Boston Spark Meetup May 24, 2016

Flux Capacitor AI Bringing AI Back to the Future!Bringing AI Back to the Future!Flux Capacitor AI

advancedspark.com


Who Am I?

2

Streaming Data EngineerNetflix OSS Committer

Data Solutions EngineerApache Contributor

Principal Data Solutions EngineerIBM Technology Center

Meetup OrganizerAdvanced Apache Meetup

Book AuthorAdvanced .

Due 2016


Advanced Apache Spark Meetuphttp://advancedspark.com

Meetup MetricsTop 10 Most-active Spark Meetup!3200+ Members in just 9 mos!!3700+ Docker downloads (demos)

Meetup MissionCode deep-dive into Spark and related open source projectsSurface key patterns and idiomsFocus on distributed systems, scale, and performance

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Live, Interactive Demo!Audience Participation Required!!Cell Phone Compatible!!!

demo.advancedspark.com4


http://demo.advancedspark.com

End User ->

ElasticSearch ->

Spark ML ->

Data Scientist ->

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<- Kafka

<- SparkStreaming

<- Cassandra,Redis

<- Zeppelin, iPython


Presentation Outline① Scaling

② Similarities

③ Recommendations

④ Approximations

⑤ Netflix Recommendations6


Scaling with Parallelism

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Peter

O(log n)O(log n)

WorkerNodes


Parallelism with ComposabilityWorker 1 Worker 2

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)

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What about Division and Average?Collect at Driver


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 + 1 + 1 + 1) 4+ (7, 1)

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values

counts

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

2 2 2 2 2

Divide, Add, Divide?Not Composable

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

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

Add, Add, Add?Composable!



② Similarities

③ Recommendations

④ Approximations



Similarities

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Euclidean SimilarityExists in Euclidean, flat spaceBased on Euclidean distance Linear measureBias towards magnitude

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Cosine SimilarityAngular measureAdjusts for Euclidean magnitude biasNormalize to unit vectors in all dimensionsUsed with real-valued vectors (versus binary)

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org.jblas.DoubleMatrix


Jaccard SimilaritySet similarity measurementSet intersection / set union Bias towards popularityWorks with binary vectors

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Log Likelihood SimilarityAdjusts for popularity biasNetflix “Shawshank” problem

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Word SimilarityEdit Distance

Misspellings and autocorrect

Word2VecSimilar words are defined by similar contexts in vector space

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English Spanish


Demo!Find Synonyms with Word2Vec

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Find Synonyms using Word2Vec

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Document SimilarityTF/IDF

Term Freq / Inverse Document FreqUsed by most search engines

Doc2VecSimilar documents are determined by similar contexts

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Bonus! Text Rank Document SummaryText Rank (aka Sentence Rank)

Surface summary sentences TF/IDF + Similarity Graph + PageRank

Most similar sentence to all other sentencesTF/IDF + Similarity Graph

Most influential sentencesPageRank

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Similarity Pathways (Recommendations)Best recommendations for 2 (or more) people

“You like Max Max. I like Message in a Bottle.We might like a movie similar to both.”

Item-to-Item Similarity Graph + Dijkstra Heaviest Path

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Demo!Similarity Pathway for Movie Recommendations

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Load Movies with Tags into DataFrame

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My Choice

TheirChoice


Item-to-Item Tag Jaccard SimilarityBased on Tags

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Calculate Jaccard Similarity(Tag Set Similarity)

Must be Above the Given Jaccard Similarity Threshold


Item-to-Item Tag Similarity Graph

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Edge Weights ==

Jaccard Similarity(Based on Tag Sets)


Use Dijkstra to Find Heaviest Pathway

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Calculating Exact SimilarityBrute-Force Similarity

Cartesian ProductO(n^2) shuffle and computeaka. All-pairs, Pair-wise,

Similarity Join

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Calculating Approximate SimilarityGoal: Reduce Shuffle

Approximate SimilaritySamplingBucketing or ClusteringIgnore low-similarity probability

