Merge branch 'livedoc' into develop

docs/manual/source/appintegration/index.html.md

@@ -20,7 +20,7 @@ Event Server listens to port 7070 by default. You can change the port with the [
 For further information, please read:
 
 * [Event Server Overview](/datacollection/)
-* [Collecting Data with REST/SDKs](datacollection/eventapi)
+* [Collecting Data with REST/SDKs](/datacollection/eventapi)
 
 ## Sending Query
 
@@ -29,4 +29,4 @@ it will wait for queries from your application and return predicted results in J
 
 For further information, please read:
 
-* [Deploying an Engine as a Web Service](/deploy/)
+* [Deploying an Engine as a Web Service](/deploy/)

docs/manual/source/evaluation/index.html.md

@@ -2,4 +2,4 @@
 title: ML Tuning and Evaluation
 ---
 
-(coming soon)
+While overview is coming soon, check out [Hyperparameter Tuning](/evaluation/paramtuning) and subsequent pages to get you going.

docs/manual/source/evaluation/paramtuning.html.md

@@ -2,6 +2,105 @@
 title: Hyperparameter Tuning
 ---
 
+
+A PredictionIO engine is governed by a set of parameters, these parameters
+determines which algorithm is used as well as the parameter for the algorithm.
+It naturally raises a question of how to choose the best set of parameters.
+The evaluation module facilitates user to *tune* the engine to obtain the best
+parameter set.
+
+## Quick Start
+
+We demonstrate the evaluation with [the classification template]
+(/templates/classification/quickstart/).
+The classification template uses naive bayesian algorithm that has a smoothing
+parameter. We evaluate the prediction quality against different parameter value
+to find the best one.
+
+### Edit the AppId
+
+Edit MyClassification/src/main/scala/***Evaluation.scala*** to specify the 
+*appId* you used to import the data.
+
+```scala
+object EngineParamsList extends EngineParamsGenerator {
+ ...
+ private[this] val baseEP = EngineParams(
+ dataSourceParams = DataSourceParams(appId = <YOUR_APP_ID>, evalK = Some(5)))
+ ...
+}
+```
+
+### Building and run the evaluation
+To run evaluation, the command `pio eval` is used. It takes to
+mandatory parameter, 
+1. the `Evaluation` object, it tells PredictionIO the engine and metric we use
+ for the evaluation; and 
+2. the `EngineParamsGenerator`, it contains a list of engine params to test
+ against. 
+The following command kickstarts the evaluation 
+workflow for the classification template.
+
+```
+$ pio build
+...
+$ pio eval org.template.classification.PrecisionEvaluation \
+ org.template.classification.EngineParamsList 
+```
+
+You will see the following output:
+
+```
+...
+[INFO] [CoreWorkflow$] CoreWorkflow.runEvaluation
+...
+[INFO] [EvaluationWorkflow$] Iteration 0
+[INFO] [EvaluationWorkflow$] EngineParams: {"dataSourceParams":{"":{"appId":18,"evalK":5}},"preparatorParams":{"":{}},"algorithmParamsList":[{"naive":{"lambda":10.0}}],"servingParams":{"":{}}}
+[INFO] [EvaluationWorkflow$] Result: 0.9281045751633987
+[INFO] [EvaluationWorkflow$] Iteration 1
+[INFO] [EvaluationWorkflow$] EngineParams: {"dataSourceParams":{"":{"appId":18,"evalK":5}},"preparatorParams":{"":{}},"algorithmParamsList":[{"naive":{"lambda":100.0}}],"servingParams":{"":{}}}
+[INFO] [EvaluationWorkflow$] Result: 0.9150326797385621
+[INFO] [EvaluationWorkflow$] Iteration 2
+[INFO] [EvaluationWorkflow$] EngineParams: {"dataSourceParams":{"":{"appId":18,"evalK":5}},"preparatorParams":{"":{}},"algorithmParamsList":[{"naive":{"lambda":1000.0}}],"servingParams":{"":{}}}
+[INFO] [EvaluationWorkflow$] Result: 0.4444444444444444
+...
+[INFO] [CoreWorkflow$] Stop spark context
+[INFO] [CoreWorkflow$] Optimal score: 0.9281045751633987
+[INFO] [CoreWorkflow$] Optimal engine params: {
+ "dataSourceParams":{
+ "":{
+ "appId":18,
+ "evalK":5
+ }
+ },
+ "preparatorParams":{
+ "":{
+ 
+ }
+ },
+ "algorithmParamsList":[
+ {
+ "naive":{
+ "lambda":10.0
+ }
+ }
+ ],
+ "servingParams":{
+ "":{
+ 
+ }
+ }
+}
+...
+```
+
+The console prints out the evaluation metric score of each engine params, and
+finally pretty print the optimal engine params.
+Amongst the 3 engine params we evaluate, *lambda = 10.0* yields the highest 
+precision score of 0.9281. 
+
+## Detailed Explanation
+
 An engine often depends on a number of parameters, for example, naive bayesian
 classification algorithm has a smoothing parameter to make the model more
 adaptive to unseen data. Comparing with parameters which are *learnt* by the
@@ -216,42 +315,20 @@ construct the list of engine params we want to evaluation by
 adding or replacing the controller parameter. Lines 13 to 16 generate 3 engine
 parameters, each has a different smoothing parameters.
 
