Remove ScalaDoc that suggests describeTopics and topDocumentsPerTopic are approximate.
cc jkbradley
Author: Meihua Wu <meihuawu@umich.edu>
Closes#7858 from rotationsymmetry/SPARK-9530 and squashes the following commits:
b574923 [Meihua Wu] Remove ScalaDoc that suggests describeTopics and topDocumentsPerTopic are approximate.
jira: https://issues.apache.org/jira/browse/SPARK-8169
stop words: http://en.wikipedia.org/wiki/Stop_words
StopWordsRemover takes a string array column and outputs a string array column with all defined stop words removed. The transformer should also come with a standard set of stop words as default.
Currently I used a minimum stop words set since on some [case](http://nlp.stanford.edu/IR-book/html/htmledition/dropping-common-terms-stop-words-1.html), small set of stop words is preferred.
ASCII char has been tested, Yet I cannot check it in due to style check.
Further thought,
1. Maybe I should use OpenHashSet. Is it recommended?
2. Currently I leave the null in input array untouched, i.e. Array(null, null) => Array(null, null).
3. If the current stop words set looks too limited, any suggestion for replacement? We can have something similar to the one in [SKlearn](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/stop_words.py).
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#6742 from hhbyyh/stopwords and squashes the following commits:
fa959d8 [Yuhao Yang] separating udf
f190217 [Yuhao Yang] replace default list and other small fix
04403ab [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into stopwords
b3aa957 [Yuhao Yang] add stopWordsRemover
mengxr Extends PrefixSpan to non-temporal itemsets. Continues work by zhangjiajin
* Internal API uses List[Set[Int]] which is likely not efficient; will need to refactor during QA
Closes#7646
Author: zhangjiajin <zhangjiajin@huawei.com>
Author: Feynman Liang <fliang@databricks.com>
Author: zhang jiajin <zhangjiajin@huawei.com>
Closes#7818 from feynmanliang/SPARK-8999-nonTemporal and squashes the following commits:
4ded81d [Feynman Liang] Replace all filters to filter nonempty
350e67e [Feynman Liang] Code review feedback
03156ca [Feynman Liang] Fix tests, drop delimiters at boundaries of sequences
d1fe0ed [Feynman Liang] Remove comments
86ca4e5 [Feynman Liang] Fix style
7c7bf39 [Feynman Liang] Fixed itemSet sequences
6073b10 [Feynman Liang] Basic itemset functionality, failing test
1a7fb48 [Feynman Liang] Add delimiter to results
5db00aa [Feynman Liang] Working for items, not itemsets
6787716 [Feynman Liang] Working on temporal sequences
f1114b9 [Feynman Liang] Add -1 delimiter
00fe756 [Feynman Liang] Reset base files for rebase
f486dcd [zhangjiajin] change maxLocalProjDBSize and fix a bug (remove -3 from frequent items).
60a0b76 [zhangjiajin] fixed a scala style error.
740c203 [zhangjiajin] fixed a scala style error.
5785cb8 [zhangjiajin] support non-temporal sequence
a5d649d [zhangjiajin] restore original version
09dc409 [zhangjiajin] Merge branch 'master' of https://github.com/apache/spark into multiItems_2
ae8c02d [zhangjiajin] Fixed some Scala style errors.
216ab0c [zhangjiajin] Support non-temporal sequence in PrefixSpan
b572f54 [zhangjiajin] initialize file before rebase.
f06772f [zhangjiajin] fix a scala style error.
a7e50d4 [zhangjiajin] Add feature: Collect enough frequent prefixes before projection in PrefixSpan.
c1d13d0 [zhang jiajin] Delete PrefixspanSuite.scala
d9d8137 [zhang jiajin] Delete Prefixspan.scala
c6ceb63 [zhangjiajin] Add new algorithm PrefixSpan and test file.
It is useful to convert the encoded indices back to their string representation for result inspection. We can add a function which creates an inverse transformation.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#6339 from holdenk/SPARK-7446-inverse-transform-for-string-indexer and squashes the following commits:
7cdf915 [Holden Karau] scala style comment fix
b9cffb6 [Holden Karau] Update the labels param to have the metadata note
6a38edb [Holden Karau] Setting the default needs to come after the value gets defined
9e241d8 [Holden Karau] use Array.empty
21c8cfa [Holden Karau] Merge branch 'master' into SPARK-7446-inverse-transform-for-string-indexer
64dd3a3 [Holden Karau] Merge branch 'master' into SPARK-7446-inverse-transform-for-string-indexer
4f06c59 [Holden Karau] Fix comment styles, use empty array as the default, etc.
