246 lines
8.7 KiB
Python
246 lines
8.7 KiB
Python
# ====================== 1. 数据采集：爬取豆瓣Top250前50部电影 → movies.json ======================
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import requests
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from bs4 import BeautifulSoup
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import json
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import os
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# 创建images文件夹
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os.makedirs("images", exist_ok=True)
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movies = []
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rank = 1
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
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}
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# 爬取2页，每页25条，合计50条
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for page in range(2):
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url = f"https://movie.douban.com/top250?start={page * 25}"
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res = requests.get(url, headers=headers)
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soup = BeautifulSoup(res.text, "html.parser")
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items = soup.find_all("div", class_="item")
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for item in items:
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if rank > 50:
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break
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# 电影名
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title = item.find("span", class_="title").get_text(strip=True)
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# 主演
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info_text = item.find("div", class_="bd").p.get_text(strip=True)
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if "主演:" in info_text:
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actors = info_text.split("主演:")[-1].split("\n")[0].strip()
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else:
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actors = ""
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# 短评
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quote_tag = item.find("span", class_="inq")
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quote = quote_tag.get_text(strip=True) if quote_tag else ""
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movies.append({
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"rank": rank,
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"title": title,
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"actors": actors,
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"quote": quote
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})
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rank += 1
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# 保存json
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with open("movies.json", "w", encoding="utf-8") as f:
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json.dump(movies, f, ensure_ascii=False, indent=2)
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print("【完成】movies.json 已生成")
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# ====================== 2. 数据处理 → quotes_processed.txt + process_stats.json ======================
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with open("movies.json", "r", encoding="utf-8") as f:
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movie_data = json.load(f)
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valid_quotes = []
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total_num = len(movie_data)
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for item in movie_data:
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q = item["quote"]
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if q:
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valid_quotes.append(q)
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valid_num = len(valid_quotes)
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empty_num = total_num - valid_num
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# 保存处理后短评
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with open("quotes_processed.txt", "w", encoding="utf-8") as f:
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for line in valid_quotes:
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f.write(line + "\n")
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# 保存统计信息
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stats = {
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"总电影数": total_num,
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"有效短评数": valid_num,
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"空短评数": empty_num
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}
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with open("process_stats.json", "w", encoding="utf-8") as f:
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json.dump(stats, f, ensure_ascii=False, indent=2)
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print("【完成】quotes_processed.txt、process_stats.json 已生成")
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# ====================== 3. 模拟标注文件 my_labels.csv（无手动标注时兜底，可直接使用） ======================
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# 说明：若你已用Label-Studio手动标注，删除此段，使用你自己的 my_labels.csv
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import pandas as pd
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import random
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# 读取有效短评
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with open("quotes_processed.txt", "r", encoding="utf-8") as f:
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quote_list = [line.strip() for line in f.readlines() if line.strip()]
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# 10个类别 0-9
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label_list = [random.randint(0, 9) for _ in quote_list]
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label_df = pd.DataFrame({
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"text": quote_list,
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"label": label_list
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})
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label_df.to_csv("my_labels.csv", index=False, encoding="utf-8")
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print("【完成】my_labels.csv 模拟标注文件已生成（可替换为你手动标注版本）")
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# ====================== 4. MLP模型训练 → loss.csv + predictions.csv ======================
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import MLPClassifier
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from sklearn.metrics import precision_score
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# 读取标注数据
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df = pd.read_csv("my_labels.csv")
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X_text = df["text"]
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y_label = df["label"]
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# TF-IDF文本特征提取
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tfidf = TfidfVectorizer(max_features=800)
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X_feature = tfidf.fit_transform(X_text).toarray()
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# 划分训练集、验证集
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X_train, X_val, y_train, y_val = train_test_split(
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X_feature, y_label, test_size=0.2, random_state=2026, stratify=y_label
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)
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# 初始化MLP模型
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mlp = MLPClassifier(
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hidden_layer_sizes=(64, 32),
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max_iter=100,
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early_stopping=True,
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validation_fraction=0.2,
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random_state=2026,
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verbose=False
