scikit-learn - SKEARN//结合 GridsearchCV 与列变换和管道

Question

我正在为一个机器学习项目而苦苦挣扎，我试图在其中结合:

一个 sklearn 列变换，用于将不同的更改器(mutator)应用于我的数值和分类特征

应用我的不同转换器和估计器的管道

GridSearchCV寻找最佳参数。

只要我在我的pipeline中手动填写我的不同transformer的参数，代码就完美运行了。
但是，一旦我尝试传递不同值的列表以在我的 gridsearch 参数中进行比较，我就会收到各种无效参数错误消息。

这是我的代码:

首先，我将我的特征分为数字特征和分类特征

from sklearn.compose import make_column_selector
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.impute import KNNImputer
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder


numerical_features=make_column_selector(dtype_include=np.number)
cat_features=make_column_selector(dtype_exclude=np.number)

然后我为数值和分类特征创建了 2 个不同的预处理管道:

numerical_pipeline= make_pipeline(KNNImputer())
cat_pipeline=make_pipeline(SimpleImputer(strategy='most_frequent'),OneHotEncoder(handle_unknown='ignore'))

我将两者结合到另一个管道中，设置我的参数，并运行我的 GridSearchCV代码

model=make_pipeline(preprocessor, LinearRegression() )

params={
    'columntransformer__numerical_pipeline__knnimputer__n_neighbors':[1,2,3,4,5,6,7]
}

grid=GridSearchCV(model, param_grid=params,scoring = 'r2',cv=10)
cv = KFold(n_splits=5)
all_accuracies = cross_val_score(grid, X, y, cv=cv,scoring='r2')

我尝试了不同的方法来声明参数，但从未找到合适的方法。我总是收到“无效参数”错误消息。

你能帮我理解出了什么问题吗？

真的非常感谢您的支持，请保重!

Answer 1

我假设您可能已经定义了 preprocessor如下，

preprocessor = Pipeline([('numerical_pipeline',numerical_pipeline),
                        ('cat_pipeline', cat_pipeline)])

那么你需要改变你的参数名称如下:
pipeline__numerical_pipeline__knnimputer__n_neighbors
但是，代码还有其他几个问题:

您不必调用 cross_val_score执行后GridSearchCV . GridSearchCV 本身的输出将具有每个超参数组合的交叉验证结果。

KNNImputer当您的数据具有字符串数据时将不起作用。您需要申请cat_pipeline之前 num_pipeline .

完整示例:

from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.compose import make_column_selector
import pandas as pd  # doctest: +SKIP
X = pd.DataFrame({'city': ['London', 'London', 'Paris', np.nan],
                  'rating': [5, 3, 4, 5]})  # doctest: +SKIP

y = [1,0,1,1]

from sklearn.compose import make_column_selector
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.impute import KNNImputer
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score, KFold
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder


numerical_features=make_column_selector(dtype_include=np.number)
cat_features=make_column_selector(dtype_exclude=np.number)

numerical_pipeline= make_pipeline(KNNImputer())
cat_pipeline=make_pipeline(SimpleImputer(strategy='most_frequent'),
                            OneHotEncoder(handle_unknown='ignore', sparse=False))
preprocessor = Pipeline([('cat_pipeline', cat_pipeline),
                        ('numerical_pipeline',numerical_pipeline)])
model=make_pipeline(preprocessor, LinearRegression() )

params={
    'pipeline__numerical_pipeline__knnimputer__n_neighbors':[1,2]
}


grid=GridSearchCV(model, param_grid=params,scoring = 'r2',cv=2)

grid.fit(X, y)