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xming521
72 lines
4.1 KiB
Python
72 lines
4.1 KiB
Python
import pytest
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from weclone.data.clean.get_score import adjust_score_tiered
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# 定义通用的参数
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THRESHOLDS = [0.6, 0.3] # 置信度阈值：>=0.6 高, >=0.3 中, <0.3 低
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DOWNGRADE_LEVELS = [0, 1, 2] # 对应降级幅度：高->0级, 中->1级, 低->2级
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THRESHOLDS_FINE = [0.7, 0.5, 0.3]
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DOWNGRADE_LEVELS_FINE = [0, 1, 2, 3] # 对应 >=0.7, >=0.5, >=0.3, <0.3
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test_cases = [
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# 案例 1: 高置信度
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(5, [0.05, 0.05, 0.1, 0.1, 0.7], THRESHOLDS, DOWNGRADE_LEVELS, 5, "高置信度"),
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# 案例 2: 中等置信度
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(4, [0.1, 0.15, 0.2, 0.45, 0.1], THRESHOLDS, DOWNGRADE_LEVELS, 3, "中等置信度"),
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# 案例 3: 低置信度
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(4, [0.15, 0.2, 0.25, 0.25, 0.15], THRESHOLDS, DOWNGRADE_LEVELS, 2, "低置信度"),
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# 案例 4: 低置信度，但原始分较低
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(2, [0.3, 0.2, 0.2, 0.15, 0.15], THRESHOLDS, DOWNGRADE_LEVELS, 1, "低置信度，原始分较低"),
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# 案例 5: 边界情况 - 刚好等于高阈值
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(3, [0.1, 0.1, 0.6, 0.1, 0.1], THRESHOLDS, DOWNGRADE_LEVELS, 3, "边界情况 - 等于高阈值"),
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# 案例 6: 边界情况 - 刚好等于中阈值
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(3, [0.2, 0.2, 0.3, 0.15, 0.15], THRESHOLDS, DOWNGRADE_LEVELS, 2, "边界情况 - 等于中阈值"),
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# 案例 7: 细分阈值 - 中高置信度
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(4, [0.1, 0.1, 0.2, 0.55, 0.05], THRESHOLDS_FINE, DOWNGRADE_LEVELS_FINE, 3, "细分阈值 - 中高置信度"),
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# 案例 8: 细分阈值 - 中低置信度
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(4, [0.15, 0.15, 0.2, 0.35, 0.15], THRESHOLDS_FINE, DOWNGRADE_LEVELS_FINE, 2, "细分阈值 - 中低置信度"),
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# 案例 9: 概率和异常 (预期行为是打印警告并继续计算)
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(3, [0.1, 0.1, 0.5, 0.1, 0.1], THRESHOLDS, DOWNGRADE_LEVELS, 3, "概率和异常"),
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]
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@pytest.mark.parametrize("initial_score, probabilities, thresholds, downgrade_levels, expected_score, description", test_cases)
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def test_adjust_score_tiered(initial_score, probabilities, thresholds, downgrade_levels, expected_score, description):
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""" 测试 adjust_score_tiered 函数在各种情况下的表现 """
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print(f"测试案例: {description}")
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print(f" 输入: score={initial_score}, probs={probabilities}, thresholds={thresholds}, levels={downgrade_levels}")
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adjusted_score = adjust_score_tiered(initial_score, probabilities, thresholds, downgrade_levels)
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print(f" 输出: adjusted_score={adjusted_score}, 预期: {expected_score}")
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assert adjusted_score == expected_score
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# 测试非法输入
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def test_adjust_score_invalid_input():
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""" 测试非法输入是否按预期引发 ValueError """
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# initial_score 无效
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with pytest.raises(ValueError, match="initial_score 必须在 1 到 5 之间"):
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adjust_score_tiered(0, [0.2]*5, THRESHOLDS, DOWNGRADE_LEVELS)
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with pytest.raises(ValueError, match="initial_score 必须在 1 到 5 之间"):
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adjust_score_tiered(6, [0.2]*5, THRESHOLDS, DOWNGRADE_LEVELS)
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# probabilities 长度无效
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with pytest.raises(ValueError, match="probabilities 列表必须包含 5 个元素"):
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adjust_score_tiered(3, [0.2]*4, THRESHOLDS, DOWNGRADE_LEVELS)
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with pytest.raises(ValueError, match="probabilities 列表必须包含 5 个元素"):
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adjust_score_tiered(3, [0.1]*6, THRESHOLDS, DOWNGRADE_LEVELS) # 总和也不为1
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# # probabilities 和不为 1 (现在是警告，不抛异常)
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# with pytest.raises(ValueError, match="probabilities 中元素的和必须接近 1.0"):
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# adjust_score_tiered(3, [0.1]*5, THRESHOLDS, DOWNGRADE_LEVELS)
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# downgrade_levels 长度无效
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with pytest.raises(ValueError, match="downgrade_levels 的长度必须比 thresholds 的长度多 1"):
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adjust_score_tiered(3, [0.2]*5, THRESHOLDS, [0, 1])
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with pytest.raises(ValueError, match="downgrade_levels 的长度必须比 thresholds 的长度多 1"):
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adjust_score_tiered(3, [0.2]*5, THRESHOLDS, [0, 1, 2, 3])
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# thresholds 不是降序
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with pytest.raises(ValueError, match="thresholds 列表必须是降序排列的"):
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adjust_score_tiered(3, [0.2]*5, [0.3, 0.6], DOWNGRADE_LEVELS)
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# downgrade_levels 包含负数
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with pytest.raises(ValueError, match="downgrade_levels 中的降级幅度不能为负数"):
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adjust_score_tiered(3, [0.2]*5, THRESHOLDS, [0, -1, 2]) |