Locality Sensitive Hashing Twitter Algebird MinHash

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BucketBy Genre



② Similarities

③ Recommendations

④ Approximations

① Netflix Recommendations30


Recommendations

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Basic TerminologyUser: User seeking recommendationsItem: Item being recommendedExplicit User Feedback: user knows they are rating or liking, can choose to dislikeImplicit User Feedback: user not explicitly aware, cannot dislike (click, hover, etc)Instances: Rows of user feedback/input dataOverfitting: Training a model too closely to the training data & hyperparametersHold Out Split: Holding out some of the instances to avoid overfittingFeatures: 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 splitFeature Engineering: Modify, reduce, combine featuresLoss Function: Function we’re trying to minimize such as least-squared error for Linear RegressionCross Entropy: Loss function used for classification algorithms such as Logistic RegressionOptimizer: Technique to optimize loss function such as Stochastic Gradient Descent (SGD)

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Stochastic Gradient Descent (SGD)Optimizes Loss Function

Least Squared Error b/w predicted and actual valueCross Entropy Log Likelihood b/w predicted and actual probability

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2-Dimensional 3-Dimensional


FeaturesBinary: True or FalseNumeric Discrete: Integers

Numeric: Real Values

Binning: Convert Continuous into Discrete (Time of Day->Morning, Afternoon)

Categorical Ordinal: Size (Small->Medium->Large), Ratings (1->5)

Categorical Nominal: Independent, Favorite Sports Teams, Dating SpotsTemporal: Time-based, Time of Day, Binge Viewing

Text: Movie Titles, Genres, Tags, Reviews (Tokenize, Stop Words, Stemming)

Media: Images, Audio, Video

Geographic: (Longitude, Latitude), Geohash

Latent: Hidden Features within Data (Collaborative Filtering)Derived: Age of Movie, Duration of User Subscription

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Feature EngineeringDimension Reduction

Reduce number of features in feature spacePrinciple Component Analysis (PCA)

Find principle features that best describe data variancePeel dimensional layers back

One-Hot EncodingConvert nominal categorical feature values into 0’s and 1’sRemove any numerical relationship between categories

Bears -> 1 Bears -> [1.0, 0.0, 0.0]49’ers -> 2 --> 49’ers -> [0.0, 1.0, 0.0]Steelers-> 3 Steelers-> [0.0, 0.0, 1.0]

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Convert Each Item to Binary Vector

with Single 1.0 Column


Feature Normalization & StandardizationGoal

Scale features to standard sizePrevent boundless featuresHelps avoid overfittingRequired by many ML algos

Normalize FeaturesCalculate L1 (or L2, etc) norm, then divide into each element

Standardize FeaturesApply standard normal transformation (mean->0, stddev->1)

org.apache.spark.ml.feature.[Normalizer, StandardScaler]36

http://www.mathsisfun.com/data/standard-normal-distribution.html


Non-Personalized Recommendations

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Cold Start Problem“Cold Start” problem

New user, don’t know their preferences, must show something!

Movies with highest-rated actorsTop K aggregations

Facebook social graphFriend-based recommendations

Most desirable singlesPageRank of likes and dislikes

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Demo!GraphFrame PageRank

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Example: Dating Site “Like” Graph

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PageRank of Top Influencers

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Personalized Recommendations

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Demo!Personalized PageRank

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Personalized PageRank: Outbound Links

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0.15 = (1 - 0.85 “Damping Factor”)85% Probability: Choose Among Outbound Network

15% Probability: Choose Self or Random

85% AmongOutboundNetwork


Personalized PageRank: No Outbound

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0.15 = (1 - 0.85 “Damping Factor”)85% Probability: Choose Among Outbound Network

15% Probability: Choose Self or Random

85% Among No

OutboundNetwork!!


