aboutsummaryrefslogtreecommitdiff
path: root/R/pkg/inst/tests
Commit message (Collapse)AuthorAgeFilesLines
* [SPARK-19828][R] Support array type in from_json in Rhyukjinkwon2017-03-141-0/+12
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since we could not directly define the array type in R, this PR proposes to support array types in R as string types that are used in `structField` as below: ```R jsonArr <- "[{\"name\":\"Bob\"}, {\"name\":\"Alice\"}]" df <- as.DataFrame(list(list("people" = jsonArr))) collect(select(df, alias(from_json(df$people, "array<struct<name:string>>"), "arrcol"))) ``` prints ```R arrcol 1 Bob, Alice ``` ## How was this patch tested? Unit tests in `test_sparkSQL.R`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #17178 from HyukjinKwon/SPARK-19828.
* [SPARK-19391][SPARKR][ML] Tweedie GLM API for SparkRactuaryzhang2017-03-141-1/+37
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Port Tweedie GLM #16344 to SparkR felixcheung yanboliang ## How was this patch tested? new test in SparkR Author: actuaryzhang <actuaryzhang10@gmail.com> Closes #16729 from actuaryzhang/sparkRTweedie.
* [SPARK-19282][ML][SPARKR] RandomForest Wrapper and GBT Wrapper return param ↵Xin Ren2017-03-121-0/+10
| | | | | | | | | | | | | | | | | | | | | | "maxDepth" to R models ## What changes were proposed in this pull request? RandomForest R Wrapper and GBT R Wrapper return param `maxDepth` to R models. Below 4 R wrappers are changed: * `RandomForestClassificationWrapper` * `RandomForestRegressionWrapper` * `GBTClassificationWrapper` * `GBTRegressionWrapper` ## How was this patch tested? Test manually on my local machine. Author: Xin Ren <iamshrek@126.com> Closes #17207 from keypointt/SPARK-19282.
* [SPARK-19601][SQL] Fix CollapseRepartition rule to preserve shuffle-enabled ↵Xiao Li2017-03-081-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | Repartition ### What changes were proposed in this pull request? Observed by felixcheung in https://github.com/apache/spark/pull/16739, when users use the shuffle-enabled `repartition` API, they expect the partition they got should be the exact number they provided, even if they call shuffle-disabled `coalesce` later. Currently, `CollapseRepartition` rule does not consider whether shuffle is enabled or not. Thus, we got the following unexpected result. ```Scala val df = spark.range(0, 10000, 1, 5) val df2 = df.repartition(10) assert(df2.coalesce(13).rdd.getNumPartitions == 5) assert(df2.coalesce(7).rdd.getNumPartitions == 5) assert(df2.coalesce(3).rdd.getNumPartitions == 3) ``` This PR is to fix the issue. We preserve shuffle-enabled Repartition. ### How was this patch tested? Added a test case Author: Xiao Li <gatorsmile@gmail.com> Closes #16933 from gatorsmile/CollapseRepartition.
* [SPARK-19818][SPARKR] rbind should check for name consistency of input data ↵actuaryzhang2017-03-061-0/+7
| | | | | | | | | | | | | | frames ## What changes were proposed in this pull request? Added checks for name consistency of input data frames in union. ## How was this patch tested? new test. Author: actuaryzhang <actuaryzhang10@gmail.com> Closes #17159 from actuaryzhang/sparkRUnion.
* [SPARK-19795][SPARKR] add column functions to_json, from_jsonFelix Cheung2017-03-051-7/+36
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add column functions: to_json, from_json, and tests covering error cases. ## How was this patch tested? unit tests, manual Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #17134 from felixcheung/rtojson.
* [DOC][MINOR][SPARKR] Update SparkR doc for names, columns and colnamesactuaryzhang2017-03-011-0/+6
| | | | | | | | | | | | Update R doc: 1. columns, names and colnames returns a vector of strings, not **list** as in current doc. 2. `colnames<-` does allow the subset assignment, so the length of `value` can be less than the number of columns, e.g., `colnames(df)[1] <- "a"`. felixcheung Author: actuaryzhang <actuaryzhang10@gmail.com> Closes #17115 from actuaryzhang/sparkRMinorDoc.
