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Posted to commits@predictionio.apache.org by gi...@apache.org on 2018/08/11 00:24:06 UTC
[07/11] predictionio-site git commit: Documentation based on
apache/predictionio#54415e1066ae2d646eef62ebfdf801ace1de2097
http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/c17b9607/sitemap.xml
----------------------------------------------------------------------
diff --git a/sitemap.xml b/sitemap.xml
index 1b416b6..c19b7a3 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,781 +2,781 @@
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
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<loc>//predictionio.apache.org/datacollection/plugin/</loc>
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</url>
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http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/c17b9607/templates/classification/quickstart/index.html
----------------------------------------------------------------------
diff --git a/templates/classification/quickstart/index.html b/templates/classification/quickstart/index.html
index 7ffd5cc..02b26cd 100644
--- a/templates/classification/quickstart/index.html
+++ b/templates/classification/quickstart/index.html
@@ -32,7 +32,7 @@ Your system is all ready to go.
<span class="o">[</span>INFO] <span class="o">[</span>App<span class="nv">$]</span> MyApp1 | 1 | 3mZWDzci2D5YsqAnqNnXH9SB6Rg3dsTBs8iHkK6X2i54IQsIZI1eEeQQyMfs7b3F | <span class="o">(</span>all<span class="o">)</span>
<span class="o">[</span>INFO] <span class="o">[</span>App<span class="nv">$]</span> MyApp2 | 2 | io5lz6Eg4m3Xe4JZTBFE13GMAf1dhFl6ZteuJfrO84XpdOz9wRCrDU44EUaYuXq5 | <span class="o">(</span>all<span class="o">)</span>
<span class="o">[</span>INFO] <span class="o">[</span>App<span class="nv">$]</span> Finished listing 2 app<span class="o">(</span>s<span class="o">)</span>.
-</pre></td></tr></tbody></table> </div> <p><a href="#"></a></p> <h2 id='4.-collecting-data' class='header-anchors'>4. Collecting Data</h2><p>Next, let's collect some training data. By default, the Classification Engine Template reads 4 properties of a user record: attr0, attr1, attr2 and plan. This templates requires '$set' user events.</p><div class="alert-message info"><p>This template can easily be customized to use different or more number of attributes.</p></div> <p>You can send these events to PredictionIO Event Server in real-time easily by making a HTTP request or through the provided SDK. Please see <a href="/appintegration/">App Integration Overview</a> for more details how to integrate your app with SDK.</p><p>Let's try sending events to EventServer with the following <code>curl</code> commands (The corresponding SDK code is showed in other tabs).</p><p>Replace <code><ACCCESS_KEY></code> by the Access Key generated in above steps. Note that <code>loc
alhost:7070</code> is the default URL of the Event Server.</p><p>For convenience, set your access key to the shell variable, run:</p><p><code>$ ACCESS_KEY=<ACCESS_KEY></code></p> <p><a href="#"></a></p> <p>To set properties "attr0", "attr1", "attr2" and "plan" for user "u0" on time <code>2014-11-02T09:39:45.618-08:00</code> (current time will be used if eventTime is not specified), you can send <code>$set</code> event for the user. To send this event, run the following <code>curl</code> command:</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a href="#tab-fd41fc24-badc-4133-8f93-7f6d77a2d6cc">REST API</a></li> <li data-lang="python"><a href="#tab-8afefce9-201f-45b9-a124-590d85e749eb">Python SDK</a></li> <li data-lang="php"><a href="#tab-859c04dd-1125-44a8-8c01-54870ed94f8d">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-031c5f48-6349-44bb-b3b1-f989fc1778c1">Ruby SDK</a></li> <li data-lang="java"><a href="#
tab-2110713f-3e42-4afb-b3d4-de1afc0cfe3e">Java SDK</a></li> </ul> <div data-tab="REST API" data-lang="json" id="tab-fd41fc24-badc-4133-8f93-7f6d77a2d6cc"> <div class="highlight shell"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td></tr></tbody></table> </div> <p><a href="#"></a></p> <h2 id='4.-collecting-data' class='header-anchors'>4. Collecting Data</h2><p>Next, let's collect some training data. By default, the Classification Engine Template reads 4 properties of a user record: attr0, attr1, attr2 and plan. This templates requires '$set' user events.</p><div class="alert-message info"><p>This template can easily be customized to use different or more number of attributes.</p></div> <p>You can send these events to PredictionIO Event Server in real-time easily by making a HTTP request or through the provided SDK. Please see <a href="/appintegration/">App Integration Overview</a> for more details how to integrate your app with SDK.</p><p>Let's try sending events to EventServer with the following <code>curl</code> commands (The corresponding SDK code is showed in other tabs).</p><p>Replace <code><ACCCESS_KEY></code> by the Access Key generated in above steps. Note that <code>loc
alhost:7070</code> is the default URL of the Event Server.</p><p>For convenience, set your access key to the shell variable, run:</p><p><code>$ ACCESS_KEY=<ACCESS_KEY></code></p> <p><a href="#"></a></p> <p>To set properties "attr0", "attr1", "attr2" and "plan" for user "u0" on time <code>2014-11-02T09:39:45.618-08:00</code> (current time will be used if eventTime is not specified), you can send <code>$set</code> event for the user. To send this event, run the following <code>curl</code> command:</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a href="#tab-85a02916-b1d2-424d-86f8-fbfad8a40e9e">REST API</a></li> <li data-lang="python"><a href="#tab-e1b69c67-a722-4821-9f64-def539a73ba7">Python SDK</a></li> <li data-lang="php"><a href="#tab-18013c0e-9b94-410f-8dfb-ea72ffc73474">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-da0c7441-fc45-4ccc-b684-7ec375ac7ff0">Ruby SDK</a></li> <li data-lang="java"><a href="#
tab-5ccfccb3-992a-44de-9ff1-67b394445237">Java SDK</a></li> </ul> <div data-tab="REST API" data-lang="json" id="tab-85a02916-b1d2-424d-86f8-fbfad8a40e9e"> <div class="highlight shell"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
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}
"eventTime" : "2014-11-02T09:39:45.618-08:00"
}'</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" data-lang="python" id="tab-8afefce9-201f-45b9-a124-590d85e749eb"> <div class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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<span class="s">"plan"</span> <span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="o"><</span><span class="n">VALUE</span> <span class="n">OF</span> <span class="n">PLAN</span><span class="o">></span><span class="p">)</span>
<span class="p">}</span>
<span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-859c04dd-1125-44a8-8c01-54870ed94f8d"> <div class="highlight php"> <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> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-18013c0e-9b94-410f-8dfb-ea72ffc73474"> <div class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -137,7 +137,7 @@ Your system is all ready to go.
