# Boston Regression

The following code illustrates how TransmogrifAI can be used to do linear regression. We use Boston dataset to predict housing prices.
The code for this example can be found [here](https://github.com/salesforce/TransmogrifAI/tree/master/helloworld/src/main/scala/com/salesforce/hw/boston), and the data over [here](https://github.com/salesforce/op/tree/master/helloworld/src/main/resources/BostonDataset).

**Define features**
```scala
val rowId = FeatureBuilder.Integral[BostonHouse].extract(_.rowId.toIntegral).asPredictor
val crim = FeatureBuilder.RealNN[BostonHouse].extract(_.crim.toRealNN).asPredictor
val zn = FeatureBuilder.RealNN[BostonHouse].extract(_.zn.toRealNN).asPredictor
val indus = FeatureBuilder.RealNN[BostonHouse].extract(_.indus.toRealNN).asPredictor
val chas = FeatureBuilder.PickList[BostonHouse].extract(x => Option(x.chas).toPickList).asPredictor
val nox = FeatureBuilder.RealNN[BostonHouse].extract(_.nox.toRealNN).asPredictor
val rm = FeatureBuilder.RealNN[BostonHouse].extract(_.rm.toRealNN).asPredictor
val age = FeatureBuilder.RealNN[BostonHouse].extract(_.age.toRealNN).asPredictor
val dis = FeatureBuilder.RealNN[BostonHouse].extract(_.dis.toRealNN).asPredictor
val rad = FeatureBuilder.Integral[BostonHouse].extract(_.rad.toIntegral).asPredictor
val tax = FeatureBuilder.RealNN[BostonHouse].extract(_.tax.toRealNN).asPredictor
val ptratio = FeatureBuilder.RealNN[BostonHouse].extract(_.ptratio.toRealNN).asPredictor
val b = FeatureBuilder.RealNN[BostonHouse].extract(_.b.toRealNN).asPredictor
val lstat = FeatureBuilder.RealNN[BostonHouse].extract(_.lstat.toRealNN).asPredictor
val medv = FeatureBuilder.RealNN[BostonHouse].extract(_.medv.toRealNN).asResponse

```
**Feature Engineering**

```scala
val houseFeatures = Seq(crim, zn, indus, chas, nox, rm, age, dis, rad, tax, ptratio, b, lstat).transmogrify()
```
**Modeling & Evaluation**
```scala
val prediction = RegressionModelSelector
  .withCrossValidation(dataSplitter = Option(DataSplitter(seed = randomSeed)), seed = randomSeed)
  .setRandomForestSeed(randomSeed)
  .setGradientBoostedTreeSeed(randomSeed)
  .setInput(medv, houseFeatures)
  .getOutput()

val workflow = new OpWorkflow().setResultFeatures(prediction)

val evaluator = Evaluators.Regression().setLabelCol(medv).setPredictionCol(prediction)

def runner(opParams: OpParams): OpWorkflowRunner =
  new OpWorkflowRunner(
    workflow = workflow,
    trainingReader = trainingReader,
    scoringReader = scoringReader,
    evaluationReader = Option(trainingReader),
    evaluator = Option(evaluator),
    scoringEvaluator = None,
    featureToComputeUpTo = Option(houseFeatures)
  )
```
You can run the code using the following commands for train, score and evaluate:
```bash
cd helloworld
./gradlew compileTestScala installDist
```
**Train**
```bash
./gradlew -q sparkSubmit -Dmain=com.salesforce.hw.boston.OpBoston -Dargs="\
--run-type=train \
--model-location=/tmp/boston-model \
--read-location BostonHouse=`pwd`/src/main/resources/BostonDataset/housing.data"
```
**Score**
```bash
./gradlew -q sparkSubmit -Dmain=com.salesforce.hw.boston.OpBoston -Dargs="\
--run-type=score \
--model-location=/tmp/boston-model \
--read-location BostonHouse=`pwd`/src/main/resources/BostonDataset/housing.data \
--write-location=/tmp/boston-scores"
```
**Evaluate**
```bash
./gradlew -q sparkSubmit -Dmain=com.salesforce.hw.boston.OpBoston -Dargs="\
--run-type=evaluate \
--read-location BostonHouse=`pwd`/src/main/resources/BostonDataset/housing.data \
--write-location=/tmp/boston-eval \
--model-location=/tmp/boston-model \
--metrics-location=/tmp/boston-metrics"
```