a60c0e3 [Holden Karau] CR feedback (remove old constructor, add a note about use of setLabels)
1987b95 [Holden Karau] Use default copy
71e8d66 [Holden Karau] Make labels a local param for StringIndexerInverse
8450d0b [Holden Karau] Use the labels param in StringIndexerInverse
7464019 [Holden Karau] Add a labels param
868b1a9 [Holden Karau] Update scaladoc since we don't have labelsCol anymore
5aa38bf [Holden Karau] Add an inverse test using only meta data, pass labels when calling inverse method
f3e0c64 [Holden Karau] CR feedback
ebed932 [Holden Karau] Add Experimental tag and some scaladocs. Also don't require that the inputCol has the metadata on it, instead have the labelsCol specified when creating the inverse.
03ebf95 [Holden Karau] Add explicit type for invert function
ecc65e0 [Holden Karau] Read the metadata correctly, use the array, pass the test
a42d773 [Holden Karau] Fix test to supply cols as per new invert method
16cc3c3 [Holden Karau] Add an invert method
d4bcb20 [Holden Karau] Make the inverse string indexer into a transformer (still needs test updates but compiles)
e8bf3ad [Holden Karau] Merge branch 'master' into SPARK-7446-inverse-transform-for-string-indexer
c3fdee1 [Holden Karau] Some WIP refactoring based on jkbradley's CR feedback. Definite work-in-progress
557bef8 [Holden Karau] Instead of using a private inverse transform, add an invert function so we can use it in a pipeline
88779c1 [Holden Karau] fix long line
78b28c1 [Holden Karau] Finish reverse part and add a test :)
bb16a6a [Holden Karau] Some progress
This PR adds a `MapData` as internal representation of map type in Spark SQL, and provides a default implementation with just 2 `ArrayData`.
After that, we have specialized getters for all internal type, so I removed generic getter in `ArrayData` and added specialized `toArray` for it.
Also did some refactor and cleanup for `InternalRow` and its subclasses.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7799 from cloud-fan/map-data and squashes the following commits:
77d482f [Wenchen Fan] fix python
e8f6682 [Wenchen Fan] skip MapData equality check in HiveInspectorSuite
40cc9db [Wenchen Fan] add toString
6e06ec9 [Wenchen Fan] some more cleanup
a90aca1 [Wenchen Fan] add MapData
Adds `alpha` (document-topic Dirichlet parameter) hyperparameter optimization to `OnlineLDAOptimizer` following Huang: Maximum Likelihood Estimation of Dirichlet Distribution Parameters. Also introduces a private `setSampleWithReplacement` to `OnlineLDAOptimizer` for unit testing purposes.
Author: Feynman Liang <fliang@databricks.com>
Closes#7836 from feynmanliang/SPARK-8936-alpha-optimize and squashes the following commits:
4bef484 [Feynman Liang] Documentation improvements
c3c6c1d [Feynman Liang] Fix docs
151e859 [Feynman Liang] Fix style
fa77518 [Feynman Liang] Hyperparameter optimization
Make NaiveBayesModel support predicting class probabilities, inherit from ProbabilisticClassificationModel.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7672 from yanboliang/spark-9308 and squashes the following commits:
25e224c [Yanbo Liang] raw2probabilityInPlace should operate in-place
3ee56d6 [Yanbo Liang] change predictRaw and raw2probabilityInPlace
c07e7a2 [Yanbo Liang] ml.NaiveBayesModel support predicting class probabilities
Add topDocumentsPerTopic to DistributedLDAModel.
Add ScalaDoc and unit tests.
Author: Meihua Wu <meihuawu@umich.edu>
Closes#7769 from rotationsymmetry/SPARK-9246 and squashes the following commits:
1029e79c [Meihua Wu] clean up code comments
a023b82 [Meihua Wu] Update tests to use Long for doc index.
91e5998 [Meihua Wu] Use Long for doc index.
b9f70cf [Meihua Wu] Revise topDocumentsPerTopic
26ff3f6 [Meihua Wu] Add topDocumentsPerTopic, scala doc and unit tests
jkbradley Exposes `bound` (variational log likelihood bound) through public API as `logLikelihood`. Also adds unit tests, some DRYing of `LDASuite`, and includes unit tests mentioned in #7760
Author: Feynman Liang <fliang@databricks.com>
Closes#7801 from feynmanliang/SPARK-9481-logLikelihood and squashes the following commits:
6d1b2c9 [Feynman Liang] Negate perplexity definition
5f62b20 [Feynman Liang] Add logLikelihood
Decision tree support predict class probabilities.
