[SPARK-5974] [SPARK-5980] [mllib] [python] [docs] Update ML guide with save/load, Python GBT

* Add GradientBoostedTrees Python examples to ML guide
  * I ran these in the pyspark shell, and they worked.
* Add save/load to examples in ML guide
* Added note to python docs about predict,transform not working within RDD actions,transformations in some cases (See SPARK-5981)

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #4750 from jkbradley/SPARK-5974 and squashes the following commits:

c410e38 [Joseph K. Bradley] Added note to LabeledPoint about attributes
bcae18b [Joseph K. Bradley] Added import of models for save/load examples in ml guide.  Fixed line length for tree.py, feature.py (but not other ML Pyspark files yet).
6d81c3e [Joseph K. Bradley] completed python GBT examples
9903309 [Joseph K. Bradley] Added note to python docs about predict,transform not working within RDD actions,transformations in some cases
c7dfad8 [Joseph K. Bradley] Added model save/load to ML guide.  Added GBT examples to ML guide

1 files changed, 20 insertions, 0 deletions

diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index 6675133a81..4695d1cde4 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -194,6 +194,7 @@ maximum tree depth of 5. The test error is calculated to measure the algorithm a
 <div data-lang="scala">
 {% highlight scala %}
 import org.apache.spark.mllib.tree.DecisionTree
+import org.apache.spark.mllib.tree.model.DecisionTreeModel
 import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file.
@@ -221,6 +222,9 @@ val labelAndPreds = testData.map { point =>
 val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count()
 println("Test Error = " + testErr)
 println("Learned classification tree model:\n" + model.toDebugString)
+
+model.save("myModelPath")
+val sameModel = DecisionTreeModel.load("myModelPath")
 {% endhighlight %}
 </div>
 
@@ -279,10 +283,16 @@ Double testErr =
   }).count() / testData.count();
 System.out.println("Test Error: " + testErr);
 System.out.println("Learned classification tree model:\n" + model.toDebugString());
+
+model.save("myModelPath");
+DecisionTreeModel sameModel = DecisionTreeModel.load("myModelPath");
 {% endhighlight %}
 </div>
 
 <div data-lang="python">
+
+Note that the Python API does not yet support model save/load but will in the future.
+
 {% highlight python %}
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib.tree import DecisionTree
@@ -324,6 +334,7 @@ depth of 5. The Mean Squared Error (MSE) is computed at the end to evaluate
 <div data-lang="scala">
 {% highlight scala %}
 import org.apache.spark.mllib.tree.DecisionTree
+import org.apache.spark.mllib.tree.model.DecisionTreeModel
 import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file.
@@ -350,6 +361,9 @@ val labelsAndPredictions = testData.map { point =>
 val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean()
 println("Test Mean Squared Error = " + testMSE)
 println("Learned regression tree model:\n" + model.toDebugString)
+
+model.save("myModelPath")
+val sameModel = DecisionTreeModel.load("myModelPath")
 {% endhighlight %}
 </div>
 
@@ -414,10 +428,16 @@ Double testMSE =
   }) / data.count();
 System.out.println("Test Mean Squared Error: " + testMSE);
 System.out.println("Learned regression tree model:\n" + model.toDebugString());
+
+model.save("myModelPath");
+DecisionTreeModel sameModel = DecisionTreeModel.load("myModelPath");
 {% endhighlight %}
 </div>
 
 <div data-lang="python">
+
+Note that the Python API does not yet support model save/load but will in the future.
+
 {% highlight python %}
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib.tree import DecisionTree