User-to-User ClusteringUser Similarity

Time-basedPattern of viewing (binge or casual)Time of viewing (am or pm)

Ratings-basedContent ratings or number of viewsAverage rating relative to others (critical or lenient)

Search-basedSearch terms

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Item-to-Item ClusteringItem Similarity

Profile text (TF/IDF, Word2Vec, NLP)Categories, tags, interests (Jaccard Similarity, LSH)Images, facial structures (Neural Nets, Eigenfaces)

Dating Site Example…

47Cluster Similar Eigen-facesCluster Similar Profiles Cluster Similar Categories


Bonus: NLP Conversation Starter Bot

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“If your responses to my generic opening lines are positive, I may read your profile.”

Spark ML, Stanford CoreNLP,TF/IDF, DecisionTrees, Sentiment

http://crockpotveggies.com/2015/02/09/automating-tinder-with-eigenfaces.html


Bonus: Demo!Spark + Stanford CoreNLP Sentiment Analysis

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Bonus: Top 100 Country Song Sentiment

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Bonus: Surprising Results…?!

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Item-to-Item Based RecommendationsBased on Metadata: Genre, Description, Cast, City

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Demo!Item-to-Item-based Recommendations

One-Hot Encoding + K-Means Clustering

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One-Hot Encode Tag Feature Vectors

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Cluster Movie Tag Feature Vectors

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HyperparameterTuning

(K Clusters?)


Analyze Movie Tag Clusters

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User-to-Item Collaborative FilteringMatrix Factorization① Factor the large matrix (left) into 2 smaller matrices (right)② Lower-rank matrices approximate original when multiplied③ Fill in the missing values of the large matrix④ Surface k (rank) latent features from user-item interactions

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Item-to-Item Collaborative FilteringFamous Amazon Paper circa 2003

ProblemAs users grew, user-to-item collaborative filtering didn’t scale

SolutionItem-to-item similarity, nearest neighbors Offline (Batch)

Generate itemId->List[userId] vectorsOnline (Real-time)

From cart, recommend nearest-neighbors in vector space58


Demo!Collaborative Filtering-based Recommendations

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Fitting the Matrix Factorization Model

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Show ItemFactors Matrix from ALS

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Show UserFactors Matrix from ALS

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Generating Individual Recommendations

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Generating Batch Recommendations

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Clustering + Collaborative Filtering RecsCluster matrix output from Matrix FactorizationLatent features derived from user-item interaction

Item-to-Item SimilarityCluster item-factor matrix->

User-to-User Similarity<-Cluster user-factor matrix

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Demo!Clustering + Collaborative Filtering-based Recommendations

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Show ItemFactors Matrix from ALS

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Convert to Item Factors -> mllib.VectorRequired by K-Means Clustering Algorithm

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Fit and Evaluate K-Means Cluster Model

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Measures ClosenessOf Points Within Clusters

K = 5 Clusters


Netflix Genres and ClustersTypical Genres

Documentary, Romance, Comedy, Horror, Action, Adventure

Latent (Hidden) ClustersEmotionally-Independent Dramas for Hopeless RomanticsWitty Dysfunctional-Family TV Animated ComediesRomantic Crime Movies based on Classic LiteratureLatin American Forbidden-Love MoviesCritically-acclaimed Emotional Drug MovieCerebral Military Movie based on Real LifeSentimental Movies about Horses for Ages 11-12Gory Canadian Revenge MoviesRaunchy Mad Scientist Comedy

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② Similarities

③ Recommendations

④ Approximations



When to Approximate?Memory or time constrained queries

Relative vs. exact counts are OK (approx # errors after a release)

Using machine learning or graph algosInherently probabilistic and approximate

Streaming aggregationsInherently sloppy collection (exactly once?)

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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”

…at the office.

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A Few Good Algorithms

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You can’t handle the approximate!


