Most data science and machine learning problems involve several steps of data preprocessing and transformation. ScikitLearn.jl provides two types to facilitate this task.
Pipelines
Pipeline can be used to chain multiple estimators into one. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification.
julia> using ScikitLearn
julia> using ScikitLearn.Pipelines: Pipeline, make_pipeline
julia> @sk_import decomposition: PCA
PyObject <class 'sklearn.decomposition._pca.PCA'>
julia> @sk_import linear_model: LogisticRegression
PyObject <class 'sklearn.linear_model._logistic.LogisticRegression'>
julia> using RDatasets: dataset
julia> iris = dataset("datasets", "iris");
julia> X = convert(Array, iris[!, [:SepalLength, :SepalWidth, :PetalLength, :PetalWidth]]);
julia> y = convert(Array, iris[!,:Species]);
julia> estimators = [("reduce_dim", PCA()), ("logistic_regression", LogisticRegression())]
2-element Array{Tuple{String,PyCall.PyObject},1}:
("reduce_dim", PyObject PCA())
("logistic_regression", PyObject LogisticRegression())
julia> clf = Pipeline(estimators)
Pipeline(Tuple{Any,Any}[("reduce_dim", PyObject PCA()), ("logistic_regression", PyObject LogisticRegression())], Any[PyObject PCA(), PyObject LogisticRegression()])
julia> fit!(clf, X, y)
Pipeline(Tuple{Any,Any}[("reduce_dim", PyObject PCA()), ("logistic_regression", PyObject LogisticRegression())], Any[PyObject PCA(), PyObject LogisticRegression()])
See ?Pipeline
, ?make_pipeline
and the user guide for details.
Examples
- Pipelining: chaining a PCA and a logistic regression
- Restricted Boltzmann Machine features for digit classification
Feature Unions
FeatureUnion combines several transformer objects into a new transformer that combines their output. A FeatureUnion takes a list of transformer objects. During fitting, each of these is fit to the data independently. For transforming data, the transformers are applied in parallel, and the sample vectors they output are concatenated end-to-end into larger vectors.
julia> using ScikitLearn.Pipelines: FeatureUnion
julia> @sk_import decomposition: KernelPCA
PyObject <class 'sklearn.decomposition._kernel_pca.KernelPCA'>
julia> estimators = [("linear_pca", PCA()), ("kernel_pca", KernelPCA())]
2-element Array{Tuple{String,PyCall.PyObject},1}:
("linear_pca", PyObject PCA())
("kernel_pca", PyObject KernelPCA())
julia> combined = FeatureUnion(estimators)
FeatureUnion(Tuple{Any,Any}[("linear_pca", PyObject PCA()), ("kernel_pca", PyObject KernelPCA())], 1, nothing)
See ?FeatureUnion
and the user guide for more.