Parallel Local Search on Optimal Classification Trees

(Shaun Fendi Gan, Arkira Tanglertsumpun, MIT Master of Business Analytics, Dec 2020)

The local-search heuristic was introduced as an attempt to remedy the limitations on the scalability of Bertsimas and Dunn’s Optimal Classification Tree mixed-integer optimization formulation. This repo examined the effectiveness of this heuristic as well as introducing four parallelized versions that could improve heuristic optimality.

Key Results

The Half-Split method was found to be the most scalable and time efficient, reducing run time by 30%.
Standard Multi-threading on random restarts did not have a significant improvement (<5%),
Deep Subtree Search Class Assignments were found to only benefit on datasets with many features (m > 15).

How to Use

Open benchmark.jl to find all functions used to test and benchmark different local search. Original local search can be found as the function LocalSearch in local_search.jl The MIO formulation implement in JuMP/ Gurobi can also be found in oct.jl

Files

local_search.jl- Main file containing serially optimized functions to run the local search heuristic
tree.jl - Serially optimized functions to create tree structures
local_search_z.jl - Contains function for Local Search with parallel Observation assignments
local_search_half.jl - Contains function for Local Search with parallel half splits
local_search_deep.jl - Contains function for Local Search for a deep subtree search
oct.jl - Contains the JuMP MIO Formulation for an Optimal Classification Tree

Testing Files

model_evaluation.jl - Contains function for calculating accuracy of a tree’s predictions
benchmark.jl - Contains code used to benchmark performance of different Local Search Functions
unit_test.jl - Contains function test_tree to verify Tree struct output from Local Search
testing OCT.ipynb - Contains IntepretableAI Optimal Classification Tree structure, used as reference
onethread_oct.ipynb - Contains MIO OCT function used to test on one thread only (Gurobi Default != 1)

Source: [Dimitris Bertsimas, Jack Dunn.Machine Learning under a ModernOptimization Lens. Dynamic Ideas LLC, Massachusetts, 2019]

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