-## Running the Evaluation
 
-It remains to run the evaluation. The command `pio eval` is used. It takes to
-mandatory parameter, 1. the `Evaluation` object, and 2. the
-`EngineParamsGenerator`. The following command kickstarts the evaluation 
-workflow for the classification template.
 
-### Building
-```
-$ pio build
-```
+## Running the Evaluation
 
-### Running the evaluation
+It remains to run the evaluation. Let's recap the quick start section above.
+The `pio eval` command kick starts the evaluation, and the result can be seen
+from the console.
 
 ```
+$ pio build
+...
 $ pio eval org.template.classification.PrecisionEvaluation \
  org.template.classification.EngineParamsList 
-```
-
-You will see the following output:
-
-```
-...
-[INFO] [CoreWorkflow$] CoreWorkflow.runEvaluation
 ...
-[INFO] [EvaluationWorkflow$] Iteration 0
-[INFO] [EvaluationWorkflow$] EngineParams: {"dataSourceParams":{"":{"appId":18,"evalK":5}},"preparatorParams":{"":{}},"algorithmParamsList":[{"naive":{"lambda":10.0}}],"servingParams":{"":{}}}
-[INFO] [EvaluationWorkflow$] Result: 0.9281045751633987
-[INFO] [EvaluationWorkflow$] Iteration 1
-[INFO] [EvaluationWorkflow$] EngineParams: {"dataSourceParams":{"":{"appId":18,"evalK":5}},"preparatorParams":{"":{}},"algorithmParamsList":[{"naive":{"lambda":100.0}}],"servingParams":{"":{}}}
-[INFO] [EvaluationWorkflow$] Result: 0.9150326797385621
-[INFO] [EvaluationWorkflow$] Iteration 2
-[INFO] [EvaluationWorkflow$] EngineParams: {"dataSourceParams":{"":{"appId":18,"evalK":5}},"preparatorParams":{"":{}},"algorithmParamsList":[{"naive":{"lambda":1000.0}}],"servingParams":{"":{}}}
-[INFO] [EvaluationWorkflow$] Result: 0.4444444444444444
-...
-[INFO] [CoreWorkflow$] Stop spark context
 [INFO] [CoreWorkflow$] Optimal score: 0.9281045751633987
 [INFO] [CoreWorkflow$] Optimal engine params: {
  "dataSourceParams":{
@@ -278,14 +355,8 @@ You will see the following output:
  }
  }
 }
-...
 ```
 
-The console prints out the evaluation metric score of each engine params, and
-finally pretty print the optimal engine params.
-Amongst the 3 engine params we evaluate, *lambda = 10.0* yields the highest 
-precision score of 0.9281. 
-
 
 ## Notes
 

docs/manual/source/install/install-linux.html.md.erb

@@ -20,7 +20,7 @@ below to setup PredictionIO and its dependencies.
 Simply download PredictionIO's binary distribution and extract it.
 
 ```
-$ wget http:https://download.prediction.io/PredictionIO-<%= data.versions.pio %>.tar.gz
+$ wget https:https://d8k1yxp8elc6b.cloudfront.net/PredictionIO-<%= data.versions.pio %>.tar.gz
 $ tar zxvf PredictionIO-<%= data.versions.pio %>.tar.gz
 $ cd PredictionIO-<%= data.versions.pio %>
 ```

docs/manual/source/partials/shared/quickstart/_install.html.md.erb

@@ -16,6 +16,9 @@ Once you have completed the installation process, please make sure all the
 components (PredictionIO Event Server, Elasticsearch, and HBase) are up and
 running.
 
+NOTE: You can skip `pio-start-all` if you have launched from the AWS
+Marketplace. All components should have been started automatically.
+
 ```
 $ pio-start-all
 ```

docs/manual/source/templates/ecommercerecommendation/how-to.html.md

@@ -0,0 +1,7 @@
+---
+title: How-To (E-Commerce Recommendation)
+---
+
+Here are the pages that show you how you can customize the E-Commerce Recommendation engine template.
+
+- [Train with Rate Event](/templates/ecommercerecommendation/train-with-rate-event/)