Python models

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

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

julia> using ScikitLearn, Random

julia> Random.seed!(11); #ensures reproducibility

julia> X = rand(20,3); y = rand([true, false], 20);

julia> @sk_import linear_model: LogisticRegression
PyObject <class 'sklearn.linear_model._logistic.LogisticRegression'>

julia> using ScikitLearn.CrossValidation: train_test_split

julia> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42);

julia> log_reg = fit!(LogisticRegression(penalty="l2"), X_train, y_train);

julia> predict(log_reg, X_test)
5-element Array{Bool,1}:

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

Installation and importing Python models

Importing the Python models requires Python 3.x 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 in-built models

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


julia> using GaussianMixtures: GMM #remember to install package first

julia> gmm = fit!(GMM(n_components=3, kind=:diag), X_train);
[ Info: Initializing GMM, 3 Gaussians diag covariance 3 dimensions using 15 data points
  Iters               objv        objv-change | affected
      0       1.462249e+00
      1       1.041033e+00      -4.212161e-01 |        2
      2       9.589243e-01      -8.210827e-02 |        2
      3       9.397430e-01      -1.918135e-02 |        0
      4       9.397430e-01       0.000000e+00 |        0
K-means converged with 4 iterations (objv = 0.9397430000827904)
┌ Info: K-means with 15 data points using 4 iterations
└ 1.3 data points per parameter

julia> predict_proba(gmm, X_test)
5×3 Array{Float64,2}:
 1.37946e-7   5.58899e-9   1.0
 0.986895     1.98749e-10  0.0131053
 0.998037     1.00296e-15  0.00196321
 2.66238e-11  0.041746     0.958254
 0.999984     4.05443e-6   1.16204e-5

Documentation at GaussianMixtures.jl. Example: density estimation


julia> using GaussianProcesses: GPE, MeanZero, SE #remember to install package first

julia> gp = fit!(GPE(; mean=MeanZero(), kernel=SE(0.0, 0.0), logNoise=-1e8), X_train, Float64.(y_train))
GP Exact object:
  Dim = 3
  Number of observations = 15
  Mean function:
    Type: MeanZero, Params: Float64[]
    Type: GaussianProcesses.SEIso{Float64}, Params: [0.0, 0.0]
  Input observations =
[0.376913304113047 0.5630896022795546 … 0.31598998347835017 0.5828199336036355; 0.50060556533132 0.4124482236437548 … 0.6750380496244157 0.6147514739028759; 0.5142063690337368 0.4774433498612982 … 0.9823652195180261 0.21010382988916376]
  Output observations = [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0]
  Variance of observation noise = 0.0
  Marginal Log-Likelihood = -749.473

julia> predict(gp, X_test)
5-element Array{Float64,1}:

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

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


  • DecisionTreeClassifier
  • DecisionTreeRegressor
  • RandomForestClassifier
  • RandomForestRegressor
  • AdaBoostStumpClassifier

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


  • 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

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 new models

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