A package that makes it trivial to create and evaluate machine learning pipeline architectures.
Author IBM
175 Stars
Updated Last
4 Months Ago
Started In
February 2020
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is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and makes it easy to discover optimal structures for machine learning prediction and classification.

To illustrate, here is a pipeline expression and evaluation of 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 (Random Forest) modeling:

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,"balanced_accuracy_score")

Just take note that + has higher priority than |> so if you are not sure, enclose the operations inside parentheses.

### these two expressions are the same
@pipeline a |> b + c; @pipeline a |> (b + c)

### these two expressions are the same
@pipeline a + b |> c; @pipeline (a + b) |> c


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 can help lessen the complexity in the management and analysis of the wide-array of choices of 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 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


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> ]
(v1.3) pkg> update
(v1.3) pkg> add AutoMLPipeline


julia> using Pkg
julia> pkg"update"
julia> pkg"add AutoMLPipeline"


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

Sample Usage

Below outlines some typical way to preprocess and model any dataset.

1. Load Data

1.1 Install CSV and DataFrames Packages
using Pkg
1.2 Load Data, Extract Input (X) and Target (Y)
# Make sure that the input feature is a dataframe and the target output is a 1-D vector.
using AutoMLPipeline
using CSV
profbdata = getprofb()
X = profbdata[:,2:end] 
Y = profbdata[:,1] |> Vector;

2. Load AutoMLPipeline Package and Submodules

using AutoMLPipeline, AutoMLPipeline.FeatureSelectors, AutoMLPipeline.EnsembleMethods
using AutoMLPipeline.CrossValidators, AutoMLPipeline.DecisionTreeLearners, AutoMLPipeline.Pipelines
using AutoMLPipeline.BaseFilters, AutoMLPipeline.SKPreprocessors, AutoMLPipeline.Utils
using AutoMLPipeline.SKLearners

3. Load Filters, Transformers, and Learners

#### Decomposition
pca = SKPreprocessor("PCA"); fa = SKPreprocessor("FactorAnalysis"); ica = SKPreprocessor("FastICA")

#### Scaler 
rb = SKPreprocessor("RobustScaler"); pt = SKPreprocessor("PowerTransformer"); 
norm = SKPreprocessor("Normalizer"); mx = SKPreprocessor("MinMaxScaler")

#### categorical preprocessing
ohe = OneHotEncoder()

#### Column selector
catf = CatFeatureSelector(); 
numf = NumFeatureSelector()

#### Learners
rf = SKLearner("RandomForestClassifier"); 
gb = SKLearner("GradientBoostingClassifier")
lsvc = SKLearner("LinearSVC");     svc = SKLearner("SVC")
mlp = SKLearner("MLPClassifier");  ada = SKLearner("AdaBoostClassifier")
jrf = RandomForest();              vote = VoteEnsemble();
stack = StackEnsemble();           best = BestLearner();

Note: You can get a listing of available SKPreprocessors and SKLearners by invoking the following functions, respectively:

  • skpreprocessors()
  • sklearners()

4. Filter categories and hot-encode them

pohe = @pipeline catf |> ohe
tr = fit_transform!(pohe,X,Y)

5. Numerical Feature Extraction Example

5.1 Filter numeric features, compute ica and pca features, and combine both features
pdec = @pipeline (numf |> pca) + (numf |> ica)
tr = fit_transform!(pdec,X,Y)
5.2 Filter numeric features, transform to robust and power transform scaling, perform ica and pca, respectively, and combine both
ppt = @pipeline (numf |> rb |> ica) + (numf |> pt |> pca)
tr = fit_transform!(ppt,X,Y)

6. A Pipeline for the Voting Ensemble Learner

# take all categorical columns and hot-bit encode each, 
# concatenate them to the numerical features,
# and feed them to the voting ensemble
pvote = @pipeline  (catf |> ohe) + (numf) |> vote
pred = fit_transform!(pvote,X,Y)
### cross-validate

Note: crossvalidate() supports the following sklearn's performance metric

  • accuracy_score, balanced_accuracy_score, cohen_kappa_score
  • jaccard_score, matthews_corrcoef, hamming_loss, zero_one_loss
  • f1_score, precision_score, recall_score

7. Use @pipelinex instead of @pipeline to print the corresponding function calls in 6

julia> @pipelinex (catf |> ohe) + (numf) |> vote
:(Pipeline(ComboPipeline(Pipeline(catf, ohe), numf), vote))

# another way is to use @macroexpand with @pipeline
julia> @macroexpand @pipeline (catf |> ohe) + (numf) |> vote
:(Pipeline(ComboPipeline(Pipeline(catf, ohe), numf), vote))

8. A Pipeline for the Random Forest (RF)

# compute the pca, ica, fa of the numerical columns,
# combine them with the hot-bit encoded categorical features
# and feed all to the random forest classifier
prf = @pipeline  (numf |> rb |> pca) + (numf |> rb |> ica) + (numf |> rb |> fa) + (catf |> ohe) |> rf
pred = fit_transform!(prf,X,Y)
score(:accuracy,pred,Y) |> println

9. A Pipeline for the Linear Support Vector for Classification (LSVC)

plsvc = @pipeline ((numf |> rb |> pca)+(numf |> rb |> fa)+(numf |> rb |> ica)+(catf |> ohe )) |> lsvc
pred = fit_transform!(plsvc,X,Y)
score(:accuracy,pred,Y) |> println

Note: More examples can be found in the test directory of the package. Since the code is written in Julia, you are highly encouraged to read the source code and feel free to extend or adapt the package to your problem. Please feel free to submit PRs to improve the package features.