* [SPARK-19682][SPARKR] Issue warning (or error) when subset method "[[" takes ↵actuaryzhang2017-02-231-0/+12
| | | | | | | | | | | | | | | | vector index ## What changes were proposed in this pull request? The `[[` method is supposed to take a single index and return a column. This is different from base R which takes a vector index. We should check for this and issue warning or error when vector index is supplied (which is very likely given the behavior in base R). Currently I'm issuing a warning message and just take the first element of the vector index. We could change this to an error it that's better. ## How was this patch tested? new tests Author: actuaryzhang <actuaryzhang10@gmail.com> Closes #17017 from actuaryzhang/sparkRSubsetter.
* [SPARK-19616][SPARKR] weightCol and aggregationDepth should be improved for ↵wm624@hotmail.com2017-02-221-1/+9
| | | | | | | | | | | | | | | | | some SparkR APIs ## What changes were proposed in this pull request? This is a follow-up PR of #16800 When doing SPARK-19456, we found that "" should be consider a NULL column name and should not be set. aggregationDepth should be exposed as an expert parameter. ## How was this patch tested? Existing tests. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16945 from wangmiao1981/svc.
* [SPARK-18285][SPARKR] SparkR approxQuantile supports input multiple columnsYanbo Liang2017-02-171-5/+13
| | | | | | | | | | | | ## What changes were proposed in this pull request? SparkR ```approxQuantile``` supports input multiple columns. ## How was this patch tested? Unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16951 from yanboliang/spark-19619.
* [SPARK-19399][SPARKR] Add R coalesce API for DataFrame and ColumnFelix Cheung2017-02-152-6/+28
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add coalesce on DataFrame for down partitioning without shuffle and coalesce on Column ## How was this patch tested? manual, unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16739 from felixcheung/rcoalesce.
* [SPARK-19456][SPARKR] Add LinearSVC R APIwm624@hotmail.com2017-02-151-0/+44
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Linear SVM classifier is newly added into ML and python API has been added. This JIRA is to add R side API. Marked as WIP, as I am designing unit tests. ## How was this patch tested? Please review http://spark.apache.org/contributing.html before opening a pull request. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16800 from wangmiao1981/svc.
* [SPARK-19342][SPARKR] bug fixed in collect method for collecting timestamp ↵titicaca2017-02-121-2/+40
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | column ## What changes were proposed in this pull request? Fix a bug in collect method for collecting timestamp column, the bug can be reproduced as shown in the following codes and outputs: ``` library(SparkR) sparkR.session(master = "local") df <- data.frame(col1 = c(0, 1, 2), col2 = c(as.POSIXct("2017-01-01 00:00:01"), NA, as.POSIXct("2017-01-01 12:00:01"))) sdf1 <- createDataFrame(df) print(dtypes(sdf1)) df1 <- collect(sdf1) print(lapply(df1, class)) sdf2 <- filter(sdf1, "col1 > 0") print(dtypes(sdf2)) df2 <- collect(sdf2) print(lapply(df2, class)) ``` As we can see from the printed output, the column type of col2 in df2 is converted to numeric unexpectedly, when NA exists at the top of the column. This is caused by method `do.call(c, list)`, if we convert a list, i.e. `do.call(c, list(NA, as.POSIXct("2017-01-01 12:00:01"))`, the class of the result is numeric instead of POSIXct. Therefore, we need to cast the data type of the vector explicitly. ## How was this patch tested? The patch can be tested manually with the same code above. Author: titicaca <fangzhou.yang@hotmail.com> Closes #16689 from titicaca/sparkr-dev.
* [SPARK-19464][BUILD][HOTFIX] run-tests should use hadoop2.6Dongjoon Hyun2017-02-081-3/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? After SPARK-19464, **SparkPullRequestBuilder** fails because it still tries to use hadoop2.3. **BEFORE** https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/72595/console ``` ======================================================================== Building Spark ======================================================================== [error] Could not find hadoop2.3 in the list. Valid options are ['hadoop2.6', 'hadoop2.7'] Attempting to post to Github... > Post successful. ``` **AFTER** https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/72595/console ``` ======================================================================== Building Spark ======================================================================== [info] Building Spark (w/Hive 1.2.1) using SBT with these arguments: -Phadoop-2.6 -Pmesos -Pkinesis-asl -Pyarn -Phive-thriftserver -Phive test:package streaming-kafka-0-8-assembly/assembly streaming-flume-assembly/assembly streaming-kinesis-asl-assembly/assembly Using /usr/java/jdk1.8.0_60 as default JAVA_HOME. Note, this will be overridden by -java-home if it is set. ``` ## How was this patch tested? Pass the existing test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #16858 from dongjoon-hyun/hotfix_run-tests.
