Data Weaving & Preparation

data_weaving_preparation.py

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+# 导入处理器
+from movielens_processor import MovieLensProcessor
+
+# 初始化处理器并指定数据路径
+processor = MovieLensProcessor(data_path='./dataset')
+
+# 1. 加载数据
+processor.load_data(verbose=True)
+
+# 2. 清洗数据
+processor.clean_data(verbose=True)
+
+# 3. 分析数据质量
+quality_metrics = processor.analyze_data_quality(plot=True)
+
+# 4. 创建评分矩阵 (稀疏表示)
+rating_sparse = processor.create_rating_matrix(sparse=True)
+
+# 5. 划分训练测试集 (基于时间)
+train_ratings, test_ratings = processor.split_train_test(
+    test_ratio=0.2, 
+    method='time'
+)
+
+# 6. 保存处理后的数据
+processor.save_processed_data(output_dir='./processed_data')

movielens_processor.py

@@ -0,0 +1,753 @@
+# -*- coding: utf-8 -*-
+"""
+MovieLens数据集编织与清洗工具
+----------------------------------------
+作者: 首席数据科学家
+版本: 1.0.0
+"""
+
+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy.sparse import csr_matrix
+from datetime import datetime
+import re
+import warnings
+from tqdm import tqdm
+
+# 设置pandas显示选项
+pd.set_option('display.max_columns', 50)
+pd.set_option('display.width', 1000)
+warnings.filterwarnings('ignore')
+
+class MovieLensProcessor:
+    """MovieLens数据集处理器"""
+    
+    def __init__(self, data_path='./'):
+        """
+        初始化处理器
+        
+        参数:
+            data_path (str): 数据文件所在目录
+        """
+        self.data_path = data_path
+        self.ratings = None
+        self.users = None
+        self.movies = None
+        self.rating_matrix = None
+        self.rating_sparse = None
+        
+        # 数据质量指标
+        self.quality_metrics = {}
+        
+    def load_data(self, verbose=True):
+        """
+        加载MovieLens数据集
+        
+        参数:
+            verbose (bool): 是否显示加载进度
+        
+        返回:
+            self: 处理器实例
+        """
+        if verbose:
+            print("开始加载MovieLens数据集...")
+        
+        # 定义最优内存数据类型
+        ratings_dtypes = {
+            'user_id': np.int32,
+            'movie_id': np.int32,
+            'rating': np.float32,
+            'timestamp': np.int64
+        }
+        
+        users_dtypes = {
+            'user_id': np.int32,
+            'gender': 'category',
+            'age': np.int8,
+            'occupation': np.int8,
+            'zip_code': 'category'
+        }
+        
+        # 使用分块读取大文件
+        # 评分数据
+        if verbose:
+            print("加载评分数据 (ratings.dat)...")
+        
+        try:
+            ratings_chunks = pd.read_csv(
+                os.path.join(self.data_path, 'ratings.dat'),
+                sep='::', 
+                names=['user_id', 'movie_id', 'rating', 'timestamp'],
+                engine='python',
+                dtype=ratings_dtypes,
+                chunksize=250000  # 分块加载，每块250K行
+            )
+            
+            # 合并数据块
+            ratings_list = []
+            for chunk in tqdm(ratings_chunks, desc="读取评分数据") if verbose else ratings_chunks:
+                ratings_list.append(chunk)
+            
+            self.ratings = pd.concat(ratings_list)
+            
+            # 转换时间戳为日期时间
+            self.ratings['datetime'] = pd.to_datetime(self.ratings['timestamp'], unit='s')
+            
+            # 提取时间特征
+            self.ratings['year'] = self.ratings['datetime'].dt.year
+            self.ratings['month'] = self.ratings['datetime'].dt.month
+            self.ratings['dayofweek'] = self.ratings['datetime'].dt.dayofweek
+            self.ratings['hour'] = self.ratings['datetime'].dt.hour
+            
+            if verbose:
+                print(f"评分数据加载完成: {len(self.ratings)} 条记录")
+        
+        except Exception as e:
+            print(f"加载评分数据时出错: {e}")
+            raise
+        
+        # 用户数据
+        if verbose:
+            print("加载用户数据 (users.dat)...")