Implement the prediction probabilities function referred the old DecisionTree API and the [sklean API](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/tree.py#L593).
I make the DecisionTreeClassificationModel inherit from ProbabilisticClassificationModel, make the predictRaw to return the raw counts vector and make raw2probabilityInPlace/predictProbability return the probabilities for each prediction.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7694 from yanboliang/spark-6885 and squashes the following commits:
08d5b7f [Yanbo Liang] fix ImpurityStats null parameters and raw2probabilityInPlace sum = 0 issue
2174278 [Yanbo Liang] solve merge conflicts
7e90ba8 [Yanbo Liang] fix typos
33ae183 [Yanbo Liang] fix annotation
ff043d3 [Yanbo Liang] raw2probabilityInPlace should operate in-place
c32d6ce [Yanbo Liang] optimize calculateImpurityStats function again
6167fb0 [Yanbo Liang] optimize calculateImpurityStats function
fbbe2ec [Yanbo Liang] eliminate duplicated struct and code
beb1634 [Yanbo Liang] try to eliminate impurityStats for each LearningNode
99e8943 [Yanbo Liang] code optimization
5ec3323 [Yanbo Liang] implement InformationGainAndImpurityStats
227c91b [Yanbo Liang] refactor LearningNode to store ImpurityCalculator
d746ffc [Yanbo Liang] decision tree support predict class probabilities
jira: https://issues.apache.org/jira/browse/SPARK-9231
Helper method in DistributedLDAModel of this form:
```
/**
* For each document, return the top k weighted topics for that document.
* return RDD of (doc ID, topic indices, topic weights)
*/
def topTopicsPerDocument(k: Int): RDD[(Long, Array[Int], Array[Double])]
```
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#7785 from hhbyyh/topTopicsPerdoc and squashes the following commits:
30ad153 [Yuhao Yang] small fix
fd24580 [Yuhao Yang] add topTopics per document to DistributedLDAModel
This pull request contains the following feature for ML:
- Multilayer Perceptron classifier
This implementation is based on our initial pull request with bgreeven: https://github.com/apache/spark/pull/1290 and inspired by very insightful suggestions from mengxr and witgo (I would like to thank all other people from the mentioned thread for useful discussions). The original code was extensively tested and benchmarked. Since then, I've addressed two main requirements that prevented the code from merging into the main branch:
- Extensible interface, so it will be easy to implement new types of networks
- Main building blocks are traits `Layer` and `LayerModel`. They are used for constructing layers of ANN. New layers can be added by extending the `Layer` and `LayerModel` traits. These traits are private in this release in order to save path to improve them based on community feedback
- Back propagation is implemented in general form, so there is no need to change it (optimization algorithm) when new layers are implemented
- Speed and scalability: this implementation has to be comparable in terms of speed to the state of the art single node implementations.
- The developed benchmark for large ANN shows that the proposed code is on par with C++ CPU implementation and scales nicely with the number of workers. Details can be found here: https://github.com/avulanov/ann-benchmark
- DBN and RBM by witgo https://github.com/witgo/spark/tree/ann-interface-gemm-dbn
- Dropout https://github.com/avulanov/spark/tree/ann-interface-gemm
mengxr and dbtsai kindly agreed to perform code review.
Author: Alexander Ulanov <nashb@yandex.ru>
Author: Bert Greevenbosch <opensrc@bertgreevenbosch.nl>
Closes#7621 from avulanov/SPARK-2352-ann and squashes the following commits:
4806b6f [Alexander Ulanov] Addressing reviewers comments.
a7e7951 [Alexander Ulanov] Default blockSize: 100. Added documentation to blockSize parameter and DataStacker class
f69bb3d [Alexander Ulanov] Addressing reviewers comments.
374bea6 [Alexander Ulanov] Moving ANN to ML package. GradientDescent constructor is now spark private.
43b0ae2 [Alexander Ulanov] Addressing reviewers comments. Adding multiclass test.
9d18469 [Alexander Ulanov] Addressing reviewers comments: unnecessary copy of data in predict
35125ab [Alexander Ulanov] Style fix in tests
e191301 [Alexander Ulanov] Apache header
a226133 [Alexander Ulanov] Multilayer Perceptron regressor and classifier
support ml.NaiveBayes for Python
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7568 from yanboliang/spark-9214 and squashes the following commits:
5ee3fd6 [Yanbo Liang] fix typos
3ecd046 [Yanbo Liang] fix typos
f9c94d1 [Yanbo Liang] change lambda_ to smoothing and fix other issues
180452a [Yanbo Liang] fix typos
7dda1f4 [Yanbo Liang] support ml.NaiveBayes for Python
Improve error message when number of examples is less than arity of high-arity categorical feature
CC jkbradley is this about what you had in mind? I know it's a starter, but was on my list to close out in the short term.
