6
我正在尝试使用scikit-learn的DBSCAN实现将一堆文档群集化。首先，我使用scikit-learn的TfidfVectorizer（它是一个类型为numpy.float64的163405x13029稀疏矩阵）创建TF-IDF矩阵。然后我尝试将这个矩阵的特定子集聚类。当子集很小时（比如说，几千行），事情就可以正常工作。但是对于大型子集（成千上万行），我得到ValueError: could not convert integer scalar。使用DBSCAN时出现“无法转换整数标量”错误
以下是完整回溯（idxs是指数列表）：
ValueError Traceback (most recent call last)
<ipython-input-1-73ee366d8de5> in <module>()
193 # use descriptions to clusterize items
194 ncm_clusterizer = DBSCAN()
--> 195 ncm_clusterizer.fit_predict(tfidf[idxs])
196 idxs_clusters = list(zip(idxs, ncm_clusterizer.labels_))
197 for e in idxs_clusters:
/usr/local/lib/python3.4/site-packages/sklearn/cluster/dbscan_.py in fit_predict(self, X, y, sample_weight)
294 cluster labels
295 """
--> 296 self.fit(X, sample_weight=sample_weight)
297 return self.labels_
/usr/local/lib/python3.4/site-packages/sklearn/cluster/dbscan_.py in fit(self, X, y, sample_weight)
264 X = check_array(X, accept_sparse='csr')
265 clust = dbscan(X, sample_weight=sample_weight,
--> 266 **self.get_params())
267 self.core_sample_indices_, self.labels_ = clust
268 if len(self.core_sample_indices_):
/usr/local/lib/python3.4/site-packages/sklearn/cluster/dbscan_.py in dbscan(X, eps, min_samples, metric, algorithm, leaf_size, p, sample_weight, n_jobs)
136 # This has worst case O(n^2) memory complexity
137 neighborhoods = neighbors_model.radius_neighbors(X, eps,
--> 138 return_distance=False)
139
140 if sample_weight is None:
/usr/local/lib/python3.4/site-packages/sklearn/neighbors/base.py in radius_neighbors(self, X, radius, return_distance)
584 if self.effective_metric_ == 'euclidean':
585 dist = pairwise_distances(X, self._fit_X, 'euclidean',
--> 586 n_jobs=self.n_jobs, squared=True)
587 radius *= radius
588 else:
/usr/local/lib/python3.4/site-packages/sklearn/metrics/pairwise.py in pairwise_distances(X, Y, metric, n_jobs, **kwds)
1238 func = partial(distance.cdist, metric=metric, **kwds)
1239
-> 1240 return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
1241
1242
/usr/local/lib/python3.4/site-packages/sklearn/metrics/pairwise.py in _parallel_pairwise(X, Y, func, n_jobs, **kwds)
1081 if n_jobs == 1:
1082 # Special case to avoid picklability checks in delayed
-> 1083 return func(X, Y, **kwds)
1084
1085 # TODO: in some cases, backend='threading' may be appropriate
/usr/local/lib/python3.4/site-packages/sklearn/metrics/pairwise.py in euclidean_distances(X, Y, Y_norm_squared, squared, X_norm_squared)
243 YY = row_norms(Y, squared=True)[np.newaxis, :]
244
--> 245 distances = safe_sparse_dot(X, Y.T, dense_output=True)
246 distances *= -2
247 distances += XX
/usr/local/lib/python3.4/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a, b, dense_output)
184 ret = a * b
185 if dense_output and hasattr(ret, "toarray"):
--> 186 ret = ret.toarray()
187 return ret
188 else:
/usr/local/lib/python3.4/site-packages/scipy/sparse/compressed.py in toarray(self, order, out)
918 def toarray(self, order=None, out=None):
919 """See the docstring for `spmatrix.toarray`."""
--> 920 return self.tocoo(copy=False).toarray(order=order, out=out)
921
922 ##############################################################
/usr/local/lib/python3.4/site-packages/scipy/sparse/coo.py in toarray(self, order, out)
256 M,N = self.shape
257 coo_todense(M, N, self.nnz, self.row, self.col, self.data,
--> 258 B.ravel('A'), fortran)
259 return B
260
ValueError: could not convert integer scalar
我使用Python 3.4.3（红帽），SciPy的0.18.1和scikit学习0.18.1。
我试过猴子补丁建议here但没有奏效。
谷歌搜索我发现一个bugfix显然解决了其他类型的稀疏矩阵（如csr）相同的问题，但不是为coo。
我试过喂DBSCAN稀疏半径邻域图（而不是一个特征矩阵），建议here，但同样的错误发生。
我试过HDBSCAN，但同样的错误发生。
我该如何解决这个问题或绕过这个？
什么是'fit_predict（tfidf [idxs]）'中的'idxs''。你只使用tfidf的一些值吗？ –
'idxs'索引列表（是的，我只使用了tfidf的一些值 - 它总共有〜163k文件，但我只使用~107k） – Parzival
您是否尝试过更新scipy和scikit版本？ –