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TPOT: A Tree-based Pipeline Optimization Tool

Trang Lê

mathematician

postdoctoral researcher

@UPenn IBI

amateur runner

@trang1618

0000-0003-3737-6565

Clean data

Select features

Preprocess features

Construct features

Select classifier

Optimize parameters

Validate model

Typical ML pipeline

Open source AutoML tools

• auto-sklearn (python) github.com/automl/auto-sklearn
  • ​Bayesian optimzation over a fixed 3-step ML pipeline

• auto-Weka (java) github.com/automl/autoweka
  • ​similar to auto-sklearn, built on top of Weka

• H20.ai (java w/ python, scala, R, web GUI) github.com/h2oai/h2o-3
  • ​basic data prep w/ grid/random search over ML algorithms

• devol (python) github.com/joeddav/devol
  • deep learning architecture search via GP

Randy Olson

• DEAP
  
  
  
  
  
  
  
  
  
   
• Objective:
  • maximize pipeline's CV classification performance
  • minimize pipeline’s complexity 
• Pareto front with NSGA-II

TPOT

Weixuan Fu

Entire data set

Entire data set

PCA

Polynomial features

Combine features

Select k best features

Logistic regression

Multiple copies of the data set can enter the pipeline for analysis

Pipeline operators modify the features

Modified data set flows through the pipeline operators

Final classification is performed on the final feature set

Genetic programming

GP primitives Dataset selectors, Feature selectors & preprocessors, Supervised classifiers

Population sequences of pipeline operators

Generations

Mutation and crossover

(a) insertion mutation

(b) deletion mutation

(c) swap mutation

(d) substitution mutation

(e) crossover

TPOT configs

• Classification
• Regression

Template + Dataset Selector

Mutation restriction