## What changes were proposed in this pull request?
It avoids counting the dataframe twice.
Author: Abou Haydar Elias <abouhaydar.elias@gmail.com>
Author: Elie A <abouhaydar.elias@gmail.com>
Closes#11491 from eliasah/quantile-discretizer-patch.
## What changes were proposed in this pull request?
This PR fixes typos in comments and testcase name of code.
## How was this patch tested?
manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11481 from dongjoon-hyun/minor_fix_typos_in_code.
## What changes were proposed in this pull request?
Remove duplicated periods at the end of some sharedParams in ScalaDoc, such as [here](https://github.com/apache/spark/pull/11344/files#diff-9edc669edcf2c0c7cf1efe4a0a57da80L367)
cc mengxr srowen
## How was this patch tested?
Documents change, no test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11344 from yanboliang/shared-cleanup.
## What changes were proposed in this pull request?
After SPARK-6990, `dev/lint-java` keeps Java code healthy and helps PR review by saving much time.
This issue aims remove unused imports from Java/Scala code and add `UnusedImports` checkstyle rule to help developers.
## How was this patch tested?
```
./dev/lint-java
./build/sbt compile
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11438 from dongjoon-hyun/SPARK-13583.
## What changes were proposed in this pull request?
Make some cross-cutting code improvements according to static analysis. These are individually up for discussion since they exist in separate commits that can be reverted. The changes are broadly:
- Inner class should be static
- Mismatched hashCode/equals
- Overflow in compareTo
- Unchecked warnings
- Misuse of assert, vs junit.assert
- get(a) + getOrElse(b) -> getOrElse(a,b)
- Array/String .size -> .length (occasionally, -> .isEmpty / .nonEmpty) to avoid implicit conversions
- Dead code
- tailrec
- exists(_ == ) -> contains find + nonEmpty -> exists filter + size -> count
- reduce(_+_) -> sum map + flatten -> map
The most controversial may be .size -> .length simply because of its size. It is intended to avoid implicits that might be expensive in some places.
## How was the this patch tested?
Existing Jenkins unit tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#11292 from srowen/SPARK-13423.
Estimator for Generalized Linear Models(GLMs) which will be solved by IRLS.
cc mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11136 from yanboliang/spark-12811.
JIRA: https://issues.apache.org/jira/browse/SPARK-13506
## What changes were proposed in this pull request?
just chang R Snippet Comment in AssociationRulesSuite
## How was this patch tested?
unit test passsed
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#11387 from zhengruifeng/ars.
## What changes were proposed in this pull request?
* The default value of ```regParam``` of PySpark MLlib ```LogisticRegressionWithLBFGS``` should be consistent with Scala which is ```0.0```. (This is also consistent with ML ```LogisticRegression```.)
* BTW, if we use a known updater(L1 or L2) for binary classification, ```LogisticRegressionWithLBFGS``` will call the ML implementation. We should update the API doc to clarifying ```numCorrections``` will have no effect if we fall into that route.
* Make a pass for all parameters of ```LogisticRegressionWithLBFGS```, others are set properly.
cc mengxr dbtsai
## How was this patch tested?
No new tests, it should pass all current tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11424 from yanboliang/spark-13545.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the tree module.
closes#10601
Author: Bryan Cutler <cutlerb@gmail.com>
Author: vijaykiran <mail@vijaykiran.com>
Closes#11353 from BryanCutler/param-desc-consistent-tree-SPARK-12634.
## What changes were proposed in this pull request?
This is another try of PR #11323.
This PR removes DataFrame RDD operations except for `foreach` and `foreachPartitions` (they are actions rather than transformations). Original calls are now replaced by calls to methods of `DataFrame.rdd`.
PR #11323 was reverted because it introduced a regression: both `DataFrame.foreach` and `DataFrame.foreachPartitions` wrap underlying RDD operations with `withNewExecutionId` to track Spark jobs. But they are removed in #11323.
## How was the this patch tested?
No extra tests are added. Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#11388 from liancheng/remove-df-rdd-ops.
jira: https://issues.apache.org/jira/browse/SPARK-13028
MaxAbsScaler works in a very similar way as MinMaxScaler, but scales in a way that the training data lies within the range [-1, 1] by dividing through the largest maximum value in each feature. The motivation to use this scaling includes robustness to very small standard deviations of features and preserving zero entries in sparse data.
