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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Release files for pamalia 0.0.2
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