# Aprender Roadmap
Next Generation Machine Learning in Pure Rust
## Version Status
| v0.1.0 | ✅ **Released** | Foundation - Linear Regression + K-Means |
| v0.2.0 | ✅ **Released** | Decision Trees, Random Forests, Cross-Validation, Serialization |
| v0.3.0 | ✅ **Released** | Regularization & Optimization |
| v0.4.0 | ✅ **Released** | TOP 10 ML Algorithms Complete |
| v0.4.1 | ✅ **Released** | Graph Algorithms, Advanced Clustering, Anomaly Detection, Stats |
| v0.5.0 | Planned | Neural Networks |
| v1.0.0 | Planned | Production Hardening |
---
## v0.2.0: Tree Models & Cross-Validation (Current)
**Target**: Decision Trees, Random Forests, Model Selection, Model Persistence
### Completed
- [x] **Tree Module**: Complete decision tree implementation
- DecisionTreeClassifier with GINI impurity
- Configurable max_depth
- Recursive tree building
- Integration tests with Iris dataset
- [x] **Random Forest**: Bootstrap aggregating ensemble
- RandomForestClassifier with configurable n_estimators
- Bootstrap sampling with replacement
- Majority voting for predictions
- Reproducible with random_state
- [x] **Cross-Validation**: Model evaluation utilities
- train_test_split() with reproducible random seeds
- KFold cross-validator with optional shuffling
- cross_validate() with statistics (mean, std, min, max)
- CrossValidationResult struct
- [x] **Model Serialization**: Save/load models to disk
- Serde + bincode integration
- Works with all models (LinearRegression, KMeans, DecisionTree, RandomForest)
- Example demonstrating persistence
- [x] **Examples**: New comprehensive examples
- decision_tree_iris.rs - Decision tree classification
- random_forest_iris.rs - Ensemble classification
- cross_validation.rs - Model evaluation workflow
- model_persistence.rs - Save/load demonstration
- [x] **Documentation**: EXTREME TDD Book
- 90+ chapter structure on GitHub Pages
- Complete case study: Cross-Validation
- RED-GREEN-REFACTOR methodology
- Live at https://paiml.github.io/aprender/
- [x] **Bug Fixes**
- Clear error messages for underdetermined systems
### Quality Metrics Achieved
- TDG Score: 93.3/100 (A grade)
- Total Tests: 184 passing (+64 from v0.1.0)
- Property Tests: 22
- Doc Tests: 16
- Test Coverage: ~97%
- Max Cyclomatic Complexity: ≤10
- Zero clippy warnings
- Zero SATD comments
### Released ✅
- [x] Published to crates.io (2024-11-18)
- [x] GitHub Release created with release notes
- [x] EXTREME TDD Book deployed
- [x] CI/CD pipeline passing
---
## v0.1.0: Foundation
**Target**: Linear Regression + K-Means (2 algorithms, viable from day one)
### Completed
- [x] Project scaffolding (Cargo workspace, quality gates)
- [x] Core primitives: Vector, Matrix (with Cholesky solver)
- [x] DataFrame (named column container with `to_matrix()`)
- [x] Linear Regression (OLS via normal equations)
- [x] K-Means clustering (k-means++ initialization + Lloyd's algorithm)
- [x] Metrics: R², MSE, RMSE, MAE, inertia, silhouette_score
- [x] Estimator/UnsupervisedEstimator/Transformer traits
- [x] Property-based tests (19 proptest properties)
- [x] Unit tests (120 tests)
- [x] Doc tests (13 tests)
- [x] Examples: boston_housing, iris_clustering, dataframe_basics
- [x] Benchmarks: linear_regression, kmeans
### Quality Metrics Achieved
- TDG Score: 95.6/100 (A+ grade)
- Repository Score: 95.0/100 (A+)
- Test Coverage: 97.72%
- Mutation Score: 85.3%
- Max Cyclomatic Complexity: 5
- Zero clippy warnings
- Zero SATD comments
- Total Tests: 149 (127 unit + 22 property)
### Released ✅
- [x] Published to crates.io (2024-11-18)
- [x] GitHub Release created with artifacts
- [x] Complete rustdoc coverage
- [x] CI/CD pipeline operational
---
## v0.3.0: Regularization & Optimization
**Target**: Regularized Linear Models, Optimizers, Advanced Model Selection
### Completed
- [x] **Regularized Linear Models**
- Ridge regression (L2 regularization)
- Lasso regression (L1 via coordinate descent)
- Elastic Net (L1 + L2 combination)
- Full builder pattern API with max_iter, tolerance
- [x] **Optimizers**
- SGD with optional momentum
- Adam with adaptive learning rates
- Trait-based design for extensibility
- [x] **Loss Functions**
- MSE (Mean Squared Error)
- MAE (Mean Absolute Error)
- Huber loss (smooth combination)
- Both functional and OOP APIs
- [x] **Advanced Model Selection**
- Stratified K-Fold cross-validation
- Grid search for hyperparameter tuning
- Works with all regularized models
- [x] **Preprocessing**
- StandardScaler (z-score normalization)
- MinMaxScaler (range scaling)
- Transformer trait integration
