Python 如何访问GridSearchCV中的ColumnTransformer元素

我想在引用param_grid中ColumnTransformer（它是管道的一部分）中包含的单个预处理器进行grid_搜索时，找出正确的命名约定

环境和样本数据：

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)

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)

管道：

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 = 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，我应该写什么来代替代码中的[某些东西]

我已经看了关于

make_pipeline

的问题的答案——因此使用类似的想法，我尝试了“preprocessor_uunum_uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu

---

谢谢你

你很接近，正确的声明方式如下：

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']}

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

以下是完整的代码：

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', 'median'],
              'preprocessor__num__discritiser__n_bins' : 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)

检查可用参数名称的一种简单方法如下：

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']}

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

这将打印出所有可用参数的列表，您可以将其直接复制到

params

字典中

我已经编写了一个实用函数，您可以通过简单地传递关键字来检查管道/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(available_params)==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

作为关键字传递即可

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']

---

很好，很有帮助。非常感谢你的帮助，穆罕默德：>@Gambit1614一个小错误。如果len（x）==0:行应替换为

如果len（可用参数）==0:

。