如何获取targetencoder的ram名称?网格拱门

Question

我有以下场景：

preprocess = make_column_transformer(
(SimpleImputer(strategy='constant',fill_value = 0),numeric_cols),
(ce.TargetEncoder(),['country'])
)
pipeline = make_pipeline(preprocess,XGBClassifier())
pipeline[0].get_params().keys()
dict_keys(['n_jobs', 'remainder', 'sparse_threshold', 'transformer_weights', 'transformers', 'verbose', 'simpleimputer', 'targetencoder', 'simpleimputer__add_indicator', 'simpleimputer__copy', 'simpleimputer__fill_value', 'simpleimputer__missing_values', 'simpleimputer__strategy', 'simpleimputer__verbose', 'targetencoder__cols', 'targetencoder__drop_invariant', 'targetencoder__handle_missing', 'targetencoder__handle_unknown', 'targetencoder__min_samples_leaf', 'targetencoder__return_df', 'targetencoder__smoothing', 'targetencoder__verbose'])

然后我想对平滑因子进行网格搜索：

所以：

param_grid =    { 
'xgbclassifier__learning_rate': [0.01,0.005,0.001],
'targetencoder__smoothing': [1, 10, 30, 50]

}
pipeline = make_pipeline(preprocess,XGBClassifier())
# Initialize Grid Search Modelg
clf = GridSearchCV(pipeline,param_grid = param_grid,scoring = 'neg_mean_squared_error',
verbose= 1,iid= True,
refit = True,cv  = 3)
clf.fit(X_train,y_train)

然而，我得到了这个错误：

ValueError：估计器管道的参数transformer_targetencoder无效(步骤=[('columnformer'，ColumnTransformer(变压器…

如何访问平滑参数？

Answer 1

使用您的示例，它将是columntransformer__targetencoder__smoothing。为了重现管道，首先我使用一个示例数据集并定义列：

from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
import category_encoders as ce
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
X_train = pd.DataFrame({'x1':np.random.normal(0,1,50),
'x2':np.random.normal(0,1,50),
'country':np.random.choice(['A','B','C'],50)})
y_train = np.random.binomial(1,0.5,50)
numeric_cols = ['x1','x2']
preprocess = make_column_transformer(
(SimpleImputer(strategy='constant',fill_value = 0),numeric_cols),
(ce.TargetEncoder(),['country'])
)
pipeline = make_pipeline(preprocess,XGBClassifier())

你应该在更高的层次上看钥匙：

pipeline.get_params().keys()

然后设置网格，确保平滑是浮动的(请参阅此问题(：

param_grid = { 'columntransformer__targetencoder__smoothing': [1.0, 10.0],
'xgbclassifier__learning_rate': [0.01,0.001]}
pipeline = make_pipeline(preprocess,XGBClassifier())
clf = GridSearchCV(pipeline,param_grid = param_grid,scoring = 'neg_mean_squared_error', 
verbose= 1,refit = True,cv  = 3)
clf.fit(X_train,y_train)

而且它应该工作