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我有一个运行预处理的管道，然后是来自 SciKit-Survival 包的随机生存森林。我正在尝试使用此处找到的 Scikit-Survival 的 as_concordance_index_ipcw_scorer()类。

我的管道如下所示：

    Pipeline(steps=[('columntransformer',
                 ColumnTransformer(transformers=[('num',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='median')),
                                                                  ('scaler',
                                                                   StandardScaler())]),
                                                  Index(['IntVar1', 'IntVar2', 'IntVar3',
       'IntVar4'],
      dtype='object')),
                                                 ('cat',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(fill_value='missing',
                                                                                 strategy='constant')),
                                                                  ('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore',
                                                                                 sparse=False))]),
                                                  Index(['CharVar1', 'CharVar2', 'CharVar3'], dtype='object'))])),
                ('randomsurvivalforest',
                 RandomSurvivalForest(max_features='sqrt',
                                      min_samples_leaf=0.005,
                                      min_samples_split=0.01, n_estimators=150,
                                      n_jobs=-1, oob_score=True,
                                      random_state=200))])

这是导致管道和管道安装的python代码：

print("Importing global DF")
print("Creating X & Y set")
X = df.iloc[:,:-2].copy()
y = Surv.from_dataframe("AliveStatus","Target_Age",df.iloc[:,-2:].copy()) ## Creates structured array for Scikit Surv

print("Defining feature categories by data type")
numerical_features = X.select_dtypes(include=['int64', 'float64']).columns
categorical_features = X.select_dtypes(include=['object']).columns

print("Splitting dataset")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5) #SkLearn splitter

print("Defining preprocessing steps using SciKitLearn pipeline...")
## Pipeline Steps
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())])


categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(sparse=False,handle_unknown='ignore'))]) ## Use "sparse=False" because Random Forests cannot take Spare Matrixes, only Dense Matrixes. 

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numerical_features),
        ('cat', categorical_transformer, categorical_features)])

## Pipeline defining
print("Defining Random Survival Forest pipeline from SciKit Survival")
rsf = make_pipeline(
    preprocessor,
    RandomSurvivalForest(n_estimators=150, ## Default 100
                        min_samples_split=0.01, ## Default 6
                        min_samples_leaf=0.005, ## Default 3
                        max_features="sqrt", ## Defaults to none when not defined
                        n_jobs=-1, ## Default -1
                        oob_score = True,
                        random_state=200) ## Random State 2020
                        )


##Fitting & Scoring
print("Fitting dataframe to RSF Pipeline")
rsf.fit(X_train,y_train)
print("Fitting completed.")

拟合完成后，我尝试运行以下命令：

as_concordance_index_ipcw_scorer(rsf).score(X_test,y_test)

之后我收到以下错误：

AttributeError                            Traceback (most recent call last)
<ipython-input-97-9a92b22d8026> in <module>
----> 1 as_concordance_index_ipcw_scorer(rsf).score(X_test,y_test)

C:\ProgramData\Anaconda3\lib\site-packages\sksurv\metrics.py in score(self, X, y)
    788         score : float
    789         """
--> 790         estimate = self._do_predict(X)
    791         score = self._score_func(
    792             survival_train=self._train_y,

C:\ProgramData\Anaconda3\lib\site-packages\sksurv\metrics.py in _do_predict(self, X)
    768 
    769     def _do_predict(self, X):
--> 770         predict_func = getattr(self.estimator_, self._predict_func)
    771         return predict_func(X)
    772 

AttributeError: 'as_concordance_index_ipcw_scorer' object has no attribute 'estimator_'

我尝试过的一个选项是指定管道的 RSF 部分，但没有成功：

as_concordance_index_ipcw_scorer(rsf[1]).score(X_test,y_test)

有什么建议么？

对长度或缺少信息表示歉意，我是管道和 ScikitSurvival 的新手，想提供尽可能多的细节。

谢谢

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1 回答 1

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as_concordance_index_ipcw_scorer需要拟合的估计器实例；在这种情况下，拟合基础估计器并没有帮助。

从（Mixin 类的）源代码中，拟合这些包装器之一适合将其保存在新属性中 estimator_的底层估计器（这是您的错误所抱怨的缺失），并且还保存了训练标签。因此，您可能能够直接创建这些属性而不会产生不利影响，但您将绕过预期的过程。

于 2022-01-09T23:08:37.453 回答