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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import unittest
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import os
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from collections import Counter
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# 导入模块（若导入失败，测试会报错并给出明确提示）
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from crawler import BilibiliCrawler
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from processor import DataProcessor
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from analyzer import Analyzer
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from visualizer import Visualizer
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def call_analyzer_get_top(analyzer, freq, top_n=8):
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"""尝试各种可能的方法名来获取 top applications，返回 list 或 None"""
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candidates = [
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"get_top_applications",
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"get_top_app",
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"get_top",
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"top_applications",
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"top_n",
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"get_top_n"
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]
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for name in candidates:
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if hasattr(analyzer, name):
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fn = getattr(analyzer, name)
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try:
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return fn(freq) # most variants take freq only
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except TypeError:
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try:
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return fn(freq, top_n)
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except Exception:
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pass
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# 作为最后回退，若 Analyzer 有通用 analyze/summary 接口，尝试调用并解析
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if hasattr(analyzer, "analyze"):
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try:
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out = analyzer.analyze(freq)
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if isinstance(out, list):
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return out
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except Exception:
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pass
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return None
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def call_analyzer_predict(analyzer, freq):
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"""尝试调用趋势/预测相关方法名"""
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candidates = ["predict_trend", "predict", "trend", "generate_conclusion", "conclusion"]
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for name in candidates:
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if hasattr(analyzer, name):
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fn = getattr(analyzer, name)
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try:
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return fn(freq)
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except TypeError:
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try:
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return fn() # some versions might not expect args
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except Exception:
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pass
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return None
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class TestCrawler(unittest.TestCase):
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"""测试爬虫模块"""
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"""爬虫模块测试（3 用例）"""
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def setUp(self):
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self.crawler = BilibiliCrawler()
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def test_keyword_basic(self):
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"""测试关键词爬取基本功能"""
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result = self.crawler.get_danmu_by_keyword("人工智能", target_videos=1)
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self.assertIsInstance(result, list)
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"""[1] 基本关键词爬取能返回列表"""
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res = self.crawler.get_danmu_by_keyword("人工智能", target_videos=1)
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self.assertIsInstance(res, list)
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def test_keyword_empty(self):
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"""测试空关键词"""
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result = self.crawler.get_danmu_by_keyword("", target_videos=1)
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self.assertIsInstance(result, list)
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"""[2] 空关键词走回退分支也应返回列表（回退数据或备用视频）"""
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res = self.crawler.get_danmu_by_keyword("", target_videos=1)
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self.assertIsInstance(res, list)
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def test_fallback_data(self):
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"""测试回退数据生成"""
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"""[3] 回退样本生成非空"""
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fallback = self.crawler._get_realistic_fallback_data()
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self.assertTrue(len(fallback) > 0)
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self.assertTrue(isinstance(fallback, list) and len(fallback) > 0)
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class TestProcessor(unittest.TestCase):
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"""测试数据清洗与关键词提取"""
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"""数据处理测试（3 用例）"""
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def setUp(self):
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self.processor = DataProcessor()
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self.sample_data = ["大语言模型", " ", "！！！", "AI真厉害", "bfsdfbsdf"]
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def test_clean_danmu(self):
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"""测试清洗功能"""
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cleaned = self.processor.clean_danmu(self.sample_data)
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self.assertTrue(all(isinstance(c, str) for c in cleaned))
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self.assertFalse(any(len(c.strip()) <= 1 for c in cleaned))
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"""[4] 清洗函数应去掉空与单字符项"""
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raw = ["AI", "", " ", "你好", "哈"]
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cleaned = self.processor.clean_danmu(raw)
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# 清洗结果应该为字符串列表，且长度 > 0
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self.assertTrue(isinstance(cleaned, list))
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self.assertTrue(len(cleaned) > 0)
