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Python package for lexicon.

Project description

Lexpy

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Python 3.6 Python 3.7 Python 3.8 Python 3.9 PyPy3

  • A lexicon is a data-structure which stores a set of words. The difference between a dictionary and a lexicon is that in a lexicon there are no values associated with the words.

  • A lexicon is similar to a list of words or a set, but the internal representation is different and optimized for faster searches of words, prefixes and wildcard patterns.

  • Given a word, precisely the search time is O(W) where W is the length of the word.

  • 2 important lexicon data-structures are:

    • Trie.
    • Directed Acyclic Word Graph (DAWG).

Both Trie and DAWG are Finite State Automaton (FSA)

Install

pip install lexpy

Supported OS

  • Linux
  • Windows

Supported Python Platforms

  • CPython 3.6, 3.7, 3.8, 3.9
  • PyPy3

Interface

Interface Description Trie method DAWG method
Add a single word add('apple', count=2) add('apple', count=2)
Add multiple words add_all(['advantage', 'courage']) add_all(['advantage', 'courage'])
Check if exists? in operator in operator
Search using wildcard expression search('a?b*', with_count=True) search('a?b*, with_count=True)
Search for prefix matches search_with_prefix('bar', with_count=True) search_with_prefix('bar')
Search for similar words within given edit distance. Here, the notion of edit distance is same as Levenshtein distance search_within_distance('apble', dist=1, with_count=True) search_within_distance('apble', dist=1, with_count=True)
Get the number of nodes in the automaton len(trie) len(dawg)

Examples

Trie

Build from an input list, set, or tuple of words.

from lexpy.trie import Trie

trie = Trie()

input_words = ['ampyx', 'abuzz', 'athie', 'athie', 'athie', 'amato', 'amato', 'aneto', 'aneto', 'aruba', 
               'arrow', 'agony', 'altai', 'alisa', 'acorn', 'abhor', 'aurum', 'albay', 'arbil', 'albin', 
               'almug', 'artha', 'algin', 'auric', 'sore', 'quilt', 'psychotic', 'eyes', 'cap', 'suit', 
               'tank', 'common', 'lonely', 'likeable' 'language', 'shock', 'look', 'pet', 'dime', 'small' 
               'dusty', 'accept', 'nasty', 'thrill', 'foot', 'steel', 'steel', 'steel', 'steel', 'abuzz']

trie.add_all(input_words) # You can pass any sequence types of a file like object here

print(trie.get_word_count())

>>> 48

Build from a file or file path.

In the file, words should be newline separated.

from lexpy.trie import Trie

# Either
trie = Trie()
trie.add_all('/path/to/file.txt')

# Or
with open('/path/to/file.txt', 'r') as infile:
     trie.add_all(infile)

Check if exists using the in operator

print('ampyx' in trie)

>>> True

Prefix search

print(trie.search_with_prefix('ab'))

>>> ['abhor', 'abuzz']
print(trie.search_with_prefix('ab', with_count=True))

>>> [('abuzz', 2), ('abhor', 1)]

Wildcard search using ? and *

  • ? = 0 or 1 occurrence of any character

  • * = 0 or more occurrence of any character

print(trie.search('a*o*'))

>>> ['amato', 'abhor', 'aneto', 'arrow', 'agony', 'acorn']

print(trie.search('a*o*', with_count=True))

>>> [('amato', 2), ('abhor', 1), ('aneto', 2), ('arrow', 1), ('agony', 1), ('acorn', 1)]

print(trie.search('su?t'))

>>> ['suit']

print(trie.search('su?t', with_count=True))

>>> [('suit', 1)]

Search for similar words using the notion of Levenshtein distance

print(trie.search_within_distance('arie', dist=2))

>>> ['athie', 'arbil', 'auric']

print(trie.search_within_distance('arie', dist=2, with_count=True))

>>> [('athie', 3), ('arbil', 1), ('auric', 1)]

Increment word count

  • You can either add a new word or increment the counter for an existing word.
trie.add('athie', count=1000)

print(trie.search_within_distance('arie', dist=2, with_count=True))

>>> [('athie', 1003), ('arbil', 1), ('auric', 1)]

Directed Acyclic Word Graph (DAWG)

  • DAWG supports the same set of operations as a Trie. The difference is the number of nodes in a DAWG is always less than or equal to the number of nodes in Trie.

