def __init__()

in LaNAS/LaNAS_NASBench101/MCTS.py [0:0]

• 32 lines of code
• 7 McCabe index (conditional complexity)

    def __init__(self, search_space, tree_height, arch_code_len ):
        assert type(search_space) == type([])
        assert len(search_space)  >= 1
        
        assert type(search_space)    == type([])
        assert type(search_space[0]) == type([])
        self.ARCH_CODE_LEN           =  arch_code_len
        self.SEARCH_COUNTER          =  0
        self.samples                 =  {}
        self.nodes                   =  []
        self.search_space            =  search_space
        self.Cp                      =  0.1
        # 49 is the length of architectuer encoding, 1 is for predicted accuracy
        #self.metaDNN                 = LinearModel(49, 1) 
        #querying the accuracy from nasbench
        self.net_trainer             = Net_Trainer( )
        
        # set random seed
        np.random.seed(seed=int(time.time() ) )
        random.seed(datetime.now() )
        
        #initialize the a full tree
        total_nodes = 2**tree_height - 1
        for i in range(1, total_nodes + 1):
            is_good_kid = False
            if (i-1)  > 0 and (i-1) % 2 == 0:
                is_good_kid = False
            elif (i -1) > 0:
                is_good_kid = True
            
            parent_id = i // 2  - 1
            if parent_id == -1:
                self.nodes.append( Node( None, is_good_kid, self.ARCH_CODE_LEN, True ) )
            else:
                self.nodes.append( Node(self.nodes[parent_id], is_good_kid, self.ARCH_CODE_LEN, False) )
        
        # self.loads_all_states()
        self.ROOT = self.nodes[0]
        self.CURT = self.ROOT
        
        print('='*10 + 'search space start' + '='*10)
        print("total architectures:", len(search_space) )
        print('='*10 + 'search space end  ' + '='*10)
        self.init_train()