Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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114 lines
3.7 KiB
114 lines
3.7 KiB
import sys
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sys.path.append('../')
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import itertools
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import json
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import typing
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import networkx as nx
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import numpy as np
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from networkx.readwrite import json_graph
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from random import choice
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from abc import ABC
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from optimizers.optimizer import Optimizer
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from estimators import structure_estimator as se
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import structure_graph.network_graph as ng
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class HillClimbing(Optimizer):
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"""
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Optimizer class that implement Hill Climbing Search
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"""
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def __init__(self,
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node_id:str,
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structure_estimator: se.StructureEstimator,
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max_parents:int = None,
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iterations_number:int= 40,
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patience:int = None
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):
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"""
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Compute Optimization process for a structure_estimator
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Parameters:
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node_id: the node label
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structure_estimator: a structure estimator object with the information about the net
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max_parents: maximum number of parents for each variable. If None, disabled
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iterations_number: maximum number of optimization algorithm's iteration
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patience: number of iteration without any improvement before to stop the search.If None, disabled
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"""
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super().__init__(node_id, structure_estimator)
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self.max_parents = max_parents
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self.iterations_number = iterations_number
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self.patience = patience
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def optimize_structure(self) -> typing.List:
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"""
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Compute Optimization process for a structure_estimator
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Parameters:
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Returns:
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the estimated structure for the node
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"""
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#'Create the graph for the single node'
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graph = ng.NetworkGraph(self.structure_estimator.sample_path.structure)
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other_nodes = [node for node in self.structure_estimator.sample_path.structure.nodes_labels if node != self.node_id]
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actual_best_score = self.structure_estimator.get_score_from_graph(graph,self.node_id)
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patince_count = 0
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for i in range(self.iterations_number):
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'choose a new random edge'
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current_new_parent = choice(other_nodes)
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current_edge = (current_new_parent,self.node_id)
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added = False
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parent_removed = None
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if graph.has_edge(current_edge):
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graph.remove_edges([current_edge])
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else:
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'check the max_parents constraint'
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if self.max_parents is not None:
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parents_list = graph.get_parents_by_id(self.node_id)
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if len(parents_list) >= self.max_parents :
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parent_removed = (choice(parents_list), self.node_id)
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graph.remove_edges([parent_removed])
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graph.add_edges([current_edge])
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added = True
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#print('**************************')
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current_score = self.structure_estimator.get_score_from_graph(graph,self.node_id)
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if current_score > actual_best_score:
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'update current best score'
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actual_best_score = current_score
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patince_count = 0
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else:
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'undo the last update'
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if added:
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graph.remove_edges([current_edge])
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'If a parent was removed, add it again to the graph'
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if parent_removed is not None:
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graph.add_edges([parent_removed])
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else:
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graph.add_edges([current_edge])
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'update patience count'
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patince_count += 1
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if self.patience is not None and patince_count > self.patience:
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break
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print(f"finito variabile: {self.node_id}")
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return graph.edges |