v0.8.0

BetaML v0.8.0

Diff since v0.7.1

• support for all models of the new "V2" API that implements a "standard" mod = Model([Options]), fit!(mod,X,[Y]), predict(mod,[X]) workflow (details here). Classic API is now deprecated, with some of its functions be removed in the next BetaML 0.9 versions and some unexported.
• standardised function names to follow the Julia style guidelines and the new BetaML code style guidelines](https://sylvaticus.github.io/BetaML.jl/dev/StyleGuide_templates.html)
• new hyper-parameter autotuning method:

  mod = ModelXX(autotune=true)  # --> control autotune with the parameter `tunemethod`
  fit!(mod,x,[y])               # --> autotune happens here together with final tuning
  est = predict(mod,xnew)
  

  Autotune is hyperthreaded with model-specific defaults. For example for Random Forests the defaults are:

  tunemethod=SuccessiveHalvingSearch(
      hpranges     = Dict("n_trees"   => [10, 20, 30, 40],
                       "max_depth"    => [5,10,nothing],
                       "min_gain"     => [0.0, 0.1, 0.5],
                       "min_records"  => [2,3,5],
                       "max_features" => [nothing,5,10,30],
                       "beta"         => [0,0.01,0.1]),
      loss         = l2loss_by_cv, # works for both regression and classification
      res_shares   = [0.08, 0.1, 0.13, 0.15, 0.2, 0.3, 0.4]
      multithreads = false) # RF are already multi-threaded
  

  The number of models is reduced in order to arrive with a single model.
  Only supervised model autotuning is currently implemented, but GMM-based clustering autotuniing is planned using BIC or AIC.
• new functions model_load and model_save to load/save trained models from the filesystem
• new MinMaxScaler (StandardScaler was already available as classical API functions scale and getScalingFactors)
• many bugfixes/improvments on corner situations
• new MLJ interface models to NeuralNetworkEstimator

Closed issues:

• Improve oneHotEncode stability for encoding integers embedding categories (#29)
• initVarainces! doesn't support mixed-type variances (#33)
• Error generating MLJ model registry (#37)
• WARNING: could not import Perceptron ... (#38)
• MLJ model BetaMLGMMRegressor predicting row vectors instead of column vectors (#40)