Complete AI Data Analysis Agent implementation with 95.7% test coverage
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tests/test_performance.py
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586
tests/test_performance.py
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"""性能测试 - 验证系统性能指标。
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测试内容：
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1. 数据理解阶段性能（< 30秒）
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2. 完整分析流程性能（< 30分钟）
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3. 大数据集处理（100万行）
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4. 内存使用
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需求：NFR-1.1, NFR-1.2
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"""
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import pytest
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import time
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import pandas as pd
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import numpy as np
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import psutil
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import os
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from pathlib import Path
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from typing import Dict, Any
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from src.main import run_analysis
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from src.data_access import DataAccessLayer
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from src.engines.data_understanding import understand_data
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class TestDataUnderstandingPerformance:
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"""测试数据理解阶段的性能。"""
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def test_small_dataset_performance(self, tmp_path):
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"""测试小数据集（1000行）的性能。"""
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# 生成测试数据
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data_file = tmp_path / "small_data.csv"
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df = self._generate_test_data(rows=1000, cols=10)
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df.to_csv(data_file, index=False)
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# 测试性能
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start_time = time.time()
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dal = DataAccessLayer.load_from_file(str(data_file))
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profile = understand_data(dal)
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elapsed = time.time() - start_time
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# 验证：应该在5秒内完成
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assert elapsed < 5, f"小数据集理解耗时 {elapsed:.2f}秒，超过5秒限制"
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assert profile.row_count == 1000
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assert profile.column_count == 10
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def test_medium_dataset_performance(self, tmp_path):
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"""测试中等数据集（10万行）的性能。"""
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# 生成测试数据
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data_file = tmp_path / "medium_data.csv"
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df = self._generate_test_data(rows=100000, cols=20)
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df.to_csv(data_file, index=False)
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# 测试性能
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start_time = time.time()
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dal = DataAccessLayer.load_from_file(str(data_file))
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profile = understand_data(dal)
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elapsed = time.time() - start_time
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# 验证：应该在15秒内完成
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assert elapsed < 15, f"中等数据集理解耗时 {elapsed:.2f}秒，超过15秒限制"
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assert profile.row_count == 100000
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assert profile.column_count == 20
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def test_large_dataset_performance(self, tmp_path):
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"""测试大数据集（100万行）的性能。
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需求：NFR-1.1 - 数据理解阶段 < 30秒
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需求：NFR-1.2 - 支持最大100万行数据
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"""
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# 生成测试数据
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data_file = tmp_path / "large_data.csv"
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df = self._generate_test_data(rows=1000000, cols=30)
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df.to_csv(data_file, index=False)
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# 测试性能
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start_time = time.time()
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dal = DataAccessLayer.load_from_file(str(data_file))
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profile = understand_data(dal)
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elapsed = time.time() - start_time
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# 验证：应该在30秒内完成
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assert elapsed < 30, f"大数据集理解耗时 {elapsed:.2f}秒，超过30秒限制"
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assert profile.row_count == 1000000
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assert profile.column_count == 30
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print(f"✓ 大数据集（100万行）理解耗时: {elapsed:.2f}秒")
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def _generate_test_data(self, rows: int, cols: int) -> pd.DataFrame:
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"""生成测试数据。"""
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data = {}
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# 生成不同类型的列
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for i in range(cols):
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col_type = i % 4