Author: Sean Owen <sowen@cloudera.com>
Closes#7800 from srowen/SPARK-9077 and squashes the following commits:
b8f6cdb [Sean Owen] Improve error message when number of examples is less than arity of high-arity categorical feature
Add checkpointing to GradientBoostedTrees, GBTClassifier, GBTRegressor
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7804 from jkbradley/gbt-checkpoint3 and squashes the following commits:
3fbd7ba [Joseph K. Bradley] tiny fix
b3e160c [Joseph K. Bradley] unset checkpoint dir after test
9cc3a04 [Joseph K. Bradley] added checkpointing to GBTs
Author: martinzapletal <zapletal-martin@email.cz>
Closes#7517 from zapletal-martin/SPARK-8671-isotonic-regression-api and squashes the following commits:
8c435c1 [martinzapletal] Review https://github.com/apache/spark/pull/7517 feedback update.
bebbb86 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-8671-isotonic-regression-api
b68efc0 [martinzapletal] Added tests for param validation.
07c12bd [martinzapletal] Comments and refactoring.
834fcf7 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-8671-isotonic-regression-api
b611fee [martinzapletal] SPARK-8671. Added first version of isotonic regression to pipeline API
See https://issues.apache.org/jira/browse/SPARK-9479 for the failure cause.
The PR includes the following changes:
1. Make ReceiverTrackerSuite create StreamingContext in the test body.
2. Fix places that don't stop StreamingContext. I verified no SparkContext was stopped in the shutdown hook locally after this fix.
3. Fix an issue that `ReceiverTracker.endpoint` may be null.
4. Make sure stopping SparkContext in non-main thread won't fail other tests.
Author: zsxwing <zsxwing@gmail.com>
Closes#7797 from zsxwing/fix-ReceiverTrackerSuite and squashes the following commits:
3a4bb98 [zsxwing] Fix another potential NPE
d7497df [zsxwing] Fix ReceiverTrackerSuite; make sure StreamingContext in tests is closed
jkbradley Changes the current hacky string-comparison for vector compares.
Author: Feynman Liang <fliang@databricks.com>
Closes#7775 from feynmanliang/SPARK-9454-ldasuite-vector-compare and squashes the following commits:
bd91a82 [Feynman Liang] Remove println
905c76e [Feynman Liang] Fix string compare in distributed EM
2f24c13 [Feynman Liang] Improve LDASuite tests
Check that SparseVector size is at least as big as the number of indices/values provided. And add tests for constructor checks.
CC MechCoder jkbradley -- I am not sure if a change needs to also happen in the Python API? I didn't see it had any similar checks to begin with, but I don't know it well.
Author: Sean Owen <sowen@cloudera.com>
Closes#7794 from srowen/SPARK-9277 and squashes the following commits:
e8dc31e [Sean Owen] Fix scalastyle
6ffe34a [Sean Owen] Check that SparseVector size is at least as big as the number of indices/values provided. And add tests for constructor checks.
Add unit tests for running LDA with empty documents.
Both EMLDAOptimizer and OnlineLDAOptimizer are tested.
feynmanliang
Author: Meihua Wu <meihuawu@umich.edu>
Closes#7620 from rotationsymmetry/SPARK-9225 and squashes the following commits:
3ed7c88 [Meihua Wu] Incorporate reviewer's further comments
f9432e8 [Meihua Wu] Incorporate reviewer's comments
8e1b9ec [Meihua Wu] Merge remote-tracking branch 'upstream/master' into SPARK-9225
ad55665 [Meihua Wu] Add unit tests for running LDA with empty documents
A trivial fix for the comments of RegexTokenizer.
