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neo4jpy2neo

Setting up a decision tree in neo4j


Say I have a decision tree that looks like this:

enter image description here

I've written a cypher query to create that decision tree:

create (_0 {`name`:"Spins?", `type`:"split"})

create (_1a {`name`:"Weight", `type`:"split"})
create (_1b {`name`:"Weight", `type`:"split"})

create (_2a {`name`:"Spider", `type`:"terminal"})
create (_2b {`name`:"Spider Man", `type`:"terminal"})
create (_2c {`name`:"Ant", `type`:"terminal"})
create (_2d {`name`:"Ant Man", `type`:"terminal"})

create (_0)-[:`CON` {`lt`:.5}]->(_1a)
create (_0)-[:`CON` {`gte`:.5}]->(_1b)

create (_1a)-[:`CON` {`lt`:200}]->(_2a)
create (_1a)-[:`CON` {`gte`:200}]->(_2b)

create (_1b)-[:`CON` {`lt`:200}]->(_2c)
create (_1b)-[:`CON` {`gte`:200}]->(_2d)
;

A couple questions:

  1. Is this the best way to set up a decision tree in neo4j?
  2. How would I write a cypher query to join to the graph and get the result node with input data? e.g. say I had the data {'Spins?' : False, 'Weight' : 500}, how would I write a query to efficiently return the "Ant Man" node?

Solution

  • Allow to make small changes to the model:

    MERGE (_0:DT:Split  {name: 'Spins', l:0, i:0})
    MERGE (_1a:DT:Split {name: 'Weight', l:1, i:0})
    MERGE (_1b:DT:Split {name: 'Weight', l:1, i:1})
    
    MERGE (_2a:DT:Terminal {name:'Spider', l:2, i:0})
    MERGE (_2b:DT:Terminal {name:'Spider Man', l:2, i:0})
    MERGE (_2c:DT:Terminal {name:'Ant', l:2, i:0})
    MERGE (_2d:DT:Terminal {name:'Ant Man', l:2, i:0})
    
    MERGE (_0)-[:DT {type:'Left', value: 0.5, propname:'Spins'}]->(_1a)
    MERGE (_0)-[:DT {type:'Right', value: 0.5, propname:'Spins'}]->(_1b)
    MERGE (_1a)-[:DT {type:'Left', value: 50, propname:'Weight'}]->(_2a)
    MERGE (_1a)-[:DT {type:'Right', value: 50, propname:'Weight'}]->(_2b)
    MERGE (_1b)-[:DT {type:'Left', value: 50, propname:'Weight'}]->(_2c)
    MERGE (_1b)-[:DT {type:'Right', value: 50, propname:'Weight'}]->(_2d)
    

    And query:

    // Input parameters:
    WITH {Spins: 0.6, Weight: 500} as Cor
    // Get all decision paths:
    MATCH p = (S:DT:Split {l:0,i:0})-[:DT*]->(T:DT:Terminal)
    // Test single decision path:
    WHERE ALL(r in relationships(p) WHERE 
            (r.type='Left' AND Cor[r.propname]<r.value) OR // Left variant
            (r.type='Right' AND Cor[r.propname]>=r.value) // Right variant
          )
    RETURN T