从Scikit学习管道传递估计器到Scikit生存as_concordance_index_ipcw_scorer

Question

我有一个管道运行预处理，然后是一个随机生存森林从SciKit生存包。我正在尝试使用Scikit-Survival的as_concordance_index_ipcw_scorer()类found 这里。

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))])

这是通向管道的python代码和管道的拟合：

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.")

在试穿完成后，我试着运行以下步骤：

as_concordance_index_ipcw_scorer(rsf).score(X_test,y_test)

我得到以下错误后：

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)

有什么建议吗？

对于长度或缺少的信息，我很抱歉，我对管道和ScikitSurvival并不熟悉，我想给出尽可能多的细节。

谢谢

Answer 1

需要对来自as_concordance_index_ipcw_scorer的估计器实例进行拟合；在这种情况下，已经安装了基本的估计器也没有帮助。

从源代码 ( Mixin类)中，安装这些包装器中的一个适合底层估计器，将其保存在新的属性estimator_中(这是您的错误抱怨丢失的地方)，还保存了培训标签。因此，您可能能够直接创建这些属性，而不会产生不良影响，但您将在预期的过程中进行操作。