559 lines
20 KiB
Python
559 lines
20 KiB
Python
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"""
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Study Blueprint
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Data structures for the study blueprint - the validated configuration
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ready for study generation.
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"""
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from dataclasses import dataclass, field, asdict
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from typing import Dict, List, Any, Optional
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import json
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@dataclass
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class DesignVariable:
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"""Design variable specification."""
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parameter: str
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current_value: float
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min_value: float
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max_value: float
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units: Optional[str] = None
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is_integer: bool = False
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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def to_config_format(self) -> Dict[str, Any]:
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"""Convert to optimization_config.json format."""
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return {
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"expression_name": self.parameter,
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"bounds": [self.min_value, self.max_value],
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"units": self.units or "",
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"is_integer": self.is_integer,
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}
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@dataclass
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class Objective:
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"""Optimization objective specification."""
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name: str
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goal: str # minimize, maximize, target
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extractor: str # Extractor ID (e.g., "E1", "E4")
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extractor_name: Optional[str] = None
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extractor_params: Optional[Dict[str, Any]] = None
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weight: float = 1.0
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target_value: Optional[float] = None # For target objectives
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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def to_config_format(self) -> Dict[str, Any]:
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"""Convert to optimization_config.json format."""
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config = {
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"name": self.name,
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"type": self.goal,
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"extractor": self.extractor,
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"weight": self.weight,
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}
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if self.extractor_params:
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config["extractor_params"] = self.extractor_params
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if self.target_value is not None:
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config["target"] = self.target_value
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return config
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@dataclass
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class Constraint:
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"""Optimization constraint specification."""
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name: str
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constraint_type: str # max, min
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threshold: float
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extractor: str # Extractor ID
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extractor_name: Optional[str] = None
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extractor_params: Optional[Dict[str, Any]] = None
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is_hard: bool = True
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penalty_weight: float = 1000.0 # For soft constraints
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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def to_config_format(self) -> Dict[str, Any]:
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"""Convert to optimization_config.json format."""
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config = {
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"name": self.name,
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"type": self.constraint_type,
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"threshold": self.threshold,
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"extractor": self.extractor,
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"hard": self.is_hard,
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}
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if self.extractor_params:
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config["extractor_params"] = self.extractor_params
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if not self.is_hard:
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config["penalty_weight"] = self.penalty_weight
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return config
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@dataclass
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class StudyBlueprint:
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"""
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Complete study blueprint ready for generation.
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This is the validated configuration that will be used to create
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the study files (optimization_config.json, run_optimization.py, etc.)
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"""
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# Study metadata
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study_name: str
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study_description: str = ""
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interview_session_id: str = ""
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# Model paths
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model_path: str = ""
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sim_path: str = ""
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fem_path: str = ""
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# Design space
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design_variables: List[DesignVariable] = field(default_factory=list)
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# Optimization goals
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objectives: List[Objective] = field(default_factory=list)
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constraints: List[Constraint] = field(default_factory=list)
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# Optimization settings
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protocol: str = "protocol_10_single" # or "protocol_11_multi"
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n_trials: int = 100
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sampler: str = "TPE"
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use_neural_acceleration: bool = False
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# Solver settings
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solver_config: Dict[str, Any] = field(default_factory=dict)
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solve_all_solutions: bool = True
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# Extractors configuration
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extractors_config: Dict[str, Any] = field(default_factory=dict)
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# Validation
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warnings_acknowledged: List[str] = field(default_factory=list)
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baseline_validated: bool = False
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baseline_results: Optional[Dict[str, Any]] = None
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def to_dict(self) -> Dict[str, Any]:
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"""Convert to dictionary."""
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return {
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"study_name": self.study_name,
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"study_description": self.study_description,
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"interview_session_id": self.interview_session_id,
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"model_path": self.model_path,
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"sim_path": self.sim_path,
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"fem_path": self.fem_path,
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"design_variables": [dv.to_dict() for dv in self.design_variables],
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"objectives": [obj.to_dict() for obj in self.objectives],
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"constraints": [con.to_dict() for con in self.constraints],
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"protocol": self.protocol,
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"n_trials": self.n_trials,
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"sampler": self.sampler,
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"use_neural_acceleration": self.use_neural_acceleration,
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"solver_config": self.solver_config,
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"solve_all_solutions": self.solve_all_solutions,
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"extractors_config": self.extractors_config,
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"warnings_acknowledged": self.warnings_acknowledged,
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"baseline_validated": self.baseline_validated,
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"baseline_results": self.baseline_results,
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}
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def to_json(self) -> str:
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"""Serialize to JSON string."""