<span class="p">)</span>
<span class="p">));</span>
<span class="cp">?></span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-031c5f48-6349-44bb-b3b1-f989fc1778c1"> <div class="highlight ruby"> <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> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-da0c7441-fc45-4ccc-b684-7ec375ac7ff0"> <div class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -168,7 +168,7 @@ Your system is all ready to go.
<span class="p">}</span>
<span class="p">}</span>
<span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" data-lang="java" id="tab-2110713f-3e42-4afb-b3d4-de1afc0cfe3e"> <div class="highlight java"> <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> </div> <div data-tab="Java SDK" data-lang="java" id="tab-5ccfccb3-992a-44de-9ff1-67b394445237"> <div class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -203,7 +203,7 @@ Your system is all ready to go.
<span class="s">"plan"</span><span class="o">,</span> <span class="o"><</span><span class="n">VALUE</span> <span class="n">OF</span> <span class="n">PLAN</span><span class="o">></span>
<span class="o">));</span>
<span class="n">client</span><span class="o">.</span><span class="na">createEvent</span><span class="o">(</span><span class="n">event</span><span class="o">);</span>
-</pre></td> </tr></tbody></table> </div> </div> </div> <p>Note that you can also set the properties for the user with multiple <code>$set</code> events (They will be aggregated during engine training).</p><p>To set properties "attr0", "attr1" and "attr2", and "plan" for user "u1" at different time, you can send follwing <code>$set</code> events for the user. To send these events, run the following <code>curl</code> command:</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a href="#tab-916a852a-bdbe-4712-916f-859f7a9328b6">REST API</a></li> <li data-lang="python"><a href="#tab-ac1784ab-fe9d-4fe5-8f66-ecc08ca85f1a">Python SDK</a></li> <li data-lang="php"><a href="#tab-a09d7f03-5f85-4893-9543-a74de040893e">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-9dd4c04e-5634-4715-97ad-d260d914f821">Ruby SDK</a></li> <li data-lang="java"><a href="#tab-20cc7bfc-d4b9-4bfd-8275-699c48b8a7aa">Java SDK</a></li> </ul> <div data-tab
="REST API" data-lang="json" id="tab-916a852a-bdbe-4712-916f-859f7a9328b6"> <div class="highlight shell"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
+</pre></td> </tr></tbody></table> </div> </div> </div> <p>Note that you can also set the properties for the user with multiple <code>$set</code> events (They will be aggregated during engine training).</p><p>To set properties "attr0", "attr1" and "attr2", and "plan" for user "u1" at different time, you can send follwing <code>$set</code> events for the user. To send these events, run the following <code>curl</code> command:</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a href="#tab-e6b078db-e93b-4dc2-ab1c-5daebe59c69a">REST API</a></li> <li data-lang="python"><a href="#tab-58c1f388-125e-4c50-b679-59fb9f5002d1">Python SDK</a></li> <li data-lang="php"><a href="#tab-8e5b56be-60ec-4b2c-a60b-f1c922887925">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-d14ceda4-c1bc-4836-a522-36788f2c235f">Ruby SDK</a></li> <li data-lang="java"><a href="#tab-3bf79f48-0ed0-4c0f-b4d7-cfcc0037edaa">Java SDK</a></li> </ul> <div data-tab
="REST API" data-lang="json" id="tab-e6b078db-e93b-4dc2-ab1c-5daebe59c69a"> <div class="highlight shell"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -274,7 +274,7 @@ Your system is all ready to go.
}
"eventTime" : "2014-11-02T09:39:45.618-08:00"
}'</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" data-lang="python" id="tab-ac1784ab-fe9d-4fe5-8f66-ecc08ca85f1a"> <div class="highlight python"> <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> </div> <div data-tab="Python SDK" data-lang="python" id="tab-58c1f388-125e-4c50-b679-59fb9f5002d1"> <div class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -327,7 +327,7 @@ Your system is all ready to go.
<span class="s">"plan"</span> <span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="o"><</span><span class="n">VALUE</span> <span class="n">OF</span> <span class="n">PLAN</span><span class="o">></span><span class="p">)</span>
<span class="p">}</span>
<span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-a09d7f03-5f85-4893-9543-a74de040893e"> <div class="highlight php"> <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> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-8e5b56be-60ec-4b2c-a60b-f1c922887925"> <div class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -390,7 +390,7 @@ Your system is all ready to go.
