In this page, we will explain how to use model mixing on Hivemall. The model mixing is useful for a better prediction performance and faster convergence in training classifiers. You can find a brief explanation of the internal design of MIX protocol in this slide.

# Prerequisite

• Hivemall v0.3 or later

We recommend to use Mixing in a cluster with fast networking. The current standard GbE is enough though.

# Running Mix Server

First, put the following files on server(s) that are accessible from Hadoop worker nodes:

Caution: hivemall-mixserv.jar is large in size and thus only used for Mix servers.

# run a Mix Server
./run_mixserv.sh


We assume in this example that Mix servers are running on host01, host03 and host03. The default port used by Mix server is 11212 and the port is configurable through "-port" option of run_mixserv.sh.

See MixServer.java to get detail of the Mix server options.

We recommended to use multiple MIX servers to get better MIX throughput (3-5 or so would be enough for normal cluster size). The MIX protocol of Hivemall is horizontally scalable by adding MIX server nodes.

# Using Mix Protocol through Hivemall

Install Hivemall on Hive.

Make sure that hivemall-with-dependencies.jar is used for installation. The jar contains minimum requirement jars (netty,jsr305) for running Hivemall on Hive.

Now, we explain that how to use mixing in an example using KDD2010a dataset.

Enabling the mixing on Hivemall is simple as follows:

use kdd2010;

create table kdd10a_pa1_model1 as
select
feature,
cast(voted_avg(weight) as float) as weight
from
(select
from
kdd10a_train_x3
) t
group by feature;


All you have to do is just adding "-mix" training option as seen in the above query.

# The effect of model mixing

In my experience, the MIX improved the prediction accuracy of the above KDD2010a PA1 training on a 32 nodes cluster from 0.844835019263103 (w/o mix) to 0.8678096499719774 (w/ mix).

The overhead of using the MIX protocol is almost negligible because the MIX communication is efficiently handled using asynchronous non-blocking I/O. Furthermore, the training time could be improved on certain settings because of the faster convergence due to mixing.