la hug 2012 02-07

Beating up on Bayesian Bandits

Mahout

• Scalable Data Mining for Everybody

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)

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)

Classification in Detail

• Naive Bayes Family– Hadoop based training

• Decision Forests– Hadoop based training

• Logistic Regression (aka SGD)– fast on-line (sequential) training

Classification in Detail

• Naive Bayes Family– Hadoop based training

• Decision Forests– Hadoop based training

• Logistic Regression (aka SGD)– fast on-line (sequential) training

Classification in Detail

• Naive Bayes Family– Hadoop based training

• Decision Forests– Hadoop based training

• Logistic Regression (aka SGD)– fast on-line (sequential) training– Now with MORE topping!

An Example

And Another

From:  Dr. Paul AcquahDear 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....

Date: Thu, May 20, 2010 at 10:51 AMFrom: 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?

Feature Encoding

Hashed Encoding

Feature Collisions

How it Works

• We are given “features”– Often binary values in a vector

• Algorithm learns weights– Weighted sum of feature * weight is the key

• Each weight is a single real value

A Quick Diversion

• You see a coin– What is the probability of heads?– Could it be larger or smaller than that?

• I flip the coin and while it is in the air ask again• I catch the coin and ask again• I look at the coin (and you don’t) and ask again• Why does the answer change?– And did it ever have a single value?

A First Conclusion

• Probability as expressed by humans is subjective and depends on information and experience

A Second Conclusion

• A single number is a bad way to express uncertain knowledge

• A distribution of values might be better

I Dunno

5 and 5

2 and 10

The Cynic Among Us

A Second Diversion

Two-armed Bandit

Which One to Play?

• One may be better than the other• The better machine pays off at some rate• Playing the other will pay off at a lesser rate– Playing the lesser machine has “opportunity cost”

• But how do we know which is which?– Explore versus Exploit!

Algorithmic Costs

• Option 1– Explicitly code the explore/exploit trade-off

• Option 2– Bayesian Bandit

Bayesian Bandit

• Compute distributions based on data• Sample p1 and p2 from these distributions

• Put a coin in bandit 1 if p1 > p2

• Else, put the coin in bandit 2

The Basic Idea

• We can encode a distribution by sampling• Sampling allows unification of exploration and

exploitation

• Can be extended to more general response models

Deployment with Storm/MapR

All state managed transactionally in MapR file system

Service Architecture

MapR Lockless Storage Services

MapR Pluggable Service Management

Storm

Hadoop

Find Out More

• Me: tdunning@mapr.com ted.dunning@gmail.com tdunning@apache.com

• MapR: http://www.mapr.com • Mahout: http://mahout.apache.org

• Code: https://github.com/tdunning