Big Data Infrastructure

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Big Data Infrastructure
CS 489/698 Big Data Infrastructure (Winter 2016)
Week 8: Data Mining (1/4)
March 1, 2016
Jimmy Lin
David R. Cheriton School of Computer Science
University of Waterloo
These slides are available at http://lintool.github.io/bigdata-2016w/
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States
See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details
Data Mining
Analyzing
Relational Data
Analyzing Graphs
Analyzing Text
Structure of the Course
“Core” framework features
and algorithm design
Supervised Machine Learning
The generic problem of function induction given
sample instances of input and output
Classification: output draws from finite discrete
labels
Regression: output is a continuous value
This is not meant to be an
exhaustive treatment of machine
Classification
Source: Wikipedia (Sorting)
Applications
Spam detection
Sentiment analysis
Content (e.g., genre) classification
Link prediction
Document ranking
Object recognition
Fraud detection
And much much more!
Supervised Machine Learning
training
testing/deployment
Model
?
Machine Learning
Feature Representations
Objects are represented in terms of features:
“Dense” features: sender IP, timestamp, # of recipients,
length of message, etc.
“Sparse” features: contains the term “viagra” in
message, contains “URGENT” in subject, etc.
Applications
Spam detection
Sentiment analysis
Content (e.g., genre) classification
Link prediction
Document ranking
Object recognition
Fraud detection
And much much more!
Components of a ML Solution
Data
Features
Model
Optimization
No data like more data!
s/knowledge/data/g;
(Banko and Brill, ACL 2001)
(Brants et al., EMNLP 2007)
Limits of Supervised Classification?

Why is this a big data problem?

Isn’t gathering labels a serious bottleneck?

Solution: crowdsourcing

Solution: bootstrapping, semi-supervised techniques

Solution: user behavior logs




Learning to rank
Computational advertising
Link recommendation
The virtuous cycle of data-driven products
Supervised Binary Classification

Restrict output label to be binary



Yes/No
1/0
Binary classifiers form a primitive building block for multiclass problems


One vs. rest classifier ensembles
Classifier cascades
The Task
label

Given
(sparse) feature vector

Induce

Such that loss is minimized
loss function

Typically, consider functions of a parametric form:
model parameters
Key insight: machine learning as an optimization problem!
(closed form solutions generally not possible)
Gradient Descent: Preliminaries

Rewrite:

Compute gradient:


“Points” to fastest increasing “direction”
So, at any point:*
*
Gradient Descent: Iterative Update

Start at an arbitrary point, iteratively update:

We have:

Lots of details:




Figuring out the step size
Getting stuck in local minima
Convergence rate
…
Gradient Descent
Repeat until convergence:
Intuition behind the math…
New weights Old weights
Update based on gradient
Gradient Descent
Source: Wikipedia (Hills)
Lots More Details…

Gradient descent is a “first order” optimization technique



Often, slow convergence
Conjugate techniques accelerate convergence
Newton and quasi-Newton methods:

Intuition: Taylor expansion

Requires the Hessian (square matrix of second order partial
derivatives): impractical to fully compute
Logistic Regression
Source: Wikipedia (Hammer)
Logistic Regression: Preliminaries

Given

Let’s define:

Interpretation:
Relation to the Logistic Function
After some algebra:

The logistic function:
1
0.9
0.8
0.7
logistic(z)

0.6
0.5
0.4
0.3
0.2
0.1
0
-8
-7
-6
-5
-4
-3
-2
-1
0
z
1
2
3
4
5
6
7
8
Training an LR Classifier

Maximize the conditional likelihood:

Define the objective in terms of conditional log likelihood:

We know

Substituting:
so:
LR Classifier Update Rule

Take the derivative:

General form for update rule:

Final update rule:
Lots more details…

Regularization

Different loss functions

…
Want more details?
Take a real machine-learning
MapReduce Implementation
mappers
single reducer
compute partial gradient
mapper
mapper
mapper
reducer
iterate until convergence
update model
mapper
Shortcomings

Hadoop is bad at iterative algorithms



High sensitivity to skew


Iteration speed bounded by slowest task
Potentially poor cluster utilization


High job startup costs
Awkward to retain state across iterations
Must shuffle all data to a single reducer
Some possible tradeoffs


Number of iterations vs. complexity of computation per iteration
E.g., L-BFGS: faster convergence, but more to compute
Spark Implementation
val points = spark.textFile(...).map(parsePoint).persist()
var w = // random initial vector
for (i <- 1 to ITERATIONS) {
val gradient = points.map{ p =>
p.x * (1/(1+exp(-p.y*(w dot p.x)))-1)*p.y
}.reduce((a,b) => a+b)
w -= gradient
}
compute partial gradient
mapper
mapper
mapper
reducer
update model
mapper
Questions?
Source: Wikipedia (Japanese rock garden)

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