754 lines
29 KiB
Python
754 lines
29 KiB
Python
# -*- coding: utf-8 -*-
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"""
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MovieLens数据集编织与清洗工具
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----------------------------------------
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作者: 首席数据科学家
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版本: 1.0.0
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"""
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import os
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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from scipy.sparse import csr_matrix
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from datetime import datetime
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import re
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import warnings
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from tqdm import tqdm
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# 设置pandas显示选项
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pd.set_option('display.max_columns', 50)
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pd.set_option('display.width', 1000)
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warnings.filterwarnings('ignore')
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class MovieLensProcessor:
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"""MovieLens数据集处理器"""
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def __init__(self, data_path='./'):
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"""
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初始化处理器
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参数:
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data_path (str): 数据文件所在目录
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"""
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self.data_path = data_path
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self.ratings = None
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self.users = None
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self.movies = None
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self.rating_matrix = None
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self.rating_sparse = None
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# 数据质量指标
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self.quality_metrics = {}
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def load_data(self, verbose=True):
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"""
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加载MovieLens数据集
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参数:
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verbose (bool): 是否显示加载进度
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返回:
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self: 处理器实例
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"""
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if verbose:
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print("开始加载MovieLens数据集...")
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# 定义最优内存数据类型
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ratings_dtypes = {
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'user_id': np.int32,
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'movie_id': np.int32,
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'rating': np.float32,
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'timestamp': np.int64
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}
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users_dtypes = {
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'user_id': np.int32,
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'gender': 'category',
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'age': np.int8,
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'occupation': np.int8,
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'zip_code': 'category'
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}
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# 使用分块读取大文件
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# 评分数据
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if verbose:
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print("加载评分数据 (ratings.dat)...")
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try:
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ratings_chunks = pd.read_csv(
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os.path.join(self.data_path, 'ratings.dat'),
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sep='::',
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names=['user_id', 'movie_id', 'rating', 'timestamp'],
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engine='python',
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dtype=ratings_dtypes,
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chunksize=250000 # 分块加载，每块250K行
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)
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# 合并数据块
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ratings_list = []
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for chunk in tqdm(ratings_chunks, desc="读取评分数据") if verbose else ratings_chunks:
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ratings_list.append(chunk)
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self.ratings = pd.concat(ratings_list)
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# 转换时间戳为日期时间
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self.ratings['datetime'] = pd.to_datetime(self.ratings['timestamp'], unit='s')
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# 提取时间特征
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self.ratings['year'] = self.ratings['datetime'].dt.year
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self.ratings['month'] = self.ratings['datetime'].dt.month
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self.ratings['dayofweek'] = self.ratings['datetime'].dt.dayofweek
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self.ratings['hour'] = self.ratings['datetime'].dt.hour
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if verbose:
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print(f"评分数据加载完成: {len(self.ratings)} 条记录")
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except Exception as e:
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print(f"加载评分数据时出错: {e}")
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raise
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# 用户数据
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if verbose:
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print("加载用户数据 (users.dat)...")
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try:
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self.users = pd.read_csv(
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os.path.join(self.data_path, 'users.dat'),
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sep='::',
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names=['user_id', 'gender', 'age', 'occupation', 'zip_code'],
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engine='python',
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dtype=users_dtypes
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)
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if verbose:
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print(f"用户数据加载完成: {len(self.users)} 条记录")
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except Exception as e:
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print(f"加载用户数据时出错: {e}")
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raise
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# 电影数据
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if verbose:
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print("加载电影数据 (movies.dat)...")
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try:
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self.movies = pd.read_csv(
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os.path.join(self.data_path, 'movies.dat'),
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sep='::',
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names=['movie_id', 'title', 'genres'],
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engine='python',
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encoding='latin-1' # 处理特殊字符
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)
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if verbose:
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print(f"电影数据加载完成: {len(self.movies)} 条记录")
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except Exception as e:
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print(f"加载电影数据时出错: {e}")
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raise
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return self
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def clean_data(self, verbose=True):
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"""
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清洗数据集
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参数:
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verbose (bool): 是否显示处理进度
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返回:
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self: 处理器实例
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"""
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if any(df is None for df in [self.ratings, self.users, self.movies]):
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raise ValueError("请先加载数据 (使用 load_data 方法)")
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if verbose:
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print("开始数据清洗流程...")
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# 1. 记录原始数据量
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original_counts = {
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'ratings': len(self.ratings),
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'users': len(self.users),
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'movies': len(self.movies)
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}
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# 2. 检测并处理重复记录
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if verbose:
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print("检测重复记录...")
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ratings_duplicates = self.ratings.duplicated(subset=['user_id', 'movie_id']).sum()
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if ratings_duplicates > 0:
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if verbose:
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print(f"发现 {ratings_duplicates} 条重复评分记录，保留最新评分...")
