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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:,

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