如何使用numb中的自定义类型为方法指定函数签名

Question

我使用numba.jitclass修饰符来标记类以进行优化。在

我不知道如何指定要优化的run方法的签名。该方法以ConvertedDocument对象数组作为参数。当我尝试在nopython模式下调用run方法时，numba似乎无法自己确定数组类型，因为出现以下错误：

Traceback (most recent call last):
  File "numba_test.py", line 53, in <module>
    print run(a)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/dispatcher.py", line 310, in _compile_for_args
    raise e
numba.errors.TypingError: Caused By:
Traceback (most recent call last):
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/compiler.py", line 230, in run
    stage()
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/compiler.py", line 444, in stage_nopython_frontend
    self.locals)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/compiler.py", line 800, in type_inference_stage
    infer.propagate()
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 767, in propagate
    raise errors[0]
TypingError: Internal error at <numba.typeinfer.ExhaustIterConstraint object at 0x788cc9572d50>:
--%<-----------------------------------------------------------------
Traceback (most recent call last):
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 128, in propagate
    constraint(typeinfer)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 264, in __call__
    raise TypingError("failed to unpack {}".format(tp), loc=self.loc)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/contextlib.py", line 35, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/errors.py", line 249, in new_error_context
    six.reraise(type(newerr), newerr, sys.exc_info()[2])
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/errors.py", line 243, in new_error_context
    yield
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 264, in __call__
    raise TypingError("failed to unpack {}".format(tp), loc=self.loc)
InternalError: local variable 'tp' referenced before assignment
[1] During: typing of exhaust iter at numba_test.py (40)
--%<-----------------------------------------------------------------

File "numba_test.py", line 40

Failed at nopython (nopython frontend)
Internal error at <numba.typeinfer.ExhaustIterConstraint object at 0x788cc9572d50>:
--%<-----------------------------------------------------------------
Traceback (most recent call last):
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 128, in propagate
    constraint(typeinfer)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 264, in __call__
    raise TypingError("failed to unpack {}".format(tp), loc=self.loc)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/contextlib.py", line 35, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/errors.py", line 249, in new_error_context
    six.reraise(type(newerr), newerr, sys.exc_info()[2])
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/errors.py", line 243, in new_error_context
    yield
  File "/home/clasocki/anaconda2/envs/my_numba_env/lib/python2.7/site-packages/numba/typeinfer.py", line 264, in __call__
    raise TypingError("failed to unpack {}".format(tp), loc=self.loc)
InternalError: local variable 'tp' referenced before assignment
[1] During: typing of exhaust iter at numba_test.py (40)
--%<-----------------------------------------------------------------

File "numba_test.py", line 40

This error may have been caused by the following argument(s):
- argument 0: Unsupported array dtype: object

下面是我如何指定numba装饰器：

^{pr2}$

这就是run方法的调用方式：

x = numpy.asarray([1.0, 2.0])
y = numpy.asarray([(1.0,2.0), (3.0,4.0)])
a = numpy.asarray([ConvertedDocument(x,y)])
print run(a)

如果将anumpy数组替换为Python列表，则异常情况如下：

Failed at nopython (nopython mode backend)
reflected list(instance.jitclass.ConvertedDocument#3bffb70<profile:array(float64, 1d, C),word_weights:array(float64, 2d, C)>): unsupported nested memory-managed object

当自定义方法的数组被使用时，是否有人知道如何使用自定义方法的数组？在

Answer 1

问题似乎是您不能对jitclass对象调用np.nditer，这是有意义的，因为jitclass是不可读取的。它将数据存储为数组（和其他数据类型）的结构，而不是结构数组。你试图用它作为后者。如果除了两个数组属性之外，还有一堆标量数据属性或大小不同的数组，那么如何迭代jitclass对象就显得模棱两可了。在

诚然，错误信息并不清楚。我的建议是直接迭代您需要的word_weights索引。在