Hivemall: Hive scalable machine learning library
Hivemall is a scalable machine learning library that runs on Apache Hive. Hivemall is designed to be scalable to the number of training instances as well as the number of training features.
Hivemall provides machine learning functionality as well as feature engineering functions through UDFs/UDAFs/UDTFs of Hive.
Passive Aggressive (PA, PA1, PA2)
Confidence Weighted (CW)
Adaptive Regularization of Weight Vectors (AROW)
Soft Confidence Weighted (SCW1, SCW2)
AdaGradRDA (with hinge loss)
My recommendation is AROW, SCW1, and AdaGradRDA, while it depends.
AdaGrad / AdaDelta (with Logistic Loss)
Passive Aggressive Regression (PA1, PA2)
My recommendation for is AROW regression, AdaDelta, and AdaGrad while it depends.
Feature Hashing (MurmurHash, SHA1)
Feature scaling (Min-Max Normalization, Z-Score)
Feature instances amplifier that reduces iterations on training
Data generator for one-vs-the-rest classifiers
- Hive 0.11 or later
Copyright (C) 2015 Makoto YUI
Copyright (C) 2013-2015 National Institute of Advanced Industrial Science and Technology (AIST)
Put the above copyrights for the services/softwares that use Hivemall.
Support is through the issue list, not by a direct e-mail.
Please refer the following paper for research uses:
Makoto Yui and Isao Kojima. ``Hivemall: Hive scalable machine learning library'' (demo), NIPS 2013 Workshop on Machine Learning Open Source Software: Towards Open Workflows, Dec 2013.
This work was supported in part by a JSPS grant-in-aid for young scientists (B) #24700111 and a JSPS grant-in-aid for scientific research (A) #24240015.