labelencoder 和 OneHotEncoder 的值错误

Posted 2023-03-12

【中文标题】labelencoder 和 OneHotEncoder 的值错误

【英文标题】：Value error with labelencoder and OneHotEncoder

【发布时间】：2017-12-10 20:14:26

【问题描述】：

我正在尝试将一个分类字符串列转换为几个虚拟变量二进制列，但我得到一个值错误。

代码如下：

import sys, os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from dateutil import parser
import math
import traceback
import logging
datasetMod = pd.read_csv('data.csv')

X = datasetMod.iloc[:, 3:6].values
y = datasetMod.iloc[:, 1].values
print(X[:, 0])

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
try:
    labelencoder_X = LabelEncoder()
    X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
    onehotencoder = OneHotEncoder(categorical_features = [0])
    X = onehotencoder.fit_transform(X).toarray()
except Exception as e:
    exc_type, exc_obj, exc_tb = sys.exc_info()
    fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
    print(exc_type, fname, exc_tb.tb_lineno)

这是错误：

<class 'ValueError'> multipleLinearRegression.py 23

该列的打印语句的结果是：

['Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Weekend' 'Workday' 'Workday' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday' 'Workday'
 'Workday' 'Workday' 'Workday' 'Workday' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend' 'Weekend'
 'Weekend' 'Weekend' 'Weekend' 'Weekend']

字符串本身似乎没有任何问题，中间没有空格，也没有类似数字的符号。所以我不明白为什么我得到一个 valuetype can't convert string to float 错误。

任何帮助将不胜感激。

更新

onehotencoder 现在工作得有些好，但最终结果是 object 类型，而它应该是 float64 类型：

labelencoder_X = LabelEncoder()
X[:, 1] = labelencoder_X.fit_transform(X[:, 1])
X[:, 2] = labelencoder_X.fit_transform(X[:, 2])
X[:, 3] = labelencoder_X.fit_transform(X[:, 3])
onehotencoder = OneHotEncoder(categorical_features = [1,2,3])
onehotencoder.fit(X[:, 1])
onehotencoder.fit(X[:, 2])
onehotencoder.fit(X[:, 3])
onehotencoder.transform(X[:, 1])
onehotencoder.transform(X[:, 2])
onehotencoder.transform(X[:, 3])
X = onehotencoder.toArray()  

更新 2

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelencoder_X = LabelEncoder()
X[:, 1] = labelencoder_X.fit_transform(X[:, 1])
X[:, 2] = labelencoder_X.fit_transform(X[:, 2])
X[:, 3] = labelencoder_X.fit_transform(X[:, 3])

onehotencoder = OneHotEncoder(categorical_features = [1,2,3])
X[:, 1] = onehotencoder.fit_transform(X[:, 1]).toarray()
X[:, 2] = onehotencoder.fit_transform(X[:, 2]).toarray()
X[:, 3] = onehotencoder.fit_transform(X[:, 3]).toarray()

print(X.dtype) #object

最终代码

由于categorical_features 已经指定了索引，我可以在整个矩阵X 上拟合_transform()。感谢@mkos 的耐心等待！

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 1] = labelencoder_X.fit_transform(X[:, 1])
X[:, 2] = labelencoder_X.fit_transform(X[:, 2])
X[:, 3] = labelencoder_X.fit_transform(X[:, 3])
onehotencoder = OneHotEncoder(categorical_features = [1,2,3])
X = onehotencoder.fit_transform(X)

【问题讨论】：

试试le.fit(X[:, 0].unique()) 然后le.transform(X[:, 0])

我按照你的建议将它们分开，它的 onehotencoder.fit(X[:, 0].unique()) 导致错误

实际上，如果我删除 unique() 它可以工作，只是带有弃用警告“如果您的数据具有单一功能或 X.reshape，请使用 X.reshape(-1, 1) 重塑您的数据(1, -1) 如果它包含单个样本。"

le意思是LabelEncoder这个需要unique;OneHotEncoder不需要

再次给它一个 valuetypeerror

【参考方案1】：

这应该可以解决问题：

onehotencoder = OneHotEncoder(categorical_features = [1,2,3])
X = onehotencoder.fit_transform(X)

您可以使用以下方式打印它：

print(X.toArray())

将X 用作稀疏矩阵还不错，因为它可以节省内存。如果你想看到它，那么你把它转换成普通的np.array 和toArray()。

【讨论】：

干杯，我如何让它成为 float64 类型？它仍然默认输入对象。（我的意思是 X）

是的，仍然是类型对象

hmm，如果我不在 onehotencoder fit_transform() 的末尾添加 .toarray()，那么我的值将变为：" ' 以 COOrdinate 格式存储 146 个元素>

如果我可以问一下，您究竟是如何检查类型的？

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