python - 生存分析：concordance_index_censored 的参数（scikit-survival）

Question

我想使用我训练的模型在我的测试集上实现 concordance_index_censored。我不明白哪个应该是我estimate对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？我试过coxnet_pred['array']但这不起作用，因为它包含步进函数。

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'])

标签： pythoncox-regressionsurvivalscikit-survival

Answer 1

estimate参数 for应该是一个数组，concordance_index_censored在您的测试数据中每个实例都有一个风险评分：

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model.score(test_X, test_y)按照 用户指南中的说明使用。