Enhanced Type Support Implementation Plan¶

🎯 Current Status¶

Overall Success: 69.1% (47/68 tests) ⬆️ +1.5% from cache fix
Target v0.7.5: 85%+ success rate
Gap to close: 16% (need 11+ more passing tests)

📊 Prioritized Fix Strategy¶

Phase 1: Quick Wins (Target: +10% success rate)¶

Priority: HIGH - Fix auto-detection gaps

1.1 Pandas DataFrame Auto-Detection (4 failing tests → potential +6%)¶

dataframe_simple: Add smart list-of-dicts → DataFrame detection
dataframe_orient_*: Fix orientation-specific reconstruction
Impact: 4 tests × 1.5% = 6% improvement

1.2 NumPy Array Auto-Detection (4 failing tests → potential +6%)¶

array_1d, array_2d, array_float32, array_int64: Add smart list → ndarray detection
Impact: 4 tests × 1.5% = 6% improvement

1.3 PyTorch Tensor Comparison Fix (2 failing tests → potential +3%)¶

Fix tensor comparison logic (currently: "Boolean value of Tensor with more than one value is ambiguous")
Impact: 2 tests × 1.5% = 3% improvement

1.4 Nested Structure Verification (1 failing test → potential +1.5%)¶

Fix audit script's verification logic for set → list conversions
Impact: 1 test × 1.5% = 1.5% improvement

Phase 1 Total: +16.5% potential → Target: 85%+ achieved ✅

Phase 2: ML Integration (Target: +5% success rate)¶

Priority: MEDIUM - Complete ML round-trip support

2.1 Sklearn Model Reconstruction (4 failing tests)¶

Fix metadata deserialization for unfitted/fitted models
Enhance _deserialize_with_type_metadata() for sklearn objects

2.2 Advanced PyTorch Support¶

Enhance tensor attribute preservation (device, dtype, etc.)
Add support for complex tensor operations

Phase 3: Advanced Features (Target: +5% success rate)¶

Priority: LOW - Edge cases and optimizations

3.1 DataFrame Orientation Mastery¶

Complete support for all pandas DataFrame orientations
Optimize for different use cases

3.2 Advanced NumPy Support¶

Enhanced dtype preservation
Complex array structures

🛠️ Implementation Strategy¶

Strategy 1: Enhanced Auto-Detection¶

Add intelligence to deserialize_fast() without breaking hot path

# In _process_dict_optimized():
# Add checks for:
# 1. List-of-dicts → DataFrame pattern
# 2. Nested lists → ndarray pattern  
# 3. Specific ML object patterns

Strategy 2: Improved Type Metadata Handling¶

Enhance _deserialize_with_type_metadata() for better ML support

# Fix issues with:
# 1. Sklearn model reconstruction
# 2. PyTorch tensor attributes
# 3. Complex nested type preservation

Strategy 3: Smart Verification¶

Update audit script verification logic

# Handle expected type conversions:
# 1. set → list (acceptable without type hints)
# 2. tuple → list (acceptable without type hints)
# 3. Complex nested structures

📁 File Organization¶

New Test Structure¶

tests/enhanced_types/
├── test_basic_type_enhancements.py      ✅ Created
├── test_pandas_auto_detection.py        🔄 Next
├── test_numpy_auto_detection.py         🔄 Next
├── test_ml_integration.py               🔄 Next
└── test_verification_improvements.py    🔄 Next

Core Implementation Files¶

datason/
├── deserializers.py                     🔄 Main enhancements
├── core.py                             🔄 Type metadata improvements  
└── ml_serializers.py                   🔄 ML-specific fixes

🎯 Success Metrics¶

v0.7.5 Targets¶

Overall Success: 85%+ (58+ tests passing)
Basic Types: 95%+ (maintain excellence)
Complex Types: 95%+ (maintain improvement)
NumPy Types: 90%+ (major improvement)
Pandas Types: 70%+ (significant improvement)
ML Types: 50%+ (basic functionality)

v0.8.0 Targets¶

Overall Success: 95%+ (65+ tests passing)
All categories: 90%+ success rates
Production-ready ML workflows

v0.8.5 Targets¶

Overall Success: 99%+ (67+ tests passing)
Perfect round-trip support for all major ML libraries
Complete edge case coverage

🚀 Implementation Order¶

Phase 1.1: Pandas DataFrame auto-detection
Phase 1.2: NumPy array auto-detection
Phase 1.3: PyTorch tensor comparison fix
Phase 1.4: Nested structure verification
Phase 2: ML integration improvements
Phase 3: Advanced features and edge cases

🔄 Testing Strategy¶

Continuous audit monitoring: Run deserialization_audit.py after each fix
Regression prevention: Maintain 1060+ passing integration tests
Performance validation: Ensure no hot path degradation
Security verification: Maintain 28/28 security tests passing