importpandasaspdfromimblearn.over_samplingimportSMOTENCXx=pd.DataFrame({'c':pd.Categorical([xforxin"abbacaba"*3]),'f':[.3,.5,.1,.2]*6,'b':[False,False,True]*8})y=pd.DataFrame({'out':[1,0,0,0,0,1,0,0,1,1,0,0]*2})# Xx.info(), y.info()SMOTENC(categorical_features=[0]).fit_resample(Xx.drop(columns=['b']),y)# OK cat + floatSMOTENC(categorical_features=[0]).fit_resample(Xx.astype({'b':float}),y)# OK cat + float + floatSMOTENC(categorical_features=[0]).fit_resample(Xx.astype({'c':str}),y)# OK 2str/cat + float + boolSMOTENC(categorical_features=[0,2]).fit_resample(Xx.astype({'b':str}),y)# OK cat + float + 2str/catSMOTENC(categorical_features=[0,2]).fit_resample(Xx.astype({'b':str}).astype({'b':'category'}),y)# OK cat + float + 2str2cat/catSMOTENC(categorical_features=[0]).fit_resample(Xx,y)# ERROR cat + float + bool# SMOTENC(categorical_features=[0,2]).fit_resample(Xx, y)                                           # ERROR cat + float + bool/cat# SMOTENC(categorical_features=[0,2]).fit_resample(Xx.astype({'b':'category'}), y)                  # ERROR cat + float + 2cat/cat

---------------------------------------------------------------------------ValueError                                Traceback (most recent call last)File ~/miniconda3/lib/python3.10/site-packages/pandas/core/arrays/categorical.py:551, in Categorical.astype(self, dtype, copy)    550 try:--> 551     new_cats = new_cats.astype(dtype=dtype, copy=copy)    552     fill_value = self.categories._na_valueValueError: could not convert string to float: 'a'During handling of the above exception, another exception occurred:ValueError                                Traceback (most recent call last)Cell In[50], line 23     20 SMOTENC(categorical_features=[0,2]).fit_resample(Xx.astype({'b':str}), y)                           # OK cat + float + 2str/cat     21 SMOTENC(categorical_features=[0,2]).fit_resample(Xx.astype({'b':str}).astype({'b':'category'}), y)  # OK cat + float + 2str2cat/cat---> 23 SMOTENC(categorical_features=[0]).fit_resample(Xx, y)                                             # ERROR cat + float + bool     24 # SMOTENC(categorical_features=[0,2]).fit_resample(Xx, y)                                           # ERROR cat + float + bool/cat     25 # SMOTENC(categorical_features=[0,2]).fit_resample(Xx.astype({'b':'category'}), y)                  # ERROR cat + float + 2cat/catFile ~/miniconda3/lib/python3.10/site-packages/imblearn/base.py:203, in BaseSampler.fit_resample(self, X, y)    182 """Resample the dataset.    183     184 Parameters   (...)    200     The corresponding label of `X_resampled`.    201 """    202 self._validate_params()--> 203 return super().fit_resample(X, y)File ~/miniconda3/lib/python3.10/site-packages/imblearn/base.py:82, in SamplerMixin.fit_resample(self, X, y)     80 check_classification_targets(y)     81 arrays_transformer = ArraysTransformer(X, y)---> 82 X, y, binarize_y = self._check_X_y(X, y)     84 self.sampling_strategy_ = check_sampling_strategy(     85     self.sampling_strategy, y, self._sampling_type     86 )     88 output = self._fit_resample(X, y)File ~/miniconda3/lib/python3.10/site-packages/imblearn/over_sampling/_smote/base.py:540, in SMOTENC._check_X_y(self, X, y)    536 """Overwrite the checking to let pass some string for categorical    537 features.    538 """    539 y, binarize_y = check_target_type(y, indicate_one_vs_all=True)--> 540 X, y = self._validate_data(    541     X, y, reset=True, dtype=None, accept_sparse=["csr", "csc"]    542 )    543 return X, y, binarize_yFile ~/miniconda3/lib/python3.10/site-packages/sklearn/base.py:554, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)    552         y = check_array(y, input_name="y", **check_y_params)    553     else:--> 554         X, y = check_X_y(X, y, **check_params)    555     out = X, y    557 if not no_val_X and check_params.get("ensure_2d", True):File ~/miniconda3/lib/python3.10/site-packages/sklearn/utils/validation.py:1104, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)   1099         estimator_name = _check_estimator_name(estimator)   1100     raise ValueError(   1101         