+        
+        try:
+            self.users = pd.read_csv(
+                os.path.join(self.data_path, 'users.dat'),
+                sep='::',
+                names=['user_id', 'gender', 'age', 'occupation', 'zip_code'],
+                engine='python',
+                dtype=users_dtypes
+            )
+            
+            if verbose:
+                print(f"用户数据加载完成: {len(self.users)} 条记录")
+        
+        except Exception as e:
+            print(f"加载用户数据时出错: {e}")
+            raise
+        
+        # 电影数据
+        if verbose:
+            print("加载电影数据 (movies.dat)...")
+            
+        try:
+            self.movies = pd.read_csv(
+                os.path.join(self.data_path, 'movies.dat'),
+                sep='::',
+                names=['movie_id', 'title', 'genres'],
+                engine='python',
+                encoding='latin-1'  # 处理特殊字符
+            )
+            
+            if verbose:
+                print(f"电影数据加载完成: {len(self.movies)} 条记录")
+        
+        except Exception as e:
+            print(f"加载电影数据时出错: {e}")
+            raise
+            
+        return self
+    
+    def clean_data(self, verbose=True):
+        """
+        清洗数据集
+        
+        参数:
+            verbose (bool): 是否显示处理进度
+            
+        返回:
+            self: 处理器实例
+        """
+        if any(df is None for df in [self.ratings, self.users, self.movies]):
+            raise ValueError("请先加载数据 (使用 load_data 方法)")
+        
+        if verbose:
+            print("开始数据清洗流程...")
+            
+        # 1. 记录原始数据量
+        original_counts = {
+            'ratings': len(self.ratings),
+            'users': len(self.users),
+            'movies': len(self.movies)
+        }
+        
+        # 2. 检测并处理重复记录
+        if verbose:
+            print("检测重复记录...")
+            
+        ratings_duplicates = self.ratings.duplicated(subset=['user_id', 'movie_id']).sum()
+        if ratings_duplicates > 0:
+            if verbose:
+                print(f"发现 {ratings_duplicates} 条重复评分记录，保留最新评分...")
+            
+            # 对于重复评分，保留最新的一条
+            self.ratings = self.ratings.sort_values('timestamp').drop_duplicates(
+                subset=['user_id', 'movie_id'], 
+                keep='last'
+            )
+        
+        # 3. 检测并处理异常值
+        if verbose:
+            print("检测评分异常值...")
+            
+        # 检查评分范围 (应在1-5之间)
+        invalid_ratings = ((self.ratings['rating'] < 1) | (self.ratings['rating'] > 5)).sum()
+        if invalid_ratings > 0:
+            if verbose:
+                print(f"发现 {invalid_ratings} 条评分超出有效范围 (1-5)...")
+                
+            # 将异常评分限制在有效范围内
+            self.ratings['rating'] = self.ratings['rating'].clip(1, 5)
+            
+        # 4. 提取电影信息
+        if verbose:
+            print("处理电影标题和年份信息...")
+            
+        # 从电影标题中提取年份
+        year_pattern = re.compile(r'\((\d{4})\)$')
+        
+        # 创建新列存储提取的信息
+        self.movies['year'] = np.nan
+        self.movies['clean_title'] = self.movies['title']
+        
+        for idx, title in enumerate(self.movies['title']):
+            match = year_pattern.search(title)
+            if match:
+                year = int(match.group(1))
+                self.movies.at[idx, 'year'] = year
+                self.movies.at[idx, 'clean_title'] = title[:match.start()].strip()
+        
+        # 将年份转换为整数类型
+        self.movies['year'] = self.movies['year'].astype('Int64')  # 允许NaN值的整数类型
+        
+        # 5. 处理流派数据
+        if verbose:
+            print("处理电影流派信息...")
+            
+        # 创建one-hot编码的流派特征
+        all_genres = set()
+        for genres in self.movies['genres']:
+            all_genres.update(genres.split('|'))
+            
+        all_genres = sorted(list(all_genres))
+        
+        # 为每个流派创建独立列
+        for genre in all_genres:
+            self.movies[f'genre_{genre}'] = self.movies['genres'].apply(
+                lambda x: 1 if genre in x.split('|') else 0
+            )
+        
+        # 计算每部电影的流派数量
+        self.movies['genre_count'] = self.movies['genres'].apply(lambda x: len(x.split('|')))
+        
+        # 6. 处理用户特征
+        if verbose:
+            print("处理用户人口统计特征...")