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return json.dumps(self.to_dict(), indent=2)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "StudyBlueprint":
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"""Create from dictionary."""
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design_variables = [
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DesignVariable(**dv) for dv in data.get("design_variables", [])
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]
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objectives = [
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Objective(**obj) for obj in data.get("objectives", [])
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]
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constraints = [
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Constraint(**con) for con in data.get("constraints", [])
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]
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return cls(
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study_name=data.get("study_name", ""),
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study_description=data.get("study_description", ""),
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interview_session_id=data.get("interview_session_id", ""),
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model_path=data.get("model_path", ""),
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sim_path=data.get("sim_path", ""),
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fem_path=data.get("fem_path", ""),
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design_variables=design_variables,
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objectives=objectives,
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constraints=constraints,
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protocol=data.get("protocol", "protocol_10_single"),
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n_trials=data.get("n_trials", 100),
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sampler=data.get("sampler", "TPE"),
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use_neural_acceleration=data.get("use_neural_acceleration", False),
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solver_config=data.get("solver_config", {}),
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solve_all_solutions=data.get("solve_all_solutions", True),
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extractors_config=data.get("extractors_config", {}),
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warnings_acknowledged=data.get("warnings_acknowledged", []),
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baseline_validated=data.get("baseline_validated", False),
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baseline_results=data.get("baseline_results"),
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)
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def to_config_json(self) -> Dict[str, Any]:
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"""
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Convert to optimization_config.json format.
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This is the format expected by the optimization runner.
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"""
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config = {
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"study_name": self.study_name,
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"description": self.study_description,
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"version": "2.0",
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"model": {
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"part_file": self.model_path,
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"sim_file": self.sim_path,
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"fem_file": self.fem_path,
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},
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"design_variables": [
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dv.to_config_format() for dv in self.design_variables
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],
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"objectives": [
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obj.to_config_format() for obj in self.objectives
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],
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"constraints": [
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con.to_config_format() for con in self.constraints
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],
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"optimization": {
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"n_trials": self.n_trials,
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"sampler": self.sampler,
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"protocol": self.protocol,
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"neural_acceleration": self.use_neural_acceleration,
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},
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"solver": {
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"solve_all": self.solve_all_solutions,
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**self.solver_config,
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},
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"extractors": self.extractors_config,
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"_metadata": {
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"interview_session_id": self.interview_session_id,
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"warnings_acknowledged": self.warnings_acknowledged,
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"baseline_validated": self.baseline_validated,
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}
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}
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return config
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def to_markdown(self) -> str:
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"""Generate human-readable markdown summary."""
