DBSCAN MemoryError

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<timed eval> in <module>()
 
z:\pyidle\lib\site-packages\sklearn\cluster\dbscan_.py in fit(self, X, y, sample_weight)
    282         X = check_array(X, accept_sparse='csr')
    283         clust = dbscan(X, sample_weight=sample_weight,
--> 284                        **self.get_params())
    285         self.core_sample_indices_, self.labels_ = clust
    286         if len(self.core_sample_indices_):
 
z:\pyidle\lib\site-packages\sklearn\cluster\dbscan_.py in dbscan(X, eps, min_samples, metric, metric_params, algorithm, leaf_size, p, sample_weight, n_jobs)
    143         # This has worst case O(n^2) memory complexity
    144         neighborhoods = neighbors_model.radius_neighbors(X, eps,
--> 145                                                          return_distance=False)
    146 
    147     if sample_weight is None:
 
z:\pyidle\lib\site-packages\sklearn\neighbors\base.py in radius_neighbors(self, X, radius, return_distance)
    588             if self.effective_metric_ == 'euclidean':
    589                 dist = pairwise_distances(X, self._fit_X, 'euclidean',
--> 590                                           n_jobs=self.n_jobs, squared=True)
    591                 radius *= radius
    592             else:
 
z:\pyidle\lib\site-packages\sklearn\metrics\pairwise.py in pairwise_distances(X, Y, metric, n_jobs, **kwds)
   1245         func = partial(distance.cdist, metric=metric, **kwds)
   1246 
-> 1247     return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
   1248 
   1249 
 
z:\pyidle\lib\site-packages\sklearn\metrics\pairwise.py in _parallel_pairwise(X, Y, func, n_jobs, **kwds)
   1088     if n_jobs == 1:
   1089         # Special case to avoid picklability checks in delayed
-> 1090         return func(X, Y, **kwds)
   1091 
   1092     # TODO: in some cases, backend='threading' may be appropriate
 
z:\pyidle\lib\site-packages\sklearn\metrics\pairwise.py in euclidean_distances(X, Y, Y_norm_squared, squared, X_norm_squared)
    244         YY = row_norms(Y, squared=True)[np.newaxis, :]
    245 
--> 246     distances = safe_sparse_dot(X, Y.T, dense_output=True)
    247     distances *= -2
    248     distances += XX
 
z:\pyidle\lib\site-packages\sklearn\utils\extmath.py in safe_sparse_dot(a, b, dense_output)
    135         ret = a * b
    136         if dense_output and hasattr(ret, "toarray"):
--> 137             ret = ret.toarray()
    138         return ret
    139     else:
 
z:\pyidle\lib\site-packages\scipy\sparse\compressed.py in toarray(self, order, out)
    941         if out is None and order is None:
    942             order = self._swap('cf')[0]
--> 943         out = self._process_toarray_args(order, out)
    944         if not (out.flags.c_contiguous or out.flags.f_contiguous):
    945             raise ValueError('Output array must be C or F contiguous')
 
z:\pyidle\lib\site-packages\scipy\sparse\base.py in _process_toarray_args(self, order, out)
   1128             return out
   1129         else:
-> 1130             return np.zeros(self.shape, dtype=self.dtype, order=order)
   1131 
   1132     def __numpy_ufunc__(self, func, method, pos, inputs, **kwargs):
 
ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.