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SciGo Development Roadmap

Overview

This document outlines the development roadmap for SciGo, a blazing-fast scikit-learn compatible ML library for Go. Our goal is to reach v1.0.0 with a stable, production-ready API and comprehensive machine learning capabilities.

Release Philosophy

  • v0.x releases: Active development, API may change
  • v1.0.0: API stability guarantee, backward compatibility commitment
  • Semantic Versioning: Following semver.org strictly

Current Status (v0.3.0)

✅ Implemented Features

  • Linear Models

    • LinearRegression (QR decomposition)
    • SGDRegressor / SGDClassifier
    • PassiveAggressive algorithms

  • Preprocessing

    • StandardScaler
    • MinMaxScaler
    • OneHotEncoder

  • Clustering

    • MiniBatch K-Means

  • Tree Models

    • LightGBM inference (Python model compatibility)

  • Advanced Features

    • Full scikit-learn API compatibility
    • Online learning / Streaming support
    • gRPC/Protobuf support
    • Model serialization framework

Roadmap to v1.0.0

Phase 1: Core ML Algorithms

v0.4.0 - Essential Classification (Target: 2-3 weeks)

  • LogisticRegression
    • Binary and multiclass classification
    • L1/L2 regularization
    • Solver options (lbfgs, liblinear, newton-cg)
  • DecisionTree
    • DecisionTreeClassifier
    • DecisionTreeRegressor
    • Feature importance calculation
    • Tree visualization support

v0.5.0 - Advanced Algorithms (Target: 2-3 weeks)

  • RandomForest
    • RandomForestClassifier
    • RandomForestRegressor
    • Out-of-bag score
    • Parallel tree training
  • SVM (Support Vector Machines)
    • SVC (Support Vector Classifier)
    • SVR (Support Vector Regressor)
    • Kernel support (linear, rbf, poly, sigmoid)

Phase 2: Ensemble Methods

v0.6.0 - XGBoost Integration (Target: 3-4 weeks)

  • XGBoost
    • XGBClassifier
    • XGBRegressor
    • Python model compatibility (.json, .ubj formats)
    • Native Go training implementation
    • GPU acceleration support (optional)

v0.7.0 - LightGBM Complete (Target: 2-3 weeks)

  • LightGBM Training
    • Native Go training implementation
    • Full feature parity with Python API
    • Categorical feature support
    • Early stopping
    • Custom objective functions

Phase 3: Stabilization

v0.8.0 - Performance & Optimization (Target: 1-2 weeks)

  • Performance Improvements
    • SIMD optimizations
    • Memory pooling
    • Parallel processing enhancements
  • Benchmarking Suite
    • Comprehensive performance tests
    • Comparison with scikit-learn
    • Memory profiling

v0.9.0 - API Finalization (Target: 1-2 weeks)

  • API Review
    • Final API adjustments
    • Deprecation of experimental features
    • Interface stabilization
  • Documentation
    • Complete API documentation
    • Migration guides
    • Best practices guide
  • Testing
    • 90%+ test coverage
    • Integration tests
    • Cross-compatibility tests with Python

Phase 4: Production Ready

v1.0.0 - Stable Release (Target: Q2 2025)

  • API Stability Guarantee
    • Backward compatibility commitment
    • Long-term support (LTS) declaration
    • Deprecation policy establishment
  • Production Features
    • Model versioning
    • A/B testing support
    • Monitoring and metrics
  • Ecosystem
    • Plugin system
    • Community contributions guide
    • Partner integrations

Feature Priority Matrix

Feature Priority Complexity Status
LogisticRegression High Medium Planned (v0.4.0)
DecisionTree High Medium Planned (v0.4.0)
RandomForest High High Planned (v0.5.0)
SVM Medium High Planned (v0.5.0)
XGBoost High Very High Planned (v0.6.0)
LightGBM Training High High Planned (v0.7.0)
Neural Networks Low Very High Post-v1.0.0
Deep Learning Low Very High Post-v1.0.0

API Stability Commitment

Pre-v1.0.0

  • APIs may change between minor versions
  • Breaking changes will be documented in CHANGELOG
  • Deprecation warnings for at least one version

Post-v1.0.0

  • No breaking changes in minor/patch releases
  • Deprecated features maintained for at least 3 minor versions
  • Clear migration paths for any future major version

Contributing

We welcome contributions! Priority areas for community help:

  1. Algorithm Implementations

    • Help implement planned algorithms
    • Optimize existing implementations

  2. Testing

    • Increase test coverage
    • Add benchmark tests
    • Cross-validation with scikit-learn

  3. Documentation

    • Improve examples
    • Add tutorials
    • Translate documentation

  4. Performance

    • SIMD optimizations
    • GPU acceleration
    • Memory optimizations

Success Metrics

  • Performance: 2-5x faster than scikit-learn for common operations
  • Compatibility: 100% API compatibility for implemented features
  • Quality: 90%+ test coverage, zero critical bugs
  • Adoption: 1000+ GitHub stars, 50+ production deployments
  • Community: 20+ active contributors

Risk Mitigation

Risk Mitigation Strategy
API design flaws discovered late Extensive testing in v0.x releases
Performance regression Continuous benchmarking CI
Breaking scikit-learn compatibility Automated compatibility tests
Slow adoption Focus on documentation and examples
Technical debt Regular refactoring sprints

Timeline Summary

  • v0.4.0: ~2-3 weeks (LogisticRegression, DecisionTree)
  • v0.5.0: ~2-3 weeks (RandomForest, SVM)
  • v0.6.0: ~3-4 weeks (XGBoost)
  • v0.7.0: ~2-3 weeks (LightGBM Training)
  • v0.8.0: ~1-2 weeks (Performance)
  • v0.9.0: ~1-2 weeks (API Finalization)
  • v1.0.0: ~2-3 months from now (estimated Q2 2025)

Contact

This roadmap is subject to change based on community feedback and project priorities. Last updated: 2025-08-07