Extract Datapoint
- class SubProblemDatapoint(**data)
Bases:
BaseModelCreate a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- double_check_statistics: DoubleCheckStatistics | None
When double-checking, we store the statistics for the solver run on the subproblem with specific aux or not.
- subproblem_str: str
The non-alphabetized subproblem JGEX string. Includes all potential auxiliary constructions.
- alphabetized_subproblem_str: str
The alphabetized subproblem JGEX string. Includes all potential auxiliary constructions.
- sub_problem_proof: ProofData
The entire proof content extracted from the subproblem.
- level_predicate_count: list[tuple[int, PredicateType, int]] | None
How many predicates of each type do we have at each DDARN level of the diagram.
- solution_natural_language: str
The proof of the subproblem in natural language.
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- larger_problem: str
The JGEX string of the larger problem that the subproblem is a part of.
- larger_nc_problem: ProblemSetup
The full setting with points coordinates of the larger problem that the subproblem is a part of.
- property has_double_checked_aux_construction: bool
Whether the subproblem has an aux construction or not
- generate_subproblems_datapoints(generation_config, seed, jgex_solver)
- Return type:
tuple[list[SubProblemDatapoint],DiagramGenerationMetadata]