文章目录

6.3. 推荐系统代码案例

import recsys.algorithm
recsys.algorithm.VERBOSE = True


from recsys.algorithm.factorize import SVD
from recsys.datamodel.data import Data
from recsys.evaluation.prediction import RMSE
import os,sys

tmpfile = "/tmp/movielens.zip"
moviefile = "./ml-1m/movies.dat"


class RecommendSystem(object):

    def __init__(self, filename, sep, **format):
        self.filename = filename
        self.sep = sep
        self.format = format

        # 训练参数
        self.k = 100
        self.min_values = 10
        self.post_normalize = True

        self.svd = SVD()

        # 判断是否加载
        self.is_load = False

        # 添加数据处理
        self.data = Data()

        # 添加模型评估
        self.rmse = RMSE()

    def get_data(self):
        """ 获取数据 :return: None """
        # 如果模型不存在
        if not os.path.exists(tmpfile):
            # 如果数据文件不存在
            if not os.path.exists(self.filename):
                sys.exit()
            # self.svd.load_data(filename=self.filename, sep=self.sep, format=self.format)
            # 使用Data()来获取数据
            self.data.load(self.filename, sep=self.sep, format=self.format)
            train, test = self.data.split_train_test(percent=80)
            return train, test
        else:
            self.svd.load_model(tmpfile)
            self.is_load = True
            return None, None


    def train(self, train):
        """ 训练模型 :param train: 训练数据 :return: None """
        if not self.is_load:
            self.svd.set_data(train)
            self.svd.compute(k=self.k, min_values=self.min_values, post_normalize=self.post_normalize, savefile=tmpfile[:-4])
        return None

    def rs_predict(self, itemid, userid):
        """ 评分预测 :param itemid: 电影id :param userid: 用户id :return: None """
        score = self.svd.predict(itemid, userid)
        print "推荐的分数为：%f" % score
        return score

    def recommend_to_user(self, userid):
        """ 推荐给用户 :param userid: 用户id :return: None """
        recommend_list = self.svd.recommend(userid, is_row=False)

        # 读取文件里的电影名称
        movie_list = []

        for line in open(moviefile, "r"):
            movie_list.append(' '.join(line.split("::")[1:2]))

        # 推荐具体电影名字和分数
        for itemid, rate in recommend_list:
            print "给您推荐了%s,我们预测分数为%s" %(movie_list[itemid],rate)
        return None

    def evaluation(self, test):
        """ 模型的评估 :param test: 测试集 :return: None """
        # 如果模型不是直接加载
        if not self.is_load:

            # 循环取出测试集里面的元组数据<评分，电影，用户>
            for value, itemid, userid in test.get():
                try:
                    predict = self.rs_predict(itemid, userid)
                    self.rmse.add(value, predict)
                except KeyError:
                    continue
            # 计算返回误差（均方误差）
            error = self.rmse.compute()

            print "模型误差为%s:" % error

        return None


if __name__ == "__main__":
    rs = RecommendSystem("./ml-1m/ratings.dat", "::", row=1, col=0, value=2, ids=int)
    train, test = rs.get_data()
    rs.train(train)
    rs.evaluation(test)
    # rs.rs_predict(1,1)
    rs.recommend_to_user(1)