sklearn：无法使 OneHotEncoder 与 Pipeline 一起使用

Question

我正在使用 ColumnTransformer 为模型构建管道。这就是我的管道的样子，

from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder,OrdinalEncoder,MinMaxScaler
from sklearn.impute import KNNImputer

imputer_transformer = ColumnTransformer([
    ('knn_imputer',KNNImputer(n_neighbors=5),[0,3,4,6,7])
],remainder='passthrough')

category_transformer = ColumnTransformer([
    ("kms_driven_engine_min_max_scaler",MinMaxScaler(),[0,6]),
    ("owner_ordinal_enc",OrdinalEncoder(categories=[['fourth','third','second','first']],handle_unknown='ignore',dtype=np.int16),[3]),
    ("brand_location_ohe",OneHotEncoder(sparse=False,handle_unknown='ignore'),[2,5]),
],remainder='passthrough')


def build_pipeline_with_estimator(estimator):
    return Pipeline([
    ('imputer',imputer_transformer),
    ('category_transformer',category_transformer),
    ('estimator',estimator),
])

这就是我的数据集的样子，

kms_driven      owner   location    mileage     power    brand              engine  age
34000.0         first       other           NaN         12.0        Yamaha          150.0     9
28000.0         first       other           72.0         7.0         Hero                100.0    16
5947.0           first       other          53.0          19.0       Bajaj                NaN       4
11000.0         first       delhi           40.0          19.8       Royal Enfield   350.0    7
13568.0         first       delhi           63.0          14.0       Suzuki             150.0     5

这就是我在管道中使用 LinearRegression 的方式。

linear_regressor = build_pipeline_with_estimator(LinearRegression())

linear_regressor.fit(X_train,y_train)

print('Linear Regression Train Performance.\n')
print(model_perf(linear_regressor,X_train,y_train))

print('Linear Regression Test Performance.\n')
print(model_perf(linear_regressor,X_test,y_test))

现在，每当我尝试对管道应用线性回归时，我都会收到此错误，

ValueError：无法将字符串转换为浮点数：“班加罗尔”

“banglore”是位置特征中的值之一，我正在尝试对其进行一次性编码，但它失败了，我无法弄清楚这里出了什么问题。任何帮助将不胜感激。

Answer 1

通过插补器后，未插补的列将移至右侧，如文档下的注释中所述：

除非在 passthrough 关键字中指定，否则原始特征矩阵中未指定的列将从生成的转换特征矩阵中删除。使用 passthrough 指定的那些列将添加到变压器输出的右侧。

我们可以先尝试使用输入器：

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, MinMaxScaler
from sklearn.impute import KNNImputer
from sklearn.linear_model import LinearRegression

imputer_transformer = ColumnTransformer([
    ('knn_imputer',KNNImputer(n_neighbors=5),[0,3,4,6,7])
],remainder='passthrough')

我们可以使用示例数据进行尝试，您将看到您的分类列现在向右移动：

X_train = pd.DataFrame({'kms':[0,1,2],'owner':['first','first','second'],
'location':['other','other','delhi'],'mileage':[9,8,np.nan],
'power':[3,2,1],'brand':['A','B','C'],'engine':[10,100,1000],'age':[3,4,5]})

imputer_transformer.fit_transform(X_train)
Out[25]: 
array([[0.0, 9.0, 3.0, 10.0, 3.0, 'first', 'other', 'A'],
       [1.0, 8.0, 2.0, 100.0, 4.0, 'first', 'other', 'B'],
       [2.0, 8.5, 1.0, 1000.0, 5.0, 'second', 'delhi', 'C']], dtype=object)

在您的情况下，您可以看到该engine列现在是第四列，而您的序数是第五列，分类的最后两列，因此一个简单的解决方案可能是：

category_transformer = ColumnTransformer([
    ("kms_driven_engine_min_max_scaler",MinMaxScaler(),[0,3]),
    ("owner_ordinal_enc",OrdinalEncoder(categories=[['fourth','third','second','first']],
handle_unknown='ignore',dtype=np.int16),[5]),
    ("brand_location_ohe",OneHotEncoder(sparse=False,handle_unknown='ignore'),[6,7]),
],remainder='passthrough')

y_train = [7,3,2]

linear_regressor = build_pipeline_with_estimator(LinearRegression())

linear_regressor.fit(X_train,y_train)