问题描述
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']