ALS推荐算法—训练并保存—加载并测试 _测试

沉舟侧畔千帆进，病树前头万木春。这篇文章主要讲述ALS推荐算法—训练并保存—加载并测试相关的知识，希望能为你提供帮助。

文章目录

??一、读取数据—清洗数据训练并保存??
??二、加载模型—预测??

【ALS推荐算法—训练并保存—加载并测试】
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一、读取数据—清洗数据训练并保存

import os
from pyspark import SparkContext,SparkConf
from pyspark.mllib.recommendation import ALS,Rating

def create_spark_context():
os.environ[java_HOME] = C:/Java/jdk1.8.0_91
os.environ[HADOOP_HOME] = C:/Java/hadoop-2.6.0-cdh5.7.6
os.environ[SPARK_HOME] = C:/Java/spark-2.2.0-bin-2.6.0-cdh5.7.6

spark_conf = SparkConf()\\
.setAppName(python_Spark_WordCount)\\
.setMaster(local[4]) \\
.set("spark.driver.extraJavaOptions", "-Xss4096k")

spark_context = SparkContext(conf=spark_conf) # 获取SparkContext实例对象,
spark_context.setLogLevel(WARN)# 设置日志级别
return spark_context

def prepare_data(spark_context):
# ------------1.读取评分数据并解析 -------------
raw_user_data = https://www.songbingjia.com/android/spark_context.textFile("../ml-100k/u.data")
raw_ratings = raw_user_data.map(lambda line: line.split("\\t")[:3])
ratings_rdd = raw_ratings.map(lambda x: Rating(int(x[0]), int(x[1]), float(x[2])))

# ------------2.数据初步统计 ----------------
num_ratings = ratings_rdd.count()
num_users = ratings_rdd.map(lambda x: x[0]).distinct().count()
num_movies = ratings_rdd.map(lambda x: x[1]).distinct().count()
print("总共: ratings: " + str(num_ratings) + ", User: " + str(num_users) + ", Moive: " + str(num_movies))
return ratings_rdd

def save_mode(spark_context,model):
try:
model.save(spark_context, "../datas/als-model")
except Exception:
print ("保存模型出错")

if __name__ =="__main__":
sc = create_spark_context()

print("==================数据准备阶段===================")
rating_rdd = prepare_data(sc)
print("==================模型训练阶段===================")
#开始使用ALS算法：rank=5",iterations = 5, lambda = 0.1
als_model = ALS.train(rating_rdd,5,iterations=5,lambda_=0.1)
print( als_model)
print("==================模型保存阶段===================")
save_mode(sc,als_model)
sc.stop()

二、加载模型—预测

from pyspark import SparkContext, SparkConf
from pyspark.mllib.recommendation import MatrixFactorizationModel
import os
import sys

def create_spark_context():
os.environ[JAVA_HOME] = C:/Java/jdk1.8.0_91
os.environ[HADOOP_HOME] = C:/Java/hadoop-2.6.0-cdh5.7.6
os.environ[SPARK_HOME] = C:/Java/spark-2.2.0-bin-2.6.0-cdh5.7.6
spark_conf = SparkConf()\\
.setAppName(Python_Spark_WordCount)\\
.setMaster(local[4]) \\
.set("spark.driver.extraJavaOptions", "-Xss4096k")

spark_context = SparkContext(conf=spark_conf) # 获取SparkContext实例对象,
spark_context.setLogLevel(WARN)# 设置日志级别
return spark_context

def prepare_data(spark_context):
item_rdd = sc.textFile("../ml-100k/u.item")# 读取 u.item 电影信息数据
movie_title = item_rdd \\# 创建电影名称与电影ID映射的字典
.map(lambda line: line.split("|")) \\
.map(lambda a: (float(a[0]), a[1]))

movie_title_dict = movie_title.collectAsMap() # 将RDD转换字典
return movie_title_dict

def load_model(spark_context):# 加载模型
try:
model = MatrixFactorizationModel.load(spark_context, ../datas/als-model)
print (model)
return model
except Exception:
print ("加载模型出错")

if __name__ =="__main__":
# 运行前点击 pycharm 菜单栏 run-> Edit configuration-> Script parameters 输入： --U 198（随意user_id）
if len(sys.argv)!=3:
print("请输入两个参数：--U user_id,--M movie_id")

def recommend_movies(als, movies, user_id):
rmd_movies = als.recommendProducts(user_id, 10)
print(推荐的电影为：.format(rmd_movies))
for rmd in rmd_movies:
print("为用户推荐的电影为：".format(rmd[0], movies[rmd[1]]))
return rmd_movies

def recommend_users(als, movies, movie_id):# 为每个电影推荐10个用户
rmd_users = als.recommendUsers(movie_id, 10)
print(针对电影ID：0,电影名:1,推荐是个用户为:.format(movie_id, movies[movie_id]))
for rmd in rmd_users:
print("推荐用户ID：,推荐评分：".format(rmd[0], rmd[2]))

def recommend(als_model, movie_dic):
if sys.argv[1] == --U:# 推荐电影给用户
recommend_movies(als_model, movie_dic, int(sys.argv[2]))
if sys.argv[1] == --M:# 推荐用户给电影
recommend_users(als_model, movie_dic, int(sys.argv[2]))

if __name__ == "__main__":
"""
1.数据准备
2.加载模型
3.预测推荐
"""
# 由于推荐的方式有两种，一个是依据用户的推荐，一个是基于商品的推荐
if len(sys.argv) != 3:
print("请输入2个参数, 要么是: --U user_id,要么是: --M movie_id")
exit(-1)
sc = create_spark_context()

# 数据准备，就是加载电影数据信息，转换字典
print(============= 数据准备 =============)
movie_title_dic = prepare_data(sc)
print(============= 加载模型 =============)
als_load_model = load_model(sc)
print(============= 预测推荐 =============)
recommend(als_load_model, movie_title_dic)