Mahout classifier tour

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The document discusses Mahout, an open source machine learning library. It describes Mahout's capabilities for recommendations, clustering, classification and other machine learning tasks. Specifically, it covers Mahout's online training algorithms like logistic regression which can train models on small to moderate sized datasets efficiently with low overhead. The document provides examples and details Mahout's approach to online learning and model training.

Technology Education

Mahout
Scalable Data Mining for Everybody
1
Wednesday, March 16, 2011

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)
2
Wednesday, March 16, 2011

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

So What?
Online training
has low
overhead for
small and
moderate size
data-sets
6
Wednesday, March 16, 2011

So What?
Online training
has low
overhead for
small and
moderate size
data-sets
big starts here
6
Wednesday, March 16, 2011

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.
...
8
Wednesday, March 16, 2011

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?
8
Wednesday, March 16, 2011

Mahout’s SGD
• Learns on-line per example
• O(1) memory
• O(1) time per training example
• Sequential implementation
• fast, but not parallel
9
Wednesday, March 16, 2011

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, ...
10
Wednesday, March 16, 2011

Feature Encoding
11
Wednesday, March 16, 2011

Hashed Encoding
12
Wednesday, March 16, 2011

Feature Collisions
13
Wednesday, March 16, 2011

Learning Rate Annealing
Learning
Rate
# training examples seen
14
Wednesday, March 16, 2011

Per-term Annealing
Learning
Rate
# training examples seen
15
Wednesday, March 16, 2011

Per-term Annealing
Learning
Rate
# training examples seen
Common
Feature
15
Wednesday, March 16, 2011

Per-term Annealing
Learning
Rate
# training examples seen
Rare
Feature
15
Wednesday, March 16, 2011

General Structure
• OnlineLogisticRegression
• Traditional logistic regression
• Stochastic Gradient Descent
• Per term annealing
• Too fast (for the disk + encoder)
16
Wednesday, March 16, 2011

Next Level
• CrossFoldLearner
• contains multiple primitive learners
• online cross validation
• 5x more work
17
Wednesday, March 16, 2011

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

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
19
Wednesday, March 16, 2011

Deployment
• Training
• ModelSerializer.writeBinary(..., model)
• Deployment
• m = ModelSerializer.readBinary(...)
• r = m.classifyScalar(featureVector)
20
Wednesday, March 16, 2011

The Upshot
• One machine can go fast
• SITM trains in 2 billion examples in 3
hours
• Deployability pays off big
• simple sample server farm
21
Wednesday, March 16, 2011

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Mahout classifier tour

1. Mahout 1 Wednesday, March 16, 2011

2. Mahout Scalable Data Mining for Everybody 1 Wednesday, March 16, 2011

3. 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) 2 Wednesday, March 16, 2011

4. 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, SVM, frequent item-set, math) 3 Wednesday, March 16, 2011

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

6. Classification in Detail • Naive Bayes Family • Hadoop based training • Decision Forests • Hadoop based training •Logistic Regression (aka SGD) •fast on-line (sequential) training 5 Wednesday, March 16, 2011

7. So What? Online training has low overhead for small and moderate size data-sets 6 Wednesday, March 16, 2011

8. So What? Online training has low overhead for small and moderate size data-sets 6 Wednesday, March 16, 2011

9. So What? Online training has low overhead for small and moderate size data-sets 6 Wednesday, March 16, 2011

10. So What? Online training has low overhead for small and moderate size data-sets 6 Wednesday, March 16, 2011

11. So What? Online training has low overhead for small and moderate size data-sets big starts here 6 Wednesday, March 16, 2011

12. An Example 7 Wednesday, March 16, 2011

13. An Example 7 Wednesday, March 16, 2011

14. An Example 7 Wednesday, March 16, 2011

15. An Example 7 Wednesday, March 16, 2011

16. An Example 7 Wednesday, March 16, 2011

17. An Example 7 Wednesday, March 16, 2011

18. An Example 7 Wednesday, March 16, 2011

19. 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. ... 8 Wednesday, March 16, 2011

20. 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? 8 Wednesday, March 16, 2011

21. 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? 8 Wednesday, March 16, 2011

22. 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? 8 Wednesday, March 16, 2011

23. Mahout’s SGD • Learns on-line per example • O(1) memory • O(1) time per training example • Sequential implementation • fast, but not parallel 9 Wednesday, March 16, 2011

24. 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, ... 10 Wednesday, March 16, 2011

25. Feature Encoding 11 Wednesday, March 16, 2011

26. Feature Encoding 11 Wednesday, March 16, 2011

27. Hashed Encoding 12 Wednesday, March 16, 2011

28. Feature Collisions 13 Wednesday, March 16, 2011

29. Learning Rate Annealing Learning Rate # training examples seen 14 Wednesday, March 16, 2011

30. Per-term Annealing Learning Rate # training examples seen 15 Wednesday, March 16, 2011

31. Per-term Annealing Learning Rate # training examples seen Common Feature 15 Wednesday, March 16, 2011

32. Per-term Annealing Learning Rate # training examples seen Rare Feature 15 Wednesday, March 16, 2011

33. General Structure • OnlineLogisticRegression • Traditional logistic regression • Stochastic Gradient Descent • Per term annealing • Too fast (for the disk + encoder) 16 Wednesday, March 16, 2011

34. Next Level • CrossFoldLearner • contains multiple primitive learners • online cross validation • 5x more work 17 Wednesday, March 16, 2011

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

36. 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 19 Wednesday, March 16, 2011

37. Deployment • Training • ModelSerializer.writeBinary(..., model) • Deployment • m = ModelSerializer.readBinary(...) • r = m.classifyScalar(featureVector) 20 Wednesday, March 16, 2011

38. The Upshot • One machine can go fast • SITM trains in 2 billion examples in 3 hours • Deployability pays off big • simple sample server farm 21 Wednesday, March 16, 2011

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