+            
+        # 性别编码
+        self.users['gender_code'] = self.users['gender'].map({'M': 1, 'F': 0})
+        
+        # 对年龄段进行标准化处理
+        age_mapping = {
+            1: 'Under 18',
+            18: '18-24',
+            25: '25-34',
+            35: '35-44',
+            45: '45-49',
+            50: '50-55',
+            56: '56+'
+        }
+        
+        self.users['age_group'] = self.users['age'].map(age_mapping)
+        
+        # 创建职业名称映射
+        occupation_mapping = {
+            0: 'other',
+            1: 'academic/educator',
+            2: 'artist',
+            3: 'clerical/admin',
+            4: 'college/grad student',
+            5: 'customer service',
+            6: 'doctor/health care',
+            7: 'executive/managerial',
+            8: 'farmer',
+            9: 'homemaker',
+            10: 'K-12 student',
+            11: 'lawyer',
+            12: 'programmer',
+            13: 'retired',
+            14: 'sales/marketing',
+            15: 'scientist',
+            16: 'self-employed',
+            17: 'technician/engineer',
+            18: 'tradesman/craftsman',
+            19: 'unemployed',
+            20: 'writer'
+        }
+        
+        self.users['occupation_name'] = self.users['occupation'].map(occupation_mapping)
+        
+        # 7. 确保引用完整性
+        if verbose:
+            print("验证数据引用完整性...")
+            
+        # 检查评分表中的用户和电影是否都存在于相应的数据集
+        ratings_users = set(self.ratings['user_id'])
+        ratings_movies = set(self.ratings['movie_id'])
+        
+        valid_users = set(self.users['user_id'])
+        valid_movies = set(self.movies['movie_id'])
+        
+        invalid_users = ratings_users - valid_users
+        invalid_movies = ratings_movies - valid_movies
+        
+        if invalid_users:
+            if verbose:
+                print(f"评分数据中发现 {len(invalid_users)} 个无效用户ID")
+            
+            # 删除包含无效用户ID的评分
+            self.ratings = self.ratings[~self.ratings['user_id'].isin(invalid_users)]
+            
+        if invalid_movies:
+            if verbose:
+                print(f"评分数据中发现 {len(invalid_movies)} 个无效电影ID")
+                
+            # 删除包含无效电影ID的评分
+            self.ratings = self.ratings[~self.ratings['movie_id'].isin(invalid_movies)]
+        
+        # 8. 计算用户和电影统计特征
+        if verbose:
+            print("计算用户和电影统计特征...")
+            
+        # 用户统计特征
+        user_stats = self.ratings.groupby('user_id').agg(
+            rating_count=('rating', 'count'),
+            avg_rating=('rating', 'mean'),
+            rating_std=('rating', 'std'),
+            min_rating=('rating', 'min'),
+            max_rating=('rating', 'max'),
+            latest_rating=('timestamp', 'max')
+        ).reset_index()
+        
+        # 处理可能的NaN值
+        user_stats['rating_std'] = user_stats['rating_std'].fillna(0)
+        
+        # 合并回用户数据集
+        self.users = pd.merge(self.users, user_stats, on='user_id', how='left')
+        
+        # 电影统计特征
+        movie_stats = self.ratings.groupby('movie_id').agg(
+            rating_count=('rating', 'count'),
+            avg_rating=('rating', 'mean'),
+            rating_std=('rating', 'std'),
+            min_rating=('rating', 'min'),
+            max_rating=('rating', 'max'),
+            latest_rating=('timestamp', 'max')
+        ).reset_index()
+        
+        # 处理可能的NaN值
+        movie_stats['rating_std'] = movie_stats['rating_std'].fillna(0)
+        
+        # 合并回电影数据集
+        self.movies = pd.merge(self.movies, movie_stats, on='movie_id', how='left')
+        
+        # 9. 记录数据清洗结果
+        cleaned_counts = {
+            'ratings': len(self.ratings),
+            'users': len(self.users),
+            'movies': len(self.movies)
+        }
+        
+        self.quality_metrics = {
+            'original_counts': original_counts,
+            'cleaned_counts': cleaned_counts,
+            'removed_ratings': original_counts['ratings'] - cleaned_counts['ratings'],
+            'duplicate_ratings': ratings_duplicates,
+            'invalid_ratings': invalid_ratings,
+            'invalid_users': len(invalid_users),
+            'invalid_movies': len(invalid_movies)
+        }
+        
+        if verbose:
+            print("数据清洗完成!")