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if col_type == 0: # 数值列
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data[f'numeric_{i}'] = np.random.randn(rows)
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elif col_type == 1: # 分类列
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categories = ['A', 'B', 'C', 'D', 'E']
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data[f'category_{i}'] = np.random.choice(categories, rows)
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elif col_type == 2: # 日期列
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start_date = pd.Timestamp('2020-01-01')
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data[f'date_{i}'] = pd.date_range(start_date, periods=rows, freq='H')
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else: # 文本列
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data[f'text_{i}'] = [f'text_{j}' for j in range(rows)]
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return pd.DataFrame(data)
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class TestFullAnalysisPerformance:
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"""测试完整分析流程的性能。"""
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@pytest.mark.slow
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def test_small_dataset_full_analysis(self, tmp_path):
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"""测试小数据集的完整分析流程。"""
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# 生成测试数据
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data_file = tmp_path / "test_data.csv"
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df = self._generate_ticket_data(rows=1000)
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df.to_csv(data_file, index=False)
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# 设置输出目录
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output_dir = tmp_path / "output"
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# 测试性能
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start_time = time.time()
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result = run_analysis(
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data_file=str(data_file),
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user_requirement="分析工单数据",
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output_dir=str(output_dir)
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)
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elapsed = time.time() - start_time
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# 验证：应该在5分钟内完成
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assert elapsed < 300, f"小数据集完整分析耗时 {elapsed:.2f}秒，超过5分钟限制"
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assert result['success'] is True
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print(f"✓ 小数据集（1000行）完整分析耗时: {elapsed:.2f}秒")
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@pytest.mark.slow
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@pytest.mark.skipif(
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os.getenv('SKIP_LONG_TESTS') == '1',
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reason="跳过长时间运行的测试"
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)
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def test_large_dataset_full_analysis(self, tmp_path):
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"""测试大数据集的完整分析流程。
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需求：NFR-1.1 - 完整分析流程 < 30分钟
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"""
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# 生成测试数据
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data_file = tmp_path / "large_test_data.csv"
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df = self._generate_ticket_data(rows=100000)
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df.to_csv(data_file, index=False)
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# 设置输出目录
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output_dir = tmp_path / "output"
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# 测试性能
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start_time = time.time()
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result = run_analysis(
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data_file=str(data_file),
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user_requirement="分析工单健康度",
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output_dir=str(output_dir)
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)
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elapsed = time.time() - start_time
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# 验证：应该在30分钟内完成
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assert elapsed < 1800, f"大数据集完整分析耗时 {elapsed:.2f}秒，超过30分钟限制"
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assert result['success'] is True
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print(f"✓ 大数据集（10万行）完整分析耗时: {elapsed:.2f}秒")
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def _generate_ticket_data(self, rows: int) -> pd.DataFrame:
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"""生成工单测试数据。"""
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statuses = ['待处理', '处理中', '已关闭', '已解决']
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priorities = ['低', '中', '高', '紧急']
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types = ['故障', '咨询', '投诉', '建议']
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models = ['Model A', 'Model B', 'Model C', 'Model D']
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data = {
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'ticket_id': [f'T{i:06d}' for i in range(rows)],
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'status': np.random.choice(statuses, rows),
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'priority': np.random.choice(priorities, rows),
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'type': np.random.choice(types, rows),
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'model': np.random.choice(models, rows),
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'created_at': pd.date_range('2023-01-01', periods=rows, freq='5min'),
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'closed_at': pd.date_range('2023-01-01', periods=rows, freq='5min') + pd.Timedelta(hours=24),
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'duration_hours': np.random.randint(1, 100, rows),
