Scikit-learn：输入包含 NaN、无穷大或对于 dtype 来说太大的值（'float64'）

Posted 2023-02-23

【中文标题】Scikit-learn：输入包含 NaN、无穷大或对于 dtype 来说太大的值（\'float64\'）

【英文标题】：Scikit-learn : Input contains NaN, infinity or a value too large for dtype ('float64')Scikit-learn：输入包含 NaN、无穷大或对于 dtype 来说太大的值（'float64'）

【发布时间】：2016-04-19 05:03:29

【问题描述】：

我正在使用 Python scikit-learn 对从 csv 获得的数据进行简单的线性回归。

reader = pandas.io.parsers.read_csv("data/all-stocks-cleaned.csv")
stock = np.array(reader)

openingPrice = stock[:, 1]
closingPrice = stock[:, 5]

print((np.min(openingPrice)))
print((np.min(closingPrice)))
print((np.max(openingPrice)))
print((np.max(closingPrice)))

peningPriceTrain, openingPriceTest, closingPriceTrain, closingPriceTest = \
    train_test_split(openingPrice, closingPrice, test_size=0.25, random_state=42)


openingPriceTrain = np.reshape(openingPriceTrain,(openingPriceTrain.size,1))

openingPriceTrain = openingPriceTrain.astype(np.float64, copy=False)
# openingPriceTrain = np.arange(openingPriceTrain, dtype=np.float64)

closingPriceTrain = np.reshape(closingPriceTrain,(closingPriceTrain.size,1))
closingPriceTrain = closingPriceTrain.astype(np.float64, copy=False)

openingPriceTest = np.reshape(openingPriceTest,(openingPriceTest.size,1))
closingPriceTest = np.reshape(closingPriceTest,(closingPriceTest.size,1))

regression = linear_model.LinearRegression()

regression.fit(openingPriceTrain, closingPriceTrain)

predicted = regression.predict(openingPriceTest)

最小值和最大值显示为 0.0 0.6 41998.0 2593.9

但我收到此错误 ValueError:Input contains NaN, infinity or a value too large for dtype('float64').

我应该如何消除这个错误？ 因为从上面的结果来看，它确实不包含无穷大或 Nan 值。

解决办法是什么？

编辑：all-stocks-cleaned.csv 位于http://www.sharecsv.com/s/cb31790afc9b9e33c5919cdc562630f3/all-stocks-cleaned.csv

【问题讨论】：

请尝试提供可重现的示例。

@iled all-stocks-cleaned.csv 可通过sharecsv.com/s/cb31790afc9b9e33c5919cdc562630f3/…获得

【参考方案1】：

您的回归问题在于 NaN 以某种方式潜入了您的数据。这可以使用以下代码 sn-p 轻松检查：

import pandas as pd
import numpy as np
from  sklearn import linear_model
from sklearn.cross_validation import train_test_split

reader = pd.io.parsers.read_csv("./data/all-stocks-cleaned.csv")
stock = np.array(reader)

openingPrice = stock[:, 1]
closingPrice = stock[:, 5]

openingPriceTrain, openingPriceTest, closingPriceTrain, closingPriceTest = \
    train_test_split(openingPrice, closingPrice, test_size=0.25, random_state=42)

openingPriceTrain = openingPriceTrain.reshape(openingPriceTrain.size,1)
openingPriceTrain = openingPriceTrain.astype(np.float64, copy=False)

closingPriceTrain = closingPriceTrain.reshape(closingPriceTrain.size,1)
closingPriceTrain = closingPriceTrain.astype(np.float64, copy=False)

openingPriceTest = openingPriceTest.reshape(openingPriceTest.size,1)
openingPriceTest = openingPriceTest.astype(np.float64, copy=False)

np.isnan(openingPriceTrain).any(), np.isnan(closingPriceTrain).any(), np.isnan(openingPriceTest).any()

(True, True, True)

如果您尝试像下面这样估算缺失值：

openingPriceTrain[np.isnan(openingPriceTrain)] = np.median(openingPriceTrain[~np.isnan(openingPriceTrain)])
closingPriceTrain[np.isnan(closingPriceTrain)] = np.median(closingPriceTrain[~np.isnan(closingPriceTrain)])
openingPriceTest[np.isnan(openingPriceTest)] = np.median(openingPriceTest[~np.isnan(openingPriceTest)])

您的回归将毫无问题地顺利进行：

regression = linear_model.LinearRegression()

regression.fit(openingPriceTrain, closingPriceTrain)

predicted = regression.predict(openingPriceTest)

predicted[:5]

array([[ 13598.74748173],
       [ 53281.04442146],
       [ 18305.4272186 ],
       [ 50753.50958453],
       [ 14937.65782778]])

简而言之：如错误消息所述，您的数据中缺少值。

编辑：：

也许更简单直接的方法是在使用 pandas 读取数据后立即检查是否有任何丢失的数据：

data = pd.read_csv('./data/all-stocks-cleaned.csv')
data.isnull().any()
Date                    False
Open                     True
High                     True
Low                      True
Last                     True
Close                    True
Total Trade Quantity     True
Turnover (Lacs)          True

然后使用以下两行中的任何一行来估算数据：

data = data.fillna(lambda x: x.median())

或

data = data.fillna(method='ffill')

【讨论】：

np.isnan(openingPriceTrain).any(), np.isnan(closurePriceTrain).any(), np.isnan(openingPriceTest).any() (True, True, True) 这部分有帮助我确定问题，非常感谢