我是 Linux 和编程世界的新手，所以我的问题可能非常基本，但我在任何地方都找不到答案。
我正在使用 shell 文件在虚拟集群上运行 python 文件（进行回归，因此我期望得到表）。该作业启动并运行良好，但我无法可视化结果（在提示时打印它们），也无法创建包含输出的文件。
我可以做什么来查看 python 文件的结果？是否有一个命令可以放入 bash 或我的 Python 代码中？
我已经尝试过一些不起作用的解决方案，例如将以下命令放入 bashpython mycode.py > SomeFile.txt这会生成一个名为 SomeFile.txt 的空 txt 文件。
我还尝试在 Python 代码中添加以下行以获取 tex 和 pickle 文件中的结果，但它也不起作用：
print(f"Output file: {results.data.htmlFileName}")
results.writeLaTeX()
print(f'LaTeX file: {results.data.latexFileName}')
results.writePickle()
print(f'Pickle file: {results.data.pickleFileName}')
预先感谢您的任何帮助 ：）
为了更好地了解我想要在集群上运行的 Python 代码，我在 mycode.py 中的脚本下方插入（小说明：因为这是我运行的第一个代码，所以作为测试，我只要求 5 次蒙特卡洛抽奖但目标是要求100000抽奖或更多）
#Import packages
import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
import biogeme.models as models
from biogeme.models import logit
from biogeme.expressions import Beta, DefineVariable, bioDraws, log, MonteCarlo
from datetime import datetime
#Prepare the data
step2_sup3_7ex = pd.read_table("step2_sup3_7ex.txt")
step2_sup3_7ex
database = db.Database("step2_sup3_7ex",step2_sup3_7ex)
# The following statement allows you to use the names of the variable
# as Python variable.
globals().update(database.variables)
#model specification
#parameters to be estimated: as a reference mode, we choose the car
#Alternative specific constant:
ASC_CAR = Beta ( "ASC_CAR" ,0 ,None ,None , 1 ) #1 as a last parameter so it will not be estimated since we decided to have the car as a reference alternative
ASC_PT = Beta ( "ASC_PT" ,0 ,None ,None , 0 )
ASC_BICYCLE = Beta ( "ASC_BICYCLE" ,0 ,None ,None , 0 )
#alternative specific attributes
#the used starting values are 0 because from exeperience with S2B3 using the starting values of prevous models gives results with low goodness of fit
B_COST = Beta("B_COST",0,None,None,0)
B_TIME_CAR = Beta("B_TIME_CAR",0,None,None,0)
B_TIME_PT = Beta("B_TIME_PT",0,None,None,0)
B_TIME_BICYCLE = Beta("B_TIME_BICYCLE",0,None,None,0)
B_POLL_mean = Beta("B_POLL_mean",0,None,None,0)
B_POLL_std = Beta("B_POLL_std",0.1,None,None,0)
B_PHYS_mean = Beta("B_PHYS_mean",0,None,None,0)
B_PHYS_std = Beta("B_PHYS_std",0.1,None,None,0)
#Individual effect of the % of people adopting a behavior
B_PRCT50 = Beta("B_PRCT50",0,None,None,1)
B_PRCT75 = Beta("B_PRCT75",0,None,None,0)
B_PRCT90 = Beta("B_PRCT90",0,None,None,0)
#we cross the level of risk of sickness due to air pollution with the % of people adopting the mobility behavior: pour chaque niveau on a un paramètre spécifique
B_POLL_PRCT50_mean = Beta("B_POLL_PRCT50_mean",0,None,None,1)
B_POLL_PRCT50_std = Beta("B_POLL_PRCT50_std",0.1,None,None,1)
B_POLL_PRCT75_mean = Beta("B_POLL_PRCT75_mean",0,None,None,0)
B_POLL_PRCT75_std = Beta("B_POLL_PRCT75_std",0.1,None,None,0)
B_POLL_PRCT90_mean = Beta("B_POLL_PRCT90_mean",0,None,None,0)
B_POLL_PRCT90_std = Beta("B_POLL_PRCT90_std",0.1,None,None,0)
#Risk variation (specific delta for each health attribute)
delta_POLL = Beta('delta_POLL',0,None,None,0) #delta is positive, varies from 0 to +infinity
delta_POLL75 = Beta('delta_POLL75',0,None,None,0)
delta_POLL90 = Beta('delta_POLL90',0,None,None,0)
delta_PHYS = Beta('delta_PHYS',0,None,None,0)
#parameters of the status quo
B_STAT_CAR = Beta("B_STAT_CAR",0,None,None,1)
B_STAT_PT = Beta("B_STAT_PT",0,None,None,0)
B_STAT_BICYCLE = Beta("B_STAT_BICYCLE",0,None,None,0)
#Socioeconomic parameters:
B_AGE = Beta('B_AGE',0,None,None,0)
B_AGE2 = Beta('B_AGE2',0,None,None,0)
