added language types

docs/abstractions/index.md

@@ -57,19 +57,19 @@ Once all the Features and Feature transformations have been defined, actual data
 
 In the example below, we would like to materialize ```bucketizedAge``` and ```nameTokens```. So we set these two Features as the result Features for a new Workflow:
 
-```
+```scala
 val workflow = new OPWorkflow().setResultFeatures(bucketizedAge, nameTokens).setReader(PassengerReader)
 ```
 
 The PassengerReader is a DataReader that essentially specifies a ```read``` method that can be used for loading the Passenger data. When we train this workflow, it reads the Passenger data and fits the bucketization estimator by determining the optimal buckets for ```age```:
 
-```
+```scala
 val workflowModel = workflow.train()
 ```
 
 The workflowModel now has a prepped DAG of Transformers. By calling the ```score``` method on the workflowModel, we can transform any data of type Passenger to a DataFrame with two columns for ```bucketizedAge``` and ```nameTokens``` 
 
-```
+```scala
 val dataFrame = workflowModel.setReader(OtherPassengerReader).score()
 ```
 

docs/examples/Bootstrap-Your-First-Project.md

@@ -18,7 +18,7 @@ Build the TransmogrifAI CLI by running:
 alias transmogrifai="java -cp `pwd`/cli/build/libs/\* com.salesforce.op.cli.CLI"
 ```
 Finally generate your Titanic model project (follow the instructions on screen):
-```
+```bash
 transmogrifai gen --input `pwd`/test-data/PassengerDataAll.csv \
  --id passengerId --response survived \
  --schema `pwd`/test-data/PassengerDataAll.avsc Titanic
@@ -29,8 +29,11 @@ If you run this command more than once, two important command line arguments wil
 - `--answers <answers_file>` will provide answers to the questions that the generator asks.
 
 e.g.
-```
-transmogrifai gen --input `pwd`/test-data/PassengerDataAll.csv --id passengerId --response survived --schema `pwd`/test-data/PassengerDataAll.avsc --answers cli/passengers.answers Titanic --overwrite
+```bash
+transmogrifai gen --input `pwd`/test-data/PassengerDataAll.csv \
+ --id passengerId --response survived \
+ --schema `pwd`/test-data/PassengerDataAll.avsc \
+ --answers cli/passengers.answers Titanic --overwrite
 ```
 will do the generation without asking you anything.
 

docs/examples/Iris-MultiClass-Classification.md

@@ -45,7 +45,7 @@ def runner(opParams: OpParams): OpWorkflowRunner =
  )
 ```
 You can run the code using the following commands for train, score and evaluate:
-```
+```bash
 cd helloworld
 ./gradlew compileTestScala installDist
 ```

docs/examples/Titanic-Binary-Classification.md

@@ -3,7 +3,7 @@
 Here we describe a very simple TransmogrifAI workflow for predicting survivors in the often-cited Titanic dataset. The code for building and applying the Titanic model can be found here: [Titanic Code](https://github.com/salesforce/TransmogrifAI/blob/master/helloworld/src/main/scala/com/salesforce/hw/OpTitanicSimple.scala), and the data can be found here: [Titanic Data](https://github.com/salesforce/op/blob/master/helloworld/src/main/resources/TitanicDataset/TitanicPassengersTrainData.csv).
 
 You can run this code as follows:
-```
+```bash
 cd helloworld
 ./gradlew compileTestScala installDist
 ./gradlew -q sparkSubmit -Dmain=com.salesforce.hw.OpTitanicSimple -Dargs="\
@@ -46,7 +46,6 @@ Basic FeatureBuilders will be created for you if you use the TransmogrifAI CLI t
 
 When defining raw features, specify the extract logic to be applied to the raw data, and also annotate the features as either predictor or response variables via the FeatureBuilders:
 ```scala
-
 // import necessary packages
 import com.salesforce.op.features.FeatureBuilder
 import com.salesforce.op.features.types._