980764f3c0
**This PR introduces an API + simple implementation for Latent Dirichlet Allocation (LDA).** The [design doc for this PR](https://docs.google.com/document/d/1kSsDqTeZMEB94Bs4GTd0mvdAmduvZSSkpoSfn-seAzo) has been updated since I initially posted it. In particular, see the API and Planning for the Future sections. * Settle on a public API which may eventually include: * more inference algorithms * more options / functionality * Have an initial easy-to-understand implementation which others may improve. * This is NOT intended to support every topic model out there. However, if there are suggestions for making this extensible or pluggable in the future, that could be nice, as long as it does not complicate the API or implementation too much. * This may not be very scalable currently. It will be important to check and improve accuracy. For correctness of the implementation, please check against the Asuncion et al. (2009) paper in the design doc. **Dependency: This makes MLlib depend on GraphX.** Files and classes: * LDA.scala (441 lines): * class LDA (main estimator class) * LDA.Document (text + document ID) * LDAModel.scala (266 lines) * abstract class LDAModel * class LocalLDAModel * class DistributedLDAModel * LDAExample.scala (245 lines): script to run LDA + a simple (private) Tokenizer * LDASuite.scala (144 lines) Data/model representation and algorithm: * Data/model: Uses GraphX, with term vertices + document vertices * Algorithm: EM, following [Asuncion, Welling, Smyth, and Teh. "On Smoothing and Inference for Topic Models." UAI, 2009.](http://arxiv-web3.library.cornell.edu/abs/1205.2662v1) * For more details, please see the description in the “DEVELOPERS NOTE” in LDA.scala Please refer to the JIRA for more discussion + the [design doc for this PR](https://docs.google.com/document/d/1kSsDqTeZMEB94Bs4GTd0mvdAmduvZSSkpoSfn-seAzo) Here, I list the main changes AFTER the design doc was posted. Design decisions: * logLikelihood() computes the log likelihood of the data and the current point estimate of parameters. This is different from the likelihood of the data given the hyperparameters, which would be harder to compute. I’d describe the current approach as more frequentist, whereas the harder approach would be more Bayesian. * The current API takes Documents as token count vectors. I believe there should be an extended API taking RDD[String] or RDD[Array[String]] in a future PR. I have sketched this out in the design doc (as well as handier versions of getTopics returning Strings). * Hyperparameters should be set differently for different inference/learning algorithms. See Asuncion et al. (2009) in the design doc for a good demonstration. I encourage good behavior via defaults and warning messages. Items planned for future PRs: * perplexity * API taking Strings * Should LDA be called LatentDirichletAllocation (and LDAModel be LatentDirichletAllocationModel)? * Pro: We may someday want LinearDiscriminantAnalysis. * Con: Very long names * Should LDA reside in clustering? Or do we want a sub-package? * mllib.topicmodel * mllib.clustering.topicmodel * Does the API seem reasonable and extensible? * Unit tests: * Should there be a test which checks a clustering results? E.g., train on a small, fake dataset with 2 very distinct topics/clusters, and ensure LDA finds those 2 topics/clusters. Does that sound useful or too flaky? This has not been tested much for scaling. I have run it on a laptop for 200 iterations on a 5MB dataset with 1000 terms and 5 topics. Running it for 500 iterations made it fail because of GC problems. I'm running larger scale tests & will put results here, but future PRs may need to improve the scaling. * dlwh for the initial implementation * + jegonzal for some code in the initial implementation * The many contributors towards topic model implementations in Spark which were referenced as a basis for this PR: akopich witgo yinxusen dlwh EntilZha jegonzal IlyaKozlov * Note: The plan is to include this full list in the authors if this PR gets merged. Please notify me if you prefer otherwise. CC: mengxr Authors: Joseph K. Bradley <joseph@databricks.com> Joseph Gonzalez <joseph.e.gonzalez@gmail.com> David Hall <david.lw.hall@gmail.com> Guoqiang Li <witgo@qq.com> Xiangrui Meng <meng@databricks.com> Pedro Rodriguez <pedro@snowgeek.org> Avanesov Valeriy <acopich@gmail.com> Xusen Yin <yinxusen@gmail.com> Closes #2388 Closes #4047 from jkbradley/davidhall-lda and squashes the following commits: 77e8814 [Joseph K. Bradley] small doc fix 5c74345 [Joseph K. Bradley] cleaned up doc based on code review 589728b [Joseph K. Bradley] Updates per code review. Main change was in LDAExample for faster vocab computation. Also updated PeriodicGraphCheckpointerSuite.scala to clean up checkpoint files at end e3980d2 [Joseph K. Bradley] cleaned up PeriodicGraphCheckpointerSuite.scala 74487e5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into davidhall-lda 4ae2a7d [Joseph K. Bradley] removed duplicate graphx dependency in mllib/pom.xml e391474 [Joseph K. Bradley] Removed LDATiming. Added PeriodicGraphCheckpointerSuite.scala. Small LDA cleanups. e8d8acf [Joseph K. Bradley] Added catch for BreakIterator exception. Improved preprocessing to reduce passes over data 1a231b4 [Joseph K. Bradley] fixed scalastyle 91aadfe [Joseph K. Bradley] Added Java-friendly run method to LDA. Added Java test suite for LDA. Changed LDAModel.describeTopics to return Java-friendly type b75472d [Joseph K. Bradley] merged improvements from LDATiming into LDAExample. Will remove LDATiming after done testing 993ca56 [Joseph K. Bradley] * Removed Document type in favor of (Long, Vector) * Changed doc ID restriction to be: id must be nonnegative and unique in the doc (instead of 0,1,2,...) * Add checks for valid ranges of eta, alpha * Rename “LearningState” to “EMOptimizer” * Renamed params: termSmoothing -> topicConcentration, topicSmoothing -> docConcentration * Also added aliases alpha, beta cb5a319 [Joseph K. Bradley] Added checkpointing to LDA * new class PeriodicGraphCheckpointer * params checkpointDir, checkpointInterval to LDA 43c1c40 [Joseph K. Bradley] small cleanup 0b90393 [Joseph K. Bradley] renamed LDA LearningState.collectTopicTotals to globalTopicTotals 77a2c85 [Joseph K. Bradley] Moved auto term,topic smoothing computation to get*Smoothing methods. Changed word to term in some places. Updated LDAExample to use default smoothing amounts. fb1e7b5 [Xiangrui Meng] minor 08d59a3 [Xiangrui Meng] reset spacing 9fe0b95 [Xiangrui Meng] optimize aggregateMessages cec0a9c [Xiangrui Meng] * -> *= 6cb11b0 [Xiangrui Meng] optimize computePTopic 9eb3d02 [Xiangrui Meng] + -> += 892530c [Xiangrui Meng] use axpy 45cc7f2 [Xiangrui Meng] mapPart -> flatMap ce53be9 [Joseph K. Bradley] fixed example name 75749e7 [Joseph K. Bradley] scala style fix 9f2a492 [Joseph K. Bradley] Unit tests and fixes for LDA, now ready for PR 377ebd9 [Joseph K. Bradley] separated LDA models into own file. more cleanups before PR 2d40006 [Joseph K. Bradley] cleanups before PR 2891e89 [Joseph K. Bradley] Prepped LDA main class for PR, but some cleanups remain 0cb7187 [Joseph K. Bradley] Added 3 files from dlwh LDA implementation |
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data/mllib | ||
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examples | ||
external | ||
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graphx | ||
mllib | ||
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CONTRIBUTING.md | ||
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README.md | ||
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tox.ini |
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run all automated tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.