Format summary of formats that can be serialized with gensim

gensim

A library of topic models implemented in Python. The details of the function are not covered here. This time, I will summarize the formats of various formats that can be converted when converting a character string to the BoW format with gensim.

Execution code

Output as Official Reference.

from gensim import corpora
from collections import defaultdict
from pprint import pprint

documents = ["Human machine interface for lab abc computer applications",
             "A survey of user opinion of computer system response time",
             "The EPS user interface management system",
             "System and human system engineering testing of EPS",
             "Relation of user perceived response time to error measurement",
             "The generation of random binary unordered trees",
             "The intersection graph of paths in trees",
             "Graph minors IV Widths of trees and well quasi ordering",
             "Graph minors A survey"]
stoplist = set('for a of the and to in'.split())
texts = [[word for word in document.lower().split() if word not in stoplist]
         for document in documents]

frequency = defaultdict(int)
for text in texts:
    for token in text:
        frequency[token] += 1

texts = [[token for token in text if frequency[token] > 1]
         for text in texts]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

corpora.MmCorpus.serialize("./corpus.mm", corpus)
corpora.BleiCorpus.serialize("./corpus.blei", corpus)
corpora.LowCorpus.serialize("./corpus.low", corpus)
corpora.SvmLightCorpus.serialize("./corpus.svmlight", corpus)
corpora.UciCorpus.serialize("./corpus.low", corpus)

pprint(texts)
print("\n")
pprint(dictionary.token2id)
print("\n")
pprint(corpus)

Output

[['human', 'interface', 'computer'],
 ['survey', 'user', 'computer', 'system', 'response', 'time'],
 ['eps', 'user', 'interface', 'system'],
 ['system', 'human', 'system', 'eps'],
 ['user', 'response', 'time'],
 ['trees'],
 ['graph', 'trees'],
 ['graph', 'minors', 'trees'],
 ['graph', 'minors', 'survey']]


{'computer': 1,
 'eps': 8,
 'graph': 10,
 'human': 2,
 'interface': 0,
 'minors': 11,
 'response': 6,
 'survey': 4,
 'system': 5,
 'time': 7,
 'trees': 9,
 'user': 3}


[[(0, 1), (1, 1), (2, 1)],
 [(1, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1)],
 [(0, 1), (3, 1), (5, 1), (8, 1)],
 [(2, 1), (5, 2), (8, 1)],
 [(3, 1), (6, 1), (7, 1)],
 [(9, 1)],
 [(9, 1), (10, 1)],
 [(9, 1), (10, 1), (11, 1)],
 [(4, 1), (10, 1), (11, 1)]]

Matrix Market format

corpus.mm


%%MatrixMarket matrix coordinate real general
9 12 28                                           
1 1 1
1 2 1
1 3 1
2 2 1
2 4 1
2 5 1
2 6 1
2 7 1
2 8 1
3 1 1
3 4 1
3 6 1
3 9 1
4 3 1
4 6 2
4 9 1
5 4 1
5 7 1
5 8 1
6 10 1
7 10 1
7 11 1
8 10 1
8 11 1
8 12 1
9 5 1
9 11 1
9 12 1

Blei format

corpus.blei


3 0:1 1:1 2:1
6 1:1 3:1 4:1 5:1 6:1 7:1
4 0:1 3:1 5:1 8:1
3 2:1 5:2 8:1
3 3:1 6:1 7:1
1 9:1
2 9:1 10:1
3 9:1 10:1 11:1
3 4:1 10:1 11:1

text:corpus.blei.vocab


0
1
2
3
4
5
6
7
8
9
10
11

UCI format

corpus.uci


9                   
12                  
28                  
1 1 1
1 2 1
1 3 1
2 2 1
2 4 1
2 5 1
2 6 1
2 7 1
2 8 1
3 1 1
3 4 1
3 6 1
3 9 1
4 3 1
4 6 2
4 9 1
5 4 1
5 7 1
5 8 1
6 10 1
7 10 1
7 11 1
8 10 1
8 11 1
8 12 1
9 5 1
9 11 1
9 12 1

text:corpus.uci.vocab


0
1
2
3
4
5
6
7
8
9
10
11

Low format

corpus.low


9
0 1 2
1 3 4 5 6 7
0 3 5 8
2 5 5 8
3 6 7
9
9 10
9 10 11
4 10 11

text:corpus.low.vocab


0
1
2
3
4
5
6
7
8
9
10
11

SvmLight format

corpus.svmlight


0 1:1 2:1 3:1
0 2:1 4:1 5:1 6:1 7:1 8:1
0 1:1 4:1 6:1 9:1
0 3:1 6:2 9:1
0 4:1 7:1 8:1
0 10:1
0 10:1 11:1
0 10:1 11:1 12:1
0 5:1 11:1 12:1

reference

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