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Decision Trees in Neo4j

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How to build expert systems using Decision Trees in Neo4j.

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Decision Trees in Neo4j

  1. 1. Decision Trees in Neo4j Build dynamic expert systems and rules engines.
  2. 2. github.com/maxdemarzi About 200 public repositories Max De Marzi Neo4j Field Engineer About Me ! 01 02 03 04 maxdemarzi.com @maxdemarzi
  3. 3. Let’s start with a Sign
  4. 4. Rule for Getting In • Anyone over 21 years old gets in. • On some nights, women 18 years or older get in. • On some nights, men 18 years or older get in. • The last two change dynamically.
  5. 5. Represent in a Graph
  6. 6. Rule Node • expression (String): • (age >= 18) && gender.equals(“female”) • parameter_names(String): • age, gender • parameter_types(String): • Int, String
  7. 7. There was a time… BEFORE
  8. 8. When the Traversal API ruled supreme
  9. 9. https://maxdemarzi.com/2015/09/04/flight-search-with-the-neo4j-traversal-api/ Learn more… or read the docs
  10. 10. The Stored Procedure
  11. 11. @Procedure(name = "com.maxdemarzi.traverse.decision_tree", mode = Mode.READ) @Description("CALL com.maxdemarzi.traverse.decision_tree(tree, facts)") public Stream<PathResult> traverseDecisionTree( @Name("tree") String id, @Name("facts") Map<String, String> facts){ // Which Decision Tree are we interested in? Node tree = db.findNode(Labels.Tree, "id", id); if ( tree != null) { // Find the paths by traversing this graph and the facts given return decisionPath(tree, facts); } return null; } Tree Facts
  12. 12. private Stream<PathResult> decisionPath(Node tree, 
 Map<String, String> facts) { TraversalDescription myTraversal = db.traversalDescription() .depthFirst() .expand(new DecisionTreeExpander(facts)) .evaluator(decisionTreeEvaluator); return myTraversal .traverse(tree) .stream() .map(PathResult::new); } Build a Traversal Description
  13. 13. public class DecisionTreeEvaluator implements PathEvaluator { @Override public Evaluation evaluate(Path path, BranchState branchState) { // If we get to an Answer stop traversing, we found a valid path. if (path.endNode().hasLabel(Labels.Answer)) { return Evaluation.INCLUDE_AND_PRUNE; } else { // If not, continue down this path 
 // to see if there is anything else to find. return Evaluation.EXCLUDE_AND_CONTINUE; } } Build a Path Evaluator
  14. 14. public class DecisionTreeExpander implements PathExpander { private Map<String, String> facts; private ExpressionEvaluator ee = new ExpressionEvaluator(); public DecisionTreeExpander(Map<String, String> facts) { this.facts = facts; ee.setExpressionType(boolean.class); } Build a Path Expander
  15. 15. @Override public Iterable<Relationship> expand(Path path, BranchState branchState) { // If we get to an Answer stop traversing, we found a valid path. if (path.endNode().hasLabel(Labels.Answer)) { return Collections.emptyList(); } // If we have Rules to evaluate, go do that. if (path.endNode() .hasRelationship(Direction.OUTGOING, RelationshipTypes.HAS)) { return path.endNode() .getRelationships(Direction.OUTGOING, RelationshipTypes.HAS); } The expand method
  16. 16. if (path.endNode().hasLabel(Labels.Rule)) { try { if (isTrue(path.endNode())) { return path.endNode() .getRelationships(Direction.OUTGOING, RelationshipTypes.IS_TRUE); } else { return path.endNode() .getRelationships(Direction.OUTGOING, RelationshipTypes.IS_FALSE); } } catch (Exception e) { // Could not continue this way! return Collections.emptyList(); } } The expand method (cont.)
  17. 17. boolean isTrue(Node rule) throws Exception { Map<String, Object> ruleProperties = rule.getAllProperties(); String[] parameterNames = Magic.explode((String) ruleProperties .get("parameter_names")); Class<?>[] parameterTypes = Magic.stringToTypes((String) ruleProperties .get("parameter_types")); The isTrue method
  18. 18. Object[] arguments = new Object[parameterNames.length]; for (int j = 0; j < parameterNames.length; ++j) { arguments[j] = Magic.createObject(parameterTypes[j], facts.get(parameterNames[j])); } ee.setParameters(parameterNames, parameterTypes); ee.cook((String)ruleProperties.get("expression")); return (boolean) ee.evaluate(arguments); The isTrue method (cont.)
  19. 19. Running it
  20. 20. CREATE (tree:Tree { id: 'bar entrance' }) CREATE (over21_rule:Rule { parameter_names: 'age', parameter_types:'int', expression:'age >= 21' }) CREATE (gender_rule:Rule { parameter_names: 'age,gender', parameter_types:'int,String', expression:'(age >= 18) && gender.equals("female")' }) CREATE (answer_yes:Answer { id: 'yes'}) CREATE (answer_no:Answer { id: 'no'}) CREATE (tree)-[:HAS]->(over21_rule) CREATE (over21_rule)-[:IS_TRUE]->(answer_yes) CREATE (over21_rule)-[:IS_FALSE]->(gender_rule) CREATE (gender_rule)-[:IS_TRUE]->(answer_yes) CREATE (gender_rule)-[:IS_FALSE]->(answer_no) Build the Tree
  21. 21. CALL com.maxdemarzi.traverse.decision_tree(
 ‘bar entrance', {gender:'male', age:'20'}) YIELD path RETURN path; Check the Facts Male, 20 years old?
  22. 22. It’s alright…
  23. 23. CALL com.maxdemarzi.traverse.decision_tree(
 ‘bar entrance', {gender:'female', age:’19'}) YIELD path RETURN path; Female, 19 years old? Check the Facts
  24. 24. OMG…
  25. 25. Learn More…
  26. 26. https://maxdemarzi.com/2018/01/14/dynamic-rule-based-decision-trees-in-neo4j/ Details:
  27. 27. What about multiple Options?
  28. 28. https://maxdemarzi.com/2018/01/26/dynamic-rule-based-decision-trees-in-neo4j-part-2/ There is a Part 2
  29. 29. Decision Streams • Paper: 
 https://arxiv.org/pdf/1704.07657.pdf • Authors:
 Dmitry Ignatov and Andrey Ignatov • Implementation: 
 https://github.com/aiff22/Decision-Stream
  30. 30. Thank you!

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