This fixes SPARK-12000, verified on my local with JDK 7. It seems that `scaladoc` try to match method names and messed up with annotations.
cc: JoshRosen jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#10114 from mengxr/SPARK-12000.2.
cc mengxr noel-smith
I worked on this issues based on https://github.com/apache/spark/pull/8729.
ehsanmok thank you for your contricution!
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Author: Ehsan M.Kermani <ehsanmo1367@gmail.com>
Closes#9338 from yu-iskw/JIRA-10266.
jira: https://issues.apache.org/jira/browse/SPARK-11898
syn0Global and sync1Global in word2vec are quite large objects with size (vocab * vectorSize * 8), yet they are passed to worker using basic task serialization.
Use broadcast can greatly improve the performance. My benchmark shows that, for 1M vocabulary and default vectorSize 100, changing to broadcast can help,
1. decrease the worker memory consumption by 45%.
2. decrease running time by 40%.
This will also help extend the upper limit for Word2Vec.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#9878 from hhbyyh/w2vBC.
Add read/write support to LDA, similar to ALS.
save/load for ml.LocalLDAModel is done.
For DistributedLDAModel, I'm not sure if we can invoke save on the mllib.DistributedLDAModel directly. I'll send update after some test.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#9894 from hhbyyh/ldaMLsave.
Doc for 1.6 that the summaries mostly ignore the weight column.
To be corrected for 1.7
CC: mengxr thunterdb
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9927 from jkbradley/linregsummary-doc.
There is an unhandled case in the transform method of VectorAssembler if one of the input columns doesn't have one of the supported type DoubleType, NumericType, BooleanType or VectorUDT.
So, if you try to transform a column of StringType you get a cryptic "scala.MatchError: StringType".
This PR aims to fix this, throwing a SparkException when dealing with an unknown column type.
Author: BenFradet <benjamin.fradet@gmail.com>
Closes#9885 from BenFradet/SPARK-11902.
Like [SPARK-11852](https://issues.apache.org/jira/browse/SPARK-11852), ```k``` is params and we should save it under ```metadata/``` rather than both under ```data/``` and ```metadata/```. Refactor the constructor of ```ml.feature.PCAModel``` to take only ```pc``` but construct ```mllib.feature.PCAModel``` inside ```transform```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9897 from yanboliang/spark-11912.
I believe this works for general estimators within CrossValidator, including compound estimators. (See the complex unit test.)
Added read/write for all 3 Evaluators as well.
CC: mengxr yanboliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9848 from jkbradley/cv-io.
```withStd``` and ```withMean``` should be params of ```StandardScaler``` and ```StandardScalerModel```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9839 from yanboliang/standardScaler-refactor.
Need to remove parent directory (```className```) rather than just tempDir (```className/random_name```)
I tested this with IDFSuite, which has 2 read/write tests, and it fixes the problem.
CC: mengxr Can you confirm this is fine? I believe it is since the same ```random_name``` is used for all tests in a suite; we basically have an extra unneeded level of nesting.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9851 from jkbradley/tempdir-cleanup.
Add read/write support to the following estimators under spark.ml:
* ChiSqSelector
* PCA
* VectorIndexer
* Word2Vec
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9838 from yanboliang/spark-11829.
Updates:
* Add repartition(1) to save() methods' saving of data for LogisticRegressionModel, LinearRegressionModel.
* Strengthen privacy to class and companion object for Writers and Readers
* Change LogisticRegressionSuite read/write test to fit intercept
* Add Since versions for read/write methods in Pipeline, LogisticRegression
* Switch from hand-written class names in Readers to using getClass
CC: mengxr
CC: yanboliang Would you mind taking a look at this PR? mengxr might not be able to soon. Thank you!
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9829 from jkbradley/ml-io-cleanups.
* add "ML" prefix to reader/writer/readable/writable to avoid name collision with java.util.*
* define `DefaultParamsReadable/Writable` and use them to save some code
* use `super.load` instead so people can jump directly to the doc of `Readable.load`, which documents the Java compatibility issues
jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#9827 from mengxr/SPARK-11839.
Add read/write support to the following estimators under spark.ml:
* CountVectorizer
* IDF
* MinMaxScaler
* StandardScaler (a little awkward because we store some params in spark.mllib model)
* StringIndexer
Added some necessary method for read/write. Maybe we should add `private[ml] trait DefaultParamsReadable` and `DefaultParamsWritable` to save some boilerplate code, though we still need to override `load` for Java compatibility.
jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#9798 from mengxr/SPARK-6787.
This PR includes:
* Update SparkR:::glm, SparkR:::summary API docs.
* Update SparkR machine learning user guide and example codes to show:
* supporting feature interaction in R formula.
* summary for gaussian GLM model.
* coefficients for binomial GLM model.
mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9727 from yanboliang/spark-11684.
jira: https://issues.apache.org/jira/browse/SPARK-11813
I found the problem during training a large corpus. Avoid serialization of vocab in Word2Vec has 2 benefits.
