def propose_rand_samples_probe()

in LA-MCTS/lamcts/Classifier.py [0:0]

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

    def propose_rand_samples_probe(self, nums_samples, path, lb, ub):

        seed   = np.random.randint(int(1e6))
        sobol  = SobolEngine(dimension = self.dims, scramble=True, seed=seed)

        center = np.mean(self.X, axis = 0)
        #check if the center located in the region
        ratio, tmp = self.get_sample_ratio_in_region( np.reshape(center, (1, len(center) ) ), path )
        if ratio == 0:
            print("==>center not in the region, using random samples")
            return self.propose_rand_samples(nums_samples, lb, ub)
        # it is possible that the selected region has no points,
        # so we need check here

        axes    = len( center )
        
        final_L = []
        for axis in range(0, axes):
            L       = np.zeros( center.shape )
            L[axis] = 0.01
            ratio   = 1
            
            while ratio >= 0.9:
                L[axis] = L[axis]*2
                if L[axis] >= (ub[axis] - lb[axis]):
                    break
                lb_     = np.clip( center - L/2, lb, ub )
                ub_     = np.clip( center + L/2, lb, ub )
                cands_  = sobol.draw(10000).to(dtype=torch.float64).cpu().detach().numpy()
                cands_  = (ub_ - lb_)*cands_ + lb_
                ratio, tmp = self.get_sample_ratio_in_region(cands_, path )
            final_L.append( L[axis] )

        final_L   = np.array( final_L )
        lb_       = np.clip( center - final_L/2, lb, ub )
        ub_       = np.clip( center + final_L/2, lb, ub )
        print("center:", center)
        print("final lb:", lb_)
        print("final ub:", ub_)
    
        count         = 0
        cands         = np.array([])
        while len(cands) < 10000:
            count    += 10000
            cands     = sobol.draw(count).to(dtype=torch.float64).cpu().detach().numpy()
        
            cands     = (ub_ - lb_)*cands + lb_
            ratio, cands = self.get_sample_ratio_in_region(cands, path)
            samples_count = len( cands )
        
        #extract candidates 
        
        return cands