# pyspellchecker
Pure Python Spell Checking based on
[Peter Norvig's](https://norvig.com/spell-correct.html) blog post on setting up
a simple spell checking algorithm.
It uses a [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance)
algorithm to find permutations within an edit distance of 2 from the
original word. It then compares all permutations (insertions, deletions,
replacements, and transpositions) to known words in a word frequency list.
Those words that are found more often in the frequency list are `more likely`
the correct results.
## Installation
The easiest method to install is using pip:
``` bash
pip install pyspellchecker
```
To install from source:
``` bash
git clone https://github.com/barrust/pyspellchecker.git
cd pyspellchecker
python setup.py install
```
As always, I highly recommend using the [Pipenv](https://github.com/pypa/pipenv)
package to help manage dependencies!
## Quickstart
After installation, using pyspellchecker should be fairly straight forward:
``` python
from spellchecker import SpellChecker
spell = SpellChecker()
# find those words that may be misspelled
misspelled = spell.unknown(['something', 'is', 'hapenning', 'here'])
for word in misspelled:
# Get the one `most likely` answer
print(spell.correction(word))
# Get a list of `likely` options
print(spell.candidates(word))
```
If the Word Frequency list is not to your liking, you can add additional text
to generate a more appropriate list for your use case.
``` python
from spellChecker import SpellChecker
spell = SpellChecker() # loads default word frequency list
spell.word_frequency.load_text_file('./my_free_text_doc.txt')
# if I just want to make sure some words are not flagged as misspelled
spell.word_frequency.load_words(['microsoft', 'apple', 'google'])
spell.known(['microsoft', 'google']) # will return both now!
```
More work in storing and loading word frequency lists is planned; stay tuned.
## Additional Methods
On-line documentation is in the future; until then you can find SpellChecker
here:
`correction(word)`: Returns the most probable result for the misspelled word
`candidates(word)`: Returns a set of possible candidates for the misspelled
word
`known([words])`: Returns those words that are in the word frequency list
`unknown([words])`: Returns those words that are not in the frequency list
`word_probability(word)`: The frequency of the given word out of all words in
the frequency list
#### The following are less likely to be needed by the user but are available:
`edit_distance_1(word)`: Returns a set of all strings at a Levenshtein Distance
of one
`edit_distance_2(word)`: Returns a set of all strings at a Levenshtein Distance
of two
Pure Python Spell Checking based on
[Peter Norvig's](https://norvig.com/spell-correct.html) blog post on setting up
a simple spell checking algorithm.
It uses a [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance)
algorithm to find permutations within an edit distance of 2 from the
original word. It then compares all permutations (insertions, deletions,
replacements, and transpositions) to known words in a word frequency list.
Those words that are found more often in the frequency list are `more likely`
the correct results.
## Installation
The easiest method to install is using pip:
``` bash
pip install pyspellchecker
```
To install from source:
``` bash
git clone https://github.com/barrust/pyspellchecker.git
cd pyspellchecker
python setup.py install
```
As always, I highly recommend using the [Pipenv](https://github.com/pypa/pipenv)
package to help manage dependencies!
## Quickstart
After installation, using pyspellchecker should be fairly straight forward:
``` python
from spellchecker import SpellChecker
spell = SpellChecker()
# find those words that may be misspelled
misspelled = spell.unknown(['something', 'is', 'hapenning', 'here'])
for word in misspelled:
# Get the one `most likely` answer
print(spell.correction(word))
# Get a list of `likely` options
print(spell.candidates(word))
```
If the Word Frequency list is not to your liking, you can add additional text
to generate a more appropriate list for your use case.
``` python
from spellChecker import SpellChecker
spell = SpellChecker() # loads default word frequency list
spell.word_frequency.load_text_file('./my_free_text_doc.txt')
# if I just want to make sure some words are not flagged as misspelled
spell.word_frequency.load_words(['microsoft', 'apple', 'google'])
spell.known(['microsoft', 'google']) # will return both now!
```
More work in storing and loading word frequency lists is planned; stay tuned.
## Additional Methods
On-line documentation is in the future; until then you can find SpellChecker
here:
`correction(word)`: Returns the most probable result for the misspelled word
`candidates(word)`: Returns a set of possible candidates for the misspelled
word
`known([words])`: Returns those words that are in the word frequency list
`unknown([words])`: Returns those words that are not in the frequency list
`word_probability(word)`: The frequency of the given word out of all words in
the frequency list
#### The following are less likely to be needed by the user but are available:
`edit_distance_1(word)`: Returns a set of all strings at a Levenshtein Distance
of one
`edit_distance_2(word)`: Returns a set of all strings at a Levenshtein Distance
of two
1 maintainer
Release files for pyspellchecker 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pyspellchecker-0.1.0.tar.gz | 2.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pyspellchecker-0.1.0-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 4.8 MB
Release files / pyspellchecker-0.1.0.tar.gz
| Download URL | pyspellchecker-0.1.0.tar.gz |
|---|---|
| Size | 2.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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No |
Release files / pyspellchecker-0.1.0-py2.py3-none-any.whl
| Download URL | pyspellchecker-0.1.0-py2.py3-none-any.whl |
|---|---|
| Size | 2.4 MB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
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|
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