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* [SPARK-18797][SPARKR] Update spark.logit in sparkr-vignetteswm624@hotmail.com2016-12-121-7/+38
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? spark.logit is added in 2.1. We need to update spark-vignettes to reflect the changes. This is part of SparkR QA work. ## How was this patch tested? Manual build html. Please see attached image for the result. ![test](https://cloud.githubusercontent.com/assets/5033592/21032237/01b565fe-bd5d-11e6-8b59-4de4b6ef611d.jpeg) Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16222 from wangmiao1981/veg.
* [SPARK-18810][SPARKR] SparkR install.spark does not work for RCs, snapshotsFelix Cheung2016-12-123-12/+51
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Support overriding the download url (include version directory) in an environment variable, `SPARKR_RELEASE_DOWNLOAD_URL` ## How was this patch tested? unit test, manually testing - snapshot build url - download when spark jar not cached - when spark jar is cached - RC build url - download when spark jar not cached - when spark jar is cached - multiple cached spark versions - starting with sparkR shell To use this, ``` SPARKR_RELEASE_DOWNLOAD_URL=http://this_is_the_url_to_spark_release_tgz R ``` then in R, ``` library(SparkR) # or specify lib.loc sparkR.session() ``` Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16248 from felixcheung/rinstallurl.
* [SPARK-18807][SPARKR] Should suppress output print for calls to JVM methods ↵Felix Cheung2016-12-094-16/+17
| | | | | | | | | | | | | | | | | | | | | | | | with void return values ## What changes were proposed in this pull request? Several SparkR API calling into JVM methods that have void return values are getting printed out, especially when running in a REPL or IDE. example: ``` > setLogLevel("WARN") NULL ``` We should fix this to make the result more clear. Also found a small change to return value of dropTempView in 2.1 - adding doc and test for it. ## How was this patch tested? manually - I didn't find a expect_*() method in testthat for this Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16237 from felixcheung/rinvis.
* [SPARK-18349][SPARKR] Update R API documentation on ml model summarywm624@hotmail.com2016-12-082-63/+86
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In this PR, the document of `summary` method is improved in the format: returns summary information of the fitted model, which is a list. The list includes ....... Since `summary` in R is mainly about the model, which is not the same as `summary` object on scala side, if there is one, the scala API doc is not pointed here. In current document, some `return` have `.` and some don't have. `.` is added to missed ones. Since spark.logit `summary` has a big refactoring, this PR doesn't include this one. It will be changed when the `spark.logit` PR is merged. ## How was this patch tested? Manual build. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16150 from wangmiao1981/audit2.
* [SPARK-18590][SPARKR] build R source package when making distributionFelix Cheung2016-12-086-11/+30
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR has 2 key changes. One, we are building source package (aka bundle package) for SparkR which could be released on CRAN. Two, we should include in the official Spark binary distributions SparkR installed from this source package instead (which would have help/vignettes rds needed for those to work when the SparkR package is loaded in R, whereas earlier approach with devtools does not) But, because of various differences in how R performs different tasks, this PR is a fair bit more complicated. More details below. This PR also includes a few minor fixes. ### more details These are the additional steps in make-distribution; please see [here](https://github.com/apache/spark/blob/master/R/CRAN_RELEASE.md) on what's going to a CRAN release, which is now run during make-distribution.sh. 1. package needs to be installed because the first code block in vignettes is `library(SparkR)` without lib path 2. `R CMD build` will build vignettes (this process runs Spark/SparkR code and captures outputs into pdf documentation) 3. `R CMD check` on the source package will install package and build vignettes again (this time from source packaged) - this is a key step required to release R package on CRAN (will skip tests here but tests will need to pass for CRAN release process to success - ideally, during release signoff we should install from the R source package and run tests) 4. `R CMD Install` on the source package (this is the only way to generate doc/vignettes rds files correctly, not in step # 1) (the output of this step is what we package into Spark dist and sparkr.zip) Alternatively, R CMD build should already be installing the package in a temp directory though it might just be finding this location and set it to lib.loc parameter; another approach is perhaps we could try calling `R CMD INSTALL --build pkg` instead. But in any case, despite installing the package multiple times this is relatively fast. Building vignettes takes a while though. ## How was this patch tested? Manually, CI. Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16014 from felixcheung/rdist.
* [SPARK-18326][SPARKR][ML] Review SparkR ML wrappers API for 2.1Yanbo Liang2016-12-072-15/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Reviewing SparkR ML wrappers API for 2.1 release, mainly two issues: * Remove ```probabilityCol``` from the argument list of ```spark.logit``` and ```spark.randomForest```. Since it was used when making prediction and should be an argument of ```predict```, and we will work on this at [SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) in the next release cycle. * Fix ```spark.als``` params to make it consistent with MLlib. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16169 from yanboliang/spark-18326.
* [SPARK-18678][ML] Skewed reservoir sampling in SamplingUtilsSean Owen2016-12-071-4/+5
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix reservoir sampling bias for small k. An off-by-one error meant that the probability of replacement was slightly too high -- k/(l-1) after l element instead of k/l, which matters for small k. ## How was this patch tested? Existing test plus new test case. Author: Sean Owen <sowen@cloudera.com> Closes #16129 from srowen/SPARK-18678.
