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* [SPARK-18239][SPARKR] Gradient Boosted Tree for RFelix Cheung2016-11-085-14/+326
| | | | | | | | | | | | | | | | | ## 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-17748][ML] Minor cleanups to one-pass linear regression with elastic netJoseph K. Bradley2016-11-083-12/+23
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? * Made SingularMatrixException private ml * WeightedLeastSquares: Changed to allow tol >= 0 instead of only tol > 0 ## How was this patch tested? existing tests Author: Joseph K. Bradley <joseph@databricks.com> Closes #15779 from jkbradley/wls-cleanups.
* [SPARK-14914][CORE] Fix Resource not closed after using, mostly for unit testsHyukjin Kwon2016-11-071-6/+10
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Close `FileStreams`, `ZipFiles` etc to release the resources after using. Not closing the resources will cause IO Exception to be raised while deleting temp files. ## How was this patch tested? Existing tests Author: U-FAREAST\tl <tl@microsoft.com> Author: hyukjinkwon <gurwls223@gmail.com> Author: Tao LI <tl@microsoft.com> Closes #15618 from HyukjinKwon/SPARK-14914-1.
* [SPARK-18291][SPARKR][ML] SparkR glm predict should output original label ↵Yanbo Liang2016-11-071-8/+69
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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.
* [SPARK-18210][ML] Pipeline.copy does not create an instance with the same UIDWojciech Szymanski2016-11-062-3/+21
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Motivation: `org.apache.spark.ml.Pipeline.copy(extra: ParamMap)` does not create an instance with the same UID. It does not conform to the method specification from its base class `org.apache.spark.ml.param.Params.copy(extra: ParamMap)` Solution: - fix for Pipeline UID - introduced new tests for `org.apache.spark.ml.Pipeline.copy` - minor improvements in test for `org.apache.spark.ml.PipelineModel.copy` ## How was this patch tested? Introduced new unit test: `org.apache.spark.ml.PipelineSuite."Pipeline.copy"` Improved existing unit test: `org.apache.spark.ml.PipelineSuite."PipelineModel.copy"` Author: Wojciech Szymanski <wk.szymanski@gmail.com> Closes #15759 from wojtek-szymanski/SPARK-18210.
* [SPARK-18276][ML] ML models should copy the training summary and set parentsethah2016-11-0512-20/+62
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Only some of the models which contain a training summary currently set the summaries in the copy method. Linear/Logistic regression do, GLR, GMM, KM, and BKM do not. Additionally, these copy methods did not set the parent pointer of the copied model. This patch modifies the copy methods of the four models mentioned above to copy the training summary and set the parent. ## How was this patch tested? Add unit tests in Linear/Logistic/GeneralizedLinear regression and GaussianMixture/KMeans/BisectingKMeans to check the parent pointer of the copied model and check that the copied model has a summary. Author: sethah <seth.hendrickson16@gmail.com> Closes #15773 from sethah/SPARK-18276.
* [SPARK-18076][CORE][SQL] Fix default Locale used in DateFormat, NumberFormat ↵Sean Owen2016-11-021-2/+2
| | | | | | | | | | | | | | | to Locale.US ## What changes were proposed in this pull request? Fix `Locale.US` for all usages of `DateFormat`, `NumberFormat` ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15610 from srowen/SPARK-18076.
* [SPARK-18088][ML] Various ChiSqSelector cleanupsJoseph K. Bradley2016-11-015-121/+139
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Renamed kbest to numTopFeatures - Renamed alpha to fpr - Added missing Since annotations - Doc cleanups ## How was this patch tested? Added new standardized unit tests for spark.ml. Improved existing unit test coverage a bit. Author: Joseph K. Bradley <joseph@databricks.com> Closes #15647 from jkbradley/chisqselector-follow-ups.
* [SPARK-17848][ML] Move LabelCol datatype cast into Predictor.fitZheng RuiFeng2016-11-019-11/+98
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1, move cast to `Predictor` 2, and then, remove unnecessary cast ## How was this patch tested? existing tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15414 from zhengruifeng/move_cast.
