Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8518#discussion_r38265176
--- Diff: docs/ml-guide.md ---
@@ -186,26 +186,18 @@ This example covers the concepts of `Estimator`,
`Transformer`, and `Param`.
<div data-lang="scala">
{% highlight scala %}
-import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.sql.{Row, SQLContext}
-
-val conf = new SparkConf().setAppName("SimpleParamsExample")
-val sc = new SparkContext(conf)
-val sqlContext = new SQLContext(sc)
-import sqlContext.implicits._
+import org.apache.spark.sql.Row
// Prepare training data.
-// We use LabeledPoint, which is a case class. Spark SQL can convert RDDs
of case classes
-// into DataFrames, where it uses the case class metadata to infer the
schema.
-val training = sc.parallelize(Seq(
- LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
- LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
- LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
- LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
+val training = sqlContext.createDataFrame(Seq(
+ (1.0, Vectors.dense(0.0, 1.1, 0.1)),
+ (0.0, Vectors.dense(2.0, 1.0, -1.0)),
+ (0.0, Vectors.dense(2.0, 1.3, 1.0)),
+ (1.0, Vectors.dense(0.0, 1.2, -0.5))
+)).toDF("label", "features")
--- End diff --
+1 to switching from reflections-based schema inference into explicitly
naming columns; should we consider deprecating `LabeledPoint` since a big use
case is schema inference?
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