Pipeline

There are three types of Pipelines: Pipeline, ComboPipeline, and Selector Pipeline. The Pipeline (linear pipeline) performs sequential evaluation of fit_transform! operation to each of its elements passing the output of previous element as input to the next element iteratively. ComboPipeline (feature union pipeline) performs dataframe concatenation of the final outputs of its elements. Selector Pipeline acts as a selector function which outputs the results of the best learner using its internal cross-validation process.

The Pipeline uses |> symbolic expression while ComboPipeline uses +. The expression, a |> b, is equivalent to Pipeline(a,b) function call while the expression, a + b, is equivalent to ComboPipeline(a,b). The elements a and b can be transformers, filters, learners or pipeline themselves.

Pipeline Structure

The linear pipeline accepts the following variables wrapped in a Dictionary type argument:

• :name -> alias name for the pipeline
• :machines -> a Vector learners/transformers/pipelines
• :machine_args -> arguments to elements of the pipeline

For ease of usage, the following function calls are supported:

• Pipeline(args::Dict) -> default init function
• Pipeline(Vector{<:Machine},args::Dict=Dict()) -> for passing vectors of learners/transformers
• Pipeline(machs::Varargs{Machine}) -> for passing learners/transformers as arguments

ComboPipeline Structure

ComboPipeline or feature union pipeline accepts similar init variables with the the linear pipeline and follows similar helper functions:

• ComboPipeline(args::Dict) -> default init function
• ComboPipeline(Vector{<:Machine},args::Dict=Dict()) -> for passing vectors of learners/transformers
• ComboPipeline(machs::Varargs{Machine}) -> for passing learners/transformers as arguments

Selector Pipeline Structure

Selector Pipeline is based on the BestLearner ensemble. Detailed explanation can be found in: BestLearner

Macro Functions

There are two macro functions available: @pipeline and @pipelinex. The @pipeline macro is used to process pipeline expression and returns the evaluation of the transformed expression. During its recursive parsing, any occurence of (|>) is converted to CombinePipeline call and + to Pipeline calls. To aid in the understanding of the worfklow, @pipelinex shows the transformed expression during the @pipeline parsing but just before the expression is evaluated. The macro @pipelinex has similar output to that of @macroexpand, i.e., pipelinex expr is equivalent to @macroexpand @pipeline expr.

Note: Please refer to the Pipeline Tutorial for their usage.