USF Seminar Series: Apache Spark, Machine Learning, Recommendations Feb 05 2016

Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc

Spark and Recommendations

Spark, Streaming, Machine Learning, Graph Processing, Approximations, Probabilistic Data Structures, NLP

USF Seminar Series Thanks, USF!!

Feb 5th, 2016

Chris Fregly Principal Data Solutions Engineer

We’re Hiring! (Only Nice People) advancedspark.com!

Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark

Who Am I?

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Streaming Data Engineer Netflix OSS Committer

Data Solutions Engineer

Apache Contributor

Principal Data Solutions Engineer IBM Technology Center

Meetup Organizer Advanced Apache Meetup

Book Author Advanced .

Due 2016


Advanced Apache Spark Meetup http://advancedspark.com Meetup Metrics Top 5 Most-active Spark Meetup! 2400+ Members in just 6 mos!! 2500+ Docker image downloads

Meetup Mission Deep-dive into Spark and related open source projects Surface key patterns and idioms Focus on distributed systems, scale, and performance

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Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc

Live, Interactive Demo!! Audience Participation Required

(cell phone or laptop)

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demo.advancedspark.com End User ->

ElasticSearch ->

Spark ML ->

Data Scientist -> 5

<- Kafka <- Spark Streaming <- Cassandra, Redis <- Zeppelin, iPython


Presentation Outline   Scaling with Parallelism and Composability

  Similarity and Recommendations

  When to Approximate

  Common Algorithms and Data Structures   Common Libraries and Tools

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Scaling with Parallelism

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Peter O(log n)

O(log n)


Scaling with Composability

Max (a max b max c max d) == (a max b) max (c max d)

Set Union (a U b U c U d) == (a U b) U (c U d)

Addition (a + b + c + d) == (a + b) + (c + d)

Multiply (a * b * c * d) == (a * b) * (c * d)

Division??

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What about Division? Division (a / b / c / d) != (a / b) / (c / d) (3 / 4 / 7 / 8) != (3 / 4) / (7 / 8) (((3 / 4) / 7) / 8) != ((3 * 8) / (4 * 7)) 0.134 != 0.857

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What were the Egyptians thinking?! Not Composable

“Divide like an Egyptian”


What about Average?

Overall AVG ( [3, 1] ((3 + 5) + (5 + 7)) 20 [5, 1] == ----------------------- == --- == 5 [5, 1] ((1 + 2) + 1) 4 [7, 1]

) 10

value

count

Pairwise AVG (3 + 5) (5 + 7) 8 12 20 ------- + ------- == --- + --- == --- == 10 != 5 2 2 2 2 2

Divide, Add, Divide? Not Composable

Single Divide at the End? Doesn’t need to be Composable!

AVG (3, 5, 5, 7) == 5

Add, Add, Add? Composable!






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Similarity

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Euclidean Similarity Exists in Euclidean, flat space Based on Euclidean distance Linear measure Bias towards magnitude

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Cosine Similarity Angular measure Adjusts for Euclidean magnitude bias

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Normalizes to unit vectors


Jaccard Similarity Set similarity measurement Set intersection / set union -> Based on Jaccard distance Bias towards popularity

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Log Likelihood Similarity Adjusts for popularity bias Netflix “Shawshank” problem

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Word Similarity Edit Distance Calculate char differences between words Deletes, transposes, replaces, inserts

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Document Similarity TD/IDF Term Freq / Inverse Document Freq Used by most search engines

Word2Vec Words embedded in vector space nearby similars

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Similarity Pathway ie. Closest recommendations between 2 people

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Calculating Similarity Exact Brute-Force “All-pairs similarity” aka “Pair-wise similarity”, “Similarity join” Cartesian O(n^2) shuffle and comparison

Approximate Sampling Bucketing (aka “Partitioning”, “Clustering”) Remove data with low probability of similarity

Reduce shuffle and comparisons 20


Bonus: Document Summary Text Rank aka “Sentence Rank” TF/IDF + Similarity Graph + PageRank

Intuition Surface summary sentences (abstract) Most similar to all others (TF/IDF + Similarity Graph) Most influential sentences (PageRank)

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Similarity Graph Vertex is movie, tag, actor, plot summary, etc. Edges are relationships and weights

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Topic-Sensitive PageRank Graph diffusion algorithm Pre-process graph, add vector of probabilities to each vertex Probability of ending up at this vertex from every other

vertex

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Recommendations

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Basic Terminology User: User seeking recommendations Item: Item being recommended Explicit User Feedback: like or rating Implicit User Feedback: search, click, hover, view, scroll Instances: Rows of user feedback/input data Overfitting: Training a model too closely to the training data & hyperparameters Hold Out Split: Holding out some of the instances to avoid overfitting Features: Columns of instance rows (of feedback/input data) Cold Start Problem: Not enough data to personalize (new) Hyperparameter: Model-specific config knobs for tuning (tree depth, iterations) Model Evaluation: Compare predictions to actual values of hold out split Feature Engineering: Modify, reduce, combine features

