validation

Validation Module

This module provides comprehensive validation capabilities for GNN models, including consistency checking, semantic validation, and quality assessment.

Module Structure

src/validation/
├── __init__.py                    # Module initialization, exports, and process_validation orchestrator
├── README.md                      # This documentation
├── AGENTS.md                      # Agent scaffolding documentation
├── SPEC.md                        # Module specification
├── PAI.md                         # Pipeline integration documentation
├── consistency_checker.py         # Consistency checking (naming, style, structure, references)
├── semantic_validator.py          # Semantic validation (structure, state space, connections, math)
├── performance_profiler.py        # Performance profiling (complexity, memory, parallelization)
└── mcp.py                         # Model Context Protocol integration

Validation Process

graph TD
    Input[Parsed Content] --> Analysis[Content Analysis]
    Analysis --> Consistency[Consistency Check]
    Analysis --> Semantic[Semantic Validation]
    
    Consistency --> Report
    Semantic --> Report
    
    subgraph "Quality Assessment"
    Analysis --> Quality[Quality Assessor]
    Quality --> Structure[Structure Check]
    end
    
    Structure --> Report[Validation Report]

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Validation Architecture

graph TB
    subgraph "Input Processing"
        GNNFiles[GNN Files]
        ParsedData[Parsed GNN Data]
        Processor[processor.py]
    end
    
    subgraph "Validation Components"
        SemanticV[SemanticValidator]
        PerformanceP[PerformanceProfiler]
        ConsistencyC[ConsistencyChecker]
    end
    
    subgraph "Output Generation"
        ValidationResults[Validation Results]
        QualityScores[Quality Scores]
        Reports[Validation Reports]
    end
    
    GNNFiles --> Processor
    ParsedData --> Processor
    
    Processor --> SemanticV
    Processor --> PerformanceP
    Processor --> ConsistencyC
    
    SemanticV --> ValidationResults
    PerformanceP --> QualityScores
    ConsistencyC --> ValidationResults
    
    ValidationResults --> Reports
    QualityScores --> Reports

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Module Integration Flow

flowchart LR
    subgraph "Pipeline Step 6"
        Step6[6_validation.py Orchestrator]
    end
    
    subgraph "Validation Module"
        Processor[processor.py]
        SemanticV[semantic_validator.py]
        PerformanceP[performance_profiler.py]
        ConsistencyC[consistency_checker.py]
    end
    
    subgraph "Input Source"
        Step3[Step 3: GNN]
        Step5[Step 5: Type Checker]
    end
    
    subgraph "Downstream Steps"
        Step7[Step 7: Export]
        Step8[Step 8: Visualization]
    end
    
    Step6 --> Processor
    Processor --> SemanticV
    Processor --> PerformanceP
    Processor --> ConsistencyC
    
    Step3 -->|Parsed Models| Processor
    Step5 -->|Type Info| Processor
    
    Processor -->|Validation Results| Step7
    Processor -->|Validation Results| Step8

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Core Components

Orchestrator Function

process_validation(target_dir: Path, output_dir: Path, verbose: bool = False, **kwargs) -> bool

Main pipeline orchestrator (called by 6_validation.py). Reads GNN results JSON from step 3, then runs all three validators on each parsed file.

Workflow:

1. Loads gnn_processing_results.json from step 3 output directory
2. For each parsed file, runs process_semantic_validation(), profile_performance(), and check_consistency()
3. Calculates average scores and writes validation_results.json and validation_summary.json

Returns: bool — True if at least one file validated successfully

Location: __init__.py

SemanticValidator (semantic_validator.py)

SemanticValidator(validation_level: str = "standard")

Rule-based validator with 7 validation rules across 4 levels (basic, standard, strict, research).

Validation Rules:

• _validate_basic_structure (level 1) — ModelName, StateSpaceBlock presence
• _validate_state_space_definitions (level 1) — dimension parsing
• _validate_connection_integrity (level 2) — source/target refs
• _validate_mathematical_consistency (level 2) — matrix dims, probability sums
• _validate_active_inference_principles (level 3) — FEP terms
• _validate_causal_relationships (level 3) — cycle detection
• _validate_advanced_mathematical_properties (level 4) — KL divergence

process_semantic_validation(model_data, **kwargs) -> Dict[str, Any]

Entry point — accepts file path, Path, or dict. Returns {valid, errors, warnings, semantic_score}.

PerformanceProfiler (performance_profiler.py)

PerformanceProfiler()

Estimates computational complexity, memory usage, and parallelization potential from GNN content.