+            print(f"原始评分数: {original_counts['ratings']}, 清洗后: {cleaned_counts['ratings']}")
+            print(f"移除的评分: {self.quality_metrics['removed_ratings']}")
+            
+        return self
+    
+    def create_rating_matrix(self, sparse=True):
+        """
+        创建用户-电影评分矩阵
+        
+        参数:
+            sparse (bool): 是否创建稀疏矩阵
+            
+        返回:
+            pd.DataFrame or scipy.sparse.csr_matrix: 评分矩阵
+        """
+        if self.ratings is None:
+            raise ValueError("请先加载和清洗数据")
+            
+        # 使用pivot创建评分矩阵
+        rating_matrix = self.ratings.pivot(
+            index='user_id', 
+            columns='movie_id', 
+            values='rating'
+        )
+        
+        # 保存原始评分矩阵
+        self.rating_matrix = rating_matrix
+        
+        # 计算稀疏度
+        sparsity = 1.0 - rating_matrix.count().sum() / (rating_matrix.shape[0] * rating_matrix.shape[1])
+        print(f"评分矩阵稀疏度: {sparsity:.4f}")
+        
+        # 如果需要稀疏表示
+        if sparse:
+            # 创建COO稀疏矩阵并转换为CSR格式
+            sparse_data = self.ratings['rating'].values
+            row_indices = self.ratings['user_id'].astype('category').cat.codes.values
+            col_indices = self.ratings['movie_id'].astype('category').cat.codes.values
+            
+            # 保存用户ID和电影ID的映射关系
+            self.user_id_map = dict(zip(
+                np.unique(self.ratings['user_id']), 
+                np.unique(row_indices)
+            ))
+            self.movie_id_map = dict(zip(
+                np.unique(self.ratings['movie_id']), 
+                np.unique(col_indices)
+            ))
+            
+            # 逆映射
+            self.user_id_reverse_map = {v: k for k, v in self.user_id_map.items()}
+            self.movie_id_reverse_map = {v: k for k, v in self.movie_id_map.items()}
+            
+            # 创建稀疏矩阵
+            self.rating_sparse = csr_matrix(
+                (sparse_data, (row_indices, col_indices)),
+                shape=(len(self.user_id_map), len(self.movie_id_map))
+            )
+            
+            print(f"稀疏矩阵大小: {self.rating_sparse.shape}, 非零元素: {self.rating_sparse.nnz}")
+            return self.rating_sparse
+        
+        return self.rating_matrix
+    
+    def analyze_data_quality(self, plot=True):
+        """
+        分析数据质量并生成报告
+        
+        参数:
+            plot (bool): 是否生成可视化图表
+            
+        返回:
+            dict: 数据质量指标
+        """
+        if any(df is None for df in [self.ratings, self.users, self.movies]):
+            raise ValueError("请先加载和清洗数据")
+            
+        print("===== 数据质量分析报告 =====")
+        
+        # 1. 基础统计
+        print("\n1. 基础统计:")
+        print(f"用户数: {len(self.users)}")
+        print(f"电影数: {len(self.movies)}")
+        print(f"评分数: {len(self.ratings)}")
+        
+        # 评分稀疏度
+        total_possible = len(self.users) * len(self.movies)
+        actual_ratings = len(self.ratings)
+        sparsity = 1 - (actual_ratings / total_possible)
+        print(f"评分稀疏度: {sparsity:.6f} ({sparsity:.2%})")
+        
+        # 2. 评分分布
+        print("\n2. 评分分布:")
+        rating_counts = self.ratings['rating'].value_counts().sort_index()
+        for rating, count in rating_counts.items():
+            print(f"  {rating}星: {count} ({count/len(self.ratings):.2%})")
+            
+        # 3. 用户行为分析
+        print("\n3. 用户行为分析:")
+        user_rating_counts = self.ratings.groupby('user_id')['rating'].count()
+        print(f"  平均每用户评分数: {user_rating_counts.mean():.2f}")
+        print(f"  中位数每用户评分数: {user_rating_counts.median():.2f}")
+        print(f"  最小每用户评分数: {user_rating_counts.min()}")
+        print(f"  最大每用户评分数: {user_rating_counts.max()}")
+        
+        # 4. 电影流行度分析
+        print("\n4. 电影流行度分析:")
