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Posted to issues@spark.apache.org by "somil deshmukh (JIRA)" <ji...@apache.org> on 2015/06/29 10:13:04 UTC
[jira] [Commented] (SPARK-8660) Update comments that contain R
statements in ml.logisticRegressionSuite
[ https://issues.apache.org/jira/browse/SPARK-8660?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14605257#comment-14605257 ]
somil deshmukh commented on SPARK-8660:
---------------------------------------
I would like to solve this issue
> Update comments that contain R statements in ml.logisticRegressionSuite
> -----------------------------------------------------------------------
>
> Key: SPARK-8660
> URL: https://issues.apache.org/jira/browse/SPARK-8660
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 1.4.0
> Reporter: Xiangrui Meng
> Priority: Trivial
> Labels: starter
> Original Estimate: 20m
> Remaining Estimate: 20m
>
> We put R statements as comments in unit test. However, there are two issues:
> 1. JavaDoc style "/** ... */" is used instead of normal multiline comment "/* ... */".
> 2. We put a leading "*" on each line. It is hard to copy & paste the commands to/from R and verify the result.
> For example, in https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala#L504
> {code}
> /**
> * Using the following R code to load the data and train the model using glmnet package.
> *
> * > library("glmnet")
> * > data <- read.csv("path", header=FALSE)
> * > label = factor(data$V1)
> * > features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
> * > weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0))
> * > weights
> * 5 x 1 sparse Matrix of class "dgCMatrix"
> * s0
> * (Intercept) -0.2480643
> * data.V2 0.0000000
> * data.V3 .
> * data.V4 .
> * data.V5 .
> */
> {code}
> should change to
> {code}
> /*
> Using the following R code to load the data and train the model using glmnet package.
>
> library("glmnet")
> data <- read.csv("path", header=FALSE)
> label = factor(data$V1)
> features = as.matrix(data.frame(data$V2, data$V3, data$V4, data$V5))
> weights = coef(glmnet(features,label, family="binomial", alpha = 1.0, lambda = 6.0))
> weights
> 5 x 1 sparse Matrix of class "dgCMatrix"
> s0
> (Intercept) -0.2480643
> data.V2 0.0000000
> data.V3 .
> data.V4 .
> data.V5 .
> */
> {code}
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