인프런 커뮤니티 질문&답변

다운그레이드 후 정상 동작합니다!! 빠른 회신과 정확한 답변에 감탄했습니다. 정말 감사합니다 👍🏻

imblearn과 scikit learn 1.1.2 가 잘 안맞는 것 같군요. 

1.1.2는 2022년 8월 출시 버전인데 최근에 사이킷런을 upgrade하신것 같군요. 

사이킷런을 설치하면 threadpoolctl 모듈도 같이 설치하는데 이게 뭔가 버전이 잘 안맞는 것 같습니다. 

pip로 설치하셨으면 pip uninstall scikit-learn으로 사이킷런을 삭제하신 후에 pip install scikit-learn==1.0.2 로 scikit learn을 downgrade 해보시지요

conda로 하셨으면 conda install scikit-learn=1.0.2 로 해보시지요. 

오류 전체 메세지 입니다!!

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [30], in <cell line: 4>()
      1 from imblearn.over_sampling import SMOTE
      3 smote = SMOTE(random_state=0)
----> 4 X_train_over, y_train_over = smote.fit_resample(X_train, y_train)
      5 print('SMOTE 적용 전 학습용 피처/레이블 데이터 세트: ', X_train.shape, y_train.shape)
      6 print('SMOTE 적용 후 학습용 피처/레이블 데이터 세트: ', X_train_over.shape, y_train_over.shape)

File ~/opt/anaconda3/lib/python3.9/site-packages/imblearn/base.py:83, in SamplerMixin.fit_resample(self, X, y)
     77 X, y, binarize_y = self._check_X_y(X, y)
     79 self.sampling_strategy_ = check_sampling_strategy(
     80     self.sampling_strategy, y, self._sampling_type
     81 )
---> 83 output = self._fit_resample(X, y)
     85 y_ = (
     86     label_binarize(output[1], classes=np.unique(y)) if binarize_y else output[1]
     87 )
     89 X_, y_ = arrays_transformer.transform(output[0], y_)

File ~/opt/anaconda3/lib/python3.9/site-packages/imblearn/over_sampling/_smote/base.py:324, in SMOTE._fit_resample(self, X, y)
    321 X_class = _safe_indexing(X, target_class_indices)
    323 self.nn_k_.fit(X_class)
--> 324 nns = self.nn_k_.kneighbors(X_class, return_distance=False)[:, 1:]
    325 X_new, y_new = self._make_samples(
    326     X_class, y.dtype, class_sample, X_class, nns, n_samples, 1.0
    327 )
    328 X_resampled.append(X_new)

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_base.py:763, in KNeighborsMixin.kneighbors(self, X, n_neighbors, return_distance)
    756 use_pairwise_distances_reductions = (
    757     self._fit_method == "brute"
    758     and PairwiseDistancesArgKmin.is_usable_for(
    759         X if X is not None else self._fit_X, self._fit_X, self.effective_metric_
    760     )
    761 )
    762 if use_pairwise_distances_reductions:
--> 763     results = PairwiseDistancesArgKmin.compute(
    764         X=X,
    765         Y=self._fit_X,
    766         k=n_neighbors,
    767         metric=self.effective_metric_,
    768         metric_kwargs=self.effective_metric_params_,
    769         strategy="auto",
    770         return_distance=return_distance,
    771     )
    773 elif (
    774     self._fit_method == "brute" and self.metric == "precomputed" and issparse(X)
    775 ):
    776     results = _kneighbors_from_graph(
    777         X, n_neighbors=n_neighbors, return_distance=return_distance
    778     )

File sklearn/metrics/_pairwise_distances_reduction.pyx:698, in sklearn.metrics._pairwise_distances_reduction.PairwiseDistancesArgKmin.compute()

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/fixes.py:151, in threadpool_limits(limits, user_api)
    149     return controller.limit(limits=limits, user_api=user_api)
    150 else:
--> 151     return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)

File ~/opt/anaconda3/lib/python3.9/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 ~/opt/anaconda3/lib/python3.9/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 ~/opt/anaconda3/lib/python3.9/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 ~/opt/anaconda3/lib/python3.9/site-packages/threadpoolctl.py:371, in _ThreadpoolInfo._load_modules(self)
    369 """Loop through loaded libraries and store supported ones"""
    370 if sys.platform == "darwin":
--> 371     self._find_modules_with_dyld()
    372 elif sys.platform == "win32":
    373     self._find_modules_with_enum_process_module_ex()

File ~/opt/anaconda3/lib/python3.9/site-packages/threadpoolctl.py:428, in _ThreadpoolInfo._find_modules_with_dyld(self)
    425 filepath = filepath.decode("utf-8")
    427 # Store the module if it is supported and selected
--> 428 self._make_module_from_path(filepath)

File ~/opt/anaconda3/lib/python3.9/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 ~/opt/anaconda3/lib/python3.9/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 ~/opt/anaconda3/lib/python3.9/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'

Imblearn과 scikit-learn 모두 Version Update 하였으며,  Version은 아래와 같습니다. 아래와 같이 동일한 오류가 발생하고 있습니다. 

imbalanced-learn : 0.9.1

scikit-learn : 1.1.2

안녕하십니까,  

아래와 같이 설치를 수행하고 버전을 확인해 보시기 바랍니다. 

pip install -U imbalanced-learn

감사합니다.