homework7

homework7/LinUCB-Homework.py

@@ -182,9 +182,9 @@ class LinUCB(object):
     @return:
     @return:
         action - true observed action for context
         action - true observed action for context
     """
     """
-    def get_reward(self, arm, action):
+    def get_reward(self, arm, action, reward):
         if arm == action:
         if arm == action:
-            return 1
+            return reward
         return 0
         return 0
 
 
     """
     """
@@ -226,6 +226,7 @@ class bandit_evaluator(object):
         self.bandit = None
         self.bandit = None
         self.cum_rewards = 0
         self.cum_rewards = 0
         self.ctr_history = []
         self.ctr_history = []
+        self.T = 1e-5
 
 
     """
     """
     calc_ctr:
     calc_ctr:
@@ -239,20 +240,21 @@ class bandit_evaluator(object):
     @return:
     @return:
         ctr - cumulative take-rate
         ctr - cumulative take-rate
     """
     """
-    def calc_ctr(self, x, action, t):
+    def calc_ctr(self, x, action, reward, t):
         assert t > 0
         assert t > 0
         pred_act = self.bandit.predict(x)
         pred_act = self.bandit.predict(x)
         ### todo
         ### todo
         if pred_act == action:
         if pred_act == action:
-            self.cum_rewards += 1
-        ctr = self.cum_rewards / t
+            self.cum_rewards += reward
+            self.T += 1
+        ctr = self.cum_rewards / self.T
         self.ctr_history.append(ctr)
         self.ctr_history.append(ctr)
         return ctr
         return ctr
 
 
 # In[20]:
 # In[20]:
 
 
 
 
-from utils import getData, getContext, getAction
+from utils import getData, getContext, getAction, getReward
 
 
 
 
 """
 """
@@ -279,16 +281,17 @@ def train(file, steps, alpha, nArms, d):
     for t in range(steps):
     for t in range(steps):
         x = getContext(data, t)
         x = getContext(data, t)
         action = getAction(data, t)
         action = getAction(data, t)
+        reward = getReward(data, t)
         arm = bandit.predict(x)
         arm = bandit.predict(x)
-        reward = bandit.get_reward(arm, action)
-        bandit.arms[arm].update_arm(reward, x)
+        reward_ = bandit.get_reward(arm, action, reward)
+        bandit.arms[arm].update_arm(reward_, x)
 
 
         if t > 0: # explore various alpha update methods to improve CTR
         if t > 0: # explore various alpha update methods to improve CTR
             # bandit.arms[arm].update_alpha(method=2) # or method=2
             # bandit.arms[arm].update_alpha(method=2) # or method=2
             bandit.arms[arm].update_alpha(3, t)
             bandit.arms[arm].update_alpha(3, t)
 
 
         if t > 0: # evaluate current bandit algorithm
         if t > 0: # evaluate current bandit algorithm
-            ctr = evaluator.calc_ctr(x, action, t)
+            ctr = evaluator.calc_ctr(x, action, reward, t)
             if t % 100 == 0:
             if t % 100 == 0:
                 print("Step:", t, end="")
                 print("Step:", t, end="")
                 print(" | CTR: {0:.02f}%".format(ctr))
                 print(" | CTR: {0:.02f}%".format(ctr))
@@ -299,7 +302,7 @@ def train(file, steps, alpha, nArms, d):
 # In[21]:
 # In[21]:
 
 
 
 
-file = "classification.txt"
+file = "dataset.txt"
 steps = 10000
 steps = 10000
 alpha = .1
 alpha = .1
 nArms = 10
 nArms = 10