FIX: reset random seed after setting random_state

smash/core/model/model.py

@@ -2591,10 +2591,10 @@ def set_nn_parameters_weight(
             self, value, initializer, random_state
         )
 
-        if (random_state is not None) and (initializer != "zeros") and (value is None):
-            np.random.seed(random_state)
-
         if value is None:
+            if random_state is not None:
+                np.random.seed(random_state)
+
             for i in range(self.setup.n_layers):
                 (n_neuron, n_in) = getattr(self._parameters.nn_parameters, f"weight_{i+1}").shape
                 setattr(
@@ -2603,6 +2603,10 @@ def set_nn_parameters_weight(
                     _initialize_nn_parameter(n_in, n_neuron, initializer),
                 )
 
+            # % Reset random seed if random_state is previously set
+            if random_state is not None:
+                np.random.seed(None)
+
         else:
             for i, val in enumerate(value):
                 setattr(self._parameters.nn_parameters, f"weight_{i+1}", val)
@@ -2679,10 +2683,10 @@ def set_nn_parameters_bias(
             self, value, initializer, random_state
         )
 
-        if (random_state is not None) and (initializer != "zeros") and (value is None):
-            np.random.seed(random_state)
-
         if value is None:
+            if random_state is not None:
+                np.random.seed(random_state)
+
             for i in range(self.setup.n_layers):
                 n_neuron = getattr(self._parameters.nn_parameters, f"bias_{i+1}").shape[0]
                 setattr(
@@ -2691,6 +2695,10 @@ def set_nn_parameters_bias(
                     _initialize_nn_parameter(1, n_neuron, initializer).flatten(),
                 )
 
+            # % Reset random seed if random_state is previously set
+            if random_state is not None:
+                np.random.seed(None)
+
         else:
             for i, val in enumerate(value):
                 setattr(self._parameters.nn_parameters, f"bias_{i+1}", val)

smash/core/simulation/optimize/_tools.py

@@ -121,6 +121,11 @@ def _merge_net_control(ret: dict, optimize_options: dict):
         for layer in net.layers:
             if hasattr(layer, "_initialize"):
                 layer._initialize(None)
+
+        # Reset random seed if random_state is previously set
+        if optimize_options["random_state"] is not None:
+            np.random.seed(None)
+
         x = _net2vect(net)
 
         ret["x"] = np.append(ret["x"], x)

smash/factory/net/net.py

@@ -510,6 +510,10 @@ def _compile(
             if hasattr(layer, "_initialize"):
                 layer._initialize(opt)
 
+        # % Reset random seed if random_state is previously set
+        if random_state is not None:
+            np.random.seed(None)
+
     def _fit_d2p(
         self,
         x_train: np.ndarray,
@@ -713,16 +717,22 @@ def set_weight(self, value: list[Any] | None = None, random_state: int | None =
 
         value, random_state = _standardize_set_weight_args(self, value, random_state)
 
-        if (random_state is not None) and (value is None):
-            np.random.seed(random_state)
+        if value is None:
+            if random_state is not None:
+                np.random.seed(random_state)
 
-        i = 0
-        for layer in self.layers:
-            if hasattr(layer, "weight"):
-                if value is None:
+            for layer in self.layers:
+                if hasattr(layer, "weight"):
                     _set_initialized_wb_to_layer(layer, "weight")
 
-                else:
+            # % Reset random seed if random_state is previously set
+            if random_state is not None:
+                np.random.seed(None)
+
+        else:
+            i = 0
+            for layer in self.layers:
+                if hasattr(layer, "weight"):
                     layer.weight = value[i]
                     i += 1
 
@@ -784,16 +794,22 @@ def set_bias(self, value: list[Any] | None = None, random_state: int | None = No
 
         value, random_state = _standardize_set_bias_args(self, value, random_state)
 
-        if (random_state is not None) and (value is None):
-            np.random.seed(random_state)
+        if value is None:
+            if random_state is not None:
+                np.random.seed(random_state)
 
-        i = 0
-        for layer in self.layers:
-            if hasattr(layer, "bias"):
-                if value is None:
+            for layer in self.layers:
+                if hasattr(layer, "bias"):
                     _set_initialized_wb_to_layer(layer, "bias")
 
-                else:
+            # % Reset random seed if random_state is previously set
+            if random_state is not None:
+                np.random.seed(None)
+
+        else:
+            i = 0
+            for layer in self.layers:
+                if hasattr(layer, "bias"):
                     layer.bias = value[i]
                     i += 1
 

smash/factory/samples/samples.py

@@ -408,4 +408,8 @@ def _generate_samples(
 
             ret_dict["_dst_" + p] = trunc_normal.pdf(ret_dict[p])
 
+    # % Reset random seed if random_state is previously set
+    if random_state is not None:
+        np.random.seed(None)
+
     return Samples(ret_dict)