Maybe this is too small, yet I just noticed it and think it can be quite misleading. I can create a jira if necessary.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#7791 from hhbyyh/docFix and squashes the following commits:
cdf2542 [Yuhao Yang] minor fix on tokenizer doc
Continuation of work by zhangjiajin
Closes#7412
Author: zhangjiajin <zhangjiajin@huawei.com>
Author: Feynman Liang <fliang@databricks.com>
Author: zhang jiajin <zhangjiajin@huawei.com>
Closes#7783 from feynmanliang/SPARK-8998-improve-distributed and squashes the following commits:
a61943d [Feynman Liang] Collect small patterns to local
4ddf479 [Feynman Liang] Parallelize freqItemCounts
ad23aa9 [zhang jiajin] Merge pull request #1 from feynmanliang/SPARK-8998-collectBeforeLocal
87fa021 [Feynman Liang] Improve extend prefix readability
c2caa5c [Feynman Liang] Readability improvements and comments
1235cfc [Feynman Liang] Use Iterable[Array[_]] over Array[Array[_]] for database
da0091b [Feynman Liang] Use lists for prefixes to reuse data
cb2a4fc [Feynman Liang] Inline code for readability
01c9ae9 [Feynman Liang] Add getters
6e149fa [Feynman Liang] Fix splitPrefixSuffixPairs
64271b3 [zhangjiajin] Modified codes according to comments.
d2250b7 [zhangjiajin] remove minPatternsBeforeLocalProcessing, add maxSuffixesBeforeLocalProcessing.
b07e20c [zhangjiajin] Merge branch 'master' of https://github.com/apache/spark into CollectEnoughPrefixes
095aa3a [zhangjiajin] Modified the code according to the review comments.
baa2885 [zhangjiajin] Modified the code according to the review comments.
6560c69 [zhangjiajin] Add feature: Collect enough frequent prefixes before projection in PrefixeSpan
a8fde87 [zhangjiajin] Merge branch 'master' of https://github.com/apache/spark
4dd1c8a [zhangjiajin] initialize file before rebase.
078d410 [zhangjiajin] fix a scala style error.
22b0ef4 [zhangjiajin] Add feature: Collect enough frequent prefixes before projection in PrefixSpan.
ca9c4c8 [zhangjiajin] Modified the code according to the review comments.
574e56c [zhangjiajin] Add new object LocalPrefixSpan, and do some optimization.
ba5df34 [zhangjiajin] Fix a Scala style error.
4c60fb3 [zhangjiajin] Fix some Scala style errors.
1dd33ad [zhangjiajin] Modified the code according to the review comments.
89bc368 [zhangjiajin] Fixed a Scala style error.
a2eb14c [zhang jiajin] Delete PrefixspanSuite.scala
951fd42 [zhang jiajin] Delete Prefixspan.scala
575995f [zhangjiajin] Modified the code according to the review comments.
91fd7e6 [zhangjiajin] Add new algorithm PrefixSpan and test file.
PeriodicGraphCheckpointer was introduced for Latent Dirichlet Allocation (LDA), but it was meant to be generalized to work with Graphs, RDDs, and other data structures based on RDDs. This PR generalizes it.
For those who are not familiar with the periodic checkpointer, it tries to automatically handle persisting/unpersisting and checkpointing/removing checkpoint files in a lineage of RDD-based objects.
I need it generalized to use with GradientBoostedTrees [https://issues.apache.org/jira/browse/SPARK-6684]. It should be useful for other iterative algorithms as well.
Changes I made:
* Copied PeriodicGraphCheckpointer to PeriodicCheckpointer.
* Within PeriodicCheckpointer, I created abstract methods for the basic operations (checkpoint, persist, etc.).
* The subclasses for Graphs and RDDs implement those abstract methods.
* I copied the test suite for the graph checkpointer and made tiny modifications to make it work for RDDs.
To review this PR, I recommend doing 2 diffs:
(1) diff between the old PeriodicGraphCheckpointer.scala and the new PeriodicCheckpointer.scala
(2) diff between the 2 test suites
CCing andrewor14 in case there are relevant changes to checkpointing.
CCing feynmanliang in case you're interested in learning about checkpointing.
CCing mengxr for final OK.
Thanks all!
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7728 from jkbradley/gbt-checkpoint and squashes the following commits:
d41902c [Joseph K. Bradley] Oops, forgot to update an extra time in the checkpointer tests, after the last commit. I'll fix that. I'll also make some of the checkpointer methods protected, which I should have done before.
32b23b8 [Joseph K. Bradley] fixed usage of checkpointer in lda
0b3dbc0 [Joseph K. Bradley] Changed checkpointer constructor not to take initial data.
568918c [Joseph K. Bradley] Generalized PeriodicGraphCheckpointer to PeriodicCheckpointer, with subclasses for RDDs and Graphs.
jira: https://issues.apache.org/jira/browse/SPARK-7368
Add QR decomposition for RowMatrix.
I'm not sure what's the blueprint about the distributed Matrix from community and whether this will be a desirable feature , so I sent a prototype for discussion. I'll go on polish the code and provide ut and performance statistics if it's acceptable.