Unlike StandardScaler and MinMaxScaler, MaxAbsScaler does not shift/center the data, and thus does not destroy any sparsity.
Something similar from sklearn:
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#10939 from hhbyyh/maxabs and squashes the following commits:
fd8bdcd [Yuhao Yang] add tag and some optimization on fit
648fced [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into maxabs
75bebc2 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into maxabs
cb10bb6 [Yuhao Yang] remove minmax
91ef8f3 [Yuhao Yang] ut added
8ab0747 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into maxabs
a9215b5 [Yuhao Yang] max abs scaler
## What changes were proposed in this pull request?
ML StringIndexer does not protect itself from column name duplication.
We should still improve a way to validate a schema of `StringIndexer` and `StringIndexerModel`. However, it would be great to fix at another issue.
## How was this patch tested?
unit test
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#11370 from yu-iskw/SPARK-12874.
## What changes were proposed in this pull request?
This PR removes DataFrame RDD operations. Original calls are now replaced by calls to methods of `DataFrame.rdd`.
## How was the this patch tested?
No extra tests are added. Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#11323 from liancheng/remove-df-rdd-ops.
## What changes were proposed in this pull request?
Like #11027 for ```LogisticRegression```, ```LinearRegression``` with L1 regularization should also cache the value of the ```standardization``` rather than re-fetching it from the ```ParamMap``` for every OWLQN iteration.
cc srowen
## How was this patch tested?
No extra tests are added. It should pass all existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11367 from yanboliang/spark-13490.
## What changes were proposed in this pull request?
Change line 113 of QuantileDiscretizer.scala to
`val requiredSamples = math.max(numBins * numBins, 10000.0)`
so that `requiredSamples` is a `Double`. This will fix the division in line 114 which currently results in zero if `requiredSamples < dataset.count`
## How was the this patch tested?
Manual tests. I was having a problems using QuantileDiscretizer with my a dataset and after making this change QuantileDiscretizer behaves as expected.
Author: Oliver Pierson <ocp@gatech.edu>
Author: Oliver Pierson <opierson@umd.edu>
Closes#11319 from oliverpierson/SPARK-13444.
`GraphImpl.fromExistingRDDs` expects preprocessed vertex RDD as input. We call it in LDA without validating this requirement. So it might introduce errors. Replacing it by `Graph.apply` would be safer and more proper because it is a public API. The tests still pass. So maybe it is safe to use `fromExistingRDDs` here (though it doesn't seem so based on the implementation) or the test cases are special. jkbradley ankurdave
Author: Xiangrui Meng <meng@databricks.com>
Closes#11226 from mengxr/SPARK-13355.
## What changes were proposed in this pull request?
In order to provide better and consistent result, let's change the default value of MLlib ```LogisticRegressionWithLBFGS convergenceTol``` from ```1E-4``` to ```1E-6``` which will be equal to ML ```LogisticRegression```.
cc dbtsai
## How was the this patch tested?
unit tests
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11299 from yanboliang/spark-13429.
As also mentioned/marked by TODO in AFTAggregator.AFTAggregator.add(data: AFTPoint) method a new array is being created for intercept value and it is being concatenated
with another array which contains the betas, the resulted Array is being converted into a Dense vector which in its turn is being converted into breeze vector.
This is expensive and not necessarily beautiful.
I've tried to solve above mentioned problem by simple algebraic decompositions - keeping and treating intercept independently.
Please let me know what do you think and if you have any questions.
Thanks,
Narine
Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com>
Closes#11179 from NarineK/survivaloptim.
ML ```KMeansModel / BisectingKMeansModel / QuantileDiscretizer``` should set parent.
cc mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11214 from yanboliang/spark-13334.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the fpm and recommendation modules.
Closes#10602Closes#10897
Author: Bryan Cutler <cutlerb@gmail.com>
Author: somideshmukh <somilde@us.ibm.com>
Closes#11186 from BryanCutler/param-desc-consistent-fpmrecc-SPARK-12632.
