python - 使用 hstack 时矩阵格式错误？

Question

我有以下矩阵:

>>> X1
shape: (2399, 39999)
type: scipy.sparse.csr.csr_matrix

和

>> X2
shape: (2399, 333534)
type: scipy.sparse.csr.csr_matrix

和

>>>X3.reshape(-1,1)
shape: (2399, 1)
type: <class 'numpy.ndarray'>

如何在右侧连接 X1 和 X2 以生成具有以下形状的新矩阵:(2399, 373534)。我知道这可以用 scipy 的 hstack 来完成或 vstack .但是，当我尝试:

X_combined = sparse.hstack([X1,X2,X3.T])

但是，我得到了一个格式错误的最终矩阵:

ValueError: all the input array dimensions except for the concatenation axis must match exactly

因此，如何在单个矩阵中正确连接？

更新

from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer(min_df=5)
X1 = count_vect.fit_transform(X)

和

from sklearn.feature_extraction.text import TfidfVectorizer
tdidf_vect = TfidfVectorizer()
X2 = tdidf_vect.fit_transform(X)

和

from hdbscan import HDBSCAN
clusterer = HDBSCAN().fit(X1)
X3 = clusterer.labels_
print(X3.shape)
print(type(X3))

然后:

在:

import scipy as sparse

X_combined = sparse.hstack([X1,X2,X3.reshape(-1,1)])

输出:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-16-14baa47e0993> in <module>()
      5 
      6 
----> 7 X_combined = sparse.hstack([X1,X2,X3.reshape(-1,1)])

/usr/local/lib/python3.5/site-packages/numpy/core/shape_base.py in hstack(tup)
    284     # As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
    285     if arrs[0].ndim == 1:
--> 286         return _nx.concatenate(arrs, 0)
    287     else:
    288         return _nx.concatenate(arrs, 1)

ValueError: all the input arrays must have same number of dimensions

Answer 1

问题是你的import，应该是

from scipy import sparse

顶级 scipy 模块(通常你不应该使用顶级 scipy 模块)导入了 numpy 函数，所以当你尝试你的版本时:

>>> import scipy as sparse
>>> sparse.hstack
<function numpy.core.shape_base.hstack>

>>> # incorrect! Correct would be

>>> from scipy import sparse
>>> sparse.hstack
<function scipy.sparse.construct.hstack>

这在他们的documentation中都提到了:

The scipy namespace itself only contains functions imported from numpy. These functions still exist for backwards compatibility, but should be imported from numpy directly.

Everything in the namespaces of scipy submodules is public. In general, it is recommended to import functions from submodule namespaces.