Complete AI Data Analysis Agent implementation with 95.7% test coverage

tests/test_data_understanding.py

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+"""数据理解引擎的单元测试。"""
+
+import pytest
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+from src.engines.data_understanding import (
+    generate_basic_stats,
+    understand_data,
+    _infer_column_type,
+    _infer_data_type,
+    _identify_key_fields,
+    _evaluate_data_quality,
+    _get_sample_values,
+    _generate_column_statistics
+)
+from src.models import DataProfile, ColumnInfo
+
+
+class TestGenerateBasicStats:
+    """测试基础统计生成。"""
+    
+    def test_basic_functionality(self):
+        """测试基本功能。"""
+        df = pd.DataFrame({
+            'id': [1, 2, 3, 4, 5],
+            'name': ['A', 'B', 'C', 'D', 'E'],
+            'value': [10.5, 20.3, 30.1, 40.8, 50.2]
+        })
+        
+        stats = generate_basic_stats(df, 'test.csv')
+        
+        assert stats['file_path'] == 'test.csv'
+        assert stats['row_count'] == 5
+        assert stats['column_count'] == 3
+        assert len(stats['columns']) == 3
+    
+    def test_empty_dataframe(self):
+        """测试空 DataFrame。"""
+        df = pd.DataFrame()
+        
+        stats = generate_basic_stats(df, 'empty.csv')
+        
+        assert stats['row_count'] == 0
+        assert stats['column_count'] == 0
+        assert len(stats['columns']) == 0
+
+
+class TestInferColumnType:
+    """测试列类型推断。"""
+    
+    def test_numeric_column(self):
+        """测试数值列。"""
+        col = pd.Series([1, 2, 3, 4, 5])
+        dtype = _infer_column_type(col)
+        assert dtype == 'numeric'
+    
+    def test_categorical_column(self):
+        """测试分类列。"""
+        col = pd.Series(['A', 'B', 'A', 'C', 'B', 'A', 'A', 'B', 'C', 'A'])  # 10个值，3个唯一值，比例30%
+        dtype = _infer_column_type(col)
+        assert dtype == 'categorical'
+    
+    def test_datetime_column(self):
+        """测试日期时间列。"""
+        col = pd.Series(pd.date_range('2020-01-01', periods=5))
+        dtype = _infer_column_type(col)
+        assert dtype == 'datetime'
+    
+    def test_text_column(self):
+        """测试文本列（唯一值多）。"""
+        col = pd.Series([f'text_{i}' for i in range(100)])
+        dtype = _infer_column_type(col)
+        assert dtype == 'text'
+
+
+class TestInferDataType:
+    """测试数据类型推断。"""
+    
+    def test_ticket_data(self):
+        """测试工单数据识别。"""
+        columns = [
+            ColumnInfo(name='ticket_id', dtype='text', missing_rate=0.0, unique_count=100),
+            ColumnInfo(name='status', dtype='categorical', missing_rate=0.0, unique_count=5),
+            ColumnInfo(name='created_at', dtype='datetime', missing_rate=0.0, unique_count=100),
+        ]
+        
+        data_type = _infer_data_type(columns)
+        assert data_type == 'ticket'
+    
+    def test_sales_data(self):
+        """测试销售数据识别。"""
+        columns = [
+            ColumnInfo(name='order_id', dtype='text', missing_rate=0.0, unique_count=100),
+            ColumnInfo(name='product', dtype='categorical', missing_rate=0.0, unique_count=10),
+            ColumnInfo(name='amount', dtype='numeric', missing_rate=0.0, unique_count=50),
+        ]
+        
+        data_type = _infer_data_type(columns)
+        assert data_type == 'sales'
+    
+    def test_user_data(self):
+        """测试用户数据识别。"""
+        columns = [
+            ColumnInfo(name='user_id', dtype='text', missing_rate=0.0, unique_count=100),
+            ColumnInfo(name='name', dtype='text', missing_rate=0.0, unique_count=100),
+            ColumnInfo(name='email', dtype='text', missing_rate=0.0, unique_count=100),
+        ]
+        
+        data_type = _infer_data_type(columns)
+        assert data_type == 'user'
+    
+    def test_unknown_data(self):
+        """测试未知数据类型。"""
+        columns = [
+            ColumnInfo(name='col1', dtype='numeric', missing_rate=0.0, unique_count=100),
+            ColumnInfo(name='col2', dtype='numeric', missing_rate=0.0, unique_count=100),
+        ]
+        
+        data_type = _infer_data_type(columns)
+        assert data_type == 'unknown'
+
+
+class TestIdentifyKeyFields:
+    """测试关键字段识别。"""
+    
+    def test_time_fields(self):
+        """测试时间字段识别。"""
+        columns = [
+            ColumnInfo(name='created_at', dtype='datetime', missing_rate=0.0, unique_count=100),
+            ColumnInfo(name='closed_at', dtype='datetime', missing_rate=0.0, unique_count=100),
+        ]
+        
+        key_fields = _identify_key_fields(columns)
+        
+        assert 'created_at' in key_fields
+        assert 'closed_at' in key_fields
+        assert '创建时间' in key_fields['created_at']
+        assert '完成时间' in key_fields['closed_at']
+    
+    def test_status_field(self):
+        """测试状态字段识别。"""
+        columns = [
+            ColumnInfo(name='status', dtype='categorical', missing_rate=0.0, unique_count=5),
+        ]
+        
+        key_fields = _identify_key_fields(columns)
+        
+        assert 'status' in key_fields
+        assert '状态' in key_fields['status']