f"{estimator_name} requires y to be passed, but the target y is None"   1102     )-> 1104 X = check_array(   1105     X,   1106     accept_sparse=accept_sparse,   1107     accept_large_sparse=accept_large_sparse,   1108     dtype=dtype,   1109     order=order,   1110     copy=copy,   1111     force_all_finite=force_all_finite,   1112     ensure_2d=ensure_2d,   1113     allow_nd=allow_nd,   1114     ensure_min_samples=ensure_min_samples,   1115     ensure_min_features=ensure_min_features,   1116     estimator=estimator,   1117     input_name="X",   1118 )   1120 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)   1122 check_consistent_length(X, y)File ~/miniconda3/lib/python3.10/site-packages/sklearn/utils/validation.py:808, 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, input_name)    803 if pandas_requires_conversion:    804     # pandas dataframe requires conversion earlier to handle extension dtypes with    805     # nans    806     # Use the original dtype for conversion if dtype is None    807     new_dtype = dtype_orig if dtype is None else dtype--> 808     array = array.astype(new_dtype)    809     # Since we converted here, we do not need to convert again later    810     dtype = NoneFile ~/miniconda3/lib/python3.10/site-packages/pandas/core/generic.py:6240, in NDFrame.astype(self, dtype, copy, errors)   6233     results = [   6234         self.iloc[:, i].astype(dtype, copy=copy)   6235         for i in range(len(self.columns))   6236     ]   6238 else:   6239     # else, only a single dtype is given-> 6240     new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)   6241     return self._constructor(new_data).__finalize__(self, method="astype")   6243 # GH 33113: handle empty frame or seriesFile ~/miniconda3/lib/python3.10/site-packages/pandas/core/internals/managers.py:450, in BaseBlockManager.astype(self, dtype, copy, errors)    449 def astype(self: T, dtype, copy: bool = False, errors: str = "raise") -> T:--> 450     return self.apply("astype", dtype=dtype, copy=copy, errors=errors)File ~/miniconda3/lib/python3.10/site-packages/pandas/core/internals/managers.py:352, in BaseBlockManager.apply(self, f, align_keys, ignore_failures, **kwargs)    350         applied = b.apply(f, **kwargs)    351     else:--> 352         applied = getattr(b, f)(**kwargs)    353 except (TypeError, NotImplementedError):    354     if not ignore_failures:File ~/miniconda3/lib/python3.10/site-packages/pandas/core/internals/blocks.py:526, in Block.astype(self, dtype, copy, errors)    508 """    509 Coerce to the new dtype.    510    (...)    522 Block    523 """    524 values = self.values--> 526 new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)    528 new_values = maybe_coerce_values(new_values)    529 newb = self.make_block(new_values)File ~/miniconda3/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:299, in astype_array_safe(values, dtype, copy, errors)    296     return values.copy()    298 try:--> 299     new_values = astype_array(values, dtype, copy=copy)    300 except (ValueError, TypeError):    301     # e.g. astype_nansafe can fail on object-dtype of strings    302     #  trying to convert to float    303     if errors == "ignore":File ~/miniconda3/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:227, in astype_array(values, dtype, copy)    223     return values    225 if not isinstance(values, np.ndarray):    226     # i.e. ExtensionArray--> 227     values = values.astype(dtype, copy=copy)    229 else:    230     values = astype_nansafe(values, dtype, copy=copy)File ~/miniconda3/lib/python3.10/site-packages/pandas/core/arrays/categorical.py:562, in Categorical.astype(self, dtype, copy)    557     except (    558         TypeError,  # downstream error msg for CategoricalIndex is misleading    559         ValueError,    560     ):    561         msg = f"Cannot cast {self.categories.dtype} dtype to {dtype}"--> 562         raise ValueError(msg)    564     result = take_nd(    565         new_cats, ensure_platform_int(self._codes), fill_value=fill_value    566     )    568 return resultValueError: Cannot cast object dtype to float64