为什么它会自动转换为 numpy.ndarray？

Question

我在我的数据库中做预测变量和 class 之间的划分，所以我意识到我必须先做 LabelEncoder 转换，然后做 OneHotEncoder，在第一个数据库中我是这样做的：

label_encoder_workclass = LabelEncoder()
label_encoder_education = LabelEncoder()
label_encoder_marital = LabelEncoder()
label_encoder_occupation = LabelEncoder()
label_encoder_relationship = LabelEncoder()
label_encoder_race = LabelEncoder()
label_encoder_sex = LabelEncoder()
label_encoder_country = LabelEncoder()

X_census[:,1] = label_encoder_workclass.fit_transform(X_census[:,1])
X_census[:,3] = label_encoder_education.fit_transform(X_census[:,3])
X_census[:,5] = label_encoder_marital.fit_transform(X_census[:,5])
X_census[:,6] = label_encoder_occupation.fit_transform(X_census[:,6])
X_census[:,7] = label_encoder_relationship.fit_transform(X_census[:,7])
X_census[:,8] = label_encoder_race.fit_transform(X_census[:,8])
X_census[:,9] = label_encoder_sex.fit_transform(X_census[:,9])
X_census[:,13] = label_encoder_country.fit_transform(X_census[:, 13])

onehotenconder_census = ColumnTransformer(transformers=[('OneHot', OneHotEncoder(), [1,3,5,6,7,8,9,13])], remainder='passthrough')

X_census = onehotenconder_census.fit_transform(X_census).toarray()

在第二个数据库中是这样的：

label_encoder_personHomeOwnership = LabelEncoder()
label_encoder_loanIntent = LabelEncoder()
label_encoder_loanGrade = LabelEncoder()
label_encoder_cbPersonDefaultOnFile = LabelEncoder()

X_credit[:,2] = label_encoder_personHomeOwnership.fit_transform(X_credit[:,2])
X_credit[:,4] = label_encoder_loanIntent.fit_transform(X_credit[:,4])
X_credit[:,5] = label_encoder_loanGrade.fit_transform(X_credit[:,5])
X_credit[:,9] = label_encoder_personHomeOwnership.fit_transform(X_credit[:,9])

oneHotEncoder_credit = ColumnTransformer(transformers=[('OneHot', OneHotEncoder(), [2,4,5,9])], remainder='passthrough')

X_credit = oneHotEncoder_credit.fit_transform(X_credit)

让我感兴趣的是，为什么在第一个中我必须使用 toarray() 方法将其转换为 numpy.ndarray 类型的对象，而在第二个中我没有，它转换了自动。

请有人帮我回答这个问题。我是不是做错了什么？

非常感谢您

Answer 1

来自 help page of ColumnTransformer :

sparse_thresholdfloat, default=0.3

If the output of the different transformers contains sparse matrices, these will be stacked as a sparse matrix if the overall density is lower than this value. Use sparse_threshold=0 to always return dense. When the transformed output consists of all dense data, the stacked result will be dense, and this keyword will be ignored.

在您的例子中，第一个示例比第二个示例具有更多的稀疏条目，因此它被转换为稀疏矩阵。 .toarray() 方法将其从稀疏转换为密集。

如果内存不是问题，将其设置为 sparse_threshold=0 将确保您每次都能获得密集矩阵。

例如，如果我们有很多类别的列：

from sklearn.compose import ColumnTransformer
import numpy as np

np.random.seed(111)

X = np.random.randint(0,10,(100,10))
ct = ColumnTransformer(transformers=[('OneHot', OneHotEncoder(),
np.arange(10))], remainder='passthrough')

type(ct.fit_transform(X))
scipy.sparse.csr.csr_matrix

ct = ColumnTransformer(transformers=[('OneHot', OneHotEncoder(),
np.arange(10))], remainder='passthrough',sparse_threshold=0)

type(ct.fit_transform(X))
numpy.ndarray

与类别较少的列相比：

X = np.random.randint(0,2,(100,10))
ct = ColumnTransformer(transformers=[('OneHot', OneHotEncoder(),
np.arange(10))], remainder='passthrough')

type(ct.fit_transform(X))
numpy.ndarray

为什么它会自动转换为 numpy.ndarray？

Why it did convert to numpy.ndarray automatically?

python

scikit-learn

data-science