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Project description

  • parallel evaluation with joblib (n_jobs)
  • implement genetic algorithm to find pipelines
  • copy parameters from tpot
  • add rules to prevent stupid things (PolynomialFeatures with many columns)
  • distribute genetic algorithms with dask
  • test joblib distributed backend with dask (nothing to do, just test)
  • fine-grained distribution with dask computation graph:
    • trivial for prediction
    • for fit, each step returns a cross-validated estimate and a fitted model. The fitted model is not used before the final step.
  • it is possible to implement cross-validation with a factor 2 improvement when the cross-val and the training folds match
  • handle timeouts: https://github.com/dask/distributed/issues/391, https://github.com/dask/dask/issues/1183

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