Python pandas:从空格分隔的'.dat'文件生成文档术语矩阵 [英] Python pandas: Generate Document-Term matrix from whitespace delimited '.dat' file
问题描述
我正在使用Python尝试使用 Okapi BM25模型对文档进行排名.. >
我认为我可以以更有效的方式来计算Score(D,Q)所需的某些术语，例如IDF(反向文档频率)(即:计算特定术语(列)的所有非零行) .此外，我可以在矩阵中为实际得分添加新列，然后以此对文档进行排名.
文档术语向量存储在.dat文件中，该文件的结构如下:
D1 7:10 2:5
D2 1:2 3:4
其中D1是文档ID，7:10表示ID 7出现10次
此刻，我正在使用以下代码将其读入列表列表:
fname = "dtv.dat"
f = open(fname, "r")
l = [x.strip(" \n").split(" ") for x in f.readlines()]
对于给定的示例将产生以下输出:
[['D1', '7:10', '2:5'],['D2' '1:2', '3:4']]
鉴于此列表格式列表，将其转换为类似于以下内容的Python pandas DataFrame的最有效方法是什么:
0 1 2 3 7
D1 0 5 0 10
D2 2 0 4 0
如果每个文档在文件中仅出现一次，您的答案似乎还可以.否则，该代码将覆盖dict d中的某些记录.
我认为以下内容会更笼统:
import numpy as np
import pandas as pd
fname = 'example.txt'
full_list = []
with open(fname, "r") as f:
for line in f:
arr = line.strip(" \n").split(" ")
for chunk in arr[1:]:
# converting numbers to ints:
int_pair = [int(x) for x in chunk.split(":")]
full_list.append([arr[0], *int_pair])
df = pd.DataFrame(full_list)
df2 = df.pivot_table(values = 2, index = 0, columns = 1, aggfunc = np.sum, fill_value = 0)
工作原理:
>>> cat 'example.txt'
D1 1:3 2:2 3:3
D2 1:4 2:7
D2 7:1
D1 2:4 4:2
D1 4:1 4:3
>>> full_list
Out[37]:
[['D1', 1, 3],
['D1', 2, 2],
['D1', 3, 3],
['D2', 1, 4],
['D2', 2, 7],
['D2', 7, 1],
['D1', 2, 4],
['D1', 4, 2],
['D1', 4, 1],
['D1', 4, 3]]
>>> df
Out[38]:
0 1 2
0 D1 1 3
1 D1 2 2
2 D1 3 3
3 D2 1 4
4 D2 2 7
5 D2 7 1
6 D1 2 4
7 D1 4 2
8 D1 4 1
9 D1 4 3
>>> df2
Out[39]:
1 1 2 3 4 7
0
D1 3 6 3 6 0
D2 4 7 0 0 1
I'm using Python to attempt to rank documents using an Okapi BM25 model.
I think that I can calculate some of the terms required for the Score(D,Q) such as the IDF (Inverse Document Frequency) in a more efficient way (i.e: Counting all non-zero rows for a particular term (column)). Furthermore, I can add a new column to the matrix for the actual Score and then sort by this to rank documents.
The document term vectors are stored in a .dat file which is structured like the following:
D1 7:10 2:5
D2 1:2 3:4
where D1 is a document ID and 7:10 represents the term with ID 7 appearing 10 times
At the moment, I am reading it into a list of lists using the following code:
fname = "dtv.dat"
f = open(fname, "r")
l = [x.strip(" \n").split(" ") for x in f.readlines()]
which yields the following output for the given example:
[['D1', '7:10', '2:5'],['D2' '1:2', '3:4']]
Given this list of list format, what is the most efficient way to convert this to a Python pandas DataFrame similar to the following:
0 1 2 3 7
D1 0 5 0 10
D2 2 0 4 0
Your answer seems to be ok if each document appears only once in the file. Otherwise, the code will overwrite some records in dict d.
I think the following would be more general:
import numpy as np
import pandas as pd
fname = 'example.txt'
full_list = []
with open(fname, "r") as f:
for line in f:
arr = line.strip(" \n").split(" ")
for chunk in arr[1:]:
# converting numbers to ints:
int_pair = [int(x) for x in chunk.split(":")]
full_list.append([arr[0], *int_pair])
df = pd.DataFrame(full_list)
df2 = df.pivot_table(values = 2, index = 0, columns = 1, aggfunc = np.sum, fill_value = 0)
How it works:
>>> cat 'example.txt'
D1 1:3 2:2 3:3
D2 1:4 2:7
D2 7:1
D1 2:4 4:2
D1 4:1 4:3
>>> full_list
Out[37]:
[['D1', 1, 3],
['D1', 2, 2],
['D1', 3, 3],
['D2', 1, 4],
['D2', 2, 7],
['D2', 7, 1],
['D1', 2, 4],
['D1', 4, 2],
['D1', 4, 1],
['D1', 4, 3]]
>>> df
Out[38]:
0 1 2
0 D1 1 3
1 D1 2 2
2 D1 3 3
3 D2 1 4
4 D2 2 7
5 D2 7 1
6 D1 2 4
7 D1 4 2
8 D1 4 1
9 D1 4 3
>>> df2
Out[39]:
1 1 2 3 4 7
0
D1 3 6 3 6 0
D2 4 7 0 0 1
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