[pythons]AttributeError: 'NoneType' object has no attribute 'split'

 

 

빅데이터 머신러닝 군집화 과정에서

최적의 K 찾기, 엘보 방법으로 할 때,

for i in range(1, 11):
    kmeans_i = KMeans(n_clusters=i, random_state=0) #모델 생성
    kmeans_i.fit(X_features_scaled)                     #모델 훈련
    distortions.append(kmeans_i.inertia_)

plt.plot(range(2,11), distortions, marker = 'o')
plt.xlabel('Number of clusters')
plt.ylabel('Distortion')
plt.show()

 

>> 에러 발생

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [87], in <cell line: 5>()
      5 for i in range(1, 11):
      6     kmeans_i = KMeans(n_clusters=i, random_state=0) #모델 생성
----> 7     kmeans_i.fit(X_features_scaled)                     #모델 훈련
      8     distortions.append(kmeans_i.inertia_)
     10 plt.plot(range(2,11), distortions, marker = 'o')

File ~\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1186, in KMeans.fit(self, X, y, sample_weight)
   1183     print("Initialization complete")
   1185 # run a k-means once
-> 1186 labels, inertia, centers, n_iter_ = kmeans_single(
   1187     X,
   1188     sample_weight,
   1189     centers_init,
   1190     max_iter=self.max_iter,
   1191     verbose=self.verbose,
   1192     tol=self._tol,
   1193     x_squared_norms=x_squared_norms,
   1194     n_threads=self._n_threads,
   1195 )
   1197 # determine if these results are the best so far
   1198 # we chose a new run if it has a better inertia and the clustering is
   1199 # different from the best so far (it's possible that the inertia is
   1200 # slightly better even if the clustering is the same with potentially
   1201 # permuted labels, due to rounding errors)
   1202 if best_inertia is None or (
   1203     inertia < best_inertia
   1204     and not _is_same_clustering(labels, best_labels, self.n_clusters)
   1205 ):

File ~\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:625, in _kmeans_single_lloyd(X, sample_weight, centers_init, max_iter, verbose, x_squared_norms, tol, n_threads)
    621 strict_convergence = False
    623 # Threadpoolctl context to limit the number of threads in second level of
    624 # nested parallelism (i.e. BLAS) to avoid oversubsciption.
--> 625 with threadpool_limits(limits=1, user_api="blas"):
    626     for i in range(max_iter):
    627         lloyd_iter(
    628             X,
    629             sample_weight,
   (...)
    636             n_threads,
    637         )

File ~\anaconda3\lib\site-packages\sklearn\utils\fixes.py:314, in threadpool_limits(limits, user_api)
    312     return controller.limit(limits=limits, user_api=user_api)
    313 else:
--> 314     return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)

File ~\anaconda3\lib\site-packages\threadpoolctl.py:171, in threadpool_limits.__init__(self, limits, user_api)
    167 def __init__(self, limits=None, user_api=None):
    168     self._limits, self._user_api, self._prefixes = \
    169         self._check_params(limits, user_api)
--> 171     self._original_info = self._set_threadpool_limits()

File ~\anaconda3\lib\site-packages\threadpoolctl.py:268, in threadpool_limits._set_threadpool_limits(self)
    265 if self._limits is None:
    266     return None
--> 268 modules = _ThreadpoolInfo(prefixes=self._prefixes,
    269                           user_api=self._user_api)
    270 for module in modules:
    271     # self._limits is a dict {key: num_threads} where key is either
    272     # a prefix or a user_api. If a module matches both, the limit
    273     # corresponding to the prefix is chosed.
    274     if module.prefix in self._limits:

File ~\anaconda3\lib\site-packages\threadpoolctl.py:340, in _ThreadpoolInfo.__init__(self, user_api, prefixes, modules)
    337     self.user_api = [] if user_api is None else user_api
    339     self.modules = []
--> 340     self._load_modules()
    341     self._warn_if_incompatible_openmp()
    342 else:

File ~\anaconda3\lib\site-packages\threadpoolctl.py:373, in _ThreadpoolInfo._load_modules(self)
    371     self._find_modules_with_dyld()
    372 elif sys.platform == "win32":
--> 373     self._find_modules_with_enum_process_module_ex()
    374 else:
    375     self._find_modules_with_dl_iterate_phdr()

File ~\anaconda3\lib\site-packages\threadpoolctl.py:485, in _ThreadpoolInfo._find_modules_with_enum_process_module_ex(self)
    482         filepath = buf.value
    484         # Store the module if it is supported and selected
--> 485         self._make_module_from_path(filepath)
    486 finally:
    487     kernel_32.CloseHandle(h_process)

File ~\anaconda3\lib\site-packages\threadpoolctl.py:515, in _ThreadpoolInfo._make_module_from_path(self, filepath)
    513 if prefix in self.prefixes or user_api in self.user_api:
    514     module_class = globals()[module_class]
--> 515     module = module_class(filepath, prefix, user_api, internal_api)
    516     self.modules.append(module)

File ~\anaconda3\lib\site-packages\threadpoolctl.py:606, in _Module.__init__(self, filepath, prefix, user_api, internal_api)
    604 self.internal_api = internal_api
    605 self._dynlib = ctypes.CDLL(filepath, mode=_RTLD_NOLOAD)
--> 606 self.version = self.get_version()
    607 self.num_threads = self.get_num_threads()
    608 self._get_extra_info()

File ~\anaconda3\lib\site-packages\threadpoolctl.py:646, in _OpenBLASModule.get_version(self)
    643 get_config = getattr(self._dynlib, "openblas_get_config",
    644                      lambda: None)
    645 get_config.restype = ctypes.c_char_p
--> 646 config = get_config().split()
    647 if config[0] == b"OpenBLAS":
    648     return config[1].decode("utf-8")

AttributeError: 'NoneType' object has no attribute 'split'

 

 

>> 해결방법

#최적의 K 찾기_1 : 엘보 방법
distortions = []

for i in range(2, 11):
    kmeans_i = KMeans(n_clusters=i, random_state=0) #모델 생성
    kmeans_i.fit(X_features_scaled)                     #모델 훈련
    distortions.append(kmeans_i.inertia_)

plt.plot(range(2,11), distortions, marker = 'o')
plt.xlabel('Number of clusters')
plt.ylabel('Distortion')
plt.show()

#범위 1부터 하니까 에러 떠서 2부터 시작하니까 됨...왜? 이유는 모르겠지만 해결은 됐음