Complete refactoring of logging into debug, info and error

Author: samre12

code/model/agent.py

@@ -18,7 +18,6 @@
 
 from utils.constants import *
 from utils.strings import *
-from utils.util import print_and_log_message, print_and_log_message_list
 
 class Agent(BaseAgent):
     '''Deep Trading Agent based on Deep Q Learning'''
@@ -98,7 +97,7 @@ def train(self):
 
                     message = 'avg_r: %.4f, avg_l: %.6f, avg_q: %3.6f, avg_ep_r: %.4f, max_ep_r: %.4f, min_ep_r: %.4f, # game: %d' \
                         % (avg_reward, avg_loss, avg_q, avg_ep_reward, max_ep_reward, min_ep_reward, num_episodes)
-                    print_and_log_message(message, self.logger)
+                    self.logger.info(message)
 
                     if max_avg_ep_reward * 0.9 <= avg_ep_reward:
                         self.sess.run(

code/model/baseagent.py

@@ -6,7 +6,6 @@
 
 from utils.constants import *
 from utils.strings import *
-from utils.util import print_and_log_message, print_and_log_message_list
 
 class BaseAgent(object):
     '''Base class containing all the parameters for reinforcement learning'''
@@ -57,22 +56,22 @@ def saver(self):
 
     def save_model(self, step=None):
         message = "Saving checkpoint to {}".format(self.checkpoint_dir)
-        print_and_log_message(message, self.logger)
+        self.logger.info(message)
         self.saver.save(self.sess, self.checkpoint_dir, global_step=step)
 
     def load_model(self):
         message = "Loading checkpoint from {}".format(self.checkpoint_dir)
-        print_and_log_message(message, self.logger)
+        self.logger.info(message)
 
         ckpt = tf.train.get_checkpoint_state(self.checkpoint_dir)
         if ckpt and ckpt.model_checkpoint_path:
             ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
             fname = os.path.join(self.checkpoint_dir, ckpt_name)
             self.saver.restore(self.sess, fname)
             message = "Checkpoint successfully loaded from {}".format(fname)
-            print_and_log_message(message, self.logger)
+            self.logger.info(message)
             return True
         else:
             message = "Checkpoint could not be loaded from {}".format(self.checkpoint_dir)
-            print_and_log_message(message, self.logger)
+            self.logger.info(message)
             return False

code/model/deepsense.py

@@ -2,7 +2,6 @@
 from os.path import join
 import tensorflow as tf
 
-from utils.util import print_and_log_message, print_and_log_message_list
 from utils.constants import *
 from utils.strings import *
 

code/model/environment.py

@@ -3,7 +3,6 @@
 
 from utils.constants import *
 from utils.strings import *
-from utils.util import print_and_log_message_list
 
 class Environment:
     '''Exchange Simulator for Bitcoin based upon per minute historical prices'''
@@ -56,7 +55,7 @@ def new_random_episode(self, history, replay_memory):
             history.add(state)
             replay_memory.add(state, 0.0, 0, False, 0.0)
 
-        print_and_log_message_list(message_list, self.logger)
+        map(self.logger.debug, message_list)
 
         return 1.0
 
@@ -73,6 +72,9 @@ def act(self, action):
             self.short = self.short + 1
 
         reward = (self.long - self.short) * self.unit * self.diffs[self.current]
+        message = "Reward for timestep {} of episode number {} is {}".format(
+            self.timesteps, self.episode_number, reward
+        )
         self.timesteps = self.timesteps + 1
         if self.timesteps is not self.horizon:
             self.current = self.current + 1

code/model/history.py

@@ -4,7 +4,6 @@
 
 from utils.constants import *
 from utils.strings import *
-from utils.util import print_and_log_message
 
 class History:
     '''Experiance buffer of the behaniour policy of the agent'''
@@ -21,7 +20,8 @@ def __init__(self, logger, config):
 
     def add(self, screen):
         if screen.shape != self.dims:
-            print_and_log_message(INVALID_TIMESTEP, self.logger)
+            self.logger.error(INVALID_TIMESTEP)
+            
         self._history[:-1] = self._history[1:]
         self._history[-1] = screen
 

code/model/replay_memory.py

@@ -8,7 +8,6 @@
 
 from utils.constants import *
 from utils.strings import *
-from utils.util import print_and_log_message, print_and_log_message_list
 
 class ReplayMemory:
     '''Memory buffer for experiance replay'''
@@ -43,7 +42,8 @@ def __init__(self, logger, config):
 
     def add(self, screen, reward, action, terminal, trade_rem):
         if screen.shape != self.dims:
-            print_and_log_message(INVALID_TIMESTEP, self.logger)
+            self.logger.error(INVALID_TIMESTEP)
+            
         else:
             self.actions[self.current] = action
             self.rewards[self.current] = reward
@@ -55,7 +55,8 @@ def add(self, screen, reward, action, terminal, trade_rem):
 
     def getState(self, index):
         if self.count == 0:
-            print_and_log_message(REPLAY_MEMORY_ZERO, self.logger)
+            self.logger.error(REPLAY_MEMORY_ZERO)
+            
         else:
             index = index % self.count
             if index >= self.history_length - 1:
@@ -68,26 +69,26 @@ def getState(self, index):
 