* [SPARK-18686][SPARKR][ML] Several cleanup and improvements for spark.logit.Yanbo Liang2016-12-072-112/+157
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Several cleanup and improvements for ```spark.logit```: * ```summary``` should return coefficients matrix, and should output labels for each class if the model is multinomial logistic regression model. * ```summary``` should not return ```areaUnderROC, roc, pr, ...```, since most of them are DataFrame which are less important for R users. Meanwhile, these metrics ignore instance weights (setting all to 1.0) which will be changed in later Spark version. In case it will introduce breaking changes, we do not expose them currently. * SparkR test improvement: comparing the training result with native R glmnet. * Remove argument ```aggregationDepth``` from ```spark.logit```, since it's an expert Param(related with Spark architecture and job execution) that would be used rarely by R users. ## How was this patch tested? Unit tests. The ```summary``` output after this change: multinomial logistic regression: ``` > df <- suppressWarnings(createDataFrame(iris)) > model <- spark.logit(df, Species ~ ., regParam = 0.5) > summary(model) $coefficients versicolor virginica setosa (Intercept) 1.514031 -2.609108 1.095077 Sepal_Length 0.02511006 0.2649821 -0.2900921 Sepal_Width -0.5291215 -0.02016446 0.549286 Petal_Length 0.03647411 0.1544119 -0.190886 Petal_Width 0.000236092 0.4195804 -0.4198165 ``` binomial logistic regression: ``` > df <- suppressWarnings(createDataFrame(iris)) > training <- df[df$Species %in% c("versicolor", "virginica"), ] > model <- spark.logit(training, Species ~ ., regParam = 0.5) > summary(model) $coefficients Estimate (Intercept) -6.053815 Sepal_Length 0.2449379 Sepal_Width 0.1648321 Petal_Length 0.4730718 Petal_Width 1.031947 ``` Author: Yanbo Liang <ybliang8@gmail.com> Closes #16117 from yanboliang/spark-18686.
* [SPARK-18643][SPARKR] SparkR hangs at session start when installed as a ↵Felix Cheung2016-12-042-3/+6
| | | | | | | | | | | | | | | | | | | | | package without Spark ## What changes were proposed in this pull request? If SparkR is running as a package and it has previously downloaded Spark Jar it should be able to run as before without having to set SPARK_HOME. Basically with this bug the auto install Spark will only work in the first session. This seems to be a regression on the earlier behavior. Fix is to always try to install or check for the cached Spark if running in an interactive session. As discussed before, we should probably only install Spark iff running in an interactive session (R shell, RStudio etc) ## How was this patch tested? Manually Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16077 from felixcheung/rsessioninteractive.
* [SPARK-18291][SPARKR][ML] Revert "[SPARK-18291][SPARKR][ML] SparkR glm ↵Yanbo Liang2016-12-021-15/+5
| | | | | | | | | | | | | | | | predict should output original label when family = binomial." ## What changes were proposed in this pull request? It's better we can fix this issue by providing an option ```type``` for users to change the ```predict``` output schema, then they could output probabilities, log-space predictions, or original labels. In order to not involve breaking API change for 2.1, so revert this change firstly and will add it back after [SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) resolved. ## How was this patch tested? Existing unit tests. This reverts commit daa975f4bfa4f904697bf3365a4be9987032e490. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16118 from yanboliang/spark-18291-revert.
* [SPARK-18476][SPARKR][ML] SparkR Logistic Regression should should support ↵wm624@hotmail.com2016-11-302-18/+27
| | | | | | | | | | | | | | | | | | output original label. ## What changes were proposed in this pull request? Similar to SPARK-18401, as a classification algorithm, logistic regression should support output original label instead of supporting index label. In this PR, original label output is supported and test cases are modified and added. Document is also modified. ## How was this patch tested? Unit tests. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #15910 from wangmiao1981/audit.
* [SPARK-18510] Fix data corruption from inferred partition column dataTypesBurak Yavuz2016-11-231-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? ### The Issue If I specify my schema when doing ```scala spark.read .schema(someSchemaWherePartitionColumnsAreStrings) ``` but if the partition inference can infer it as IntegerType or I assume LongType or DoubleType (basically fixed size types), then once UnsafeRows are generated, your data will be corrupted. ### Proposed solution The partition handling code path is kind of a mess. In my fix I'm probably adding to the mess, but at least trying to standardize the code path. The real issue is that a user that uses the `spark.read` code path can never clearly specify what the partition columns are. If you try to specify the fields in `schema`, we practically ignore what the user provides, and fall back to our inferred data types. What happens in the end is data corruption. My solution tries to fix this by always trying to infer partition columns the first time you specify the table. Once we find what the partition columns are, we try to find them in the user specified schema and use the dataType provided there, or fall back to the smallest common data type. We will ALWAYS append partition columns to the user's schema, even if they didn't ask for it. We will only use the data type they provided if they specified it. While this is confusing, this has been the behavior since Spark 1.6, and I didn't want to change this behavior in the QA period of Spark 2.1. We may revisit this decision later. A side effect of this PR is that we won't need https://github.com/apache/spark/pull/15942 if this PR goes in. ## How was this patch tested? Regression tests Author: Burak Yavuz <brkyvz@gmail.com> Closes #15951 from brkyvz/partition-corruption.
* [SPARK-18073][DOCS][WIP] Migrate wiki to spark.apache.org web siteSean Owen2016-11-232-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Updates links to the wiki to links to the new location of content on spark.apache.org. ## How was this patch tested? Doc builds Author: Sean Owen <sowen@cloudera.com> Closes #15967 from srowen/SPARK-18073.1.
* [SPARK-18501][ML][SPARKR] Fix spark.glm errors when fitting on collinear dataYanbo Liang2016-11-222-5/+25
| | | | | | | | | | | | | ## What changes were proposed in this pull request? * Fix SparkR ```spark.glm``` errors when fitting on collinear data, since ```standard error of coefficients, t value and p value``` are not available in this condition. * Scala/Python GLM summary should throw exception if users get ```standard error of coefficients, t value and p value``` but the underlying WLS was solved by local "l-bfgs". ## How was this patch tested? Add unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15930 from yanboliang/spark-18501.
* [SPARK-18514][DOCS] Fix the markdown for `Note:`/`NOTE:`/`Note that` across ↵hyukjinkwon2016-11-222-5/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | R API documentation ## What changes were proposed in this pull request? It seems in R, there are - `Note:` - `NOTE:` - `Note that` This PR proposes to fix those to `Note:` to be consistent. **Before** ![2016-11-21 11 30 07](https://cloud.githubusercontent.com/assets/6477701/20468848/2f27b0fa-afde-11e6-89e3-993701269dbe.png) **After** ![2016-11-21 11 29 44](https://cloud.githubusercontent.com/assets/6477701/20468851/39469664-afde-11e6-9929-ad80be7fc405.png) ## How was this patch tested? The notes were found via ```bash grep -r "NOTE: " . grep -r "Note that " . ``` And then fixed one by one comparing with API documentation. After that, manually tested via `sh create-docs.sh` under `./R`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15952 from HyukjinKwon/SPARK-18514.
* [SPARK-18444][SPARKR] SparkR running in yarn-cluster mode should not ↵Yanbo Liang2016-11-223-6/+64
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | download Spark package. ## What changes were proposed in this pull request? When running SparkR job in yarn-cluster mode, it will download Spark package from apache website which is not necessary. ``` ./bin/spark-submit --master yarn-cluster ./examples/src/main/r/dataframe.R ``` The following is output: ``` Attaching package: ‘SparkR’ The following objects are masked from ‘package:stats’: cov, filter, lag, na.omit, predict, sd, var, window The following objects are masked from ‘package:base’: as.data.frame, colnames, colnames<-, drop, endsWith, intersect, rank, rbind, sample, startsWith, subset, summary, transform, union Spark not found in SPARK_HOME: Spark not found in the cache directory. Installation will start. MirrorUrl not provided. Looking for preferred site from apache website... ...... ``` There's no ```SPARK_HOME``` in yarn-cluster mode since the R process is in a remote host of the yarn cluster rather than in the client host. The JVM comes up first and the R process then connects to it. So in such cases we should never have to download Spark as Spark is already running. ## How was this patch tested? Offline test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15888 from yanboliang/spark-18444.
* [SPARK-18365][DOCS] Improve Sample Method Documentationanabranch2016-11-171-1/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? I found the documentation for the sample method to be confusing, this adds more clarification across all languages. - [x] Scala - [x] Python - [x] R - [x] RDD Scala - [ ] RDD Python with SEED - [X] RDD Java - [x] RDD Java with SEED - [x] RDD Python ## How was this patch tested? NA Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request. Author: anabranch <wac.chambers@gmail.com> Author: Bill Chambers <bill@databricks.com> Closes #15815 from anabranch/SPARK-18365.
* [SPARK-18438][SPARKR][ML] spark.mlp should support RFormula.Yanbo Liang2016-11-163-33/+62
| | | | | | | | | | | | | ## What changes were proposed in this pull request? ```spark.mlp``` should support ```RFormula``` like other ML algorithm wrappers. BTW, I did some cleanup and improvement for ```spark.mlp```. ## How was this patch tested? Unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15883 from yanboliang/spark-18438.
* [SPARK-18412][SPARKR][ML] Fix exception for some SparkR ML algorithms ↵Yanbo Liang2016-11-131-3/+15
| | | | | | | | | | | | | | | | | | | | training on libsvm data ## What changes were proposed in this pull request? * Fix the following exceptions which throws when ```spark.randomForest```(classification), ```spark.gbt```(classification), ```spark.naiveBayes``` and ```spark.glm```(binomial family) were fitted on libsvm data. ``` java.lang.IllegalArgumentException: requirement failed: If label column already exists, forceIndexLabel can not be set with true. ``` See [SPARK-18412](https://issues.apache.org/jira/browse/SPARK-18412) for more detail about how to reproduce this bug. * Refactor out ```getFeaturesAndLabels``` to RWrapperUtils, since lots of ML algorithm wrappers use this function. * Drop some unwanted columns when making prediction. ## How was this patch tested? Add unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15851 from yanboliang/spark-18412.
* [SPARK-18264][SPARKR] build vignettes with package, update vignettes for ↵Felix Cheung2016-11-116-35/+134
| | | | | | | | | | | | | | | | | | | | | | | | | | | | CRAN release build and add info on release ## What changes were proposed in this pull request? Changes to DESCRIPTION to build vignettes. Changes the metadata for vignettes to generate the recommended format (which is about <10% of size before). Unfortunately it does not look as nice (before - left, after - right) ![image](https://cloud.githubusercontent.com/assets/8969467/20040492/b75883e6-a40d-11e6-9534-25cdd5d59a8b.png) ![image](https://cloud.githubusercontent.com/assets/8969467/20040490/a40f4d42-a40d-11e6-8c91-af00ddcbdad9.png) Also add information on how to run build/release to CRAN later. ## How was this patch tested? manually, unit tests shivaram We need this for branch-2.1 Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15790 from felixcheung/rpkgvignettes.