* [SPARK-18024][SQL] Introduce an internal commit protocol APIReynold Xin2016-10-311-10/+7
| | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch introduces an internal commit protocol API that is used by the batch data source to do write commits. It currently has only one implementation that uses Hadoop MapReduce's OutputCommitter API. In the future, this commit API can be used to unify streaming and batch commits. ## How was this patch tested? Should be covered by existing write tests. Author: Reynold Xin <rxin@databricks.com> Author: Eric Liang <ekl@databricks.com> Closes #15707 from rxin/SPARK-18024-2.
* [SPARK-16137][SPARKR] randomForest for RFelix Cheung2016-10-303-0/+295
| | | | | | | | | | | | | | | ## 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-3261][MLLIB] KMeans clusterer can return duplicate cluster centersSean Owen2016-10-303-65/+85
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Return potentially fewer than k cluster centers in cases where k distinct centroids aren't available or aren't selected. ## How was this patch tested? Existing tests Author: Sean Owen <sowen@cloudera.com> Closes #15450 from srowen/SPARK-3261.
* [SPARK-5992][ML] Locality Sensitive HashingYunni2016-10-286-0/+1208
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Implement Locality Sensitive Hashing along with approximate nearest neighbors and approximate similarity join based on the [design doc](https://docs.google.com/document/d/1D15DTDMF_UWTTyWqXfG7y76iZalky4QmifUYQ6lH5GM/edit). Detailed changes are as follows: (1) Implement abstract LSH, LSHModel classes as Estimator-Model (2) Implement approxNearestNeighbors and approxSimilarityJoin in the abstract LSHModel (3) Implement Random Projection as LSH subclass for Euclidean distance, Min Hash for Jaccard Distance (4) Implement unit test utility methods including checkLshProperty, checkNearestNeighbor and checkSimilarityJoin Things that will be implemented in a follow-up PR: - Bit Sampling for Hamming Distance, SignRandomProjection for Cosine Distance - PySpark Integration for the scala classes and methods. ## How was this patch tested? Unit test is implemented for all the implemented classes and algorithms. A scalability test on Uber's dataset was performed internally. Tested the methods on [WEX dataset](https://aws.amazon.com/items/2345) from AWS, with the steps and results [here](https://docs.google.com/document/d/19BXg-67U83NVB3M0I84HVBVg3baAVaESD_mrg_-vLro/edit). ## References Gionis, Aristides, Piotr Indyk, and Rajeev Motwani. "Similarity search in high dimensions via hashing." VLDB 7 Sep. 1999: 518-529. Wang, Jingdong et al. "Hashing for similarity search: A survey." arXiv preprint arXiv:1408.2927 (2014). Author: Yunni <Euler57721@gmail.com> Author: Yun Ni <yunn@uber.com> Closes #15148 from Yunni/SPARK-5992-yunn-lsh.
* [SPARK-18109][ML] Add instrumentation to GMMZheng RuiFeng2016-10-281-0/+6
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add instrumentation to GMM ## How was this patch tested? Test in spark-shell Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15636 from zhengruifeng/gmm_instr.
* [SPARK-17219][ML] enhanced NaN value handling in BucketizerVinceShieh2016-10-274-32/+147
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR is an enhancement of PR with commit ID:57dc326bd00cf0a49da971e9c573c48ae28acaa2. NaN is a special type of value which is commonly seen as invalid. But We find that there are certain cases where NaN are also valuable, thus need special handling. We provided user when dealing NaN values with 3 options, to either reserve an extra bucket for NaN values, or remove the NaN values, or report an error, by setting handleNaN "keep", "skip", or "error"(default) respectively. '''Before: val bucketizer: Bucketizer = new Bucketizer() .setInputCol("feature") .setOutputCol("result") .setSplits(splits) '''After: val bucketizer: Bucketizer = new Bucketizer() .setInputCol("feature") .setOutputCol("result") .setSplits(splits) .setHandleNaN("keep") ## How was this patch tested? Tests added in QuantileDiscretizerSuite, BucketizerSuite and DataFrameStatSuite Signed-off-by: VinceShieh <vincent.xieintel.com> Author: VinceShieh <vincent.xie@intel.com> Author: Vincent Xie <vincent.xie@intel.com> Author: Joseph K. Bradley <joseph@databricks.com> Closes #15428 from VinceShieh/spark-17219_followup.
