如何访问 GridSearchCV 中的 ColumnTransformer 元素

Question

当引用 grid_search 的 param_grid 中的 ColumnTransformer （它是管道的一部分）中包含的单个预处理器时，我想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)

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，我应该在代码中写什么而不是[SOMETHING]？

我看过这个答案，它回答了这个问题make_pipeline- 所以使用类似的想法，我尝试了“preprocessor__num__”，“preprocessor__num_”，“pipeline__num__”，“pipeline__num_” - 到目前为止没有运气。

谢谢

Answer 1

你很接近，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', '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：

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字典中params。

我编写了一个实用函数，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(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imputer作为关键字传递

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']