我正在嘗試使用管道和 GridSearchCV 將嶺回歸模型擬合到我的資料中。
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
X = transformed_data.iloc[:, :-1]
y = transformed_data['class']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 1)
params = {}
params['ridge__alpha'] = np.arange(0, 100, 1).tolist()
t = [('labelenc',LabelEncoder() , [0]), ('stand', StandardScaler(), [1,2,3,4,5,6]), ('poly'),PolynomialFeatures(degree=2),[1,2,3,4,5,6] ]
transformer = ColumnTransformer(transformers=t)
pipe = Pipeline(steps=[('t', transformer), ('m',Ridge())])
#grid_ridge2_r2 = GridSearchCV(pipe, params, cv=10, scoring='r2', n_jobs=-1)
#results_ridge2_r2 = grid_ridge2_r2.fit(X_train,y_train)
grid_ridge2_rmse = GridSearchCV(pipe, params, cv=10, scoring='neg_root_mean_squared_error', n_jobs=-1)
results_ridge2_rmse = grid_ridge2_rmse.fit(X_train,y_train)
我不斷得到
ValueError: too many values to unpack (expected 3)
在最后一行grid_ridge2_rmse.fit(X_train,y_train)。我的直覺是我拆分資料集的方式有問題。
uj5u.com熱心網友回復：
您的管道中有一些錯誤。
FirstLabelEncoder不能在 scikit-learn 管道中使用，因為它用于修改ynot X。假設您想對特征的分類值進行編碼，則應將其替換為OrdinalEncoder.
然后，要設定 grid 引數，它必須使用以下命名約定命名<step>__<hyperparameter。在您的情況下設定 ridge 引數應該是m__alpha.
可以使用pipe.get_params().
我會這樣做：
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures, OrdinalEncoder, StandardScaler
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
import numpy as np
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 1)
params = {'m__alpha' : np.arange(0, 100, 1).tolist()}
t = [
('labelenc',OrdinalEncoder() , [0]),
('stand', StandardScaler(), [1,2,3,4,5,6]),
('poly', PolynomialFeatures(degree=2), [1,2,3,4,5,6])
]
transformer = ColumnTransformer(transformers=t)
pipe = Pipeline(steps=[('t', transformer), ('m',Ridge())])
grid_ridge2_rmse = GridSearchCV(pipe, params, cv=10, scoring='neg_root_mean_squared_error', n_jobs=-1)
results_ridge2_rmse = grid_ridge2_rmse.fit(X_train,y_train)
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