[SPARK-10518] [DOCS] Update code examples in spark.ml user guide to use LIBSVM data source instead of MLUtils

I fixed to use LIBSVM data source in the example code in spark.ml instead of MLUtils

Author: y-shimizu <y.shimizu0429@gmail.com>

Closes #8697 from y-shimizu/SPARK-10518.

3 files changed, 47 insertions, 104 deletions

diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md
index 62749909e0..58f566c9b4 100644
--- a/docs/ml-ensembles.md
+++ b/docs/ml-ensembles.md
@@ -121,10 +121,9 @@ import org.apache.spark.ml.classification.RandomForestClassifier
 import org.apache.spark.ml.classification.RandomForestClassificationModel
 import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -193,14 +192,11 @@ import org.apache.spark.ml.classification.RandomForestClassifier;
 import org.apache.spark.ml.classification.RandomForestClassificationModel;
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
 import org.apache.spark.ml.feature.*;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -268,10 +264,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.classification import RandomForestClassifier
 from pyspark.ml.feature import StringIndexer, VectorIndexer
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Index labels, adding metadata to the label column.
 # Fit on whole dataset to include all labels in index.
@@ -327,10 +322,9 @@ import org.apache.spark.ml.regression.RandomForestRegressor
 import org.apache.spark.ml.regression.RandomForestRegressionModel
 import org.apache.spark.ml.feature.VectorIndexer
 import org.apache.spark.ml.evaluation.RegressionEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -387,14 +381,11 @@ import org.apache.spark.ml.feature.VectorIndexer;
 import org.apache.spark.ml.feature.VectorIndexerModel;
 import org.apache.spark.ml.regression.RandomForestRegressionModel;
 import org.apache.spark.ml.regression.RandomForestRegressor;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -450,10 +441,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.regression import RandomForestRegressor
 from pyspark.ml.feature import VectorIndexer
 from pyspark.ml.evaluation import RegressionEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Automatically identify categorical features, and index them.
 # Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -576,10 +566,9 @@ import org.apache.spark.ml.classification.GBTClassifier
 import org.apache.spark.ml.classification.GBTClassificationModel
 import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -648,14 +637,10 @@ import org.apache.spark.ml.classification.GBTClassifier;
 import org.apache.spark.ml.classification.GBTClassificationModel;
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
 import org.apache.spark.ml.feature.*;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt");
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -724,10 +709,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.classification import GBTClassifier
 from pyspark.ml.feature import StringIndexer, VectorIndexer
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Index labels, adding metadata to the label column.
 # Fit on whole dataset to include all labels in index.
@@ -783,10 +767,9 @@ import org.apache.spark.ml.regression.GBTRegressor
 import org.apache.spark.ml.regression.GBTRegressionModel
 import org.apache.spark.ml.feature.VectorIndexer
 import org.apache.spark.ml.evaluation.RegressionEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -844,14 +827,10 @@ import org.apache.spark.ml.feature.VectorIndexer;
 import org.apache.spark.ml.feature.VectorIndexerModel;
 import org.apache.spark.ml.regression.GBTRegressionModel;
 import org.apache.spark.ml.regression.GBTRegressor;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt");
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -908,10 +887,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.regression import GBTRegressor
 from pyspark.ml.feature import VectorIndexer
 from pyspark.ml.evaluation import RegressionEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Automatically identify categorical features, and index them.
 # Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -970,15 +948,14 @@ Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classifie
 {% highlight scala %}
 import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
-import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.sql.{Row, SQLContext}
 
 val sqlContext = new SQLContext(sc)
 
 // parse data into dataframe
-val data = MLUtils.loadLibSVMFile(sc, 
-  "data/mllib/sample_multiclass_classification_data.txt")
-val Array(train, test) = data.toDF().randomSplit(Array(0.7, 0.3))
+val data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_multiclass_classification_data.txt")
+val Array(train, test) = data.randomSplit(Array(0.7, 0.3))
 
 // instantiate multiclass learner and train
 val ovr = new OneVsRest().setClassifier(new LogisticRegression)
@@ -1016,9 +993,6 @@ import org.apache.spark.ml.classification.OneVsRest;
 import org.apache.spark.ml.classification.OneVsRestModel;
 import org.apache.spark.mllib.evaluation.MulticlassMetrics;
 import org.apache.spark.mllib.linalg.Matrix;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 
@@ -1026,10 +1000,9 @@ SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample");
 JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
 
-RDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(),
-  "data/mllib/sample_multiclass_classification_data.txt");
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_multiclass_classification_data.txt");
 
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
 DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345);
 DataFrame train = splits[0];
 DataFrame test = splits[1];
diff --git a/docs/ml-features.md b/docs/ml-features.md
index 58b31a5a5c..a414c21b5c 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1179,9 +1179,9 @@ In the example below, we read in a dataset of labeled points and then use `Vecto
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.feature.VectorIndexer
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 val indexer = new VectorIndexer()
   .setInputCol("features")
   .setOutputCol("indexed")
@@ -1200,16 +1200,12 @@ val indexedData = indexerModel.transform(data)
 {% highlight java %}
 import java.util.Map;
 
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.VectorIndexer;
 import org.apache.spark.ml.feature.VectorIndexerModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(),
-  "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame data = sqlContext.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 VectorIndexer indexer = new VectorIndexer()
   .setInputCol("features")
   .setOutputCol("indexed")
@@ -1230,9 +1226,9 @@ DataFrame indexedData = indexerModel.transform(data);
 <div data-lang="python" markdown="1">
 {% highlight python %}
 from pyspark.ml.feature import VectorIndexer
-from pyspark.mllib.util import MLUtils
 