* [SPARK-17157][SPARKR] Add multiclass logistic regression SparkR Wrapperwm624@hotmail.com2016-10-262-0/+159
| | | | | | | | | | | | | | | | ## 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.
* [MINOR][ML] Refactor clustering summary.Yanbo Liang2016-10-264-83/+80
| | | | | | | | | | | | ## What changes were proposed in this pull request? Abstract ```ClusteringSummary``` from ```KMeansSummary```, ```GaussianMixtureSummary``` and ```BisectingSummary```, and eliminate duplicated pieces of code. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15555 from yanboliang/clustering-summary.
* [SPARK-17748][FOLLOW-UP][ML] Reorg variables of WeightedLeastSquares.Yanbo Liang2016-10-262-68/+86
| | | | | | | | | | | | | ## What changes were proposed in this pull request? This is follow-up work of #15394. Reorg some variables of ```WeightedLeastSquares``` and fix one minor issue of ```WeightedLeastSquaresSuite```. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15621 from yanboliang/spark-17748.
* [SPARK-18007][SPARKR][ML] update SparkR MLP - add initalWeights parameterWeichenXu2016-10-251-1/+8
| | | | | | | | | | | | | | ## 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.
* [SPARK-18019][ML] Add instrumentation to GBTssethah2016-10-252-2/+17
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add instrumentation for logging in ML GBT, part of umbrella ticket [SPARK-14567](https://issues.apache.org/jira/browse/SPARK-14567) ## How was this patch tested? Tested locally: ```` 16/10/20 10:24:51 INFO Instrumentation: GBTRegressor-gbtr_2b460d3e2e93-1207021668-45: training: numPartitions=1 storageLevel=StorageLevel(1 replicas) 16/10/20 10:24:51 INFO Instrumentation: GBTRegressor-gbtr_2b460d3e2e93-1207021668-45: {"maxIter":1} 16/10/20 10:24:51 INFO Instrumentation: GBTRegressor-gbtr_2b460d3e2e93-1207021668-45: {"numFeatures":2} 16/10/20 10:24:51 INFO Instrumentation: GBTRegressor-gbtr_2b460d3e2e93-1207021668-45: {"numClasses":0} ... 16/10/20 15:54:21 INFO Instrumentation: GBTRegressor-gbtr_065fad465377-1922077832-22: training finished ```` Author: sethah <seth.hendrickson16@gmail.com> Closes #15574 from sethah/gbt_instr.
* [SPARK-17748][FOLLOW-UP][ML] Fix build error for Scala 2.10.Yanbo Liang2016-10-251-6/+7
| | | | | | | | | | | | ## What changes were proposed in this pull request? #15394 introduced build error for Scala 2.10, this PR fix it. ## How was this patch tested? Existing test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15625 from yanboliang/spark-17748-scala.
* [SPARK-14634][ML][FOLLOWUP] Delete superfluous line in BisectingKMeansZheng RuiFeng2016-10-252-6/+5
| | | | | | | | | | | | ## What changes were proposed in this pull request? As commented by jkbradley in https://github.com/apache/spark/pull/12394, `model.setSummary(summary)` is superfluous ## How was this patch tested? existing tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15619 from zhengruifeng/del_superfluous.
