Sklearn Pipeline - 尝试计算估算器被调用的次数

Question

我正在尝试计算在此管道中调用 LogisticRegression 的次数，因此我扩展了 class 并覆盖了 .fit()。它本来应该很简单，但它产生了这个奇怪的错误：

TypeError: float() 参数必须是字符串或数字，而不是 'MyLogistic'

其中 MyLogistic 是新的 class。如果您复制并粘贴代码，您应该能够重现整个过程。

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import (GridSearchCV, StratifiedKFold)
from sklearn.feature_selection import SelectFromModel
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris
import numpy as np

class MyLogistic(LogisticRegression):
    __call_counter = 0
    def fit(X, y, sample_weight=None):
        print("MyLogistic fit is called.")
        MyLogistic._MyLogistic__call_counter += 1
        # fit() returns self.
        return super().fit(X, y, sample_weight)

# If I use this "extension", everything works fine.
#class MyLogistic(LogisticRegression):
#    pass
    
initial_logistic = MyLogistic(solver="liblinear", random_state = np.random.RandomState(18))
final_logistic = LogisticRegression(solver="liblinear", random_state = np.random.RandomState(20))
# prefit = False by default
select_best = SelectFromModel(estimator = initial_logistic, threshold = -np.inf)

select_k_best_pipeline = Pipeline(steps=[
    ('first_scaler', StandardScaler(with_mean = False)),
    # initial_logistic will be called from select_best, prefit = false by default.
    ('select_k_best', select_best),
    ('final_logit', final_logistic)
])

select_best_grid = {'select_k_best__estimator__C' : [0.02, 0.03],
                    'select_k_best__max_features': [1, 2],
                    'final_logit__C' : [0.01, 0.5, 1.0]}

skf = StratifiedKFold(n_splits = 3, shuffle = True, random_state = 17)

logit_best_searcher = GridSearchCV(estimator = select_k_best_pipeline, param_grid = select_best_grid, cv = skf, 
                               scoring = "roc_auc", n_jobs = 6, verbose = 4)

X, y = load_iris(return_X_y=True)
logit_best_searcher.fit(X, y > 0)
print("Best hyperparams: ", logit_best_searcher.best_params_)

Answer 1

您只是忘记将 self 作为 fit 签名的第一个参数。因此调用正在 X=self，并且在尝试检查输入时 X 它在某个时候尝试转换为浮点数，因此出现错误消息。

并行化仍然有些奇怪；我得到的计数器等于 1。改为设置 n_jobs=1，我得到正确的计数器 37（x3 折叠上的 2x2x3 超参数候选，最终改装 +1）。

Sklearn Pipeline - 尝试计算估算器被调用的次数

Sklearn Pipeline - trying to count the number of times an estimator is called

machine-learning

scikit-learn

logistic-regression

cross-validation

gridsearchcv