生存障碍:一致性指数传感器参数(scikit生存率)

Question

我想使用我训练的模型在测试集上实现concordance_index_censored。我不明白哪个应该是我在concordance_index_censored()中的estimate参数中的输入。

它在的某个位置吗？如果没有，我应该从哪里得到它？我尝试了coxnet_pred[‘array’]，但它不起作用，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'])

Answer 1

concordance_index_censored的estimate参数应该是一个数组，在测试数据中每个实例都有一个风险分数：

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，您也可以使用model.score(test_X, test_y)，如用户指南。