import sys
import random
import math
import os
import numpy as np
import pandas as pd
from operator import itemgetter

from collections import defaultdict

random.seed(0)

class UserBasedCF(object):
    """
    TopN recommendation - User Based Collaborative Filtering
    """

    def __init__(self):
        self.trainset = {}
        self.testset = {}

        self.n_sim_user = 20
        self.n_rec_movie = 5

        self.user_sim_mat = {}
        self.movie_popular = {}
        self.movie_count = 0

        print('Similar user number = %d' % self.n_sim_user, file=sys.stderr)
        print('recommended movie number = %d' %
               self.n_rec_movie, file=sys.stderr)

    @staticmethod
    def loadfile(filename):
        """
        load a file, return a generator.
        """
        fp = open(filename, 'r')
        for i, line in enumerate(fp):
            yield line.strip('\r\n')
            if i % 100000 == 0:
                print('loading %s(%s)' % (filename, i), file=sys.stderr)
        fp.close()
        print('load %s successfully' % filename, file=sys.stderr)

    def generate_dataset(self, filename, pivot=0.7):
        """
        load rating data and split it to training set and test set
        """
        trainset_len = 0
        testset_len = 0

        for line in self.loadfile(filename):
            user, movie, rating, _ = line.split('::')
            # split the data by pivot
            if random.random() < pivot:
                self.trainset.setdefault(user, {})
                self.trainset[user][movie] = int(rating)
                trainset_len += 1
            else:
                self.testset.setdefault(user, {})
                self.testset[user][movie] = int(rating)
                testset_len += 1

        print('split training set and test set successfully', file=sys.stderr)
        print('train set = %s' % trainset_len, file=sys.stderr)
        print('test set = %s' % testset_len, file=sys.stderr)

    def calc_user_sim(self):
        """
        calculate user similarity matrix
        """
        # build inverse table for item-users
        # key=movieID, value=list of userIDs who have seen this movie
        print('building movie-users inverse table...', file=sys.stderr)
        movie2users = dict()

        for user, movies in self.trainset.items():
            for movie in movies:
                # inverse table for item-users
                if movie not in movie2users:
                    movie2users[movie] = set()
                movie2users[movie].add(user)
                # count item popularity at the same time
                if movie not in self.movie_popular:
                    self.movie_popular[movie] = 0
                self.movie_popular[movie] += 1
        print('build movie-users inverse table successfully', file=sys.stderr)

        # save the total movie number, which will be used in evaluation
        self.movie_count = len(movie2users)
        print('total movie number = %d' % self.movie_count, file=sys.stderr)

        # count co-rated items between users
        usersim_mat = self.user_sim_mat
        print('building user co-rated movies matrix...', file=sys.stderr)

        for movie, users in movie2users.items():
            for u in users:
                usersim_mat.setdefault(u, defaultdict(int))
                for v in users:
                    if u == v:
                        continue
                    usersim_mat[u][v] += 1
        print('build user co-rated movies matrix successfully', file=sys.stderr)

        # calculate similarity matrix
        print('calculating user similarity matrix...', file=sys.stderr)
        simfactor_count = 0
        PRINT_STEP = 2000000

        for u, related_users in usersim_mat.items():
            for v, count in related_users.items():
                usersim_mat[u][v] = count / math.sqrt(
                    len(self.trainset[u]) * len(self.trainset[v]))
                simfactor_count += 1
                if simfactor_count % PRINT_STEP == 0:
                    print('calculating user similarity factor(%d)' %
                           simfactor_count, file=sys.stderr)

        print('calculate user similarity matrix(similarity factor) successfully',
               file=sys.stderr)
        print('Total similarity factor number = %d' %
               simfactor_count, file=sys.stderr)
        print(pd.DataFrame(usersim_mat))

    def recommend(self, user):
        """
        Find K similar users and recommend N movies.
        """
        K = self.n_sim_user
        N = self.n_rec_movie
        rank = dict()
        watched_movies = self.trainset[user]

        for similar_user, similarity_factor in sorted(self.user_sim_mat[user].items(),
                                                      key=itemgetter(1), reverse=True)[0:K]:
            for movie in self.trainset[similar_user]:
                if movie in watched_movies:
                    continue
                # predict the user's "interest" for each movie
                rank.setdefault(movie, 0)
                rank[movie] += similarity_factor
        # return the N best movies
        rec_movies = sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]
        return rec_movies

    def evaluate(self):
        """
        print evaluation result: precision, recall, coverage and popularity
        """
        print('Evaluation start...', file=sys.stderr)

        N = self.n_rec_movie
        #  varables for precision and recall
        hit = 0
        rec_count = 0
        test_count = 0
        # varables for coverage
        all_rec_movies = set()
        # varables for popularity
        popular_sum = 0

        for i, user in enumerate(self.trainset):
            if i % 500 == 0:
                print('recommended for %d users' % i, file=sys.stderr)
            test_movies = self.testset.get(user, {})
            rec_movies = self.recommend(user)
            for movie, _ in rec_movies:
                if movie in test_movies:
                    hit += 1
                all_rec_movies.add(movie)
                popular_sum += math.log(1 + self.movie_popular[movie])
            rec_count += N
            test_count += len(test_movies)

        precision = hit / (1.0 * rec_count)
        recall = hit / (1.0 * test_count)
        coverage = len(all_rec_movies) / (1.0 * self.movie_count)
        popularity = popular_sum / (1.0 * rec_count)

        print('precision=%.4f\trecall=%.4f\tcoverage=%.4f\tpopularity=%.4f' %
               (precision, recall, coverage, popularity), file=sys.stderr)

读取数据：

ratingfile = os.path.join('ml-1m', 'ratings.dat')

现假定给用户i推荐电影，那么方案是：在用户i未观看过的电影中，以和用户i最相似的前20个用户为准，预测用户i会对这些电影的评分值，然后选出评分值最高的前5部电影。

usercf = UserBasedCF()

划分训练集和测试集： 

usercf.generate_dataset(ratingfile)

 

计算用户-用户相似度矩阵：

usercf.calc_user_sim()

模型评估：

usercf.evaluate()

推荐示例：

Recommendations = usercf.recommend('2')
print(Recommendations)

括号中第一个值表示电影编号，第二个值表示预测评分值。