usability improvements and saving function

testing for bo and manual usage completed

src/active_bo_msgs/CMakeLists.txt

@@ -30,6 +30,7 @@ rosidl_generate_interfaces(${PROJECT_NAME}
     "msg/ActiveRLResponse.msg"
     "msg/ActiveRL.msg"
     "msg/ImageFeedback.msg"
+    "msg/ActiveBOState.msg"
 
 )
 

src/active_bo_msgs/msg/ActiveBORequest.msg

@@ -6,4 +6,5 @@ uint16 max_steps
 uint16 nr_episodes
 uint16 nr_runs
 string acquisition_function
-float32 metric_parameter
+float32 metric_parameter
+bool save_result

src/active_bo_msgs/msg/ActiveBOState.msg

@@ -0,0 +1,4 @@
+uint16 current_run
+uint16 current_episode
+float32 best_reward
+float32 last_user_reward

src/active_bo_ros/active_bo_ros/active_bo_topic.py

@@ -2,6 +2,7 @@ from active_bo_msgs.msg import ActiveBORequest
 from active_bo_msgs.msg import ActiveBOResponse
 from active_bo_msgs.msg import ActiveRL
 from active_bo_msgs.msg import ActiveRLResponse
+from active_bo_msgs.msg import ActiveBOState
 
 import rclpy
 from rclpy.node import Node
@@ -22,6 +23,7 @@ from active_bo_ros.UserQuery.max_acq_query import MaxAcqQuery
 
 import numpy as np
 import time
+import os
 
 
 class ActiveBOTopic(Node):
@@ -47,14 +49,15 @@ class ActiveBOTopic(Node):
         self.bo_metric = None
         self.bo_fixed_seed = False
         self.bo_nr_weights = None
-        self.bo_steps = None
+        self.bo_steps = 0
-        self.bo_episodes = None
+        self.bo_episodes = 0
-        self.bo_runs = None
+        self.bo_runs = 0
         self.bo_acq_fcn = None
         self.bo_metric_parameter = None
         self.current_run = 0
         self.current_episode = 0
         self.seed = None
+        self.save_result = False
 
         # Active Reinforcement Learning Publisher, Subscriber and Message attributes
         self.active_rl_pub = self.create_publisher(ActiveRL,
@@ -68,7 +71,10 @@ class ActiveBOTopic(Node):
         self.active_rl_pending = False
         self.rl_weights = None
         self.rl_final_step = None
-        self.rl_reward = None
+        self.rl_reward = 0.0
+
+        # State Publisher
+        self.state_pub = self.create_publisher(ActiveBOState, 'active_bo_state', 1)
 
         # RL Environments and BO
         self.env = None
@@ -76,6 +82,7 @@ class ActiveBOTopic(Node):
         self.BO = None
         self.nr_init = 3
         self.reward = None
+        self.best_reward = 0.0
         self.best_pol_reward = None
         self.best_policy = None
         self.best_weights = None
@@ -91,13 +98,14 @@ class ActiveBOTopic(Node):
         self.bo_metric = None
         self.bo_fixed_seed = False
         self.bo_nr_weights = None
-        self.bo_steps = None
+        self.bo_steps = 0
-        self.bo_episodes = None
+        self.bo_episodes = 0
-        self.bo_runs = None
+        self.bo_runs = 0
         self.bo_acq_fcn = None
         self.bo_metric_parameter = None
         self.current_run = 0
         self.current_episode = 0
+        self.save_result = False
 
     def active_bo_callback(self, msg):
         if not self.active_bo_pending:
@@ -112,6 +120,7 @@ class ActiveBOTopic(Node):
             self.bo_runs = msg.nr_runs
             self.bo_acq_fcn = msg.acquisition_function
             self.bo_metric_parameter = msg.metric_parameter
+            self.save_result = msg.save_result
 
             # initialize
             self.reward = np.zeros((self.bo_episodes, self.bo_runs))
@@ -128,7 +137,6 @@ class ActiveBOTopic(Node):
     def reset_rl_response(self):
         self.rl_weights = None
         self.rl_final_step = None
-        self.rl_reward = None
 
     def active_rl_callback(self, msg):
         if self.active_rl_pending:
@@ -180,6 +188,18 @@ class ActiveBOTopic(Node):
                 bo_response.reward_mean = np.mean(self.reward, axis=1).tolist()
                 bo_response.reward_std = np.std(self.reward, axis=1).tolist()
 
+                if self.save_result:
+                    home_dir = os.path.expanduser('~')
+                    file_path = os.path.join(home_dir, 'Documents/IntRLResults')
+                    filename = self.bo_metric + '-'\
+                               + str(self.bo_metric_parameter) + '-' \
+                               + str(self.bo_nr_weights) + '-' + str(time.time())
+                    filename = filename.replace('.', '_') + '.csv'
+                    path = os.path.join(file_path, filename)
+                    self.get_logger().info(path)
+
+                    np.savetxt(path, self.reward, delimiter=',')
+
                 self.get_logger().info('Responding: Active BO')
                 self.active_bo_pub.publish(bo_response)
                 self.reset_bo_request()
@@ -249,6 +269,18 @@ class ActiveBOTopic(Node):
                         self.current_run += 1
                         self.get_logger().info(f'Current Run: {self.current_run}')
 
+            # send the current states
+
+            if self.BO is not None and self.BO.Y is not None:
+                self.best_reward = np.max(self.BO.Y)
+
+            state_msg = ActiveBOState()
+            state_msg.current_run = self.current_run + 1 if self.current_run < self.bo_runs else self.bo_runs
+            state_msg.current_episode = self.current_episode + 1\
+                if self.current_episode < self.bo_episodes else self.bo_episodes
+            state_msg.best_reward = self.best_reward
+            state_msg.last_user_reward = self.rl_reward
+            self.state_pub.publish(state_msg)
 
 def main(args=None):
     rclpy.init(args=args)

src/active_bo_ros/active_bo_ros/active_rl_topic.py

@@ -172,6 +172,7 @@ class ActiveRLService(Node):
                 if done:
                     self.best_pol_shown = True
                     self.rl_step = 0
+                    self.rl_reward = 0.0
 
             elif self.best_pol_shown:
                 if not self.eval_response_received: