在 pandas-ta 库中使用多处理
using multiproccesing with the pandas-ta library
我正在尝试使用技术分析库 pandas-ta 的多处理功能。
该示例在此处描述 https://pythonrepo.com/repo/twopirllc-pandas-ta-python-deep-learning#multiprocessing
但是我不能让这个例子太有效，错误信息给出了一个明确的提示，说明可能是什么地方出了问题“已尝试在
当前进程已完成其引导阶段。"
我需要帮助破译错误消息，我在这个例子中做错了什么？
df1 = pd.DataFrame() # Empty DataFrame
# Load data
df1 = pd.read_csv("I Provide data further down.csv", sep=",")
def cleanUp(frame):
frame = frame.iloc[:, :6]
frame.columns = ['Time', 'Open', 'High', 'Low', 'Close', 'Volume']
frame[['Open', 'High', 'Low', 'Close', 'Volume']] = frame[['Open', 'High', 'Low', 'Close', 'Volume']].astype(float)
#frame.Time = pd.to_datetime(frame.Time, unit='ms')
return frame
df = cleanUp(df1)
df.set_index(pd.DatetimeIndex(df["datetime"]), inplace=True)
df.ta.strategy()
df.ta.strategy(verbose=True)
df.ta.strategy(timed=True)
# Choose the number of cores to use. Default is all available cores.
# For no multiprocessing, set this value to 0.
df.ta.cores = 4
print(df.columns)
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 268, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\jeppe\PycharmProjects\pandas-ta-trial\main.py", line 47, in <module>
df.ta.strategy()
File "C:\Users\jeppe\PycharmProjects\test\pandas-ta-trial\lib\site-packages\pandas_ta\core.py", line 725, in strategy
with Pool(self.cores) as pool:
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\context.py", line 119, in Pool
return Pool(processes, initializer, initargs, maxtasksperchild,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\pool.py", line 212, in __init__
self._repopulate_pool()
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\pool.py", line 303, in _repopulate_pool
return self._repopulate_pool_static(self._ctx, self.Process,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\pool.py", line 326, in _repopulate_pool_static
w.start()
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\context.py", line 327, in _Popen
return Popen(process_obj)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
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1577847900.0,7235.18000000,7235.66000000,7220.10000000,7224.98000000,108.38500300,1577848199999,783396.65916757,817,32.88055900,237655.73210438,0
1577848200.0,7224.98000000,7229.70000000,7223.43000000,7228.50000000,75.37889900,1577848499999,544807.54913650,1115,58.98292400,426315.39696847,0
1577848500.0,7227.43000000,7235.50000000,7227.19000000,7235.50000000,76.26424900,1577848799999,551573.18631218,943,37.74926700,273025.08551912,0```
从堆栈跟踪来看，您的代码似乎在 Windows 下 运行，并且在 Windows 上使用多处理代码时，它必须由 if __name__ == "__main__":[= 保护12=]
将您的代码包装在 main() 函数中，然后在 top-level:
处调用它会更安全
if __name__ == "__main__":
main()
错误描述中也给出了相同的建议。
我正在尝试使用技术分析库 pandas-ta 的多处理功能。 该示例在此处描述 https://pythonrepo.com/repo/twopirllc-pandas-ta-python-deep-learning#multiprocessing
但是我不能让这个例子太有效，错误信息给出了一个明确的提示，说明可能是什么地方出了问题“已尝试在 当前进程已完成其引导阶段。"
我需要帮助破译错误消息，我在这个例子中做错了什么？
df1 = pd.DataFrame() # Empty DataFrame
# Load data
df1 = pd.read_csv("I Provide data further down.csv", sep=",")
def cleanUp(frame):
frame = frame.iloc[:, :6]
frame.columns = ['Time', 'Open', 'High', 'Low', 'Close', 'Volume']
frame[['Open', 'High', 'Low', 'Close', 'Volume']] = frame[['Open', 'High', 'Low', 'Close', 'Volume']].astype(float)
#frame.Time = pd.to_datetime(frame.Time, unit='ms')
return frame
df = cleanUp(df1)
df.set_index(pd.DatetimeIndex(df["datetime"]), inplace=True)
df.ta.strategy()
df.ta.strategy(verbose=True)
df.ta.strategy(timed=True)