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Feature Engineering Dimension Reduction Reduce number of features (aka “feature space”)

Principle Component Analysis (PCA) Find principle features that describe the data in terms of variance Peel the dimensional layers back until you describe the data

Example: One-Hot Encoding Convert categorical feature values to 0’s, 1’s Remove any hint of a relationship between the categories Bears -> 1 Bears -> [1,0,0] 49’ers -> 2 --> 49’ers -> [0,1,0] Steelers-> 3 Steelers-> [0,0,1]

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1 binary column per category


Features Binary Features: True or False

Numeric Discrete Features: Integers

Numeric Features: Real values

Ordinal Features: Maintains order (S -> M -> L -> XL -> XXL)

Temporal Features: Time-based (Time of Day, Binge Watching)

Categorical Features: Finite, unique set of categories (NFL teams)

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Non-Personalized Recommendations

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Cold Start Problem “Cold Start” problem New user, don’t know their pref, must show them something!

Movies with highest-rated actors Top K Aggregations

Most desirable singles PageRank of like activity

Facebook social graph Recommend friend activity

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Personalized Recommendations

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Clustering (aka. Nearest Neighbors) User-to-User Clustering Similar movies watched or rated Similar wiewing pattern (ie. binge or casual)

Item-to-Item Clustering Similar tags/genres on movies Similar textual description (TF/IDF, Word2Vec, NLP, Image)

31 http://crockpotveggies.com/2015/02/09/automating-tinder-with-eigenfaces.html!My OKCupid Profile! My Hinge Profile!


User-to-Item Collaborative Filtering Matrix Factorization ①  Factor the large matrix (left) into 2 smaller matrices (right) ②  Fill in the missing values with in the large matrix

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Item-to-Item Collaborative Filtering Made famous by Amazon Paper ~2003 Problem As # of users grew, Matrix Factorization couldn’t scale

Solution Offline/Batch Generate itemId -> List[customerId] vectors

Online/Real-time For each item in cart, recommend similar items from vector space

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When to Approximate? Memory or time constrained queries Relative vs. exact counts are OK (# errors between then and now)

Using machine learning or graph algos Inherently probabilistic and approximate Finding topics in documents (LDA) Finding similar pairs of users, items, words at scale (LSH) Finding top influencers (PageRank)

Streaming aggregations (distinct count or top k) Inherently sloppy means of collecting (at least once delivery)

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Approximate as much as you can get away with! Ask for forgiveness later !!


When NOT to Approximate? If you’ve ever heard the term…

“Sarbanes-Oxley”

…in-that-order, at the office, after 2002.

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A Few Good Algorithms

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You can’t handle the approximate!


Common to These Algos & Data Structs Low, fixed size in memory Known error bounds Store large amount of data Less memory than Java/Scala collections Tunable tradeoff between size and error Rely on multiple hash functions or operations Size of hash range defines error

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Bloom Filter Set.contains(key): Boolean

“Hash Multiple Times and Flip the Bits Wherever You Land”

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Bloom Filter Approximate set membership for key False positive: expect contains(), actual !contains() True negative: expect !contains(), actual !contains()

Elements only added, never removed

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Bloom Filter in Action

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set(key) contains(key): Boolean

Images by @avibryant

TRUE -> maybe contains FALSE -> definitely does not contain.


CountMin Sketch Frequency Count and TopK

“Hash Multiple Times and Add 1 Wherever You Land”

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CountMin Sketch (CMS) Approximate frequency count and TopK for key ie. “Heavy Hitters” on Twitter

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Johnny Hallyday Martin Odersky Donald Trump


CountMin Sketch In Action

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Images derived from @avibryant

Find minimum of all rows

… …

Can overestimate, but never underestimate

Multiple hash functions (1 hash function per row)

Binary hash output (1 element per column)

x 2 occurrences of “Top Gun” for slightly additional complexity

Top Gun

Top Gun

Top Gun (x 2)

A FewGood Men

Taps

Top Gun (x 2)

add(Top Gun, 2)

getCount(Top Gun): Long

Use Case: TopK movies using total views

add(A Few Good Men, 1)

add(Taps, 1)

A FewGood Men

Taps

…

…

Overlap Top Gun

Overlap A Few Good Men


HyperLogLog Count Distinct

“Hash Multiple Times and Uniformly Distribute Where You Land”

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HyperLogLog (HLL) Approximate count distinct Slight twist Special hash function creates uniform distribution

Error estimate 14 bits for size of range m = 2^14 = 16,384 slots error = 1.04/(sqrt(16,384)) = .81%

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Not many of these


HyperLogLog In Action Use Case: Distinct number of views per movie

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0 32 Top Gun: Hour 2 user2001

user 4009

user 3002

user 7002

user 1005

user 6001

User 8001

User 8002

user 1001

user 2009

user 3005

user 3003

Top Gun: Hour 1 user 3001

user 7009

0 16

Uniform Distribution: Estimate distinct # of users by inspecting just the beginning

Uniform Distribution: Estimate distinct # of users

by inspecting just the beginning

Composable: Hour 1 + 2 (lose a bit of precision)


Locality Sensitive Hashing Set Similarity

“Pre-process Items into Buckets, Compare Within Buckets”

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Locality Sensitive Hashing (LSH) Approximate set similarity Hash designed to cluster similar items Avoids cartesian all-pairs comparison Pre-process m rows into b buckets b << m

Hash items multiple times Similar items hash to overlapping buckets Compare just contents of buckets Much smaller cartesian … and parallel !!

