feat: Add configuration validation system for MVP stability (Phase 1.2)
Implements JSON Schema validation for optimization configurations to ensure
consistency across all studies and prevent configuration errors.
Added:
- optimization_engine/schemas/optimization_config_schema.json
- Comprehensive schema for Protocol 10 & 11 configurations
- Validates objectives, constraints, design variables, simulation settings
- Enforces standard field names (goal, bounds, parameter, threshold)
- optimization_engine/config_manager.py
- ConfigManager class with schema validation
- CLI tool: python config_manager.py <config.json>
- Type-safe accessor methods for config elements
- Custom validations: bounds check, multi-objective consistency, location check
- optimization_engine/schemas/README.md
- Complete documentation of standard configuration format
- Validation examples and common error fixes
- Migration guidance for legacy configs
- docs/07_DEVELOPMENT/Phase_1_2_Implementation_Plan.md
- Detailed implementation plan for remaining Phase 1.2 tasks
- Migration tool design, integration guide, testing plan
Testing:
- Validated drone_gimbal_arm_optimization config successfully
- ConfigManager works with drone_gimbal format (new standard)
- Identifies legacy format issues in bracket studies
Standards Established:
- Configuration location: studies/{name}/1_setup/
- Objective direction: "goal" not "type"
- Design var bounds: "bounds": [min, max] not "min"/"max"
- Design var name: "parameter" not "name"
- Constraint threshold: "threshold" not "value"
Next Steps (Phase 1.2.1+):
- Config migration tool for legacy studies
- Integration with run_optimization.py
- Update create-study Claude skill with schema reference
- Migrate bracket studies to new format
Relates to: Phase 1.2 MVP Development Plan
🤖 Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
244
optimization_engine/config_manager.py
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244
optimization_engine/config_manager.py
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"""Configuration validation and management for Atomizer studies.
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This module provides schema-based validation for optimization configuration files,
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ensuring consistency across all studies.
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Usage:
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# In run_optimization.py
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from optimization_engine.config_manager import ConfigManager
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config_manager = ConfigManager(Path(__file__).parent / "1_setup" / "optimization_config.json")
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config_manager.load_config()
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if not config_manager.validate():
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print(config_manager.get_validation_report())
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sys.exit(1)
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# Access validated configuration
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design_vars = config_manager.get_design_variables()
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objectives = config_manager.get_objectives()
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"""
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import json
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from pathlib import Path
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from typing import Dict, List, Any, Optional
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try:
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import jsonschema
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JSONSCHEMA_AVAILABLE = True
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except ImportError:
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JSONSCHEMA_AVAILABLE = False
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print("Warning: jsonschema not installed. Install with: pip install jsonschema>=4.17.0")
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class ConfigValidationError(Exception):
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"""Raised when configuration validation fails."""
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pass
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class ConfigManager:
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"""Manages and validates optimization configuration files."""
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def __init__(self, config_path: Path):
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"""
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Initialize ConfigManager with path to optimization_config.json.
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Args:
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config_path: Path to optimization_config.json file
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"""
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self.config_path = Path(config_path)
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self.schema_path = Path(__file__).parent / "schemas" / "optimization_config_schema.json"
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self.config: Optional[Dict[str, Any]] = None
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self.validation_errors: List[str] = []
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def load_schema(self) -> Dict[str, Any]:
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"""Load JSON schema for validation."""
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if not self.schema_path.exists():
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raise FileNotFoundError(f"Schema file not found: {self.schema_path}")
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with open(self.schema_path, 'r') as f:
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return json.load(f)
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def load_config(self) -> Dict[str, Any]:
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"""Load configuration file."""
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if not self.config_path.exists():
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raise FileNotFoundError(f"Config file not found: {self.config_path}")
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with open(self.config_path, 'r') as f:
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self.config = json.load(f)
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return self.config
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def validate(self, strict: bool = True) -> bool:
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"""
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Validate configuration against schema.
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Args:
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strict: If True, enforce all validations. If False, only warn on non-critical issues.