# Choose the number of cores to use. Default is all available cores.
# For no multiprocessing, set this value to 0.
df.ta.cores = 4
print(df.columns)
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 125, in _main
prepare(preparation_data)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 268, in run_path
return _run_module_code(code, init_globals, run_name,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\jeppe\PycharmProjects\pandas-ta-trial\main.py", line 47, in <module>
df.ta.strategy()
File "C:\Users\jeppe\PycharmProjects\test\pandas-ta-trial\lib\site-packages\pandas_ta\core.py", line 725, in strategy
with Pool(self.cores) as pool:
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\context.py", line 119, in Pool
return Pool(processes, initializer, initargs, maxtasksperchild,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\pool.py", line 212, in __init__
self._repopulate_pool()
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\pool.py", line 303, in _repopulate_pool
return self._repopulate_pool_static(self._ctx, self.Process,
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\pool.py", line 326, in _repopulate_pool_static
w.start()
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\context.py", line 327, in _Popen
return Popen(process_obj)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "C:\Users\jeppe\AppData\Local\Programs\Python\Python39\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
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1577843700.0,7209.75000000,7216.41000000,7207.39000000,7216.27000000,38.26293000,1577843999999,275874.49091546,519,24.45966300,176357.76159813,0
1577844000.0,7215.52000000,7223.37000000,7214.09000000,7217.25000000,62.72749700,1577844299999,452754.08488518,809,34.32410800,247754.49464240,0
1577844300.0,7217.21000000,7223.80000000,7214.27000000,7217.18000000,40.37927000,1577844599999,291472.74386075,450,22.14185300,159841.97734924,0
1577844600.0,7217.73000000,7219.03000000,7211.41000000,7211.97000000,30.65388600,1577844899999,221159.98976269,440,11.44721300,82588.05896068,0
1577844900.0,7212.87000000,7225.00000000,7212.15000000,7224.20000000,32.41522200,1577845199999,234036.30960380,410,26.24226500,189467.38539295,0
1577845200.0,7224.20000000,7230.00000000,7218.97000000,7229.32000000,36.71092700,1577845499999,265247.94179368,540,15.18070600,109691.04940257,0
1577845500.0,7229.50000000,7238.88000000,7224.91000000,7228.09000000,106.13135500,1577845799999,767622.84737177,1031,45.92922600,332190.77953536,0
1577845800.0,7227.12000000,7236.07000000,7226.79000000,7234.76000000,56.68833600,1577846099999,409950.24832819,629,33.52172000,242416.94296405,0
1577846100.0,7233.83000000,7235.83000000,7228.08000000,7232.21000000,73.99718000,1577846399999,535146.93433720,660,41.72203500,301706.93949934,0
1577846400.0,7232.75000000,7237.99000000,7229.65000000,7237.63000000,42.63381900,1577846699999,308344.14964985,462,25.20989500,182352.19988289,0
1577846700.0,7237.42000000,7237.81000000,7231.93000000,7236.13000000,38.42991400,1577846999999,278030.89841814,517,17.19293100,124376.96447313,0
1577847000.0,7237.21000000,7244.20000000,7232.20000000,7243.65000000,89.94917000,1577847299999,651134.69258648,964,62.15063300,449941.31467835,0
1577847300.0,7243.07000000,7244.87000000,7238.00000000,7242.85000000,44.44023300,1577847599999,321818.54759365,554,22.11767800,160185.71830680,0
1577847600.0,7242.66000000,7245.00000000,7230.25000000,7235.33000000,87.25912300,1577847899999,631447.62459136,879,37.94202400,274571.31474658,0
1577847900.0,7235.18000000,7235.66000000,7220.10000000,7224.98000000,108.38500300,1577848199999,783396.65916757,817,32.88055900,237655.73210438,0
1577848200.0,7224.98000000,7229.70000000,7223.43000000,7228.50000000,75.37889900,1577848499999,544807.54913650,1115,58.98292400,426315.39696847,0
1577848500.0,7227.43000000,7235.50000000,7227.19000000,7235.50000000,76.26424900,1577848799999,551573.18631218,943,37.74926700,273025.08551912,0```
从堆栈跟踪来看，您的代码似乎在 Windows 下 运行，并且在 Windows 上使用多处理代码时，它必须由 if __name__ == "__main__":[= 保护12=]
将您的代码包装在 main() 函数中，然后在 top-level:
处调用它会更安全if __name__ == "__main__":
main()
错误描述中也给出了相同的建议。