[SPARK-4587] [mllib] [docs] Fixed save,load calls in ML guide examples

Should pass spark context to save/load

CC: mengxr

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

Closes #4816 from jkbradley/ml-io-doc-fix and squashes the following commits:

83d369d [Joseph K. Bradley] added comment to save,load parts of ML guide examples
2841170 [Joseph K. Bradley] Fixed save,load calls in ML guide examples

1 files changed, 24 insertions, 16 deletions

diff --git a/docs/mllib-ensembles.md b/docs/mllib-ensembles.md
index ddae84165f..ec1ef38b45 100644
--- a/docs/mllib-ensembles.md
+++ b/docs/mllib-ensembles.md
@@ -129,8 +129,9 @@ val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.
 println("Test Error = " + testErr)
 println("Learned classification forest model:\n" + model.toDebugString)
 
-model.save("myModelPath")
-val sameModel = RandomForestModel.load("myModelPath")
+// Save and load model
+model.save(sc, "myModelPath")
+val sameModel = RandomForestModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 
@@ -193,8 +194,9 @@ Double testErr =
 System.out.println("Test Error: " + testErr);
 System.out.println("Learned classification forest model:\n" + model.toDebugString());
 
-model.save("myModelPath");
-RandomForestModel sameModel = RandomForestModel.load("myModelPath");
+// Save and load model
+model.save(sc.sc(), "myModelPath");
+RandomForestModel sameModel = RandomForestModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
 </div>
 
@@ -276,8 +278,9 @@ val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean
 println("Test Mean Squared Error = " + testMSE)
 println("Learned regression forest model:\n" + model.toDebugString)
 
-model.save("myModelPath")
-val sameModel = RandomForestModel.load("myModelPath")
+// Save and load model
+model.save(sc, "myModelPath")
+val sameModel = RandomForestModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 
@@ -343,8 +346,9 @@ Double testMSE =
 System.out.println("Test Mean Squared Error: " + testMSE);
 System.out.println("Learned regression forest model:\n" + model.toDebugString());
 
-model.save("myModelPath");
-RandomForestModel sameModel = RandomForestModel.load("myModelPath");
+// Save and load model
+model.save(sc.sc(), "myModelPath");
+RandomForestModel sameModel = RandomForestModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
 </div>
 
@@ -504,8 +508,9 @@ val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.
 println("Test Error = " + testErr)
 println("Learned classification GBT model:\n" + model.toDebugString)
 
-model.save("myModelPath")
-val sameModel = GradientBoostedTreesModel.load("myModelPath")
+// Save and load model
+model.save(sc, "myModelPath")
+val sameModel = GradientBoostedTreesModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 
@@ -568,8 +573,9 @@ Double testErr =
 System.out.println("Test Error: " + testErr);
 System.out.println("Learned classification GBT model:\n" + model.toDebugString());
 
-model.save("myModelPath");
-GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load("myModelPath");
+// Save and load model
+model.save(sc.sc(), "myModelPath");
+GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
 </div>
 
@@ -647,8 +653,9 @@ val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean
 println("Test Mean Squared Error = " + testMSE)
 println("Learned regression GBT model:\n" + model.toDebugString)
 
-model.save("myModelPath")
-val sameModel = GradientBoostedTreesModel.load("myModelPath")
+// Save and load model
+model.save(sc, "myModelPath")
+val sameModel = GradientBoostedTreesModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 
@@ -717,8 +724,9 @@ Double testMSE =
 System.out.println("Test Mean Squared Error: " + testMSE);
 System.out.println("Learned regression GBT model:\n" + model.toDebugString());
 
-model.save("myModelPath");
-GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load("myModelPath");
+// Save and load model
+model.save(sc.sc(), "myModelPath");
+GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
 </div>