我想使用我训练的模型在我的测试集上实现concordance_index_censored。我不明白哪一个应该是我输入到estimate
参数中的concordance_index_censored()
。
它在coxnet_pred的某个地方吗?如果没有,我应该从哪里得到它?我尝试了coxnet_pred‘’array‘,但这不起作用,因为它包含步骤函数。
代码如下
from sksurv.linear_model import CoxnetSurvivalAnalysis
from sksurv.metrics import concordance_index_censored
from sksurv.util import Surv
y=Surv.from_arrays(np.array(survival_status_training), np.array(survival_time_training), name_event="event",name_time ="time")
cox_lasso_model = CoxnetSurvivalAnalysis(l1_ratio=1.0, fit_baseline_model=True)
cox_lasso_trained = cox_lasso_model.fit(training_data, y)
coxnet_pred=cox_lasso_trained.predict_survival_function(np.array(test_data))
training_cindex = concordance_index_censored(event_indicator=np.array(survival_status_training),event_time=np.array(survival_time_training), estimate=coxnet_pred['array'])
发布于 2021-03-21 12:44:58
用于estimate
的concordance_index_censored
参数应该是一个数组,每个实例在测试数据中都有一个风险评分:
from sksurv.linear_model import CoxnetSurvivalAnalysis
from sksurv.metrics import concordance_index_censored
from sksurv.util import Surv
train_y = Surv.from_arrays(
survival_status_training,
survival_time_training
)
test_y = Surv.from_arrays(
survival_status_test,
survival_time_test
)
model = CoxnetSurvivalAnalysis()
model.fit(train_X, train_y)
test_risk_scores = model.predict(test_X)
cindex = concordance_index_censored(
event_indicator=test_y["event"],
event_time=test_y["time"],
estimate=test_risk_scores)
或者,您可以使用model.score(test_X, test_y)
,如用户指南中所解释的那样。
https://stackoverflow.com/questions/65191111
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