* [SPARK-17748][ML] One pass solver for Weighted Least Squares with ElasticNetsethah2016-10-249-308/+994
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. Make a pluggable solver interface for `WeightedLeastSquares` 2. Add a `QuasiNewton` solver to handle elastic net regularization for `WeightedLeastSquares` 3. Add method `BLAS.dspmv` used by QN solver 4. Add mechanism for WLS to handle singular covariance matrices by falling back to QN solver when Cholesky fails. ## How was this patch tested? Unit tests - see below. ## Design choices **Pluggable Normal Solver** Before, the `WeightedLeastSquares` package always used the Cholesky decomposition solver to compute the solution to the normal equations. Now, we specify the solver as a constructor argument to the `WeightedLeastSquares`. We introduce a new trait: ````scala private[ml] sealed trait NormalEquationSolver { def solve( bBar: Double, bbBar: Double, abBar: DenseVector, aaBar: DenseVector, aBar: DenseVector): NormalEquationSolution } ```` We extend this trait for different variants of normal equation solvers. In the future, we can easily add others (like QR) using this interface. **Always train in the standardized space** The normal solver did not previously standardize the data, but this patch introduces a change such that we always solve the normal equations in the standardized space. We convert back to the original space in the same way that is done for distributed L-BFGS/OWL-QN. We add test cases for zero variance features/labels. **Use L-BFGS locally to solve normal equations for singular matrix** When linear regression with the normal solver is called for a singular matrix, we initially try to solve with Cholesky. We use the output of `lapack.dppsv` to determine if the matrix is singular. If it is, we fall back to using L-BFGS locally to solve the normal equations. We add test cases for this as well. ## Test cases I found it helpful to enumerate some of the test cases and hopefully it makes review easier. **WeightedLeastSquares** 1. Constant columns - Cholesky solver fails with no regularization, Auto solver falls back to QN, and QN trains successfully. 2. Collinear features - Cholesky solver fails with no regularization, Auto solver falls back to QN, and QN trains successfully. 3. Label is constant zero - no training is performed regardless of intercept. Coefficients are zero and intercept is zero. 4. Label is constant - if fitIntercept, then no training is performed and intercept equals label mean. If not fitIntercept, then we train and return an answer that matches R's lm package. 5. Test with L1 - go through various combinations of L1/L2, standardization, fitIntercept and verify that output matches glmnet. 6. Initial intercept - verify that setting the initial intercept to label mean is correct by training model with strong L1 regularization so that all coefficients are zero and intercept converges to label mean. 7. Test diagInvAtWA - since we are standardizing features now during training, we should test that the inverse is computed to match R. **LinearRegression** 1. For all existing L1 test cases, test the "normal" solver too. 2. Check that using the normal solver now handles singular matrices. 3. Check that using the normal solver with L1 produces an objective history in the model summary, but does not produce the inverse of AtA. **BLAS** 1. Test new method `dspmv`. ## Performance Testing This patch will speed up linear regression with L1/elasticnet penalties when the feature size is < 4096. I have not conducted performance tests at scale, only observed by testing locally that there is a speed improvement. We should decide if this PR needs to be blocked before performance testing is conducted. Author: sethah <seth.hendrickson16@gmail.com> Closes #15394 from sethah/SPARK-17748.
* [SPARK-18049][MLLIB][TEST] Add missing tests for truePositiveRate and ↵Zheng RuiFeng2016-10-245-5/+13
| | | | | | | | | | | | | | weightedTruePositiveRate ## What changes were proposed in this pull request? Add missing tests for `truePositiveRate` and `weightedTruePositiveRate` in `MulticlassMetricsSuite` ## How was this patch tested? added testing Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15585 from zhengruifeng/mc_missing_test.
* [SPARK-17986][ML] SQLTransformer should remove temporary tablesDrew Robb2016-10-222-1/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? A call to the method `SQLTransformer.transform` previously would create a temporary table and never delete it. This change adds a call to `dropTempView()` that deletes this temporary table before returning the result so that the table will not remain in spark's table catalog. Because `tableName` is randomized and not exposed, there should be no expected use of this table outside of the `transform` method. ## How was this patch tested? A single new assertion was added to the existing test of the `SQLTransformer.transform` method that all temporary tables are removed. Without the corresponding code change, this new assertion fails. I am not aware of any circumstances in which removing this temporary view would be bad for performance or correctness in other ways, but some expertise here would be helpful. Author: Drew Robb <drewrobb@gmail.com> Closes #15526 from drewrobb/SPARK-17986.
