如何在GridSearchCV中访问ColumnTransformer元素

如何解决如何在GridSearchCV中访问ColumnTransformer元素

当我要为grid_search引用param_grid的ColumnTransformer（属于管道的一部分）中包含的各个预处理器时，我想找出正确的命名约定。

环境和示例数据：

import seaborn as sns
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder,KBinsDiscretizer,MinMaxScaler
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

df = sns.load_dataset('titanic')[['survived','age','embarked']]
X_train,X_test,y_train,y_test = train_test_split(df.drop(columns='survived'),df['survived'],test_size=0.2,random_state=123)

管道：

num = ['age']
cat = ['embarked']

num_transformer = Pipeline(steps=[('imputer',SimpleImputer()),('discritiser',KBinsDiscretizer(encode='ordinal',strategy='uniform')),('scaler',MinMaxScaler())])

cat_transformer = Pipeline(steps=[('imputer',SimpleImputer(strategy='constant',fill_value='missing')),('onehot',OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(transformers=[('num',num_transformer,num),('cat',cat_transformer,cat)])

pipe = Pipeline(steps=[('preprocessor',preprocessor),('classiffier',LogisticRegression(random_state=1,max_iter=10000))])

param_grid = dict([SOMETHING]imputer__strategy = ['mean','median'],[SOMETHING]discritiser__nbins = range(5,10),classiffier__C = [0.1,10,100],classiffier__solver = ['liblinear','saga'])
grid_search = GridSearchCV(pipe,param_grid=param_grid,cv=10)
grid_search.fit(X_train,y_train)

基本上，我应该写什么而不是代码中的[SOMETHING]？

我看过this answer，它回答了make_pipeline的问题-因此，使用类似的想法，我尝试了'preprocessor__num __'，'preprocessor__num _'，'pipeline__num __'，'pipeline__num_'-没办法远。

谢谢

解决方法

您亲近了，正确的声明方式是这样的：

param_grid = {'preprocessor__num__imputer__strategy' : ['mean','median'],'preprocessor__num__discritiser__n_bins' : range(5,10),'classiffier__C' : [0.1,10,100],'classiffier__solver' : ['liblinear','saga']}

这是完整的代码：

import seaborn as sns
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder,KBinsDiscretizer,MinMaxScaler
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

df = sns.load_dataset('titanic')[['survived','age','embarked']]
X_train,X_test,y_train,y_test = train_test_split(df.drop(columns='survived'),df['survived'],test_size=0.2,random_state=123)
num = ['age']
cat = ['embarked']

num_transformer = Pipeline(steps=[('imputer',SimpleImputer()),('discritiser',KBinsDiscretizer(encode='ordinal',strategy='uniform')),('scaler',MinMaxScaler())])

cat_transformer = Pipeline(steps=[('imputer',SimpleImputer(strategy='constant',fill_value='missing')),('onehot',OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(transformers=[('num',num_transformer,num),('cat',cat_transformer,cat)])

pipe = Pipeline(steps=[('preprocessor',preprocessor),('classiffier',LogisticRegression(random_state=1,max_iter=10000))])

param_grid = {'preprocessor__num__imputer__strategy' : ['mean','saga']}
grid_search = GridSearchCV(pipe,param_grid=param_grid,cv=10)
grid_search.fit(X_train,y_train)

一种简单的检查可用参数名称的方法是这样的：

print(pipe.get_params().keys())

这将打印出所有可用参数的列表，您可以将这些参数直接复制到params词典中。

我已经编写了一个实用程序函数，您可以通过简单地传入关键字来检查管道/分类器中是否存在参数。

def check_params_exist(esitmator,params_keyword):
    all_params = esitmator.get_params().keys()
    available_params = [x for x in all_params if params_keyword in x]
    if len(x)==0:
        return "No matching params found!"
    else:
        return available_params

现在，如果您不确定确切的名称，只需将imputer作为关键字

print(check_params_exist(pipe,'imputer'))

这将打印以下列表：

['preprocessor__num__imputer','preprocessor__num__imputer__add_indicator','preprocessor__num__imputer__copy','preprocessor__num__imputer__fill_value','preprocessor__num__imputer__missing_values','preprocessor__num__imputer__strategy','preprocessor__num__imputer__verbose','preprocessor__cat__imputer','preprocessor__cat__imputer__add_indicator','preprocessor__cat__imputer__copy','preprocessor__cat__imputer__fill_value','preprocessor__cat__imputer__missing_values','preprocessor__cat__imputer__strategy','preprocessor__cat__imputer__verbose']