## What changes were proposed in this pull request?
This PR tries to fix all typos in all markdown files under `docs` module,
and fixes similar typos in other comments, too.
## How was the this patch tested?
manual tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11300 from dongjoon-hyun/minor_fix_typos.
add support of arbitrary length sentence by using the nature representation of sentences in the input.
add new similarity functions and add normalization option for distances in synonym finding
add new accessor for internal structure(the vocabulary and wordindex) for convenience
need instructions about how to set value for the Since annotation for newly added public functions. 1.5.3?
jira link: https://issues.apache.org/jira/browse/SPARK-12153
Author: Yong Gang Cao <ygcao@amazon.com>
Author: Yong-Gang Cao <ygcao@users.noreply.github.com>
Closes#10152 from ygcao/improvementForSentenceBoundary.
## What changes were proposed in this pull request?
Fix MLlib LogisticRegressionWithLBFGS regularization map as:
```SquaredL2Updater``` -> ```elasticNetParam = 0.0```
```L1Updater``` -> ```elasticNetParam = 1.0```
cc dbtsai
## How was the this patch tested?
unit tests
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11258 from yanboliang/spark-13379.
This PR fixes some warnings found by `build/sbt mllib/test:compile`.
Author: Xiangrui Meng <meng@databricks.com>
Closes#11227 from mengxr/fix-mllib-warnings-201602.
This documents the implementation of ALS in `spark.ml` with example code in scala, java and python.
Author: BenFradet <benjamin.fradet@gmail.com>
Closes#10411 from BenFradet/SPARK-12247.
This enhancement extends the existing SparkML Binarizer [SPARK-5891] to allow Vector in addition to the existing Double input column type.
A use case for this enhancement is for when a user wants to Binarize many similar feature columns at once using the same threshold value (for example a binary threshold applied to many pixels in an image).
This contribution is my original work and I license the work to the project under the project's open source license.
viirya mengxr
Author: seddonm1 <seddonm1@gmail.com>
Closes#10976 from seddonm1/master.
JIRA: https://issues.apache.org/jira/browse/SPARK-12363
This issue is pointed by yanboliang. When `setRuns` is removed from PowerIterationClustering, one of the tests will be failed. I found that some `dstAttr`s of the normalized graph are not correct values but 0.0. By setting `TripletFields.All` in `mapTriplets` it can work.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#10539 from viirya/fix-poweriter.
jkbradley I tried to improve the function to export a model. When I tried to export a model to S3 under Spark 1.6, we couldn't do that. So, it should offer S3 besides HDFS. Can you review it when you have time? Thanks!
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#11151 from yu-iskw/SPARK-13265.
In spark-env.sh.template, there are multi-byte characters, this PR will remove it.
Author: Sasaki Toru <sasakitoa@nttdata.co.jp>
Closes#11149 from sasakitoa/remove_multibyte_in_sparkenv.
JIRA: https://issues.apache.org/jira/browse/SPARK-10524
Currently we use the hard prediction (`ImpurityCalculator.predict`) to order categories' bins. But we should use the soft prediction.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8734 from viirya/dt-soft-centroids.
KMeans:
Make a private non-deprecated version of setRuns API so that we can call it from the PythonAPI without deprecation warnings in our own build. Also use it internally when being called from train. Add a logWarning for non-1 values
MFDataGenerator:
Apparently we are calling round on an integer which now in Scala 2.11 results in a warning (it didn't make any sense before either). Figure out if this is a mistake we can just remove or if we got the types wrong somewhere.
I put these two together since they are both deprecation fixes in MLlib and pretty small, but I can split them up if we would prefer it that way.
Author: Holden Karau <holden@us.ibm.com>
Closes#11112 from holdenk/SPARK-13201-non-deprecated-setRuns-SPARK-mathround-integer.
cache the value of the standardization Param in LogisticRegression, rather than re-fetching it from the ParamMap for every index and every optimization step in the quasi-newton optimizer
also, fix Param#toString to cache the stringified representation, rather than re-interpolating it on every call, so any other implementations that have similar repeated access patterns will see a benefit.
this change improves training times for one of my test sets from ~7m30s to ~4m30s
Author: Gary King <gary@idibon.com>
Closes#11027 from idigary/spark-13132-optimize-logistic-regression.