* [SPARK-18401][SPARKR][ML] SparkR random forest should support output ↵Yanbo Liang2016-11-101-0/+24
| | | | | | | | | | | | | | original label. ## What changes were proposed in this pull request? SparkR ```spark.randomForest``` classification prediction should output original label rather than the indexed label. This issue is very similar with [SPARK-18291](https://issues.apache.org/jira/browse/SPARK-18291). ## How was this patch tested? Add unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15842 from yanboliang/spark-18401.
* [SPARK-18239][SPARKR] Gradient Boosted Tree for RFelix Cheung2016-11-084-47/+365
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Gradient Boosted Tree in R. With a few minor improvements to RandomForest in R. Since this is relatively isolated I'd like to target this for branch-2.1 ## How was this patch tested? manual tests, unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15746 from felixcheung/rgbt.
* [SPARK-18291][SPARKR][ML] SparkR glm predict should output original label ↵Yanbo Liang2016-11-071-5/+15
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | when family = binomial. ## What changes were proposed in this pull request? SparkR ```spark.glm``` predict should output original label when family = "binomial". ## How was this patch tested? Add unit test. You can also run the following code to test: ```R training <- suppressWarnings(createDataFrame(iris)) training <- training[training$Species %in% c("versicolor", "virginica"), ] model <- spark.glm(training, Species ~ Sepal_Length + Sepal_Width,family = binomial(link = "logit")) showDF(predict(model, training)) ``` Before this change: ``` +------------+-----------+------------+-----------+----------+-----+-------------------+ |Sepal_Length|Sepal_Width|Petal_Length|Petal_Width| Species|label| prediction| +------------+-----------+------------+-----------+----------+-----+-------------------+ | 7.0| 3.2| 4.7| 1.4|versicolor| 0.0| 0.8271421517601544| | 6.4| 3.2| 4.5| 1.5|versicolor| 0.0| 0.6044595910413112| | 6.9| 3.1| 4.9| 1.5|versicolor| 0.0| 0.7916340858281998| | 5.5| 2.3| 4.0| 1.3|versicolor| 0.0|0.16080518180591158| | 6.5| 2.8| 4.6| 1.5|versicolor| 0.0| 0.6112229217050189| | 5.7| 2.8| 4.5| 1.3|versicolor| 0.0| 0.2555087295500885| | 6.3| 3.3| 4.7| 1.6|versicolor| 0.0| 0.5681507664364834| | 4.9| 2.4| 3.3| 1.0|versicolor| 0.0|0.05990570219972002| | 6.6| 2.9| 4.6| 1.3|versicolor| 0.0| 0.6644434078306246| | 5.2| 2.7| 3.9| 1.4|versicolor| 0.0|0.11293577405862379| | 5.0| 2.0| 3.5| 1.0|versicolor| 0.0|0.06152372321585971| | 5.9| 3.0| 4.2| 1.5|versicolor| 0.0|0.35250697207602555| | 6.0| 2.2| 4.0| 1.0|versicolor| 0.0|0.32267018290814303| | 6.1| 2.9| 4.7| 1.4|versicolor| 0.0| 0.433391153814592| | 5.6| 2.9| 3.6| 1.3|versicolor| 0.0| 0.2280744262436993| | 6.7| 3.1| 4.4| 1.4|versicolor| 0.0| 0.7219848389339459| | 5.6| 3.0| 4.5| 1.5|versicolor| 0.0|0.23527698971404695| | 5.8| 2.7| 4.1| 1.0|versicolor| 0.0| 0.285024533520016| | 6.2| 2.2| 4.5| 1.5|versicolor| 0.0| 0.4107047877447493| | 5.6| 2.5| 3.9| 1.1|versicolor| 0.0|0.20083561961645083| +------------+-----------+------------+-----------+----------+-----+-------------------+ ``` After this change: ``` +------------+-----------+------------+-----------+----------+-----+----------+ |Sepal_Length|Sepal_Width|Petal_Length|Petal_Width| Species|label|prediction| +------------+-----------+------------+-----------+----------+-----+----------+ | 7.0| 3.2| 4.7| 1.4|versicolor| 0.0| virginica| | 6.4| 3.2| 4.5| 1.5|versicolor| 0.0| virginica| | 6.9| 3.1| 4.9| 1.5|versicolor| 0.0| virginica| | 5.5| 2.3| 4.0| 1.3|versicolor| 0.0|versicolor| | 6.5| 2.8| 4.6| 1.5|versicolor| 0.0| virginica| | 5.7| 2.8| 4.5| 1.3|versicolor| 0.0|versicolor| | 6.3| 3.3| 4.7| 1.6|versicolor| 0.0| virginica| | 4.9| 2.4| 3.3| 1.0|versicolor| 0.0|versicolor| | 6.6| 2.9| 4.6| 1.3|versicolor| 0.0| virginica| | 5.2| 2.7| 3.9| 1.4|versicolor| 0.0|versicolor| | 5.0| 2.0| 3.5| 1.0|versicolor| 0.0|versicolor| | 5.9| 3.0| 4.2| 1.5|versicolor| 0.0|versicolor| | 6.0| 2.2| 4.0| 1.0|versicolor| 0.0|versicolor| | 6.1| 2.9| 4.7| 1.4|versicolor| 0.0|versicolor| | 5.6| 2.9| 3.6| 1.3|versicolor| 0.0|versicolor| | 6.7| 3.1| 4.4| 1.4|versicolor| 0.0| virginica| | 5.6| 3.0| 4.5| 1.5|versicolor| 0.0|versicolor| | 5.8| 2.7| 4.1| 1.0|versicolor| 0.0|versicolor| | 6.2| 2.2| 4.5| 1.5|versicolor| 0.0|versicolor| | 5.6| 2.5| 3.9| 1.1|versicolor| 0.0|versicolor| +------------+-----------+------------+-----------+----------+-----+----------+ ``` Author: Yanbo Liang <ybliang8@gmail.com> Closes #15788 from yanboliang/spark-18291.
