如何将G-均值集成到cross_validate学习函数中？
如何将G-均值集成到cross_validate学习函数中？
提问于 2021-04-02 14:25:46
from sklearn.model_selection import cross_validate
scores = cross_validate(LogisticRegression(class_weight='balanced',max_iter=100000),
X,y, cv=5, scoring=('roc_auc', 'average_precision','f1','recall','balanced_accuracy'))
scores['test_roc_auc'].mean(), scores['test_average_precision'].mean(),scores['test_f1'].mean(),scores['test_recall'].mean(),scores['test_balanced_accuracy'].mean()如何在上述交叉验证评分参数下计算下列G-均值：
from imblearn.metrics import geometric_mean_score
print('The geometric mean is {}'.format(geometric_mean_score(y_test, y_test_pred)))或
from sklearn.metrics import accuracy_score
g_mean = 1.0
#
for label in np.unique(y_test):
idx = (y_test == label)
g_mean *= accuracy_score(y_test[idx], y_test_pred[idx])
#
g_mean = np.sqrt(g_mean)
score = g_mean
print(score)回答 2
Stack Overflow用户
回答已采纳
发布于 2021-04-03 16:09:06
只是把它当作一个定制的得分手。
from sklearn.metrics import make_scorer
from imblearn.metrics import geometric_mean_score
gm_scorer = make_scorer(geometric_mean_score, greater_is_better=True, average='binary')将greater_is_better=True设置为最佳值更接近1。geometrics_mean_score的附加参数可以直接传递给make_scorer。
完整示例
from sklearn.model_selection import cross_validate
from sklearn.metrics import make_scorer
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from imblearn.metrics import geometric_mean_score
X, y = load_breast_cancer(return_X_y=True)
gm_scorer = make_scorer(geometric_mean_score, greater_is_better=True)
scores = cross_validate(
LogisticRegression(class_weight='balanced',max_iter=100000),
X,y,
cv=5,
scoring=gm_scorer
)
scores
>>>
{'fit_time': array([0.76488066, 0.69808364, 1.22158527, 0.94157672, 1.01577377]),
'score_time': array([0.00103951, 0.00100923, 0.00065804, 0.00071168, 0.00068736]),
'test_score': array([0.91499142, 0.93884403, 0.9860133 , 0.92439026, 0.9525989 ])}编辑
若要指定多个指标，请将一个dict传递给scoring参数
scores = cross_validate(
LogisticRegression(class_weight='balanced',max_iter=100000),
X,y,
cv=5,
scoring={'gm_scorer': gm_scorer, 'AUC': 'roc_auc', 'Avg_Precision': 'average_precision'}
)
scores
>>>
{'fit_time': array([1.03509665, 0.96399784, 1.49760461, 1.13874388, 1.32006526]),
'score_time': array([0.00560617, 0.00357151, 0.0057447 , 0.00566769, 0.00549698]),
'test_gm_scorer': array([0.91499142, 0.93884403, 0.9860133 , 0.92439026, 0.9525989 ]),
'test_AUC': array([0.99443171, 0.99344907, 0.99801587, 0.97949735, 0.99765258]),
'test_Avg_Precision': array([0.99670544, 0.99623085, 0.99893162, 0.98640759, 0.99861043])}Stack Overflow用户
发布于 2021-04-02 14:42:17
您需要定制一个记分员，下面是一个例子：https://stackoverflow.com/a/53850851/12384070，如果它是您唯一想要的得分手，您可以这样做：
scores = cross_validate(LogisticRegression(class_weight='balanced',max_iter=100000),
X,y, cv=5, scoring=your_custom_function)我想你可以用另一个得分手，正如医生所解释的：
If scoring reprents multiple scores, one can use:
a list or tuple of unique strings;
a callable returning a dictionary where the keys are the metric names and the values are the metric scores;
a dictionary with metric names as keys and callables a values.页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/66920782
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