Fixed the bug in linear regression train for the case when the target variable is constant. The two cases for `fitIntercept=true` or `fitIntercept=false` should be treated differently.
Author: Imran Younus <iyounus@us.ibm.com>
Closes#10702 from iyounus/SPARK-12732_bug_fix_in_linear_regression_train.
Fixes problem and verifies fix by test suite.
Also - adds optional parameter: nullable (Boolean) to: SchemaUtils.appendColumn
and deduplicates SchemaUtils.appendColumn functions.
Author: Grzegorz Chilkiewicz <grzegorz.chilkiewicz@codilime.com>
Closes#10741 from grzegorz-chilkiewicz/master.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the clustering module.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#10610 from BryanCutler/param-desc-consistent-cluster-SPARK-12631.
This patch changes Spark's build to make Scala 2.11 the default Scala version. To be clear, this does not mean that Spark will stop supporting Scala 2.10: users will still be able to compile Spark for Scala 2.10 by following the instructions on the "Building Spark" page; however, it does mean that Scala 2.11 will be the default Scala version used by our CI builds (including pull request builds).
The Scala 2.11 compiler is faster than 2.10, so I think we'll be able to look forward to a slight speedup in our CI builds (it looks like it's about 2X faster for the Maven compile-only builds, for instance).
After this patch is merged, I'll update Jenkins to add new compile-only jobs to ensure that Scala 2.10 compilation doesn't break.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10608 from JoshRosen/SPARK-6363.
Implement ```IterativelyReweightedLeastSquares``` solver for GLM. I consider it as a solver rather than estimator, it only used internal so I keep it ```private[ml]```.
There are two limitations in the current implementation compared with R:
* It can not support ```Tuple``` as response for ```Binomial``` family, such as the following code:
```
glm( cbind(using, notUsing) ~ age + education + wantsMore , family = binomial)
```
* It does not support ```offset```.
Because I considered that ```RFormula``` did not support ```Tuple``` as label and ```offset``` keyword, so I simplified the implementation. But to add support for these two functions is not very hard, I can do it in follow-up PR if it is necessary. Meanwhile, we can also add R-like statistic summary for IRLS.
The implementation refers R, [statsmodels](https://github.com/statsmodels/statsmodels) and [sparkGLM](https://github.com/AlteryxLabs/sparkGLM).
Please focus on the main structure and overpass minor issues/docs that I will update later. Any comments and opinions will be appreciated.
cc mengxr jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10639 from yanboliang/spark-9835.
The intercept in Logistic Regression represents a prior on categories which should not be regularized. In MLlib, the regularization is handled through Updater, and the Updater penalizes all the components without excluding the intercept which resulting poor training accuracy with regularization.
The new implementation in ML framework handles this properly, and we should call the implementation in ML from MLlib since majority of users are still using MLlib api.
Note that both of them are doing feature scalings to improve the convergence, and the only difference is ML version doesn't regularize the intercept. As a result, when lambda is zero, they will converge to the same solution.
Previously partially reviewed at https://github.com/apache/spark/pull/6386#issuecomment-168781424 re-opening for dbtsai to review.
Author: Holden Karau <holden@us.ibm.com>
Author: Holden Karau <holden@pigscanfly.ca>
Closes#10788 from holdenk/SPARK-7780-intercept-in-logisticregressionwithLBFGS-should-not-be-regularized.
… Add LibSVMOutputWriter
The behavior of LibSVMRelation is not changed except adding LibSVMOutputWriter
* Partition is still not supported
* Multiple input paths is not supported
Author: Jeff Zhang <zjffdu@apache.org>
Closes#9595 from zjffdu/SPARK-11622.
https://issues.apache.org/jira/browse/SPARK-12834
We use `SerDe.dumps()` to serialize `JavaArray` and `JavaList` in `PythonMLLibAPI`, then deserialize them with `PickleSerializer` in Python side. However, there is no need to transform them in such an inefficient way. Instead of it, we can use type conversion to convert them, e.g. `list(JavaArray)` or `list(JavaList)`. What's more, there is an issue to Ser/De Scala Array as I said in https://issues.apache.org/jira/browse/SPARK-12780
Author: Xusen Yin <yinxusen@gmail.com>
Closes#10772 from yinxusen/SPARK-12834.
