# Sklears Python Bindings - TODO
## 0.1.0-alpha.2 progress checklist (2025-12-22)
- [x] Validated the sklears python module as part of the 10,013 passing workspace tests (69 skipped).
- [x] Published refreshed README and release notes for the alpha drop.
- [ ] Beta focus: prioritize the items outlined below.
## Priority 1: Critical Implementations
### Linear Models (CRITICAL - Currently stub implementations)
- [ ] **LinearRegression**: Replace stub with proper OLS implementation using sklears-linear
- [ ] **Ridge**: Implement L2 regularized linear regression with cross-validation
- [ ] **Lasso**: Implement L1 regularized linear regression with coordinate descent
- [ ] **LogisticRegression**: Complete classification implementation with multiple solvers
- [ ] **ElasticNet**: Add combined L1+L2 regularization (missing from current impl)
- [ ] **BayesianRidge**: Add Bayesian Ridge regression variant
- [ ] **ARDRegression**: Automatic Relevance Determination regression
### Core Infrastructure
- [ ] **SciRS2 Integration**: Replace direct ndarray/rand usage with scirs2-core
- [ ] Replace `use ndarray` with `scirs2_autograd::ndarray`
- [ ] Replace rand usage with `scirs2_core::random`
- [ ] Integrate SIMD operations from `scirs2_core::simd_ops`
- [ ] Add GPU acceleration support from `scirs2_core::gpu`
- [ ] **Error Handling**: Comprehensive error mapping between Rust and Python
- [ ] **Memory Management**: Optimize NumPy array conversions and memory usage
- [ ] **Parallel Processing**: Leverage rayon/scirs2 parallel operations
## Priority 2: Algorithm Completions
### Tree-Based Models
- [ ] **DecisionTreeClassifier**: Complete implementation with all splitting criteria
- [ ] **DecisionTreeRegressor**: Regression variant with MSE/MAE criteria
- [ ] **RandomForestClassifier**: Ensemble of decision trees for classification
- [ ] **RandomForestRegressor**: Regression variant of random forest
- [ ] **ExtraTreesClassifier**: Extremely randomized trees classifier
- [ ] **ExtraTreesRegressor**: Extremely randomized trees regressor
- [ ] **GradientBoostingClassifier**: Gradient boosting for classification
- [ ] **GradientBoostingRegressor**: Gradient boosting for regression
- [ ] **XGBoost Integration**: Optional XGBoost backend support
### Neural Networks
- [ ] **MLPClassifier**: Multi-layer Perceptron classifier completion
- [ ] **MLPRegressor**: Multi-layer Perceptron regressor completion
- [ ] **Advanced Optimizers**: Adam, RMSprop, AdaGrad implementations
- [ ] **Regularization**: Dropout, batch normalization, weight decay
- [ ] **Advanced Architectures**: CNN, RNN, LSTM, Transformer support
### Support Vector Machines
- [ ] **SVC**: Support Vector Classification with multiple kernels
- [ ] **SVR**: Support Vector Regression
- [ ] **LinearSVC**: Linear SVM classifier
- [ ] **LinearSVR**: Linear SVM regressor
- [ ] **NuSVC**: Nu-Support Vector Classification
- [ ] **NuSVR**: Nu-Support Vector Regression
- [ ] **OneClassSVM**: One-class SVM for outlier detection
### Clustering Algorithms
- [ ] **KMeans**: Complete K-means clustering with kmeans++ initialization
- [ ] **DBSCAN**: Density-based clustering completion
- [ ] **AgglomerativeClustering**: Hierarchical clustering
- [ ] **SpectralClustering**: Spectral clustering algorithm
- [ ] **OPTICS**: Ordering points clustering algorithm
- [ ] **GaussianMixture**: Gaussian Mixture Model clustering
- [ ] **BirchClustering**: BIRCH clustering algorithm
### Dimensionality Reduction
- [ ] **PCA**: Principal Component Analysis
- [ ] **TruncatedSVD**: Singular Value Decomposition
- [ ] **FastICA**: Independent Component Analysis
- [ ] **FactorAnalysis**: Factor Analysis
- [ ] **TSNE**: t-distributed Stochastic Neighbor Embedding
- [ ] **UMAP**: Uniform Manifold Approximation and Projection
- [ ] **LLE**: Locally Linear Embedding
- [ ] **Isomap**: Isometric mapping
- [ ] **MDS**: Multidimensional Scaling