* [SPARK-18042][SQL] OutputWriter should expose file path writtenReynold Xin2016-10-211-1/+7
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch adds a new "path" method on OutputWriter that returns the path of the file written by the OutputWriter. This is part of the necessary work to consolidate structured streaming and batch write paths. The batch write path has a nice feature that each data source can define the extension of the files, and allow Spark to specify the staging directory and the prefix for the files. However, in the streaming path we need to collect the list of files written, and there is no interface right now to do that. ## How was this patch tested? N/A - there is no behavior change and this should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #15580 from rxin/SPARK-18042.
* [SPARK-17331][FOLLOWUP][ML][CORE] Avoid allocating 0-length arraysZheng RuiFeng2016-10-219-23/+24
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `Array[T]()` -> `Array.empty[T]` to avoid allocating 0-length arrays. Use regex `find . -name '*.scala' | xargs -i bash -c 'egrep "Array\[[A-Za-z]+\]\(\)" -n {} && echo {}'` to find modification candidates. cc srowen ## How was this patch tested? existing tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15564 from zhengruifeng/avoid_0_length_array.
* [SPARK-18021][SQL] Refactor file name specification for data sourcesReynold Xin2016-10-201-10/+6
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently each data source OutputWriter is responsible for specifying the entire file name for each file output. This, however, does not make any sense because we rely on file naming schemes for certain behaviors in Spark SQL, e.g. bucket id. The current approach allows individual data sources to break the implementation of bucketing. On the flip side, we also don't want to move file naming entirely out of data sources, because different data sources do want to specify different extensions. This patch divides file name specification into two parts: the first part is a prefix specified by the caller of OutputWriter (in WriteOutput), and the second part is the suffix that can be specified by the OutputWriter itself. Note that a side effect of this change is that now all file based data sources also support bucketing automatically. There are also some other minor cleanups: - Removed the UUID passed through generic Configuration string - Some minor rewrites for better clarity - Renamed "path" in multiple places to "stagingDir", to more accurately reflect its meaning ## How was this patch tested? This should be covered by existing data source tests. Author: Reynold Xin <rxin@databricks.com> Closes #15562 from rxin/SPARK-18021.
* [SPARK-17941][ML][TEST] Logistic regression tests should use sample weights.sethah2016-10-141-745/+748
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The sample weight testing for logistic regressions is not robust. Logistic regression suite already has many test cases comparing results to R glmnet. Since both libraries support sample weights, we should use sample weights in the test to increase coverage for sample weighting. This patch doesn't really add any code and makes the testing more complete. Also fixed some errors with the R code that was referenced in the test suit. Changed `standardization=T` to `standardize=T` since the former is invalid. ## How was this patch tested? Existing unit tests are modified. No non-test code is touched. Author: sethah <seth.hendrickson16@gmail.com> Closes #15488 from sethah/logreg_weight_tests.
* [SPARK-17870][MLLIB][ML] Change statistic to pValue for SelectKBest and ↵Peng2016-10-143-9/+9
| | | | | | | | | | | | | | | | | SelectPercentile because of DoF difference ## What changes were proposed in this pull request? For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic (ChiSqure value) to select the features. It select the features with the largest ChiSqure value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD), and for different df, you cannot base on ChiSqure value to select features. So we change statistic to pValue for SelectKBest and SelectPercentile ## How was this patch tested? change existing test Author: Peng <peng.meng@intel.com> Closes #15444 from mpjlu/chisqure-bug.
* [SPARK-14634][ML] Add BisectingKMeansSummaryZheng RuiFeng2016-10-144-5/+91
| | | | | | | | | | | | ## What changes were proposed in this pull request? Add BisectingKMeansSummary ## How was this patch tested? unit test Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #12394 from zhengruifeng/biKMSummary.