+        movie_rating_counts = self.ratings.groupby('movie_id')['rating'].count()
+        print(f"  平均每电影评分数: {movie_rating_counts.mean():.2f}")
+        print(f"  中位数每电影评分数: {movie_rating_counts.median():.2f}")
+        print(f"  最小每电影评分数: {movie_rating_counts.min()}")
+        print(f"  最大每电影评分数: {movie_rating_counts.max()}")
+        
+        # 计算低评分电影比例 (长尾分布)
+        low_rated_movies = (movie_rating_counts < 20).sum()
+        print(f"  评分少于20次的电影: {low_rated_movies} ({low_rated_movies/len(movie_rating_counts):.2%})")
+        
+        # 5. 时间趋势分析
+        print("\n5. 时间趋势分析:")
+        min_date = self.ratings['datetime'].min()
+        max_date = self.ratings['datetime'].max()
+        print(f"  数据时间范围: {min_date.date()} 至 {max_date.date()}")
+        
+        # 按月统计评分数量
+        monthly_ratings = self.ratings.set_index('datetime').resample('M')['rating'].count()
+        print(f"  月均评分数: {monthly_ratings.mean():.2f}")
+        
+        # 6. 用户人口统计
+        print("\n6. 用户人口统计:")
+        gender_dist = self.users['gender'].value_counts()
+        print(f"  性别分布: 男 {gender_dist.get('M', 0)} ({gender_dist.get('M', 0)/len(self.users):.2%}), " 
+              f"女 {gender_dist.get('F', 0)} ({gender_dist.get('F', 0)/len(self.users):.2%})")
+              
+        age_dist = self.users['age_group'].value_counts().sort_index()
+        print("  年龄分布:")
+        for age_group, count in age_dist.items():
+            print(f"    {age_group}: {count} ({count/len(self.users):.2%})")
+            
+        # 7. 电影类型分析
+        print("\n7. 电影类型分析:")
+        genre_columns = [col for col in self.movies.columns if col.startswith('genre_') and col != 'genre_count']
+        genre_counts = self.movies[genre_columns].sum().sort_values(ascending=False)
+        
+        print("  流派分布 (前10):")
+        for genre, count in genre_counts.iloc[:10].items():
+            genre_name = genre.replace('genre_', '')
+            print(f"    {genre_name}: {count} ({count/len(self.movies):.2%})")
+            
+        # 生成可视化图表
+        if plot:
+            print("\n生成数据质量可视化图表...")
+            
+            # 创建4x2网格布局
+            fig, axs = plt.subplots(4, 2, figsize=(18, 20))
+            fig.suptitle('MovieLens数据集质量分析', fontsize=16)
+            
+            # 1. 评分分布直方图
+            sns.histplot(self.ratings['rating'], bins=9, kde=True, ax=axs[0, 0])
+            axs[0, 0].set_title('评分分布')
+            axs[0, 0].set_xlabel('评分值')
+            axs[0, 0].set_ylabel('频次')
+            
+            # 2. 用户评分数量分布
+            sns.histplot(user_rating_counts, bins=50, kde=True, ax=axs[0, 1])
+            axs[0, 1].set_title('用户评分数量分布')
+            axs[0, 1].set_xlabel('每用户评分数')
+            axs[0, 1].set_ylabel('用户数')
+            axs[0, 1].set_yscale('log')
+            
+            # 3. 电影评分数量分布
+            sns.histplot(movie_rating_counts, bins=50, kde=True, ax=axs[1, 0])
+            axs[1, 0].set_title('电影评分数量分布')
+            axs[1, 0].set_xlabel('每电影评分数')
+            axs[1, 0].set_ylabel('电影数')
+            axs[1, 0].set_yscale('log')
+            
+            # 4. 月度评分数量趋势
+            monthly_ratings.plot(ax=axs[1, 1])
+            axs[1, 1].set_title('月度评分数量趋势')
+            axs[1, 1].set_xlabel('日期')
+            axs[1, 1].set_ylabel('评分数')
+            
+            # 5. 用户年龄分布
+            sns.countplot(y='age_group', data=self.users, ax=axs[2, 0], order=age_dist.index)
+            axs[2, 0].set_title('用户年龄分布')
+            axs[2, 0].set_xlabel('用户数')
+            axs[2, 0].set_ylabel('年龄组')
+            
+            # 6. 用户职业分布
+            occupation_counts = self.users['occupation_name'].value_counts().sort_values(ascending=False).head(15)
+            sns.barplot(y=occupation_counts.index, x=occupation_counts.values, ax=axs[2, 1])
+            axs[2, 1].set_title('用户职业分布 (前15)')