B_GENDER = Beta('B_GENDER',0,None,None,0)
# Random parameters
B_PHYS_random = B_PHYS_mean + B_PHYS_std * bioDraws('B_PHYS_random','NORMAL')
B_POLL_random = B_POLL_mean + B_POLL_std * bioDraws('B_POLL_random','NORMAL')
B_POLL_PRCT50_random = B_POLL_PRCT50_mean + B_POLL_PRCT50_std * bioDraws('B_POLL_PRCT50_random', 'NORMAL')
B_POLL_PRCT75_random = B_POLL_PRCT75_mean + B_POLL_PRCT75_std * bioDraws('B_POLL_PRCT75_random', 'NORMAL')
B_POLL_PRCT90_random = B_POLL_PRCT90_mean + B_POLL_PRCT90_std * bioDraws('B_POLL_PRCT90_random', 'NORMAL')
#Utilities
U_CAR = ASC_CAR + B_TIME_CAR * temps1_C1Q + B_COST * cout1_C1Q
U_PT = (ASC_PT + B_TIME_PT * temps2_C1Q + B_COST * cout2_C1Q +
B_PHYS_random * (phys_act1_C1Q - phys_act2_C1Q)**delta_PHYS +
(B_POLL_random + B_POLL_PRCT75_random * prct75 + B_POLL_PRCT90_random * prct90 ) * (poll1_C1Q - poll2_C1Q)**(delta_POLL + delta_POLL75 * prct75 + delta_POLL90 * prct90) +
B_PRCT75 * prct75 + B_PRCT90 * prct90 +
B_STAT_PT * stat_TC + B_STAT_BICYCLE * stat_velo +
B_AGE * age_cont + B_AGE2 * age2_cont + B_GENDER * homme)
U_BICYCLE = (ASC_BICYCLE + B_TIME_BICYCLE * temps3_C1Q + B_COST * cout3_C1Q +
B_PHYS_random * (phys_act1_C1Q - phys_act3_C1Q)**delta_PHYS +
(B_POLL_random + B_POLL_PRCT75_random * prct75 + B_POLL_PRCT90_random * prct90 ) * (poll1_C1Q - poll3_C1Q)**(delta_POLL + delta_POLL75 * prct75 + delta_POLL90 * prct90) +
B_PRCT75 * prct75 + B_PRCT90 * prct90 +
B_STAT_PT * stat_TC + B_STAT_BICYCLE * stat_velo +
B_AGE * age_cont + B_AGE2 * age2_cont + B_GENDER * homme)
#associating the utility fcts with the numbering of the altrnatives
V = {5: U_CAR,
4: U_PT,
1: U_BICYCLE,
} #V is a python dictionnary
#associate the avaiability conditions with the alternatives
av = {5: 5,
4: 4,
1: 1,
} #dictionnary
# Choice model (Random coefficients/ mixed logit model)
prob = logit(V,av,choix_C1Q)
logprob = log(MonteCarlo(prob))
biogeme = bio.BIOGEME(database,logprob,numberOfDraws=5)
biogeme.modelName = "S2A3_MLdelta_5MC"
start_time = datetime.now()
# As the estimation may take a while and risk to be interrupted, we save the iterations,
# and restore them before the estimation.
fname = "__MLdelta5MC.iters"
biogeme.loadSavedIteration(filename=fname)
# Estimate the parameters.
results = biogeme.estimate(saveIterations=True, file_iterations=fname)
#read the results
print(f"Estimation time: {datetime.now() - start_time}")
# Get the results in a pandas table
pandasResults = results.getEstimatedParameters()
print(pandasResults)
print(f"Nbr of observations: {database.getNumberOfObservations()}")
print(f"LL(0) = {results.data.initLogLike:.3f}")
print(f"LL(beta) = {results.data.logLike:.3f}")
print(f"rho bar square = {results.data.rhoBarSquare:.3g}")
print(f"Output file: {results.data.htmlFileName}")
results.writeLaTeX()
print(f'LaTeX file: {results.data.latexFileName}')
results.writePickle()
print(f'Pickle file: {results.data.pickleFileName}')
答案1
可能已经太晚了，没有用处，但我认为您正在寻找的是 '-u' 争论。
$ man python
。 。 。
-u Force stdin, stdout and stderr to be totally unbuffered. On systems where it matters, also put stdin, stdout and stderr in binary mode. Note that there is internal buffering in xreadlines(), readlines() and file-object iterators ("for line in sys.stdin") which is not influenced by this option. To work around this, you will want to use "sys.stdin.readline()" inside a "while 1:" loop.
。 。 。
因此，在您的工作文件中，您应该放置：
python -u mycode.py > Somefile.text
或者，我建议您使用 python 文件处理来更好地管理数据。但如果由于某种原因你不能，那么上述就是可行的方法。