* [SPARK-17835][ML][MLLIB] Optimize NaiveBayes mllib wrapper to eliminate ↵Yanbo Liang2016-10-122-18/+43
| | | | | | | | | | | | | | | | | | | | extra pass on data ## What changes were proposed in this pull request? [SPARK-14077](https://issues.apache.org/jira/browse/SPARK-14077) copied the ```NaiveBayes``` implementation from mllib to ml and left mllib as a wrapper. However, there are some difference between mllib and ml to handle labels: * mllib allow input labels as {-1, +1}, however, ml assumes the input labels in range [0, numClasses). * mllib ```NaiveBayesModel``` expose ```labels``` but ml did not due to the assumption mention above. During the copy in [SPARK-14077](https://issues.apache.org/jira/browse/SPARK-14077), we use ```val labels = data.map(_.label).distinct().collect().sorted``` to get the distinct labels firstly, and then encode the labels for training. It involves extra Spark job compared with the original implementation. Since ```NaiveBayes``` only do one pass aggregation during training, adding another one seems less efficient. We can get the labels in a single pass along with ```NaiveBayes``` training and send them to MLlib side. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15402 from yanboliang/spark-17835.
* [SPARK-11560][MLLIB] Optimize KMeans implementation / remove 'runs'Sean Owen2016-10-121-164/+132
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is a revival of https://github.com/apache/spark/pull/14948 and related to https://github.com/apache/spark/pull/14937. This removes the 'runs' parameter, which has already been disabled, from the K-means implementation and further deprecates API methods that involve it. This also happens to resolve the issue that K-means should not return duplicate centers, meaning that it may return less than k centroids if not enough data is available. ## How was this patch tested? Existing tests Author: Sean Owen <sowen@cloudera.com> Closes #15342 from srowen/SPARK-11560.
* [SPARK-15153][ML][SPARKR] Fix SparkR spark.naiveBayes error when label is ↵Yanbo Liang2016-10-111-0/+1
| | | | | | | | | | | | | | | 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-15957][ML] RFormula supports forcing to index labelYanbo Liang2016-10-102-4/+52
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? ```RFormula``` will index label only when it is string type currently. If the label is numeric type and we use ```RFormula``` to present a classification model, there is no label attributes in label column metadata. The label attributes are useful when making prediction for classification, so we can force to index label by ```StringIndexer``` whether it is numeric or string type for classification. Then SparkR wrappers can extract label attributes from label column metadata successfully. This feature can help us to fix bug similar with [SPARK-15153](https://issues.apache.org/jira/browse/SPARK-15153). For regression, we will still to keep label as numeric type. In this PR, we add a param ```indexLabel``` to control whether to force to index label for ```RFormula```. ## How was this patch tested? Unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13675 from yanboliang/spark-15957.
* [SPARK-14610][ML] Remove superfluous split for continuous features in ↵sethah2016-10-102-23/+52
| | | | | | | | | | | | | | | | decision tree training ## What changes were proposed in this pull request? A nonsensical split is produced from method `findSplitsForContinuousFeature` for decision trees. This PR removes the superfluous split and updates unit tests accordingly. Additionally, an assertion to check that the number of found splits is `> 0` is removed, and instead features with zero possible splits are ignored. ## How was this patch tested? A unit test was added to check that finding splits for a constant feature produces an empty array. Author: sethah <seth.hendrickson16@gmail.com> Closes #12374 from sethah/SPARK-14610.
* [MINOR][ML] remove redundant comment in LogisticRegressionwm624@hotmail.com2016-10-071-1/+0
| | | | | | | | | | | | ## What changes were proposed in this pull request? While adding R wrapper for LogisticRegression, I found one extra comment. It is minor and I just remove it. ## How was this patch tested? Unit tests Author: wm624@hotmail.com <wm624@hotmail.com> Closes #15391 from wangmiao1981/mlordoc.
* [SPARK-17761][SQL] Remove MutableRowHerman van Hovell2016-10-074-10/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In practice we cannot guarantee that an `InternalRow` is immutable. This makes the `MutableRow` almost redundant. This PR folds `MutableRow` into `InternalRow`. The code below illustrates the immutability issue with InternalRow: ```scala import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.GenericMutableRow val struct = new GenericMutableRow(1) val row = InternalRow(struct, 1) println(row) scala> [[null], 1] struct.setInt(0, 42) println(row) scala> [[42], 1] ``` This might be somewhat controversial, so feedback is appreciated. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15333 from hvanhovell/SPARK-17761.