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Returns:
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True if valid, False otherwise
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"""
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if self.config is None:
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self.load_config()
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self.validation_errors = []
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# JSON Schema validation
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if JSONSCHEMA_AVAILABLE:
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schema = self.load_schema()
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try:
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jsonschema.validate(instance=self.config, schema=schema)
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except jsonschema.ValidationError as e:
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self.validation_errors.append(f"Schema validation failed: {e.message}")
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if strict:
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return False
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else:
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self.validation_errors.append("jsonschema not installed - schema validation skipped")
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# Custom validations
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self._validate_design_variable_bounds()
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self._validate_multi_objective_consistency()
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self._validate_file_locations()
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self._validate_extraction_consistency()
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return len(self.validation_errors) == 0
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def _validate_design_variable_bounds(self):
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"""Ensure bounds are valid (min < max)."""
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for dv in self.config.get("design_variables", []):
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bounds = dv.get("bounds", [])
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if len(bounds) == 2 and bounds[0] >= bounds[1]:
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self.validation_errors.append(
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f"Design variable '{dv.get('parameter', 'unknown')}': "
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f"min ({bounds[0]}) must be < max ({bounds[1]})"
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)
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def _validate_multi_objective_consistency(self):
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"""Validate multi-objective settings consistency."""
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n_objectives = len(self.config.get("objectives", []))
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protocol = self.config.get("optimization_settings", {}).get("protocol")
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sampler = self.config.get("optimization_settings", {}).get("sampler")
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if n_objectives > 1:
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# Multi-objective should use protocol_11 and NSGA-II
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if protocol and protocol != "protocol_11_multi_objective":
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self.validation_errors.append(
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f"Multi-objective optimization ({n_objectives} objectives) "
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f"should use protocol_11_multi_objective (got {protocol})"
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)
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if sampler and sampler != "NSGAIISampler":
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self.validation_errors.append(
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f"Multi-objective optimization should use NSGAIISampler (got {sampler})"
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)
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elif n_objectives == 1:
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# Single-objective should not use NSGA-II
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if sampler == "NSGAIISampler":
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self.validation_errors.append(
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"Single-objective optimization should not use NSGAIISampler "
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"(use TPESampler or CmaEsSampler)"
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)
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def _validate_file_locations(self):
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"""Check if config is in correct location (1_setup/)."""
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if "1_setup" not in str(self.config_path.parent):
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self.validation_errors.append(
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f"Warning: Config should be in '1_setup/' directory, "
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f"found in {self.config_path.parent}"
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)
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def _validate_extraction_consistency(self):
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"""Validate extraction specifications."""
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# Check objectives have extraction specs
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for obj in self.config.get("objectives", []):
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if "extraction" not in obj:
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self.validation_errors.append(
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f"Objective '{obj.get('name', 'unknown')}' missing extraction specification"
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)
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# Check constraints have extraction specs
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for constraint in self.config.get("constraints", []):
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if "extraction" not in constraint:
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self.validation_errors.append(
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f"Constraint '{constraint.get('name', 'unknown')}' missing extraction specification"
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)
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def get_validation_report(self) -> str:
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"""Get human-readable validation report."""
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if not self.validation_errors:
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return "[OK] Configuration is valid"
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report = "[FAIL] Configuration validation failed:\n"
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for i, error in enumerate(self.validation_errors, 1):
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report += f" {i}. {error}\n"
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return report
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# Type-safe accessor methods
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def get_design_variables(self) -> List[Dict[str, Any]]:
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"""Get design variables with validated structure."""
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if self.config is None:
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self.load_config()
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return self.config.get("design_variables", [])
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def get_objectives(self) -> List[Dict[str, Any]]:
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"""Get objectives with validated structure."""
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if self.config is None:
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self.load_config()
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return self.config.get("objectives", [])
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def get_constraints(self) -> List[Dict[str, Any]]:
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"""Get constraints with validated structure."""
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if self.config is None:
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self.load_config()
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return self.config.get("constraints", [])
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def get_simulation_settings(self) -> Dict[str, Any]:
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"""Get simulation settings."""
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if self.config is None:
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self.load_config()
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return self.config.get("simulation", {})
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def get_optimization_settings(self) -> Dict[str, Any]:
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"""Get optimization settings."""
