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考虑以下代码片段：

import random
from uncertainties import unumpy, ufloat

x = [random.uniform(0,1) for p in range(1,8200)]
y = [random.randrange(0,1000) for p in range(1,8200)]
xerr = [random.uniform(0,1)/1000 for p in range(1,8200)]
yerr = [random.uniform(0,1)*10 for p in range(1,8200)]

x = unumpy.uarray(x, xerr)
y = unumpy.uarray(y, yerr)
diff = sum(x*y)
u = ufloat(0.0, 0.0)
for k in range(len(x)):
    u+= (diff-x[k])**2 * y[k]  

print(u)

如果我尝试在我的计算机上运行它，最多需要 10 分钟才能产生结果。我不太确定为什么会这样，并希望得到某种解释。如果我不得不猜测，我会说不确定性的计算由于某种原因比人们想象的要复杂，但就像我说的那样，这只是一个猜测。有趣的是，如果最后删除print指令，代码几乎立即完成，老实说，这让我感到困惑，而不是帮助......

如果您不知道，这是不确定性库的存储库。

4

1 回答 1

1

我可以复制这个，打印是永远的。或者更确切地说，它是 print 隐式调用的字符串转换。我使用line_profiler__format__来测量AffineScalarFunc. （由__str__调用，由 print 调用）我将数组大小从 8200 减小到 1000，以使其运行得更快一些。这是结果（为便于阅读而修剪）：

Timer unit: 1e-06 s

Total time: 29.1365 s
File: /home/veith/Projects/stackoverflow/test/lib/python3.6/site-packages/uncertainties/core.py
Function: __format__ at line 1813

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
  1813                                               @profile
  1814                                               def __format__(self, format_spec):

  1960                                           
  1961                                                   # Since the '%' (percentage) format specification can change
  1962                                                   # the value to be displayed, this value must first be
  1963                                                   # calculated. Calculating the standard deviation is also an
  1964                                                   # optimization: the standard deviation is generally
  1965                                                   # calculated: it is calculated only once, here:
  1966         1          2.0      2.0      0.0          nom_val = self.nominal_value
  1967         1   29133097.0 29133097.0    100.0          std_dev = self.std_dev
  1968

您可以看到几乎所有时间都发生在第 1967 行，计算标准差。如果再深入一点，你会发现问题是属性error_components，问题在哪里，问题在哪里。如果您对此进行分析，您就会开始找到问题的根源。这里的大多数工作都是均匀分布的：derivatives_linear_part.expand()

Function: expand at line 1481

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
  1481                                               @profile
  1482                                               def expand(self):
  1483                                                   """
  1484                                                   Expand the linear combination.
  1485                                           
  1486                                                   The expansion is a collections.defaultdict(float).
  1487                                           
  1488                                                   This should only be called if the linear combination is not
  1489                                                   yet expanded.
  1490                                                   """
  1491                                           
  1492                                                   # The derivatives are built progressively by expanding each
  1493                                                   # term of the linear combination until there is no linear
  1494                                                   # combination to be expanded.
  1495                                           
  1496                                                   # Final derivatives, constructed progressively:
  1497         1          2.0      2.0      0.0          derivatives = collections.defaultdict(float)
  1498                                           
  1499  15995999    4942237.0      0.3      9.7          while self.linear_combo:  # The list of terms is emptied progressively
  1500                                           
  1501                                                       # One of the terms is expanded or, if no expansion is
  1502                                                       # needed, simply added to the existing derivatives.
  1503                                                       #
  1504                                                       # Optimization note: since Python's operations are
  1505                                                       # left-associative, a long sum of Variables can be built
  1506                                                       # such that the last term is essentially a Variable (and
  1507                                                       # not a NestedLinearCombination): popping from the
  1508                                                       # remaining terms allows this term to be quickly put in
  1509                                                       # the final result, which limits the number of terms
  1510                                                       # remaining (and whose size can temporarily grow):
  1511  15995998    6235033.0      0.4     12.2              (main_factor, main_expr) = self.linear_combo.pop()
  1512                                           
  1513                                                       # print "MAINS", main_factor, main_expr
  1514                                           
  1515  15995998   10572206.0      0.7     20.8              if main_expr.expanded():
  1516  15992002    6822093.0      0.4     13.4                  for (var, factor) in main_expr.linear_combo.items():
  1517   7996001    8070250.0      1.0     15.8                      derivatives[var] += main_factor*factor
  1518                                           
  1519                                                       else:  # Non-expanded form
  1520  23995993    8084949.0      0.3     15.9                  for (factor, expr) in main_expr.linear_combo:
  1521                                                               # The main_factor is applied to expr:
  1522  15995996    6208091.0      0.4     12.2                      self.linear_combo.append((main_factor*factor, expr))
  1523                                           
  1524                                                       # print "DERIV", derivatives
  1525                                           
  1526         1          2.0      2.0      0.0          self.linear_combo = derivatives

可以看到有很多调用expanded，哪个调用isinstance，哪个慢。另请注意注释，其中暗示该库实际上仅在需要时计算导数（并且知道否则它真的很慢）。这就是为什么转换成字符串需要这么长的时间，而之前没有这个时间。

在：__init___AffineScalarFunc

# In order to have a linear execution time for long sums, the
# _linear_part is generally left as is (otherwise, each
# successive term would expand to a linearly growing sum of
# terms: efficiently handling such terms [so, without copies]
# is not obvious, when the algorithm should work for all
# functions beyond sums).

在：std_dev_AffineScalarFunc

#! It would be possible to not allow the user to update the
#std dev of Variable objects, in which case AffineScalarFunc
#objects could have a pre-calculated or, better, cached
#std_dev value (in fact, many intermediate AffineScalarFunc do
#not need to have their std_dev calculated: only the final
#AffineScalarFunc returned to the user does).

在：expand_LinearCombination

   # The derivatives are built progressively by expanding each
    # term of the linear combination until there is no linear
    # combination to be expanded.

所以总而言之，这在某种程度上是意料之中的，因为库处理这些需要大量操作来处理的非本地数字（显然）。

于 2018-11-02T22:15:34.117 回答