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lines = []
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lines.append(f"# Study Blueprint: {self.study_name}")
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lines.append("")
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if self.study_description:
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lines.append(f"**Description**: {self.study_description}")
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lines.append("")
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# Design Variables
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lines.append(f"## Design Variables ({len(self.design_variables)})")
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lines.append("")
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lines.append("| Parameter | Current | Min | Max | Units |")
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lines.append("|-----------|---------|-----|-----|-------|")
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for dv in self.design_variables:
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lines.append(f"| {dv.parameter} | {dv.current_value} | {dv.min_value} | {dv.max_value} | {dv.units or '-'} |")
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lines.append("")
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# Objectives
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lines.append(f"## Objectives ({len(self.objectives)})")
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lines.append("")
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lines.append("| Name | Goal | Extractor | Weight |")
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lines.append("|------|------|-----------|--------|")
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for obj in self.objectives:
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lines.append(f"| {obj.name} | {obj.goal} | {obj.extractor} | {obj.weight} |")
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lines.append("")
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# Constraints
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if self.constraints:
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lines.append(f"## Constraints ({len(self.constraints)})")
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lines.append("")
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lines.append("| Name | Type | Threshold | Extractor | Hard? |")
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lines.append("|------|------|-----------|-----------|-------|")
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for con in self.constraints:
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op = "<=" if con.constraint_type == "max" else ">="
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lines.append(f"| {con.name} | {op} | {con.threshold} | {con.extractor} | {'Yes' if con.is_hard else 'No'} |")
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lines.append("")
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# Settings
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lines.append("## Optimization Settings")
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lines.append("")
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lines.append(f"- **Protocol**: {self.protocol}")
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lines.append(f"- **Trials**: {self.n_trials}")
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lines.append(f"- **Sampler**: {self.sampler}")
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lines.append(f"- **Neural Acceleration**: {'Enabled' if self.use_neural_acceleration else 'Disabled'}")
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lines.append("")
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# Validation
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lines.append("## Validation")
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lines.append("")
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lines.append(f"- **Baseline Validated**: {'Yes' if self.baseline_validated else 'No'}")
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if self.warnings_acknowledged:
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lines.append(f"- **Warnings Acknowledged**: {len(self.warnings_acknowledged)}")
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for w in self.warnings_acknowledged:
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lines.append(f" - {w}")
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lines.append("")
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return "\n".join(lines)
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def validate(self) -> List[str]:
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"""
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Validate blueprint completeness.
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Returns:
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List of validation errors (empty if valid)
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"""
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errors = []
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if not self.study_name:
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errors.append("Study name is required")