## Priority 3: Feature Enhancements
### Preprocessing & Feature Engineering
- [ ] **StandardScaler**: Complete standardization implementation
- [ ] **MinMaxScaler**: Complete min-max scaling implementation
- [ ] **RobustScaler**: Robust scaling using median and IQR
- [ ] **MaxAbsScaler**: Maximum absolute scaling
- [ ] **Normalizer**: L1/L2/max normalization
- [ ] **QuantileTransformer**: Quantile-based feature transformation
- [ ] **PowerTransformer**: Box-Cox and Yeo-Johnson transformations
- [ ] **PolynomialFeatures**: Polynomial feature generation
- [ ] **OneHotEncoder**: One-hot encoding for categorical features
- [ ] **LabelEncoder**: Complete label encoding implementation
- [ ] **OrdinalEncoder**: Ordinal encoding for categorical features
- [ ] **BinaryEncoder**: Binary encoding for high-cardinality categoricals
- [ ] **TargetEncoder**: Target-based encoding methods
### Feature Selection
- [ ] **SelectKBest**: Select K best features using statistical tests
- [ ] **SelectPercentile**: Select top percentile of features
- [ ] **SelectFpr**: Select features based on FPR
- [ ] **SelectFdr**: Select features based on FDR
- [ ] **SelectFwe**: Select features based on FWE
- [ ] **RFE**: Recursive Feature Elimination
- [ ] **RFECV**: RFE with cross-validation
- [ ] **SelectFromModel**: Select features based on model coefficients
- [ ] **VarianceThreshold**: Remove low-variance features
- [ ] **GenericUnivariateSelect**: Generic univariate feature selection
### Model Selection & Validation
- [ ] **GridSearchCV**: Grid search with cross-validation
- [ ] **RandomizedSearchCV**: Randomized parameter search
- [ ] **BayesSearchCV**: Bayesian optimization for hyperparameters
- [ ] **HalvingGridSearchCV**: Successive halving grid search
- [ ] **HalvingRandomSearchCV**: Successive halving random search
- [ ] **cross_val_score**: Cross-validation scoring
- [ ] **cross_val_predict**: Cross-validation predictions
- [ ] **cross_validate**: Extended cross-validation with multiple metrics
- [ ] **StratifiedKFold**: Stratified K-fold cross-validation
- [ ] **GroupKFold**: Group-based K-fold cross-validation
- [ ] **TimeSeriesSplit**: Time series cross-validation
- [ ] **LeaveOneOut**: Leave-one-out cross-validation
- [ ] **LeavePOut**: Leave-P-out cross-validation
- [ ] **ShuffleSplit**: Shuffle split cross-validation
- [ ] **StratifiedShuffleSplit**: Stratified shuffle split
### Advanced Metrics
- [ ] **Classification Metrics**:
- [ ] ROC AUC (one-vs-rest, one-vs-one)
- [ ] Precision-Recall AUC
- [ ] Top-K accuracy
- [ ] Balanced accuracy
- [ ] Matthews correlation coefficient
- [ ] Cohen's kappa
- [ ] Hamming loss
- [ ] Jaccard score
- [ ] Log loss
- [ ] Hinge loss
- [ ] **Regression Metrics**:
- [ ] Mean Poisson deviance
- [ ] Mean gamma deviance
- [ ] Mean tweedie deviance
- [ ] D² score variants
- [ ] Explained variance score
- [ ] Max error
- [ ] Mean squared log error (safe version)
- [ ] **Clustering Metrics**:
- [ ] Adjusted Rand Index
- [ ] Normalized Mutual Information
- [ ] Homogeneity score
- [ ] Completeness score
- [ ] V-measure
- [ ] Silhouette analysis
- [ ] Calinski-Harabasz index
- [ ] Davies-Bouldin index
## Priority 4: Advanced Features
### Multioutput & Multilabel
- [ ] **MultiOutputClassifier**: Multi-output classification wrapper
- [ ] **MultiOutputRegressor**: Multi-output regression wrapper
- [ ] **ClassifierChain**: Classifier chains for multilabel
- [ ] **RegressorChain**: Regressor chains for multioutput
- [ ] **MultiLabelBinarizer**: Multilabel binarization
### Imbalanced Learning Integration
- [ ] **SMOTE**: Synthetic Minority Over-sampling Technique
- [ ] **ADASYN**: Adaptive Synthetic Sampling
- [ ] **BorderlineSMOTE**: Borderline SMOTE variant
- [ ] **RandomUnderSampler**: Random under-sampling
- [ ] **RandomOverSampler**: Random over-sampling
- [ ] **TomekLinks**: Tomek links cleaning
- [ ] **EditedNearestNeighbours**: Edited nearest neighbours