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if self.config is None:
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self.load_config()
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return self.config.get("optimization_settings", {})
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# CLI tool for validation
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if __name__ == "__main__":
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import sys
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if len(sys.argv) < 2:
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print("Usage: python config_manager.py <path_to_optimization_config.json>")
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print("\nExample:")
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print(" python config_manager.py studies/drone_gimbal_arm_optimization/1_setup/optimization_config.json")
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sys.exit(1)
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config_path = Path(sys.argv[1])
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print(f"Validating configuration: {config_path}")
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print("=" * 60)
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manager = ConfigManager(config_path)
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try:
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manager.load_config()
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print("[OK] Config loaded successfully")
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is_valid = manager.validate()
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print(manager.get_validation_report())
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if is_valid:
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print("\n" + "=" * 60)
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print("Configuration Summary:")
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print(f" Study: {manager.config.get('study_name')}")
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print(f" Protocol: {manager.get_optimization_settings().get('protocol')}")
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print(f" Design Variables: {len(manager.get_design_variables())}")
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print(f" Objectives: {len(manager.get_objectives())}")
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print(f" Constraints: {len(manager.get_constraints())}")
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sys.exit(0 if is_valid else 1)
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except Exception as e:
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print(f"[ERROR] {e}")
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sys.exit(1)
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213
optimization_engine/schemas/README.md
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optimization_engine/schemas/README.md
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# Atomizer Configuration Schemas
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This directory contains JSON Schema files for validating Atomizer optimization study configurations.
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## Files
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- **optimization_config_schema.json** - Schema for `optimization_config.json` files
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- Validates study configuration including objectives, constraints, design variables
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- Ensures consistency across all studies
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- Based on Protocol 10 (single-objective) and Protocol 11 (multi-objective) standards
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## Usage
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### Validate a Configuration
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Use the ConfigManager CLI tool:
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```bash
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python optimization_engine/config_manager.py studies/your_study/1_setup/optimization_config.json
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```
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### In Python Code
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```python
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from pathlib import Path
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from optimization_engine.config_manager import ConfigManager
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config_path = Path("studies/my_study/1_setup/optimization_config.json")
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manager = ConfigManager(config_path)
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manager.load_config()
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if manager.validate():
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print("Configuration is valid")
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design_vars = manager.get_design_variables()
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objectives = manager.get_objectives()
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else:
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print(manager.get_validation_report())
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```
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## Schema Standards
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### Standard Configuration Format
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Based on the drone_gimbal_arm_optimization study, the standard format is:
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```json
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{
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"study_name": "study_name_lowercase_with_underscores",
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"description": "Brief description of optimization problem",
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"engineering_context": "Real-world scenario and requirements",
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"optimization_settings": {
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"protocol": "protocol_11_multi_objective", // or protocol_10_single_objective
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"n_trials": 30,
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"sampler": "NSGAIISampler", // or TPESampler, CmaEsSampler
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"pruner": null,
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"timeout_per_trial": 600
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},
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"design_variables": [
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{
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"parameter": "nx_expression_name",
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"bounds": [min, max],
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"description": "What this parameter controls"
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}
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],
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"objectives": [
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{
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"name": "objective_name",
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"goal": "minimize", // or "maximize"
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"weight": 1.0,
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"description": "What this measures",
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"target": 100.0, // optional
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"extraction": {
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"action": "extract_mass", // or extract_stress, extract_displacement, etc.
