[SPARK-8308] [MLLIB] add missing save load for python example

jira: https://issues.apache.org/jira/browse/SPARK-8308

1. add some missing save/load in python examples. , LogisticRegression, LinearRegression and NaiveBayes
2. tune down iterations for MatrixFactorization, since current number will trigger StackOverflow for default java configuration (>1M)

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #6760 from hhbyyh/docUpdate and squashes the following commits:

9bd3383 [Yuhao Yang] update scala example
8a44692 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into docUpdate
077cbb8 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into docUpdate
3e948dc [Yuhao Yang] add missing save load for python example

1 files changed, 10 insertions, 2 deletions

diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 2a2a7c1318..3927d65fbf 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -499,7 +499,7 @@ Note that the Python API does not yet support multiclass classification and mode
 will in the future.
 
 {% highlight python %}
-from pyspark.mllib.classification import LogisticRegressionWithLBFGS
+from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel
 from pyspark.mllib.regression import LabeledPoint
 from numpy import array
 
@@ -518,6 +518,10 @@ model = LogisticRegressionWithLBFGS.train(parsedData)
 labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
 trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())
 print("Training Error = " + str(trainErr))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = LogisticRegressionModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 </div>
@@ -668,7 +672,7 @@ values. We compute the mean squared error at the end to evaluate
 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, LinearRegressionWithSGD
+from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD, LinearRegressionModel
 from numpy import array
 
 # Load and parse the data
@@ -686,6 +690,10 @@ model = LinearRegressionWithSGD.train(parsedData)
 valuesAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))
 MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y) / valuesAndPreds.count()
 print("Mean Squared Error = " + str(MSE))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = LinearRegressionModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 </div>