PUB-565 Document use of scoring engine, pojo export and use

h2o-docs/source/userguide/score.rst

@@ -1,11 +1,12 @@
 
 Score
-========
+=====
 
 .. toctree::
    :maxdepth: 1
 
    scoreMulitModel
+   scorePOJO
    scorePCAscore
    scoreauc
    scoreconfusionmatrix

h2o-docs/source/userguide/scoreMulitModel.rst

@@ -1,3 +1,5 @@
 
-Score: Multi Model Scoring
-=============================
+Score: Multmodel Scoring
+========================
+
+Please refer to :ref:`STEAM_tutorial` for a walk-through of H\ :sub:`2`\ O's scoring engine.

h2o-docs/source/userguide/scorePCAscore.rst

@@ -1,6 +1,6 @@
 
 Score: Principal Components Analysis Scoring
-==============================================
+============================================
 
 
 **Destination Key** 

h2o-docs/source/userguide/scorePOJO.rst

@@ -0,0 +1,75 @@
+
+Score: POJO
+===========
+
+One of value add of using H\ :sub:`2`\ O is the ability to code in any front end API but have the model exportable as
+a POJO (Plain Old Java Object). This allows the user the flexibility to take the model outside of H\ :sub:`2`\ O either
+to run standalone or integrating the Java Object into a platform like Hadoop's Storm. The walkthrough below will detail
+the steps required via the command line to export a model object and use it to score using a sample class object.
+
+The working example or unit test for scoring using the Java code is available on `github <https://github.com/0xdata/h2o/blob/1516535e6c9358667369074a17a4f25821b281e2/R/tests/Utils/shared_javapredict_GBM.R>`_.
+
+Walk-through
+""""""""""""
+
+**Step 1**
+
+For a H\ :sub:`2`\ O instance sitting on localhost:54321 by default and GBM model with 50 trees that you want to
+export, run the command to grab the h2o-model jar file as well as the Java code for the example model GBM_a2647515ded07d5b710c82015a6842a9.
+It is recommended to create a new directory for each model.
+
+::
+
+  $ mkdir GBM_a2647515ded07d5b710c82015a6842a9
+  $ cd GBM_a2647515ded07d5b710c82015a6842a9
+  $ curl http://localhost:54321/h2o-model.jar > h2o-model.jar
+  $ curl http://localhost:54321/2/GBMModelView.java?_modelKey=GBM_a2647515ded07d5b710c82015a6842a9 >
+  GBM_a2647515ded07d5b710c82015a6842a9.java
+
+
+**Step 2**
+
+Download from git the `PredictCSV <https://github.com/0xdata/h2o/blob/master/R/tests/testdir_javapredict/PredictCSV.java>`_  class object that
+will be used to compile the model object. The user can certainly write their own script; the one available in git is working example that is
+tested on all the builds at 0xdata. It will take four arguments:
+
+    *- -header*
+        |   specify argument if the input data set has headers
+
+    *- -model*
+        |   model key name used to score on the input file
+
+    *- -input*
+        |   the input data that will be scored
+
+    *- -output*
+        |   the resulting output csv file with all the s scores for each entry of the input data
+
+
+**Step 3**
+
+Next set up a java instance to compile model object with PredictCSV.java which should generate in this case over 50 trees class objects.
+
+    ::
+
+      $ javac -cp h2o-model.jar -J-Xmx2g -J-XX:MaxPermSize=256m PredictCSV.java
+      GBM_a2647515ded07d5b710c82015a6842a9.java
+
+
+**Step 4**
+
+Finally feed in testing data to be scored by running the following command:
+
+    ::
+
+      $ java -ea -cp h2o-model.jar -Xmx4g -XX:MaxPermSize=256m -XX:ReservedCodeCacheSize=256m
+      PredictCSV.java --header --model GBM_a2647515ded07d5b710c82015a6842a9 --input iris_test.csv
+      --output out_pojo.csv
+
+More generic sample command:
+
+    ::
+
+      $ java -ea -cp h2o-model.jar -Xmx4g -XX:MaxPermSize=256m -XX:ReservedCodeCacheSize=256m
+      PredictCSV.java --header --model <model key> --input <path to input data>
+      --output <path to output csv>