From 0674700303da3e4737d73f5fabd2a925ec712f63 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Sun, 31 May 2015 11:51:49 -0700
Subject: [SPARK-7949] [MLLIB] [DOC] update document with some missing
 save/load

add save load for examples:
KMeansModel
PowerIterationClusteringModel
Word2VecModel
IsotonicRegressionModel

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #6498 from hhbyyh/docSaveLoad and squashes the following commits:

7f9f06d [Yuhao Yang] add missing imports
c604cad [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into docSaveLoad
1dd77cc [Yuhao Yang] update document with some missing save/load
---
 docs/mllib-isotonic-regression.md | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

(limited to 'docs/mllib-isotonic-regression.md')

diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md
index b521c2f27c..5732bc4c7e 100644
--- a/docs/mllib-isotonic-regression.md
+++ b/docs/mllib-isotonic-regression.md
@@ -60,7 +60,7 @@ Model is created using the training set and a mean squared error is calculated f
 labels and real labels in the test set.
 
 {% highlight scala %}
-import org.apache.spark.mllib.regression.IsotonicRegression
+import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegressionModel}
 
 val data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt")
 
@@ -88,6 +88,10 @@ val predictionAndLabel = test.map { point =>
 // Calculate mean squared error between predicted and real labels.
 val meanSquaredError = predictionAndLabel.map{case(p, l) => math.pow((p - l), 2)}.mean()
 println("Mean Squared Error = " + meanSquaredError)
+
+// Save and load model
+model.save(sc, "myModelPath")
+val sameModel = IsotonicRegressionModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 
@@ -150,6 +154,10 @@ Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map(
 ).rdd()).mean();
 
 System.out.println("Mean Squared Error = " + meanSquaredError);
+
+// Save and load model
+model.save(sc.sc(), "myModelPath");
+IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
 </div>
 </div>
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