python - Movie File 将文件解析为以下形式的字典

Question

1.6。推荐电影创建一个函数来计算一组电影评论中有多少个相似的关键字并推荐关键词数量最相似的电影。此任务的解决方案需要使用字典。电影评论和关键字位于名为 film_reviews.txt 的文件中，以逗号分隔。第一个术语是电影名称，其余术语是电影的关键字标签(即“惊人的”、“诗意的”、“可怕的”等等)。

函数名称:similar_movie()

参数/参数:电影名称

返回:与作为参数传递的电影相似的电影列表

film_reviews.txt -

7 Days in Entebbe,fun,foreign,sad,boring,slow,romance
12 Strong,war,violence,foreign,sad,action,romance,bloody
A Fantastic Woman,fun,foreign,sad,romance
A Wrinkle in Time,book,witty,historical,boring,slow,romance
Acts of Violence,war,violence,historical,action
Annihilation,fun,war,violence,gore,action
Armed,foreign,sad,war,violence,cgi,fancy,action,bloody
Black '47,fun,clever,witty,boring,slow,action,bloody
Black Panther,war,violence,comicbook,expensive,action,bloody

Answer 1

我认为这对你有用

film_data = {'films': {}} 
with open('film_reviews.txt', 'r') as f:
    for line in f.readlines():
        data = line.split(',')
        data[-1] = data[-1].strip() # removing new line character
        film_data['films'][data[0].lower()] = data[1:]

def get_smilar_movie(name):
    if name.lower() in film_data['films'].keys():
        original_review = film_data['films'][name.lower()]
        similarities = dict()
        for key in film_data['films']:
            if key == name.lower():
                continue
            else:
                similar_movie_review = set(film_data['films'][key])
                overlap = set(original_review) & similar_movie_review
                universe = set(original_review) | similar_movie_review
                # % of overlap  compared to the first movie = output1
                output1 = float(len(overlap)) / len(set(original_review)) * 100 
                # % of overlap compared to the second movie = output2
                output2 = float(len(overlap)) / len(similar_movie_review) * 100 
                # % of overlap compared to universe
                output3 = float(len(overlap)) / len(universe) * 100 
                similarities[output1 + output2 + output3] = dict()
                similarities[output1 + output2 + output3]['reviews'] = film_data['films'][key]
                similarities[output1 + output2 + output3]['movie'] = key 
        max_similarity = max(similarities.keys())
        movie2 = similarities[max_similarity]
        print(name,' reviews ',film_data['films'][name.lower()])
        print('similar movie ',movie2)
        print('Similarity = {0:.2f}/100'.format(max_similarity/3))
        return movie2['movie']
    return None

get_similar_movie 函数将从 film_data dict 返回最相似的电影。该函数将采用电影名称作为参数。