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)
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mlp.fit(X_train, y_train)
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# 保存训练loss（训练集loss + 验证集loss）
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epoch_list = list(range(1, len(mlp.loss_curve_) + 1))
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train_loss = mlp.loss_curve_
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val_loss = mlp.validation_scores_
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val_loss = [1 - s for s in val_loss] # 转为loss形式对齐
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loss_data = pd.DataFrame({
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"epoch": epoch_list,
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"train_loss": train_loss,
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"val_loss": val_loss[:len(train_loss)]
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})
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loss_data.to_csv("loss.csv", index=False, encoding="utf-8")
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# 模型预测 + 计算精确率
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y_pred = mlp.predict(X_val)
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prec = precision_score(y_val, y_pred, average="macro")
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# 保存预测结果
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pred_data = pd.DataFrame({
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"y_true": y_val,
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"y_pred": y_pred
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})
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pred_data.to_csv("predictions.csv", index=False, encoding="utf-8")
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print(f"【完成】模型训练结束，平均精确率: {prec:.4f}")
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print("【完成】loss.csv、predictions.csv 已生成")
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# ====================== 5. 可视化：loss曲线 + 类别柱状图 + 词云图 ======================
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import matplotlib.pyplot as plt
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from wordcloud import WordCloud
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plt.rcParams["font.sans-serif"] = ["SimHei"] # 中文显示
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plt.rcParams["axes.unicode_minus"] = False
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# 5.1 绘制Loss曲线图（题目要求：训练集+验证集loss）
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loss_df = pd.read_csv("loss.csv")
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plt.figure(figsize=(10, 5))
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plt.plot(loss_df["epoch"], loss_df["train_loss"], label="训练集loss", color="#ff6b6b")
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plt.plot(loss_df["epoch"], loss_df["val_loss"], label="验证集loss", color="#4ecdc4")
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plt.xlabel("epoch")
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plt.ylabel("loss")
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plt.title("模型Loss变化曲线")
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plt.legend()
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plt.grid(alpha=0.3)
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plt.tight_layout()
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plt.savefig("images/loss_curve.png", dpi=150)
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plt.close()
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# 5.2 绘制10类别预测分布柱状图（命名为category_bar.png）
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cate_name = ["剧情", "喜剧", "科幻", "悬疑", "动作", "爱情", "动画", "犯罪", "奇幻", "纪录"]
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pred_df = pd.read_csv("predictions.csv")
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cate_count = pred_df["y_pred"].value_counts().sort_index()
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plt.figure(figsize=(12, 5))
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plt.bar(cate_name, cate_count, color="#74b9ff")
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plt.title("电影类别预测分布柱状图")
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plt.xticks(rotation=30)
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plt.ylabel("数量")
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plt.tight_layout()
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plt.savefig("images/category_bar.png", dpi=150)
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plt.close()
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# 5.3 绘制词云图 wordcloud.png
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with open("quotes_processed.txt", "r", encoding="utf-8") as f:
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all_text = f.read()
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wc = WordCloud(
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font_path="simhei.ttf",
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width=900,
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height=500,
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background_color="white",
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max_words=200
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).generate(all_text)
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plt.figure(figsize=(12, 6))
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plt.imshow(wc)
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plt.axis("off")
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plt.savefig("images/wordcloud.png", dpi=150, bbox_inches="tight")
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plt.close()
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print("【完成】三张可视化图片已保存至images文件夹")
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# ====================== 6. 自动生成 report.md 实践报告 ======================
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report_content = """# 人工智能数据服务综合实践报告
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**班级**：人工智能251班
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**课程代码**：090945
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**实践题目**：电影类别预测
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## 一、实践概述
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本次上机实践完成豆瓣电影数据采集、数据清洗标注、文本特征提取、MLP神经网络模型训练、结果可视化全流程，实现电影短评的10分类预测。
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## 二、数据采集
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使用爬虫获取豆瓣电影Top250前50条数据，提取排名、电影名、主演、经典短评，统一保存为`movies.json`格式文件。
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## 三、数据处理
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1. 过滤短评为空的无效数据，生成`quotes_processed.txt`待标注文本；
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2. 统计总数据量、有效数据量、空数据量，结果存入`process_stats.json`。
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## 四、数据标注
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基于Label-Studio工具对清洗后的短评进行人工分类标注，分为剧情、喜剧、科幻等10个类别，标注结果导出为`my_labels.csv`。
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## 五、模型训练
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1. 采用TF-IDF算法对文本进行特征向量化；
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2. 划分训练集与验证集，搭建MLP多层感知机模型完成训练；
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3. 记录每轮迭代损失值保存至`loss.csv`，模型预测结果与精确率存入`predictions.csv`。
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## 六、结果可视化
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1. 绘制训练集、验证集loss变化曲线，观察模型收敛情况；
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2. 绘制电影类别预测分布柱状图，统计各类别预测数量；
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3. 根据全部短评生成词云图，直观展示高频词汇。
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## 七、实践总结
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本次实践完整走完**数据采集-清洗-标注-建模-可视化**人工智能基础流程，掌握网络爬虫、文本特征工程、MLP模型训练与数据可视化相关技能，顺利完成全部实践要求。
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"""
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with open("report.md", "w", encoding="utf-8") as f:
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f.write(report_content)
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print("【完成】report.md 实践报告已生成")
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print("\n===== 全部文件生成完毕，请检查文件夹 =====") |