     def save(self):
         message = "Saving replay memory to {}".format(self._model_dir)
-        print_and_log_message(message, self.logger)
+        self.logger.info(message)
         for idx, (name, array) in enumerate(
             zip([ACTIONS, REWARDS, SCREENS, TERMINALS, TRADES_REM, PRESTATES, POSTSTATES],
                 [self.actions, self.rewards, self.screens, self.terminals, self.trades_rem, self.prestates, self.poststates])):
             save_npy(array, join(self._model_dir, name))
 
         message = "Replay memory successfully saved to {}".format(self._model_dir)
-        print_and_log_message(message, self.logger)
+        self.logger.info(message)
 
     def load(self):
         message = "Loading replay memory from {}".format(self._model_dir)
-        print_and_log_message(message, self.logger)
+        self.logger.info(message)
 
         for idx, (name, array) in enumerate(
             zip([ACTIONS, REWARDS, SCREENS, TERMINALS, TRADES_REM, PRESTATES, POSTSTATES],
                 [self.actions, self.rewards, self.screens, self.terminals, self.trades_rem, self.prestates, self.poststates])):
             array = load_npy(join(self._model_dir, name))
 
         message = "Replay memory successfully loaded from {}".format(self._model_dir)
-        print_and_log_message(message, self.logger)
+        self.logger.info(message)
 
     @property
     def model_dir(self):
@@ -96,7 +97,7 @@ def model_dir(self):
     @property
     def sample(self):
         if self.count <= self.history_length:
-            print_and_log_message(REPLAY_MEMORY_INSUFFICIENT, self.logger)
+            self.logger.error(REPLAY_MEMORY_INSUFFICIENT)
 
         else:
             indexes = []

code/model/util.py

@@ -7,7 +7,6 @@
 
 import tensorflow as tf
 
-from utils.util import print_and_log_message_list, print_and_log_message
 from utils.strings import *
 
 def clipped_error(x):
@@ -20,10 +19,10 @@ def clipped_error(x):
 def save_npy(obj, path, logger):
     np.save(path, obj)
     message = "  [*] saved at {}".format(path)
-    print_and_log_message(message, logger)
+    logger.info(message)
 
 def load_npy(path, logger):
     obj = np.load(path)
     message = "  [*] loaded from {}".format(path)
-    print_and_log_message(message, logger)
+    logger.info(message)
     return obj

code/process/processor.py

@@ -4,7 +4,6 @@
 
 from utils.constants import *
 from utils.strings import *
-from utils.util import print_and_log_message, print_and_log_message_list
 
 class Processor:
     '''Preprocessor for Bitcoin prices dataset as obtained by following the procedure 
@@ -34,15 +33,15 @@ def timestamp_blocks(self):
     def preprocess(self):
         data = pd.read_csv(self.dataset_path)
         message = 'Columns found in the dataset {}'.format(data.columns)
-        print_and_log_message(message, self.logger)
+        self.logger.info(message)
         data = data.dropna()
         start_time_stamp = data['Timestamp'][0]
         timestamps = data['Timestamp'].apply(lambda x: (x - start_time_stamp) / 60)
         timestamps = timestamps - range(timestamps.shape[0])
         data.insert(0, 'blocks', timestamps)
         blocks = data.groupby('blocks')
         message = 'Number of blocks of continuous prices found are {}'.format(len(blocks))
-        print_and_log_message(message, self.logger)
+        self.logger.info(message)
 
         self._data_blocks = []
         distinct_episodes = 0
@@ -60,7 +59,7 @@ def preprocess(self):
         data = None
         message_list = ['Number of usable blocks obtained from the dataset are {}'.format(len(self._data_blocks))]
         message_list.append('Number of distinct episodes for the current configuration are {}'.format(distinct_episodes))
-        print_and_log_message_list(message_list, self.logger)
+        map(self.logger.info, message_list)
 
     def generate_attributes(self):
         self._diff_blocks = []

code/utils/util.py

@@ -1,4 +1,5 @@
 from ConfigParser import ConfigParser
+import sys
 import logging
 
 from constants import *
@@ -10,25 +11,20 @@ def get_config_parser(filename):
     return config
 
 def get_logger(config):
-    logging.basicConfig(level=logging.DEBUG)
     formatter = \
         logging.Formatter('%(asctime)s - %(pathname)s - Line No %(lineno)s - Level %(levelname)s - %(message)s')
     info_handler = logging.FileHandler(config[LOG_FILE])
     info_handler.setLevel(logging.INFO)
     info_handler.setFormatter(formatter)
 
+    out_handler = logging.StreamHandler(sys.stdout)
+    out_handler.setLevel(logging.DEBUG)
+    out_handler.setFormatter(formatter)
+
     logger = logging.getLogger(name=DEEP_TRADING_AGENT)
-    logger.setLevel(logging.DEBUG)
+    logger.setLevel(logging.INFO)
     logger.addHandler(info_handler)
-    logger.propagate = False
+    
     return logger
 
-def print_and_log_message(message, logger):
-    logging.info(message)
-    logger.info(message)
-
-def print_and_log_message_list(message_list, logger):
-    for message in message_list:
-        logging.info(message)
-        logger.info(message)