Change function according to huawei-noah/HEBO#61 (comment) but leave …

…the function `default_kern_rd` in the file as well.

HEBO/hebo/models/gp/gp_util.py

@@ -10,14 +10,14 @@
 import numpy as np
 import torch
 import torch.nn as nn
-from gpytorch.kernels import (AdditiveKernel, MaternKernel, ProductKernel,
-                              ScaleKernel)
-from gpytorch.priors import GammaPrior
 from torch import FloatTensor, LongTensor
 
-from ..layers import EmbTransform
-from ..util import get_random_graph
 
+from gpytorch.kernels import MaternKernel, ScaleKernel, ProductKernel
+from gpytorch.priors  import GammaPrior
+from gpytorch.constraints.constraints import LessThan
+
+from ..layers import EmbTransform
 
 class DummyFeatureExtractor(nn.Module):
     def __init__(self, num_cont, num_enum, num_uniqs = None, emb_sizes = None):
@@ -43,7 +43,8 @@ def default_kern(x, xe, y, total_dim = None, ard_kernel = True, fe = None, max_x
         kerns    = []
         if has_num:
             ard_num_dims = x.shape[1] if ard_kernel else None
-            kernel       = MaternKernel(nu = 1.5, ard_num_dims = ard_num_dims, active_dims = torch.arange(x.shape[1]))
+            kernel       = MaternKernel(nu = 1.5, ard_num_dims = ard_num_dims, active_dims = torch.arange(x.shape[1]),
+                                        lengthscale_constraint=LessThan(5))
             if ard_kernel:
                 lscales = kernel.lengthscale.detach().clone().view(1, -1)
                 for i in range(x.shape[1]):
@@ -52,19 +53,21 @@ def default_kern(x, xe, y, total_dim = None, ard_kernel = True, fe = None, max_x
                 kernel.lengthscale = lscales
             kerns.append(kernel)
         if has_enum:
-            kernel = MaternKernel(nu = 1.5, active_dims = torch.arange(x.shape[1], total_dim))
+            kernel = MaternKernel(nu = 1.5, active_dims = torch.arange(x.shape[1], total_dim),
+                                        lengthscale_constraint=LessThan(5))
             kerns.append(kernel)
         final_kern = ScaleKernel(ProductKernel(*kerns), outputscale_prior = GammaPrior(0.5, 0.5))
         final_kern.outputscale = y[torch.isfinite(y)].var()
         return final_kern
     else:
         if ard_kernel:
-            kernel = ScaleKernel(MaternKernel(nu = 1.5, ard_num_dims = total_dim))
+            kernel = ScaleKernel(MaternKernel(nu = 1.5, ard_num_dims = total_dim,
+                                        lengthscale_constraint=LessThan(5)))
         else:
             kernel = ScaleKernel(MaternKernel(nu = 1.5))
         kernel.outputscale = y[torch.isfinite(y)].var()
         return kernel
-    
+
 def default_kern_rd(x, xe, y, total_dim = None, ard_kernel = True, fe = None, max_x = 1000, E=0.2):
     '''
     Get a default kernel with random decompositons. 0 <= E <=1 specifies random tree conectivity.