```PCAModel``` can output ```explainedVariance``` at Python side.
cc mengxr srowen
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10830 from yanboliang/spark-12905.
Update user guide for RFormula feature interactions. Meanwhile we also update other new features such as supporting string label in Spark 1.6.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10222 from yanboliang/spark-11965.
- Remove Akka dependency from core. Note: the streaming-akka project still uses Akka.
- Remove HttpFileServer
- Remove Akka configs from SparkConf and SSLOptions
- Rename `spark.akka.frameSize` to `spark.rpc.message.maxSize`. I think it's still worth to keep this config because using `DirectTaskResult` or `IndirectTaskResult` depends on it.
- Update comments and docs
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10854 from zsxwing/remove-akka.
When all labels are the same, it's a dangerous ground for LogisticRegression without intercept to converge. GLMNET doesn't support this case, and will just exit. GLM can train, but will have a warning message saying the algorithm doesn't converge.
Author: DB Tsai <dbt@netflix.com>
Closes#10862 from dbtsai/add-tests.
Add Since annotations to ml.param and ml.*
Author: Takahashi Hiroshi <takahashi.hiroshi@lab.ntt.co.jp>
Author: Hiroshi Takahashi <takahashi.hiroshi@lab.ntt.co.jp>
Closes#8935 from taishi-oss/issue10263.
This fixes the behavior of WeightedLeastSquars.fit() when the standard deviation of the target variable is zero. If the fitIntercept is true, there is no need to train.
Author: Imran Younus <iyounus@us.ibm.com>
Closes#10274 from iyounus/SPARK-12230_bug_fix_in_weighted_least_squares.
This PR aims to allow the prediction column of `BinaryClassificationEvaluator` to be of double type.
Author: BenFradet <benjamin.fradet@gmail.com>
Closes#10472 from BenFradet/SPARK-9716.
From the coverage issues for 1.6 : Add Python API for mllib.clustering.BisectingKMeans.
Author: Holden Karau <holden@us.ibm.com>
Closes#10150 from holdenk/SPARK-11937-python-api-coverage-SPARK-11944-python-mllib.clustering.BisectingKMeans.
Change assertion's message so it's consistent with the code. The old message says that the invoked method was lapack.dports, where in fact it was lapack.dppsv method.
Author: Wojciech Jurczyk <wojtek.jurczyk@gmail.com>
Closes#10818 from wjur/wjur/rename_error_message.
Currently `summary()` fails on a GLM model fitted over a vector feature missing ML attrs, since the output feature attrs will also have no name. We can avoid this situation by forcing `VectorAssembler` to make up suitable names when inputs are missing names.
cc mengxr
Author: Eric Liang <ekl@databricks.com>
Closes#10323 from ericl/spark-12346.
I create new pr since original pr long time no update.
Please help to review.
srowen
Author: Tommy YU <tummyyu@163.com>
Closes#10756 from Wenpei/add_since_to_recomm.
jira: https://issues.apache.org/jira/browse/SPARK-12026
The issue is valid as features.toArray.view.zipWithIndex.slice(startCol, endCol) becomes slower as startCol gets larger.
I tested on local and the change can improve the performance and the running time was stable.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#10146 from hhbyyh/chiSq.
jira: https://issues.apache.org/jira/browse/SPARK-10809
We could provide a single-document topicDistributions method for LocalLDAModel to allow for quick queries which avoid RDD operations. Currently, the user must use an RDD of documents.
add some missing assert too.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#9484 from hhbyyh/ldaTopicPre.
jira: https://issues.apache.org/jira/browse/SPARK-12685
the log of `word2vec` reports
trainWordsCount = -785727483
during computation over a large dataset.
Update the priority as it will affect the computation process.
`alpha = learningRate * (1 - numPartitions * wordCount.toDouble / (trainWordsCount + 1))`
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#10627 from hhbyyh/w2voverflow.
PySpark MLlib ```GaussianMixtureModel``` should support single instance ```predict/predictSoft``` just like Scala do.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10552 from yanboliang/spark-12603.