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if not self.design_variables:
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errors.append("At least one design variable is required")
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if not self.objectives:
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errors.append("At least one objective is required")
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for dv in self.design_variables:
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if dv.min_value >= dv.max_value:
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errors.append(f"Invalid bounds for {dv.parameter}: min >= max")
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return errors
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def is_multi_objective(self) -> bool:
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"""Check if this is a multi-objective study."""
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return len(self.objectives) > 1
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def get_objective_count(self) -> int:
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"""Get number of objectives."""
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return len(self.objectives)
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def get_constraint_count(self) -> int:
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"""Get number of constraints."""
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return len(self.constraints)
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def get_design_variable_count(self) -> int:
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"""Get number of design variables."""
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return len(self.design_variables)
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class BlueprintBuilder:
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"""
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Helper class for building StudyBlueprint from interview state.
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"""
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def __init__(self):
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"""Initialize builder."""
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from .interview_intelligence import InterviewIntelligence
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self.intelligence = InterviewIntelligence()
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def from_interview_state(
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self,
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state: "InterviewState",
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introspection: Optional[Dict[str, Any]] = None
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) -> StudyBlueprint:
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"""
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Build StudyBlueprint from completed interview state.
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Args:
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state: Completed interview state
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introspection: Optional introspection results
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Returns:
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StudyBlueprint ready for generation
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"""
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|
|
answers = state.answers
|
||
|
|
intro = introspection or state.introspection
|
||
|
|
|
||
|
|
# Build design variables
|
||
|
|
design_variables = []
|
||
|
|
for dv_data in answers.get("design_variables", []):
|
||
|
|
if isinstance(dv_data, dict):
|
||
|
|
dv = DesignVariable(
|
||
|
|
parameter=dv_data.get("parameter", ""),
|
||
|
|
current_value=dv_data.get("current_value", 0),
|
||
|
|
min_value=dv_data.get("min_value", 0),
|
||
|
|
max_value=dv_data.get("max_value", 1),
|
||
|
|
units=dv_data.get("units"),
|
||
|
|
is_integer=dv_data.get("is_integer", False),
|
||
|
|
)
|
||
|
|
design_variables.append(dv)
|
||
|
|
elif isinstance(dv_data, str):
|
||
|
|
# Just a parameter name - look up in introspection
|
||
|
|
expr = self._find_expression(dv_data, intro.get("expressions", []))
|
||
|
|
if expr:
|
||
|
|
value = expr.get("value", 0)
|
||
|
|
dv = DesignVariable(
|
||
|
|
parameter=dv_data,
|
||
|
|
current_value=value,
|
||
|
|
min_value=value * 0.5 if value > 0 else value * 1.5,
|
||
|
|
max_value=value * 1.5 if value > 0 else value * 0.5,
|
||
|
|
)
|
||
|
|
design_variables.append(dv)
|
||
|
|
|
||
|
|
# Build objectives
|
||
|
|
objectives = []
|
||
|
|
primary_goal = answers.get("objectives", [{}])
|
||
|
|
if isinstance(primary_goal, list) and primary_goal:
|
||
|
|
primary = primary_goal[0] if isinstance(primary_goal[0], dict) else {"goal": primary_goal[0]}
|
||
|
|
else:
|
||
|
|
primary = {"goal": str(primary_goal)}
|
||
|
|
|
||
|
|
# Map to extractor
|
||
|
|
extractor_sel = self.intelligence.extractor_mapper.map_goal_to_extractor(
|
||
|
|
primary.get("goal", ""),
|
||
|
|
intro
|
||
|
|
)
|
||
|
|
|
||
|
|
objectives.append(Objective(
|
||
|
|
name=primary.get("name", "primary_objective"),
|
||
|
|
goal=self._normalize_goal(primary.get("goal", "")),
|
||
|
|
extractor=extractor_sel.extractor_id,