10. Performance Comparison of Several Learners

10.1 Sequential Processing
using Random
using DataFrames

jrf = RandomForest()
ada = SKLearner("AdaBoostClassifier")
sgd = SKLearner("SGDClassifier")
tree = PrunedTree()
std = SKPreprocessor("StandardScaler")
disc = CatNumDiscriminator()
lsvc = SKLearner("LinearSVC")

learners = DataFrame()
for learner in [jrf,ada,sgd,tree,lsvc]
  pcmc = @pipeline disc |> ((catf |> ohe) + (numf |> std)) |> learner
  mean,sd,_ = crossvalidate(pcmc,X,Y,"accuracy_score",10)
  global learners = vcat(learners,DataFrame(,mean=mean,sd=sd))
@show learners;
10.2 Parallel Processing
using Random
using DataFrames
using Distributed

nprocs() == 1 && addprocs()
@everywhere using DataFrames
@everywhere using AutoMLPipeline

jrf = RandomForest()
ada = SKLearner("AdaBoostClassifier")
sgd = SKLearner("SGDClassifier")
tree = PrunedTree()
std = SKPreprocessor("StandardScaler")
disc = CatNumDiscriminator()
lsvc = SKLearner("LinearSVC")

learners = @distributed (vcat) for learner in [jrf,ada,sgd,tree,lsvc]
  pcmc = @pipeline disc |> ((catf |> ohe) + (numf |> std)) |> learner
  mean,sd,_ = crossvalidate(pcmc,X,Y,"accuracy_score",10)
@show learners;

11. Automatic Selection of Best Learner

You can use * operation as a selector function which outputs the result of the best learner. If we use the same pre-processing pipeline in 10, we expect that the average performance of best learner which is lsvc will be around 73.0.

pcmc = @pipeline disc |> ((catf |> ohe) + (numf |> std)) |> (jrf * ada * sgd * tree * lsvc)

12. Learners as Transformers

It is also possible to use learners in the middle of expression to serve as transformers and their outputs become inputs to the final learner as illustrated below.

expr = @pipeline ( 
                   ((numf |> rb)+(catf |> ohe) |> gb) + ((numf |> rb)+(catf |> ohe) |> rf) 
                 ) |> ohe |> ada;

One can even include selector function as part of transformer preprocessing routine:

pjrf = @pipeline disc |> ((catf |> ohe) + (numf |> std)) |> 
                 ((jrf * ada ) + (sgd * tree * lsvc)) |> ohe |> ada


Note: The ohe is necessary in both examples because the outputs of the learners and selector function are categorical values that need to be hot-bit encoded before feeding to the final ada learner.

13. Tree Visualization of the Pipeline Structure

You can visualize the pipeline by using AbstractTrees Julia package.

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

# 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

Extending AutoMLPipeline

# If you want to add your own filter/transformer/learner, it is trivial. 
# Just take note that filters and transformers process the first 
# input features and ignores the target output while learners process both 
# the input features and target output arguments of the fit! function. 
# transform! function always expect one input argument in all cases. 

# First, import the abstract types and define your own mutable structure 
# as subtype of either Learner or Transformer. Also import the fit! and
# transform! functions to be overloaded. Also load the DataFrames package
# as the main data interchange format.

using DataFrames
using AutoMLPipeline.AbsTypes, AutoMLPipeline.Utils

import AutoMLPipeline.AbsTypes: fit!, transform!  #for function overloading 

export fit!, transform!, MyFilter

# define your filter structure
mutable struct MyFilter <: Transformer
  function MyFilter(args::Dict())

# define your fit! function. 
# filters and transformer ignore the target argument. 
# learners process both the input features and target argument.
function fit!(fl::MyFilter, inputfeatures::DataFrame, target::Vector=Vector())

#define your transform! function
function transform!(fl::MyFilter, inputfeatures::DataFrame)::DataFrame

# Note that the main data interchange format is a dataframe so transform! 
# output should always be a dataframe as well as the input for fit! and transform!.
# This is necessary so that the pipeline passes the dataframe format consistently to
# its filters/transformers/learners. Once you have this filter, you can use 
# it as part of the pipeline together with the other learners and filters.

Feature Requests and Contributions

We welcome contributions, feature requests, and suggestions. Here is the link to open an issue for any problems you encounter. If you want to contribute, please follow the guidelines in contributors page.

Help usage

Usage questions can be posted in:

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