参数具有弹性值误差的交叉Val得分

如何解决参数具有弹性值误差的交叉Val得分

我试图使用cross_val_score()函数和嵌套交叉验证在python中实现一个简单的Elastic Net Regression，但它不允许我传递参数。它会一直从l1_ratio的无效参数中声明ValueError，我不明白为什么给定它在0到1之间。

ValueError: Invalid parameter l1_ratio for estimator Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('cat',OneHotEncoder(),[0]),('num',StandardScaler(),slice(1,37,None))])),('model',ElasticNet(random_state=42))]).
Check the list of available parameters with `estimator.get_params().keys()`.

我的代码：

cv_outer=KFold(10,shuffle=True)
cv_inner=KFold(10,shuffle=True)
models_params = {
    'en': (LREN(random_state=42),# Elastic Net
        {'l1_ratio': [0,0.25,0.5,0.75,1],'alpha':[1e-2,1e-1,1,1e1]})
# My first column is Categorical,the other 36 are numerical
preprocessor = ColumnTransformer(
                    transformers=[
                        ('cat',37))
                    ],remainder = 'passthrough')
# Store Results
average_scores = dict()
for name,(model,params) in models_params.items():
    mymodel = Pipeline(steps = [('preprocessor',preprocessor),model)
                                ])
        # this object is a regressor that also happens to choose
        # its hyperparameters automatically using `inner_cv`
    optimize_hparams = gridsearchcv(
            estimator = mymodel,param_grid=params,n_jobs = -1,cv=cv_inner,scoring='neg_mean_absolute_error')
# estimate generalization error on the outer-fold splits of the data
    outer_folds_scores = cross_val_score(
        optimize_hparams,X,y,cv=cv_outer,scoring='neg_mean_absolute_error')

解决方法

您可以尝试定义网格，例如：

models_params = {
        {'model__l1_ratio': [0,0.25,0.5,0.75,1],'model__alpha':[1e-2,1e-1,1,1e1]}
    }