio.incubator.apache.org";>http://predictionio.incubator.apache.org</a>
 site, and any other documentation support files.</p><h3 id='examples' 
class='header-anchors'>examples</h3><p>Complete code examples showing Apache 
PredictionIO (incubating)&#39;s application.</p><h3 id='sbt
 ' class='header-anchors'>sbt</h3><p>Embedded SBT (Simple Build Tool) 
launcher.</p><h3 id='storage' class='header-anchors'>storage</h3><p>Storage 
implementations.</p><h3 id='tools' class='header-anchors'>tools</h3><p>Tools 
for running Apache PredictionIO (incubating). Contains primarily the CLI 
(command-line interface) and its supporting code, and the experimental 
evaluation dashboard.</p></div></div></div></div><footer><div 
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all newly accepted projects until a further review indicates that the 
infrastructure, communications, and decision making process have stabilized in 
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