The implementation refers to the [paper: https://www.cs.purdue.edu/homes/dgleich/publications/Benson%202013%20-%20direct-tsqr.pdf]
Austin R. Benson, David F. Gleich, James Demmel. "Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures", 2013 IEEE International Conference on Big Data, which is a stable algorithm with good scalability.
Currently I tried it on a 400000 * 500 rowMatrix (16 partitions) and it can bring down the computation time from 8.8 mins (using breeze.linalg.qr.reduced) to 2.6 mins on a 4 worker cluster. I think there will still be some room for performance improvement.
Any trial and suggestion is welcome.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#5909 from hhbyyh/qrDecomposition and squashes the following commits:
cec797b [Yuhao Yang] remove unnecessary qr
0fb1012 [Yuhao Yang] hierarchy R computing
3fbdb61 [Yuhao Yang] update qr to indirect and add ut
0d913d3 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into qrDecomposition
39213c3 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into qrDecomposition
c0fc0c7 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into qrDecomposition
39b0b22 [Yuhao Yang] initial draft for discussion
jkbradley MechCoder
Resolves blocking issue for SPARK-6793. Please review after #7705 is merged.
Author: Feynman Liang <fliang@databricks.com>
Closes#7757 from feynmanliang/SPARK-9940-localSaveLoad and squashes the following commits:
d0d8cf4 [Feynman Liang] Fix thisClassName
0f30109 [Feynman Liang] Fix tests after changing LDAModel public API
dc61981 [Feynman Liang] Add hyperparams to LocalLDAModel save/load
Implement the classification trait for RandomForestClassifiers. The plan is to use this in the future to providing thresholding for RandomForestClassifiers (as well as other classifiers that implement that trait).
Author: Holden Karau <holden@pigscanfly.ca>
Closes#7432 from holdenk/SPARK-9016-make-random-forest-classifiers-implement-classification-trait and squashes the following commits:
bf22fa6 [Holden Karau] Add missing imports for testing suite
e948f0d [Holden Karau] Check the prediction generation from rawprediciton
25320c3 [Holden Karau] Don't supply numClasses when not needed, assert model classes are as expected
1a67e04 [Holden Karau] Use old decission tree stuff instead
673e0c3 [Holden Karau] Merge branch 'master' into SPARK-9016-make-random-forest-classifiers-implement-classification-trait
0d15b96 [Holden Karau] FIx typo
5eafad4 [Holden Karau] add a constructor for rootnode + num classes
fc6156f [Holden Karau] scala style fix
2597915 [Holden Karau] take num classes in constructor
3ccfe4a [Holden Karau] Merge in master, make pass numClasses through randomforest for training
222a10b [Holden Karau] Increase numtrees to 3 in the python test since before the two were equal and the argmax was selecting the last one
16aea1c [Holden Karau] Make tests match the new models
b454a02 [Holden Karau] Make the Tree classifiers extends the Classifier base class
77b4114 [Holden Karau] Import vectors lib
Author: Bimal Tandel <bimal@bimal-MBP.local>
Closes#7730 from BimalTandel/branch_spark_8921 and squashes the following commits:
3ea230a [Bimal Tandel] Spark 8921 add @since tags
This PR introduces save / load for GMM's in python API.
Also I refactored `GaussianMixtureModel` and inherited it from `JavaModelWrapper` with model being `GaussianMixtureModelWrapper`, a wrapper which provides convenience methods to `GaussianMixtureModel` (due to serialization and deserialization issues) and I moved the creation of gaussians to the scala backend.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7617 from MechCoder/python_gmm_save_load and squashes the following commits:
9c305aa [MechCoder] [SPARK-7105] [PySpark] [MLlib] Support model save/load in GMM
Author: vinodkc <vinod.kc.in@gmail.com>
Closes#7325 from vinodkc/add_since_mllib.recommendation and squashes the following commits:
93156f2 [vinodkc] Changed 0.8.0 to 0.9.1
c413350 [vinodkc] Added @since
This adds StringType feature support via OneHotEncoder. As part of this task it was necessary to change RFormula to an Estimator, so that factor levels could be determined from the training dataset.
