增加一些评价函数

Author: =

Recommend/test.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 #-*-coding:utf-8-*-
 import random  
+import math
 #统计各类数量  
 def addValueToMat(theMat,key,value,incr):  
     if key not in theMat: #如果key没出先在theMat中  
@@ -16,6 +17,7 @@ def addValueToMat(theMat,key,value,incr):
 tag_items = dict();  
 user_items = dict();  
 user_items_test = dict();#测试集数据字典  
+item_tags = dict()        #用于多样性测试
 
 #初始化，进行各种统计  
 def InitStat():  
@@ -29,7 +31,8 @@ def InitStat():
             tag=terms[2];  
             addValueToMat(user_tags,user,tag,1)  
             addValueToMat(tag_items,tag,item,1)  
-            addValueToMat(user_items,user,item,1)  
+            addValueToMat(user_items,user,item,1)
+            addValueToMat(item_tags,item,tag,1)  
             line = data_file.readline();  
         else:  
             addValueToMat(user_items_test,user,item,1)  
@@ -48,8 +51,56 @@ def Recommend(usr):
                     recommend_list[item_]+=wut*wit;  
     return sorted(recommend_list.iteritems(), key=lambda a:a[1],reverse=True)
 
+#统计标签流行度
+def TagPopularity():
+    tagfreq = {}
+    for user in user_tags.keys():
+        for tag in user_tags[user].keys():
+            if tag not in tagfreq:
+                tagfreq[tag] = 1
+            else:
+                tagfreq[tag] +=1
+    return sorted(tagfreq.iteritems(), key=lambda a:a[1],reverse=True)
+
+#计算余弦相似度
+def CosineSim(item_tags,i,j):
+    ret = 0
+    for b,wib in item_tags[i].items():     #求物品i,j的标签交集数目
+        if b in item_tags[j]:
+            ret += wib * item_tags[j][b]
+    ni = 0
+    nj = 0
+    for b, w in item_tags[i].items():      #统计 i 的标签数目
+        ni += w * w
+    for b, w in item_tags[j].items():      #统计 j 的标签数目
+        nj += w * w
+    if ret == 0:
+        return 0
+    return ret/math.sqrt(ni * nj)          #返回余弦值       
+
+#计算推荐列表多样性
+def Diversity(item_tags,recommend_items):
+    ret = 0
+    n = 0
+    for i in dict(recommend_items).keys():
+        for j in dict(recommend_items).keys():
+            if i == j:
+                continue
+            ret += CosineSim(item_tags,i,j)
+            n += 1
+    return ret/(n * 1.0)
+
 InitStat()
 recommend_list = Recommend("48411")
 # print recommend_list
 for recommend in recommend_list[:10]:  #兴趣度最高的十个itemid
-    print recommend
+    print recommend
+
+#标签流行度统计    
+tagFreq = TagPopularity()
+for tag in tagFreq[:20]:
+    print tag
+
+#推荐列表多样性,计算时间较长
+diversityNum = Diversity(item_tags, recommend_list)
+print diversityNum