从numpy python中的稀疏矩阵生成密集矩阵
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Generating a dense matrix from a sparse matrix in numpy python
提问by
I have a Sqlite database that contains following type of schema:
我有一个包含以下类型架构的 Sqlite 数据库：
termcount(doc_num, term , count)
This table contains terms with their respective counts in the document. like
此表包含在文档中具有各自计数的术语。喜欢
(doc1 , term1 ,12)
(doc1, term 22, 2)
.
.
(docn,term1 , 10)
This matrix can be considered as sparse matrix as each documents contains very few terms that will have a non-zero value.
该矩阵可以被视为稀疏矩阵，因为每个文档都包含非常少的非零值项。
How would I create a dense matrix from this sparse matrix using numpy as I have to calculate the similarity among documents using cosine similarity.
我将如何使用 numpy 从这个稀疏矩阵创建一个密集矩阵，因为我必须使用余弦相似度计算文档之间的相似度。
This dense matrix will look like a table that have docid as the first column and all the terms will be listed as the first row.and remaining cells will contain counts.
这个密集矩阵看起来像一个表格，第一列是 docid，所有的术语都列在第一行。剩余的单元格将包含计数。
采纳答案by Rachel Gallen
I solved this problem using Pandas. Because we want to keep the document ids and term ids.
我使用 Pandas 解决了这个问题。因为我们要保留文档 ID 和术语 ID。
from pandas import DataFrame
# A sparse matrix in dictionary form (can be a SQLite database). Tuples contains doc_id and term_id.
doc_term_dict={('d1','t1'):12, ('d2','t3'):10, ('d3','t2'):5}
#extract all unique documents and terms ids and intialize a empty dataframe.
rows = set([d for (d,t) in doc_term_dict.keys()])
cols = set([t for (d,t) in doc_term_dict.keys()])
df = DataFrame(index = rows, columns = cols )
df = df.fillna(0)
#assign all nonzero values in dataframe
for key, value in doc_term_dict.items():
df[key[1]][key[0]] = value
print df
Output:
输出：
t2 t3 t1
d2 0 10 0
d3 5 0 0
d1 0 0 12
回答by Rachel Gallen
from scipy.sparse import csr_matrix
A = csr_matrix([[1,0,2],[0,3,0]])
>>>A
<2x3 sparse matrix of type '<type 'numpy.int64'>'
with 3 stored elements in Compressed Sparse Row format>
>>> A.todense()
matrix([[1, 0, 2],
[0, 3, 0]])
>>> A.toarray()
array([[1, 0, 2],
[0, 3, 0]])
this is an example of how to convert a sparse matrix to a dense matrix taken from scipy