Not sure if I am using uids correctly here, would be good to get reviewer help on that.
cc mengxr
Umbrella design doc: https://docs.google.com/document/d/10NZNSEurN2EdWM31uFYsgayIPfCFHiuIu3pCWrUmP_c/edit#
Author: Eric Liang <ekl@databricks.com>
Closes#7574 from ericl/string-features and squashes the following commits:
f99131a [Eric Liang] comments
0bf3c26 [Eric Liang] update docs
c302a2c [Eric Liang] fix tests
9d1ac82 [Eric Liang] Merge remote-tracking branch 'upstream/master' into string-features
e713da3 [Eric Liang] comments
4d79193 [Eric Liang] revert to seq + distinct
169a085 [Eric Liang] tweak functional test
a230a47 [Eric Liang] Merge branch 'master' into string-features
72bd6f3 [Eric Liang] fix merge
d841cec [Eric Liang] Merge branch 'master' into string-features
5b2c4a2 [Eric Liang] Mon Jul 20 18:45:33 PDT 2015
b01c7c5 [Eric Liang] add test
8a637db [Eric Liang] encoder wip
a1d03f4 [Eric Liang] refactor into estimator
Use Vector.argmax call instead of converting to dense vector before calculating predictions.
Author: George Dittmar <georgedittmar@gmail.com>
Closes#7670 from GeorgeDittmar/sprk-7423 and squashes the following commits:
e796747 [George Dittmar] Changing ClassificationModel and ProbabilisticClassificationModel to use Vector.argmax instead of converting to DenseVector
jira: https://issues.apache.org/jira/browse/SPARK-9337
Word2Vec should throw exception when vocabulary is empty
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#7660 from hhbyyh/ut4Word2vec and squashes the following commits:
17a18cb [Yuhao Yang] add ut for word2vec
Currently UnsafeRow cannot support a generic getter. However, if the data type is known, we can support a generic getter.
Author: Reynold Xin <rxin@databricks.com>
Closes#7666 from rxin/generic-getter-with-datatype and squashes the following commits:
ee2874c [Reynold Xin] Add a default implementation for getStruct.
1e109a0 [Reynold Xin] [SPARK-9350][SQL] Introduce an InternalRow generic getter that requires a DataType.
033ee88 [Reynold Xin] Removed getAs in non test code.
Word2Vec used to convert from an Array[Float] representation to a Map[String, Array[Float]] and then back to an Array[Float] through Word2VecModel.
This prevents this conversion while still supporting the older method of supplying a Map.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#5748 from MechCoder/spark-7045 and squashes the following commits:
e308913 [MechCoder] move docs
5703116 [MechCoder] minor
fa04313 [MechCoder] style fixes
b1d61c4 [MechCoder] better errors and tests
3b32c8c [MechCoder] [SPARK-7045] Avoid intermediate representation when creating model
This makes it easier to test all the class variables of the DistributedLDAmodel.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7573 from MechCoder/lda_test and squashes the following commits:
2f1a293 [MechCoder] [SPARK-9222] [MLlib] Make class instantiation variables in DistributedLDAModel private[clustering]
I also changed InternalRow's size/length function to numFields, to make it more obvious that it is not about bytes, but the number of fields.
Author: Reynold Xin <rxin@databricks.com>
Closes#7626 from rxin/internalRow and squashes the following commits:
e124daf [Reynold Xin] Fixed test case.
805ceb7 [Reynold Xin] Commented out the failed test suite.
f8a9ca5 [Reynold Xin] Fixed more bugs. Still at least one more remaining.
76d9081 [Reynold Xin] Fixed data sources.
7807f70 [Reynold Xin] Fixed DataFrameSuite.
cb60cd2 [Reynold Xin] Code review & small bug fixes.
0a2948b [Reynold Xin] Fixed style.
3280d03 [Reynold Xin] [SPARK-9285][SQL] Remove InternalRow's inheritance from Row.
The base classifier input and output columns are ignored in favor of the ones specified in OneVsRest.
Author: Ram Sriharsha <rsriharsha@hw11853.local>
Closes#6631 from harsha2010/SPARK-8092 and squashes the following commits:
6591dc6 [Ram Sriharsha] add documentation for params
b7024b1 [Ram Sriharsha] cleanup
f0e2bfb [Ram Sriharsha] merge with master
108d3d7 [Ram Sriharsha] merge with master
4f74126 [Ram Sriharsha] Allow label/ features columns to be configurable
Romove Decimal.Unlimited (change to support precision up to 38, to match with Hive and other databases).
In order to keep backward source compatibility, Decimal.Unlimited is still there, but change to Decimal(38, 18).
If no precision and scale is provide, it's Decimal(10, 0) as before.
Author: Davies Liu <davies@databricks.com>
Closes#7605 from davies/decimal_unlimited and squashes the following commits:
aa3f115 [Davies Liu] fix tests and style
fb0d20d [Davies Liu] address comments
bfaae35 [Davies Liu] fix style
df93657 [Davies Liu] address comments and clean up
06727fd [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_unlimited
4c28969 [Davies Liu] fix tests
8d783cc [Davies Liu] fix tests
788631c [Davies Liu] fix double with decimal in Union/except
1779bde [Davies Liu] fix scala style
c9c7c78 [Davies Liu] remove Decimal.Unlimited
Removed varargs annotation from Params.setDefault taking multiple params.
