"""Heuristic functions."""
from typing import Final
import numpy as np
from .abstracts import AbstractHeuristic, AbstractOEHeuristicNode, EncodedLearningProblem
from .learning_problem import EncodedPosNegUndLP, EncodedPosNegLPStandard
from .metrics import Accuracy
from .search import LBLNode, RL_State
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class CELOEHeuristic(AbstractHeuristic[AbstractOEHeuristicNode]):
"""Heuristic like the CELOE Heuristic in DL-Learner."""
__slots__ = 'gainBonusFactor', 'startNodeBonus', 'nodeRefinementPenalty', 'expansionPenaltyFactor'
name: Final = 'CELOE_Heuristic'
gainBonusFactor: Final[float]
startNodeBonus: Final[float]
nodeRefinementPenalty: Final[float]
expansionPenaltyFactor: Final[float]
def __init__(self, *,
gainBonusFactor: float = 0.3,
startNodeBonus: float = 0.1,
nodeRefinementPenalty: float = 0.001,
expansionPenaltyFactor: float = 0.1):
"""Create a new CELOE Heuristic.
Args:
gainBonusFactor: Factor that weighs the increase in quality compared to the parent node.
startNodeBonus: Special value added to the root node.
nodeRefinementPenalty: Value that is subtracted from the heuristic for each refinement attempt of this node.
expansionPenaltyFactor: Value that is subtracted from the heuristic for each horizontal expansion of this
node.
"""
self.gainBonusFactor = gainBonusFactor
self.startNodeBonus = startNodeBonus
self.nodeRefinementPenalty = nodeRefinementPenalty
self.expansionPenaltyFactor = expansionPenaltyFactor
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def apply(self, node: AbstractOEHeuristicNode, instances, learning_problem: EncodedLearningProblem):
heuristic_val = 0
heuristic_val += node.quality
if node.is_root:
heuristic_val += self.startNodeBonus
else:
heuristic_val += (node.quality - node.parent_node.quality) * self.gainBonusFactor
# penalty for horizontal expansion
heuristic_val -= (node.h_exp - 1) * self.expansionPenaltyFactor
# // penalty for having many child nodes (stuck prevention)
heuristic_val -= node.refinement_count * self.nodeRefinementPenalty
node.heuristic = round(heuristic_val, 5)
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class DLFOILHeuristic(AbstractHeuristic):
"""DLFOIL Heuristic."""
__slots__ = ()
name: Final = 'custom_dl_foil'
def __init__(self):
# @todo Needs to be tested.
...
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def apply(self, node, instances, learning_problem: EncodedPosNegUndLP):
instances = node.concept.instances
if len(instances) == 0:
node.heuristic = 0
return False
p_1 = len(learning_problem.kb_pos.intersection(instances)) # number of positive examples covered by the concept
n_1 = len(learning_problem.kb_neg.intersection(instances)) # number of negative examples covered by the concept
u_1 = len(learning_problem.kb_unlabelled.intersection(instances))
term1 = np.log(p_1 / (p_1 + n_1 + u_1))
if node.parent_node:
parent_inst = node.parent_node.individuals
p_0 = len(
learning_problem.kb_pos.intersection(parent_inst)) # number of positive examples covered by the concept
n_0 = len(
learning_problem.kb_neg.intersection(parent_inst)) # number of negative examples covered by the concept
u_0 = len(learning_problem.kb_unlabelled.intersection(parent_inst))
term2 = np.log(p_0 / (p_0 + n_0 + u_0))
else:
term2 = 0
gain = round(p_1 * (term1 - term2), 5)
node.heuristic = gain
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class OCELHeuristic(AbstractHeuristic):
"""OCEL Heuristic."""
__slots__ = 'accuracy_method', 'gainBonusFactor', 'expansionPenaltyFactor'
name: Final = 'OCEL_Heuristic'
def __init__(self, *, gainBonusFactor: float = 0.5,
expansionPenaltyFactor: float = 0.02):
super().__init__()
self.accuracy_method = Accuracy()
self.gainBonusFactor = gainBonusFactor # called alpha in the paper and gainBonusFactor in the original code
self.expansionPenaltyFactor = expansionPenaltyFactor # called beta in the paper
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def apply(self, node: LBLNode, instances, learning_problem: EncodedPosNegLPStandard):
assert isinstance(node, LBLNode), "OCEL Heuristic requires instances information of a node"
heuristic_val = 0
accuracy_gain = 0
_, accuracy = self.accuracy_method.score_elp(node.individuals, learning_problem)
if node.parent_node is not None:
_, parent_accuracy = self.accuracy_method.score_elp(node.parent_node.individuals, learning_problem)
accuracy_gain = accuracy - parent_accuracy
heuristic_val += accuracy + self.gainBonusFactor * accuracy_gain - node.h_exp * self.expansionPenaltyFactor
node.heuristic = round(heuristic_val, 5)
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class CeloeBasedReward:
"""Reward function for DRILL."""
def __init__(self, reward_of_goal=5.0, beta=.04, alpha=.5):
self.name = 'DRILL_Reward'
self.lp = None
self.reward_of_goal = reward_of_goal
self.beta = beta
self.alpha = alpha
@property
def learning_problem(self):
return self.lp
@learning_problem.setter
def learning_problem(self, x):
assert isinstance(x, EncodedLearningProblem)
self.lp = x
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def apply(self, rl_state: RL_State, next_rl_state: RL_State):
assert next_rl_state.quality is not None
assert rl_state.quality is not None
reward = next_rl_state.quality
if next_rl_state.quality == 1.0:
reward = self.reward_of_goal
else:
# Reward => being better than parent.
reward += (next_rl_state.quality - rl_state.quality) * self.alpha
# Regret => Length penalization.
reward -= next_rl_state.length * self.beta
return max(reward, 0)