<span class="p">));</span>
<span class="cp">?></span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-9dd4c04e-5634-4715-97ad-d260d914f821"> <div class="highlight ruby"> <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> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-d14ceda4-c1bc-4836-a522-36788f2c235f"> <div class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -433,7 +433,7 @@ Your system is all ready to go.
<span class="p">)</span>
<span class="c1"># Etc...</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" data-lang="java" id="tab-20cc7bfc-d4b9-4bfd-8275-699c48b8a7aa"> <div class="highlight java"> <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> </div> <div data-tab="Java SDK" data-lang="java" id="tab-3bf79f48-0ed0-4c0f-b4d7-cfcc0037edaa"> <div class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -497,17 +497,17 @@ Your system is all ready to go.
</pre></td></tr></tbody></table> </div> <p>When the engine is deployed successfully and running, you should see a console message similar to the following:</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</pre></td><td class="code"><pre><span class="o">[</span>INFO] <span class="o">[</span>HttpListener] Bound to /0.0.0.0:8000
<span class="o">[</span>INFO] <span class="o">[</span>MasterActor] Bind successful. Ready to serve.
-</pre></td></tr></tbody></table> </div> <p>Do not kill the deployed engine process.</p><p>By default, the deployed engine binds to <a href="http://localhost:8000"><a href="http://localhost:8000">http://localhost:8000</a></a>. You can visit that page in your web browser to check its status.</p><p><img alt="Engine Status" src="/images/engine-server-3246414b.png"/></p></p><h2 id='6.-use-the-engine' class='header-anchors'>6. Use the Engine</h2><p>Now, You can try to retrieve predicted results. For example, to predict the label (i.e. <em>plan</em> in this case) of a user with attr0=2, attr1=0 and attr2=0, you send this JSON <code>{ "attr0":2, "attr1":0, "attr2":0 }</code> to the deployed engine and it will return a JSON of the predicted plan. Simply send a query by making a HTTP request or through the <code>EngineClient</code> of an SDK.</p><p>With the deployed engine running, open another terminal and run the following <code>curl</code> command or use SDK t
o send the query:</p><div class="tabs"> <ul class="control"> <li data-lang="bash"><a href="#tab-ff5a583d-5f52-4939-820b-788c48a83ca0">REST API</a></li> <li data-lang="python"><a href="#tab-7d6fb6ca-3b8e-4523-a7b7-388f5550f080">Python SDK</a></li> <li data-lang="php"><a href="#tab-1dfd85e1-43b9-4cd0-a89b-987d4c49be0d">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-ace019fe-6b72-4863-b168-0d75817edd15">Ruby SDK</a></li> <li data-lang="java"><a href="#tab-24d898c6-674f-4dbb-b34a-e941e7e7a16c">Java SDK</a></li> </ul> <div data-tab="REST API" data-lang="bash" id="tab-ff5a583d-5f52-4939-820b-788c48a83ca0"> <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></tr></tbody></table> </div> <p>Do not kill the deployed engine process.</p><p>By default, the deployed engine binds to <a href="http://localhost:8000"><a href="http://localhost:8000">http://localhost:8000</a></a>. You can visit that page in your web browser to check its status.</p><p><img alt="Engine Status" src="/images/engine-server-3246414b.png"/></p></p><h2 id='6.-use-the-engine' class='header-anchors'>6. Use the Engine</h2><p>Now, You can try to retrieve predicted results. For example, to predict the label (i.e. <em>plan</em> in this case) of a user with attr0=2, attr1=0 and attr2=0, you send this JSON <code>{ "attr0":2, "attr1":0, "attr2":0 }</code> to the deployed engine and it will return a JSON of the predicted plan. Simply send a query by making a HTTP request or through the <code>EngineClient</code> of an SDK.</p><p>With the deployed engine running, open another terminal and run the following <code>curl</code> command or use SDK t
o send the query:</p><div class="tabs"> <ul class="control"> <li data-lang="bash"><a href="#tab-15014cc7-016e-4a18-86ae-0855ec182b83">REST API</a></li> <li data-lang="python"><a href="#tab-641eef27-9813-446c-867d-29cb967c15a6">Python SDK</a></li> <li data-lang="php"><a href="#tab-721f686e-ca25-4a9a-8be2-96caef35f063">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-f1474ecf-36c1-4273-abee-f86ca558d3d2">Ruby SDK</a></li> <li data-lang="java"><a href="#tab-7fb18661-b682-44da-8136-16fd689397d9">Java SDK</a></li> </ul> <div data-tab="REST API" data-lang="bash" id="tab-15014cc7-016e-4a18-86ae-0855ec182b83"> <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>curl -H <span class="s2">"Content-Type: application/json"</span> <span class="se">\</span>
-d <span class="s1">'{ "attr0":2, "attr1":0, "attr2":0 }'</span> http://localhost:8000/queries.json
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" data-lang="python" id="tab-7d6fb6ca-3b8e-4523-a7b7-388f5550f080"> <div class="highlight python"> <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> </div> <div data-tab="Python SDK" data-lang="python" id="tab-641eef27-9813-446c-867d-29cb967c15a6"> <div class="highlight python"> <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="kn">import</span> <span class="nn">predictionio</span>
<span class="n">engine_client</span> <span class="o">=</span> <span class="n">predictionio</span><span class="o">.</span><span class="n">EngineClient</span><span class="p">(</span><span class="n">url</span><span class="o">=</span><span class="s">"http://localhost:8000"</span><span class="p">)</span>
<span class="k">print</span> <span class="n">engine_client</span><span class="o">.</span><span class="n">send_query</span><span class="p">({</span><span class="s">"attr0"</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span> <span class="s">"attr1"</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s">"attr2"</span><span class="p">:</span><span class="mi">0</span><span class="p">})</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-1dfd85e1-43b9-4cd0-a89b-987d4c49be0d"> <div class="highlight php"> <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> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-721f686e-ca25-4a9a-8be2-96caef35f063"> <div class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -526,7 +526,7 @@ Your system is all ready to go.
