Spark的39个机器学习库

jopen 7年前

Apache Spark itself

1. MLlib

AMPLab

Spark originally came out of Berkeley AMPLab and even today AMPLab projects, even though they are not in Apache Spark Foundation, enjoy a status a bit over your everyday github project.

ML Base

Spark's own MLLib forms the bottom layer of the three-layer ML Base, with MLI being the middle layer and ML Optimizer being the most abstract layer.

2. MLI

3. ML Optimizer (aka Ghostface)

Ghostware was described in 2014 but never released. Of the 39 machine learning libraries, this is the only one that is vaporware, and is included only due to its AMPLab and ML Base status.

Other than ML Base

4. Splash

A recent project from June, 2015, this set of stochastic learning algorithms claims 25x - 75x faster performance than Spark MLlib on Stochastic Gradient Descent (SGD). Plus it's an AMPLab project that begins with the letters "sp", so it's worth watching.

5. Keystone ML

Brought machine learning pipelines to Spark, but pipelines have matured in recent versions of Spark. Also promises some computer vision capability, but there are limitations I previously blogged about.

6. Velox

A server to manage a large collection of machine learning models.

7. CoCoA

Faster machine learning on Spark by optimizing communication patterns and shuffles, as described in the paper Communication-Efficient Distributed Dual Coordinate Ascent

Frameworks

GPU-based

8. DeepLearning4j


I previously blogged DeepLearning4j Adds Spark GPU Support

9. Elephas

Brand new and frankly why I started this list for this blog post. Provides an interface to Keras.

Non-GPU-based

10. DistML

Parameter server for model-parallel rather than data-parallel (as Spark's MLlib is).

11. Aerosolve

From Airbnb, used in their automated pricing

12. Zen

Logistic regression, LDA, Factorization machines, Neural Network, Restricted Boltzmann Machines

13. Distributed Data Frame

Similar to Spark DataFrames, but agnostic to engine (i.e. will run on engines other than Spark in the future). Includes cross-validation and interfaces to external machine learning libraries.

Interfaces to other Machine Learning systems

14. spark-corenlp

Wraps Stanford CoreNLP.

15. Sparkit-learn

Interface to Python's Scikit-learn

16. Sparkling Water

Interface to H2O

17. hivemall-spark

Wraps Hivemall, machine learning in Hive

18. spark-pmml-exporter-validator

Export PMML, an industry standard XML format for transporting machine learning models.

Add-ons that enhance MLlib's existing algorithms

19. MLlib-dropout

Adds dropout capability to Spark MLLib, based on the paper Dropout: A simple way to prevent neural networks from overfitting.

20. generalized-kmeans-clustering

Adds arbitrary distance functions to K-Means

21. spark-ml-streaming

Visualize the Streaming Machine Learning algorithms built into Spark MLlib

Algorithms

Supervised learning

22. spark-libFM

Factorization Machines

23. ScalaNetwork

Recursive Neural Networks (RNNs)

24. dissolve-struct

SVM based on the performant Spark communication framework CoCoA listed above.

25. Sparkling Ferns

Based on Image Classification using Random Forests and Ferns

26. streaming-matrix-factorization

Matrix Factorization Recommendation System

Unsupervised learning

27. PatchWork

40x faster clustering than Spark MLlib K-Means

28. Bisecting K-Meams Clustering

K-Means that produces more uniformly-sized clusters, based on A Comparison of Document Clustering Techniques

29. spark-knn-graphs

Build graphs using k-nearest-neighbors and locality sensitive hashing (LSH)

30. TopicModeling

Online Latent Dirichlet Allocation (LDA), Gibbs Sampling LDA, Online Hierarchical Dirichlet Process (HDP)

Algorithm building blocks

31. sparkboost

Adaboost and MP-Boost

32. spark-tfocs

Port to Spark of TFOCS: Templates for First-Order Conic Solvers. If your machine learning cost function happens to be convex, then TFOCS can solve it.

33. lazy-linalg

Linear algebra operators to work with Spark MLlib's linalg package

Feature extractors

34. spark-infotheoretic-feature-selection

Information-theoretic basis for feature selection, based on Conditional likelihood maximisation: a unifying framework for information theoretic feature selection

35. spark-MDLP-discretization

Given labeled data, "discretize" one of the continuous numeric dimensions such that each bin is relatively homogenous in terms of data classes. This is a foundational idea CART and ID3 algorithms to generate decision trees. Based on Multi-interval discretization of continuous-valued attributes for classification learning.

36. spark-tsne

Distributed t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction.

37. modelmatrix

Sparse feature vectors

Domain-specific

38. Spatial and time-series data

K-Means, Regression, and Statistics

39. 推ter data

</div> </div> </div> </div> 来自:http://datascienceassn.org/content/39-machine-learning-libraries-spark-categorized