+            axs[2, 1].set_xlabel('用户数')
+            axs[2, 1].set_ylabel('职业')
+            
+            # 7. 电影流派分布
+            top_genres = genre_counts.head(15).sort_values()
+            sns.barplot(y=top_genres.index.str.replace('genre_', ''), x=top_genres.values, ax=axs[3, 0])
+            axs[3, 0].set_title('电影流派分布 (前15)')
+            axs[3, 0].set_xlabel('电影数')
+            axs[3, 0].set_ylabel('流派')
+            
+            # 8. 电影发行年份分布
+            decade_bins = pd.cut(self.movies['year'].dropna(), 
+                                bins=[1900, 1950, 1960, 1970, 1980, 1990, 2000],
+                                labels=['1950前', '1950s', '1960s', '1970s', '1980s', '1990s'])
+            decade_counts = decade_bins.value_counts().sort_index()
+            sns.barplot(x=decade_counts.index, y=decade_counts.values, ax=axs[3, 1])
+            axs[3, 1].set_title('电影发行年代分布')
+            axs[3, 1].set_xlabel('年代')
+            axs[3, 1].set_ylabel('电影数')
+            
+            plt.tight_layout(rect=[0, 0, 1, 0.97])
+            plt.savefig('movielens_data_quality.png', dpi=300, bbox_inches='tight')
+            plt.show()
+            
+        # 构建并返回质量指标
+        quality_metrics = {
+            'users_count': len(self.users),
+            'movies_count': len(self.movies),
+            'ratings_count': len(self.ratings),
+            'sparsity': sparsity,
+            'avg_rating': self.ratings['rating'].mean(),
+            'median_rating': self.ratings['rating'].median(),
+            'ratings_per_user': {
+                'mean': user_rating_counts.mean(),
+                'median': user_rating_counts.median(),
+                'min': user_rating_counts.min(),
+                'max': user_rating_counts.max()
+            },
+            'ratings_per_movie': {
+                'mean': movie_rating_counts.mean(),
+                'median': movie_rating_counts.median(),
+                'min': movie_rating_counts.min(),
+                'max': movie_rating_counts.max()
+            },
+            'rating_distribution': rating_counts.to_dict(),
+            'low_rated_movies_ratio': low_rated_movies/len(movie_rating_counts),
+            'date_range': {
+                'start': min_date.strftime('%Y-%m-%d'),
+                'end': max_date.strftime('%Y-%m-%d'),
+                'days': (max_date - min_date).days
+            },
+            'gender_ratio': {
+                'male': gender_dist.get('M', 0) / len(self.users),
+                'female': gender_dist.get('F', 0) / len(self.users),
+            }
+        }
+        
+        # 更新对象的质量指标
+        self.quality_metrics.update(quality_metrics)
+        
+        return self.quality_metrics
+    
+    def split_train_test(self, test_ratio=0.2, method='time', seed=42):
+        """
+        将数据集拆分为训练集和测试集
+        
+        参数:
+            test_ratio (float): 测试集比例 (0-1之间)
+            method (str): 拆分方法 ('random', 'time', 'user')
+            seed (int): 随机种子
+            
+        返回:
+            tuple: (train_ratings, test_ratings)
+        """
+        if self.ratings is None:
+            raise ValueError("请先加载数据")
+            
+        np.random.seed(seed)
+        
+        if method == 'time':
+            # 基于时间的拆分
+            time_threshold = self.ratings['timestamp'].quantile(1 - test_ratio)
+            train_ratings = self.ratings[self.ratings['timestamp'] < time_threshold]
+            test_ratings = self.ratings[self.ratings['timestamp'] >= time_threshold]
+            
+        elif method == 'user':
+            # 对每个用户的评分进行拆分
+            train_ratings = pd.DataFrame()
+            test_ratings = pd.DataFrame()
+            
+            for user_id, user_ratings in self.ratings.groupby('user_id'):
+                # 对每个用户，确保至少有10个评分用于训练
