JuliaCon 2020 (times are in UTC)

The typical workflow in machine learning
classification or prediction requires
some or combination of the following
preprocessing steps together with modeling:
- feature extraction (e.g. ica, pca, svd)
- feature transformation (e.g. normalization, scaling, ohe)
- feature selection (anova, correlation)
- modeling (rf, adaboost, xgboost, lm, svm, mlp)

Each step has several choices of functions
to use together with their corresponding
parameters. Optimizing the performance of the
entire pipeline is a combinatorial search
of the proper order and combination of preprocessing
steps, optimization of their corresponding
parameters, together with searching for
the optimal model and its hyper-parameters.

Because of close dependencies among various
steps, we can consider the entire process
to be a pipeline optimization problem (POP).
POP requires simultaneous optimization of pipeline
structure and parameter adaptation of its elements.
As a consequence, having an elegant way to
express pipeline structure helps in the analysis
and implementation of the optimization routines.

Package Features

• Pipeline API that allows high-level description of processing workflow
• Common API wrappers for ML libs including Scikitlearn, DecisionTree, etc
• Symbolic pipeline parsing for easy expression
  of complexed pipeline structures
• Easily extensible architecture by overloading just two main interfaces: fit! and transform!
• Meta-ensembles that allow composition of
  ensembles of ensembles (recursively if needed)
  for robust prediction routines
• Categorical and numerical feature selectors for
  specialized preprocessing routines based on types

To illustrate, a typical machine learning workflow that extracts
numerical features (numf) for ICA (independent component analysis) and
PCA (principal component analysis) transformations, respectively,
concatentated with the hot-bit encoding (ohe) of categorical
features (catf) of a given data for RF modeling can be expressed
in AMLP as:

julia> model = @pipeline (catf |> ohe) + (numf |> pca) + (numf |> ica) |> rf
julia> fit!(model,Xtrain,Ytrain)
julia> prediction = transform!(model,Xtest)
julia> score(:accuracy,prediction,Ytest)