* [SPARK-16609] Add to_date/to_timestamp with format functionsanabranch2017-02-071-0/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This pull request adds two new user facing functions: - `to_date` which accepts an expression and a format and returns a date. - `to_timestamp` which accepts an expression and a format and returns a timestamp. For example, Given a date in format: `2016-21-05`. (YYYY-dd-MM) ### Date Function *Previously* ``` to_date(unix_timestamp(lit("2016-21-05"), "yyyy-dd-MM").cast("timestamp")) ``` *Current* ``` to_date(lit("2016-21-05"), "yyyy-dd-MM") ``` ### Timestamp Function *Previously* ``` unix_timestamp(lit("2016-21-05"), "yyyy-dd-MM").cast("timestamp") ``` *Current* ``` to_timestamp(lit("2016-21-05"), "yyyy-dd-MM") ``` ### Tasks - [X] Add `to_date` to Scala Functions - [x] Add `to_date` to Python Functions - [x] Add `to_date` to SQL Functions - [X] Add `to_timestamp` to Scala Functions - [x] Add `to_timestamp` to Python Functions - [x] Add `to_timestamp` to SQL Functions - [x] Add function to R ## How was this patch tested? - [x] Add Functions to `DateFunctionsSuite` - Test new `ParseToTimestamp` Expression (*not necessary*) - Test new `ParseToDate` Expression (*not necessary*) - [x] Add test for R - [x] Add test for Python in test.py Please review http://spark.apache.org/contributing.html before opening a pull request. Author: anabranch <wac.chambers@gmail.com> Author: Bill Chambers <bill@databricks.com> Author: anabranch <bill@databricks.com> Closes #16138 from anabranch/SPARK-16609.
* [SPARK-19452][SPARKR] Fix bug in the name assignment methodactuaryzhang2017-02-051-0/+8
| | | | | | | | | | | | ## What changes were proposed in this pull request? The names method fails to check for validity of the assignment values. This can be fixed by calling colnames within names. ## How was this patch tested? new tests. Author: actuaryzhang <actuaryzhang10@gmail.com> Closes #16794 from actuaryzhang/sparkRNames.
* [SPARK-19319][SPARKR] SparkR Kmeans summary returns error when the cluster ↵wm624@hotmail.com2017-01-311-4/+11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | size doesn't equal to k ## What changes were proposed in this pull request When Kmeans using initMode = "random" and some random seed, it is possible the actual cluster size doesn't equal to the configured `k`. In this case, summary(model) returns error due to the number of cols of coefficient matrix doesn't equal to k. Example: > col1 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0) > col2 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0) > col3 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0) > cols <- as.data.frame(cbind(col1, col2, col3)) > df <- createDataFrame(cols) > > model2 <- spark.kmeans(data = df, ~ ., k = 5, maxIter = 10, initMode = "random", seed = 22222, tol = 1E-5) > > summary(model2) Error in `colnames<-`(`*tmp*`, value = c("col1", "col2", "col3")) : length of 'dimnames' [2] not equal to array extent In addition: Warning message: In matrix(coefficients, ncol = k) : data length [9] is not a sub-multiple or multiple of the number of rows [2] Fix: Get the actual cluster size in the summary and use it to build the coefficient matrix. ## How was this patch tested? Add unit tests. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16666 from wangmiao1981/kmeans.
* [SPARK-19395][SPARKR] Convert coefficients in summary to matrixactuaryzhang2017-01-313-18/+30
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The `coefficients` component in model summary should be 'matrix' but the underlying structure is indeed list. This affects several models except for 'AFTSurvivalRegressionModel' which has the correct implementation. The fix is to first `unlist` the coefficients returned from the `callJMethod` before converting to matrix. An example illustrates the issues: ``` data(iris) df <- createDataFrame(iris) model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family = "gaussian") s <- summary(model) > str(s$coefficients) List of 8 $ : num 6.53 $ : num -0.223 $ : num 0.479 $ : num 0.155 $ : num 13.6 $ : num -1.44 $ : num 0 $ : num 0.152 - attr(*, "dim")= int [1:2] 2 4 - attr(*, "dimnames")=List of 2 ..$ : chr [1:2] "(Intercept)" "Sepal_Width" ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)" > s$coefficients[, 2] $`(Intercept)` [1] 0.4788963 $Sepal_Width [1] 0.1550809 ``` This shows that the underlying structure of coefficients is still `list`. felixcheung wangmiao1981 Author: actuaryzhang <actuaryzhang10@gmail.com> Closes #16730 from actuaryzhang/sparkRCoef.