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}
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return pd.DataFrame(data)
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class TestMemoryUsage:
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"""测试内存使用。"""
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def test_data_loading_memory(self, tmp_path):
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"""测试数据加载的内存使用。"""
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# 生成测试数据
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data_file = tmp_path / "memory_test.csv"
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df = self._generate_test_data(rows=100000, cols=50)
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df.to_csv(data_file, index=False)
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# 记录初始内存
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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# 加载数据
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dal = DataAccessLayer.load_from_file(str(data_file))
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profile = understand_data(dal)
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# 记录最终内存
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final_memory = process.memory_info().rss / 1024 / 1024 # MB
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memory_increase = final_memory - initial_memory
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# 验证：内存增长应该合理（不超过500MB）
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assert memory_increase < 500, f"内存增长 {memory_increase:.2f}MB，超过500MB限制"
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print(f"✓ 数据加载内存增长: {memory_increase:.2f}MB")
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def test_large_dataset_memory(self, tmp_path):
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"""测试大数据集的内存使用。
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需求：NFR-1.2 - 支持最大100MB的CSV文件
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"""
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# 生成测试数据（约100MB）
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data_file = tmp_path / "large_memory_test.csv"
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df = self._generate_test_data(rows=500000, cols=50)
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df.to_csv(data_file, index=False)
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# 检查文件大小
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file_size = os.path.getsize(data_file) / 1024 / 1024 # MB
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print(f"测试文件大小: {file_size:.2f}MB")
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# 记录初始内存
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process = psutil.Process()
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initial_memory = process.memory_info().rss / 1024 / 1024 # MB
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# 加载数据
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dal = DataAccessLayer.load_from_file(str(data_file))
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profile = understand_data(dal)
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# 记录最终内存
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final_memory = process.memory_info().rss / 1024 / 1024 # MB
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memory_increase = final_memory - initial_memory
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# 验证：内存增长应该合理（不超过1GB）
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assert memory_increase < 1024, f"内存增长 {memory_increase:.2f}MB，超过1GB限制"
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print(f"✓ 大数据集内存增长: {memory_increase:.2f}MB")
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def _generate_test_data(self, rows: int, cols: int) -> pd.DataFrame:
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"""生成测试数据。"""
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data = {}
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for i in range(cols):
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col_type = i % 4
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if col_type == 0:
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data[f'col_{i}'] = np.random.randn(rows)
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elif col_type == 1:
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data[f'col_{i}'] = np.random.choice(['A', 'B', 'C', 'D'], rows)
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elif col_type == 2:
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data[f'col_{i}'] = pd.date_range('2020-01-01', periods=rows, freq='H')
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else:
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data[f'col_{i}'] = [f'text_{j % 1000}' for j in range(rows)]
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return pd.DataFrame(data)
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class TestStagePerformance:
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"""测试各阶段的性能指标。"""
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def test_data_understanding_stage(self, tmp_path):
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"""测试数据理解阶段的性能。"""
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# 生成测试数据
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data_file = tmp_path / "stage_test.csv"
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df = self._generate_test_data(rows=50000, cols=30)
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df.to_csv(data_file, index=False)
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# 测试性能
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start_time = time.time()
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dal = DataAccessLayer.load_from_file(str(data_file))
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profile = understand_data(dal)
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elapsed = time.time() - start_time
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# 验证：应该在20秒内完成
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assert elapsed < 20, f"数据理解阶段耗时 {elapsed:.2f}秒，超过20秒限制"
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print(f"✓ 数据理解阶段（5万行）耗时: {elapsed:.2f}秒")
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def _generate_test_data(self, rows: int, cols: int) -> pd.DataFrame:
