我需要在 nvidia 的 rapids cudf 库的定义块中为 sklearn 的内核密度函数指定 dtype（数据类型）。在 Python 3.7 中，我能够找到类型信息，但由于某种原因，它不被认为是 nvidia 的 rapids def 块接受的数据类型。我在下面包含了我的代码和错误消息，以便任何人都可以重现错误消息。
下面是内核密度函数的典型实现代码：
from sklearn.neighbors import KernelDensity
import numpy as np
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
kde = KernelDensity(kernel='gaussian', bandwidth=0.2).fit(X)
kde.score_samples(X)
array([-0.41075698, -0.41075698, -0.41076071, -0.41075698, -0.41075698,
-0.41076071])
type(kde)
<class 'sklearn.neighbors.kde.KernelDensity'>
这是我与 Sklearn 的内核密度函数一起使用的 NVIDIA Rapids Def 块：
import cudf, math
import numpy as np
df = cudf.DataFrame()
nelem = 10
df['in1'] = np.arange(nelem) * 1.5
df['in2'] = np.arange(nelem) * 1.45
#Define input columns for the kernel
in1 = df['in1']
in2 = df['in2']
def kernel(in1, in2, out1, out2, out3, out4, kwarg1, kwarg2):
for i, (x, y) in enumerate(zip(in1, in2)):
out1[i] = [math.tan(i) for i in x]
out2[i] = np.array(out1[i].to_pandas())
out3[i] = ((KernelDensity(kernel='gaussian', bandwidth=kwarg1).fit(out2[i])).score_samples(out2[i]))
out4[i] = [i >= kwarg2 for i in out3[i]]
Results = cudf.DataFrame()
Results = df.apply_rows(kernel, incols=['in1','in2'], outcols=dict(out1='float', out2='float64', out3='float64', out4='float'), kwargs=dict(kwarg1=0.1, kwarg2=0.33))
这是错误消息（也许如果我得到正确的 x 和 out3 的 dtype，这将解决所有错误）：
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/cudf/dataframe/dataframe.py", line 2707, in apply_rows
self, func, incols, outcols, kwargs, cache_key=cache_key
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/cudf/utils/applyutils.py", line 64, in apply_rows return applyrows.run(df)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/cudf/utils/applyutils.py", line 128, in run self.launch_kernel(df, bound.args, **launch_params)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/cudf/utils/applyutils.py", line 152, in launch_kernel self.kernel[blkct, blksz](*args)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/cuda/compiler.py", line 806, in __call__ kernel = self.specialize(*args)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/cuda/compiler.py", line 817, in specialize kernel = self.compile(argtypes)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/cuda/compiler.py", line 833, in compile **self.targetoptions)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler_lock.py", line 32, in _acquire_compile_lock return func(*args, **kwargs)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/cuda/compiler.py", line 62, in compile_kernel
cres = compile_cuda(pyfunc, types.void, args, debug=debug, inline=inline)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler_lock.py", line 32, in _acquire_compile_lock, return func(*args, **kwargs)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/cuda/compiler.py", line 51, in compile_cuda, locals={})
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler.py", line 972, in compile_extra, return pipeline.compile_extra(func)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler.py", line 390, in compile_extra, return self._compile_bytecode()
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler.py", line 903, in _compile_bytecode, return self._compile_core()
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler.py", line 890, in _compile_core, res = pm.run(self.status)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler_lock.py", line 32, in _acquire_compile_lock, return func(*args, **kwargs)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler.py", line 266, in run
raise patched_exception
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler.py", line 257, in run
stage()
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler.py", line 515, in stage_nopython_frontend self.locals)
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/compiler.py", line 1124, in type_inference_stage, infer.propagate()
File "/anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/typeinfer.py", line 927, in propagate, raise errors[0]
numba.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Invalid use of Function(<numba.cuda.compiler.DeviceFunctionTemplate object at 0x7f2679e6f9e8>) with argument(s) of type(s): (array(float64, 1d, A), array(float64, 1d, A), array(float64, 1d, A), array(float64, 1d, A), array(float64, 1d, A), array(float64, 1d, A), float64, float64) * parameterized
In definition 0:
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Untyped global name 'x': cannot determine Numba type of <class 'numba.ir.UndefinedType'>
File "<stdin>", line 2:
<source missing, REPL/exec in use?>
raised from /anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/typeinfer.py:1254
In definition 1:
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Untyped global name 'x': cannot determine Numba type of <class 'numba.ir.UndefinedType'>
File "<stdin>", line 2:
<source missing, REPL/exec in use?>
raised from /anaconda3/envs/rapidsAI/lib/python3.7/site-packages/numba/typeinfer.py:1254
This error is usually caused by passing an argument of a type that is unsupported by the named function.
[1] During: resolving callee type: Function(<numba.cuda.compiler.DeviceFunctionTemplate object at 0x7f2679e6f9e8>)
[2] During: typing of call at <string> (11)
File "<string>", line 11:
<source missing, REPL/exec in use?>