Though varargs is technically correct, it often requires that developers do clean assembly, rather than (not clean) assembly, which is a nuisance during development.
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#7604 from jkbradley/params-setdefault-varargs and squashes the following commits:
6016dc6 [Joseph K. Bradley] removed varargs annotation from Params.setDefault taking multiple params
Spark has an option called spark.localExecution.enabled; according to the docs:
> Enables Spark to run certain jobs, such as first() or take() on the driver, without sending tasks to the cluster. This can make certain jobs execute very quickly, but may require shipping a whole partition of data to the driver.
This feature ends up adding quite a bit of complexity to DAGScheduler, especially in the runLocallyWithinThread method, but as far as I know nobody uses this feature (I searched the mailing list and haven't seen any recent mentions of the configuration nor stacktraces including the runLocally method). As a step towards scheduler complexity reduction, I propose that we remove this feature and all code related to it for Spark 1.5.
This pull request simply brings #7484 up to date.
Author: Josh Rosen <joshrosen@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#7585 from rxin/remove-local-exec and squashes the following commits:
84bd10e [Reynold Xin] Python fix.
1d9739a [Reynold Xin] Merge pull request #7484 from JoshRosen/remove-localexecution
eec39fa [Josh Rosen] Remove allowLocal(); deprecate user-facing uses of it.
b0835dc [Josh Rosen] Remove local execution code in DAGScheduler
8975d96 [Josh Rosen] Remove local execution tests.
ffa8c9b [Josh Rosen] Remove documentation for configuration
I've seen a few cases in the past few weeks that the compiler is throwing warnings that are caused by legitimate bugs. This patch upgrades warnings to errors, except deprecation warnings.
Note that ideally we should be able to mark deprecation warnings as errors as well. However, due to the lack of ability to suppress individual warning messages in the Scala compiler, we cannot do that (since we do need to access deprecated APIs in Hadoop).
Most of the work are done by ericl.
Author: Reynold Xin <rxin@databricks.com>
Author: Eric Liang <ekl@databricks.com>
Closes#7598 from rxin/warnings and squashes the following commits:
beb311b [Reynold Xin] Fixed tests.
542c031 [Reynold Xin] Fixed one more warning.
87c354a [Reynold Xin] Fixed all non-deprecation warnings.
78660ac [Eric Liang] first effort to fix warnings
- [X] Added TrainValidationSplit for hyper-parameter tuning. It randomly splits the input dataset into train and validation and use evaluation metric on the validation set to select the best model. It should be similar to CrossValidator, but simpler and less expensive.
- [X] Simplified replacement of https://github.com/apache/spark/pull/6996
Author: martinzapletal <zapletal-martin@email.cz>
Closes#7337 from zapletal-martin/SPARK-8484-TrainValidationSplit and squashes the following commits:
cafc949 [martinzapletal] Review comments https://github.com/apache/spark/pull/7337.
511b398 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-8484-TrainValidationSplit
f4fc9c4 [martinzapletal] SPARK-8484 Resolved feedback to https://github.com/apache/spark/pull/7337
00c4f5a [martinzapletal] SPARK-8484. Styling.
d699506 [martinzapletal] SPARK-8484. Styling.
93ed2ee [martinzapletal] Styling.
3bc1853 [martinzapletal] SPARK-8484. Styling.
2aa6f43 [martinzapletal] SPARK-8484. Added TrainValidationSplit for hyper-parameter tuning. It randomly splits the input dataset into train and validation and use evaluation metric on the validation set to select the best model.
21662eb [martinzapletal] SPARK-8484. Added TrainValidationSplit for hyper-parameter tuning. It randomly splits the input dataset into train and validation and use evaluation metric on the validation set to select the best model.
There are a few memory limits that people hit often and that we could
make higher, especially now that memory sizes have grown.
- spark.akka.frameSize: This defaults at 10 but is often hit for map
output statuses in large shuffles. This memory is not fully allocated
up-front, so we can just make this larger and still not affect jobs
that never sent a status that large. We increase it to 128.
- spark.executor.memory: Defaults at 512m, which is really small. We
increase it to 1g.
Author: Matei Zaharia <matei@databricks.com>
Closes#7586 from mateiz/configs and squashes the following commits:
ce0038a [Matei Zaharia] [SPARK-9244] Increase some memory defaults
