Scikit学习:输入包含NaN,无穷大或对于dtype太大的值('float64')
machine-learning
numpy
python
scikit-learn
5
0

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

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

我应该如何清除此错误?因为从上面的结果来看,它不包含无限或Nan值是正确的。

有什么解决方案?

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

参考资料:
Stack Overflow
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共 1 个回答
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您的回归问题是NaN某种程度上潜入了您的数据。可以使用以下代码段轻松地检查这一点:

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)

如果您尝试估算缺少的值,如下所示:

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)])

您的回归将顺利运行而不会出现问题:

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:如错误消息所示,您的数据中缺少值。

编辑

也许更简单直接的方法是使用熊猫读取数据后立即检查是否有丢失的数据:

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条来估算数据:

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

要么

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