* [SPARK-17792][ML] L-BFGS solver for linear regression does not accept ↵sethah2016-10-062-14/+11
| | | | | | | | | | | | | | | | general numeric label column types ## What changes were proposed in this pull request? Before, we computed `instances` in LinearRegression in two spots, even though they did the same thing. One of them did not cast the label column to `DoubleType`. This patch consolidates the computation and always casts the label column to `DoubleType`. ## How was this patch tested? Added a unit test to check all solvers. This test failed before this patch. Author: sethah <seth.hendrickson16@gmail.com> Closes #15364 from sethah/linreg_numeric_type.
* [MINOR][ML] Avoid 2D array flatten in NB training.Yanbo Liang2016-10-051-4/+4
| | | | | | | | | | | | ## What changes were proposed in this pull request? Avoid 2D array flatten in ```NaiveBayes``` training, since flatten method might be expensive (It will create another array and copy data there). ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15359 from yanboliang/nb-theta.
* [SPARK-17744][ML] Parity check between the ml and mllib test suites for NBZheng RuiFeng2016-10-046-11/+70
| | | | | | | | | | | | | ## What changes were proposed in this pull request? 1,parity check and add missing test suites for ml's NB 2,remove some unused imports ## How was this patch tested? manual tests in spark-shell Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15312 from zhengruifeng/nb_test_parity.
* [SPARK-17559][MLLIB] persist edges if their storage level is non in ↵ding2016-10-041-1/+4
| | | | | | | | | | | | | | PeriodicGraphCheckpointer ## What changes were proposed in this pull request? When use PeriodicGraphCheckpointer to persist graph, sometimes the edges isn't persisted. As currently only when vertices's storage level is none, graph is persisted. However there is a chance vertices's storage level is not none while edges's is none. Eg. graph created by a outerJoinVertices operation, vertices is automatically cached while edges is not. In this way, edges will not be persisted if we use PeriodicGraphCheckpointer do persist. We need separately check edges's storage level and persisted it if it's none. ## How was this patch tested? manual tests Author: ding <ding@localhost.localdomain> Closes #15124 from dding3/spark-persisitEdge.
* [SPARK-17704][ML][MLLIB] ChiSqSelector performance improvement.Sean Owen2016-10-012-12/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Partial revert of #15277 to instead sort and store input to model rather than require sorted input ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15299 from srowen/SPARK-17704.2.
* [SPARK-14077][ML][FOLLOW-UP] Revert change for NB Model's Load to maintain ↵Zheng RuiFeng2016-09-301-4/+7
| | | | | | | | | | | | | | compatibility with the model stored before 2.0 ## What changes were proposed in this pull request? Revert change for NB Model's Load to maintain compatibility with the model stored before 2.0 ## How was this patch tested? local build Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15313 from zhengruifeng/revert_save_load.
* [SPARK-14077][ML] Refactor NaiveBayes to support weighted instancesZheng RuiFeng2016-09-293-112/+191
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1,support weighted data 2,use dataset/dataframe instead of rdd 3,make mllib as a wrapper to call ml ## How was this patch tested? local manual tests in spark-shell unit tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #12819 from zhengruifeng/weighted_nb.
* [SPARK-17697][ML] Fixed bug in summary calculations that pattern match ↵Bryan Cutler2016-09-294-7/+49
| | | | | | | | | | | | | | against label without casting ## What changes were proposed in this pull request? In calling LogisticRegression.evaluate and GeneralizedLinearRegression.evaluate using a Dataset where the Label is not of a double type, calculations pattern match against a double and throw a MatchError. This fix casts the Label column to a DoubleType to ensure there is no MatchError. ## How was this patch tested? Added unit tests to call evaluate with a dataset that has Label as other numeric types. Author: Bryan Cutler <cutlerb@gmail.com> Closes #15288 from BryanCutler/binaryLOR-numericCheck-SPARK-17697.
