Oscon data-2011-ted-dunning
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Oscon data-2011-ted-dunning



These are the slides for my half of the OSCON Mahout tutorial.

These are the slides for my half of the OSCON Mahout tutorial.



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Oscon data-2011-ted-dunning Oscon data-2011-ted-dunning Presentation Transcript

  • Hands-on Classification
  • Preliminaries• Code is available from github: – git@github.com:tdunning/Chapter-16.git• EC2 instances available• Thumb drives also available• Email to ted.dunning@gmail.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?
  • Data FlowNot quite so simple
  • 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
  • Data Flow
  • Classifiable Data Vectors
  • Hashed Encoding
  • What about collisions?
  • 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
  • K-means using target
  • K-means features
  • More code!(cue relaxing background music)
  • 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