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| author | wm624@hotmail.com <wm624@hotmail.com> | 2016-05-26 12:36:36 +0200 |
|---|---|---|
| committer | Nick Pentreath <nickp@za.ibm.com> | 2016-05-26 12:36:36 +0200 |
| commit | e451f7f0c3857cdbbca98e66928a97f797f2fc6b (patch) | |
| tree | e77848ac0a4d1f5c2b5044b8f3374626b167d042 /examples/src | |
| parent | 53d4abe9e996e53c1bdcd5ac4cb8cbf08b9ec8b5 (diff) | |
| download | spark-e451f7f0c3857cdbbca98e66928a97f797f2fc6b.tar.gz spark-e451f7f0c3857cdbbca98e66928a97f797f2fc6b.tar.bz2 spark-e451f7f0c3857cdbbca98e66928a97f797f2fc6b.zip | |
[SPARK-15492][ML][DOC] Binarization scala example copy & paste to spark-shell error
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
The Binarization scala example val dataFrame : Dataframe = spark.createDataFrame(data).toDF("label", "feature"), which can't be pasted in the spark-shell as Dataframe is not imported. Compared with other examples, this explicit type is not required.
So I removed Dataframe in the code.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Manually tested
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes #13266 from wangmiao1981/unit.
Diffstat (limited to 'examples/src')
| -rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/ml/BinarizerExample.scala | 4 | ||||
| -rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala | 3 |
2 files changed, 3 insertions, 4 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/BinarizerExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/BinarizerExample.scala index 82bc14789b..5cd13ad64c 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/BinarizerExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/BinarizerExample.scala @@ -21,7 +21,7 @@ package org.apache.spark.examples.ml // $example on$ import org.apache.spark.ml.feature.Binarizer // $example off$ -import org.apache.spark.sql.{DataFrame, SparkSession} +import org.apache.spark.sql.{SparkSession} object BinarizerExample { def main(args: Array[String]): Unit = { @@ -31,7 +31,7 @@ object BinarizerExample { .getOrCreate() // $example on$ val data = Array((0, 0.1), (1, 0.8), (2, 0.2)) - val dataFrame: DataFrame = spark.createDataFrame(data).toDF("label", "feature") + val dataFrame = spark.createDataFrame(data).toDF("label", "feature") val binarizer: Binarizer = new Binarizer() .setInputCol("feature") diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala index 0b333cf629..0da8e3137a 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.scala @@ -21,7 +21,6 @@ package org.apache.spark.examples.ml // $example on$ import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest} import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator -import org.apache.spark.sql.DataFrame // $example off$ import org.apache.spark.sql.SparkSession @@ -43,7 +42,7 @@ object OneVsRestExample { // $example on$ // load data file. - val inputData: DataFrame = spark.read.format("libsvm") + val inputData = spark.read.format("libsvm") .load("data/mllib/sample_multiclass_classification_data.txt") // generate the train/test split. |