- [x] **Examples**: New comprehensive demonstrations
- regularized_regression.rs - Ridge, Lasso, ElasticNet with grid search
- optimizer_demo.rs - SGD and Adam optimization
- [x] **Chaos Engineering**
- ChaosConfig from renacer integration
- Property-based testing with proptest
- Fuzz testing infrastructure with cargo-fuzz
- 41 new tests (6 property + 14 integration + 1 fuzz target)
- [x] **Refactoring**
- Reduced tree module complexity: 10 → 7 cyclomatic, 22 → 13 cognitive
- Reduced grid search complexity: 9 → 4 cyclomatic, 23 → 6 cognitive
- Extracted 6 helper functions for better maintainability
### Quality Metrics Achieved
- TDG Score: 95.6/100 (A+ grade)
- Total Tests: 498 passing (+314 from v0.2.0)
- Property Tests: 32 (+10)
- Doc Tests: 49 (+33)
- Integration Tests: 6
- Unit Tests: 387 (+203)
- Max Cyclomatic Complexity: 7 (down from 14)
- Max Cognitive Complexity: 13 (down from 23)
- Zero clippy warnings
- Zero SATD comments
- All quality gates passing
### Released ✅
- [x] All 10 features implemented and tested
- [x] Chaos engineering infrastructure integrated
- [x] Code complexity significantly reduced
- [x] 9 comprehensive examples running
- [x] All quality gates passing
---
## v0.4.0: TOP 10 ML Algorithms Complete
**Target**: Industry's most popular machine learning algorithms with comprehensive testing
### Completed
- [x] **Logistic Regression** (Issue #12)
- Binary and multi-class classification
- Gradient descent optimization with learning rate decay
- L2 regularization support
- predict_proba() for probability estimates
- [x] **K-Nearest Neighbors** (Issue #23)
- Distance-based classification
- Configurable k parameter
- Brute-force and optimized distance computation
- Works with any distance metric
- [x] **Support Vector Machine** (Issue #24)
- Linear SVM with hinge loss
- Subgradient descent optimizer
- C regularization parameter
- decision_function() for margin-based predictions
- [x] **Naive Bayes** (Issue #25)
- GaussianNB with probabilistic classification
- Variance smoothing parameter
- predict_proba() returns class probabilities
- Fast training O(n·d)
- [x] **Gradient Boosting Machine** (Issue #26)
- Adaptive boosting with residual learning
- Configurable n_estimators and learning_rate
- Tree-based weak learners
- Feature importance scores
- [x] **Decision Trees & Random Forests** (v0.2.0)
- GINI impurity-based splitting
- Bootstrap aggregating for Random Forest
- Configurable max_depth and n_estimators
- [x] **Linear Regression** (v0.1.0)
- OLS via normal equations
- Ridge/Lasso/ElasticNet (v0.3.0)
- [x] **K-Means Clustering** (v0.1.0)
- k-means++ initialization
- Lloyd's algorithm
- Configurable max_iter
- [x] **Principal Component Analysis** (Issue #13)
- Eigendecomposition-based dimensionality reduction
- Configurable n_components
- explained_variance_ratio for feature analysis
- transform() for new data projection
- [x] **Classification Metrics**
- accuracy_score, precision_score, recall_score
- f1_score, confusion_matrix
- Multi-class support (macro/micro averaging)
### Quality Metrics Achieved
- Total Tests: 528 passing
- Zero clippy warnings
- Zero SATD violations
- All quality gates passing
- Comprehensive documentation with examples
- 10/10 TOP algorithms implemented ✅
### Released ✅
- [x] Published to crates.io as v0.4.0 (2024-11-19)
- [x] All TOP 10 algorithms tested and documented
- [x] Examples for each algorithm
- [x] Book chapters for theory + case studies
---
## v0.4.1: Graph Algorithms, Advanced Clustering & Statistics
**Target**: Expand beyond TOP 10 with graph theory, advanced clustering, anomaly detection, and statistical analysis
### Completed
- [x] **Graph Algorithms** (Issue #9)
- Betweenness Centrality (shortest path counting)
- PageRank (iterative power method)
- Graph data structure with adjacency list
- Weighted and unweighted edge support
- [x] **Community Detection** (Issue #22)
- Louvain algorithm for modularity optimization
- Modularity computation Q = (1/2m) Σ[A_ij - k_i*k_j/2m] δ(c_i, c_j)
- Detects densely connected groups in networks
- O(m·log n) complexity
- [x] **Advanced Clustering**
- DBSCAN (Issue #14) - Density-based clustering
- Hierarchical Clustering (Issue #15) - Agglomerative with linkage methods
- Gaussian Mixture Models (Issue #16) - EM algorithm for soft clustering
- Spectral Clustering (Issue #19) - Graph Laplacian eigendecomposition
- [x] **Anomaly Detection**
- Isolation Forest (Issue #17) - Ensemble of isolation trees
- Local Outlier Factor (Issue #20) - Density-based outlier detection
