我正在使用多种评分方法来处理 cross_validate。该results变量是一个字典列表。我正在尝试使用不同模型访问 F1 分数，results['test_f1_score']但我得到了TypeError: list indices must be integers or slices, not tuple. 从列表中获取 F1 分数的最佳方法是什么？这是从 results 变量生成的列表：[{'fit_time': array([3.25437379, 3.31360006, 4.55807948, 4.13171291, 4.19844866, 5.00323677, 4.02107215, 4.18458176, 4.82133555, 4.25998092, 3.76690507, 3.97073889, 4.82378697, 4.32887745, 4.66021585, 4.54414296, 5.0830574 , 4.85126257, 5.40511918, 5.04520845, 5.11681008, 4.93635201, 4.74664378, 5.08661175, 4.32240748, 4.10375977, 4.31480002, 4.04965734, 3.78688979, 3.88911653]), 'score_time': array([0.14319539, 0.12677526, 0.10101032, 0.09385777, 0.06942415, 0.11760497, 0.06548786, 0.05601525, 0.07890821, 0.07988477, 0.06272411, 0.07337427, 0.07825518, 0.04193068, 0.04330111, 0.04155302, 0.05201483, 0.0699327 , 0.05248165, 0.04978824, 0.05156302, 0.05214596, 0.07210374, 0.06283927, 0.04857302, 0.05679178, 0.04323983, 0.04890585, 0.04407573, 0.05073118]), 'test_f1_score': array([0.03381643, 0.06428571, 0.01939058, 0.02870813, 0.05673759, 0.05128205, 0.06306306, 0.01066667, 0.08275862, 0.0180791 , 0.03755869, 0.04013378, 0.04255319, 0.07619048, 0.04494382, 0.0818118, 0.02181818, 0.02171291, 0.03367003, 0.04195804, 0.01532567, 0.05687204, 0.0591716 , 0.05825243, 0.07659574, 0.04848485, 0.01724138, 0.02247191, 0.01233046, 0.01920439]), 'test_f05_measure': array([0.02168525, 0.04174397, 0.01229796, 0.01840491, 0.03683241, 0.03355705, 0.04137116, 0.00676133, 0.05576208, 0.01143511, 0.02386635, 0.02599653, 0.02873563, 0.05012531, 0.02985075, 0.05369928, 0.01390176, 0.01374465, 0.02181501, 0.02722323, 0.00967586, 0.03740648, 0.03816794, 0.03836317, 0.05011136, 0.0312989 , 0.01089918, 0.01447178, 0.00780762, 0.01217815]), 'test_sensitivity': array([0.5 , 0.64285714, 0.5 , 0.42857143, 0.57142857,
2 回答
炎炎设计
TA贡献1808条经验 获得超4个赞
您必须迭代您的列表：
def get_values(res, k)
out = []
for v in res:
if k in v
out.append(v[k])
return out
out = get_values(results, 'test_f1_score')
或者单行：
out = [v['test_f1_score'] for v in results if 'test_f1_score' in v]
茅侃侃
TA贡献1842条经验 获得超21个赞
使用列表理解来检查列表项，并获取它的每个字典'test_f1_score'：
查看结果：
for item in results验证当前结果以“test_f1_score”为键：
if 'test_f1_score' in item.keys()
2.1。如果它有“test_f1_score”，请将其添加到列表中：item['test_f1_score']
f1_scores = [item['test_f1_score'] for item in results if 'test_f1_score' in item.keys()]
输出：
[array([0.03381643, 0.06428571, 0.01939058, 0.02870813, 0.05673759,
0.05128205, 0.06306306, 0.01066667, 0.08275862, 0.0180791 ,
0.03755869, 0.04013378, 0.04255319, 0.07619048, 0.04494382,
0.08181818, 0.02181818, 0.02171291, 0.03367003, 0.04195804,
0.01532567, 0.05687204, 0.0591716 , 0.05825243, 0.07659574,
0.04848485, 0.01724138, 0.02247191, 0.01233046, 0.01920439]), array([0.63636364, 0.92307692, 0.96296296, 0.75 , 0.92857143,
0.83333333, 0.92307692, 0.83333333, 0.92307692, 0.92857143,
0.75 , 0.72727273, 0.8 , 0.96296296, 0.88 ,
0.88888889, 0.92307692, 0.92307692, 0.88888889, 0.88 ,
0.83333333, 0.88888889, 0.96551724, 0.88 , 0.96296296,
0.88 , 0.92307692, 0.75 , 0.83333333, 0.66666667]), array([0.00107335, 0.00068248, 0.00094757, 0.00183083, 0.00252127,
0.00114827, 0.00064725, 0.00101868, 0.00189095, 0.00243717,
0.00251467, 0.00230814, 0.00161264, 0.00114833, 0.00164609,
0.00261584, 0.00213311, 0.00060588, 0.00067877, 0.00191205,
0.00125274, 0.00163043, 0.00184945, 0.00174004, 0.00291333,
0.00147438, 0.00357782, 0.00063331, 0.00130506, 0.00108421])]
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