<span class="nb">print_r</span><span class="p">(</span><span class="nv">$response</span><span class="p">);</span>
<span class="cp">?></span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-ace019fe-6b72-4863-b168-0d75817edd15"> <div class="highlight ruby"> <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> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-f1474ecf-36c1-4273-abee-f86ca558d3d2"> <div class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -539,7 +539,7 @@ Your system is all ready to go.
<span class="n">response</span> <span class="o">=</span> <span class="n">client</span><span class="p">.</span><span class="nf">send_query</span><span class="p">(</span><span class="s1">'attr0'</span> <span class="o">=></span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'attr1'</span> <span class="o">=></span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'attr2'</span> <span class="o">=></span> <span class="mi">0</span><span class="p">)</span>
<span class="nb">puts</span> <span class="n">response</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" data-lang="java" id="tab-24d898c6-674f-4dbb-b34a-e941e7e7a16c"> <div class="highlight java"> <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> </div> <div data-tab="Java SDK" data-lang="java" id="tab-7fb18661-b682-44da-8136-16fd689397d9"> <div class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/c17b9607/templates/complementarypurchase/dase/index.html
----------------------------------------------------------------------
diff --git a/templates/complementarypurchase/dase/index.html b/templates/complementarypurchase/dase/index.html
index 175ef6f..cf8bce0 100644
--- a/templates/complementarypurchase/dase/index.html
+++ b/templates/complementarypurchase/dase/index.html
@@ -212,7 +212,7 @@
<span class="n">maxNumRulesPerCond</span><span class="k">:</span> <span class="kt">Int</span> <span class="c1">// max number of rules per condition
</span> <span class="o">)</span> <span class="k">extends</span> <span class="nc">Params</span>
-</pre></td></tr></tbody></table> </div> <p>Parameter description:</p> <ul> <li><strong>basketWindow</strong>: The buy event is considered as the same basket as previous one if the time difference is within this window (in unit of seconds). For example, if it's set to 120, it means that if the user buys item B within 2 minutes of previous purchase (item A), then the item set [A, B] is considered as the same basket. The purchase of this <em>basket</em> is referred as one <em>transaction</em>.</li> <li><strong>maxRuleLength</strong>: The maximum length of the association rule length. Must be at least 2. For example, rule of "A implies B" has length of 2 while rule "A, B implies C" has a length of 3. Increasing this number will incrase the training time significantly because more combinations are considered.</li> <li><strong>minSupport</strong>: The minimum required <em>support</em> for the item set to be considered as rule (valid range is 0 to 1). It's the p
ercentage of the item set appearing among all transcations. This is used to filter out infrequent item set. For example, setting to 0.1 means that the item set must appear in 10 % of all transactions.</li> <li><strong>minConfidence</strong>: The minimum <em>confidence</em> required for the rules (valid range is 0 to 1). The confidence indicates the probability of the condition and conseuquence appear in the same transaction. For example, if A appears in 30 transactions and the item set [A, B] appears in 20 transactions, then the rule "A implies B" has confidence of 0.66.</li> <li><strong>minLift</strong>: The minimum <em>lift</em> required for the rule. It should be set to 1 to find high quality rule. It's the confidence of the rule divided by the support of the consequence. It is used to filter out rules that the consequence is very frequent anyway regardless of the condition.</li> <li><strong>minBasketSize</strong>: The minimum number of items in basket to be conside
red by algorithm. This value must be at least 2.</li> <li><strong>maxNumRulesPerCond</strong>: Maximum number of rules generated per condition and stored in the model. By default, the top rules are sorted by <em>lift</em> score.</li> </ul> <div class="alert-message info"><p>If you import your own data and the engine doesn't return any results, it could be caused by the following reasons: (1) the algorithm parameter constraint is too high and the algo couldn't find rules that satisfy the condition. you could try setting the following param to 0: <strong>minSupport</strong>, <strong>minConfidence</strong>, <strong>minLift</strong> and then see if anything returned (regardless of recommendation quality), and then adjust the parameter accordingly. (2) the complementary purchase engine requires buy event with correct eventTime. If you import data without specifying eventTime, the SDK will use current time because it assumes the event happens in real time (which is not the case if