- score_samples() and predict() methods
- Contamination parameter for threshold setting
- [x] **Dimensionality Reduction**
- t-SNE (Issue #18) - Non-linear visualization
- Perplexity-based similarity computation
- KL divergence minimization via gradient descent
- 2D/3D embedding support
- [x] **Association Rule Mining**
- Apriori Algorithm (Issue #21) - Frequent itemset mining
- Support, confidence, and lift metrics
- Market basket analysis support
- Efficient pruning with apriori property
- [x] **Descriptive Statistics** (Issue #9)
- Mean, median, mode, variance, std deviation
- Quartiles (Q1, Q2, Q3), IQR
- Histograms with multiple binning strategies
- Five-number summary (min, Q1, median, Q3, max)
### Quality Metrics Achieved
- Total Tests: 683 passing (+155 from v0.4.0)
- Zero clippy warnings
- Zero critical SATD violations (1 low-priority Bayesian Blocks TODO)
- All quality gates passing
- Comprehensive EXTREME TDD book with case studies
- mdbook tests: 0 failures across 119 chapters
### Released ✅
- [x] Published to crates.io as v0.4.1 (2024-11-21)
- [x] All 6 advanced clustering algorithms implemented
- [x] Graph algorithms with social network examples
- [x] Complete anomaly detection suite
- [x] Association rule mining for market basket analysis
- [x] Comprehensive book chapters for all algorithms
---
## v0.5.0: Regression Trees & Advanced Ensemble Methods
- [ ] Decision tree regression (CART algorithm)
- [ ] Random Forest regression
- [ ] Out-of-bag error estimation for Random Forests
- [ ] Feature importance visualization
- [ ] XGBoost-style optimizations (histogram binning, approximate split finding)
---
## v0.6.0: Neural Networks
- [ ] Autodiff integration (feature-gated)
- [ ] Dense layers (fully connected)
- [ ] Activation functions (ReLU, Leaky ReLU, ELU, SELU)
- [ ] Optimizers: SGD, Adam, AdaGrad, RMSprop
- [ ] Batch normalization
- [ ] Dropout regularization
- [ ] Sequential model API
---
## v0.7.0: Advanced Statistics & Inference
- [ ] Generalized Linear Models (GLM)
- [ ] Statistical tests: t-test, chi-square, ANOVA, F-test
- [ ] Covariance/correlation matrices
- [ ] Independent Component Analysis (ICA)
- [ ] Factor Analysis
- [ ] Hypothesis testing framework
---
## v0.8.0: Showcase & QA Protocol (Completed)
**Target**: Unified Inference Architecture (GGUF/SafeTensors/APR) & Severe Testing Protocol
### Completed
- [x] **Unified Inference Architecture**
- Multi-format support: GGUF, SafeTensors, APR
- Hybrid backend: CPU + GPU (CUDA)
- Rosetta ML Diagnostics for format conversion
- `apr run`, `apr chat`, `apr serve` commands
- [x] **Severe Testing Protocol (PMAT-QA-PROTOCOL-001)**
- Hang Detection: 60s timeout wrapper for all tests
- Garbage Detection: Strict Level 5 verification (regex + patterns)
- Zombie Mitigation: SIGINT resiliency for `apr serve`
- RAII Model Fixtures: Automated setup/teardown
- [x] **QA Matrix**
- 21-cell test matrix (Modality × Format × Trace)
- Full traceability with `--trace` flag
- Performance regression baselines
- [x] **Documentation**
- Updated showcase spec (v1.7.0) with Dr. Popper's audit
- Falsification suite (qa_falsify.rs)
- Comprehensive QA Report
### Quality Metrics Achieved
- QA Pass Rate: 100% (21/21 matrix cells)
- Falsification Coverage: 5 attack vectors verified
- Zombie Processes: 0 (verified by SIGINT tests)
- Documentation: Epistemologically audited (Corroborated)
### Released ✅
- [x] QA Protocol fully operational
- [x] Showcase demo verified
- [x] Falsification suite integrated
---
## v0.8.1: Time Series (Planned)
- [ ] ARIMA models
- [ ] Exponential smoothing (Holt-Winters)
- [ ] Seasonal decomposition
- [ ] Forecasting metrics: MAPE, SMAPE
---
## v1.0.0: Production Hardening
- [ ] GPU benchmarks and optimization
- [ ] WASM examples (in-browser training)
- [ ] Model serialization versioning
- [ ] Complete EXTREME TDD Book content
- [ ] Performance whitepaper
- [ ] Production deployment examples
---
## Quality Targets
All releases must meet:
- **TDG Score**: A+ (95.0+/100)
- **Test Coverage**: 95%+ line coverage
- **Mutation Score**: 85%+ (cargo-mutants)
- **Complexity**: ≤10 cyclomatic per function
- **Documentation**: 100% rustdoc coverage
---
## Contributing
Contributions welcome! Please ensure:
- All tests pass: `cargo test --all`
- No clippy warnings: `cargo clippy --all-targets`
- Code is formatted: `cargo fmt`
Priorities:
1. Bug fixes and test coverage improvements
2. Documentation and examples
3. Performance optimizations
4. New algorithms (must include tests and benchmarks)