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我在 Docker 容器中运行一些 Python statsmodel 代码。当我在两台不同的计算机上运行此代码时（使用从 DockerHub 中提取的同一个 Docker 容器，而不是在本地构建 2x），我得到了不同的结果。差异很小 - 第 10 位或第 15 位发生变化。但它正在破坏我们可重现的构建。这是 Python statsmodel 问题吗？一个 Docker 问题？

我认为这是 Python，因为在从这些 Docker 映像生成的容器中运行着 1000 多条其他行，并且它们是可重现的。

这是一个 MWE，以及差异示例：

import numpy as np
import pandas as pd
import statsmodels
import statsmodels.api as sm
from statsmodels.sandbox.regression.predstd import wls_prediction_std
np.random.seed(42)

df = pd.DataFrame(columns=['foo', 'bar'], data=np.random.random((1000, 2)))

y = (df['bar'])
X = np.log10(df['foo'])
X = sm.add_constant(X)
model = sm.OLS(y, X)
fits = model.fit()
predictions = fits.predict(X)

XX = np.linspace(X['foo'].min(), X['foo'].max(), 50)
XX = sm.add_constant(XX)
yy = fits.predict(XX)
sdev, lower, upper = wls_prediction_std(fits, exog=XX, alpha=0.05)

bad = df.loc[df['bar'] < 50,'bar']

df.loc[df['bar'] < 50,'bar'] = fits.predict(sm.add_constant(np.log10(bad)))

fits.summary()

with open("output.txt", "w") as text_file:
    text_file.write(fits.summary().as_csv())

df.to_csv('out.csv', index=False)

并且差异out.csv很小。例如，

$ sdiff <(cat out.csv) <(ssh remote_server cat out.csv) | tail

显示以下内容。请注意，只有最后一位数字发生了变化。

0.18610141784627732,0.5081884090422659                        | 0.18610141784627732,0.5081884090422658
0.45818688673789265,0.5082792408801786                        | 0.45818688673789265,0.5082792408801785
0.13347997241594378,0.5085994020210153                        | 0.13347997241594378,0.5085994020210152
0.7279393069737652,0.5082743139146337                         | 0.7279393069737652,0.5082743139146336
0.43685070261517955,0.5082054932289445                        | 0.43685070261517955,0.5082054932289444
0.7655128989911097,0.5084780190581778                         | 0.7655128989911097,0.5084780190581777
0.6102251494776413,0.5085067071667805                         | 0.6102251494776413,0.5085067071667804
0.7513750860290457,0.5082242252400639                           0.7513750860290457,0.5082242252400639
0.956614621083458,0.5086273010565618                            0.956614621083458,0.5086273010565618
0.05705472115125432,0.5083753342014574                        | 0.05705472115125432,0.5083753342014573
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