Pass self.rng to SubModel.GenModel

Author: yuta-nakahara

bayesml/metatree/_metatree_x_discrete.py

@@ -159,7 +159,7 @@ def __init__(
             self.c_num_children,
             list(range(self.c_k)),
             self.h_g,
-            sub_model=self.SubModel.GenModel(**self.sub_h_params),
+            sub_model=self.SubModel.GenModel(seed=self.rng,**self.sub_h_params),
             leaf=True
             )
 
@@ -181,7 +181,7 @@ def _gen_params_recursion(self,node:_Node,h_node:_Node,feature_fix):
             else:
                 node.h_g = self.h_g
             # node.sub_model.set_h_params(**self.sub_h_params)
-            node.sub_model = self.SubModel.GenModel(**self.sub_h_params)
+            node.sub_model = self.SubModel.GenModel(seed=self.rng,**self.sub_h_params)
             if node.depth == self.c_d_max or node.depth == self.c_k or self.rng.random() > self.h_g:  # leaf node
                 node.sub_model.gen_params()
                 node.leaf = True
@@ -198,7 +198,7 @@ def _gen_params_recursion(self,node:_Node,h_node:_Node,feature_fix):
                             node.depth+1,
                             self.c_num_children,
                             h_g=self.h_g,
-                            sub_model=self.SubModel.GenModel(**self.sub_h_params),
+                            sub_model=self.SubModel.GenModel(seed=self.rng,**self.sub_h_params),
                             )
                     node.children[i].k_candidates = child_k_candidates
                     self._gen_params_recursion(node.children[i],None,feature_fix)
@@ -228,7 +228,7 @@ def _gen_params_recursion(self,node:_Node,h_node:_Node,feature_fix):
                             node.depth+1,
                             self.c_num_children,
                             h_g=self.h_g,
-                            sub_model=self.SubModel.GenModel(**self.sub_h_params),
+                            sub_model=self.SubModel.GenModel(seed=self.rng,**self.sub_h_params),
                             )
                     node.children[i].k_candidates = child_k_candidates
                     self._gen_params_recursion(node.children[i],h_node.children[i],feature_fix)
@@ -249,7 +249,7 @@ def _gen_params_recursion_tree_fix(self,node:_Node,h_node:_Node,feature_fix):
             else:
                 node.h_g = self.h_g
             # node.sub_model.set_h_params(**self.sub_h_params)
-            node.sub_model = self.SubModel.GenModel(**self.sub_h_params)
+            node.sub_model = self.SubModel.GenModel(seed=self.rng,**self.sub_h_params)
             if node.leaf:  # leaf node
                 node.sub_model.gen_params()
                 node.leaf = True
@@ -325,7 +325,7 @@ def _set_params_recursion(self,node:_Node,original_tree_node:_Node):
                     self.c_num_children,
                     child_k_candidates,
                     self.h_g,
-                    sub_model=self.SubModel.GenModel(**self.sub_h_params)
+                    sub_model=self.SubModel.GenModel(seed=self.rng,**self.sub_h_params)
                     )
                 self._set_params_recursion(node.children[i],original_tree_node.children[i])
 
@@ -401,7 +401,7 @@ def _set_h_params_recursion(self,node:_Node,original_tree_node:_Node):
             else:
                 node.h_g = self.h_g
             # node.sub_model.set_h_params(**self.sub_h_params)
-            node.sub_model = self.SubModel.GenModel(**self.sub_h_params)
+            node.sub_model = self.SubModel.GenModel(seed=self.rng,**self.sub_h_params)
             for i in range(self.c_num_children):
                 if node.children[i] is not None:
                     self._set_h_params_recursion(node.children[i],None)
@@ -424,7 +424,7 @@ def _set_h_params_recursion(self,node:_Node,original_tree_node:_Node):
                         node.children[i] = _Node(
                             node.depth+1,
                             self.c_num_children,
-                            sub_model=self.SubModel.GenModel(**self.sub_h_params),
+                            sub_model=self.SubModel.GenModel(seed=self.rng,**self.sub_h_params),
                             )
                     self._set_h_params_recursion(node.children[i],original_tree_node.children[i])
 
@@ -471,7 +471,7 @@ def set_h_params(self,
                     self._set_h_params_recursion(h_root,None)
 
         if sub_h_params is not None:
-            self.SubModel.GenModel(**sub_h_params)
+            self.SubModel.GenModel(seed=self.rng,**sub_h_params)
             self.sub_h_params = copy.deepcopy(sub_h_params)
             if self.h_metatree_list:
                 for h_root in self.h_metatree_list:
@@ -587,7 +587,7 @@ def set_params(self,root=None):
                 self.c_num_children,
                 list(range(self.c_k)),
                 self.h_g,
-                sub_model=self.SubModel.GenModel(**self.sub_h_params),
+                sub_model=self.SubModel.GenModel(seed=self.rng,**self.sub_h_params),
                 leaf=True
                 )
             self._set_params_recursion(self.root,root)

bayesml/metatree/metatree_test.py

@@ -5,17 +5,15 @@
 import numpy as np
 import copy
 
-gen_model = GenModel(4,3,2,h_g=0.75)
-gen_model.gen_params()
-gen_model.visualize_model('tree.pdf')
-x,y = gen_model.gen_sample(1000)
+gen_model1 = GenModel(4,3,2,h_g=0.75,seed=0)
+gen_model1.gen_params()
+gen_model1.visualize_model('tree.pdf')
+x1,y1 = gen_model1.gen_sample(10)
 
-learn_model = LearnModel(4,3,2)
-learn_model.update_posterior(x,y,n_estimators=1)
-learn_model.visualize_posterior('tree2.pdf')
+gen_model2 = GenModel(4,3,2,h_g=0.75,seed=0)
+gen_model2.gen_params()
+gen_model2.visualize_model('tree2.pdf')
+x2,y2 = gen_model2.gen_sample(10)
 
-learn_model.calc_pred_dist(np.zeros(4,dtype=int))
-print(learn_model.make_prediction(loss='squared'))
-
-learn_model.calc_pred_dist(np.ones(4,dtype=int))
-print(learn_model.make_prediction(loss='squared'))
+print(x1-x2)
+print(y1-y2)