Merge pull request udacity#54 from udacity/hotfix/smartcab/remove-incomplete-analysis

Remove unfinished smartcab analysis code, leave in other fixes

Author: jared-weed

projects/smartcab/smartcab/analysis.py

@@ -65,23 +65,6 @@ def __init__(self, num_dummies=3):
         self.primary_agent = None  # to be set explicitly
         self.enforce_deadline = False
 
-        # Step data (updated after each environment step)
-        self.step_data = {
-            't': 0,
-            'deadline': 0,
-            'waypoint': None,
-            'inputs': None,
-            'action': None,
-            'reward': 0.0
-        }
-
-        # Trial data (updated at the end of each trial)
-        self.trial_data = {
-            'net_reward': 0.0,  # total reward earned in current trial
-            'final_deadline': None,  # deadline value (time remaining)
-            'success': 0  # whether the agent reached the destination in time
-        }
-
     def create_agent(self, agent_class, *args, **kwargs):
         agent = agent_class(self, *args, **kwargs)
         self.agent_states[agent] = {'location': random.choice(self.intersections.keys()), 'heading': (0, 1)}
@@ -120,11 +103,6 @@ def reset(self):
                 'destination': destination if agent is self.primary_agent else None,
                 'deadline': deadline if agent is self.primary_agent else None}
             agent.reset(destination=(destination if agent is self.primary_agent else None))
-            if agent is self.primary_agent:
-                # Reset metrics for this trial (step data will be set during the step)
-                self.trial_data['net_reward'] = 0.0
-                self.trial_data['final_deadline'] = deadline
-                self.trial_data['success'] = 0
 
     def step(self):
         #print "Environment.step(): t = {}".format(self.t)  # [debug]
@@ -231,22 +209,11 @@ def act(self, agent, action):
             if state['location'] == state['destination']:
                 if state['deadline'] >= 0:
                     reward += 10  # bonus
-                    self.trial_data['success'] = 1
                 self.done = True
                 print "Environment.act(): Primary agent has reached destination!"  # [debug]
             self.status_text = "state: {}\naction: {}\nreward: {}".format(agent.get_state(), action, reward)
             #print "Environment.act() [POST]: location: {}, heading: {}, action: {}, reward: {}".format(location, heading, action, reward)  # [debug]
 
-            # Update metrics
-            self.step_data['t'] = self.t
-            self.trial_data['final_deadline'] = self.step_data['deadline'] = state['deadline']
-            self.step_data['waypoint'] = agent.get_next_waypoint()
-            self.step_data['inputs'] = inputs
-            self.step_data['action'] = action
-            self.step_data['reward'] = reward
-            self.trial_data['net_reward'] += reward
-            print "Environment.act(): Step data: {}".format(self.step_data)  # [debug]
-
         return reward
 
     def compute_dist(self, a, b):

projects/smartcab/smartcab/environment.py

@@ -3,10 +3,6 @@
 import random
 import importlib
 
-import numpy as np
-
-from analysis import Reporter
-
 class Simulator(object):
     """Simulates agents in a dynamic smartcab environment.
 
@@ -25,7 +21,7 @@ class Simulator(object):
         'orange'  : (255, 128,   0)
     }
 
-    def __init__(self, env, size=None, update_delay=1.0, display=True, live_plot=False):
+    def __init__(self, env, size=None, update_delay=1.0, display=True):
         self.env = env
         self.size = size if size is not None else ((self.env.grid_size[0] + 1) * self.env.block_size, (self.env.grid_size[1] + 1) * self.env.block_size)
         self.width, self.height = self.size
@@ -63,14 +59,8 @@ def __init__(self, env, size=None, update_delay=1.0, display=True, live_plot=Fal
                 self.display = False
                 print "Simulator.__init__(): Error initializing GUI objects; display disabled.\n{}: {}".format(e.__class__.__name__, e)
 
-        # Setup metrics to report
-        self.live_plot = live_plot
-        self.rep = Reporter(metrics=['net_reward', 'avg_net_reward', 'final_deadline', 'success'], live_plot=self.live_plot)
-        self.avg_net_reward_window = 10
-
     def run(self, n_trials=1):
         self.quit = False
-        self.rep.reset()
         for trial in xrange(n_trials):
             print "Simulator.run(): Trial {}".format(trial)  # [debug]
             self.env.reset()
@@ -100,7 +90,6 @@ def run(self, n_trials=1):
                     # Update environment
                     if self.current_time - self.last_updated >= self.update_delay:
                         self.env.step()
-                        # TODO: Log step data
                         self.last_updated = self.current_time
 
                     # Render GUI and sleep
@@ -116,22 +105,6 @@ def run(self, n_trials=1):
             if self.quit:
                 break
 
-            # Collect/update metrics
-            self.rep.collect('net_reward', trial, self.env.trial_data['net_reward'])  # total reward obtained in this trial
-            self.rep.collect('avg_net_reward', trial, np.mean(self.rep.metrics['net_reward'].ydata[-self.avg_net_reward_window:]))  # rolling mean of reward
-            self.rep.collect('final_deadline', trial, self.env.trial_data['final_deadline'])  # final deadline value (time remaining)
-            self.rep.collect('success', trial, self.env.trial_data['success'])
-            if self.live_plot:
-                self.rep.refresh_plot()  # autoscales axes, draws stuff and flushes events
-
-        # Report final metrics
-        if self.display:
-            self.pygame.display.quit()  # need to shutdown pygame before showing metrics plot
-            # TODO: Figure out why having both game and plot displays makes things crash!
-
-        if self.live_plot:
-            self.rep.show_plot()  # holds till user closes plot window
-
     def render(self):
         # Clear screen
         self.screen.fill(self.bg_color)