* [MINOR][DOCUMENTATION] Fix some minor descriptions in functions consistently ↵hyukjinkwon2016-11-051-9/+13
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | with expressions ## What changes were proposed in this pull request? This PR proposes to improve documentation and fix some descriptions equivalent to several minor fixes identified in https://github.com/apache/spark/pull/15677 Also, this suggests to change `Note:` and `NOTE:` to `.. note::` consistently with the others which marks up pretty. ## How was this patch tested? Jenkins tests and manually. For PySpark, `Note:` and `NOTE:` to `.. note::` make the document as below: **From** ![2016-11-04 6 53 35](https://cloud.githubusercontent.com/assets/6477701/20002648/42989922-a2c5-11e6-8a32-b73eda49e8c3.png) ![2016-11-04 6 53 45](https://cloud.githubusercontent.com/assets/6477701/20002650/429fb310-a2c5-11e6-926b-e030d7eb0185.png) ![2016-11-04 6 54 11](https://cloud.githubusercontent.com/assets/6477701/20002649/429d570a-a2c5-11e6-9e7e-44090f337e32.png) ![2016-11-04 6 53 51](https://cloud.githubusercontent.com/assets/6477701/20002647/4297fc74-a2c5-11e6-801a-b89fbcbfca44.png) ![2016-11-04 6 53 51](https://cloud.githubusercontent.com/assets/6477701/20002697/749f5780-a2c5-11e6-835f-022e1f2f82e3.png) **To** ![2016-11-04 7 03 48](https://cloud.githubusercontent.com/assets/6477701/20002659/4961b504-a2c5-11e6-9ee0-ef0751482f47.png) ![2016-11-04 7 04 03](https://cloud.githubusercontent.com/assets/6477701/20002660/49871d3a-a2c5-11e6-85ea-d9a5d11efeff.png) ![2016-11-04 7 04 28](https://cloud.githubusercontent.com/assets/6477701/20002662/498e0f14-a2c5-11e6-803d-c0c5aeda4153.png) ![2016-11-04 7 33 39](https://cloud.githubusercontent.com/assets/6477701/20002731/a76e30d2-a2c5-11e6-993b-0481b8342d6b.png) ![2016-11-04 7 33 39](https://cloud.githubusercontent.com/assets/6477701/20002731/a76e30d2-a2c5-11e6-993b-0481b8342d6b.png) Author: hyukjinkwon <gurwls223@gmail.com> Closes #15765 from HyukjinKwon/minor-function-doc.
* [SPARK-14393][SQL][DOC] update doc for python and RFelix Cheung2016-11-031-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? minor doc update that should go to master & branch-2.1 ## How was this patch tested? manual Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15747 from felixcheung/pySPARK-14393.
* [SPARKR][TEST] remove unnecessary suppressWarningswm624@hotmail.com2016-11-031-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? In test_mllib.R, there are two unnecessary suppressWarnings. This PR just removes them. ## How was this patch tested? Existing unit tests. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #15697 from wangmiao1981/rtest.
* [SPARK-17470][SQL] unify path for data source table and locationUri for hive ↵Wenchen Fan2016-11-021-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | serde table ## What changes were proposed in this pull request? Due to a limitation of hive metastore(table location must be directory path, not file path), we always store `path` for data source table in storage properties, instead of the `locationUri` field. However, we should not expose this difference to `CatalogTable` level, but just treat it as a hack in `HiveExternalCatalog`, like we store table schema of data source table in table properties. This PR unifies `path` and `locationUri` outside of `HiveExternalCatalog`, both data source table and hive serde table should use the `locationUri` field. This PR also unifies the way we handle default table location for managed table. Previously, the default table location of hive serde managed table is set by external catalog, but the one of data source table is set by command. After this PR, we follow the hive way and the default table location is always set by external catalog. For managed non-file-based tables, we will assign a default table location and create an empty directory for it, the table location will be removed when the table is dropped. This is reasonable as metastore doesn't care about whether a table is file-based or not, and an empty table directory has no harm. For external non-file-based tables, ideally we can omit the table location, but due to a hive metastore issue, we will assign a random location to it, and remove it right after the table is created. See SPARK-15269 for more details. This is fine as it's well isolated in `HiveExternalCatalog`. To keep the existing behaviour of the `path` option, in this PR we always add the `locationUri` to storage properties using key `path`, before passing storage properties to `DataSource` as data source options. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15024 from cloud-fan/path.
* [SPARK-16839][SQL] Simplify Struct creation code patheyal farago2016-11-021-6/+6
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`. This PR includes: 1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`). 2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees. 3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`. 4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved. 5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns. ## How was this patch tested? Running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully. Modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`. Author: eyal farago <eyal farago> Author: Herman van Hovell <hvanhovell@databricks.com> Author: eyal farago <eyal.farago@gmail.com> Author: Eyal Farago <eyal.farago@actimize.com> Author: Hyukjin Kwon <gurwls223@gmail.com> Author: eyalfa <eyal.farago@gmail.com> Closes #15718 from hvanhovell/SPARK-16839-2.
* [SPARK-17838][SPARKR] Check named arguments for options and use formatted R ↵hyukjinkwon2016-11-015-25/+64
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | friendly message from JVM exception message ## What changes were proposed in this pull request? This PR proposes to - improve the R-friendly error messages rather than raw JVM exception one. As `read.json`, `read.text`, `read.orc`, `read.parquet` and `read.jdbc` are executed in the same path with `read.df`, and `write.json`, `write.text`, `write.orc`, `write.parquet` and `write.jdbc` shares the same path with `write.df`, it seems it is safe to call `handledCallJMethod` to handle JVM messages. - prevent `zero-length variable name` and prints the ignored options as an warning message. **Before** ``` r > read.json("path", a = 1, 2, 3, "a") Error in env[[name]] <- value : zero-length variable name ``` ``` r > read.json("arbitrary_path") Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) : org.apache.spark.sql.AnalysisException: Path does not exist: file:/...; at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:398) ... > read.orc("arbitrary_path") Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) : org.apache.spark.sql.AnalysisException: Path does not exist: file:/...; at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:398) ... > read.text("arbitrary_path") Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) : org.apache.spark.sql.AnalysisException: Path does not exist: file:/...; at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:398) ... > read.parquet("arbitrary_path") Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) : org.apache.spark.sql.AnalysisException: Path does not exist: file:/...; at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:398) ... ``` ``` r > write.json(df, "existing_path") Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) : org.apache.spark.sql.AnalysisException: path file:/... already exists.; at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:68) > write.orc(df, "existing_path") Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) : org.apache.spark.sql.AnalysisException: path file:/... already exists.; at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:68) > write.text(df, "existing_path") Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) : org.apache.spark.sql.AnalysisException: path file:/... already exists.; at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:68) > write.parquet(df, "existing_path") Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) : org.apache.spark.sql.AnalysisException: path file:/... already exists.; at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:68) ``` **After** ``` r read.json("arbitrary_path", a = 1, 2, 3, "a") Unnamed arguments ignored: 2, 3, a. ``` ``` r > read.json("arbitrary_path") Error in json : analysis error - Path does not exist: file:/... > read.orc("arbitrary_path") Error in orc : analysis error - Path does not exist: file:/... > read.text("arbitrary_path") Error in text : analysis error - Path does not exist: file:/... > read.parquet("arbitrary_path") Error in parquet : analysis error - Path does not exist: file:/... ``` ``` r > write.json(df, "existing_path") Error in json : analysis error - path file:/... already exists.; > write.orc(df, "existing_path") Error in orc : analysis error - path file:/... already exists.; > write.text(df, "existing_path") Error in text : analysis error - path file:/... already exists.; > write.parquet(df, "existing_path") Error in parquet : analysis error - path file:/... already exists.; ``` ## How was this patch tested? Unit tests in `test_utils.R` and `test_sparkSQL.R`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15608 from HyukjinKwon/SPARK-17838.
* Revert "[SPARK-16839][SQL] redundant aliases after cleanupAliases"Herman van Hovell2016-11-011-6/+6
| | | | This reverts commit 5441a6269e00e3903ae6c1ea8deb4ddf3d2e9975.
* [SPARK-16839][SQL] redundant aliases after cleanupAliaseseyal farago2016-11-011-6/+6
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`. This PR includes: 1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`). 2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees. 3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`. 4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved. 5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns. ## How was this patch tested? running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully. modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`. Credit goes to hvanhovell for assisting with this PR. Author: eyal farago <eyal farago> Author: eyal farago <eyal.farago@gmail.com> Author: Herman van Hovell <hvanhovell@databricks.com> Author: Eyal Farago <eyal.farago@actimize.com> Author: Hyukjin Kwon <gurwls223@gmail.com> Author: eyalfa <eyal.farago@gmail.com> Closes #14444 from eyalfa/SPARK-16839_redundant_aliases_after_cleanupAliases.
* [SPARK-16137][SPARKR] randomForest for RFelix Cheung2016-10-304-34/+361
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Random Forest Regression and Classification for R Clean-up/reordering generics.R ## How was this patch tested? manual tests, unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15607 from felixcheung/rrandomforest.
* [SPARK-17919] Make timeout to RBackend configurable in SparkRHossein2016-10-305-9/+32
| | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch makes RBackend connection timeout configurable by user. ## How was this patch tested? N/A Author: Hossein <hossein@databricks.com> Closes #15471 from falaki/SPARK-17919.