Turn import ordering violations into build errors, plus a few adjustments
to account for how the checker behaves. I'm a little on the fence about
whether the existing code is right, but it's easier to appease the checker
than to discuss what's the more correct order here.
Plus a few fixes to imports that cropped in since my recent cleanups.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#10612 from vanzin/SPARK-3873-enable.
Fix the style violation (space before , and :).
This PR is a followup for #10643.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10684 from sarutak/SPARK-12692-followup-mllib.
Fix most build warnings: mostly deprecated API usages. I'll annotate some of the changes below. CC rxin who is leading the charge to remove the deprecated APIs.
Author: Sean Owen <sowen@cloudera.com>
Closes#10570 from srowen/SPARK-12618.
For the BinaryClassificationEvaluator, the scaladoc doesn't mention that "areaUnderPR" is supported, only that the default is "areadUnderROC".
Also, in the documentation, it is said that:
"The default metric used to choose the best ParamMap can be overriden by the setMetric method in each of these evaluators."
However, the method is called setMetricName.
This PR aims to fix both issues.
Author: BenFradet <benjamin.fradet@gmail.com>
Closes#10328 from BenFradet/SPARK-12368.
Modified the definition of R^2 for regression through origin. Added modified test for regression metrics.
Author: Imran Younus <iyounus@us.ibm.com>
Author: Imran Younus <imranyounus@gmail.com>
Closes#10384 from iyounus/SPARK_12331_R2_for_regression_through_origin.
DecisionTreeRegressor will provide variance of prediction as a Double column.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8866 from yanboliang/spark-9622.
callUDF has been deprecated. However, we do not have an alternative for users to specify the output data type without type tags. This pull request introduced a new API for that, and replaces the invocation of the deprecated callUDF with that.
Author: Reynold Xin <rxin@databricks.com>
Closes#10547 from rxin/SPARK-12599.
A slight adjustment to the checker configuration was needed; there is
a handful of warnings still left, but those are because of a bug in
the checker that I'll fix separately (before enabling errors for the
checker, of course).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#10535 from vanzin/SPARK-3873-mllib.
Include the following changes:
1. Close `java.sql.Statement`
2. Fix incorrect `asInstanceOf`.
3. Remove unnecessary `synchronized` and `ReentrantLock`.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10440 from zsxwing/findbugs.
ParamMap#filter uses `mutable.Map#filterKeys`. The return type of `filterKey` is collection.Map, not mutable.Map but the result is casted to mutable.Map using `asInstanceOf` so we get `ClassCastException`.
Also, the return type of Map#filterKeys is not Serializable. It's the issue of Scala (https://issues.scala-lang.org/browse/SI-6654).
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#10381 from sarutak/SPARK-12424.
Restore the original value of os.arch property after each test
Since some of tests forced to set the specific value to os.arch property, we need to set the original value.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#10289 from kiszk/SPARK-12311.
Only load explainedVariance in PCAModel if it was written with Spark > 1.6.x
jkbradley is this kind of what you had in mind?
Author: Sean Owen <sowen@cloudera.com>
Closes#10327 from srowen/SPARK-12349.
Added catch for casting Long to Int exception when PySpark ALS Ratings are serialized. It is easy to accidentally use Long IDs for user/product and before, it would fail with a somewhat cryptic "ClassCastException: java.lang.Long cannot be cast to java.lang.Integer." Now if this is done, a more descriptive error is shown, e.g. "PickleException: Ratings id 1205640308657491975 exceeds max integer value of 2147483647."
Author: Bryan Cutler <bjcutler@us.ibm.com>
Closes#9361 from BryanCutler/als-pyspark-long-id-error-SPARK-10158.
Use ```sqlContext``` from ```MLlibTestSparkContext``` rather than creating new one for spark.ml test suites. I have checked thoroughly and found there are four test cases need to update.
cc mengxr jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10279 from yanboliang/spark-12309.
JIRA: https://issues.apache.org/jira/browse/SPARK-12016
We should not directly use Word2VecModel in pyspark. We need to wrap it in a Word2VecModelWrapper when loading it in pyspark.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#10100 from viirya/fix-load-py-wordvecmodel.
