Python models

scikit-learn has on the order of 100 to 200 models (more generally called "estimators"), split into three categories:

• Supervised Learning (linear regression, support vector machines, random forest, neural nets, ...)
• Unsupervised Learning (clustering, PCA, mixture models, manifold learning, ...)
• Dataset Transformation (preprocessing, text feature extraction, one-hot encoding, ...)

All of those estimators will work with ScikitLearn.jl. They are imported with @sk_import. For example, here's how to import and fit sklearn.linear_regression.LogisticRegression

using ScikitLearn
@sk_import linear_model: LogisticRegression

log_reg = fit!(LogisticRegression(penalty="l1"), X_train, y_train)
predict(X_test)

Reminder: ?LogisticRegression contains a lot of information about the model parameters.

Installation

Importing the Python models requires Python 2.7 with numpy, and the scikit-learn library. This is easiest to get through Conda.jl, which is already installed on your system. Calling @sk_import linear_model: LinearRegression should automatically install everything. You can also install scikit-learn manually with Conda.add("scikit-learn"). If you have other issues, please refer to PyCall.jl, or post an issue

Julia models

Julia models are hosted in other packages, and need to be installed separately with Pkg.add or Pkg.checkout (to get the latest version - sometimes necessary). They all implement the common api, and provide hyperparameter information in their ?docstrings.

Unfortunately, some packages export a fit! function that conflicts with ScikitLearn's fit!. This can be fixed by adding this line:

using ScikitLearn: fit!, predict

ScikitLearn models

• ScikitLearn.Models.LinearRegression() implements linear regression using \, optimized for speed. See ?LinearRegression for fitting options.

GaussianMixtures.jl

Pkg.checkout("GaussianMixtures.jl")   # install the package
using GaussianMixtures: GMM
using ScikitLearn

gmm = fit!(GMM(n_components=3, # number of Gaussians to fit
               kind=:diag), # diagonal covariance matrix (other option: :full)
           X)
predict_proba(gmm, X)

Documentation at GaussianMixtures.jl. Example: density estimation

GaussianProcesses.jl

Pkg.checkout("GaussianProcesses.jl")   # install the package
using GaussianProcesses: GP
using ScikitLearn

gp = fit!(GP(; m=MeanZero(), k=SE(0.0, 0.0), logNoise=-1e8),
          X, y)
predict(gp, X)

Documentation at GaussianProcesses.jl and in the ?GP docstring. Example: Gaussian Processes

Gaussian Processes have a lot of hyperparameters, see get_params(GP) for a list. They can all be tuned

DecisionTree.jl

• DecisionTreeClassifier
• DecisionTreeRegressor
• RandomForestClassifier
• RandomForestRegressor
• AdaBoostStumpClassifier

Documentation at DecisionTree.jl. Examples: Classifier Comparison, Decision Tree Regression notebooks.

LowRankModels.jl

• SkGLRM: Generalized Low Rank Model
• PCA: Principal Component Analysis
• QPCA: Quadratically Regularized PCA
• RPCA: Robust PCA
• NNMF: Non-negative matrix factorization
• KMeans: The k-means algorithm

Please note that these algorithms are all special cases of the Generalized Low Rank Model algorithm, whose main goal is to provide flexible loss and regularization for heterogeneous data. Specialized algorithms will achieve faster convergence in general.

Documentation at LowRankModels.jl. Example: KMeans Digit Classifier.

Contributing

To make your Julia model compatible with ScikitLearn.jl, you need to implement the scikit-learn interface. See ScikitLearnBase.jl