- [ ] **NeighbourhoodCleaningRule**: Neighbourhood cleaning rule
### Pipeline & Composition
- [ ] **Pipeline**: ML pipeline implementation
- [ ] **FeatureUnion**: Feature union for parallel transformations
- [ ] **ColumnTransformer**: Column-wise transformations
- [ ] **TransformedTargetRegressor**: Target transformation wrapper
- [ ] **VotingClassifier**: Voting ensemble classifier
- [ ] **VotingRegressor**: Voting ensemble regressor
- [ ] **StackingClassifier**: Stacking ensemble classifier
- [ ] **StackingRegressor**: Stacking ensemble regressor
- [ ] **BaggingClassifier**: Bagging ensemble classifier
- [ ] **BaggingRegressor**: Bagging ensemble regressor
### Semi-Supervised Learning
- [ ] **SelfTrainingClassifier**: Self-training with confident predictions
- [ ] **LabelPropagation**: Label propagation algorithm
- [ ] **LabelSpreading**: Label spreading algorithm
### Gaussian Processes
- [ ] **GaussianProcessClassifier**: GP for classification
- [ ] **GaussianProcessRegressor**: GP for regression
- [ ] **Kernels**: RBF, Matern, White noise, etc.
### Discriminant Analysis
- [ ] **LinearDiscriminantAnalysis**: LDA implementation
- [ ] **QuadraticDiscriminantAnalysis**: QDA implementation
### Outlier Detection
- [ ] **IsolationForest**: Isolation forest for anomaly detection
- [ ] **LocalOutlierFactor**: Local outlier factor
- [ ] **OneClassSVM**: One-class SVM for outliers
- [ ] **EllipticEnvelope**: Robust covariance estimation
### Calibration
- [ ] **CalibratedClassifierCV**: Probability calibration
- [ ] **IsotonicRegression**: Isotonic regression for calibration
- [ ] **calibration_curve**: Calibration curve calculation
## Priority 5: Infrastructure & Quality
### Testing & Benchmarking
- [ ] **Unit Tests**: Comprehensive unit test coverage (>90%)
- [ ] **Integration Tests**: End-to-end testing with real datasets
- [ ] **Property Tests**: Property-based testing with proptest
- [ ] **Regression Tests**: Prevent performance/accuracy regressions
- [ ] **Memory Tests**: Memory leak detection and profiling
- [ ] **Performance Benchmarks**: Automated benchmarking vs scikit-learn
- [ ] **Correctness Tests**: Numerical accuracy validation
- [ ] **Edge Case Tests**: Boundary condition testing
### Documentation & Examples
- [ ] **API Documentation**: Complete docstrings for all classes/functions
- [ ] **User Guide**: Comprehensive usage examples
- [ ] **Migration Guide**: Scikit-learn to sklears migration
- [ ] **Performance Guide**: Optimization best practices
- [ ] **Jupyter Notebooks**: Interactive tutorials and examples
- [ ] **Benchmark Reports**: Performance comparison documentation
### Build & Distribution
- [ ] **Wheel Building**: Automated wheel building for PyPI
- [ ] **CI/CD Pipeline**: Comprehensive testing automation
- [ ] **Release Automation**: Automated versioning and releases
- [ ] **Cross-Platform**: Support for Linux, macOS, Windows
- [ ] **Python Versions**: Support for Python 3.8-3.12
- [ ] **ARM64 Support**: Native ARM64 wheels for Apple Silicon
### Developer Experience
- [ ] **Type Stubs**: Python type hints for better IDE support
- [ ] **Debug Mode**: Enhanced debugging with verbose logging
- [ ] **Profiling Integration**: Built-in performance profiling
- [ ] **Memory Tracking**: Memory usage monitoring and reporting
- [ ] **Warning System**: Consistent warning messages
- [ ] **Configuration**: Global configuration system
## Technical Debt & Refactoring
### Code Quality
- [ ] **Remove Stub Implementations**: Replace all placeholder code
- [ ] **Consistent Error Handling**: Standardize error types and messages
- [ ] **Code Duplication**: Eliminate repeated code patterns
- [ ] **Magic Numbers**: Replace with named constants
- [ ] **TODO Comments**: Resolve all remaining TODO items
- [ ] **Dead Code**: Remove unused code and imports
- [ ] **Consistent Naming**: Follow Python/Rust naming conventions
### Performance Optimization
- [ ] **Memory Allocations**: Reduce unnecessary allocations