* [SPARK-19324][SPARKR] Spark VJM stdout output is getting dropped in SparkRFelix Cheung2017-01-271-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This affects mostly running job from the driver in client mode when results are expected to be through stdout (which should be somewhat rare, but possible) Before: ``` > a <- as.DataFrame(cars) > b <- group_by(a, "dist") > c <- count(b) > sparkR.callJMethod(c$countjc, "explain", TRUE) NULL ``` After: ``` > a <- as.DataFrame(cars) > b <- group_by(a, "dist") > c <- count(b) > sparkR.callJMethod(c$countjc, "explain", TRUE) count#11L NULL ``` Now, `column.explain()` doesn't seem very useful (we can get more extensive output with `DataFrame.explain()`) but there are other more complex examples with calls of `println` in Scala/JVM side, that are getting dropped. ## How was this patch tested? manual Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16670 from felixcheung/rjvmstdout.
* [SPARK-18788][SPARKR] Add API for getNumPartitionsFelix Cheung2017-01-262-12/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? With doc to say this would convert DF into RDD ## How was this patch tested? unit tests, manual tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16668 from felixcheung/rgetnumpartitions.
* [SPARK-18821][SPARKR] Bisecting k-means wrapper in SparkRwm624@hotmail.com2017-01-261-0/+40
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add R wrapper for bisecting Kmeans. As JIRA is down, I will update title to link with corresponding JIRA later. ## How was this patch tested? Add new unit tests. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16566 from wangmiao1981/bk.
* [SPARK-18823][SPARKR] add support for assigning to columnFelix Cheung2017-01-241-0/+20
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Support for ``` df[[myname]] <- 1 df[[2]] <- df$eruptions ``` ## How was this patch tested? manual tests, unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16663 from felixcheung/rcolset.
* [SPARK-19291][SPARKR][ML] spark.gaussianMixture supports output log-likelihood.Yanbo Liang2017-01-211-0/+7
| | | | | | | | | | | | ## What changes were proposed in this pull request? ```spark.gaussianMixture``` supports output total log-likelihood for the model like R ```mvnormalmixEM```. ## How was this patch tested? R unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16646 from yanboliang/spark-19291.
* [SPARK-19066][SPARKR] SparkR LDA doesn't set optimizer correctlywm624@hotmail.com2017-01-162-3/+14
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? spark.lda passes the optimizer "em" or "online" as a string to the backend. However, LDAWrapper doesn't set optimizer based on the value from R. Therefore, for optimizer "em", the `isDistributed` field is FALSE, which should be TRUE based on scala code. In addition, the `summary` method should bring back the results related to `DistributedLDAModel`. ## How was this patch tested? Manual tests by comparing with scala example. Modified the current unit test: fix the incorrect unit test and add necessary tests for `summary` method. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16464 from wangmiao1981/new.
* [SPARK-18335][SPARKR] createDataFrame to support numPartitions parameterFelix Cheung2017-01-132-3/+24
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? To allow specifying number of partitions when the DataFrame is created ## How was this patch tested? manual, unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16512 from felixcheung/rnumpart.
* [SPARK-19142][SPARKR] spark.kmeans should take seed, initSteps, and tol as ↵wm624@hotmail.com2017-01-121-0/+20
| | | | | | | | | | | | | | | parameters ## What changes were proposed in this pull request? spark.kmeans doesn't have interface to set initSteps, seed and tol. As Spark Kmeans algorithm doesn't take the same set of parameters as R kmeans, we should maintain a different interface in spark.kmeans. Add missing parameters and corresponding document. Modified existing unit tests to take additional parameters. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16523 from wangmiao1981/kmeans.
