python - 创建 |N| x |M|来自哈希表的矩阵

Question

假设我有一个由字符串对(键)及其各自的概率(值)组成的字典/哈希表:

import numpy as np
import random
import uuid

# Creating the N vocabulary and M vocabulary
max_word_len = 20
n_vocab_size = random.randint(8000,10000)
m_vocab_size = random.randint(8000,10000)

def random_word(): 
    return str(uuid.uuid4().get_hex().upper()[0:random.randint(1,max_word_len)])

# Generate some random words.
n_vocab = [random_word() for i in range(n_vocab_size)]
m_vocab = [random_word() for i in range(m_vocab_size)]


# Let's hallucinate probabilities for each word pair.
hashes =  {(n, m): random.random() for n in n_vocab for m in m_vocab}

hashes 哈希表看起来像这样:

{('585F', 'B4867'): 0.7582038699473549,
 ('69', 'D98B23C5809A'): 0.7341569569849136,
 ('4D30CB2BF4134', '82ED5FA3A00E4728AC'): 0.9106077161619021,
 ('DD8F8AFA5CF', 'CB'): 0.4609114677237601,
...
}

假设这是我将从 CSV 文件中读取的输入哈希表，第一列和第二列是哈希表的词对(键)，第三列是概率

如果我要将概率放入某种 numpy 矩阵中，我将不得不从哈希表中执行此操作:

 n_words, m_words = zip(*hashes.keys())
 probs = np.array([[hashes[(n, m)] for n in n_vocab] for m in m_vocab])

是否有另一种方法让prob进入|N| * |米|不通过 m_vocab 和 n_vocab 进行嵌套循环的情况下从哈希表获取矩阵？

(注意:我在这里创建随机词和随机概率，但假设我已经从文件中读取哈希表并将其读入该哈希表结构)

---

假设这两种情况，其中:

1. 哈希表来自 csv 文件(@bunji 的回答解决了这个问题)
2. 哈希表来自 pickled 字典。或者在到达需要将其转换为矩阵的部分之前，以其他方式计算哈希表。

---

重要的是最终矩阵需要是可查询的，以下是不可取的:

$ echo -e 'abc\txyz\t0.9\nefg\txyz\t0.3\nlmn\topq\t\0.23\nabc\tjkl\t0.5\n' > test.txt

$ cat test.txt
abc xyz 0.9
efg xyz 0.3
lmn opq .23
abc jkl 0.5


$ python
Python 2.7.10 (default, Jul 30 2016, 18:31:42) 
[GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.34)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pt = pd.read_csv('test.txt', index_col=[0,1], header=None, delimiter='\t').unstack().as_matrix()
>>> pt
array([[ 0.5,  nan,  0.9],
       [ nan,  nan,  0.3],
       [ nan,  nan,  nan]])
>>> pd.read_csv('test.txt', index_col=[0,1], header=None, delimiter='\t').unstack()
       2         
1    jkl opq  xyz
0                
abc  0.5 NaN  0.9
efg  NaN NaN  0.3
lmn  NaN NaN  NaN

>>> df = pd.read_csv('test.txt', index_col=[0,1], header=None, delimiter='\t').unstack()

>>> df
       2         
1    jkl opq  xyz
0                
abc  0.5 NaN  0.9
efg  NaN NaN  0.3
lmn  NaN NaN  NaN

>>> df['abc', 'jkl']
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2055, in __getitem__
    return self._getitem_multilevel(key)
  File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2099, in _getitem_multilevel
    loc = self.columns.get_loc(key)
  File "/Library/Python/2.7/site-packages/pandas/indexes/multi.py", line 1617, in get_loc
    return self._engine.get_loc(key)
  File "pandas/index.pyx", line 139, in pandas.index.IndexEngine.get_loc (pandas/index.c:4160)
  File "pandas/index.pyx", line 161, in pandas.index.IndexEngine.get_loc (pandas/index.c:4024)
  File "pandas/src/hashtable_class_helper.pxi", line 732, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13161)
  File "pandas/src/hashtable_class_helper.pxi", line 740, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13115)
KeyError: ('abc', 'jkl')
>>> df['abc']['jkl']
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2055, in __getitem__
    return self._getitem_multilevel(key)
  File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2099, in _getitem_multilevel
    loc = self.columns.get_loc(key)
  File "/Library/Python/2.7/site-packages/pandas/indexes/multi.py", line 1597, in get_loc
    loc = self._get_level_indexer(key, level=0)
  File "/Library/Python/2.7/site-packages/pandas/indexes/multi.py", line 1859, in _get_level_indexer
    loc = level_index.get_loc(key)
  File "/Library/Python/2.7/site-packages/pandas/indexes/base.py", line 2106, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))
  File "pandas/index.pyx", line 139, in pandas.index.IndexEngine.get_loc (pandas/index.c:4160)
  File "pandas/index.pyx", line 163, in pandas.index.IndexEngine.get_loc (pandas/index.c:4090)
KeyError: 'abc'

>>> df[0][2]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2055, in __getitem__
    return self._getitem_multilevel(key)
  File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2099, in _getitem_multilevel
    loc = self.columns.get_loc(key)
  File "/Library/Python/2.7/site-packages/pandas/indexes/multi.py", line 1597, in get_loc
    loc = self._get_level_indexer(key, level=0)
  File "/Library/Python/2.7/site-packages/pandas/indexes/multi.py", line 1859, in _get_level_indexer
    loc = level_index.get_loc(key)
  File "/Library/Python/2.7/site-packages/pandas/indexes/base.py", line 2106, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))
  File "pandas/index.pyx", line 139, in pandas.index.IndexEngine.get_loc (pandas/index.c:4160)
  File "pandas/index.pyx", line 161, in pandas.index.IndexEngine.get_loc (pandas/index.c:4024)
  File "pandas/src/hashtable_class_helper.pxi", line 404, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8141)
  File "pandas/src/hashtable_class_helper.pxi", line 410, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8085)
KeyError: 0

>>> df[0]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2055, in __getitem__
    return self._getitem_multilevel(key)
  File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 2099, in _getitem_multilevel
    loc = self.columns.get_loc(key)
  File "/Library/Python/2.7/site-packages/pandas/indexes/multi.py", line 1597, in get_loc
    loc = self._get_level_indexer(key, level=0)
  File "/Library/Python/2.7/site-packages/pandas/indexes/multi.py", line 1859, in _get_level_indexer
    loc = level_index.get_loc(key)
  File "/Library/Python/2.7/site-packages/pandas/indexes/base.py", line 2106, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))
  File "pandas/index.pyx", line 139, in pandas.index.IndexEngine.get_loc (pandas/index.c:4160)
  File "pandas/index.pyx", line 161, in pandas.index.IndexEngine.get_loc (pandas/index.c:4024)
  File "pandas/src/hashtable_class_helper.pxi", line 404, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8141)
  File "pandas/src/hashtable_class_helper.pxi", line 410, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8085)
KeyError: 0

生成的矩阵/数据帧应该是可查询的，即能够做类似的事情:

probs[('585F', 'B4867')] = 0.7582038699473549

Answer 1

我不确定是否有办法完全避免循环，但我想可以通过使用 itertools 对其进行优化。 :

import itertools
nested_loop_iter = itertools.product(n_vocab,m_vocab)
#note that because it iterates over n_vocab first we will need to transpose it at the end
probs = np.fromiter(map(hashes.get, nested_loop_iter),dtype=float)
probs.resize((len(n_vocab),len(m_vocab)))
probs = probs.T