you import as batch offline), resulting in that all buy events are treated as one big transcation while they should be treated as multiple transcations.</p></div><p>The values of these parameters can be specified in <em>algorithms</em> of MyComplementaryPurchase/<strong><em>engine.json</em></strong>:</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></tr></tbody></table> </div> <p>Parameter description:</p> <ul> <li><strong>basketWindow</strong>: The buy event is considered as the same basket as previous one if the time difference is within this window (in unit of seconds). For example, if it's set to 120, it means that if the user buys item B within 2 minutes of previous purchase (item A), then the item set [A, B] is considered as the same basket. The purchase of this <em>basket</em> is referred as one <em>transaction</em>.</li> <li><strong>maxRuleLength</strong>: The maximum length of the association rule length. Must be at least 2. For example, rule of "A implies B" has length of 2 while rule "A, B implies C" has a length of 3. Increasing this number will incrase the training time significantly because more combinations are considered.</li> <li><strong>minSupport</strong>: The minimum required <em>support</em> for the item set to be considered as rule (valid range is 0 to 1). It's the p
ercentage of the item set appearing among all transactions. This is used to filter out infrequent item set. For example, setting to 0.1 means that the item set must appear in 10 % of all transactions.</li> <li><strong>minConfidence</strong>: The minimum <em>confidence</em> required for the rules (valid range is 0 to 1). The confidence indicates the probability of the condition and conseuquence appear in the same transaction. For example, if A appears in 30 transactions and the item set [A, B] appears in 20 transactions, then the rule "A implies B" has confidence of 0.66.</li> <li><strong>minLift</strong>: The minimum <em>lift</em> required for the rule. It should be set to 1 to find high quality rule. It's the confidence of the rule divided by the support of the consequence. It is used to filter out rules that the consequence is very frequent anyway regardless of the condition.</li> <li><strong>minBasketSize</strong>: The minimum number of items in basket to be conside
red by algorithm. This value must be at least 2.</li> <li><strong>maxNumRulesPerCond</strong>: Maximum number of rules generated per condition and stored in the model. By default, the top rules are sorted by <em>lift</em> score.</li> </ul> <div class="alert-message info"><p>If you import your own data and the engine doesn't return any results, it could be caused by the following reasons: (1) the algorithm parameter constraint is too high and the algo couldn't find rules that satisfy the condition. you could try setting the following param to 0: <strong>minSupport</strong>, <strong>minConfidence</strong>, <strong>minLift</strong> and then see if anything returned (regardless of recommendation quality), and then adjust the parameter accordingly. (2) the complementary purchase engine requires buy event with correct eventTime. If you import data without specifying eventTime, the SDK will use current time because it assumes the event happens in real time (which is not the case if
you import as batch offline), resulting in that all buy events are treated as one big transaction while they should be treated as multiple transactions.</p></div><p>The values of these parameters can be specified in <em>algorithms</em> of MyComplementaryPurchase/<strong><em>engine.json</em></strong>:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -254,7 +254,7 @@
<span class="k">extends</span> <span class="n">P2LAlgorithm</span><span class="o">[</span><span class="kt">PreparedData</span>, <span class="kt">Model</span>, <span class="kt">Query</span>, <span class="kt">PredictedResult</span><span class="o">]</span> <span class="o">{</span>
<span class="o">...</span>
<span class="o">}</span>
-</pre></td></tr></tbody></table> </div> <h3 id='train(...)' class='header-anchors'>train(...)</h3><p><code>train</code> is called when you run <strong>pio train</strong> to train a predictive model. The algorithm first find all basket transcations, generates and filters the association rules based on the algorithm parameters:</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> <h3 id='train(...)' class='header-anchors'>train(...)</h3><p><code>train</code> is called when you run <strong>pio train</strong> to train a predictive model. The algorithm first find all basket transactions, generates and filters the association rules based on the algorithm parameters:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/c17b9607/templates/complementarypurchase/quickstart/index.html
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index b967e07..4896bd2 100644
--- a/templates/complementarypurchase/quickstart/index.html
+++ b/templates/complementarypurchase/quickstart/index.html
@@ -32,7 +32,7 @@ Your system is all ready to go.
<span class="o">[</span>INFO] <span class="o">[</span>App<span class="nv">$]</span> MyApp1 | 1 | 3mZWDzci2D5YsqAnqNnXH9SB6Rg3dsTBs8iHkK6X2i54IQsIZI1eEeQQyMfs7b3F | <span class="o">(</span>all<span class="o">)</span>
<span class="o">[</span>INFO] <span class="o">[</span>App<span class="nv">$]</span> MyApp2 | 2 | io5lz6Eg4m3Xe4JZTBFE13GMAf1dhFl6ZteuJfrO84XpdOz9wRCrDU44EUaYuXq5 | <span class="o">(</span>all<span class="o">)</span>
<span class="o">[</span>INFO] <span class="o">[</span>App<span class="nv">$]</span> Finished listing 2 app<span class="o">(</span>s<span class="o">)</span>.
