made sobo and randombo more consistent

bo/random.py

@@ -9,71 +9,44 @@ class RandomBOEngine(BOEngine):
     def __init__(self, config):
         super().__init__(config)
 
-        bo_params = [
-            key for key, spec in config["bo"]["params"].items() if spec is not None
-        ]
+        self.params = [key for key, spec in config["bo"]["params"].items() if spec is not None]
+        self.param_types = {key: config["bo"]["params"][key].get("type", "float") for key in self.params}
+        self.integer_indices = [i for i, param in enumerate(self.params) if self.param_types[param] == 'int']
+        self.bounds = np.empty((2, len(self.params)))
 
-        bo_param_types = {
-            key: config["bo"]["params"][key].get("type", "float") for key in bo_params
-        }
-
-        integer_params = [
-            key for key in bo_params if bo_param_types[key] == "int"
-        ]
-
-        integer_indices = [
-            bo_params.index(key) for key in integer_params
-        ]
-
-        bounds = np.empty((2, len(bo_params)))
-
-        for i, param in enumerate(bo_params):
+        for i, param in enumerate(self.params):
             center = config["ptycho"]["params"][param]
             radius = config["bo"]["params"][param]["radius"]
-            bounds[0, i] = center - radius
-            bounds[1, i] = center + radius
+            self.bounds[0, i] = center - radius
+            self.bounds[1, i] = center + radius
 
-        state = {
-            "method": "random",
-            "acquisition": "",
-            "params": bo_params,
-            "param_types": bo_param_types,
-            "integer_params": integer_params,
-            "integer_indices": integer_indices,
-            "bounds": bounds,                          # shape: (2, BOparam)
-            "train_x": np.empty((0, len(bo_params))),  # shape: (BOiter, BOparam)
-            "train_y": np.empty((0,)),                 # shape: (BOiter,)
-            "train_info": []                           # shape: (BOiter,)
-        }
+        self.train_x = np.empty((0, len(self.params)))  # shape: (BOiter, BOparam)
+        self.train_y = np.empty((0,))                   # shape: (BOiter,)
 
         train_x_path = config["bo"].get("train_x")
         train_y_path = config["bo"].get("train_y")
-
         if train_x_path is not None and train_y_path is not None:
             if os.path.exists(train_x_path) and os.path.exists(train_y_path):
                 train_x = np.load(train_x_path)
                 train_y = np.load(train_y_path)
                 assert train_x.ndim == 2,                    "loaded train_x must be 2D"
-                assert train_x.shape[1] == len(bo_params),   "loaded train_x shape(1) does not match number of variable parameters"
+                assert train_x.shape[1] == len(self.params), "loaded train_x shape(1) does not match number of variable parameters"
                 assert train_y.ndim == 1,                    "loaded train_y must be 1D"
                 assert train_y.shape[0] == train_x.shape[0], "loaded train_x and train_y shape(0) have unequal iterations"
-                state["train_x"] = train_x
-                state["train_y"] = train_y
-
-        self.state = state
-
+                self.train_x = train_x
+                self.train_y = train_y
+        
 
     def ask(self):
         config = self.config
-        state = self.state
         next_config = copy.deepcopy(config)
 
-        for param in state['params']:
+        for param in self.params:
             radius = config['bo']['params'][param]['radius']
             center = config['ptycho']['params'][param]
             modulation = radius * (np.random.rand() - 0.5) * 2
             next_value = center + modulation
-            if state['param_types'][param] == 'int':
+            if self.param_types[param] == 'int':
                 next_value = round(next_value)
             next_config['ptycho']['params'][param] = next_value
 
@@ -82,31 +55,28 @@ class RandomBOEngine(BOEngine):
 
     def tell(self, job_config, y_value):
         config = self.config
-        state = self.state
 
         x_value = []
-        for param in state['params']:
+        for param in self.params:
             x_value.append(job_config['ptycho']['params'][param])
 
-        state['train_x'] = np.vstack([
-            state['train_x'],
+        self.train_x = np.vstack([
+            self.train_x,
             np.array(x_value).reshape(1, -1)
         ])
 
-        state['train_y'] = np.concatenate([
-            state['train_y'],
+        self.train_y = np.concatenate([
+            self.train_y,
             np.array([y_value])
         ])
 
-        state['train_info'].append(state['method'])
-
 
         train_x_path = config['bo'].get('train_x')
         train_y_path = config['bo'].get('train_y')
         if train_x_path is not None and train_y_path is not None:
-            np.save(train_x_path, state['train_x'])
-            np.save(train_y_path, state['train_y'])
+            np.save(train_x_path, self.train_x)
+            np.save(train_y_path, self.train_y)
         else:
             result_dir = config['io']['result_dir']
-            np.save(os.path.join(result_dir, 'train_x.npy'), state['train_x'])
-            np.save(os.path.join(result_dir, 'train_y.npy'), state['train_y'])
+            np.save(os.path.join(result_dir, 'train_x.npy'), self.train_x)
+            np.save(os.path.join(result_dir, 'train_y.npy'), self.train_y)

bo/sobo.py

@@ -38,7 +38,6 @@ class SingleObjectiveBOEngine(BOEngine):
 
         train_x_path = config["bo"].get("train_x")
         train_y_path = config["bo"].get("train_y")
-
         if train_x_path is not None and train_y_path is not None:
             if os.path.exists(train_x_path) and os.path.exists(train_y_path):
                 train_x = np.load(train_x_path)
@@ -168,7 +167,7 @@ class SingleObjectiveBOEngine(BOEngine):
         ])
 
         
-        train_x_path = config['bo'].get('train_x')
+        train_x_path = config['bo'].get('tain_x')
         train_y_path = config['bo'].get('train_y')
         if train_x_path is not None and train_y_path is not None:
             np.save(train_x_path, self.train_x)