fromcollectionsimportCounterfromsklearn.datasetsimportmake_classificationfromimblearn.over_samplingimportSVMSMOTE# doctest: +NORMALIZE_WHITESPACEX,y=make_classification(n_classes=3,class_sep=0,weights=[0.004,0.451,0.545],n_informative=3,n_redundant=0,flip_y=0,n_features=3,n_clusters_per_class=2,n_samples=1000,random_state=10)print('Original dataset shape %s'%Counter(y))sm=SVMSMOTE(random_state=42,k_neighbors=4)X_res,y_res=sm.fit_resample(X,y)print('Resampled dataset shape %s'%Counter(y_res))

Original dataset shape Counter({2: 544, 1: 451, 0: 5})---------------------------------------------------------------------------ValueError                                Traceback (most recent call last)<ipython-input-78-8f5d2308c2bd> in <module>()     10      11 sm = SVMSMOTE(random_state=42, k_neighbors=4)---> 12 X_res, y_res = sm.fit_resample(X, y)     13 print('Resampled dataset shape %s' % Counter(y_res))~/anaconda3/lib/python3.6/site-packages/imblearn/base.py in fit_resample(self, X, y)     82             self.sampling_strategy, y, self._sampling_type)     83 ---> 84         output = self._fit_resample(X, y)     85      86         if binarize_y:~/anaconda3/lib/python3.6/site-packages/imblearn/over_sampling/_smote.py in _fit_resample(self, X, y)    530     def _fit_resample(self, X, y):    531         # print("_fit_resample X shape", X.shape)--> 532         return self._sample(X, y)    533     534     def _sample(self, X, y):~/anaconda3/lib/python3.6/site-packages/imblearn/over_sampling/_smote.py in _sample(self, X, y)    569     570             danger_bool = self._in_danger_noise(--> 571                 self.nn_m_, support_vector, class_sample, y, kind='danger')    572             safety_bool = np.logical_not(danger_bool)    573 ~/anaconda3/lib/python3.6/site-packages/imblearn/over_sampling/_smote.py in _in_danger_noise(self, nn_estimator, samples, target_class, y, kind)    213         # print("kind", kind)    214         # print("_in_danger_noise samples shape", samples.shape)--> 215         x = nn_estimator.kneighbors(samples, return_distance=False)[:, 1:]    216         # print("x", x)    217         nn_label = (y[x] != target_class).astype(int)~/anaconda3/lib/python3.6/site-packages/sklearn/neighbors/base.py in kneighbors(self, X, n_neighbors, return_distance)    400         if X is not None:    401             query_is_train = False--> 402             X = check_array(X, accept_sparse='csr')    403         else:    404             query_is_train = True~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py 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, warn_on_dtype, estimator)    548                              " minimum of %d is required%s."    549                              % (n_samples, array.shape, ensure_min_samples,--> 550                                 context))    551     552     if ensure_min_features > 0 and array.ndim == 2:ValueError: Found array with 0 sample(s) (shape=(0, 3)) while a minimum of 1 is required.