* [SPARK-19130][SPARKR] Support setting literal value as column implicitlyFelix Cheung2017-01-111-0/+18
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? ``` df$foo <- 1 ``` instead of ``` df$foo <- lit(1) ``` ## How was this patch tested? unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16510 from felixcheung/rlitcol.
* [SPARK-19133][SPARKR][ML] fix glm for Gamma, clarify glm family supportedFelix Cheung2017-01-101-9/+17
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? R family is a longer list than what Spark supports. ## How was this patch tested? manual Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16511 from felixcheung/rdocglmfamily.
* [SPARK-18862][SPARKR][ML] Split SparkR mllib.R into multiple filesYanbo Liang2017-01-087-1170/+1303
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? SparkR ```mllib.R``` is getting bigger as we add more ML wrappers, I'd like to split it into multiple files to make us easy to maintain: * mllib_classification.R * mllib_clustering.R * mllib_recommendation.R * mllib_regression.R * mllib_stat.R * mllib_tree.R * mllib_utils.R Note: Only reorg, no actual code change. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16312 from yanboliang/spark-18862.
* [SPARK-18958][SPARKR] R API toJSON on DataFrameFelix Cheung2016-12-221-6/+7
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? It would make it easier to integrate with other component expecting row-based JSON format. This replaces the non-public toJSON RDD API. ## How was this patch tested? manual, unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16368 from felixcheung/rJSON.
* [SPARK-18903][SPARKR] Add API to get SparkUI URLFelix Cheung2016-12-211-1/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? API for SparkUI URL from SparkContext ## How was this patch tested? manual, unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16367 from felixcheung/rwebui.
* [SPARK-18897][SPARKR] Fix SparkR SQL Test to drop test tableDongjoon Hyun2016-12-161-0/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? SparkR tests, `R/run-tests.sh`, succeeds only once because `test_sparkSQL.R` does not clean up the test table, `people`. As a result, the rows in `people` table are accumulated at every run and the test cases fail. The following is the failure result for the second run. ```r Failed ------------------------------------------------------------------------- 1. Failure: create DataFrame from RDD (test_sparkSQL.R#204) ------------------- collect(sql("SELECT age from people WHERE name = 'Bob'"))$age not equal to c(16). Lengths differ: 2 vs 1 2. Failure: create DataFrame from RDD (test_sparkSQL.R#206) ------------------- collect(sql("SELECT height from people WHERE name ='Bob'"))$height not equal to c(176.5). Lengths differ: 2 vs 1 ``` ## How was this patch tested? Manual. Run `run-tests.sh` twice and check if it passes without failures. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #16310 from dongjoon-hyun/SPARK-18897.
* [MINOR][SPARKR] fix kstest example error and add unit testwm624@hotmail.com2016-12-131-0/+6
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? While adding vignettes for kstest, I found some errors in the example: 1. There is a typo of kstest; 2. print.summary.KStest doesn't work with the example; Fix the example errors; Add a new unit test for print.summary.KStest; ## How was this patch tested? Manual test; Add new unit test; Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16259 from wangmiao1981/ks.
* [SPARK-18810][SPARKR] SparkR install.spark does not work for RCs, snapshotsFelix Cheung2016-12-121-0/+11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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 Cheung2016-12-091-7/+7
| | | | | | | | | | | | | | | | | | | | | | | | 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.com2016-12-081-0/+2
| | | | | | | | | | | | | | | | | | | | | ## 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-18326][SPARKR][ML] Review SparkR ML wrappers API for 2.1Yanbo Liang2016-12-071-2/+2
| | | | | | | | | | | | | | ## 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 Liang2016-12-071-55/+128
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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-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.com2016-11-301-8/+18
| | | | | | | | | | | | | | | | | | 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-18501][ML][SPARKR] Fix spark.glm errors when fitting on collinear dataYanbo Liang2016-11-221-0/+9
| | | | | | | | | | | | | ## 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-18444][SPARKR] SparkR running in yarn-cluster mode should not ↵Yanbo Liang2016-11-221-0/+46
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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-18438][SPARKR][ML] spark.mlp should support RFormula.Yanbo Liang2016-11-161-20/+43
| | | | | | | | | | | | | ## 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-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 Cheung2016-11-081-0/+68
| | | | | | | | | | | | | | | | | ## 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.
* [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.
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-11 10:32:48 +0000