Windows 10和Linux Ubuntu 16.04在运行时间上的巨大差异，用于ExtraTreesClassifier训练和预测（Python）

我使用

extratreeClassifier

进行训练和预测。我在Windows10和LinuxUbuntu16.04的相同数据集上执行相同的源代码，令人惊讶的是，我在执行时间上得到了巨大的差异

结果是：

+---------------+-----------+----------+----------+---------+ | Dataset in Mo | Win Train | Win Pred | Ub Train | Ub Pred | +---------------+-----------+----------+----------+---------+ | 430 | 104 | 11 | 2420 | 2019 | +---------------+-----------+----------+----------+---------+ | 530 | 122 | 14 | 2948 | 2162 | +---------------+-----------+----------+----------+---------+ | 699 | 140 | 18 | 3672 | 2500 | +---------------+-----------+----------+----------+---------+ 所使用的操作系统信息和库版本如下表所示。我更新了所有使用过的库

+---------------------------------------+-------------------------------------------+ | Windows 10 | Ubuntu 16.04 | | Intel i7-8550U CPU @ 1.80Ghz 1.99Ghz | Intel(R) Xeon(R) CPU E5-2660 v4 @ 2.00GHz | | cpu cores : 4 | cpu cores : 1 | | 64 bit OS | 64 bit OS | | RAM 16 Go | RAM 1007 Go | +---------------------------------------+-------------------------------------------+ | Python 3.7.7 | Python 3.5.2 | | ----------------- | ------------- | | biopython==1.77 | biopython==1.73 | | datatable==0.11.0a0+pr2536.12 | datatable==0.10.1 | | numpy==1.19.0 | numpy==1.18.5 | | pandas==1.0.5 | pandas==0.24.2 | | pyahocorasick==1.4.0 | pyahocorasick==1.4.0 | | scikit-learn==0.23.1 | scikit-learn==0.22.2.post1 | | scipy==1.5.0 | scipy==1.4.1 | | suffix-trees==0.3.0 | suffix-trees==0.3.0 | +---------------------------------------+-------------------------------------------+ +---------------------------------------+-------------------------------------------+ |Windows 10 | Ubuntu 16.04| |英特尔i7-8550U CPU@1.80Ghz 1.99Ghz |英特尔（R）至强（R）CPU E5-2660 v4@2.00GHz| |cpu核数：4 | cpu核数：1| |64位操作系统| 64位操作系统| |RAM 16通电| RAM 1007通电| +---------------------------------------+-------------------------------------------+ |Python 3.7.7 | Python 3.5.2| | ----------------- | ------------- | |生物皮顿==1.77 |生物皮顿==1.73| |数据表==0.11.0a0+pr2536.12 |数据表==0.10.1| |numpy==1.19.0 | numpy==1.18.5| |熊猫==1.0.5 |熊猫==0.24.2| |pyahocorasick==1.4.0 | pyahocorasick==1.4.0| |scikit学习==0.23.1 | scikit学习==0.22.2.1| |scipy==1.5.0 | scipy==1.4.1| |后缀树==0.3.0 |后缀树==0.3.0| +---------------------------------------+-------------------------------------------+ 使用cprofile：

1619734 function calls (1589052 primitive calls) in 6495.451 seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 4828 6248.349 1.294 6248.349 1.294 {built-in method numpy.core.multiarray.array} 100 130.458 1.305 130.458 1.305 {method 'build' of 'sklearn.tree._tree.DepthFirstTreeBuilder' objects} 1 48.288 48.288 48.288 48.288 {built-in method datatable.lib._datatable.gread} 2 21.834 10.917 25.749 12.874 Main.py:40(get_matrix_nbOcrrs_listStr_AhoCorasick) 2 20.747 10.374 2570.626 1285.313 model.py:233(test_groupe_score_dt) 4365 6.476 0.001 6.476 0.001 {method 'reduce' of 'numpy.ufunc' objects} 1 5.851 5.851 6492.121 6492.121 Main.py:309(main) 6710 3.705 0.001 3.705 0.001 {method 'copy' of 'list' objects} 400 2.548 0.006 2.548 0.006 {method 'predict' of 'sklearn.tree._tree.Tree' objects} 1 2.288 2.288 6495.453 6495.453 Main.py:1() 1 1.334 1.334 3889.596 3889.596 model.py:189(train_with_dt) 400 0.827 0.002 3.628 0.009 _classes.py:880(predict_proba) 4 0.522 0.131 4936.793 1234.198 _forest.py:591(predict) 400 0.354 0.001 3.982 0.010 _forest.py:442(_accumulate_prediction) 376662 0.150 0.000 0.150 0.000 {method 'add_word' of 'ahocorasick.Automaton' objects} 803 0.120 0.000 0.120 0.000 {built-in method marshal.loads} 2272/2260 0.070 0.000 0.144 0.000 {built-in method builtins.__build_class__} 1081/1 0.069 0.000 6495.453 6495.453 {built-in method builtins.exec} 143/119 0.064 0.000 0.116 0.001 {built-in method _imp.create_dynamic} 2 0.046 0.023 0.046 0.023 {method 'make_automaton' of 'ahocorasick.Automaton' objects} ...etc 6495.451秒内完成1619734次函数调用（1589052次基元调用） 订购人：内部时间 ncalls tottime percall cumtime percall文件名：lineno（函数） 4828 6248.349 1.294 6248.349 1.294{内置方法numpy.core.multiarray.array} 100 130.458 1.305 130.458 1.305{“sklearn.tree.\u tree.DepthFirstTreeBuilder”对象的方法“build”} 148.28848.28848.28848.28848{内置方法datatable.lib.\u datatable.gread} 221.83410.91725.74912.874 Main.py:40（获取矩阵 220.747 10.374 2570.626 1285.313型号。py:233（测试组分数） 4365 6.476 0.001 6.476 0.001{“numpy.ufunc”对象的方法“reduce”} 1 5.851 5.851 6492.121 6492.121 Main.py:309（Main） 6710 3.705 0.001 3.705 0.001{“列表”对象的方法“复制”} 400 2.548 0.006 2.548 0.006{“sklearn.tree.\u tree.tree”对象的“预测”方法} 1 2.288 2.288 6495.453 6495.453 Main.py:1（） 1 1.334 1.334 3889.596 3889.596型号。py:189（带dt的列车） 400 0.827 0.002 3.628 0.009（预测概率） 40.5220.131 4936.793 1234.198_森林。py:591（预测） 400 0.354 0.001 3.982 0.010 _forest.py:442（累积预测） 376662 0.150 0.000 0.150 0.000{“ahocarasick.Automaton”对象的“添加单词”方法} 803 0.120 0.000 0.120 0.000{内置方法marshal.loads} 2272/2260.070 0.000 0.144 0.000{内置方法内置。uuu构建uuu类uuuu} 1081/1 0.069 0.000 6495.453 6495.453{内置方法builtins.exec} 143/119 0.064 0.000 0.116 0.001{内置方法_imp.create_dynamic} 2 0.046 0.023 0.046 0.023{方法'make_automaton'的'ahocorasick.automaton'对象} 等

