MAHOUT classifier tour

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Ted Dunning

This ta

Technologie

Mahout
Scalable Data Mining for Everybody

Wednesday, March 16, 2011 1

What is Mahout
• Recommendations (people who x this also
x that)
• Clustering (segment data into groups of)
• Classiﬁcation (learn decision making from
examples)
• Stuff (LDA, SVD, frequent item-set, math)

Wednesday, March 16, 2011 2

Classiﬁcation in Detail
• Naive Bayes Family
• Hadoop based training
• Decision Forests
• Hadoop based training
• Logistic Regression (aka SGD)
• fast on-line (sequential) training
Wednesday, March 16, 2011 4

So What?
Online training
has low
overhead for
small and
moderate size
data-sets

Wednesday, March 16, 2011 6

So What?
big starts here

Online training
has low
overhead for
small and
moderate size
data-sets

Wednesday, March 16, 2011 6

And Another
From: Dr. Paul Acquah
Dear Sir,
Re: Proposal for over-invoice Contract Benevolence

Based on information gathered from the India
hospital directory, I am pleased to propose a
confidential business deal for our mutual benefit.
I have in my possession, instruments
(documentation) to transfer the sum of
33,100,000.00 eur thirty-three million one hundred
thousand euros, only) into a foreign company's bank
account for our favor.
...

Wednesday, March 16, 2011 8

And Another
Date: Thu, May 20, 2010 at 10:51 AM
From: George <george@fumble-tech.com>

Hi Ted, was a pleasure talking to you last night
at the Hadoop User Group. I liked the idea of
going for lunch together. Are you available
tomorrow (Friday) at noon?

Wednesday, March 16, 2011 8

Mahout’s SGD

• Learns on-line per example
• O(1) memory
• O(1) time per training example
• Sequential implementation
• fast, but not parallel

Wednesday, March 16, 2011 9

Special Features
• Hashed feature encoding
• Per-term annealing
• learn the boring stuff once
• Auto-magical learning knob turning
• learns correct learning rate, learns
correct learning rate for learning learning
rate, ...

Wednesday, March 16, 2011 10

Feature Encoding

Wednesday, March 16, 2011 11

Hashed Encoding

Wednesday, March 16, 2011 12

Feature Collisions

Wednesday, March 16, 2011 13

Learning Rate Annealing
Learning Rate

# training examples seen

Wednesday, March 16, 2011 14

Learning Rate Per-term Annealing

# training examples seen

Wednesday, March 16, 2011 15

Learning Rate Per-term Annealing

Common
Feature

# training examples seen

Wednesday, March 16, 2011 15

Learning Rate Per-term Annealing

Rare
Feature

# training examples seen

Wednesday, March 16, 2011 15

General Structure

• OnlineLogisticRegression
• Traditional logistic regression
• Stochastic Gradient Descent
• Per term annealing
• Too fast (for the disk + encoder)

Wednesday, March 16, 2011 16

Next Level

• CrossFoldLearner
• contains multiple primitive learners
• online cross validation
• 5x more work

Wednesday, March 16, 2011 17

And again
• AdaptiveLogisticRegression
• 20 x CrossFoldLearner
• evolves good learning and regularization
rates
• 100 x more work than basic learner
• still faster than disk + encoding
Wednesday, March 16, 2011 18

A comparison
• Traditional view
• 400 x (read + OLR)
• Revised Mahout view
• 1 x (read + mu x 100 x OLR) x eta
• mu = efﬁciency from killing losers early
• eta = efﬁciency from stopping early
Wednesday, March 16, 2011 19

Deployment

• Training
• ModelSerializer.writeBinary(..., model)
• Deployment
• m = ModelSerializer.readBinary(...)
• r = m.classifyScalar(featureVector)

Wednesday, March 16, 2011 20

The Upshot

• One machine can go fast
• SITM trains in 2 billion examples in 3
hours
• Deployability pays off big
• simple sample server farm

Wednesday, March 16, 2011 21

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