python - 将graphlab sframe转换为{key : values}的字典

Question

给定一个 SFrame:

+------+-----------+-----------+-----------+-----------+-----------+-----------+
|  X1  |     X2    |     X3    |     X4    |     X5    |     X6    |     X7    |
+------+-----------+-----------+-----------+-----------+-----------+-----------+
| the  | -0.060292 |  0.06763  | -0.036891 |  0.066684 |  0.024045 |  0.099091 |
|  ,   |  0.026625 |  0.073101 | -0.027073 | -0.019504 |  0.04173  |  0.038811 |
|  .   | -0.005893 |  0.093791 |  0.015333 |  0.046226 |  0.032791 |  0.110069 |
|  of  | -0.050371 |  0.031452 |  0.04091  |  0.033255 | -0.009195 |  0.061086 |
| and  |  0.005456 |  0.063237 | -0.075793 | -0.000819 |  0.003407 |  0.053554 |
|  to  |  0.01347  |  0.043712 | -0.087122 |  0.015258 |  0.08834  |  0.139644 |
|  in  | -0.019466 |  0.077509 | -0.102543 |  0.034337 |  0.130886 |  0.032195 |
|  a   | -0.072288 | -0.017494 | -0.018383 |  0.001857 |  -0.04645 |  0.133424 |
|  is  |  0.052726 |  0.041903 |  0.163781 |  0.006887 |  -0.07533 |  0.108394 |
| for  | -0.004082 | -0.024244 |  0.042166 |  0.007032 | -0.081243 |  0.026162 |
|  on  | -0.023709 | -0.038306 |  -0.16072 | -0.171599 |  0.150983 |  0.042044 |
| that |  0.062037 |  0.100348 | -0.059753 | -0.041444 |  0.041156 |  0.166704 |
|  )   |  0.052312 |  0.072473 |  -0.02067 | -0.015581 |  0.063368 | -0.017216 |
|  (   |  0.051408 |  0.186162 |  0.03028  | -0.048425 |  0.051376 |  0.004989 |
| with |  0.091825 | -0.081649 | -0.087926 | -0.061273 |  0.043528 |  0.107864 |
| was  |  0.046042 | -0.058529 |  0.040581 |  0.067748 |  0.053724 |  0.041067 |
|  as  |  0.025248 | -0.012519 | -0.054685 | -0.040581 |  0.051061 |  0.114956 |
|  it  |  0.028606 |  0.106391 |  0.025065 |  0.023486 |  0.011184 |  0.016715 |
|  by  | -0.096704 |  0.150165 |  -0.01775 |  -0.07178 |  0.004458 |  0.098807 |
|  be  | -0.109489 | -0.025908 |  0.025608 |  0.076263 | -0.047246 |  0.100489 |
+------+-----------+-----------+-----------+-----------+-----------+-----------+

我如何将 SFrame 转换成字典，这样 X1 列是键，X2 到 X7 作为 np.array()?

我尝试逐行遍历原始 SFrame 并执行如下操作:

>>> import graphlab as gl
>>> import numpy as np
>>> x = gl.SFrame()
>>> a = np.array([1,2,3])
>>> w = 'foo'
>>> x.append(gl.SFrame({'word':[w], 'vector':[a]}))
Columns:
    vector  array
    word    str

Rows: 1

Data:
+-----------------+------+
|      vector     | word |
+-----------------+------+
| [1.0, 2.0, 3.0] | foo  |
+-----------------+------+
[1 rows x 2 columns]

还有其他方法可以做到吗？

---

已编辑

尝试@papayawarrior 解决方案后，如果我可以将整个数据帧加载到内存中，它就可以工作，但有一些奇怪的地方让它变得奇怪。

假设我对 SFrame 的原始输入如上所示(有 501 列)但在 .csv 文件中，我有将它们读入所需字典的代码:

def get_embeddings(embedding_gzip, size):
    coltypes = [str] + [float] * size
    sf = gl.SFrame.read_csv('compose-vectors/' + embedding_gzip, delimiter='\t', column_type_hints=coltypes, header=False, quote_char='\0')
    sf = sf.pack_columns(['X'+str(i) for i in range(2, size+1)])
    df = sf.to_dataframe().set_index('X1')
    print list(df)
    return df.to_dict(orient='dict')['X2']

但奇怪的是它给出了这个错误:

  File "sts_compose.py", line 28, in get_embeddings
    return df.to_dict(orient='dict')['X2']
KeyError: 'X2'

所以当我在转换为字典之前检查列名时，我发现我的列名不是 'X1' 和 'X2' 而是 list(df) 打印 [' X501', 'X3'].

我转换 graphlab.SFrame -> pandas.DataFrame -> dict 的方式有问题吗？

我知道我可以通过这样做来解决问题，但问题仍然存在，“列名怎么变得如此奇怪？”:

def get_embeddings(embedding_gzip, size):
    coltypes = [str] + [float] * size
    sf = gl.SFrame.read_csv('compose-vectors/' + embedding_gzip, delimiter='\t', column_type_hints=coltypes, header=False, quote_char='\0')
    sf = sf.pack_columns(['X'+str(i) for i in range(2, size+1)])
    df = sf.to_dataframe().set_index('X1')
    col_names = list(df)
    return df.to_dict(orient='dict')[col_names[1]]

Answer 1

是否有另一种方法可以做到这一点？是的，您可以使用 SFrame 类中的 pack_columns 方法。

import graphlab as gl
data = gl.SFrame()
data.add_column(gl.SArray(['foo', 'bar']), 'X1')
data.add_column(gl.SArray([1., 3.]), 'X2')
data.add_column(gl.SArray([2., 4.]), 'X3')
print data
+-----+-----+-----+
|  X1 |  X2 |  X3 |
+-----+-----+-----+
| foo | 1.0 | 2.0 |
| bar | 3.0 | 4.0 |
+-----+-----+-----+
[2 rows x 3 columns]

import array
data = data.pack_columns(['X2', 'X3'], dtype=array.array, new_column_name='vector')
data = data.rename({'X1':'word'})
print data
+------+------------+
| word |   vector   |
+------+------------+
| foo  | [1.0, 2.0] |
| bar  | [3.0, 4.0] |
+------+------------+
[2 rows x 2 columns]

b=data['vector'][0]
print type(b)
<type 'array.array'>

如何将 SFrame 转换为字典，使 X1 列成为键，X2 到 X7 作为 np.array()？

我没有找到任何将 SFrame 转换为字典的内置方法。您可以尝试以下方法(可能会很慢):

a={}
def dump_sframe_to_dict(row, a):
    a[row['word']]=row['vector']
data.apply(lambda x: dump_sframe_to_dict(x, a))
print a
{'foo': array('d', [1.0, 2.0]), 'bar': array('d', [3.0, 4.0])}