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DIMSUM Set Similarity

“Pre-process Items into Buckets, Compare Within Buckets”

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DIMSUM “Dimension Independent Matrix Square Using MR” Remove vectors with low probability of similarity RowMatrix.columnSimiliarites(threshold)

Twitter DIMSUM Case Study 40% efficiency gain over bruce-force cosine sim

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Common Tools to Approximate

Twitter Algebird

Redis

Apache Spark

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Composable Library

Distributed Cache

Big Data Processing


Twitter Algebird Rooted in Algebraic Fundamentals! Parallel Associative Composable Examples Min, Max, Avg BloomFilter (Set.contains(key)) HyperLogLog (Count Distinct) CountMin Sketch (TopK Count)

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Redis Implementation of HyperLogLog (Count Distinct) 12KB per item count 2^64 max # of items 0.81% error (Tunable) Add user views for given movie

PFADD TopGun_HLL user1001 user2009 user3005 PFADD TopGun_HLL user3003 user1001

Get distinct count (cardinality) of set

PFCOUNT TopGun_HLL Returns: 4 (distinct users viewed this movie)

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ignore duplicates

Tunable

Union 2 HyperLogLog Data Structures PFMERGE TopGun_HLL Taps_HLL


Spark Approximations Spark Core

RDD.count*Approx() Spark SQL

PartialResult HyperLogLogPlus approxCountDistinct(column)

Spark ML Stratified sampling PairRDD.sampleByKey(fractions: Double[ ]) DIMSUM sampling Probabilistic sampling reduces amount of comparison shuffle RowMatrix.columnSimilarities(threshold)

Spark Streaming A/B testing StreamingTest.setTestMethod(“welch”).registerStream(dstream)

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Demos!

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Counting Exact Count vs. Approx HyperLogLog, CountMin Sketch

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HashSet vs. HyperLogLog

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HashSet vs. CountMin Sketch

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Set Similarity Exact Jaccard Similarity vs. Approx Locality Sensitive Hashing

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Brute Force Cartesian All Pair Similarity

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90 mins!


All Pairs & Locality Sensitive Hashing

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<< 90 mins!


Many More Demos Available! http://advancedspark.com

Download Docker or Clone Github

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Bonus: Netflix Recommendations From Offline DVD Ratings to Real-time Trending Now

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$1 Million Netflix Prize (2006-2009) Goal Improve movie predictions by 10% (RMSE)

Dataset (userId, movieId, rating, timestamp) Test data withheld to calculate RMSE upon submission

Winning algorithm 10.06% improvement (RMSE) Ensemble of 500+ ML Combined using GBDT’s Computationally impractical

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Secret to the Winning Algorithms Adjust for the following… Human bias “Alice effect”: Alice tends to rate lower than average user “Inception effect”: Inception is rated higher than average “Alice-Inception effect”: Combo of Alice and Inception Time-based bias Number of days since a user’s first rating Number of days since a movie’s first rating Number of people who have rated a movie A movie’s overall mean rating

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Current Netflix Recommendations

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Throw away loffline-generated user factors (U)


Netflix Common ML Algorithms Logistic Regression Linear Regression Gradient Boosted Decision Trees Random Forest Matrix Factorization SVD Restricted Boltzmann Machines Deep Neural Nets Markov Models LDA Clustering … 70


Bonus: Netflix Search No results? No problem… Show similar results! Used as implicit feedback for future decision making

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Netflix and Data Netflix has a lot of data about a lot of users and a lot of movies. Netflix can use this data to buy new movies. Netflix is global. Netflix can use this data to choose original programming. Netflix knows that a lot of people like Politics and Kevin Spacey.

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The UK doesn’t have any White Castles. So they renamed my favourite movie, “Harold and Kumar Get the Munchies”

(This broke all of my unit tests.)

My favorite movie, “Harold and Kumar Go to White Castle”

Summary: Buy NFLX Stock!


Thank You!! Chris Fregly @cfregly IBM Spark Tech Center http://spark.tc San Francisco, California, USA

http://advancedspark.com Sign up for the Meetup and Book Contribute to Github Repo Run all Demos using Docker

Find me: LinkedIn, Twitter, Github, Email, Fax 73

Image derived from http://www.duchess-france.org/

Power of data. Simplicity of design. Speed of innovation. IBM Spark spark.tc

Power of data. Simplicity of design. Speed of innovation.

IBM Spark

advancedspark.com @cfregly

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USF Seminar Series: Apache Spark, Machine Learning, Recommendations Feb 05 2016

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