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"domain": "result_extraction",
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"params": {
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"result_type": "mass",
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"metric": "total"
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}
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}
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}
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],
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"constraints": [
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{
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"name": "constraint_name",
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"type": "less_than", // or "greater_than"
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"threshold": 100.0,
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"description": "Engineering justification",
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"extraction": {
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"action": "extract_displacement",
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"domain": "result_extraction",
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"params": {
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"result_type": "displacement",
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"metric": "max"
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}
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}
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}
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],
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"simulation": {
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"model_file": "Model.prt",
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"sim_file": "Model_sim1.sim",
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"fem_file": "Model_fem1.fem",
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"solver": "nastran",
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"analysis_types": ["static", "modal"]
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},
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"reporting": {
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"generate_plots": true,
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"save_incremental": true,
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"llm_summary": false
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}
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}
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```
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## Field Name Standards
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**IMPORTANT**: Use these field names (not legacy alternatives):
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| Field | Standard Name | Legacy (DON'T USE) |
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|-------|--------------|-------------------|
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| Objective direction | `"goal"` | `"type"` |
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| Design var bounds | `"bounds": [min, max]` | `"min": X, "max": Y"` |
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| Design var name | `"parameter"` | `"name"` |
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| Constraint limit | `"threshold"` | `"value"` |
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## File Location Standard
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Configuration files **MUST** be in the `1_setup/` directory:
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```
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studies/your_study/
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├── 1_setup/
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│ ├── optimization_config.json ← HERE
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│ └── workflow_config.json
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├── 2_results/
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└── run_optimization.py
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```
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## Validation Rules
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The schema enforces:
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1. **Study Name**: Lowercase with underscores, 3-100 characters
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2. **Design Variable Bounds**: min < max
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3. **Multi-Objective Consistency**:
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- 2-3 objectives → protocol_11_multi_objective + NSGAIISampler
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- 1 objective → protocol_10_single_objective + TPESampler/CmaEsSampler
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4. **Extraction Specs**: All objectives and constraints must have extraction blocks
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5. **File Extensions**: .prt, .sim, .fem
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6. **Analysis Types**: static, modal, thermal, or buckling
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## Common Validation Errors
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### Error: "min must be < max"
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```json
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// BAD
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{"parameter": "thickness", "bounds": [10, 5]}
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// GOOD
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{"parameter": "thickness", "bounds": [5, 10]}
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```
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### Error: "Multi-objective should use NSGAIISampler"
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```json
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// BAD
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{
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"objectives": [{...}, {...}], // 2 objectives
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"optimization_settings": {"sampler": "TPESampler"} // Wrong sampler!
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}
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// GOOD
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{
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"objectives": [{...}, {...}],
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"optimization_settings": {"sampler": "NSGAIISampler"}
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}
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```
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### Error: "Config should be in 1_setup/"
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Move your configuration file from study root to `1_setup/` directory.
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## Dependencies
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The ConfigManager requires:
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```bash
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pip install jsonschema>=4.17.0
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```
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## Migration from Legacy Format
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If you have old configuration files with:
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- `"type"` instead of `"goal"` in objectives
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- `"min"/"max"` instead of `"bounds"` in design variables
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- `"name"` instead of `"parameter"` in design variables
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- `"value"` instead of `"threshold"` in constraints
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See the migration tool (coming in Phase 1.2.1):
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```bash
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python optimization_engine/config_migrator.py studies/old_study/optimization_config.json
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```
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## References
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||||
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- [Phase 1.2 Implementation Plan](../../docs/07_DEVELOPMENT/Phase_1_2_Implementation_Plan.md)
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- [create-study Claude Skill](../../.claude/skills/create-study.md)
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- [drone_gimbal_arm Example](../../studies/drone_gimbal_arm_optimization/1_setup/optimization_config.json)
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## Questions?
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For MVP development questions, refer to [DEVELOPMENT.md](../../DEVELOPMENT.md) or the MVP plan in `docs/07_DEVELOPMENT/Today_Todo.md`.