* [SPARK-17721][MLLIB][ML] Fix for multiplying transposed SparseMatrix with ↵Bjarne Fruergaard2016-09-292-2/+23
| | | | | | | | | | | | | | | | | | | | | | | | SparseVector ## What changes were proposed in this pull request? * changes the implementation of gemv with transposed SparseMatrix and SparseVector both in mllib-local and mllib (identical) * adds a test that was failing before this change, but succeeds with these changes. The problem in the previous implementation was that it only increments `i`, that is enumerating the columns of a row in the SparseMatrix, when the row-index of the vector matches the column-index of the SparseMatrix. In cases where a particular row of the SparseMatrix has non-zero values at column-indices lower than corresponding non-zero row-indices of the SparseVector, the non-zero values of the SparseVector are enumerated without ever matching the column-index at index `i` and the remaining column-indices i+1,...,indEnd-1 are never attempted. The test cases in this PR illustrate this issue. ## How was this patch tested? I have run the specific `gemv` tests in both mllib-local and mllib. I am currently still running `./dev/run-tests`. ## ___ As per instructions, I hereby state that this is my original work and that I license the work to the project (Apache Spark) under the project's open source license. Mentioning dbtsai, viirya and brkyvz whom I can see have worked/authored on these parts before. Author: Bjarne Fruergaard <bwahlgreen@gmail.com> Closes #15296 from bwahlgreen/bugfix-spark-17721.
* [SPARK-17704][ML][MLLIB] ChiSqSelector performance improvement.Yanbo Liang2016-09-291-15/+30
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Several performance improvement for ```ChiSqSelector```: 1, Keep ```selectedFeatures``` ordered ascendent. ```ChiSqSelectorModel.transform``` need ```selectedFeatures``` ordered to make prediction. We should sort it when training model rather than making prediction, since users usually train model once and use the model to do prediction multiple times. 2, When training ```fpr``` type ```ChiSqSelectorModel```, it's not necessary to sort the ChiSq test result by statistic. ## How was this patch tested? Existing unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15277 from yanboliang/spark-17704.
* [SPARK-16356][FOLLOW-UP][ML] Enforce ML test of exception for ↵Yanbo Liang2016-09-291-4/+9
| | | | | | | | | | | | | | | local/distributed Dataset. ## What changes were proposed in this pull request? #14035 added ```testImplicits``` to ML unit tests and promoted ```toDF()```, but left one minor issue at ```VectorIndexerSuite```. If we create the DataFrame by ```Seq(...).toDF()```, it will throw different error/exception compared with ```sc.parallelize(Seq(...)).toDF()``` for one of the test cases. After in-depth study, I found it was caused by different behavior of local and distributed Dataset if the UDF failed at ```assert```. If the data is local Dataset, it throws ```AssertionError``` directly; If the data is distributed Dataset, it throws ```SparkException``` which is the wrapper of ```AssertionError```. I think we should enforce this test to cover both case. ## How was this patch tested? Unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15261 from yanboliang/spark-16356.
* [SPARK-17666] Ensure that RecordReaders are closed by data source file scansJosh Rosen2016-09-271-2/+5
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch addresses a potential cause of resource leaks in data source file scans. As reported in [SPARK-17666](https://issues.apache.org/jira/browse/SPARK-17666), tasks which do not fully-consume their input may cause file handles / network connections (e.g. S3 connections) to be leaked. Spark's `NewHadoopRDD` uses a TaskContext callback to [close its record readers](https://github.com/apache/spark/blame/master/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala#L208), but the new data source file scans will only close record readers once their iterators are fully-consumed. This patch modifies `RecordReaderIterator` and `HadoopFileLinesReader` to add `close()` methods and modifies all six implementations of `FileFormat.buildReader()` to register TaskContext task completion callbacks to guarantee that cleanup is eventually performed. ## How was this patch tested? Tested manually for now. Author: Josh Rosen <joshrosen@databricks.com> Closes #15245 from JoshRosen/SPARK-17666-close-recordreader.
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