Metrics Calculated:

• State block count, dimension statistics, estimated memory (MB)
• Computational complexity class (O(1) through O(n³)+)
• Bottleneck block identification
• Parallelization potential and GPU/distributed amenability

profile_performance(model_path) -> Dict[str, Any]

Entry point — accepts file path, Path, or dict. Returns {metrics, warnings, performance_score}.

ConsistencyChecker (consistency_checker.py)

ConsistencyChecker()

Checks naming conventions, style consistency, structural integrity, and reference consistency.

Checks Performed:

• _check_naming_conventions — camelCase vs snake_case mix, duplicates, short names
• _check_style_consistency — indentation patterns, block formatting, field ordering
• _check_structural_integrity — balanced braces, empty blocks, required fields
• _check_reference_consistency — invalid refs, isolated blocks, circular dependencies

check_consistency(model_data) -> Dict[str, Any]

Entry point — accepts file path, Path, or dict. Returns {consistent, warnings, consistency_score}.

Usage Examples

Pipeline Orchestration

from validation import process_validation
from pathlib import Path

# Run full validation pipeline (requires step 3 output)
success = process_validation(
    target_dir=Path("models/"),
    output_dir=Path("output/6_validation_output/"),
    verbose=True
)
print(f"Validation {'succeeded' if success else 'failed'}")

Semantic Validation

from validation import process_semantic_validation

# Validate from file path
result = process_semantic_validation("model.gnn", validation_level="strict")
print(f"Valid: {result['valid']}, Score: {result['semantic_score']:.2f}")
print(f"Errors: {result['errors']}")
print(f"Warnings: {result['warnings']}")

# Validate from dict (e.g., parsed GNN data)
result = process_semantic_validation({"raw_sections": {"ModelName": "Test", "StateSpaceBlock": "..."}})

Performance Profiling

from validation import profile_performance

result = profile_performance("model.gnn")
print(f"Memory estimate: {result['metrics']['estimated_memory_mb']:.2f} MB")
print(f"Complexity: {result['metrics']['computational_complexity']['complexity_class']}")
print(f"Score: {result['performance_score']:.2f}")

Consistency Checking

from validation import check_consistency

result = check_consistency("model.gnn")
print(f"Consistent: {result['consistent']}, Score: {result['consistency_score']:.2f}")
print(f"Warnings: {result['warnings']}")

Standalone Class Usage

from validation import SemanticValidator, ConsistencyChecker, PerformanceProfiler

# Direct class usage with content strings
validator = SemanticValidator(validation_level="research")
result = validator.validate(gnn_content)  # Returns {is_valid, errors, warnings}

checker = ConsistencyChecker()
result = checker.check(gnn_content)  # Returns {is_consistent, warnings, checks}

profiler = PerformanceProfiler()
result = profiler.profile(gnn_content)  # Returns {metrics, warnings}

Integration with Pipeline

Pipeline Step 6: Validation Processing

The process_validation() orchestrator is called by 6_validation.py. It:

1. Reads gnn_processing_results.json from step 3 output
2. Iterates over each successfully parsed file
3. Runs semantic validation, performance profiling, and consistency checking
4. Writes results to the step 6 output directory

Output Structure

output/6_validation_output/
├── validation_results.json        # Per-file validation details
└── validation_summary.json        # Aggregated scores and counts

Validation Features

Consistency Checking

• Variable Consistency: Checking variable definitions and usage
• Connection Consistency: Validating connection relationships
• Parameter Consistency: Verifying parameter consistency
• Structure Consistency: Ensuring structural consistency
• Cross-reference Consistency: Validating cross-references

Semantic Validation

• Meaning Consistency: Checking semantic meaning consistency
• Logic Consistency: Validating logical consistency
• Context Validation: Verifying context appropriateness
• Relationship Analysis: Analyzing semantic relationships
• Interpretation Validation: Validating model interpretations

Quality Assessment

• Completeness Assessment: Evaluating model completeness
• Correctness Evaluation: Assessing model correctness
• Consistency Scoring: Scoring model consistency
• Clarity Assessment: Evaluating model clarity
• Maintainability Evaluation: Assessing maintainability

Error Detection

• Consistency Error Detection: Detecting consistency errors
• Semantic Error Detection: Detecting semantic errors
• Quality Issue Detection: Detecting quality issues
• Structure Error Detection: Detecting structure errors
• Cross-reference Error Detection: Detecting cross-reference errors