|
||
|
|
extractor_name=extractor_sel.extractor_name,
|
||
|
|
extractor_params=extractor_sel.params,
|
||
|
|
weight=primary.get("weight", 1.0),
|
||
|
|
))
|
||
|
|
|
||
|
|
# Add secondary objectives
|
||
|
|
secondary = answers.get("objectives_secondary", [])
|
||
|
|
for sec_goal in secondary:
|
||
|
|
if sec_goal == "none" or not sec_goal:
|
||
|
|
continue
|
||
|
|
|
||
|
|
sec_sel = self.intelligence.extractor_mapper.map_goal_to_extractor(
|
||
|
|
sec_goal, intro
|
||
|
|
)
|
||
|
|
|
||
|
|
objectives.append(Objective(
|
||
|
|
name=f"secondary_{sec_goal}",
|
||
|
|
goal=self._normalize_goal(sec_goal),
|
||
|
|
extractor=sec_sel.extractor_id,
|
||
|
|
extractor_name=sec_sel.extractor_name,
|
||
|
|
extractor_params=sec_sel.params,
|
||
|
|
weight=0.5, # Default lower weight for secondary
|
||
|
|
))
|
||
|
|
|
||
|
|
# Build constraints
|
||
|
|
constraints = []
|
||
|
|
constraint_answers = answers.get("constraints", {})
|
||
|
|
constraint_handling = answers.get("constraint_handling", "hard")
|
||
|
|
|
||
|
|
if "max_stress" in constraint_answers and constraint_answers["max_stress"]:
|
||
|
|
stress_sel = self.intelligence.extractor_mapper.map_constraint_to_extractor("stress", intro)
|
||
|
|
constraints.append(Constraint(
|
||
|
|
name="max_stress",
|
||
|
|
constraint_type="max",
|
||
|
|
threshold=constraint_answers["max_stress"],
|
||
|
|
extractor=stress_sel.extractor_id,
|
||
|
|
extractor_name=stress_sel.extractor_name,
|
||
|
|
extractor_params=stress_sel.params,
|
||
|
|
is_hard=constraint_handling != "soft",
|
||
|
|
))
|
||
|
|
|
||
|
|
if "max_displacement" in constraint_answers and constraint_answers["max_displacement"]:
|
||
|
|
disp_sel = self.intelligence.extractor_mapper.map_constraint_to_extractor("displacement", intro)
|
||
|
|
constraints.append(Constraint(
|
||
|
|
name="max_displacement",
|
||
|
|
constraint_type="max",
|
||
|
|
threshold=constraint_answers["max_displacement"],
|
||
|
|
extractor=disp_sel.extractor_id,
|
||
|
|
extractor_name=disp_sel.extractor_name,
|
||
|
|
extractor_params=disp_sel.params,
|
||
|
|
is_hard=constraint_handling != "soft",
|
||
|
|
))
|
||
|
|
|
||
|
|
if "min_frequency" in constraint_answers and constraint_answers["min_frequency"]:
|
||
|
|
freq_sel = self.intelligence.extractor_mapper.map_constraint_to_extractor("frequency", intro)
|
||
|
|
constraints.append(Constraint(
|
||
|
|
name="min_frequency",
|
||
|
|
constraint_type="min",
|
||
|
|
threshold=constraint_answers["min_frequency"],
|
||
|
|
extractor=freq_sel.extractor_id,
|
||
|
|
extractor_name=freq_sel.extractor_name,
|
||
|
|
extractor_params=freq_sel.params,
|
||
|
|
is_hard=constraint_handling != "soft",
|
||
|
|
))
|
||
|
|
|
||
|
|
if "max_mass" in constraint_answers and constraint_answers["max_mass"]:
|
||
|
|
mass_sel = self.intelligence.extractor_mapper.map_constraint_to_extractor("mass", intro)
|
||
|
|
constraints.append(Constraint(
|
||
|
|
name="max_mass",
|
||
|
|
constraint_type="max",
|
||
|
|
threshold=constraint_answers["max_mass"],
|
||
|
|
extractor=mass_sel.extractor_id,
|
||
|
|
extractor_name=mass_sel.extractor_name,
|
||
|
|
is_hard=constraint_handling != "soft",
|
||
|
|
))
|
||
|
|
|
||
|
|
# Determine protocol
|
||
|
|
protocol = "protocol_11_multi" if len(objectives) > 1 else "protocol_10_single"
|
||
|
|
|
||
|
|
# Get settings
|
||
|
|
n_trials = answers.get("n_trials", 100)
|
||
|
|
if n_trials == "custom":
|
||
|
|
n_trials = 100 # Default
|
||
|
|
|
||
|
|
# Build blueprint
|
||
|
|
blueprint = StudyBlueprint(
|
||
|
|
study_name=state.study_name,
|
||
|
|
study_description=answers.get("problem_description", ""),
|
||
|
|
interview_session_id=state.session_id,
|
||
|
|
model_path=intro.get("part_file", ""),
|
||
|
|
sim_path=intro.get("sim_file", ""),
|
||
|
|
fem_path=intro.get("fem_file", ""),
|
||
|
|
design_variables=design_variables,
|
||
|
|
objectives=objectives,
|
||
|
|
constraints=constraints,
|
||
|
|
protocol=protocol,
|
||
|
|
n_trials=int(n_trials) if isinstance(n_trials, (int, float)) else 100,
|
||
|
|
sampler=self.intelligence.suggest_sampler(len(objectives), len(design_variables)),
|
||
|
|
use_neural_acceleration=answers.get("use_neural_acceleration", False),
|
||
|
|
solve_all_solutions=answers.get("solve_all_solutions", True),
|
||
|
|
warnings_acknowledged=state.warnings_acknowledged,
|
||
|
|
baseline_validated=answers.get("run_baseline_validation", False),
|
||
|
|
)
|
||
|
|
|
||
|
|
return blueprint
|
||
|
|
|
||
|
|
def _find_expression(self, name: str, expressions: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
|
||
|
|
"""Find expression by name."""
|
||
|
|
for expr in expressions:
|
||
|
|
if expr.get("name") == name:
|
||
|
|
return expr
|
||
|
|
return None
|
||
|
|
|
||
|
|
def _normalize_goal(self, goal: str) -> str:
|
||
|
|
"""Normalize goal string to standard format."""
|
||
|
|
goal_lower = goal.lower()
|
||
|
|
|
||
|
|
if "minimize" in goal_lower or "reduce" in goal_lower:
|
||
|
|
return "minimize"
|
||
|
|
elif "maximize" in goal_lower or "increase" in goal_lower:
|
||
|
|
return "maximize"
|
||
|
|
elif "target" in goal_lower:
|
||
|
|
return "target"
|
||
|
|
else:
|
||
|
|
return goal
|
||
|
|
|
||
|
|
|
||
|
|
# Import for type hints
|
||
|
|
from typing import TYPE_CHECKING
|
||
|
|
if TYPE_CHECKING:
|
||
|
|
from .interview_state import InterviewState
|