推荐系统---surprise库的测试

1：加载数据集

def load_format2trainset():
    file_path = "F:\\ML\\recommendation_data\\music_playlist_farmat.txt"
    # 指定文件格式
    reader = Reader(line_format='user item rating timestamp', sep=',')
    # 从文件读取数据
    music_data = Dataset.load_from_file(file_path, reader=reader)
    print("构建数据集...")
    retrainset = music_data.build_full_trainset()
    return retrainset

主要用的到的类有：Reader --- 解析包含评分的文件  reader类

                             Dataset--- 包含一些数据集操作，主要方法有load_builtion('数据集名')  #加载内置数据集

                                                                                              load_from_df()  #加载pandas结构数据

                                                                                              load_from_file() #加载用户自己的数据

                                                                                              load_from_folds() #加载多个数据，例如

# folds_files is a list of tuples containing file paths:
# [(u1.base, u1.test), (u2.base, u2.test), ... (u5.base, u5.test)]
train_file = files_dir + 'u%d.base'
test_file = files_dir + 'u%d.test'
folds_files = [(train_file % i, test_file % i) for i in (1, 2, 3, 4, 5)]

data = Dataset.load_from_folds(folds_files, reader=reader)

对数据集的操作包括：

build_full_trainset()   #不对数据集做切分，返回整个数据
split(n_folds=5, shuffle=True)  #切分数据集

2：算法选择，surprise库包含了基于协同过滤的和基于矩阵分解的两大类算法。

random_pred.NormalPredictor	Algorithm predicting a random rating based on the distribution of the training set, which is assumed to be normal.