sklearn-java

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.

Why sklearn-java?

• 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

Modules

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

Quick Start

// 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);

Building

export JAVA_HOME=/opt/homebrew/opt/openjdk@21
./gradlew build

Testing

./gradlew test
./gradlew jacocoTestReport  # coverage report at build/reports/

Contributing

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.

Roadmap

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.

License

Apache 2.0 — see LICENSE.

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