added comment to save,load parts of ML guide examples

Author: jkbradley

docs/mllib-collaborative-filtering.md

@@ -97,6 +97,7 @@ val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
 }.mean()
 println("Mean Squared Error = " + MSE)
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = MatrixFactorizationModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -186,6 +187,7 @@ public class CollaborativeFiltering {
     ).rdd()).mean();
     System.out.println("Mean Squared Error = " + MSE);
 
+    // Save and load model
     model.save(sc.sc(), "myModelPath");
     MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(sc.sc(), "myModelPath");
   }

docs/mllib-decision-tree.md

@@ -223,6 +223,7 @@ val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.
 println("Test Error = " + testErr)
 println("Learned classification tree model:\n" + model.toDebugString)
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = DecisionTreeModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -284,6 +285,7 @@ Double testErr =
 System.out.println("Test Error: " + testErr);
 System.out.println("Learned classification tree model:\n" + model.toDebugString());
 
+// Save and load model
 model.save(sc.sc(), "myModelPath");
 DecisionTreeModel sameModel = DecisionTreeModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
@@ -362,6 +364,7 @@ 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)
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = DecisionTreeModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -429,6 +432,7 @@ Double testMSE =
 System.out.println("Test Mean Squared Error: " + testMSE);
 System.out.println("Learned regression tree model:\n" + model.toDebugString());
 
+// Save and load model
 model.save(sc.sc(), "myModelPath");
 DecisionTreeModel sameModel = DecisionTreeModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}

docs/mllib-ensembles.md

@@ -129,6 +129,7 @@ val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.
 println("Test Error = " + testErr)
 println("Learned classification forest model:\n" + model.toDebugString)
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = RandomForestModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -193,6 +194,7 @@ Double testErr =
 System.out.println("Test Error: " + testErr);
 System.out.println("Learned classification forest model:\n" + model.toDebugString());
 
+// Save and load model
 model.save(sc.sc(), "myModelPath");
 RandomForestModel sameModel = RandomForestModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
@@ -276,6 +278,7 @@ 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)
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = RandomForestModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -343,6 +346,7 @@ Double testMSE =
 System.out.println("Test Mean Squared Error: " + testMSE);
 System.out.println("Learned regression forest model:\n" + model.toDebugString());
 
+// Save and load model
 model.save(sc.sc(), "myModelPath");
 RandomForestModel sameModel = RandomForestModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
@@ -504,6 +508,7 @@ val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.
 println("Test Error = " + testErr)
 println("Learned classification GBT model:\n" + model.toDebugString)
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = GradientBoostedTreesModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -568,6 +573,7 @@ Double testErr =
 System.out.println("Test Error: " + testErr);
 System.out.println("Learned classification GBT model:\n" + model.toDebugString());
 
+// Save and load model
 model.save(sc.sc(), "myModelPath");
 GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
@@ -647,6 +653,7 @@ 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)
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = GradientBoostedTreesModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -717,6 +724,7 @@ Double testMSE =
 System.out.println("Test Mean Squared Error: " + testMSE);
 System.out.println("Learned regression GBT model:\n" + model.toDebugString());
 
+// Save and load model
 model.save(sc.sc(), "myModelPath");
 GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}

docs/mllib-linear-methods.md

@@ -223,6 +223,7 @@ val auROC = metrics.areaUnderROC()
 
 println("Area under ROC = " + auROC)
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = SVMModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -308,6 +309,7 @@ public class SVMClassifier {
 
     System.out.println("Area under ROC = " + auROC);
 
+    // Save and load model
     model.save(sc.sc(), "myModelPath");
     SVMModel sameModel = SVMModel.load(sc.sc(), "myModelPath");
   }
@@ -423,6 +425,7 @@ val valuesAndPreds = parsedData.map { point =>
 val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
 println("training Mean Squared Error = " + MSE)
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = LinearRegressionModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -496,6 +499,7 @@ public class LinearRegression {
     ).rdd()).mean();
     System.out.println("training Mean Squared Error = " + MSE);
 
+    // Save and load model
     model.save(sc.sc(), "myModelPath");
     LinearRegressionModel sameModel = LinearRegressionModel.load(sc.sc(), "myModelPath");
   }

docs/mllib-naive-bayes.md

@@ -56,6 +56,7 @@ val model = NaiveBayes.train(training, lambda = 1.0)
 val predictionAndLabel = test.map(p => (model.predict(p.features), p.label))
 val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()
 
+// Save and load model
 model.save(sc, "myModelPath")
 val sameModel = NaiveBayesModel.load(sc, "myModelPath")
 {% endhighlight %}
@@ -97,6 +98,7 @@ double accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>,
     }
   }).count() / (double) test.count();
 
+// Save and load model
 model.save(sc.sc(), "myModelPath");
 NaiveBayesModel sameModel = NaiveBayesModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}