AutoMLPipeline (AMLP)

is a package that makes it trivial to create complex ML pipeline structures using simple expressions. AMLP leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and automatically discover optimal structures for machine learning prediction and classification.

To illustrate, a typical machine learning workflow that extracts numerical features (numf) for ICA (independent component analysis) and PCA (principal component analysis) transformations, respectively, concatenated 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)
julia> crossvalidate(model,X,Y,"accuracy_score")
julia> crossvalidate(model,X,Y,"balanced_accuracy_score")

You can visualize the pipeline by using AbstractTrees Julia package.

# package installation
julia> using Pkg
julia> Pkg.add("AbstractTrees")
julia> Pkg.add("AutoMLPipeline")

# load the packages
julia> using AbstractTrees
julia> using AutoMLPipeline

julia> expr = @pipelinex (catf |> ohe) + (numf |> pca) + (numf |> ica) |> rf
:(Pipeline(ComboPipeline(Pipeline(catf, ohe), Pipeline(numf, pca), Pipeline(numf, ica)), rf))

julia> print_tree(stdout, expr)
:(Pipeline(ComboPipeline(Pipeline(catf, ohe), Pipeline(numf, pca), Pipeline(numf, ica)), rf))
├─ :Pipeline
├─ :(ComboPipeline(Pipeline(catf, ohe), Pipeline(numf, pca), Pipeline(numf, ica)))
│  ├─ :ComboPipeline
│  ├─ :(Pipeline(catf, ohe))
│  │  ├─ :Pipeline
│  │  ├─ :catf
│  │  └─ :ohe
│  ├─ :(Pipeline(numf, pca))
│  │  ├─ :Pipeline
│  │  ├─ :numf
│  │  └─ :pca
│  └─ :(Pipeline(numf, ica))
│     ├─ :Pipeline
│     ├─ :numf
│     └─ :ica
└─ :rf


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.

The target of future work will be the implementations of different pipeline optimization algorithms ranging from evolutionary approaches, integer programming (discrete choices of POP elements), tree/graph search, and hyper-parameter search.

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 complex pipeline structures
  • Easily extensible architecture by overloading just two main interfaces: fit! and transform!
  • Meta-ensembles that allows composition of ensembles of ensembles (recursively if needed) for robust prediction routines
  • Categorical and numerical feature selectors for specialized preprocessing routines based on types


AutoMLPipeline is in the Julia Official package registry. The latest release can be installed at the Julia prompt using Julia's package management which is triggered by pressing ] at the julia prompt:

julia> ]
(v1.0) pkg> add AutoMLPipeline


julia> using Pkg
julia> pkg"add AutoMLPipeline"


julia> using Pkg
julia> Pkg.add("AutoMLPipeline")

Once AutoMLPipeline is installed, you can load it by:

julia> using AutoMLPipeline


julia> import AutoMLPipeline

Generally, you will need the different learners/transformers and utils in AMLP for to carry-out the processing and modeling routines.

using AutoMLPipeline 
using AutoMLPipeline.FeatureSelectors
using AutoMLPipeline.EnsembleMethods
using AutoMLPipeline.CrossValidators 
using AutoMLPipeline.DecisionTreeLearners
using AutoMLPipeline.Pipelines
using AutoMLPipeline.BaseFilters
using AutoMLPipeline.SKPreprocessors 
using AutoMLPipeline.Utils`

CSV and DataFrames will be needed in the succeeding examples and should be installed:

using Pkg

Tutorial Outline

Manual Outline

ML Library