To fit geographically weighted model, geographically and temporally weighted regression model and multiscale geographically and temporally weighted regression model. You can read example.ipynb to know how to use it.
When trying to use parallel processing, model.py is the function that is called. The original code was using multiprocessing.So based on cProfile and the pstats library ran statistics, it is found thread.lock to be the main time consuming task,which causes parallel processing to be slower than non-parallel processing. So attempts were made to compare the processing of ThreadPoolExecutor from concurrent.futures and joblib, and ultimately joblib was found to be the fastest case.All of the result was run on the provided example.csv. As you can see, joblib greatly reduces the parallel processing time, and also has a large improvement compared to orginal, however, it should be noted that {method 'acquire' of '_thread.lock' objects} is still the most time-consuming task, and how to solve this is beyond my ability to do.
Related computer configuration: 12t Gen_Intel(R) Core(TM) i5-12600KF(6+4 for core 16 of threads) Crucial 16GB DDR5-4800 UDIMM (My computer configuration isn't that low also so I'm very sad why I have to run for 11 minutes when it's only 6 minutes in the example)
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Orginal(thread=1) time cost: 0:11:3.748 636805417 function calls (613137518 primitive calls) in 663.749 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function) 2151360 169.201 0.000 225.785 0.000 d:\anaconda\Lib\site-packages\scipy\linalg_basic.py:40(solve) 19328497/15022891 35.541 0.000 81.668 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
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Original(thread=15) time cost: 0:24:29.669 6022657 function calls (6019718 primitive calls) in 1469.671 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function) 23663 1397.118 0.059 1397.118 0.059 {method 'acquire' of '_thread.lock' objects}
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ThreadPoolExecutor(thread=15) time cost: 0:07:44.877 99635100 function calls (99632161 primitive calls) in 464.878 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function) 10310899 409.240 0.000 409.240 0.000 {method 'acquire' of '_thread.lock' objects}
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joblib(thread=15) time cost: 0:03:40.609 12395230 function calls (12381086 primitive calls) in 220.610 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function) 902982 187.731 0.000 187.731 0.000 {method 'acquire' of '_thread.lock' objects} 1245 22.471 0.018 215.907 0.173 d:\anaconda\Lib\site-packages\joblib\parallel.py:960(retrieve) 403539 2.014 0.000 192.678 0.000 d:\anaconda\Lib\concurrent\futures_base.py:428(result) 1220 1.293 0.001 215.684 0.177 C:\Users\34456\AppData\Roaming\Python\Python311\site-packages\mgtwr\model.py:450(cal_aic)