root/wd/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:746: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
 array = np.asarray(array, order=order, dtype=dtype)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [132], in ()
 6 # Fit the model to the data
 7 print(type(X[0]))
----> 8 model.fit(X)
 10 # Get the cluster labels
 11 labels = model.labels\_

File ~/wd/venv/lib/python3.8/site-packages/sklearn/cluster/\_dbscan.py:346, in DBSCAN.fit(self, X, y, sample\_weight)
 321 def fit(self, X, y=None, sample\_weight=None):
 322 """Perform DBSCAN clustering from features, or distance matrix.
 323 
 324 Parameters
 (...)
 344 Returns a fitted instance of self.
 345 """
--> 346 X = self.\_validate\_data(X, accept\_sparse="csr")
 348 if not self.eps > 0.0:
 349 raise ValueError("eps must be positive.")

File ~/wd/venv/lib/python3.8/site-packages/sklearn/base.py:566, in BaseEstimator.\_validate\_data(self, X, y, reset, validate\_separately, \*\*check\_params)
 564 raise ValueError("Validation should be done on X, y or both.")
 565 elif not no\_val\_X and no\_val\_y:
--> 566 X = check\_array(X, \*\*check\_params)
 567 out = X
 568 elif no\_val\_X and not no\_val\_y:

File ~/wd/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:769, in check\_array(array, accept\_sparse, accept\_large\_sparse, dtype, order, copy, force\_all\_finite, ensure\_2d, allow\_nd, ensure\_min\_samples, ensure\_min\_features, estimator)
 767 # If input is 1D raise error
 768 if array.ndim == 1:
--> 769 raise ValueError(
 770 "Expected 2D array, got 1D array instead:\narray={}.\n"
 771 "Reshape your data either using array.reshape(-1, 1) if "
 772 "your data has a single feature or array.reshape(1, -1) "
 773 "if it contains a single sample.".format(array)
 774 )
 776 # make sure we actually converted to numeric:
 777 if dtype\_numeric and array.dtype.kind in "OUSV":

ValueError: Expected 2D array, got 1D array instead:
array=[array([ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
 -2.44249065e-15, 1.33226763e-15, -1.77635684e-15, 1.00000000e+00,
 1.00000000e+00, 1.00000000e+00])
 array([ 1.00087825, 0.99922117, 1.00750659, 1.00253534, -0.00165708,
 0.00828542, -0.00497125, 0.99834437, 1.00829187, 0.99506579])
 array([ 1.00000000e+00, 1.00000000e+00, 9.96504068e-01, 6.53445290e-01,
 4.88498131e-15, -3.49593230e-03, -3.43058777e-01, 1.00000000e+00,
 9.96504068e-01, 6.55737705e-01])
 ...
 array([ 1.50457269, 0.50457269, 1. , 1.00238771, -1. ,
 0.49542731, 0.00238771, 0.33535946, 1.981875 , 1.00238771])
 array([ 2.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
 -1.00000000e+00, 4.99600361e-15, 0.00000000e+00, 5.00000000e-01,
 1.00000000e+00, 1.00000000e+00])
 array([ 1.00076749e+00, 1.00000000e+00, 4.51467269e-01, 5.50022573e-01,
 -7.67494357e-04, -5.48532731e-01, 9.85553047e-02, 9.99233094e-01,
 4.51467269e-01, 1.21830000e+00]) ].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
oot/wd/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:746: VisibleDeprecationWarning: 고르지 않게 중첩된 시퀀스(목록 또는 튜플의 목록 또는 튜플 또는 길이 또는 모양이 다른 배열)로부터 배열을 만드는 것은 더 이상 사용되지 않습니다. 이 작업을 수행하려면 배열을 만들 때 'dtype=object'를 지정해야 합니다. array = np.asarray(array, order=order, dtype=dtype)---------------------------------------------------------------------------ValueError 트레이스백(가장 최근 호출 마지막)입력 In [132], in () 6 # 데이터에 모델 맞추기 7 print(type(X[0]))----> 8 model.fit(X) 10 # 클러스터 레이블 가져오기 11 labels = model.labels\_파일 ~/wd/venv/lib/python3.8/site-packages/sklearn/cluster/\_dbscan.py:346, in DBSCAN.fit(self, X, y, sample\_weight) 321 def fit(self, X, y=None, sample\_weight=None): 322 """특징 또는 거리 행렬에서 DBSCAN 클러스터링을 수행합니다. 323  324 파라미터 (...) 344 자의 적합 인스턴스를 반환합니다. 345 """--> 346 X = self.\_validate\_data(X, accept\_sparse="csr") 348 if not self.eps > 0.0: 349 raise ValueError("eps는 양수여야 합니다.")파일 ~/wd/venv/lib/python3.8/site-packages/sklearn/base.py:566, in BaseEstimator.\_validate\_data(self, X, y, reset, validate\_separately, \*\*check\_params) 564 raise ValueError("유효성 검사는 X, y 또는 둘 다에서 수행되어야 합니다.") 565 elif no\_val\_X와 no\_val\_y가 아니라면:--> 566 X = check\_array(X, \*\*check\_params) 567 out = X 568 no\_val\_X이고 no\_val\_y가 아닌 경우:파일 ~/wd/venv/lib/python3.8/site-packages/sklearn/utils/validation.py:769, in check\_array(array, accept\_sparse, accept\_large\_sparse, dtype, order, copy, force\_all\_finite, ensure\_2d, allow\_nd, ensure\_min\_samples, ensure\_min\_features, estimator) 767 # 입력이 1D이면 에러 발생 768 array.ndim == 1:--> 769 raise ValueError( 770 "2D 배열이 예상되었으나 대신 1D 배열을 얻었습니다:\narray={}.\n" 771 "array.reshape(-1, 1)를 사용하여 데이터를 재형성하십시오." 772 "데이터에 단일 피처가 있거나 array.reshape(1, -1) " 773 "단일 샘플을 포함하는 경우.".format(array) 774 ) 776 # 실제로 숫자로 변환되었는지 확인합니다: 777 dtype\_numeric 및 array.dtype.kind가 "OUSV"인 경우:ValueError: 2D 배열을 기대했는데, 대신 1D 배열을 가져왔습니다:array=[array([ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, -2.44249065e-15, 1.33226763e-15, -1.77635684e-15, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]) array([ 1.00087825, 0.99922117, 1.00750659, 1.00253534, -0.00165708, 0.00828542, -0.00497125, 0.99834437, 1.00829187, 0.99506579]) array([ 1.00000000e+00, 1.00000000e+00, 9.96504068e-01, 6.53445290e-01, 4.88498131e-15, -3.49593230e-03, -3.43058777e-01, 1.00000000e+00, 9.96504068e-01, 6.55737705e-01]) ... array([ 1.50457269, 0.50457269, 1. , 1.00238771, -1. , 0.49542731, 0.00238771, 0.33535946, 1.981875 , 1.00238771]) array([ 2.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, -1.00000000e+00, 4.99600361e-15, 0.00000000e+00, 5.00000000e-01, 1.00000000e+00, 1.00000000e+00]) array([ 1.00076749E+00, 1.00000000E+00, 4.51467269E-01, 5.50022573E-01, -7.67494357e-04, -5.48532731e-01, 9.85553047e-02, 9.99233094e-01, 4.51467269e-01, 1.21830000e+00]) ].데이터에 단일 피처가 있는 경우 array.reshape(-1, 1)을, 단일 샘플이 포함된 경우 array.reshape(1, -1)을 사용하여 데이터를 재구성합니다.