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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(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False))
 ("logistic_regression", PyObject LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False))

julia> clf = Pipeline(estimators)
Pipeline(Tuple{Any,Any}[("reduce_dim", PyObject PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False)), ("logistic_regression", PyObject LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False))], Any[PyObject PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False), PyObject LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)])

julia> fit!(clf, X, y)
Pipeline(Tuple{Any,Any}[("reduce_dim", PyObject PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False)), ("logistic_regression", PyObject LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False))], Any[PyObject PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False), PyObject LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)])

See ?Pipeline, ?make_pipeline and the user guide for details.

Examples

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(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False))
 ("kernel_pca", PyObject KernelPCA(alpha=1.0, coef0=1, copy_X=True, degree=3, eigen_solver='auto',
          fit_inverse_transform=False, gamma=None, kernel='linear',
          kernel_params=None, max_iter=None, n_components=None, n_jobs=None,
          random_state=None, remove_zero_eig=False, tol=0))

julia> combined = FeatureUnion(estimators)
FeatureUnion(Tuple{Any,Any}[("linear_pca", PyObject PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
    svd_solver='auto', tol=0.0, whiten=False)), ("kernel_pca", PyObject KernelPCA(alpha=1.0, coef0=1, copy_X=True, degree=3, eigen_solver='auto',
          fit_inverse_transform=False, gamma=None, kernel='linear',
          kernel_params=None, max_iter=None, n_components=None, n_jobs=None,
          random_state=None, remove_zero_eig=False, tol=0))], 1, nothing)

See ?FeatureUnion and the user guide for more.

Examples