+                n_user_ratings = len(user_ratings)
+                n_test = int(test_ratio * n_user_ratings)
+                
+                # 确保测试集不为空且训练集至少有10个样本
+                n_test = min(max(1, n_test), n_user_ratings - 10)
+                
+                # 随机选择测试评分
+                test_indices = np.random.choice(user_ratings.index, size=n_test, replace=False)
+                
+                user_test = user_ratings.loc[test_indices]
+                user_train = user_ratings.drop(test_indices)
+                
+                train_ratings = pd.concat([train_ratings, user_train])
+                test_ratings = pd.concat([test_ratings, user_test])
+                
+        else:  # 默认随机拆分
+            # 随机拆分
+            shuffled_indices = np.random.permutation(len(self.ratings))
+            test_size = int(test_ratio * len(self.ratings))
+            
+            test_indices = shuffled_indices[:test_size]
+            train_indices = shuffled_indices[test_size:]
+            
+            train_ratings = self.ratings.iloc[train_indices]
+            test_ratings = self.ratings.iloc[test_indices]
+        
+        print(f"训练集: {len(train_ratings)} 评分 ({len(train_ratings)/len(self.ratings):.2%})")
+        print(f"测试集: {len(test_ratings)} 评分 ({len(test_ratings)/len(self.ratings):.2%})")
+        
+        # 验证拆分的有效性
+        train_users = set(train_ratings['user_id'])
+        train_movies = set(train_ratings['movie_id'])
+        
+        test_users = set(test_ratings['user_id'])
+        test_movies = set(test_ratings['movie_id'])
+        
+        missing_users = test_users - train_users
+        missing_movies = test_movies - train_movies
+        
+        if missing_users:
+            print(f"警告: 测试集中有 {len(missing_users)} 个用户在训练集中不存在")
+            
+        if missing_movies:
+            print(f"警告: 测试集中有 {len(missing_movies)} 部电影在训练集中不存在")
+        
+        return train_ratings, test_ratings
+    
+    def save_processed_data(self, output_dir='./processed'):
+        """
+        保存处理后的数据
+        
+        参数:
+            output_dir (str): 输出目录
+            
+        返回:
+            bool: 是否保存成功
+        """
+        if any(df is None for df in [self.ratings, self.users, self.movies]):
+            raise ValueError("请先加载和清洗数据")
+            
+        # 创建输出目录
+        os.makedirs(output_dir, exist_ok=True)
+        
+        try:
+            # 保存DataFrame到CSV
+            self.ratings.to_csv(os.path.join(output_dir, 'ratings_processed.csv'), index=False)
+            self.users.to_csv(os.path.join(output_dir, 'users_processed.csv'), index=False)
+            self.movies.to_csv(os.path.join(output_dir, 'movies_processed.csv'), index=False)
+            
+            # 保存评分矩阵
+            if self.rating_matrix is not None:
+                self.rating_matrix.to_pickle(os.path.join(output_dir, 'rating_matrix.pkl'))
+                
+            # 保存稀疏矩阵 (如果已创建)
+            if self.rating_sparse is not None:
+                import scipy.sparse as sp
+                sp.save_npz(os.path.join(output_dir, 'rating_sparse.npz'), self.rating_sparse)
+                
+                # 保存ID映射
+                np.save(os.path.join(output_dir, 'user_id_map.npy'), self.user_id_map)
+                np.save(os.path.join(output_dir, 'movie_id_map.npy'), self.movie_id_map)
+            
+            # 保存质量报告
+            if self.quality_metrics:
+                import json
+                with open(os.path.join(output_dir, 'quality_metrics.json'), 'w') as f:
+                    json.dump(self.quality_metrics, f, indent=4, default=str)
+                    
+            print(f"处理后的数据已保存至 {output_dir}")
+            return True
+            
+        except Exception as e:
+            print(f"保存数据时出错: {e}")
+            return False