* [SQL][DOC] updating doc for JSON source to link to jsonlines.orgFelix Cheung2016-10-262-2/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? API and programming guide doc changes for Scala, Python and R. ## How was this patch tested? manual test Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15629 from felixcheung/jsondoc.
* [SPARK-17157][SPARKR][FOLLOW-UP] doc fixesFelix Cheung2016-10-261-15/+11
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? a couple of small late finding fixes for doc ## How was this patch tested? manually wangmiao1981 Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15650 from felixcheung/logitfix.
* [SPARK-17157][SPARKR] Add multiclass logistic regression SparkR Wrapperwm624@hotmail.com2016-10-264-3/+251
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As we discussed in #14818, I added a separate R wrapper spark.logit for logistic regression. This single interface supports both binary and multinomial logistic regression. It also has "predict" and "summary" for binary logistic regression. ## How was this patch tested? New unit tests are added. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #15365 from wangmiao1981/glm.
* [SPARK-17961][SPARKR][SQL] Add storageLevel to DataFrame for SparkRWeichenXu2016-10-266-4/+79
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add storageLevel to DataFrame for SparkR. This is similar to this RP: https://github.com/apache/spark/pull/13780 but in R I do not make a class for `StorageLevel` but add a method `storageToString` ## How was this patch tested? test added. Author: WeichenXu <WeichenXu123@outlook.com> Closes #15516 from WeichenXu123/storageLevel_df_r.
* [SPARK-18007][SPARKR][ML] update SparkR MLP - add initalWeights parameterWeichenXu2016-10-252-4/+25
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? update SparkR MLP, add initalWeights parameter. ## How was this patch tested? test added. Author: WeichenXu <WeichenXu123@outlook.com> Closes #15552 from WeichenXu123/mlp_r_add_initialWeight_param.
* [SPARKR][BRANCH-2.0] R merge API doc and example fixFelix Cheung2016-10-232-2/+2
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fixes for R doc ## How was this patch tested? N/A Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15589 from felixcheung/rdocmergefix. (cherry picked from commit 0e0d83a597885ab1773cb69d6dcc10346d6976a3) Signed-off-by: Felix Cheung <felixcheung@apache.org>
* [SPARK-17811] SparkR cannot parallelize data.frame with NA or NULL in Date ↵Hossein2016-10-211-0/+13
| | | | | | | | | | | | | | columns ## What changes were proposed in this pull request? NA date values are serialized as "NA" and NA time values are serialized as NaN from R. In the backend we did not have proper logic to deal with them. As a result we got an IllegalArgumentException for Date and wrong value for time. This PR adds support for deserializing NA as Date and Time. ## How was this patch tested? * [x] TODO Author: Hossein <hossein@databricks.com> Closes #15421 from falaki/SPARK-17811.
* [SPARK-18013][SPARKR] add crossJoin APIFelix Cheung2016-10-214-15/+60
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add crossJoin and do not default to cross join if joinExpr is left out ## How was this patch tested? unit test Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15559 from felixcheung/rcrossjoin.
* [SPARK-17674][SPARKR] check for warning in test outputFelix Cheung2016-10-211-2/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? testthat library we are using for testing R is redirecting warning (and disabling `options("warn" = 2)`), we need to have a way to detect any new warning and fail ## How was this patch tested? manual testing, Jenkins Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15576 from felixcheung/rtestwarning.
* [SPARKR] fix warningsFelix Cheung2016-10-202-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix for a bunch of test warnings that were added recently. We need to investigate why warnings are not turning into errors. ``` Warnings ----------------------------------------------------------------------- 1. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Sepal_Length instead of Sepal.Length as column name 2. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Sepal_Width instead of Sepal.Width as column name 3. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Petal_Length instead of Petal.Length as column name 4. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Petal_Width instead of Petal.Width as column name Consider adding importFrom("utils", "object.size") to your NAMESPACE file. ``` ## How was this patch tested? unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15560 from felixcheung/rwarnings.
* [SPARK-17790][SPARKR] Support for parallelizing R data.frame larger than 2GBHossein2016-10-122-2/+54
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? If the R data structure that is being parallelized is larger than `INT_MAX` we use files to transfer data to JVM. The serialization protocol mimics Python pickling. This allows us to simply call `PythonRDD.readRDDFromFile` to create the RDD. I tested this on my MacBook. Following code works with this patch: ```R intMax <- .Machine$integer.max largeVec <- 1:intMax rdd <- SparkR:::parallelize(sc, largeVec, 2) ``` ## How was this patch tested? * [x] Unit tests Author: Hossein <hossein@databricks.com> Closes #15375 from falaki/SPARK-17790.
* [SPARK-17720][SQL] introduce static SQL confWenchen Fan2016-10-111-1/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897. Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf. ## How was this patch tested? new tests in SQLConfSuite Author: Wenchen Fan <wenchen@databricks.com> Closes #15295 from cloud-fan/global-conf.
* [SPARK-15153][ML][SPARKR] Fix SparkR spark.naiveBayes error when label is ↵Yanbo Liang2016-10-111-0/+10
| | | | | | | | | | | | | | | numeric type ## What changes were proposed in this pull request? Fix SparkR ```spark.naiveBayes``` error when response variable of dataset is numeric type. See details and how to reproduce this bug at [SPARK-15153](https://issues.apache.org/jira/browse/SPARK-15153). ## How was this patch tested? Add unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15431 from yanboliang/spark-15153-2.