-</pre></td></tr></tbody></table> </div> <p><a href="#"></a></p> <h2 id='4.-collecting-data' class='header-anchors'>4. Collecting Data</h2><p>Next, let's collect training data for this Engine. By default, Complementary Purchase Engine Template supports the following entities: <strong>user</strong>, <strong>item</strong>. A user buys an item. This template requires user-buy-item events.</p><p>Note that the engine requires correct buy event time being used in order to determine if the items being bought are in the same 'basket', which is configured by the 'basketWindow' parameter. Using an unreal event time for the buy events will cause an incorrect model. If you use SDK, the current time is used as event time by default.</p><div class="alert-message warning"><p>In particular, make sure correct event time is specified if you import data in batch (i.e. not in real time). If the event time is omitted, the SDK will use <strong>current time</strong> as event time which
is not the actual time of the buy event in this case!</p></div> <p>You can send these events to PredictionIO Event Server in real-time easily by making a HTTP request or through the provided SDK. Please see <a href="/appintegration/">App Integration Overview</a> for more details how to integrate your app with SDK.</p><p>Let's try sending events to EventServer with the following <code>curl</code> commands (The corresponding SDK code is showed in other tabs).</p><p>Replace <code><ACCCESS_KEY></code> by the Access Key generated in above steps. Note that <code>localhost:7070</code> is the default URL of the Event Server.</p><p>For convenience, set your access key to the shell variable, run:</p><p><code>$ ACCESS_KEY=<ACCESS_KEY></code></p> <p><a href="#"></a></p> <p>When an user u0 buys item i0 on time <code>2014-11-02T09:39:45.618-08:00</code> (current time will be used if eventTime is not specified), you can send a buy event. Run the following <code>curl</code> command:
</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a href="#tab-1ddd62c1-70c3-402c-bd0c-8de709f36da4">REST API</a></li> <li data-lang="python"><a href="#tab-4677c753-0aa9-4ffd-a94f-8a49c1d48df8">Python SDK</a></li> <li data-lang="php"><a href="#tab-9e9f0f51-54c4-4bfe-87c1-ac67afe144bf">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-531f4729-3ddc-464d-891c-87309671ffb6">Ruby SDK</a></li> <li data-lang="java"><a href="#tab-22bcb5d4-1c87-4501-9aef-f3b5cc1d32b6">Java SDK</a></li> </ul> <div data-tab="REST API" data-lang="json" id="tab-1ddd62c1-70c3-402c-bd0c-8de709f36da4"> <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></tr></tbody></table> </div> <p><a href="#"></a></p> <h2 id='4.-collecting-data' class='header-anchors'>4. Collecting Data</h2><p>Next, let's collect training data for this Engine. By default, Complementary Purchase Engine Template supports the following entities: <strong>user</strong>, <strong>item</strong>. A user buys an item. This template requires user-buy-item events.</p><p>Note that the engine requires correct buy event time being used in order to determine if the items being bought are in the same 'basket', which is configured by the 'basketWindow' parameter. Using an unreal event time for the buy events will cause an incorrect model. If you use SDK, the current time is used as event time by default.</p><div class="alert-message warning"><p>In particular, make sure correct event time is specified if you import data in batch (i.e. not in real time). If the event time is omitted, the SDK will use <strong>current time</strong> as event time which
is not the actual time of the buy event in this case!</p></div> <p>You can send these events to PredictionIO Event Server in real-time easily by making a HTTP request or through the provided SDK. Please see <a href="/appintegration/">App Integration Overview</a> for more details how to integrate your app with SDK.</p><p>Let's try sending events to EventServer with the following <code>curl</code> commands (The corresponding SDK code is showed in other tabs).</p><p>Replace <code><ACCCESS_KEY></code> by the Access Key generated in above steps. Note that <code>localhost:7070</code> is the default URL of the Event Server.</p><p>For convenience, set your access key to the shell variable, run:</p><p><code>$ ACCESS_KEY=<ACCESS_KEY></code></p> <p><a href="#"></a></p> <p>When an user u0 buys item i0 on time <code>2014-11-02T09:39:45.618-08:00</code> (current time will be used if eventTime is not specified), you can send a buy event. Run the following <code>curl</code> command:
</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a href="#tab-1cd406c2-3d16-4a20-92b5-994c3e2f06f9">REST API</a></li> <li data-lang="python"><a href="#tab-51598266-3d11-4c07-b267-ff74d1155e2a">Python SDK</a></li> <li data-lang="php"><a href="#tab-d3120489-ac8a-4c94-a862-169717028ff4">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-3a3b6e8f-f4be-4617-91c7-c1b8e6623c43">Ruby SDK</a></li> <li data-lang="java"><a href="#tab-36697a24-75b8-4b7d-8bf5-88782f7b48fe">Java SDK</a></li> </ul> <div data-tab="REST API" data-lang="json" id="tab-1cd406c2-3d16-4a20-92b5-994c3e2f06f9"> <div class="highlight shell"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -51,7 +51,7 @@ Your system is all ready to go.
"targetEntityId" : "i0",
"eventTime" : "2014-11-02T09:39:45.618-08:00"
}'</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" data-lang="python" id="tab-4677c753-0aa9-4ffd-a94f-8a49c1d48df8"> <div class="highlight python"> <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> </div> <div data-tab="Python SDK" data-lang="python" id="tab-51598266-3d11-4c07-b267-ff74d1155e2a"> <div class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -104,7 +104,7 @@ Your system is all ready to go.
<span class="n">target_entity_id</span><span class="o">=<</span><span class="n">ITEM</span> <span class="n">ID</span><span class="o">></span><span class="p">,</span>
<span class="n">event_time</span><span class="o">=<</span><span class="n">EVENT_TIME</span><span class="o">></span>
<span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-9e9f0f51-54c4-4bfe-87c1-ac67afe144bf"> <div class="highlight php"> <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> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-d3120489-ac8a-4c94-a862-169717028ff4"> <div class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -155,7 +155,7 @@ Your system is all ready to go.
<span class="p">));</span>
<span class="cp">?></span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-531f4729-3ddc-464d-891c-87309671ffb6"> <div class="highlight ruby"> <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> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-3a3b6e8f-f4be-4617-91c7-c1b8e6623c43"> <div class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -200,7 +200,7 @@ Your system is all ready to go.
