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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/python/logistic_regression.py | |
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/python/logistic_regression.py')
-rwxr-xr-x | examples/src/main/python/logistic_regression.py | 7 |
1 files changed, 4 insertions, 3 deletions
diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py index b318b7d87b..7d33be7e81 100755 --- a/examples/src/main/python/logistic_regression.py +++ b/examples/src/main/python/logistic_regression.py @@ -20,7 +20,7 @@ A logistic regression implementation that uses NumPy (http://www.numpy.org) to act on batches of input data using efficient matrix operations. In practice, one may prefer to use the LogisticRegression algorithm in -MLlib, as shown in examples/src/main/python/mllib/logistic_regression.py. +ML, as shown in examples/src/main/python/ml/logistic_regression_with_elastic_net.py. """ from __future__ import print_function @@ -51,8 +51,9 @@ if __name__ == "__main__": exit(-1) print("""WARN: This is a naive implementation of Logistic Regression and is - given as an example! Please refer to examples/src/main/python/mllib/logistic_regression.py - to see how MLlib's implementation is used.""", file=sys.stderr) + given as an example! + Please refer to examples/src/main/python/ml/logistic_regression_with_elastic_net.py + to see how ML's implementation is used.""", file=sys.stderr) sc = SparkContext(appName="PythonLR") points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache() |