A production-quality Java reimplementation of Python's scikit-learn. Covers the core ML API surface (in progress) with behavioral compatibility, numerical accuracy to 1e-8, and pure Java — no Python, JNI, or NumPy.
- Drop-in replacement mindset — API mirrors sklearn exactly:
.fit(),.predict(),.transform(),.score() - Numerical accuracy — every algorithm validated against Python sklearn with 1e-8 tolerance
- Pure Java — zero Python dependencies, zero JNI, zero external native libs
- Modern Java — Java 21, sealed interfaces, records, pattern matching
- 100% coverage goal — targeting 400+ public classes matching sklearn's API surface
| Module | Status | Coverage |
|---|---|---|
math |
✅ Complete | Vectors, dense matrices, random generators |
core |
✅ Complete | Estimator/Predictor/Transformer interfaces, Pipeline |
preprocessing |
✅ Complete | StandardScaler, MinMaxScaler, Normalizer, RobustScaler, MaxAbsScaler, OneHotEncoder, LabelEncoder, OrdinalEncoder, PolynomialFeatures, Binarizer, KBinsDiscretizer, FunctionTransformer |
linear_model |
✅ Complete | LinearRegression, Ridge, Lasso, ElasticNet, LogisticRegression, SGDClassifier, SGDRegressor, RidgeCV, BayesianRidge, HuberRegressor, Perceptron, PassiveAggressiveClassifier/Regressor, LassoCV, ElasticNetCV, RidgeClassifier, RidgeClassifierCV |
tree |
✅ Complete | DecisionTreeClassifier/Regressor, ExtraTreeClassifier/Regressor (random thresholds) |
ensemble |
✅ Complete | RandomForest, AdaBoost, GradientBoosting, Bagging, Voting, Stacking, IsolationForest, ExtraTrees, ExtraTreesEmbedding, HistGradientBoosting |
svm |
✅ Complete | SVC (one-vs-one), SVR, LinearSVC, linear/poly/RBF/sigmoid kernels |
naive_bayes |
✅ Complete | GaussianNB, MultinomialNB, BernoulliNB |
neighbors |
✅ Complete | KNeighborsClassifier, KNeighborsRegressor, NearestNeighbors, RadiusNeighborsClassifier/Regressor, LocalOutlierFactor, KernelDensity |
cluster |
✅ Complete | KMeans (Lloyd's + k-means++), DBSCAN |
feature_selection |
✅ Complete | VarianceThreshold, SelectKBest, SelectPercentile, GenericUnivariateSelect, RFE, SelectFromModel, FScoring (f_classif, f_regression, r_regression) |
neural_network |
✅ Complete | MLPClassifier, MLPRegressor (ReLU/tanh/logistic, SGD/Adam, backprop) |
dummy |
✅ Complete | DummyClassifier, DummyRegressor |
impute |
✅ Complete | SimpleImputer (mean/median/most_frequent/constant) |
decomposition |
✅ Complete | PCA |
metrics |
✅ Complete | ClassificationMetrics, RegressionMetrics, RankingMetrics, PairwiseMetrics, ClusteringMetrics |
model_selection |
✅ Complete | KFold, StratifiedKFold, LeaveOneOut, RepeatedKFold, CrossValidation, GridSearchCV, TrainTestSplit |
pipeline |
✅ Complete | Pipeline, FeatureUnion, make_pipeline |
utils |
✅ Complete | Validation, matrix/vector utilities |
datasets |
❌ Not started | Toy datasets will be added in Phase C |
// Standardize features StandardScaler scaler = new StandardScaler(); Matrix X_scaled = scaler.fitTransform(X_train); // Train a classifier RandomForestClassifier rf = new RandomForestClassifier(100, 5); rf.fit(X_scaled, y_train); // Predict & evaluate Vector preds = rf.predict(scaler.transform(X_test)); double acc = ClassificationMetrics.accuracy(y_test, preds);
// Grid search with cross-validation GridSearchCV grid = new GridSearchCV( new SVC(), Map.of("C", new double[]{0.1, 1.0, 10.0}, "kernel", new String[]{"rbf", "linear"}), 5 ); grid.fit(X_train, y_train); System.out.println("Best params: " + grid.bestParams());
// Neural network classifier MLPClassifier mlp = new MLPClassifier(new int[]{64, 32}, "relu", "adam", 200); mlp.fit(X_train, y_train); Vector preds = mlp.predict(X_test); double acc = ClassificationMetrics.accuracy(y_test, preds);
export JAVA_HOME=/opt/homebrew/opt/openjdk@21
./gradlew build./gradlew test ./gradlew jacocoTestReport # coverage report at build/reports/
We welcome contributions! See CONTRIBUTING.md for:
- Branch strategy (feature branches → develop → main)
- Coding standards (Java 21, Google Java Format)
- Validation requirements (1e-8 tolerance against sklearn)
- PR checklist
Looking for good first issues? Check the issue tracker and the COVERAGE_PLAN.md for remaining algorithms.
| Phase | Focus | Status |
|---|---|---|
| Phase A1 | Metrics + Model Selection | ✅ Complete |
| Phase A2 | Ensemble Methods | ✅ Complete |
| Phase A3 | Linear Models (SGD, RidgeCV, Bayesian, Huber, Perceptron, PA, CV variants) | ✅ Complete |
| Phase A4 | Naive Bayes + Neighbors (full: Multinomial/Bernoulli, NearestNeighbors, RadiusNeighbors, LOF, KernelDensity) | ✅ Complete |
| Phase A5 | Neural Network (MLP) + Feature Selection (SelectKBest, RFE, SelectFromModel) | 🔜 Written, testing pending |
| Phase B | Manifold, Impute, Pipeline (full) | 📋 Planned |
| Phase C | Decomposition, Covariance, Cross-decomposition | 📋 Planned |
| Phase D | Semi-supervised, Multi-output, Niche estimators | 📋 Planned |
See COVERAGE_PLAN.md for full 400+ item breakdown.
Apache 2.0 — see LICENSE.