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 indexer = VectorIndexer(inputCol="features", outputCol="indexed", maxCategories=10)
 indexerModel = indexer.fit(data)
 
@@ -1253,10 +1249,9 @@ The following example demonstrates how to load a dataset in libsvm format and th
 <div data-lang="scala">
 {% highlight scala %}
 import org.apache.spark.ml.feature.Normalizer
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-val dataFrame = sqlContext.createDataFrame(data)
+val dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 
 // Normalize each Vector using $L^1$ norm.
 val normalizer = new Normalizer()
@@ -1272,15 +1267,11 @@ val lInfNormData = normalizer.transform(dataFrame, normalizer.p -> Double.Positi
 
 <div data-lang="java">
 {% highlight java %}
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.Normalizer;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> data =
-  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 
 // Normalize each Vector using $L^1$ norm.
 Normalizer normalizer = new Normalizer()
@@ -1297,11 +1288,10 @@ DataFrame lInfNormData =
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark.mllib.util import MLUtils
 from pyspark.ml.feature import Normalizer
 
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-dataFrame = sqlContext.createDataFrame(data)
+dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 
 # Normalize each Vector using $L^1$ norm.
 normalizer = Normalizer(inputCol="features", outputCol="normFeatures", p=1.0)
@@ -1335,10 +1325,9 @@ The following example demonstrates how to load a dataset in libsvm format and th
 <div data-lang="scala">
 {% highlight scala %}
 import org.apache.spark.ml.feature.StandardScaler
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-val dataFrame = sqlContext.createDataFrame(data)
+val dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 val scaler = new StandardScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures")
@@ -1355,16 +1344,12 @@ val scaledData = scalerModel.transform(dataFrame)
 
 <div data-lang="java">
 {% highlight java %}
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.StandardScaler;
 import org.apache.spark.ml.feature.StandardScalerModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> data =
-  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 StandardScaler scaler = new StandardScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures")
@@ -1381,11 +1366,10 @@ DataFrame scaledData = scalerModel.transform(dataFrame);
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark.mllib.util import MLUtils
 from pyspark.ml.feature import StandardScaler
 
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-dataFrame = sqlContext.createDataFrame(data)
+dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures",
                         withStd=True, withMean=False)
 
@@ -1424,10 +1408,9 @@ More details can be found in the API docs for
 [MinMaxScalerModel](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScalerModel).
 {% highlight scala %}
 import org.apache.spark.ml.feature.MinMaxScaler
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-val dataFrame = sqlContext.createDataFrame(data)
+val dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 val scaler = new MinMaxScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures")
@@ -1448,13 +1431,10 @@ More details can be found in the API docs for
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.MinMaxScaler;
 import org.apache.spark.ml.feature.MinMaxScalerModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> data =
-  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 MinMaxScaler scaler = new MinMaxScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures");
diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md
index cdd9d4999f..4e94e2f9c7 100644
--- a/docs/ml-linear-methods.md
+++ b/docs/ml-linear-methods.md
@@ -59,10 +59,9 @@ $\alpha$ and `regParam` corresponds to $\lambda$.
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.classification.LogisticRegression
-import org.apache.spark.mllib.util.MLUtils
 
 // Load training data
-val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 val lr = new LogisticRegression()
   .setMaxIter(10)
@@ -81,8 +80,6 @@ println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
 {% highlight java %}
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.SparkConf;
 import org.apache.spark.SparkContext;
 import org.apache.spark.sql.DataFrame;
@@ -98,7 +95,7 @@ public class LogisticRegressionWithElasticNetExample {
     String path = "data/mllib/sample_libsvm_data.txt";
 
     // Load training data
-    DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
+    DataFrame training = sqlContext.read.format("libsvm").load(path);
 
     LogisticRegression lr = new LogisticRegression()
       .setMaxIter(10)
@@ -118,11 +115,9 @@ public class LogisticRegressionWithElasticNetExample {
 <div data-lang="python" markdown="1">
 {% highlight python %}
 from pyspark.ml.classification import LogisticRegression
-from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib.util import MLUtils
 
 # Load training data
-training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
 
@@ -251,10 +246,9 @@ regression model and extracting model summary statistics.
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.regression.LinearRegression
-import org.apache.spark.mllib.util.MLUtils
 
 // Load training data
-val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 val lr = new LinearRegression()
   .setMaxIter(10)
@@ -283,8 +277,6 @@ import org.apache.spark.ml.regression.LinearRegression;
 import org.apache.spark.ml.regression.LinearRegressionModel;
 import org.apache.spark.ml.regression.LinearRegressionTrainingSummary;
 import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.SparkConf;
 import org.apache.spark.SparkContext;
 import org.apache.spark.sql.DataFrame;
@@ -300,7 +292,7 @@ public class LinearRegressionWithElasticNetExample {
     String path = "data/mllib/sample_libsvm_data.txt";
 
     // Load training data
-    DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
+    DataFrame training = sqlContext.read.format("libsvm").load(path);
 
     LinearRegression lr = new LinearRegression()
       .setMaxIter(10)
@@ -329,11 +321,9 @@ public class LinearRegressionWithElasticNetExample {
 <!--- TODO: Add python model summaries once implemented -->
 {% highlight python %}
 from pyspark.ml.regression import LinearRegression
-from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib.util import MLUtils
 
 # Load training data
-training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)