Common to These Algos & Data StructsLow, fixed size in memoryStore large amount of dataKnown error boundsTunable tradeoff between size and errorLess memory than Java/Scala collectionsRely on multiple hash functions or operationsSize of hash range defines error

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Bloom FilterSet.contains(key): Boolean

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

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Bloom FilterApproximate Set.contains(key)

No means No, Yes means Maybe

Elements can only be addedNever updated or removed

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Bloom Filter in Action

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set(key) contains(key): Boolean

Images by @avibryant

Set.contains(key): TRUE -> maybe contains (other key hashes may overlap)Set.contains(key): FALSE -> definitely does not contain (no key flipped all bits)


CountMin SketchFrequency 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 keyie. “Heavy Hitters” on Twitter

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Matei Zaharia Martin Odersky Donald Trump


CountMin Sketch In Action (TopK Count)

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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 GunTop 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


HyperLogLogCount Distinct

“Hash Multiple Times and Uniformly Distribute Where You Land”

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HyperLogLog (HLL)Approximate count distinctSlight twist

Special hash function creates uniform distributionHash subsets of data with single, special hash func

Error estimate14 bits for size of rangem = 2^14 = 16,384 hash slotserror = 1.04/(sqrt(16,384)) = .81%

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Not many of these


HyperLogLog In Action (Count Distinct)Use Case: Number of distinct users who view a movie

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0 32

Top Gun: Hour 2user2001

user4009

user3002

user7002

user1005

user6001

User8001

User8002

user1001

user2009

user3005

user3003

Top Gun: Hour 1user3001

user7009

0 16

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

0 32

Top Gun: Hour 1 + 2user2001

user4009

user3002

user7002

user1005

user6001

User8001

User8002

Combine across different scales

user7009

user1001

user2009

user3005

user3003

user3001


Locality Sensitive HashingSet Similarity

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

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Locality Sensitive Hashing (LSH)Approximate set similarityPre-process m rows into b buckets

b << m; b = buckets, m = rowsHash items multiple times

** Similar items hash to overlapping buckets** Hash designed to cluster similar items

Compare just contents of bucketsMuch smaller cartesian compare ** Compare in parallel !!

Avoids huge cartesian all-pairs compare86

Chapter 3: LSH


DIMSUMSet Similarity

“Pre-process and ignore data that is unlikely to be similar.”

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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 AlgebirdAlgebraic Fundamentals

Parallel

Associative

ComposableExamples

Min, Max, AvgBloomFilter (Set.contains(key))HyperLogLog (Count Distinct)CountMin Sketch (TopK Count)

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RedisImplementation of HyperLogLog (Count Distinct)

12KB per item count2^64 max # of items0.81% error

Add user views for given moviePFADD TopGun_Hour1_HLL user1001 user2009 user3005PFADD TopGun_Hour1_HLL user3003 user1001

Get distinct count (cardinality) of setPFCOUNT TopGun_Hour1_HLLReturns: 4 (distinct users viewed this movie)

Union 2 HyperLogLog Data StructuresPFMERGE TopGun_Hour1_HLL TopGun_Hour2_HLL

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ignore duplicates

Tunable


Approximations in Spark LibrariesSpark Core

countByKeyApprox(timeout: Long, confidence: Double)PartialResult

Spark SQLapproxCountDistinct(column: Column, targetResidual: Float)approxQuantile(column: Column, quantiles: Seq[Float], targetResidual: Float)

Spark MLStratified sampling

sampleByKey(fractions: Map[K, Double])DIMSUM sampling

Probabilistic sampling reduces amount of shuffleRowMatrix.columnSimilarities(threshold: Double)

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Demo!Exact Count vs. Approximate HLL and CMS Count

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HashSet vs. HyperLogLog (Memory)

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HashSet vs. CountMin Sketch (Memory)

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Demo!Exact Similarity vs. Approximate LSH Similarity

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Brute Force Cartesian All Pair Similarity

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47 seconds


Locality Sensitive Hash All Pair Similarity

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6 seconds


Many More Demos!

or

Download Docker Clone on Github

99

http://advancedspark.com



② Similarities

③ Recommendations

④ Approximations



Netflix RecommendationsFrom Ratings to Real-time

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Netflix Has a Lot of DataNetflix 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 White Castle.Renamed my favourite movie to:

“Harold and Kumar Get the Munchies”

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

Summary: Buy NFLX Stock!

