import sys sys.path.append('../') import itertools import json import typing import networkx as nx import numpy as np from networkx.readwrite import json_graph from random import choice from abc import ABC import copy from optimizers.optimizer import Optimizer from estimators import structure_estimator as se import structure_graph.network_graph as ng class ConstraintBasedOptimizer(Optimizer): """ Optimizer class that implement Hill Climbing Search """ def __init__(self, node_id:str, structure_estimator: se.StructureEstimator, tot_vars_count:int ): """ Compute Optimization process for a structure_estimator """ super().__init__(node_id, structure_estimator) self.tot_vars_count = tot_vars_count def optimize_structure(self): """ Compute Optimization process for a structure_estimator Parameters: Returns: the estimated structure for the node """ print("##################TESTING VAR################", self.node_id) graph = ng.NetworkGraph(self.structure_estimator._sample_path.structure) other_nodes = [node for node in self.structure_estimator._sample_path.structure.nodes_labels if node != self.node_id] for possible_parent in other_nodes: graph.add_edges([(possible_parent,self.node_id)]) u = other_nodes #tests_parents_numb = len(u) #complete_frame = self.complete_graph_frame #test_frame = complete_frame.loc[complete_frame['To'].isin([self.node_id])] child_states_numb = self.structure_estimator._sample_path.structure.get_states_number(self.node_id) b = 0 while b < len(u): parent_indx = 0 while parent_indx < len(u): removed = False S = self.structure_estimator.generate_possible_sub_sets_of_size(u, b, u[parent_indx]) test_parent = u[parent_indx] for parents_set in S: if self.structure_estimator.complete_test(test_parent, self.node_id, parents_set, child_states_numb, self.tot_vars_count): graph.remove_edges([(test_parent, self.node_id)]) u.remove(test_parent) removed = True break if not removed: parent_indx += 1 b += 1 self.structure_estimator.cache.clear() return graph.edges