- [ ] **SIMD Usage**: Leverage SIMD instructions where possible
- [ ] **Parallel Algorithms**: Parallelize computationally intensive operations
- [ ] **GPU Acceleration**: Integrate GPU backends where beneficial
- [ ] **Cache Optimization**: Improve data locality and cache usage
- [ ] **Vectorization**: Replace loops with vectorized operations
### Architecture Improvements
- [ ] **Trait Consistency**: Ensure consistent trait implementations
- [ ] **State Management**: Improve untrained/trained state handling
- [ ] **Resource Management**: Better memory and compute resource handling
- [ ] **Plugin Architecture**: Support for custom algorithms and extensions
- [ ] **Serialization**: Model persistence and loading
- [ ] **Backward Compatibility**: API stability guarantees
## Integration Goals
### Ecosystem Integration
- [ ] **NumPy**: Full NumPy array protocol support
- [ ] **Pandas**: Native DataFrame support
- [ ] **Polars**: Alternative DataFrame backend
- [ ] **Apache Arrow**: Zero-copy data interchange
- [ ] **Dask**: Distributed computing integration
- [ ] **Ray**: Scalable ML workflow support
- [ ] **MLflow**: Experiment tracking integration
- [ ] **Weights & Biases**: Advanced experiment tracking
### Scikit-learn Compatibility
- [ ] **API Parity**: 100% API compatibility with scikit-learn
- [ ] **Parameter Names**: Identical parameter naming
- [ ] **Return Values**: Consistent return value formats
- [ ] **Exception Types**: Compatible exception hierarchy
- [ ] **Random State**: Reproducible random number generation
- [ ] **Fit/Transform**: Identical fit/transform behavior
- [ ] **Pickle Support**: Model serialization compatibility
## Long-term Vision
### Advanced ML Features
- [ ] **AutoML**: Automated machine learning capabilities
- [ ] **Neural Architecture Search**: NAS integration
- [ ] **Federated Learning**: Distributed learning support
- [ ] **Online Learning**: Streaming/incremental learning
- [ ] **Multi-GPU**: Multi-GPU training and inference
- [ ] **Distributed Training**: Cluster-based training
- [ ] **Edge Deployment**: Optimized models for edge devices
- [ ] **Quantization**: Model quantization for deployment
### Research Integration
- [ ] **Latest Algorithms**: Integration of cutting-edge algorithms
- [ ] **Research Collaboration**: Academic research partnership
- [ ] **Benchmark Suite**: Comprehensive ML benchmark suite
- [ ] **Paper Implementations**: Reference implementations of research papers
## Completion Metrics
### Coverage Targets
- [ ] **Algorithm Coverage**: >80% of scikit-learn algorithms
- [ ] **Test Coverage**: >90% code coverage
- [ ] **Documentation Coverage**: 100% API documentation
- [ ] **Performance Target**: 3-100x speedup over scikit-learn
- [ ] **Memory Efficiency**: <50% memory usage vs scikit-learn
- [ ] **API Compatibility**: >95% drop-in replacement
### Quality Gates
- [ ] **Zero Critical Bugs**: No known critical issues
- [ ] **Performance Regression**: <5% performance degradation
- [ ] **Memory Leaks**: Zero memory leaks detected
- [ ] **Thread Safety**: Full thread safety where applicable
- [ ] **Numerical Stability**: Robust numerical implementations
- [ ] **Cross-Platform**: Consistent behavior across platforms
---
## Notes
- This TODO represents a comprehensive roadmap spanning multiple releases
- Priority 1 items are critical for a usable v0.1.0 release
- Priority 2-3 items target feature completeness for v1.0.0
- Priority 4-5 items focus on production readiness and ecosystem integration
- Long-term vision items are for future major versions
## Contributing
When working on items from this TODO:
1. Move items to "in progress" when starting work
2. Create tests before implementing features
3. Update documentation alongside code changes
4. Ensure SciRS2 integration compliance
5. Maintain backward compatibility
6. Add performance benchmarks for new features
Last updated: 2025-09-26