diff options
author | Zheng RuiFeng <ruifengz@foxmail.com> | 2016-05-19 23:26:11 -0700 |
---|---|---|
committer | Xiangrui Meng <meng@databricks.com> | 2016-05-19 23:26:11 -0700 |
commit | 47a2940da97caa55bbb8bb8ec1d51c9f6d5041c6 (patch) | |
tree | 2cad1dcab525990a7b18e6800634e45edcc36197 /examples/src/main/scala | |
parent | 4c7a6b385c79f4de07a89495afce4f8e73b06086 (diff) | |
download | spark-47a2940da97caa55bbb8bb8ec1d51c9f6d5041c6.tar.gz spark-47a2940da97caa55bbb8bb8ec1d51c9f6d5041c6.tar.bz2 spark-47a2940da97caa55bbb8bb8ec1d51c9f6d5041c6.zip |
[SPARK-15398][ML] Update the warning message to recommend ML usage
## What changes were proposed in this pull request?
MLlib are not recommended to use, and some methods are even deprecated.
Update the warning message to recommend ML usage.
```
def showWarning() {
System.err.println(
"""WARN: This is a naive implementation of Logistic Regression and is given as an example!
|Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or
|org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
|for more conventional use.
""".stripMargin)
}
```
To
```
def showWarning() {
System.err.println(
"""WARN: This is a naive implementation of Logistic Regression and is given as an example!
|Please use org.apache.spark.ml.classification.LogisticRegression
|for more conventional use.
""".stripMargin)
}
```
## How was this patch tested?
local build
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes #13190 from zhengruifeng/update_recd.
Diffstat (limited to 'examples/src/main/scala')
8 files changed, 16 insertions, 24 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala index fa10101955..97aefac025 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalALS.scala @@ -24,7 +24,7 @@ import org.apache.commons.math3.linear._ * Alternating least squares matrix factorization. * * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to org.apache.spark.mllib.recommendation.ALS + * please refer to org.apache.spark.ml.recommendation.ALS. */ object LocalALS { @@ -96,7 +96,7 @@ object LocalALS { def showWarning() { System.err.println( """WARN: This is a naive implementation of ALS and is given as an example! - |Please use the ALS method found in org.apache.spark.mllib.recommendation + |Please use org.apache.spark.ml.recommendation.ALS |for more conventional use. """.stripMargin) } diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala index bec89f7c3d..3d02ce0561 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala @@ -26,8 +26,7 @@ import breeze.linalg.{DenseVector, Vector} * Logistic regression based classification. * * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or - * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs. + * please refer to org.apache.spark.ml.classification.LogisticRegression. */ object LocalFileLR { val D = 10 // Number of dimensions @@ -43,8 +42,7 @@ object LocalFileLR { def showWarning() { System.err.println( """WARN: This is a naive implementation of Logistic Regression and is given as an example! - |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or - |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS + |Please use org.apache.spark.ml.classification.LogisticRegression |for more conventional use. """.stripMargin) } diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala index f8961847f3..fca585c2a3 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala @@ -29,7 +29,7 @@ import breeze.linalg.{squaredDistance, DenseVector, Vector} * K-means clustering. * * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to org.apache.spark.mllib.clustering.KMeans + * please refer to org.apache.spark.ml.clustering.KMeans. */ object LocalKMeans { val N = 1000 @@ -66,7 +66,7 @@ object LocalKMeans { def showWarning() { System.err.println( """WARN: This is a naive implementation of KMeans Clustering and is given as an example! - |Please use the KMeans method found in org.apache.spark.mllib.clustering + |Please use org.apache.spark.ml.clustering.KMeans |for more conventional use. """.stripMargin) } diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala index 0baf6db607..13ccc2ae7c 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala @@ -26,8 +26,7 @@ import breeze.linalg.{DenseVector, Vector} * Logistic regression based classification. * * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or - * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs. + * please refer to org.apache.spark.ml.classification.LogisticRegression. */ object LocalLR { val N = 10000 // Number of data points @@ -50,8 +49,7 @@ object LocalLR { def showWarning() { System.err.println( """WARN: This is a naive implementation of Logistic Regression and is given as an example! - |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or - |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS + |Please use org.apache.spark.ml.classification.LogisticRegression |for more conventional use. """.stripMargin) } diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala index 4263680c6f..b06c629802 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala @@ -26,7 +26,7 @@ import org.apache.spark._ * Alternating least squares matrix factorization. * * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to org.apache.spark.mllib.recommendation.ALS + * please refer to org.apache.spark.ml.recommendation.ALS. */ object SparkALS { @@ -81,7 +81,7 @@ object SparkALS { def showWarning() { System.err.println( """WARN: This is a naive implementation of ALS and is given as an example! - |Please use the ALS method found in org.apache.spark.mllib.recommendation + |Please use org.apache.spark.ml.recommendation.ALS |for more conventional use. """.stripMargin) } diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala index 7463b868ff..c514eb0fa5 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala @@ -31,8 +31,7 @@ import org.apache.spark._ * Logistic regression based classification. * * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or - * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs. + * please refer to org.apache.spark.ml.classification.LogisticRegression. */ object SparkHdfsLR { val D = 10 // Number of dimensions @@ -54,8 +53,7 @@ object SparkHdfsLR { def showWarning() { System.err.println( """WARN: This is a naive implementation of Logistic Regression and is given as an example! - |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or - |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS + |Please use org.apache.spark.ml.classification.LogisticRegression |for more conventional use. """.stripMargin) } diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala index d9f94a42b1..676164806e 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala @@ -26,7 +26,7 @@ import org.apache.spark.{SparkConf, SparkContext} * K-means clustering. * * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to org.apache.spark.mllib.clustering.KMeans + * please refer to org.apache.spark.ml.clustering.KMeans. */ object SparkKMeans { @@ -52,7 +52,7 @@ object SparkKMeans { def showWarning() { System.err.println( """WARN: This is a naive implementation of KMeans Clustering and is given as an example! - |Please use the KMeans method found in org.apache.spark.mllib.clustering + |Please use org.apache.spark.ml.clustering.KMeans |for more conventional use. """.stripMargin) } diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala index acd8656b65..718f84f645 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala @@ -31,8 +31,7 @@ import org.apache.spark._ * Usage: SparkLR [slices] * * This is an example implementation for learning how to use Spark. For more conventional use, - * please refer to either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or - * org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS based on your needs. + * please refer to org.apache.spark.ml.classification.LogisticRegression. */ object SparkLR { val N = 10000 // Number of data points @@ -55,8 +54,7 @@ object SparkLR { def showWarning() { System.err.println( """WARN: This is a naive implementation of Logistic Regression and is given as an example! - |Please use either org.apache.spark.mllib.classification.LogisticRegressionWithSGD or - |org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS + |Please use org.apache.spark.ml.classification.LogisticRegression |for more conventional use. """.stripMargin) } |