Preliminaries• Code is available from github: – firstname.lastname@example.org:tdunning/Chapter-16.git• EC2 instances available• Thumb drives also available• Email to email@example.com• Twitter @ted_dunning
A Quick Review• What is classification? – goes-ins: predictors – goes-outs: target variable• What is classifiable data? – continuous, categorical, word-like, text-like – uniform schema• How do we convert from classifiable data to feature vector?
Classifiable Data• Continuous – A number that represents a quantity, not an id – Blood pressure, stock price, latitude, mass• Categorical – One of a known, small set (color, shape)• Word-like – One of a possibly unknown, possibly large set• Text-like – Many word-like things, usually unordered
But that isn’t quite there• Learning algorithms need feature vectors – Have to convert from data to vector• Can assign one location per feature – or category – or word• Can assign one or more locations with hashing – scary – but safe on average
Let’s write some code (cue relaxing background music)
Generating new features• Sometimes the existing features are difficult to use• Restating the geometry using new reference points may help• Automatic reference points using k-means can be better than manual references
Integration Issues• Feature extraction is ideal for map-reduce – Side data adds some complexity• Clustering works great with map-reduce – Cluster centroids to HDFS• Model training works better sequentially – Need centroids in normal files• Model deployment shouldn’t depend on HDFS
Parallel Stochastic Gradient Descent Model I n Train Average p sub models u model t
Variational Dirichlet Assignment Model I n Gather Update p sufficient model u statistics t
Old tricks, new dogs Read from local disk• Mapper from distributed cache – Assign point to cluster Read from – Emit cluster id, (1, point) HDFS to local disk• Combiner and reducer by distributed cache – Sum counts, weighted sum of points – Emit cluster id, (n, sum/n) Written by• Output to HDFS map-reduce
Old tricks, new dogs• Mapper – Assign point to cluster Read from – Emit cluster id, 1, point NFS• Combiner and reducer – Sum counts, weighted sum of points – Emit cluster id, n, sum/n Written by map-reduce• Output to HDFS MapR FS
Modeling architecture Side-data Now via NFSI Featuren Sequential extraction Datap SGD and joinu Learning downt sampling Map-reduce
A particular slide catching your eye?
Clipping is a handy way to collect important slides you want to go back to later.