跟进自 here ，我不断溢出。所以我试图提出一个异常(exception)，以便我确切地知道哪里出了问题。

我有这样的东西:

@jit
def train_function(X, y, H):
     np.seterr(over="raise", under="raise", invalid="raise")
     # do some stuff, start a double loop, and then do:
     try: 
            z[i,j] = math.exp(-beta[j,i])
     except OverflowError:
            print "Calculation failed! z[i,j] = math.exp(-beta[j,i]), j: " + str(j) + ", i: " +str(i) + ", b: " + str(beta[j,i]) + ", omb: " + str(oneminusbeta[j,i])
            raise    


class MyClass(object):
     # init and other methods
     def train(self, X, y, H):
          train_function(X, y, H)

但是我得到这个错误:

Traceback (most recent call last):
  File "C:\work_asaaki\code\gbc_classifier_train_7.py", line 55, in <module>
    gentlebooster.train(X_train, y_train, boosting_rounds)
  File "C:\work_asaaki\code\gentleboost_c_class_jit_v7_nolimit.py", line 297, in train
    self.g_per_round, self.g = train_function(X, y, H)  
  File "C:\Anaconda\lib\site-packages\numba\dispatcher.py", line 152, in _compile_for_args
    return self.jit(sig)
  File "C:\Anaconda\lib\site-packages\numba\dispatcher.py", line 143, in jit
    return self.compile(sig, **kws)
  File "C:\Anaconda\lib\site-packages\numba\dispatcher.py", line 131, in compile
    flags=flags, locals=locs)
  File "C:\Anaconda\lib\site-packages\numba\compiler.py", line 103, in compile_extra
    bc = bytecode.ByteCode(func=func)
  File "C:\Anaconda\lib\site-packages\numba\bytecode.py", line 305, in __init__
    table = utils.SortedMap(ByteCodeIter(code))
  File "C:\Anaconda\lib\site-packages\numba\utils.py", line 70, in __init__
    for i, (k, v) in enumerate(sorted(seq)):
  File "C:\Anaconda\lib\site-packages\numba\bytecode.py", line 219, in next
    raise NotImplementedError(ts % tv)
NotImplementedError: offset=742 opcode=0x79 opname=SETUP_EXCEPT

我不能在使用 numba 时引发异常吗？我在 64 位机器上使用带有 Numba 0.13.x 和 Numpy 1.8.x 的 Anaconda 2.0.1。

最佳答案

http://numba.pydata.org/numba-doc/dev/reference/pysupported.html

2.6.1.1。构建

Numba strives to support as much of the Python language as possible, but some language features are not available inside Numba-compiled functions. The following Python language features are not currently supported:

Class definition
Exception handling (try .. except, try .. finally)
Context management (the with statement)

The raise statement is supported in several forms:

raise (to re-raise the current exception)
raise SomeException
raise SomeException(<arguments>)

所以我们留在这里:

z[i,j] = math.exp(-beta[j,i])

关于 exp(-1000) 的任何负值；真的非常小将评估为零而不会溢出

math.exp(-1000000000)“有效”并且可能不是您的问题(虽然它会返回 0.0，但它不是“真正的”零)

那么什么会导致失败呢？我们知道:

print(math.exp(100))
>>>
2.6881171418161356e+43

傻大了，远不止于此……可能会溢出

果然如此

print(math.exp(1000))
>>>
OverflowError: math range error

我没有引文，但我认为有效范围是 -700 到 700，从双 float 的角度有效评估为 0 和无穷大(溢出)

处理我们窗口函数:

n = beta
if n > 100:
    n = 100
z = math.exp(n)

但这也行不通，因为 math.exp(n) 只接受 float ，而您的 beta 似乎是一个列表；你必须使用 numpy.exp(n) 和 numpy.clip() 来窗口

b = numpy.array(-beta[j,i])
n = numpy.clip(b, a_max=100)
z = numpy.exp(n)

或者引发溢出异常:

b = numpy.array(-beta[j,i])
n = numpy.clip(b, a_max=100)
if b != n:
    print (j,i,-beta[j,i])
    raise OverflowError
else:
    z = numpy.exp(n)

关于python - 使用 numba 时引发异常，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/25821596/