The main goal this Python module is to provide functions to apply Text Classification.
Project description
Introduction
A Python module that allows you to create and manage a collection of occurrence counts of words without regard to grammar. The main purpose is provide a set of classes to manage several document classifieds by category in order to apply Text Classification.
You can make use via API or via Command Line. For example, you can generate your classified documents (learn) via Command Line and after via API classify an input document.
Third parties modules
Module uses three third parties modules
The first module is used in stop_words filter, the second module is used in stemming filter. If you don’t use these two filters, you don’t need install them.
Installation
Install it via pip
$ [sudo] pip install bagofwords
Or download zip and then install it by running
$ [sudo] python setup.py install
You can test it by running
$ [sudo] python setup.py test
Uninstallation
$ [sudo] pip uninstall bagofwords
Python API
Methods
document_classifier(document, **classifieds) Text classification based on an implementation of Naive Bayes
Module contains two main classes DocumentClass and Document and four secondary classes BagOfWords, WordFilters, TextFilters and Tokenizer
Main classes
DocumentClass Implementing a bag of words collection where all the bags of words are the same category, as well as a bag of words with the entire collection of words. Each bag of words has an identifier otherwise it’s assigned an calculated identifier. Retrieves the text of a file, folder, url or zip, and also allows save or retrieve the collection in json format.
Document Implementing a bag of words where all words are of the same category. Retrieves the text of a file, folder, url or zip, and also allows save or retrieve the Document in json format.
Secondary classes
BagOfWords Implementing a bag of words with their frequency of usages.
TextFilters Filters for transforming a text. It’s used in Tokenizer class. Including filters upper lower invalid_chars and html_to_text
WordFilters Filters for transforming a set of words. It’s used in Tokenizer class. Including filters stemming stopwords and normalize
Tokenizer Allows to break a string into tokens (set of words). Optionally allows you to set filters before (TextFilters) and after (WordFilters) breaking the string into tokens.
Subclasses
Tokenizer subclasses DefaultTokenizer SimpleTokenizer and HtmlTokenizer that implements the more common filters and overwriting after_tokenizer and berofe_tokenizer methods
Document subclasses DefaultDocument SimpleDocument and HtmlDocument
DocumentClass subclasses DefaultDocumentClass SimpleDocumentClass and HtmlDocumentClass
Command Line Tool
usage: bow [-h] [--version] {create,learn,show,classify} ...
Manage several document to apply text classification.
positional arguments:
{create,learn,show,classify}
create create classifier
learn add words learned a classifier
show show classifier info
classify Naive Bayes text classification
optional arguments:
-h, --help show this help message and exit
--version show version and exit
Create Command
usage: bow create [-h] [--lang-filter LANG_FILTER]
[--stemming-filter STEMMING_FILTER]
{text,html} filename
positional arguments:
{text,html} filter type
filename file to be created where words learned are saved
optional arguments:
-h, --help show this help message and exit
--lang-filter LANG_FILTER
language text where remove empty words
--stemming-filter STEMMING_FILTER
number loops of lemmatizing
Learn Command
usage: bow learn [-h] [--file FILE [FILE ...]] [--dir DIR [DIR ...]]
[--url URL [URL ...]] [--zip ZIP [ZIP ...]] [--no-learn]
[--rewrite] [--list-top-words LIST_TOP_WORDS]
filename
positional arguments:
filename file to write words learned
optional arguments:
-h, --help show this help message and exit
--file FILE [FILE ...]
filenames to learn
--dir DIR [DIR ...] directories to learn
--url URL [URL ...] url resources to learn
--zip ZIP [ZIP ...] zip filenames to learn
--no-learn not write to file the words learned
--rewrite overwrite the file
--list-top-words LIST_TOP_WORDS
maximum number of words to list, 50 by default, -1
list all
Show Command
usage: bow show [-h] [--list-top-words LIST_TOP_WORDS] filename
positional arguments:
filename filename
optional arguments:
-h, --help show this help message and exit
--list-top-words LIST_TOP_WORDS
maximum number of words to list, 50 by default, -1
list all
Classify Command
usage: bow classify [-h] [--file FILE] [--url URL] [--text TEXT]
classifiers [classifiers ...]
positional arguments:
classifiers classifiers
optional arguments:
-h, --help show this help message and exit
--file FILE file to classify
--url URL url resource to classify
--text TEXT text to classify
Example
Previously you need to download a spam corpus enron-spam dataset. For example you can download a compressed file that includes a directory with 1500 spam emails and a directory with 4012 ham emails.
