Class LogicTree

opencompass/datasets/musr/tree.py:158–517

Main datastructure used when creating a MuSR example. It's basically a standard tree with some parameters controlling the shape.

class LogicTree:
    """Main datastructure used when creating a MuSR example.

    It's basically a standard tree with some parameters controlling the shape.
    """

    nodes: List[LogicNode]

    chance_of_or: float
    chance_of_cs_fact: float
    depth: int
    chance_to_prune: float
    chance_to_prune_all: float
    bf_factor: Dict[int, float]
    deduction_type_sample_rate: Dict[LogicNodeDeductionType, float]
    root_structure: List[List[LogicNode]] = ()

    def __init__(self,
                 chance_of_or: float = 0.3,
                 chance_of_cs_fact: float = 0.1,
                 depth: int = 2,
                 chance_to_prune: float = 0.6,
                 chance_to_prune_all: float = 0.2,
                 bf_factor: Dict[int, float] = None,
                 deduction_type_sample_rate: Dict[LogicNodeDeductionType,
                                                  float] = None,
                 enforce_cs_fact_per_level: bool = False,
                 root_structure: List[Any] = (),
                 nodes: List[LogicNode] = (),
                 populate: bool = True,
                 prune: bool = True):
        """
        :param chance_of_or: (not used) how often should a node with children be an OR
        :param chance_of_cs_fact: (not used) how often should there be a commonsense node
        :param depth: How deep should a tree go
        :param chance_to_prune: Percentage chance of pruning a node
        :param chance_to_prune_all: Percentage chance of pruning all children from a node.
        :param bf_factor: Branching factor (dictionary of percentages {1: 0.33, 2:0.33, 3:0.33} for example.
        :param deduction_type_sample_rate: (not used, see bf_factor and LogicNodeDeductionType)
        :param enforce_cs_fact_per_level: Enforce 1 commonsense fact per level in the tree (we use this instead of chance_of_cs_fact)
        :param root_structure: List of LogicNodes to build off of.
        :param nodes: List of LogicNodes to define the LogicTree on (we will not populate/prune the tree if this is filled)
        :param populate: Should we populate children for the tree according to the other parameters?
        :param prune: Should we prune the children for the tree according to the other parameters?
        """
        self.chance_of_or = chance_of_or
        self.chance_of_cs_fact = chance_of_cs_fact
        self.depth = depth
        self.chance_to_prune = chance_to_prune
        self.chance_to_prune_all = chance_to_prune_all
        self.bf_factor = bf_factor
        self.enforce_cs_fact_per_level = enforce_cs_fact_per_level

        if not bf_factor:
            self.bf_factor = {2: 0.8, 3: 0.2}
        if not deduction_type_sample_rate:
            deduction_type_sample_rate = {
                LogicNodeDeductionType.SYLLOGISM: 1.0