python - Python - 如何在通过 train_test_split 拆分后反转使用 LabelEncoder 编码的数据的编码？

Question

我正在尝试导出使用LabelEncoder编码的数据集的未编码版本（从sklearn.preprocessing，以启用机器学习算法的应用），然后拆分为训练和测试数据集（使用train_test_split）。

我想将测试数据导出到 excel 但使用原始值。到目前为止，我找到的示例仅在一个变量上使用了LabelEncoderinverse_transform的方法。我想将它自动应用于最初编码的多个列。

这是一个示例数据：

# data
code = ('A B C D A B C D E F').split()
sp = ('animal bird animal animal animal bird animal animal bird thing').split()
res = ('yes, yes, yes, yes, no, no, yes, no, yes, no').split(", ")
 
data =pd.DataFrame({'code':code, 'sp':sp, 'res':res})
data

假设“res”是目标变量，“code”和“sp”是特征。

Answer 1

干得好：

# data
code = ('A B C D A B C D E F').split()
sp = ('animal bird animal animal animal bird animal animal bird thing').split()
res = ('yes, yes, yes, yes, no, no, yes, no, yes, no').split(", ")
 
data = pd.DataFrame({'code':code, 'sp':sp, 'res':res})
data

# creating LabelEncoder object
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()

# encoding
dfe = pd.DataFrame()    # created empty dataframe for saving encoded values
for column in data.columns:
    dfe[column] = le.fit_transform(data[column])
dfe

# saving features
X = dfe[['code','sp']]

# saving target
y = dfe['res']

# splitting into training & test data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=13)

X_train

# reversal of encoding
dfr_train = X_train.copy()
for column in X.columns:
    le.fit(data[column])   # you fit the column before it was encoded here

# now that python has the above encoding in its memory, we can ask it to reverse such 
# encoding in the corresponding column having encoded values of the split dataset

    dfr_train[column] = le.inverse_transform(X_train[column])
dfr_train

您可以对测试数据执行相同的操作。

# reversal of encoding of data
dfr_test = X_test.copy()
for column in X.columns:
    le.fit(data[column])
    dfr_test[column] = le.inverse_transform(X_test[column])
dfr_test

这是用于导出的完整训练数据（特征 + 变量）：

# reverse encoding of target variable y
le.fit(data['res'])
dfr_train['res'] = le.inverse_transform(y_train)
dfr_train     # unencoded training data, ready for export