http://www.aueb.gr/users/ion/data/enron-spam/preprocessed/enron3.tar.gz
Now we will create the spam and ham classifiers
$ bow create text spam
* filename: spam
* filter:
type: DefaultDocument
lang: english
stemming: 1
* total words: 0
* total docs: 0
$ bow create text ham
* filename: ham
* filter:
type: DefaultDocument
lang: english
stemming: 1
* total words: 0
* total docs: 0
It’s time to learn
$ bow learn spam --dir enron3/spam
current
=======
* filename: spam
* filter:
type: DefaultDocument
lang: english
stemming: 1
* total words: 0
* total docs: 0
updated
=======
* filename: spam
* filter:
type: DefaultDocument
lang: english
stemming: 1
* total words: 223145
* total docs: 1500
* pos | word (top 50) | occurrence | rate
--- | ----------------------------------- | ---------- | ----------
1 | " | 2438 | 0.01092563
2 | subject | 1662 | 0.00744807
3 | compani | 1659 | 0.00743463
4 | s | 1499 | 0.00671761
5 | will | 1194 | 0.00535078
6 | com | 978 | 0.00438280
7 | statement | 935 | 0.00419010
8 | secur | 908 | 0.00406910
9 | inform | 880 | 0.00394362
10 | e | 802 | 0.00359408
11 | can | 798 | 0.00357615
12 | http | 779 | 0.00349100
13 | pleas | 743 | 0.00332967
14 | invest | 740 | 0.00331623
15 | de | 739 | 0.00331175
16 | o | 733 | 0.00328486
17 | 1 | 732 | 0.00328038
18 | 2 | 709 | 0.00317731
19 | stock | 700 | 0.00313697
20 | price | 664 | 0.00297564
....
$ bow learn ham --dir enron3/ham
current
=======
* filename: ham
* filter:
type: DefaultDocument
lang: english
stemming: 1
* total words: 0
* total docs: 0
updated
=======
* filename: ham
* filter:
type: DefaultDocument
lang: english
stemming: 1
* total words: 1293023
* total docs: 4012
* pos | word (top 50) | occurrence | rate
--- | ----------------------------------- | ---------- | ----------
1 | enron | 29805 | 0.02305063
2 | s | 22438 | 0.01735313
3 | " | 15712 | 0.01215137
4 | compani | 12039 | 0.00931074
5 | said | 9470 | 0.00732392
6 | will | 8862 | 0.00685371
7 | 2001 | 8293 | 0.00641365
8 | subject | 7167 | 0.00554282
9 | 1 | 5887 | 0.00455290
10 | trade | 5718 | 0.00442220
11 | energi | 5599 | 0.00433016
12 | market | 5498 | 0.00425205
13 | new | 5278 | 0.00408191
14 | 2 | 4742 | 0.00366737
15 | dynegi | 4651 | 0.00359700
16 | stock | 4594 | 0.00355291
17 | 10 | 4545 | 0.00351502
18 | year | 4517 | 0.00349336
19 | power | 4503 | 0.00348254
20 | share | 4393 | 0.00339746
....
Finally, we can classify a text file or url
$ bow classify spam ham --text "company" * classifier | rate ----------------------------------- | ---------- ham | 0.87888743 spam | 0.12111257
$ bow classify spam ham --text "new lottery" * classifier | rate ----------------------------------- | ---------- spam | 0.96633627 ham | 0.03366373
$ bow classify spam ham --text "Subject: a friendly professional online pharmacy focused on you !" * classifier | rate ----------------------------------- | ---------- spam | 0.99671480 ham | 0.00328520
You should know that it is also possible to classify from python code
import bow
spam = bow.Document.load('spam')
ham = bow.Document.load('ham')
dc = bow.DefaultDocument()
dc.read_text("company")
result = bow.document_classifier(dc, spam=spam, ham=ham)
print result
Result
[('ham', 0.8788874288217258), ('spam', 0.12111257117827418)]
Others examples
Join several bag of words
from bow import BagOfWords
a = BagOfWords('car', 'chair', 'chicken')
b = BagOfWords({'chicken':2}, ['eye', 'ugly'])
c = BagOfWords('plane')
print a + b + c
print a - b - c
Result
{'eye': 1, 'car': 1, 'ugly': 1, 'plane': 1, 'chair': 1, 'chicken': 3}
{'car': 1, 'chair': 1}
HTML document class
from bow import HtmlDocumentClass
html_one = '''
<!DOCTYPE html>
<html lang="en">
<head>
<title>bag of words demo</title>
<link rel="stylesheet" href="css/mycss.css">
<script src="js/myjs.js"></script>
</head>
<body>
<style> #demo {background: #c00; color: #fff; padding: 10px;}</style>
<!--my comment section -->
<h2>This is a demo</h2>
<p id="demo">This a text example of my bag of words demo!</p>
I hope this demo is useful for you
<script type="text/javascript"> alert('But wait, it\'s a demo...');</script>
</body>
</html>
'''
html_two = '''
<!DOCTYPE html>
<html lang="en">
<head> </head>
<body> Another silly example. </body>
</html>
'''
dclass = HtmlDocumentClass(lang='english', stemming=0)
dclass(id_='doc1', text=html_one)
dclass(id_='doc2', text=html_two)
print 'docs \n', dclass.docs
print 'total \n', dclass
print 'rates \n', dclass.rates
Result
>>>
docs
{
'doc2': {u'silly': 1, u'example': 1, u'another': 1},
'doc1': {u'useful': 1, u'text': 1, u'bag': 2, u'words': 2, u'demo': 4, u'example': 1, u'hope': 1}
}
total
{
u'useful': 1, u'another': 1, u'text': 1, u'bag': 2, u'silly': 1, u'words': 2,
u'demo': 4, u'example': 2, u'hope': 1
}
rates
{
u'useful': 0.06666666666666667, u'another': 0.06666666666666667, u'text': 0.06666666666666667,
u'bag': 0.13333333333333333, u'silly': 0.06666666666666667, u'words': 0.13333333333333333,
u'demo': 0.26666666666666666, u'example': 0.13333333333333333, u'hope': 0.06666666666666667
}
>>>
License
MIT License, see LICENSE
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