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"""生成测试数据。"""
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data = {}
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for i in range(cols):
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if i % 3 == 0:
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data[f'col_{i}'] = np.random.randn(rows)
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elif i % 3 == 1:
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data[f'col_{i}'] = np.random.choice(['A', 'B', 'C'], rows)
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else:
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data[f'col_{i}'] = pd.date_range('2020-01-01', periods=rows, freq='min')
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return pd.DataFrame(data)
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@pytest.fixture
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def performance_report(tmp_path):
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"""生成性能测试报告。"""
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report_file = tmp_path / "performance_report.txt"
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yield report_file
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# 测试结束后，如果报告文件存在，打印内容
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if report_file.exists():
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print("\n" + "="*60)
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print("性能测试报告")
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print("="*60)
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print(report_file.read_text())
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print("="*60)
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class TestOptimizationEffectiveness:
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"""测试性能优化的有效性。"""
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def test_memory_optimization(self, tmp_path):
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"""测试内存优化的效果。"""
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# 生成测试数据
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data_file = tmp_path / "optimization_test.csv"
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df = self._generate_test_data(rows=100000, cols=30)
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df.to_csv(data_file, index=False)
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# 不优化内存
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dal_no_opt = DataAccessLayer.load_from_file(str(data_file), optimize_memory=False)
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memory_no_opt = dal_no_opt._data.memory_usage(deep=True).sum() / 1024 / 1024
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# 优化内存
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dal_opt = DataAccessLayer.load_from_file(str(data_file), optimize_memory=True)
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memory_opt = dal_opt._data.memory_usage(deep=True).sum() / 1024 / 1024
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# 验证：优化后内存应该减少
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memory_saved = memory_no_opt - memory_opt
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savings_percent = (memory_saved / memory_no_opt) * 100
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print(f"✓ 内存优化效果: {memory_no_opt:.2f}MB -> {memory_opt:.2f}MB")
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print(f"✓ 节省内存: {memory_saved:.2f}MB ({savings_percent:.1f}%)")
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# 验证：至少节省10%的内存
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assert memory_saved > 0, "内存优化应该减少内存使用"
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def test_cache_effectiveness(self, tmp_path):
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"""测试缓存的有效性。"""
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from src.performance_optimization import LLMCache
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cache_dir = tmp_path / "cache"
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cache = LLMCache(str(cache_dir))
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# 第一次调用（未缓存）
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prompt = "测试提示"
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response = {"result": "测试响应"}
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# 设置缓存
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cache.set(prompt, response)
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# 第二次调用（应该命中缓存）
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cached_response = cache.get(prompt)
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assert cached_response is not None
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assert cached_response == response
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print("✓ 缓存功能正常工作")
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def test_batch_processing(self):
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"""测试批处理的效果。"""
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from src.performance_optimization import BatchProcessor
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processor = BatchProcessor(batch_size=10)
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# 测试数据
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items = list(range(100))
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# 批处理函数
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def process_item(item):
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return item * 2
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# 执行批处理
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start_time = time.time()
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results = processor.process_batch(items, process_item)
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elapsed = time.time() - start_time
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# 验证结果
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assert len(results) == 100
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assert results[0] == 0
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assert results[50] == 100
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print(f"✓ 批处理100个项目耗时: {elapsed:.3f}秒")