<span class="s1">'eventTime'</span> <span class="o">=></span> <span class="o"><</span><span class="no">EVENT_TIME</span><span class="o">></span>
<span class="p">}</span>
<span class="p">)</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" data-lang="java" id="tab-22bcb5d4-1c87-4501-9aef-f3b5cc1d32b6"> <div class="highlight java"> <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> </div> <div data-tab="Java SDK" data-lang="java" id="tab-36697a24-75b8-4b7d-8bf5-88782f7b48fe"> <div class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -290,7 +290,7 @@ User u10 buys item s2i1 at 2014-10-19 15:43:15.618000-07:53
</pre></td></tr></tbody></table> </div> <p>When the engine is deployed successfully and running, you should see a console message similar to the following:</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</pre></td><td class="code"><pre><span class="o">[</span>INFO] <span class="o">[</span>HttpListener] Bound to /0.0.0.0:8000
<span class="o">[</span>INFO] <span class="o">[</span>MasterActor] Bind successful. Ready to serve.
-</pre></td></tr></tbody></table> </div> <p>Do not kill the deployed engine process.</p><p>By default, the deployed engine binds to <a href="http://localhost:8000"><a href="http://localhost:8000">http://localhost:8000</a></a>. You can visit that page in your web browser to check its status.</p><p><img alt="Engine Status" src="/images/engine-server-3246414b.png"/></p></p><h2 id='6.-use-the-engine' class='header-anchors'>6. Use the Engine</h2><p>Now, You can query the engine. For example, return top 3 items which are frequently bought with item "s2i1". You can sending this JSON '{ "items" : ["s2i1"], "num" : 3 }' to the deployed engine. The engine will return a JSON with the recommeded items.</p><p>If you include one or more items in the query, the engine will use each combination of the query items as condition, and return recommended items if there is any for this condition. For example, if you query items are ["A", "B&qu
ot;], then the engine will use ["A"], ["B"], and ["A", "B"] as condition and try to find top n recommended items for each combination.</p><p>You can simply send a query by making a HTTP request or through the <code>EngineClient</code> of an SDK.</p><p>With the deployed engine running, open another terminal and run the following <code>curl</code> command or use SDK to send the query:</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a href="#tab-8363ff8b-1bae-404a-b990-ba72d129eda5">REST API</a></li> <li data-lang="python"><a href="#tab-b18dc13b-fc11-4163-8ce3-919f99dc9c39">Python SDK</a></li> <li data-lang="php"><a href="#tab-a999d660-c7a0-4eef-afa5-c0c548f9d073">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-4c314941-e697-417e-9ef2-8ab45df12f59">Ruby SDK</a></li> <li data-lang="java"><a href="#tab-0acc4930-9a61-43b1-89ce-3248f6efaca9">Java SDK</a></li> </ul> <div data-tab="REST API" data-lang="json" id="tab-8363ff8b-1bae-4
04a-b990-ba72d129eda5"> <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></tr></tbody></table> </div> <p>Do not kill the deployed engine process.</p><p>By default, the deployed engine binds to <a href="http://localhost:8000"><a href="http://localhost:8000">http://localhost:8000</a></a>. You can visit that page in your web browser to check its status.</p><p><img alt="Engine Status" src="/images/engine-server-3246414b.png"/></p></p><h2 id='6.-use-the-engine' class='header-anchors'>6. Use the Engine</h2><p>Now, You can query the engine. For example, return top 3 items which are frequently bought with item "s2i1". You can sending this JSON '{ "items" : ["s2i1"], "num" : 3 }' to the deployed engine. The engine will return a JSON with the recommended items.</p><p>If you include one or more items in the query, the engine will use each combination of the query items as condition, and return recommended items if there is any for this condition. For example, if you query items are ["A", "B&q
uot;], then the engine will use ["A"], ["B"], and ["A", "B"] as condition and try to find top n recommended items for each combination.</p><p>You can simply send a query by making a HTTP request or through the <code>EngineClient</code> of an SDK.</p><p>With the deployed engine running, open another terminal and run the following <code>curl</code> command or use SDK to send the query:</p><div class="tabs"> <ul class="control"> <li data-lang="json"><a href="#tab-a97c0a9f-957b-41ac-8fa9-b000a033998d">REST API</a></li> <li data-lang="python"><a href="#tab-f2100c58-a059-4365-9dee-071a3425302f">Python SDK</a></li> <li data-lang="php"><a href="#tab-bf12d27a-b88d-4662-9768-f330ba10d55c">PHP SDK</a></li> <li data-lang="ruby"><a href="#tab-609535cf-8f86-40df-a3be-a7b299eda5d3">Ruby SDK</a></li> <li data-lang="java"><a href="#tab-9df879b9-d04e-4fbf-b14e-5cbacdab91f1">Java SDK</a></li> </ul> <div data-tab="REST API" data-lang="json" id="tab-a97c0a9f-957b-
41ac-8fa9-b000a033998d"> <div class="highlight shell"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -303,7 +303,7 @@ User u10 buys item s2i1 at 2014-10-19 15:43:15.618000-07:53
}'</span> <span class="se">\</span>
http://localhost:8000/queries.json
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Python SDK" data-lang="python" id="tab-b18dc13b-fc11-4163-8ce3-919f99dc9c39"> <div class="highlight python"> <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> </div> <div data-tab="Python SDK" data-lang="python" id="tab-f2100c58-a059-4365-9dee-071a3425302f"> <div class="highlight python"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -314,7 +314,7 @@ http://localhost:8000/queries.json
<span class="s">"items"</span> <span class="p">:</span> <span class="p">[</span><span class="s">"s2i1"</span><span class="p">],</span>
<span class="s">"num"</span> <span class="p">:</span> <span class="mi">3</span>