As noted in PR #9441, implementing `tallSkinnyQR` uncovered a bug with our PySpark `RowMatrix` constructor. As discussed on the dev list [here](http://apache-spark-developers-list.1001551.n3.nabble.com/K-Means-And-Class-Tags-td10038.html), there appears to be an issue with type erasure with RDDs coming from Java, and by extension from PySpark. Although we are attempting to construct a `RowMatrix` from an `RDD[Vector]` in [PythonMLlibAPI](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala#L1115), the `Vector` type is erased, resulting in an `RDD[Object]`. Thus, when calling Scala's `tallSkinnyQR` from PySpark, we get a Java `ClassCastException` in which an `Object` cannot be cast to a Spark `Vector`. As noted in the aforementioned dev list thread, this issue was also encountered with `DecisionTrees`, and the fix involved an explicit `retag` of the RDD with a `Vector` type. `IndexedRowMatrix` and `CoordinateMatrix` do not appear to have this issue likely due to their related helper functions in `PythonMLlibAPI` creating the RDDs explicitly from DataFrames with pattern matching, thus preserving the types.
This PR currently contains that retagging fix applied to the `createRowMatrix` helper function in `PythonMLlibAPI`. This PR blocks #9441, so once this is merged, the other can be rebased.
cc holdenk
Author: Mike Dusenberry <mwdusenb@us.ibm.com>
Closes#9458 from dusenberrymw/SPARK-11497_PySpark_RowMatrix_Constructor_Has_Type_Erasure_Issue.
LogisticRegression training summary should still function if the predictionCol is set to an empty string or otherwise unset (related too https://issues.apache.org/jira/browse/SPARK-9718 )
Author: Holden Karau <holden@pigscanfly.ca>
Author: Holden Karau <holden@us.ibm.com>
Closes#9037 from holdenk/SPARK-10991-LogisticRegressionTrainingSummary-handle-empty-prediction-col.
Add `computePrincipalComponentsAndVariance` to also compute PCA's explained variance.
CC mengxr
Author: Sean Owen <sowen@cloudera.com>
Closes#9736 from srowen/SPARK-11530.
Currently word2vec has the window hard coded at 5, some users may want different sizes (for example if using on n-gram input or similar). User request comes from http://stackoverflow.com/questions/32231975/spark-word2vec-window-size .
Author: Holden Karau <holden@us.ibm.com>
Author: Holden Karau <holden@pigscanfly.ca>
Closes#8513 from holdenk/SPARK-10299-word2vec-should-allow-users-to-specify-the-window-size.
felixcheung , mengxr
Just added a message to require()
Author: Dominik Dahlem <dominik.dahlem@gmail.combination>
Closes#9598 from dahlem/ddahlem_regression_evaluator_double_predictions_message_04112015.
jira: https://issues.apache.org/jira/browse/SPARK-11605
Check Java compatibility for MLlib for this release.
fix:
1. `StreamingTest.registerStream` needs java friendly interface.
2. `GradientBoostedTreesModel.computeInitialPredictionAndError` and `GradientBoostedTreesModel.updatePredictionError` has java compatibility issue. Mark them as `developerAPI`.
TBD:
[updated] no fix for now per discussion.
`org.apache.spark.mllib.classification.LogisticRegressionModel`
`public scala.Option<java.lang.Object> getThreshold();` has wrong return type for Java invocation.
`SVMModel` has the similar issue.
Yet adding a `scala.Option<java.util.Double> getThreshold()` would result in an overloading error due to the same function signature. And adding a new function with different name seems to be not necessary.
cc jkbradley feynmanliang
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#10102 from hhbyyh/javaAPI.
Sparse feature generated in LinearDataGenerator does not create dense vectors as an intermediate any more.
Author: Nakul Jindal <njindal@us.ibm.com>
Closes#9756 from nakul02/SPARK-11439_sparse_without_creating_dense_feature.
Add ```SQLTransformer``` user guide, example code and make Scala API doc more clear.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10006 from yanboliang/spark-11958.
Switched from using SQLContext constructor to using getOrCreate, mainly in model save/load methods.
This covers all instances in spark.mllib. There were no uses of the constructor in spark.ml.
CC: mengxr yhuai
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#10161 from jkbradley/mllib-sqlcontext-fix.