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279
optimization_engine/schemas/optimization_config_schema.json
Normal file
279
optimization_engine/schemas/optimization_config_schema.json
Normal file
@@ -0,0 +1,279 @@
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{
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"$schema": "http://json-schema.org/draft-07/schema#",
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"$id": "https://atomizer.io/schemas/optimization_config.json",
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"title": "Atomizer Optimization Configuration",
|
||||
"description": "Schema for Atomizer optimization study configuration files",
|
||||
"type": "object",
|
||||
"required": ["study_name", "description", "optimization_settings", "design_variables", "objectives", "simulation"],
|
||||
|
||||
"properties": {
|
||||
"study_name": {
|
||||
"type": "string",
|
||||
"description": "Unique identifier for this optimization study",
|
||||
"pattern": "^[a-z0-9_]+$",
|
||||
"minLength": 3,
|
||||
"maxLength": 100
|
||||
},
|
||||
|
||||
"description": {
|
||||
"type": "string",
|
||||
"description": "Brief description of the optimization problem",
|
||||
"minLength": 10,
|
||||
"maxLength": 500
|
||||
},
|
||||
|
||||
"engineering_context": {
|
||||
"type": "string",
|
||||
"description": "Real-world engineering scenario and requirements (optional but recommended)",
|
||||
"minLength": 20
|
||||
},
|
||||
|
||||
"optimization_settings": {
|
||||
"type": "object",
|
||||
"required": ["protocol", "n_trials"],
|
||||
"properties": {
|
||||
"protocol": {
|
||||
"type": "string",
|
||||
"description": "Optimization protocol to use",
|
||||
"enum": [
|
||||
"protocol_10_single_objective",
|
||||
"protocol_11_multi_objective",
|
||||
"legacy"
|
||||
]
|
||||
},
|
||||
"n_trials": {
|
||||
"type": "integer",
|
||||
"description": "Number of optimization trials to run",
|
||||
"minimum": 1,
|
||||
"maximum": 10000
|
||||
},
|
||||
"sampler": {
|
||||
"type": "string",
|
||||
"description": "Optuna sampler algorithm",
|
||||
"enum": ["TPESampler", "NSGAIISampler", "CmaEsSampler", "RandomSampler"]
|
||||
},
|
||||
"pruner": {
|
||||
"type": ["string", "null"],
|
||||
"description": "Optuna pruner (null for no pruning)",
|
||||
"enum": ["MedianPruner", "HyperbandPruner", "SuccessiveHalvingPruner", null]
|
||||
},
|
||||
"timeout_per_trial": {
|
||||
"type": "integer",
|
||||
"description": "Maximum time per trial in seconds",
|
||||
"minimum": 60,
|
||||
"maximum": 7200
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"design_variables": {
|
||||
"type": "array",
|
||||
"description": "List of design variables (NX expressions) to optimize",
|
||||
"minItems": 1,
|
||||
"maxItems": 50,
|
||||
"items": {
|
||||
"type": "object",
|
||||
"required": ["parameter", "bounds", "description"],
|
||||
"properties": {
|
||||
"parameter": {
|
||||
"type": "string",
|
||||
"description": "NX expression name",
|
||||
"pattern": "^[a-zA-Z_][a-zA-Z0-9_]*$"
|
||||
},
|
||||
"bounds": {
|
||||
"type": "array",
|
||||
"description": "[min, max] bounds for this parameter",
|
||||
"minItems": 2,
|
||||
"maxItems": 2,
|
||||
"items": {
|
||||
"type": "number"
|
||||
}
|
||||
},
|
||||
"description": {
|
||||
"type": "string",
|
||||
"description": "What this parameter controls"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"objectives": {
|
||||
"type": "array",
|
||||
"description": "Optimization objectives (1 for single-objective, 2-3 for multi-objective)",
|
||||
"minItems": 1,
|
||||
"maxItems": 3,
|
||||
"items": {
|
||||
"type": "object",
|
||||
"required": ["name", "goal", "weight", "description", "extraction"],
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "Objective name (e.g., 'mass', 'stiffness', 'frequency')"
|
||||
},
|
||||
"goal": {
|
||||
"type": "string",
|
||||
"description": "Optimization direction",
|
||||
"enum": ["minimize", "maximize"]
|
||||
},
|
||||
"weight": {
|
||||
"type": "number",
|
||||
"description": "Relative importance weight",
|
||||
"minimum": 0,
|
||||
"maximum": 100
|
||||
},
|
||||
"description": {
|
||||
"type": "string",