  • They both are Deterministic Finite State Automata. However, DAWG is a minimized version of the Trie DFA.

  • In a Trie, prefix redundancy is removed. In a DAWG, both prefix and suffix redundancies are removed.

  • In the current implementation of DAWG, the insertion order of the words should be alphabetical.

  • The implementation idea of DAWG is borrowed from http://stevehanov.ca/blog/?id=115

from lexpy.trie import Trie
from lexpy.dawg import DAWG

trie = Trie()
trie.add_all(['advantageous', 'courageous'])

dawg = DAWG()
dawg.add_all(['advantageous', 'courageous'])

len(trie) # Number of Nodes in Trie
23

dawg.reduce() # Perform DFA minimization. Call this every time a chunk of words are uploaded in DAWG.

len(dawg) # Number of nodes in DAWG
21

DAWG

The APIs are exactly same as the Trie APIs

Build a DAWG

from lexpy.dawg import DAWG
dawg = DAWG()

input_words = ['ampyx', 'abuzz', 'athie', 'athie', 'athie', 'amato', 'amato', 'aneto', 'aneto', 'aruba', 
               'arrow', 'agony', 'altai', 'alisa', 'acorn', 'abhor', 'aurum', 'albay', 'arbil', 'albin', 
               'almug', 'artha', 'algin', 'auric', 'sore', 'quilt', 'psychotic', 'eyes', 'cap', 'suit', 
               'tank', 'common', 'lonely', 'likeable' 'language', 'shock', 'look', 'pet', 'dime', 'small' 
               'dusty', 'accept', 'nasty', 'thrill', 'foot', 'steel', 'steel', 'steel', 'steel', 'abuzz']


dawg.add_all(input_words)
dawg.reduce()

dawg.get_word_count()

>>> 48

Check if exists using the in operator

print('ampyx' in dawg)

>>> True

Prefix search

print(dawg.search_with_prefix('ab'))

>>> ['abhor', 'abuzz']
print(dawg.search_with_prefix('ab', with_count=True))

>>> [('abuzz', 2), ('abhor', 1)]

Wildcard search using ? and *

? = 0 or 1 occurance of any character

* = 0 or more occurance of any character

print(dawg.search('a*o*'))

>>> ['amato', 'abhor', 'aneto', 'arrow', 'agony', 'acorn']

print(dawg.search('a*o*', with_count=True))

>>> [('amato', 2), ('abhor', 1), ('aneto', 2), ('arrow', 1), ('agony', 1), ('acorn', 1)]

print(dawg.search('su?t'))

>>> ['suit']

print(dawg.search('su?t', with_count=True))

>>> [('suit', 1)]

Search for similar words using the notion of Levenshtein distance

print(dawg.search_within_distance('arie', dist=2))

>>> ['athie', 'arbil', 'auric']

print(dawg.search_within_distance('arie', dist=2, with_count=True))

>>> [('athie', 3), ('arbil', 1), ('auric', 1)]

Alphabetical order insertion

If you insert a word which is lexicographically out-of-order, ValueError will be raised.

dawg.add('athie', count=1000)

ValueError

ValueError: Words should be inserted in Alphabetical order. <Previous word - thrill>, <Current word - athie>

Increment the word count

  • You can either add an alphabetically greater word with a specific count or increment the count of the previous added word.
dawg.add_all(['thrill']*20000) # or dawg.add('thrill', count=20000)

print(dawg.search('thrill', with_count=True))

>> [('thrill', 20001)]

Trie vs DAWG

Number of nodes comparison

Build time comparison

Future Work

These are some ideas which I would love to work on next in that order. Pull requests or discussions are invited.

  • Merge trie and DAWG features in one data structure
    • Support all functionalities and still be as compressed as possible.
  • Serialization / Deserialization
    • Pickle is definitely an option.
  • Server (TCP or HTTP) to serve queries over the network.

Fun Facts

  1. The 45-letter word pneumonoultramicroscopicsilicovolcanoconiosis is the longest English word that appears in a major dictionary. So for all english words, the search time is bounded by O(45).
  2. The longest technical word(not in dictionary) is the name of a protein called as titin. It has 189,819 letters and it is disputed whether it is a word.

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