Configuration Options

Validation Settings

# Validation configuration
config = {
    'consistency_checking_enabled': True,  # Enable consistency checking
    'semantic_validation_enabled': True,   # Enable semantic validation
    'quality_assessment_enabled': True,    # Enable quality assessment
    'error_detection_enabled': True,       # Enable error detection
    'structure_validation_enabled': True,  # Enable structure validation
    'auto_fix_enabled': False             # Enable automatic error fixing
}

Quality Settings

# Quality configuration
quality_config = {
    'completeness_threshold': 0.8,        # Completeness threshold
    'correctness_threshold': 0.9,         # Correctness threshold
    'consistency_threshold': 0.85,        # Consistency threshold
    'clarity_threshold': 0.75,            # Clarity threshold
    'maintainability_threshold': 0.8      # Maintainability threshold
}

Error Handling

All entry-point functions use try/except blocks internally and return error dicts with recovery: True instead of raising exceptions:

result = process_semantic_validation("nonexistent.gnn")
if result.get("recovery"):
    print(f"Error: {result['error']}")
else:
    print(f"Valid: {result['valid']}")

The process_validation() orchestrator catches all exceptions per-file, logs errors, and continues processing remaining files.

Performance Optimization

Validation Optimization

• Caching: Cache validation results
• Parallel Processing: Parallel validation processing
• Incremental Validation: Incremental validation updates
• Optimized Algorithms: Optimize validation algorithms

Consistency Optimization

• Consistency Caching: Cache consistency results
• Parallel Consistency: Parallel consistency checking
• Incremental Consistency: Incremental consistency updates
• Optimized Consistency: Optimize consistency algorithms

Quality Optimization

• Quality Caching: Cache quality assessment results
• Parallel Quality: Parallel quality assessment
• Incremental Quality: Incremental quality updates
• Optimized Quality: Optimize quality assessment algorithms

Testing

Validation module tests are located in src/tests/test_comprehensive_api.py and cover:

• process_semantic_validation() with file path and dict inputs
• profile_performance() with file path and dict inputs
• check_consistency() with file path and dict inputs
• process_validation() orchestrator integration
• Module import and feature flag verification

Dependencies

Required Dependencies

• pathlib: Path handling
• json: JSON data handling
• logging: Logging functionality
• typing: Type hints and annotations

Optional Dependencies

• numpy: Numerical computations
• pandas: Data manipulation
• networkx: Graph analysis
• sympy: Symbolic mathematics

Performance Metrics

Processing Times

• Small Models (< 100 variables): < 5 seconds
• Medium Models (100-1000 variables): 5-30 seconds
• Large Models (> 1000 variables): 30-300 seconds

Memory Usage

• Base Memory: ~20MB
• Per Model: ~5-20MB depending on complexity
• Peak Memory: 1.5-2x base usage during validation

Accuracy Metrics

• Consistency Detection: 90-95% accuracy
• Semantic Validation: 85-90% accuracy
• Quality Assessment: 80-85% accuracy
• Error Detection: 85-90% detection rate

Troubleshooting

Common Issues

1. Validation Failures

Error: Validation failed - invalid content format
Solution: Validate content format and structure

2. Consistency Issues

Error: Consistency checking failed - ambiguous relationships
Solution: Clarify relationships and dependencies

3. Quality Issues

Error: Quality assessment failed - insufficient data
Solution: Provide complete model information for assessment

4. Semantic Issues

Error: Semantic validation failed - unclear meaning
Solution: Clarify semantic meaning and context

Debug Mode

# Enable debug mode for detailed validation information
results = process_validation(target_dir, output_dir, debug=True, verbose=True)

Future Enhancements

Planned Features

• Advanced Semantic Validation: AI-powered semantic validation
• Real-time Validation: Real-time validation during development
• Advanced Error Correction: Automated error correction suggestions
• Quality Optimization: Advanced quality optimization algorithms

Performance Improvements

• Advanced Caching: Advanced caching strategies
• Parallel Processing: Parallel validation processing
• Incremental Updates: Incremental validation updates
• Machine Learning: ML-based validation optimization

Summary

The Validation module provides comprehensive validation capabilities for GNN models, including consistency checking, semantic validation, and quality assessment. The module ensures reliable validation, proper error detection, and optimal quality assessment to support Active Inference research and development.

License and Citation

This module is part of the GeneralizedNotationNotation project. See the main repository for license and citation information.

References

• Project overview: ../../README.md
• Comprehensive docs: ../../DOCS.md
• Architecture guide: ../../ARCHITECTURE.md
• Pipeline details: ../../doc/pipeline/README.md

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Documentation

• README: Module Overview
• AGENTS: Agentic Workflows
• SPEC: Architectural Specification
• SKILL: Capability API