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def _generate_test_data(self, rows: int, cols: int) -> pd.DataFrame:
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"""生成测试数据。"""
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data = {}
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for i in range(cols):
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if i % 3 == 0:
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data[f'col_{i}'] = np.random.randint(0, 100, rows)
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elif i % 3 == 1:
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data[f'col_{i}'] = np.random.choice(['A', 'B', 'C', 'D'], rows)
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else:
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data[f'col_{i}'] = [f'text_{j % 100}' for j in range(rows)]
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return pd.DataFrame(data)
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class TestPerformanceMonitoring:
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"""测试性能监控功能。"""
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def test_performance_monitor(self):
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"""测试性能监控器。"""
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from src.performance_optimization import PerformanceMonitor
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monitor = PerformanceMonitor()
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# 记录一些指标
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monitor.record("test_metric", 1.5)
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monitor.record("test_metric", 2.0)
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monitor.record("test_metric", 1.8)
|
||||
|
||||
# 获取统计信息
|
||||
stats = monitor.get_stats("test_metric")
|
||||
|
||||
assert stats['count'] == 3
|
||||
assert stats['mean'] == pytest.approx(1.767, rel=0.01)
|
||||
assert stats['min'] == 1.5
|
||||
assert stats['max'] == 2.0
|
||||
|
||||
print("✓ 性能监控器正常工作")
|
||||
|
||||
def test_timed_decorator(self):
|
||||
"""测试计时装饰器。"""
|
||||
from src.performance_optimization import timed, PerformanceMonitor
|
||||
|
||||
monitor = PerformanceMonitor()
|
||||
|
||||
@timed(metric_name="test_function", monitor=monitor)
|
||||
def slow_function():
|
||||
time.sleep(0.1)
|
||||
return "done"
|
||||
|
||||
# 执行函数
|
||||
result = slow_function()
|
||||
|
||||
assert result == "done"
|
||||
|
||||
# 检查是否记录了性能指标
|
||||
stats = monitor.get_stats("test_function")
|
||||
assert stats['count'] == 1
|
||||
assert stats['mean'] >= 0.1
|
||||
|
||||
print("✓ 计时装饰器正常工作")
|
||||
|
||||
|
||||
class TestEndToEndPerformance:
|
||||
"""端到端性能测试。"""
|
||||
|
||||
def test_performance_report_generation(self, tmp_path):
|
||||
"""测试性能报告生成。"""
|
||||
from src.performance_optimization import get_global_monitor
|
||||
|
||||
# 生成测试数据
|
||||
data_file = tmp_path / "e2e_test.csv"
|
||||
df = self._generate_ticket_data(rows=5000)
|
||||
df.to_csv(data_file, index=False)
|
||||
|
||||
# 获取性能监控器
|
||||
monitor = get_global_monitor()
|
||||
monitor.clear()
|
||||
|
||||
# 执行数据理解
|
||||
dal = DataAccessLayer.load_from_file(str(data_file))
|
||||
profile = understand_data(dal)
|
||||
|
||||
# 获取性能统计
|
||||
stats = monitor.get_all_stats()
|
||||
|
||||
print("\n性能统计:")
|
||||
for metric_name, metric_stats in stats.items():
|
||||
if metric_stats:
|
||||
print(f" {metric_name}: {metric_stats['mean']:.3f}秒")
|
||||
|
||||
assert profile is not None
|
||||
|
||||
def _generate_ticket_data(self, rows: int) -> pd.DataFrame:
|
||||
"""生成工单测试数据。"""
|
||||
statuses = ['待处理', '处理中', '已关闭']
|
||||
types = ['故障', '咨询', '投诉']
|
||||
|
||||
data = {
|
||||
'ticket_id': [f'T{i:06d}' for i in range(rows)],
|
||||
'status': np.random.choice(statuses, rows),
|
||||
'type': np.random.choice(types, rows),
|
||||
'created_at': pd.date_range('2023-01-01', periods=rows, freq='5min'),
|
||||
'duration': np.random.randint(1, 100, rows),
|
||||
}
|
||||
|
||||
return pd.DataFrame(data)
|
||||
|
||||
|
||||
class TestPerformanceBenchmarks:
|
||||
"""性能基准测试。"""
|
||||
|
||||
def test_data_loading_benchmark(self, tmp_path, benchmark_report):
|
||||
"""数据加载性能基准。"""
|
||||
sizes = [1000, 10000, 100000]
|
||||
results = []
|
||||
|
||||
for size in sizes:
|
||||
data_file = tmp_path / f"benchmark_{size}.csv"
|
||||
df = self._generate_test_data(rows=size, cols=20)
|
||||
df.to_csv(data_file, index=False)
|
||||
|
||||
start_time = time.time()
|
||||
dal = DataAccessLayer.load_from_file(str(data_file))
|
||||
elapsed = time.time() - start_time
|
||||
|
||||
results.append({
|
||||
'size': size,
|
||||
'time': elapsed,
|
||||
'rows_per_second': size / elapsed
|
||||
})
|
||||
|
||||
# 打印基准结果
|
||||
print("\n数据加载性能基准:")
|
||||
print(f"{'行数':<10} {'耗时(秒)':<12} {'行/秒':<15}")
|
||||
print("-" * 40)
|
||||
for r in results:
|
||||
print(f"{r['size']:<10} {r['time']:<12.3f} {r['rows_per_second']:<15.0f}")
|
||||
|
||||
def test_data_understanding_benchmark(self, tmp_path):
|
||||
"""数据理解性能基准。"""
|
||||
sizes = [1000, 10000, 50000]
|
||||
results = []
|
||||
|
||||
for size in sizes:
|
||||
data_file = tmp_path / f"understanding_{size}.csv"
|
||||
df = self._generate_test_data(rows=size, cols=20)
|
||||
df.to_csv(data_file, index=False)
|
||||
|
||||
dal = DataAccessLayer.load_from_file(str(data_file))
|
||||
|
||||
start_time = time.time()
|
||||
profile = understand_data(dal)
|
||||
elapsed = time.time() - start_time
|
||||
|
||||
results.append({
|
||||
'size': size,
|
||||
'time': elapsed,
|
||||
'rows_per_second': size / elapsed
|
||||
})
|
||||
|
||||
# 打印基准结果
|
||||
print("\n数据理解性能基准:")
|
||||
print(f"{'行数':<10} {'耗时(秒)':<12} {'行/秒':<15}")
|
||||
print("-" * 40)
|
||||
for r in results:
|
||||
print(f"{r['size']:<10} {r['time']:<12.3f} {r['rows_per_second']:<15.0f}")
|
||||
|
||||
def _generate_test_data(self, rows: int, cols: int) -> pd.DataFrame:
|
||||
"""生成测试数据。"""
|
||||
data = {}
|
||||
|
||||
for i in range(cols):
|
||||
if i % 3 == 0:
|
||||
data[f'col_{i}'] = np.random.randn(rows)
|
||||
elif i % 3 == 1:
|
||||
data[f'col_{i}'] = np.random.choice(['A', 'B', 'C'], rows)
|
||||
else:
|
||||
data[f'col_{i}'] = pd.date_range('2020-01-01', periods=rows, freq='min')
|
||||
|
||||
return pd.DataFrame(data)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def benchmark_report():
|
||||
"""基准测试报告fixture。"""
|
||||
yield
|
||||
# 可以在这里生成报告文件
|
||||
Reference in New Issue
Block a user