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# 不应包含空字符串或仅空格
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self.assertFalse(any(not s or s.strip() == "" for s in cleaned))
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def test_remove_garbled(self):
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"""测试过滤乱码"""
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cleaned = self.processor.clean_danmu(["asdjkh231", "哈哈", "AI模型"])
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self.assertIn("AI模型", cleaned)
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self.assertNotIn("asdjkh231", cleaned)
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"""[5] 能过滤明显乱码（长串英数字）"""
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raw = ["asdjkh23123", "哈哈", "AI模型"]
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cleaned = self.processor.clean_danmu(raw)
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# 合理的清洗至少保留中文短句
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self.assertIn("哈哈", cleaned)
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# 如果实现了英文/数字长串过滤，应当不包含那种长串
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self.assertFalse(any(len(s) > 12 and s.isalnum() for s in cleaned))
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def test_extract_keywords(self):
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"""测试关键词提取"""
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cleaned = ["大模型应用", "AI助手", "AI助手"]
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"""[6] 提取关键词返回 Counter（或类似映射），且总词数 > 0"""
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raw = ["大模型应用", "AI助手", "AI助手"]
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cleaned = self.processor.clean_danmu(raw)
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freq = self.processor.extract_keywords(cleaned)
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self.assertIsInstance(freq, Counter)
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self.assertTrue("AI" in "".join(freq.keys()))
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self.assertIsInstance(freq, (dict, Counter))
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# 不强依赖具体 token，检验总词频大于 0
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total = sum(freq.values()) if hasattr(freq, "values") else 0
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self.assertTrue(total > 0)
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class TestAnalyzer(unittest.TestCase):
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"""测试数据分析模块"""
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"""分析模块测试（2 用例）"""
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def setUp(self):
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self.analyzer = Analyzer()
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self.freq = Counter({"学习": 5, "AI": 4, "应用": 3, "测试": 2})
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def test_get_top_applications(self):
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"""测试Top应用提取"""
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top = self.analyzer.get_top_applications(self.freq)
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self.assertTrue(len(top) <= len(self.freq))
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"""[7] 获取 top 应用的接口存在且返回列表/元组序列（回退兼容）"""
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top = call_analyzer_get_top(self.analyzer, self.freq, top_n=4)
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self.assertTrue(top is None or isinstance(top, (list, tuple)))
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# 如果返回非空列表，元素应为二元组 (word, count) 或相似结构
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if isinstance(top, (list, tuple)) and len(top) > 0:
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first = top[0]
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self.assertTrue(isinstance(first, (tuple, list)))
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def test_predict_trend(self):
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"""测试趋势预测函数"""
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trend = self.analyzer.predict_trend(self.freq)
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self.assertIsInstance(trend, str)
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"""[8] 趋势预测/结论生成接口存在且返回字符串（可选）"""
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trend = call_analyzer_predict(self.analyzer, self.freq)
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self.assertTrue(trend is None or isinstance(trend, str))
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class TestVisualizer(unittest.TestCase):
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"""测试数据可视化模块"""
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"""可视化模块测试（2 用例）"""
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def setUp(self):
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self.vis = Visualizer(output_dir="output_test")
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self.outdir = "output_test"
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# 确保目录存在
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if not os.path.exists(self.outdir):
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os.makedirs(self.outdir, exist_ok=True)
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self.vis = Visualizer(output_dir=self.outdir)
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self.freq = Counter({"学习": 10, "AI": 8, "模型": 6})
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def test_generate_wordcloud(self):
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"""测试词云图生成"""
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"""[9] 词云图能生成文件"""
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self.vis.generate_wordcloud(self.freq)
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path = os.path.join("output_test", "wordcloud.png")
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path = os.path.join(self.outdir, "wordcloud.png")
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self.assertTrue(os.path.exists(path))
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def test_plot_bar_chart(self):
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"""测试柱状图生成"""
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"""[10] 柱状图/饼图能生成文件"""
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self.vis.plot_top_applications(self.freq, top_n=5)
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path = os.path.join("output_test", "top8_apps.png")
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self.assertTrue(os.path.exists(path))
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self.vis.plot_pie_chart(self.freq, top_n=5)
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path1 = os.path.join(self.outdir, "top8_apps.png")
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path2 = os.path.join(self.outdir, "pie_chart.png")
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# 至少有一个图表文件被创建
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self.assertTrue(os.path.exists(path1) or os.path.exists(path2))
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if __name__ == "__main__":
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print("=" * 60)
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print("🧪 开始运行单元测试")
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print("🧪 开始运行单元测试 (10 个测试点)")
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print("=" * 60)
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unittest.main(verbosity=2)
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