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# 对于重复评分，保留最新的一条
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self.ratings = self.ratings.sort_values('timestamp').drop_duplicates(
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subset=['user_id', 'movie_id'],
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keep='last'
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)
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# 3. 检测并处理异常值
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if verbose:
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print("检测评分异常值...")
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# 检查评分范围 (应在1-5之间)
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invalid_ratings = ((self.ratings['rating'] < 1) | (self.ratings['rating'] > 5)).sum()
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if invalid_ratings > 0:
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if verbose:
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print(f"发现 {invalid_ratings} 条评分超出有效范围 (1-5)...")
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# 将异常评分限制在有效范围内
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self.ratings['rating'] = self.ratings['rating'].clip(1, 5)
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# 4. 提取电影信息
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if verbose:
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print("处理电影标题和年份信息...")
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# 从电影标题中提取年份
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year_pattern = re.compile(r'\((\d{4})\)$')
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# 创建新列存储提取的信息
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self.movies['year'] = np.nan
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self.movies['clean_title'] = self.movies['title']
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for idx, title in enumerate(self.movies['title']):
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match = year_pattern.search(title)
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if match:
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year = int(match.group(1))
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self.movies.at[idx, 'year'] = year
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self.movies.at[idx, 'clean_title'] = title[:match.start()].strip()
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# 将年份转换为整数类型
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self.movies['year'] = self.movies['year'].astype('Int64') # 允许NaN值的整数类型
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# 5. 处理流派数据
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if verbose:
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print("处理电影流派信息...")
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# 创建one-hot编码的流派特征
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all_genres = set()
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for genres in self.movies['genres']:
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all_genres.update(genres.split('|'))
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all_genres = sorted(list(all_genres))
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# 为每个流派创建独立列
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for genre in all_genres:
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self.movies[f'genre_{genre}'] = self.movies['genres'].apply(
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lambda x: 1 if genre in x.split('|') else 0
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)
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# 计算每部电影的流派数量
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self.movies['genre_count'] = self.movies['genres'].apply(lambda x: len(x.split('|')))
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# 6. 处理用户特征
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if verbose:
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print("处理用户人口统计特征...")
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# 性别编码
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self.users['gender_code'] = self.users['gender'].map({'M': 1, 'F': 0})
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# 对年龄段进行标准化处理
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age_mapping = {
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1: 'Under 18',
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18: '18-24',
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25: '25-34',
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35: '35-44',
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45: '45-49',
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50: '50-55',
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56: '56+'
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}
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self.users['age_group'] = self.users['age'].map(age_mapping)
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# 创建职业名称映射
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occupation_mapping = {
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0: 'other',
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1: 'academic/educator',
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2: 'artist',
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3: 'clerical/admin',
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4: 'college/grad student',
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5: 'customer service',
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6: 'doctor/health care',
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7: 'executive/managerial',
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8: 'farmer',
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9: 'homemaker',
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10: 'K-12 student',
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11: 'lawyer',
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12: 'programmer',
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13: 'retired',
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14: 'sales/marketing',
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15: 'scientist',
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16: 'self-employed',
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17: 'technician/engineer',
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18: 'tradesman/craftsman',
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19: 'unemployed',
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20: 'writer'
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}
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self.users['occupation_name'] = self.users['occupation'].map(occupation_mapping)
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# 7. 确保引用完整性
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if verbose:
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print("验证数据引用完整性...")
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# 检查评分表中的用户和电影是否都存在于相应的数据集
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ratings_users = set(self.ratings['user_id'])
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ratings_movies = set(self.ratings['movie_id'])
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valid_users = set(self.users['user_id'])
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valid_movies = set(self.movies['movie_id'])
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invalid_users = ratings_users - valid_users
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invalid_movies = ratings_movies - valid_movies
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if invalid_users:
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if verbose:
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print(f"评分数据中发现 {len(invalid_users)} 个无效用户ID")
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# 删除包含无效用户ID的评分
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self.ratings = self.ratings[~self.ratings['user_id'].isin(invalid_users)]
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if invalid_movies:
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if verbose:
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print(f"评分数据中发现 {len(invalid_movies)} 个无效电影ID")
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# 删除包含无效电影ID的评分
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self.ratings = self.ratings[~self.ratings['movie_id'].isin(invalid_movies)]
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# 8. 计算用户和电影统计特征
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if verbose:
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print("计算用户和电影统计特征...")
