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python - 如何水平堆叠csr矩阵和numpy.ndarry？

问题描述

我有一个问题，我必须堆叠一个numpy.ndarray （其中包含字符串值）和一个csr 矩阵（其中包含浮点值）

我尝试执行以下操作

1)

from scipy.sparse import hstack
from scipy import sparse
temp = hstack((image_features,sparse.csr_matrix(feature_names)))
print(temp.shape)
print(type(temp))

这给了我以下错误

TypeError: no supported conversion for types: (dtype('O'),)

2)

from scipy.sparse import hstack
from scipy import sparse
temp = hstack((image_features.astype(object),feature_names))
print(temp.shape)
print(type(temp))

由于两个矩阵的大小，这给了我一个内存错误

print(type(image_features))
--> <class 'scipy.sparse.csr.csr_matrix'>
print(type(feature_names))
--> <class 'numpy.ndarray'>

print(image_features.shape)
--> (140047, 34464)
print(feature_names.shape)
--> (140047, 2)

两个矩阵的第一行供参考 

print(image_features[0].toarray())
--> array([[0.       , 0.       , 0.       , ..., 0.       , 0.6384238,
        0.       ]])
print(feature_names[0])
--> array(['00007787805e474ea3f33c722178f550', 'Men'], dtype=object)

更新：

输出：

标签： pythonnumpyscipyconcatenation

解决方案

解决方案

尝试以下方法之一/或。我认为第二种方法适用于您的情况。

import numpy as np

# Method-1: 2 elements' tuple array data for feature_names
np.hstack([image_features.astype('O'), feature_names[:, np.newaxis])

# Method-2: 2 columns array data for feature_names
np.hstack([image_features.astype('O'), 
           np.array(feature_names.tolist()).reshape(-1,2).astype('O')])

# Method-3: using pandas
import pandas as pd
df1 = pd.DataFrame(image_features)
df2 = pd.DataFrame(np.array(feature_names.tolist()).reshape(-1,2).astype('O'), columns=['ID', 'Gender'])
df = pd.merge(df1, df2, left_index=True, right_index=True)
#df.head()
df.to_numpy()

例子

我们将制作一些虚拟数据并测试上面给出的解决方案。

import numpy as np

image_features = np.random.rand(2,10).round(3)
feature_names = np.array([('00007787805e474ea3f33c722178f550', 'Male'), 
                          ('00007787805e474ea3f33c722223f550', 'Female')], 
                        dtype=[('ID', 'O'),('Gender', 'O')])
print('Shape BEFORE newaxis addition: {}'.format((image_features.shape, 
                                                  feature_names.shape)))
feature_names = feature_names[:, np.newaxis]
print('Shape AFTER newaxis addition: {}'.format((image_features.shape, 
                                                 feature_names.shape)))
stacked = np.hstack([image_features.astype('O'), feature_names])
print(stacked)

输出： 

Shape BEFORE newaxis addition: ((2, 10), (2,))
Shape AFTER newaxis addition: ((2, 10), (2, 1))
[[0.335 0.576 0.769 0.442 0.34 0.938 0.745 0.085 0.617 0.643
  ('00007787805e474ea3f33c722178f550', 'Male')]
 [0.689 0.959 0.57 0.122 0.328 0.421 0.176 0.797 0.364 0.495
  ('00007787805e474ea3f33c722223f550', 'Female')]]

为了更清楚，让我们使用 pandas 来展示这一点： 

import pandas as pd
print(pd.DataFrame(stacked))

      0      1   ...     9                                           10
0  0.335  0.576  ...  0.643    (00007787805e474ea3f33c722178f550, Male)
1  0.689  0.959  ...  0.495  (00007787805e474ea3f33c722223f550, Female)

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