Create personal_rank.py

Author: zhaozhiyong19890102

Chapter_16 PersonalRank/personal_rank.py

@@ -0,0 +1,130 @@
+# coding=utf-8
+'''
+Date:20160930
+@author: zhaozhiyong
+'''
+
+import numpy as np
+
+def load_data(file_path):
+	'''导入用户商品数据
+	input:  file_path(string):用户商品数据存储的文件
+	output: data(mat):用户商品矩阵
+	'''
+	f = open(file_path)
+	data = []
+	for line in f.readlines():
+		lines = line.strip().split("\t")
+		tmp = []
+		for x in lines:
+			if x != "-":
+				tmp.append(1) # 打过分记为1
+			else:
+				tmp.append(0) # 未打分记为0
+		data.append(tmp)
+	f.close()
+	return np.mat(data)
+
+def generate_dict(dataTmp):
+	'''将用户-商品矩阵转换成二部图的表示
+	input:  dataTmp(mat):用户商品矩阵
+	output: data_dict(dict):图的表示
+	'''
+	m, n = np.shape(dataTmp)
+	
+	data_dict = {}
+	# 对每一个用户生成节点
+	for i in xrange(m):
+		tmp_dict = {}
+		for j in xrange(n):
+			if dataTmp[i, j] != 0:
+				tmp_dict["D_" + str(j)] = dataTmp[i, j]
+		data_dict["U_" + str(i)] = tmp_dict
+	
+	# 对每一个商品生成节点	
+	for j in xrange(n):
+		tmp_dict = {}
+		for i in xrange(m):
+			if dataTmp[i, j] != 0:
+				tmp_dict["U_" + str(i)] = dataTmp[i, j]
+		data_dict["D_" + str(j)] = tmp_dict
+	return data_dict
+		
+
+def PersonalRank(data_dict, alpha, user, maxCycles):
+	'''利用PersonalRank打分
+	input:  data_dict(dict):用户-商品的二部图表示
+			alpha(float):概率
+			user(string):指定用户
+			maxCycles(int):最大的迭代次数
+	output: rank(dict):打分的列表
+	'''
+	# 1、初始化打分
+	rank = {}  
+	for x in data_dict.keys():
+		rank[x] = 0
+	rank[user] = 1 # 从user开始游走
+	
+	# 2、迭代
+	step = 0
+	while step < maxCycles:
+		tmp = {}
+		for x in data_dict.keys():
+			tmp[x] = 0
+			
+		for i, ri in data_dict.items():
+			for j in ri.keys():
+				if j not in tmp:
+					tmp[j] = 0
+				tmp[j] += alpha * rank[i] / (1.0 * len(ri))  
+				if j == user:
+					tmp[j] += (1 - alpha)
+		# 判断是否收敛
+		check = []
+		for k in tmp.keys():
+			check.append(tmp[k] - rank[k])	
+		if sum(check) <= 0.0001:
+			break
+		rank = tmp
+		if step % 20 == 0:
+			print "iter: ", step
+		step = step + 1
+	return rank
+
+def recommend(data_dict, rank, user):
+	'''得到最终的推荐列表
+	input:  data_dict(dict):用户-商品的二部图表示
+			rank(dict):打分的结果
+			user(string):用户
+	output: result(dict):推荐结果
+	'''
+	items_dict = {}
+	# 1、用户user已打过分的项
+	items = []
+	for k in data_dict[user].keys():
+		items.append(k)
+		
+	# 2、从rank取出商品的打分
+	for k in rank.keys():
+		if k.startswith("D_"): # 商品
+			if k not in items: # 排除已经互动过的商品
+				items_dict[k] = rank[k]
+			
+	# 3、按打分的降序排序
+	result = sorted(items_dict.items(), key=lambda d: d[1], reverse=True)
+	return result
+
+if __name__ == "__main__":
+	# 1、导入用户商品矩阵
+	print "------------ 1.load data -------------"
+	dataMat = load_data("data.txt")
+	# 2、将用户商品矩阵转换成邻接表的存储
+	print "------------ 2.generate dict --------------"
+	data_dict = generate_dict(dataMat)
+	# 3、利用PersonalRank计算
+	print "------------ 3.PersonalRank --------------"
+	rank = PersonalRank(data_dict, 0.85, "U_0", 500)
+	# 4、根据rank结果进行商品推荐
+	print "------------ 4.recommend -------------"
+	result = recommend(data_dict, rank, "U_0")
+	print result