This broke my unit tests!


Netflix Data Pipeline - Then

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v1.0

v2.0


Netflix Data Pipeline – Now (Keystone)

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v3.0

9 million events per second22 GB per second!!

EC2 D2XLDisk: 6 TB, 475 MB/sRAM: 30 GNetwork: 700 Mbps

Auto-scaling,Fault tolerance

A/B Tests,Trending Now

SAMZA

Splits high andnormal priority


Netflix Recommendation Data Pipeline

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Throw away batch user factors (U)

Keep batch video factors (V)


Netflix Trending Now (Time-based Recs)Uses Spark StreamingPersonalized to user (viewing history, past ratings)Learns and adapts to events (Valentine’s Day)

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“VHS”

Number of Plays

Number of Impressions

CalculateTake Rate


Bonus: Pandora Time-based RecsWork Days

Play familiar musicUser is less likely accept new music

Evenings and WeekendsPlay new musicMore like to accept new music

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$1 Million Netflix Prize (2006-2009)Goal

Improve movie predictions by 10% (Root Mean Sq Error)Test data withheld to calculate RMSE upon submission

5-star Ratings Dataset(userId, movieId, rating, timestamp)

Winning algorithm(s)10.06% improvement (RMSE)Ensemble of 500+ ML combined with GBDT’sComputationally impractical

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Secrets to the Winning AlgorithmsAdjust for the following human bias…① Alice effect: user rates lower than avg② Inception effect: movie rated higher than avg③ Overall mean rating of a movie④ Number of people who have rated a movie⑤ Number of days since user’s first rating⑥ Number of days since movie’s first rating⑦ Mood, time of day, day of week, season, weather

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Netflix Common ML AlgorithmsLogistic RegressionLinear RegressionGradient Boosted Decision TreesRandom ForestMatrix FactorizationSVDRestricted Boltzmann MachinesDeep Neural NetsMarkov ModelsLDAClustering

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Ensembles!


Netflix Genres and ClustersTypical Genres

Documentaries, Romance Comedies, Horror, Action, Adventure

Latent (Hidden) ClustersEmotionally-Independent Dramas for Hopeless RomanticsWitty Dysfunctional-Family TV Animated ComediesRomantic Crime Movies based on Classic LiteratureLatin American Forbidden-Love MoviesCritically-acclaimed Emotional Drug MovieCerebral Military Movie based on Real LifeSentimental Movies about Horses for Ages 11-12Gory Canadian Revenge MoviesRaunchy Mad Scientist Comedy

111


Netflix Social IntegrationPost to Facebook after movie start (5 mins)Recommend to new users based on friendsHelps with Cold Start problem

112


Netflix SearchNo results? No problem… Show similar results!

Utilize extensive DVD CatalogMetadata search (ElasticSearch)Named entity recognition (NLP)

Empty searches are opportunity!Explicit feedback for future recommendationsContent to buy and produce!

113


Netflix A/B TestsUsers tend to click on images featuring…

Faces with strong emotional expressionsVillains over heroesSmall number of cast members

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Netflix Recommendation Serving LayerUse Case: Recommendation service depends on EVCacheProblem: EVCache cluster does down or becomes latent!?Answer: github.com/Netflix/Hystrix Circuit Breaker!

Circuit StatesClosed: Service OK

Open: Service DOWNFallback to Static

115


Why Higher Average Ratings 2004+?2004, Netflix noticed higher ratings on averageSome possible reasons why…

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① Significant UI improvements deployed② New recommendation engine deployed③


Thank You, Everyone!!Chris Fregly @cfreglyResearch Scientist @ Flux Capacitor AISan Francisco, California, USA

http://fluxcapacitor.comSign up for the Meetup and BookContribute to Github RepoRun all Demos using Docker

Find me LinkedIn, Twitter, Github, Email, Fax117

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

Flux Capacitor AI Bringing AI Back to the Future!

Bringing AI Back to the Future!

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Boston Spark Meetup May 24, 2016

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