List all movies and their ratings in percent

I'm sorry, but there are too many movies and their ratings to list them all. Can you please provide me with a specific list of movies or a genre so that I can provide you with the ratings?

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import pandas as pd from mlxtend.frequent_patterns import apriori from mlxtend.frequent_patterns import association_rules # 读取数据 ratings = pd.read_csv('ratings.csv') movies = pd.read_csv('movies.csv') # 筛选评分大于3的电影信息 ratings = ratings[ratings['rating'] > 3] # 汇总每个用户的电影评分信息 user_ratings = ratings.groupby('userId')['movieId'].apply(list).reset_index(name='movieIds') # 统计每部电影被用户评论的次数 movie_counts = ratings.groupby('movieId')['userId'].count().reset_index(name='count') # 设置mini_support=50% min_support = 0.5 # 生成频繁项集 frequent_itemsets = apriori(user_ratings['movieIds'].tolist(), min_support=min_support, max_len=3, use_colnames=True) # 生成关联规则 rules = association_rules(frequent_itemsets, metric='confidence', min_threshold=0.5) # 将电影ID替换为电影名称 rules['antecedents'] = rules['antecedents'].apply(lambda x: ', '.join(movies[movies['movieId'].isin(x)]['title'].tolist())) rules['consequents'] = rules['consequents'].apply(lambda x: ', '.join(movies[movies['movieId'].isin(x)]['title'].tolist())) # 打印结果 print(rules[['antecedents', 'consequents', 'confidence']])In[4], line 16'list' object has no attribute 'size'

这个错误可能是由于apriori函数期望的输入类型与你提供的数据不匹配导致的。apriori函数期望的输入是一个包含多个事务（transaction）的列表，每个事务由多个元素组成。而在你的代码中，输入参数是一个列表，每个元素又是一个列表，这可能导致了错误。

尝试将第16行的输入参数改为`user_ratings['movieIds'].values.tolist()`，即将Series对象转换为numpy数组，再转换为列表。这样可以确保输入参数的正确格式。

This code block seems to be evaluating a trained PyTorch model on a test set and calculating the Root Mean Squared Error (RMSE) of the model's predictions. The with torch.no_grad() statement is used to turn off the gradient calculation during testing, since we do not need to backpropagate the error. This can save memory and speed up evaluation. Next, the user IDs, item IDs, and corresponding ratings are extracted from the test set and converted to PyTorch tensors using the LongTensor and FloatTensor functions. The model object is then called with the user and item tensors as inputs to get the predicted ratings. The criterion function calculates the loss between the predicted ratings and actual ratings, and the RMSE is computed by taking the square root of the loss. Finally, the RMSE value is printed using string formatting to display only 3 decimal places.翻译

这段代码似乎是在测试集上评估训练好的 PyTorch 模型，并计算模型预测的均方根误差（RMSE）。使用 `with torch.no_grad()` 语句可以在测试期间关闭梯度计算，因为我们不需要反向传播误差。这可以节省内存并加快评估速度。接下来，从测试集中提取用户 ID、物品 ID 和相应的评分，并使用 `LongTensor` 和 `FloatTensor` 函数将它们转换为 PyTorch 张量。然后，将用户和物品张量作为输入调用模型对象以获取预测评分。`criterion` 函数计算预测评分和实际评分之间的损失，然后通过对损失进行平方根运算计算 RMSE。最后，使用字符串格式化打印 RMSE 值，仅显示 3 位小数。