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# 用户统计特征
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user_stats = self.ratings.groupby('user_id').agg(
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rating_count=('rating', 'count'),
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avg_rating=('rating', 'mean'),
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rating_std=('rating', 'std'),
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min_rating=('rating', 'min'),
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max_rating=('rating', 'max'),
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latest_rating=('timestamp', 'max')
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).reset_index()
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# 处理可能的NaN值
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user_stats['rating_std'] = user_stats['rating_std'].fillna(0)
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# 合并回用户数据集
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self.users = pd.merge(self.users, user_stats, on='user_id', how='left')
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# 电影统计特征
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movie_stats = self.ratings.groupby('movie_id').agg(
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rating_count=('rating', 'count'),
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avg_rating=('rating', 'mean'),
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rating_std=('rating', 'std'),
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min_rating=('rating', 'min'),
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max_rating=('rating', 'max'),
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latest_rating=('timestamp', 'max')
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).reset_index()
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# 处理可能的NaN值
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movie_stats['rating_std'] = movie_stats['rating_std'].fillna(0)
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# 合并回电影数据集
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self.movies = pd.merge(self.movies, movie_stats, on='movie_id', how='left')
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# 9. 记录数据清洗结果
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cleaned_counts = {
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'ratings': len(self.ratings),
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'users': len(self.users),
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'movies': len(self.movies)
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}
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self.quality_metrics = {
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'original_counts': original_counts,
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'cleaned_counts': cleaned_counts,
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'removed_ratings': original_counts['ratings'] - cleaned_counts['ratings'],
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'duplicate_ratings': ratings_duplicates,
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'invalid_ratings': invalid_ratings,
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'invalid_users': len(invalid_users),
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'invalid_movies': len(invalid_movies)
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}
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if verbose:
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print("数据清洗完成!")
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print(f"原始评分数: {original_counts['ratings']}, 清洗后: {cleaned_counts['ratings']}")
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print(f"移除的评分: {self.quality_metrics['removed_ratings']}")
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return self
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def create_rating_matrix(self, sparse=True):
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"""
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创建用户-电影评分矩阵
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参数:
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sparse (bool): 是否创建稀疏矩阵
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返回:
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pd.DataFrame or scipy.sparse.csr_matrix: 评分矩阵
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"""
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if self.ratings is None:
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raise ValueError("请先加载和清洗数据")
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# 使用pivot创建评分矩阵
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rating_matrix = self.ratings.pivot(
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index='user_id',
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columns='movie_id',
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values='rating'
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)
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# 保存原始评分矩阵
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self.rating_matrix = rating_matrix
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# 计算稀疏度
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sparsity = 1.0 - rating_matrix.count().sum() / (rating_matrix.shape[0] * rating_matrix.shape[1])
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print(f"评分矩阵稀疏度: {sparsity:.4f}")
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# 如果需要稀疏表示
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if sparse:
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# 创建COO稀疏矩阵并转换为CSR格式
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sparse_data = self.ratings['rating'].values
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row_indices = self.ratings['user_id'].astype('category').cat.codes.values
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col_indices = self.ratings['movie_id'].astype('category').cat.codes.values
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# 保存用户ID和电影ID的映射关系
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self.user_id_map = dict(zip(
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np.unique(self.ratings['user_id']),
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np.unique(row_indices)
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))
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self.movie_id_map = dict(zip(
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np.unique(self.ratings['movie_id']),
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np.unique(col_indices)
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))
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# 逆映射
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self.user_id_reverse_map = {v: k for k, v in self.user_id_map.items()}
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self.movie_id_reverse_map = {v: k for k, v in self.movie_id_map.items()}
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# 创建稀疏矩阵
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self.rating_sparse = csr_matrix(
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(sparse_data, (row_indices, col_indices)),
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shape=(len(self.user_id_map), len(self.movie_id_map))
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)
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print(f"稀疏矩阵大小: {self.rating_sparse.shape}, 非零元素: {self.rating_sparse.nnz}")
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return self.rating_sparse
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return self.rating_matrix
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def analyze_data_quality(self, plot=True):
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"""
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分析数据质量并生成报告
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参数:
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plot (bool): 是否生成可视化图表
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返回:
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dict: 数据质量指标
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"""
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if any(df is None for df in [self.ratings, self.users, self.movies]):
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raise ValueError("请先加载和清洗数据")
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print("===== 数据质量分析报告 =====")
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# 1. 基础统计
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print("\n1. 基础统计:")
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print(f"用户数: {len(self.users)}")
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print(f"电影数: {len(self.movies)}")
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print(f"评分数: {len(self.ratings)}")
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# 评分稀疏度
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total_possible = len(self.users) * len(self.movies)
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actual_ratings = len(self.ratings)
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sparsity = 1 - (actual_ratings / total_possible)
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print(f"评分稀疏度: {sparsity:.6f} ({sparsity:.2%})")
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# 2. 评分分布
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print("\n2. 评分分布:")
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rating_counts = self.ratings['rating'].value_counts().sort_index()
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for rating, count in rating_counts.items():
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print(f" {rating}星: {count} ({count/len(self.ratings):.2%})")
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# 3. 用户行为分析
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print("\n3. 用户行为分析:")
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user_rating_counts = self.ratings.groupby('user_id')['rating'].count()
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print(f" 平均每用户评分数: {user_rating_counts.mean():.2f}")
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print(f" 中位数每用户评分数: {user_rating_counts.median():.2f}")
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print(f" 最小每用户评分数: {user_rating_counts.min()}")
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print(f" 最大每用户评分数: {user_rating_counts.max()}")
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|
||
# 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
|