1. Performance improvement for less serialization.
2. Increase the capacity of Word2Vec a lot.
Currently in the fit of word2vec, the closure mainly includes serialization of Word2Vec and 2 global table.
the main part of Word2vec is the vocab of size: vocab * 40 * 2 * 4 = 320 vocab
2 global table: vocab * vectorSize * 8. If vectorSize = 20, that's 160 vocab.
Their sum cannot exceed Int.max due to the restriction of ByteArrayOutputStream. In any case, avoiding serialization of vocab helps decrease the size of the closure serialization, especially when vectorSize is small, thus to allow larger vocabulary.
Actually there's another possible fix, make local copy of fields to avoid including Word2Vec in the closure. Let me know if that's preferred.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#9803 from hhbyyh/w2vVocab.
Also modifies DefaultParamsWriter.saveMetadata to take optional extra metadata.
CC: mengxr yanboliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9786 from jkbradley/als-io.
This replaces [https://github.com/apache/spark/pull/9656] with updates.
fayeshine should be the main author when this PR is committed.
CC: mengxr fayeshine
Author: Wenjian Huang <nextrush@163.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9814 from jkbradley/fayeshine-patch-6790.
I have added unit test for ML's StandardScaler By comparing with R's output, please review for me.
Thx.
Author: RoyGaoVLIS <roygao@zju.edu.cn>
Closes#6665 from RoyGao/7013.
This PR makes the default read/write work with simple transformers/estimators that have params of type `Param[Vector]`. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#9776 from mengxr/SPARK-11764.
Add save/load to LogisticRegression Estimator, and refactor tests a little to make it easier to add similar support to other Estimator, Model pairs.
Moved LogisticRegressionReader/Writer to within LogisticRegressionModel
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9749 from jkbradley/lr-io-2.
This excludes Estimators and ones which include Vector and other non-basic types for Params or data. This adds:
* Bucketizer
* DCT
* HashingTF
* Interaction
* NGram
* Normalizer
* OneHotEncoder
* PolynomialExpansion
* QuantileDiscretizer
* RFormula
* SQLTransformer
* StopWordsRemover
* StringIndexer
* Tokenizer
* VectorAssembler
* VectorSlicer
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9755 from jkbradley/transformer-io.
This is to support JSON serialization of Param[Vector] in the pipeline API. It could be used for other purposes too. The schema is the same as `VectorUDT`. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#9751 from mengxr/SPARK-11766.
Pipeline and PipelineModel extend Readable and Writable. Persistence succeeds only when all stages are Writable.
Note: This PR reinstates tests for other read/write functionality. It should probably not get merged until [https://issues.apache.org/jira/browse/SPARK-11672] gets fixed.
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9674 from jkbradley/pipeline-io.
Use LibSVM data source rather than MLUtils.loadLibSVMFile to load DataFrame, include:
* Use libSVM data source for all example codes under examples/ml, and remove unused import.
* Use libSVM data source for user guides under ml-*** which were omitted by #8697.
* Fix bug: We should use ```sqlContext.read().format("libsvm").load(path)``` at Java side, but the API doc and user guides misuse as ```sqlContext.read.format("libsvm").load(path)```.
* Code cleanup.
mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9690 from yanboliang/spark-11723.
We set `sqlContext = null` in `afterAll`. However, this doesn't change `SQLContext.activeContext` and then `SQLContext.getOrCreate` might use the `SparkContext` from previous test suite and hence causes the error. This PR calls `clearActive` in `beforeAll` and `afterAll` to avoid using an old context from other test suites.
cc: yhuai
Author: Xiangrui Meng <meng@databricks.com>
Closes#9677 from mengxr/SPARK-11672.2.
Per discussion in the initial Pipelines LDA PR [https://github.com/apache/spark/pull/9513], we should make LDAModel abstract and create a LocalLDAModel. This code simplification should be done before the 1.6 release to ensure API compatibility in future releases.
CC feynmanliang mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9678 from jkbradley/lda-pipelines-2.
This causes compile failure with Scala 2.11. See https://issues.scala-lang.org/browse/SI-8813. (Jenkins won't test Scala 2.11. I tested compile locally.) JoshRosen
Author: Xiangrui Meng <meng@databricks.com>
Closes#9644 from mengxr/SPARK-11674.
org.apache.spark.ml.feature.Word2Vec.transform() very slow. we should not read broadcast every sentence.
Author: Yuming Wang <q79969786@gmail.com>
Author: yuming.wang <q79969786@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#9592 from 979969786/master.
This PR adds model save/load for spark.ml's LogisticRegressionModel. It also does minor refactoring of the default save/load classes to reuse code.
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9606 from jkbradley/logreg-io2.
This adds LDA to spark.ml, the Pipelines API. It follows the design doc in the JIRA: [https://issues.apache.org/jira/browse/SPARK-5565], with one major change:
* I eliminated doc IDs. These are not necessary with DataFrames since the user can add an ID column as needed.
Note: This will conflict with [https://github.com/apache/spark/pull/9484], but I'll try to merge [https://github.com/apache/spark/pull/9484] first and then rebase this PR.
CC: hhbyyh feynmanliang If you have a chance to make a pass, that'd be really helpful--thanks! Now that I'm done traveling & this PR is almost ready, I'll see about reviewing other PRs critical for 1.6.
