Spark的39个机器学习库
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 paperCommunication-Efficient Distributed Dual Coordinate Ascent
Frameworks
GPU-based
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
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
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
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. Twitter data
来自：http://datascienceassn.org/content/39-machine-learning-libraries-spark-categorized