|
||||
"description": "What this objective measures"
|
||||
},
|
||||
"target": {
|
||||
"type": ["number", "null"],
|
||||
"description": "Target value (optional)"
|
||||
},
|
||||
"extraction": {
|
||||
"$ref": "#/definitions/extraction_spec"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"constraints": {
|
||||
"type": "array",
|
||||
"description": "Constraints that must be satisfied",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"required": ["name", "type", "threshold", "description", "extraction"],
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "Constraint name"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"description": "Constraint type",
|
||||
"enum": ["less_than", "greater_than", "equal_to"]
|
||||
},
|
||||
"threshold": {
|
||||
"type": "number",
|
||||
"description": "Threshold value for constraint"
|
||||
},
|
||||
"description": {
|
||||
"type": "string",
|
||||
"description": "Engineering justification for this constraint"
|
||||
},
|
||||
"extraction": {
|
||||
"$ref": "#/definitions/extraction_spec"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"simulation": {
|
||||
"type": "object",
|
||||
"required": ["model_file", "sim_file", "fem_file", "solver"],
|
||||
"properties": {
|
||||
"model_file": {
|
||||
"type": "string",
|
||||
"description": "NX Part file name (e.g., 'Beam.prt')",
|
||||
"pattern": "\\.(prt|PRT)$"
|
||||
},
|
||||
"sim_file": {
|
||||
"type": "string",
|
||||
"description": "NX Simulation file name (e.g., 'Beam_sim1.sim')",
|
||||
"pattern": "\\.(sim|SIM)$"
|
||||
},
|
||||
"fem_file": {
|
||||
"type": "string",
|
||||
"description": "FEM mesh file name (e.g., 'Beam_fem1.fem')",
|
||||
"pattern": "\\.(fem|FEM)$"
|
||||
},
|
||||
"solver": {
|
||||
"type": "string",
|
||||
"description": "FEA solver type",
|
||||
"enum": ["nastran", "NX_Nastran"]
|
||||
},
|
||||
"analysis_types": {
|
||||
"type": "array",
|
||||
"description": "Types of analysis required",
|
||||
"items": {
|
||||
"type": "string",
|
||||
"enum": ["static", "modal", "thermal", "buckling"]
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"reporting": {
|
||||
"type": "object",
|
||||
"description": "Result reporting settings (optional)",
|
||||
"properties": {
|
||||
"generate_plots": {
|
||||
"type": "boolean",
|
||||
"default": true
|
||||
},
|
||||
"save_incremental": {
|
||||
"type": "boolean",
|
||||
"default": true
|
||||
},
|
||||
"llm_summary": {
|
||||
"type": "boolean",
|
||||
"default": false,
|
||||
"description": "Generate LLM summaries (experimental, requires API setup)"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"definitions": {
|
||||
"extraction_spec": {
|
||||
"type": "object",
|
||||
"required": ["action", "domain", "params"],
|
||||
"properties": {
|
||||
"action": {
|
||||
"type": "string",
|
||||
"description": "Extractor function to call",
|
||||
"enum": [
|
||||
"extract_mass",
|
||||
"extract_stress",
|
||||
"extract_displacement",
|
||||
"extract_frequency",
|
||||
"extract_stiffness"
|
||||
]
|
||||
},
|
||||
"domain": {
|
||||
"type": "string",
|
||||
"description": "Extraction domain (always 'result_extraction' for MVP)",
|
||||
"enum": ["result_extraction"]
|
||||
},
|
||||
"params": {
|
||||
"type": "object",
|
||||
"description": "Parameters for the extractor function",
|
||||
"required": ["result_type"],
|
||||
"properties": {
|
||||
"result_type": {
|
||||
"type": "string",
|
||||
"description": "Type of result to extract",
|
||||
"enum": ["mass", "stress", "displacement", "frequency", "stiffness"]
|
||||
},
|
||||
"metric": {
|
||||
"type": "string",
|
||||
"description": "Specific metric to extract (e.g., 'max', 'total', 'max_von_mises')"
|
||||
},
|
||||
"mode_number": {
|
||||
"type": "integer",
|
||||
"description": "Mode number for frequency extraction",
|
||||
"minimum": 1
|
||||
},
|
||||
"subcase": {
|
||||
"type": "integer",
|
||||
"description": "Subcase number for result extraction"
|
||||
},
|
||||
"element_type": {
|
||||
"type": "string",
|
||||
"description": "Element type for stress extraction (e.g., 'CTETRA', 'CHEXA')"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user