Generating Learned NB Model with 2 Mapper-CPUs
In this process, we utilize two mapper-CPUs to generate the learned Naive Bayes (NB) model. The mappers are configured to handle different aspects such as color, type, and origin of stolen items. The model is constructed based on the data collected on color, type, and origin, splitting into categories of 'yes' and 'no' to determine probabilities. The iterative process involves mapping, emitting successor nodes, calculating edge costs, and identifying maximum probabilities from predecessors. Adjustments are made iteratively until convergence, and finally, postprocessing involves the removal of adjacency lists to optimize the model.
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Presentation Transcript
Show how we generate the learned NB model using 2 mapper-CPUs
Mapper-2 Mapper-1 Stolen? Colour | Stolen? Type | Stolen? Origin | Stolen?
Mapper-1 Class: yes=3, no=2, total=5 Colour Colour| yes: red=2, yellow=1, total=3 Colour| no: red=1, yellow=1, total=2 Type Type| yes: sports=3, SUV=0, total=3 Type| no: sports=2, SUV=0, total=2 Origin Origin| yes: domestic=2, imported=1, total=3 Origin| no: domestic=2, imported=0, total=2
C .8 A .75 uv .9 .5 H D .7 .8 B .9 .9 E A D H B C E A D H B C E
C 1 C 1 .8 .8 A 1 A .9 .75 .9 .75 .9 .5 .5 D 1 D 1 H H .7 B 1 .7 B .8 .8 .9 .8 .9 E 1 .9 E 1 .9 1. 2. C .72 C .72 .8 .8 A .9 A .9 .75 .9 .75 .9 .5 .5 D H D .56 H .64 .7 B .8 .7 .8 B .8 .9 .8 .9 E .9 E .9 3. .72 4. .72
init: For each node, node ID <1, -, {<succ-node-ID,edge-cost>}> map: take node ID < , next, {<succ-node-ID,edge-cost>}> For each succ-node-ID: emit succ-node ID {<node ID, * edge-cost>} emit node ID ,{<succ-node-ID,edge-cost>} reduce: := max prob. from a predecessor; next := that predec. emit node ID < , next, {<succ-node-ID,edge-cost>}> Repeat until no changes Postprocessing: Remove adjacency lists