* [SPARK-17665][SPARKR] Support options/mode all for read/write APIs and ↵hyukjinkwon2016-10-076-22/+160
| | | | | | | | | | | | | | | | | | | | options in other types ## What changes were proposed in this pull request? This PR includes the changes below: - Support `mode`/`options` in `read.parquet`, `write.parquet`, `read.orc`, `write.orc`, `read.text`, `write.text`, `read.json` and `write.json` APIs - Support other types (logical, numeric and string) as options for `write.df`, `read.df`, `read.parquet`, `write.parquet`, `read.orc`, `write.orc`, `read.text`, `write.text`, `read.json` and `write.json` ## How was this patch tested? Unit tests in `test_sparkSQL.R`/ `utils.R`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15239 from HyukjinKwon/SPARK-17665.
* [SPARK-17658][SPARKR] read.df/write.df API taking path optionally in SparkRhyukjinkwon2016-10-046-13/+127
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `write.df`/`read.df` API require path which is not actually always necessary in Spark. Currently, it only affects the datasources implementing `CreatableRelationProvider`. Currently, Spark currently does not have internal data sources implementing this but it'd affect other external datasources. In addition we'd be able to use this way in Spark's JDBC datasource after https://github.com/apache/spark/pull/12601 is merged. **Before** - `read.df` ```r > read.df(source = "json") Error in dispatchFunc("read.df(path = NULL, source = NULL, schema = NULL, ...)", : argument "x" is missing with no default ``` ```r > read.df(path = c(1, 2)) Error in dispatchFunc("read.df(path = NULL, source = NULL, schema = NULL, ...)", : argument "x" is missing with no default ``` ```r > read.df(c(1, 2)) Error in invokeJava(isStatic = TRUE, className, methodName, ...) : java.lang.ClassCastException: java.lang.Double cannot be cast to java.lang.String at org.apache.spark.sql.execution.datasources.DataSource.hasMetadata(DataSource.scala:300) at ... In if (is.na(object)) { : ... ``` - `write.df` ```r > write.df(df, source = "json") Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘write.df’ for signature ‘"function", "missing"’ ``` ```r > write.df(df, source = c(1, 2)) Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘write.df’ for signature ‘"SparkDataFrame", "missing"’ ``` ```r > write.df(df, mode = TRUE) Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘write.df’ for signature ‘"SparkDataFrame", "missing"’ ``` **After** - `read.df` ```r > read.df(source = "json") Error in loadDF : analysis error - Unable to infer schema for JSON at . It must be specified manually; ``` ```r > read.df(path = c(1, 2)) Error in f(x, ...) : path should be charactor, null or omitted. ``` ```r > read.df(c(1, 2)) Error in f(x, ...) : path should be charactor, null or omitted. ``` - `write.df` ```r > write.df(df, source = "json") Error in save : illegal argument - 'path' is not specified ``` ```r > write.df(df, source = c(1, 2)) Error in .local(df, path, ...) : source should be charactor, null or omitted. It is 'parquet' by default. ``` ```r > write.df(df, mode = TRUE) Error in .local(df, path, ...) : mode should be charactor or omitted. It is 'error' by default. ``` ## How was this patch tested? Unit tests in `test_sparkSQL.R` Author: hyukjinkwon <gurwls223@gmail.com> Closes #15231 from HyukjinKwon/write-default-r.
* [SPARKR][DOC] minor formatting and output cleanup for R vignettesFelix Cheung2016-10-041-11/+20
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Clean up output, format table, truncate long example output, hide warnings (new - Left; existing - Right) ![image](https://cloud.githubusercontent.com/assets/8969467/19064018/5dcde4d0-89bc-11e6-857b-052df3f52a4e.png) ![image](https://cloud.githubusercontent.com/assets/8969467/19064034/6db09956-89bc-11e6-8e43-232d5c3fe5e6.png) ![image](https://cloud.githubusercontent.com/assets/8969467/19064058/88f09590-89bc-11e6-9993-61639e29dfdd.png) ![image](https://cloud.githubusercontent.com/assets/8969467/19064066/95ccbf64-89bc-11e6-877f-45af03ddcadc.png) ![image](https://cloud.githubusercontent.com/assets/8969467/19064082/a8445404-89bc-11e6-8532-26d8bc9b206f.png) ## How was this patch tested? Run create-doc.sh manually Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15340 from felixcheung/vignettes.
* [SPARK-17499][SPARKR][FOLLOWUP] Check null first for layers in spark.mlp to ↵hyukjinkwon2016-09-271-0/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | avoid warnings in test results ## What changes were proposed in this pull request? Some tests in `test_mllib.r` are as below: ```r expect_error(spark.mlp(df, layers = NULL), "layers must be a integer vector with length > 1.") expect_error(spark.mlp(df, layers = c()), "layers must be a integer vector with length > 1.") ``` The problem is, `is.na` is internally called via `na.omit` in `spark.mlp` which causes warnings as below: ``` Warnings ----------------------------------------------------------------------- 1. spark.mlp (test_mllib.R#400) - is.na() applied to non-(list or vector) of type 'NULL' 2. spark.mlp (test_mllib.R#401) - is.na() applied to non-(list or vector) of type 'NULL' ``` ## How was this patch tested? Manually tested. Also, Jenkins tests and AppVeyor. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15232 from HyukjinKwon/remove-warnnings.
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