Apriori算法作电影推荐

Apriori算法是一种挖掘关联规则的频繁项集算法，其核心思想是通过候选集生成和情节的向下封闭检测两个阶段来挖掘频繁项集。

思想：
1、生成L1；
2、Lk生成Ck+1;
3、得到频繁项集；（前面只用到支持度）
4、通过频繁项集生成关联规则。（用到置信度）

import os,sysimport pandas as pdfrom collections import defaultdict from operator import itemgetterdata_fold = "ml-100k"#对原始数据进行处理def data_deal():    ratings_filename = os.path.join(data_fold,"u.data")     all_ratings = pd.read_csv(ratings_filename, delimiter="\t",header=None,names=["UserID","MovieID","Rating","DateTime"])    all_ratings["DateTime"] = pd.to_datetime(all_ratings["DateTime"],unit="s")    #为了判断用户是否喜欢某一部电影，增加一个属性Favorable(打分大于3则表示喜欢)    all_ratings["Favorable"] = all_ratings["Rating"]>3    return all_ratings#定义属性作为推荐的依据def getFeature(ratings):    #筛选只包含喜欢电影的用户数据集    favorate_ratings = ratings[ratings["Favorable"]]    #下面需要知道每个用户喜欢什么电影，即按照用户ID进行分组,格式为：用户ID：{电影ID集合}    favorate_reviews_by_users = dict( (k,frozenset(v.values)) for k,v in favorate_ratings.groupby("UserID")["MovieID"])    #定义每部电影的影迷数    num_favorable_by_movie = ratings[["MovieID", "Favorable"]].groupby("MovieID").sum()    #按照影迷数进行排序    num_favorable_by_movie = num_favorable_by_movie.sort_values("Favorable", ascending=False)    return favorate_reviews_by_users,num_favorable_by_movie  #生成频繁项集def find_frequent_itemsets(favorable_reviews_by_users, k_1_itemsets, min_support):    counts = defaultdict(int)    for user, reviews in favorable_reviews_by_users.items():         for itemset in k_1_itemsets:             #判断是否为集合的子集            if itemset.issubset(reviews):                #由LK生成Ck+1                for other_reviewed_movie in reviews - itemset:                     current_superset = itemset | frozenset((other_reviewed_movie,))                    counts[current_superset] += 1    #由CK+1生成Lk+1              return dict([(itemset, frequency) for itemset, frequency in counts.items() if frequency >= min_support]) #通过电影ID获取电影名称def get_movie_name(movie_id):    movie_name_filename = os.path.join(data_fold, "u.item")    movie_name_data = pd.read_csv(movie_name_filename, delimiter="|",  header=None, encoding = "mac-roman")     movie_name_data.columns = ["MovieID", "Title", "Release Date",  "Video Release", "IMDB", "<UNK>", "Action",                            "Adventure",  "Animation", "Children's", "Comedy", "Crime", "Documentary",  "Drama",                            "Fantasy", "Film-Noir",  "Horror", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller",                             "War", "Western"]        title_object = movie_name_data[movie_name_data["MovieID"] ==  movie_id]["Title"]    title = title_object.values[0]       return title #下面将通过频繁项集来生成关联规则。def generateRule(frequent_itemsets,favorate_reviews_by_users):    candidate_rules = []    for itemset_length, itemset_counts in frequent_itemsets.items():        #itemset存放频繁项集        for itemset in itemset_counts.keys():             for conclusion in itemset:                 premise = itemset - set((conclusion,))                 #这样就得到大量的规则，但还需要进一步的筛选                candidate_rules.append((premise, conclusion))         correct_counts = defaultdict(int)        incorrect_counts = defaultdict(int)        for user, reviews in favorate_reviews_by_users.items():              for candidate_rule in candidate_rules:                 premise, conclusion = candidate_rule                #判断用户是否喜欢前提里的所有电影,只有都喜欢，规则才适用。                if premise.issubset(reviews):                    #判断结论里所有的电影，用户是否喜欢                    if conclusion in reviews:                        correct_counts[candidate_rule]+=1                    else:                        incorrect_counts[candidate_rule] += 1      #计算置信度：用规则应验的次数除以前提条件出现的总次数。 格式为{规则：置信度}    rule_confidence = {candidate_rule: correct_counts[candidate_rule]/ float(correct_counts[candidate_rule] +  incorrect_counts[candidate_rule])                         for candidate_rule in candidate_rules}    #规则按照置信度大小进行排序。    sorted_confidence = sorted(rule_confidence.items(),  key=itemgetter(1), reverse=True)    return sorted_confidence#按照指定的格式打印前5条规则def print_rule_top5(sorted_confidence):    for index in range(5):         print("Rule #{0}".format(index + 1))          (premise, conclusion) = sorted_confidence[index][0]        premise_names = ", ".join(get_movie_name(idx) for idx in premise)         conclusion_name = get_movie_name(conclusion)          print("Rule: If a person recommends {0} they will also  recommend {1}".format(premise_names, conclusion_name))         print(" - Confidence:{0:.3f}".format(sorted_confidence[index][1])) def get_rule(ratings):    frequent_itemsets = {}    min_support = 50    favorate_reviews_by_users,num_favorable_by_movie = getFeature(ratings)    #定义频繁一项集    frequent_itemsets[1] = dict( (frozenset((movie_id,)),row["Favorable"]) for movie_id,row in num_favorable_by_movie.iterrows() if row["Favorable"]>min_support  )     #生成频繁项集    for k in range(2, 20):        cur_frequent_itemsets = find_frequent_itemsets(favorate_reviews_by_users,frequent_itemsets[k-1],min_support)        frequent_itemsets[k] = cur_frequent_itemsets        if len(cur_frequent_itemsets) == 0:              sys.stdout.flush()                      break         else:            sys.stdout.flush()           #删除频繁一项集,因为频繁一项集不能生成关联规则    del frequent_itemsets[1]      #生成关联规则    sorted_confidence = generateRule(frequent_itemsets,favorate_reviews_by_users)    return sorted_confidenceif __name__ == "__main__":    #获取处理后的数据    all_ratings = data_deal()    #由于电脑配置问题，只选择用户ID为1到200的用户，这样出结果相对较快    ratings_test = all_ratings[all_ratings["UserID"].isin(range(201))]     sorted_confidence_test = get_rule(ratings_test)    print_rule_top5(sorted_confidence_test)

运行结果截图：

总结：当数据量比较庞大的时候，算法的优化显得格外重要，上面的实现还有一些缺陷，对于大量数据的运行将花费较长时间，所以只选择了前200个用户的相关信息作为原始数据集。