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9513 from jkbradley/lda-pipelines.
Implementation of step capability for sliding window function in MLlib's RDD.
Though one can use current sliding window with step 1 and then filter every Nth window, it will take more time and space (N*data.count times more than needed). For example, below are the results for various windows and steps on 10M data points:
Window | Step | Time | Windows produced
------------ | ------------- | ---------- | ----------
128 | 1 | 6.38 | 9999873
128 | 10 | 0.9 | 999988
128 | 100 | 0.41 | 99999
1024 | 1 | 44.67 | 9998977
1024 | 10 | 4.74 | 999898
1024 | 100 | 0.78 | 99990
```
import org.apache.spark.mllib.rdd.RDDFunctions._
val rdd = sc.parallelize(1 to 10000000, 10)
rdd.count
val window = 1024
val step = 1
val t = System.nanoTime(); val windows = rdd.sliding(window, step); println(windows.count); println((System.nanoTime() - t) / 1e9)
```
Author: unknown <ulanov@ULANOV3.americas.hpqcorp.net>
Author: Alexander Ulanov <nashb@yandex.ru>
Author: Xiangrui Meng <meng@databricks.com>
Closes#5855 from avulanov/SPARK-7316-sliding.
Refactoring
* separated overwrite and param save logic in DefaultParamsWriter
* added sparkVersion to DefaultParamsWriter
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9587 from jkbradley/logreg-io.
jira: https://issues.apache.org/jira/browse/SPARK-11069
quotes from jira:
Tokenizer converts strings to lowercase automatically, but RegexTokenizer does not. It would be nice to add an option to RegexTokenizer to convert to lowercase. Proposal:
call the Boolean Param "toLowercase"
set default to false (so behavior does not change)
Actually sklearn converts to lowercase before tokenizing too
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#9092 from hhbyyh/tokenLower.
I implemented a hierarchical clustering algorithm again. This PR doesn't include examples, documentation and spark.ml APIs. I am going to send another PRs later.
https://issues.apache.org/jira/browse/SPARK-6517
- This implementation based on a bi-sectiong K-means clustering.
- It derives from the freeman-lab 's implementation
- The basic idea is not changed from the previous version. (#2906)
- However, It is 1000x faster than the previous version through parallel processing.
Thank you for your great cooperation, RJ Nowling(rnowling), Jeremy Freeman(freeman-lab), Xiangrui Meng(mengxr) and Sean Owen(srowen).
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Yu ISHIKAWA <yu-iskw@users.noreply.github.com>
Closes#5267 from yu-iskw/new-hierarchical-clustering.
The current pmml models generated do not specify the pmml version in its root node. This is a problem when using this pmml model in other tools because they expect the version attribute to be set explicitly. This fix adds the pmml version attribute to the generated pmml models and specifies its value as 4.2.
Author: fazlan-nazeem <fazlann@wso2.com>
Closes#9558 from fazlan-nazeem/master.
Expose R-like summary statistics in SparkR::glm for linear regression, the output of ```summary``` like
```Java
$DevianceResiduals
Min Max
-0.9509607 0.7291832
$Coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6765 0.2353597 7.123139 4.456124e-11
Sepal_Length 0.3498801 0.04630128 7.556598 4.187317e-12
Species_versicolor -0.9833885 0.07207471 -13.64402 0
Species_virginica -1.00751 0.09330565 -10.79796 0
```
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9561 from yanboliang/spark-11494.
Could jkbradley and davies review it?
- Create a wrapper class: `LDAModelWrapper` for `LDAModel`. Because we can't deal with the return value of`describeTopics` in Scala from pyspark directly. `Array[(Array[Int], Array[Double])]` is too complicated to convert it.
- Add `loadLDAModel` in `PythonMLlibAPI`. Since `LDAModel` in Scala is an abstract class and we need to call `load` of `DistributedLDAModel`.
[[SPARK-8467] Add LDAModel.describeTopics() in Python - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8467)
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#8643 from yu-iskw/SPARK-8467-2.
This PR implements the default save/load for non-meta estimators and transformers using the JSON serialization of param values. The saved metadata includes:
* class name
* uid
* timestamp
* paramMap
The save/load interface is similar to DataFrames. We use the current active context by default, which should be sufficient for most use cases.
~~~scala
instance.save("path")
instance.write.context(sqlContext).overwrite().save("path")
Instance.load("path")
~~~
The param handling is different from the design doc. We didn't save default and user-set params separately, and when we load it back, all parameters are user-set. This does cause issues. But it also cause other issues if we modify the default params.
TODOs:
* [x] Java test
* [ ] a follow-up PR to implement default save/load for all non-meta estimators and transformers
cc jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#9454 from mengxr/SPARK-11217.
https://issues.apache.org/jira/browse/SPARK-10116
This is really trivial, just happened to notice it -- if `XORShiftRandom.hashSeed` is really supposed to have random bits throughout (as the comment implies), it needs to do something for the conversion to `long`.
mengxr mkolod
Author: Imran Rashid <irashid@cloudera.com>
Closes#8314 from squito/SPARK-10116.