+    
+    def test_id_field(self):
+        """测试ID字段识别。"""
+        columns = [
+            ColumnInfo(name='ticket_id', dtype='text', missing_rate=0.0, unique_count=100),
+        ]
+        
+        key_fields = _identify_key_fields(columns)
+        
+        assert 'ticket_id' in key_fields
+        assert '标识符' in key_fields['ticket_id']
+
+
+class TestEvaluateDataQuality:
+    """测试数据质量评估。"""
+    
+    def test_high_quality_data(self):
+        """测试高质量数据。"""
+        columns = [
+            ColumnInfo(name='col1', dtype='numeric', missing_rate=0.0, unique_count=100),
+            ColumnInfo(name='col2', dtype='categorical', missing_rate=0.0, unique_count=5),
+        ]
+        
+        quality_score = _evaluate_data_quality(columns, row_count=100)
+        
+        assert quality_score >= 80
+    
+    def test_low_quality_data(self):
+        """测试低质量数据（高缺失率）。"""
+        columns = [
+            ColumnInfo(name='col1', dtype='numeric', missing_rate=0.8, unique_count=20),
+            ColumnInfo(name='col2', dtype='categorical', missing_rate=0.9, unique_count=2),
+        ]
+        
+        quality_score = _evaluate_data_quality(columns, row_count=100)
+        
+        assert quality_score < 50
+    
+    def test_empty_data(self):
+        """测试空数据。"""
+        columns = []
+        
+        quality_score = _evaluate_data_quality(columns, row_count=0)
+        
+        assert quality_score == 0.0
+
+
+class TestGetSampleValues:
+    """测试示例值获取。"""
+    
+    def test_basic_functionality(self):
+        """测试基本功能。"""
+        col = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        
+        samples = _get_sample_values(col, max_samples=5)
+        
+        assert len(samples) <= 5
+        assert all(isinstance(s, (int, float)) for s in samples)
+    
+    def test_with_null_values(self):
+        """测试包含空值的情况。"""
+        col = pd.Series([1, 2, None, 4, None, 6])
+        
+        samples = _get_sample_values(col, max_samples=5)
+        
+        assert len(samples) <= 4  # 排除了空值
+    
+    def test_datetime_values(self):
+        """测试日期时间值。"""
+        col = pd.Series(pd.date_range('2020-01-01', periods=5))
+        
+        samples = _get_sample_values(col, max_samples=3)
+        
+        assert len(samples) <= 3
+        assert all(isinstance(s, str) for s in samples)
+
+
+class TestGenerateColumnStatistics:
+    """测试列统计信息生成。"""
+    
+    def test_numeric_statistics(self):
+        """测试数值列统计。"""
+        col = pd.Series([1, 2, 3, 4, 5])
+        
+        stats = _generate_column_statistics(col, 'numeric')
+        
+        assert 'mean' in stats
+        assert 'median' in stats
+        assert 'std' in stats
+        assert 'min' in stats
+        assert 'max' in stats
+        assert stats['mean'] == 3.0
+        assert stats['min'] == 1.0
+        assert stats['max'] == 5.0
+    
+    def test_categorical_statistics(self):
+        """测试分类列统计。"""
+        col = pd.Series(['A', 'B', 'A', 'C', 'A'])
+        
+        stats = _generate_column_statistics(col, 'categorical')
+        
+        assert 'most_common' in stats
+        assert 'most_common_count' in stats
+        assert stats['most_common'] == 'A'
+        assert stats['most_common_count'] == 3
+    
+    def test_datetime_statistics(self):
+        """测试日期时间列统计。"""
+        col = pd.Series(pd.date_range('2020-01-01', periods=10))
+        
+        stats = _generate_column_statistics(col, 'datetime')
+        
+        assert 'min_date' in stats
+        assert 'max_date' in stats
+        assert 'date_range_days' in stats
+    
+    def test_text_statistics(self):
+        """测试文本列统计。"""
+        col = pd.Series(['hello', 'world', 'test'])
+        
+        stats = _generate_column_statistics(col, 'text')
+        
+        assert 'avg_length' in stats
+        assert 'max_length' in stats
+
+
+class TestUnderstandData:
+    """测试完整的数据理解流程。"""
+    
+    def test_basic_functionality(self):
+        """测试基本功能。"""
+        df = pd.DataFrame({
+            'ticket_id': [1, 2, 3, 4, 5],
+            'status': ['open', 'closed', 'open', 'pending', 'closed'],
+            'created_at': pd.date_range('2020-01-01', periods=5),
+            'amount': [100, 200, 150, 300, 250]
+        })
+        
+        profile = understand_data('test.csv', data=df)
+        
+        assert isinstance(profile, DataProfile)
+        assert profile.row_count == 5
+        assert profile.column_count == 4
+        assert len(profile.columns) == 4
+        assert profile.inferred_type in ['ticket', 'sales', 'user', 'unknown']
+        assert 0 <= profile.quality_score <= 100
+        assert len(profile.summary) > 0
+    
+    def test_with_missing_values(self):
+        """测试包含缺失值的数据。"""
+        df = pd.DataFrame({
+            'col1': [1, 2, None, 4, 5],
+            'col2': ['A', None, 'C', 'D', None]
+        })
+        
+        profile = understand_data('test.csv', data=df)
+        
+        assert profile.row_count == 5
+        # 质量分数应该因为缺失值而降低
+        assert profile.quality_score < 100