<span class="p">})</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-a999d660-c7a0-4eef-afa5-c0c548f9d073"> <div class="highlight php"> <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> </div> <div data-tab="PHP SDK" data-lang="php" id="tab-bf12d27a-b88d-4662-9768-f330ba10d55c"> <div class="highlight php"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -341,7 +341,7 @@ http://localhost:8000/queries.json
<span class="nb">print_r</span><span class="p">(</span><span class="nv">$response</span><span class="p">);</span>
<span class="cp">?></span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-4c314941-e697-417e-9ef2-8ab45df12f59"> <div class="highlight ruby"> <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> </div> <div data-tab="Ruby SDK" data-lang="ruby" id="tab-609535cf-8f86-40df-a3be-a7b299eda5d3"> <div class="highlight ruby"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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@@ -360,7 +360,7 @@ http://localhost:8000/queries.json
<span class="p">)</span>
<span class="nb">puts</span> <span class="n">response</span>
-</pre></td> </tr></tbody></table> </div> </div> <div data-tab="Java SDK" data-lang="java" id="tab-0acc4930-9a61-43b1-89ce-3248f6efaca9"> <div class="highlight java"> <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> </div> <div data-tab="Java SDK" data-lang="java" id="tab-9df879b9-d04e-4fbf-b14e-5cbacdab91f1"> <div class="highlight java"> <table style="border-spacing: 0"><tbody><tr> <td class="gutter gl" style="text-align: right"><pre class="lineno">1
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http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/c17b9607/templates/ecommercerecommendation/dase/index.html
----------------------------------------------------------------------
diff --git a/templates/ecommercerecommendation/dase/index.html b/templates/ecommercerecommendation/dase/index.html
index ddac180..681b9bf 100644
--- a/templates/ecommercerecommendation/dase/index.html
+++ b/templates/ecommercerecommendation/dase/index.html
@@ -361,7 +361,7 @@
<span class="n">lambda</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span>
<span class="n">seed</span><span class="k">:</span> <span class="kt">Option</span><span class="o">[</span><span class="kt">Long</span><span class="o">]</span>
<span class="o">)</span> <span class="k">extends</span> <span class="nc">Params</span>
-</pre></td></tr></tbody></table> </div> <p>Parameter description:</p> <ul> <li><strong>appName</strong>: Your App name. Events defined by "seenEvents" and "similarEvents" will be read from this app during <code>predict</code>.</li> <li><strong>unseenOnly</strong>: true or false. Set to true if you want to recommmend unseen items only. Seen items are defined by <em>seenEvents</em> which mean if the user has these events on the items, then it's treated as <em>seen</em>.</li> <li><strong>seenEvents</strong>: A list of user-to-item events which will be treated as <em>seen</em> events. Used when <em>unseenOnly</em> is set to true.</li> <li><strong>similarEvents</strong>: A list of user-item-item events which will be used to find similar items to the items which the user has performend these events on.</li> <li><strong>rank</strong>: Parameter of the MLlib ALS algorithm. Number of latent features.</li> <li><strong>numIterations</strong>: Parameter of the MLlib ALS
algorithm. Number of iterations.</li> <li><strong>lambda</strong>: Regularization parameter of the MLlib ALS algorithm.</li> <li><strong>seed</strong>: Optional. A random seed of the MLlib ALS algorithm. Specify a fixed value if want to have deterministic result.</li> </ul> <h3 id='train(...)' class='header-anchors'>train(...)</h3><p><code>train</code> is called when you run <strong>pio train</strong>. This is where MLlib ALS algorithm, i.e. <code>ALS.trainImplicit()</code>, is used to train a predictive model. In addition, we also count the number of items being bought for each item as default model which will be used when there is no ALS model avaiable or other useful information about the user is avaiable during <code>predict</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>Parameter description:</p> <ul> <li><strong>appName</strong>: Your App name. Events defined by "seenEvents" and "similarEvents" will be read from this app during <code>predict</code>.</li> <li><strong>unseenOnly</strong>: true or false. Set to true if you want to recommmend unseen items only. Seen items are defined by <em>seenEvents</em> which mean if the user has these events on the items, then it's treated as <em>seen</em>.</li> <li><strong>seenEvents</strong>: A list of user-to-item events which will be treated as <em>seen</em> events. Used when <em>unseenOnly</em> is set to true.</li> <li><strong>similarEvents</strong>: A list of user-item-item events which will be used to find similar items to the items which the user has performend these events on.</li> <li><strong>rank</strong>: Parameter of the MLlib ALS algorithm. Number of latent features.</li> <li><strong>numIterations</strong>: Parameter of the MLlib ALS
algorithm. Number of iterations.</li> <li><strong>lambda</strong>: Regularization parameter of the MLlib ALS algorithm.</li> <li><strong>seed</strong>: Optional. A random seed of the MLlib ALS algorithm. Specify a fixed value if want to have deterministic result.</li> </ul> <h3 id='train(...)' class='header-anchors'>train(...)</h3><p><code>train</code> is called when you run <strong>pio train</strong>. This is where MLlib ALS algorithm, i.e. <code>ALS.trainImplicit()</code>, is used to train a predictive model. In addition, we also count the number of items being bought for each item as default model which will be used when there is no ALS model available or other useful information about the user is available during <code>predict</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
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