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感谢您的帮助。

我发现了导致windows和Linux在执行时间上存在巨大差异的问题。
当我对python包进行更新时，系统说，需求已经满足了…，所以我认为包已经更新了。在@0x5453的注释之后，我尝试再次更新包，但没有成功，我在（）中发现了一个关于更新问题的问题。
解决方案：我在Python3.5的旁边安装了Python3.8，并创建了一个虚拟环境，在那里安装了所需软件包的最新版本。而且它大大缩短了执行时间。

+---------------+-----------+----------+------------------+----------------+- | Dataset in Mo | Win Train | Win Pred | Ub Train (Py3.5) | Ub Pred (3.5) | +---------------+-----------+----------+------------------+----------------+- | 430 | 104 | 11 | 2420 (win x23) | 2019 (win x153)| +---------------+-----------+----------+------------------+----------------+- | 530 | 122 | 14 | 2948 (win x24) | 2162 (win x154)| +---------------+-----------+----------+------------------+----------------+- | 699 | 140 | 18 | 3672 (win x26) | 2500 (win x204)| +---------------+-----------+----------+------------------+----------------+- -+------------------+-----------------+ | Ub Train (Py3.8) | Ub Pred (Py3.8) | -+------------------+-----------------+ | 353 (win x3) | 270 (win x24) | -+------------------+-----------------+ | 771 (win x6) | 646 (win x46) | -+------------------+-----------------+ | 901 (win x6) | 430 (win x23) | -+------------------+-----------------+ +---------------+-----------+----------+------------------+----------------+- |Mo | Win Train | Win Pred | Ub Train（Py3.5）| Ub Pred（3.5）|中的数据集 +---------------+-----------+----------+------------------+----------------+- |430 | 104 | 11 | 2420（胜利x23）| 2019（胜利x153）| +---------------+-----------+----------+------------------+----------------+- |530 | 122 | 14 | 2948 | 2162 | x154 | +---------------+-----------+----------+------------------+----------------+- |699 | 140 | 18 | 3672（赢x26）| 2500（赢x204）| +---------------+-----------+----------+------------------+----------------+- -+------------------+-----------------+ |Ub列车（Py3.8）| Ub Pred（Py3.8）| -+------------------+-----------------+ |353（赢x3）| 270（赢x24）| -+------------------+-----------------+ |771（win x6）| 646（win x46）| -+------------------+-----------------+ |901（赢x6）| 430（赢x23）| -+------------------+-----------------+

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现在，windows和Linux Python 3.8之间的剩余差异可以用所用机器的差异来解释。

我发现了导致windows和Linux之间执行时间巨大差异的问题。
当我对python包进行更新时，系统会说Req +---------------+-----------+----------+------------------+----------------+- | Dataset in Mo | Win Train | Win Pred | Ub Train (Py3.5) | Ub Pred (3.5) | +---------------+-----------+----------+------------------+----------------+- | 430 | 104 | 11 | 2420 (win x23) | 2019 (win x153)| +---------------+-----------+----------+------------------+----------------+- | 530 | 122 | 14 | 2948 (win x24) | 2162 (win x154)| +---------------+-----------+----------+------------------+----------------+- | 699 | 140 | 18 | 3672 (win x26) | 2500 (win x204)| +---------------+-----------+----------+------------------+----------------+- -+------------------+-----------------+ | Ub Train (Py3.8) | Ub Pred (Py3.8) | -+------------------+-----------------+ | 353 (win x3) | 270 (win x24) | -+------------------+-----------------+ | 771 (win x6) | 646 (win x46) | -+------------------+-----------------+ | 901 (win x6) | 430 (win x23) | -+------------------+-----------------+