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'''
symspell_python.py
################
To run, execute python symspell_python.py at the prompt.
Make sure the dictionary "big.txt" is in the current working directory.
Enter word to correct when prompted.
################
v 1.2 last revised 25 Jul 2016
For further info, check out spark-n-spell.com
or e-mail mail@k-lo.ca
(maintained by ppgmg)
License:
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License,
version 3.0 (LGPL-3.0) as published by the Free Software Foundation.
http://www.opensource.org/licenses/LGPL-3.0
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
See the GNU General Public License for more details.
Please acknowledge Wolf Garbe, as the original creator of SymSpell,
(see note below) in any use.
################
This program is a Python version of a spellchecker based on SymSpell,
a Symmetric Delete spelling correction algorithm developed by Wolf Garbe
and originally written in C#.
From the original SymSpell documentation:
"The Symmetric Delete spelling correction algorithm reduces the complexity
of edit candidate generation and dictionary lookup for a given Damerau-
Levenshtein distance. It is six orders of magnitude faster and language
independent. Opposite to other algorithms only deletes are required,
no transposes + replaces + inserts. Transposes + replaces + inserts of the
input term are transformed into deletes of the dictionary term.
Replaces and inserts are expensive and language dependent:
e.g. Chinese has 70,000 Unicode Han characters!"
For further information on SymSpell, please consult the original
documentation:
URL: blog.faroo.com/2012/06/07/improved-edit-distance-based-spelling-correction/
Description: blog.faroo.com/2012/06/07/improved-edit-distance-based-spelling-correction/
The current version of this program will output all possible suggestions for
corrections up to an edit distance (configurable) of max_edit_distance = 3.
With the exception of the use of a third-party method for calculating
Demerau-Levenshtein distance between two strings, we have largely followed
the structure and spirit of the original SymSpell algorithm and have not
introduced any major optimizations or improvements.
Changes in version (1.1):
We implement allowing for less verbose options: e.g. when only a single
recommended correction is required, the search may terminate early, thereby
enhancing performance.
Changes in this version (1.2):
Removed unnecessary condition in create_dictionary_entry
#################
Sample output:
Please wait...
Creating dictionary...
total words processed: 1105285
total unique words in corpus: 29157
total items in dictionary (corpus words and deletions): 2151998
edit distance for deletions: 3
length of longest word in corpus: 18
Word correction
---------------
Enter your input (or enter to exit): there
('there', (2972, 0))
Enter your input (or enter to exit): hellot
('hello', (1, 1))
Enter your input (or enter to exit): accomodation
('accommodation', (5, 1))
Enter your input (or enter to exit):
goodbye
'''
import re
max_edit_distance = 3
verbose = 0
# 0: top suggestion
# 1: all suggestions of smallest edit distance
# 2: all suggestions <= max_edit_distance (slower, no early termination)
dictionary = {}
longest_word_length = 0
def get_deletes_list(w):
'''given a word, derive strings with up to max_edit_distance characters
deleted'''
deletes = []
queue = [w]
for d in range(max_edit_distance):
temp_queue = []
for word in queue:
if len(word)>1:
for c in range(len(word)): # character index
word_minus_c = word[:c] + word[c+1:]
if word_minus_c not in deletes:
deletes.append(word_minus_c)
if word_minus_c not in temp_queue:
temp_queue.append(word_minus_c)
queue = temp_queue
return deletes
def create_dictionary_entry(w):
'''add word and its derived deletions to dictionary'''
# check if word is already in dictionary
# dictionary entries are in the form: (list of suggested corrections,
# frequency of word in corpus)
global longest_word_length
new_real_word_added = False
if w in dictionary:
# increment count of word in corpus
dictionary[w] = (dictionary[w][0], dictionary[w][1] + 1)
else:
dictionary[w] = ([], 1)
longest_word_length = max(longest_word_length, len(w))
if dictionary[w][1]==1:
# first appearance of word in corpus
# n.b. word may already be in dictionary as a derived word
# (deleting character from a real word)
# but counter of frequency of word in corpus is not incremented
# in those cases)
new_real_word_added = True
deletes = get_deletes_list(w)
for item in deletes:
if item in dictionary:
# add (correct) word to delete's suggested correction list
dictionary[item][0].append(w)
else:
# note frequency of word in corpus is not incremented
dictionary[item] = ([w], 0)
return new_real_word_added
def create_dictionary(fname):
total_word_count = 0
unique_word_count = 0
with open(fname) as file:
print "Creating dictionary..."
for line in file:
# separate by words by non-alphabetical characters
words = re.findall('[a-z]+', line.lower())
for word in words:
total_word_count += 1
if create_dictionary_entry(word):
unique_word_count += 1
print "total words processed: %i" % total_word_count
print "total unique words in corpus: %i" % unique_word_count
print "total items in dictionary (corpus words and deletions): %i" % len(dictionary)
print " edit distance for deletions: %i" % max_edit_distance
print " length of longest word in corpus: %i" % longest_word_length
return dictionary
def dameraulevenshtein(seq1, seq2):
"""Calculate the Damerau-Levenshtein distance between sequences.
This method has not been modified from the original.
Source: http://mwh.geek.nz/2009/04/26/python-damerau-levenshtein-distance/
This distance is the number of additions, deletions, substitutions,
and transpositions needed to transform the first sequence into the
second. Although generally used with strings, any sequences of
comparable objects will work.
Transpositions are exchanges of *consecutive* characters; all other
operations are self-explanatory.
This implementation is O(N*M) time and O(M) space, for N and M the
lengths of the two sequences.
>>> dameraulevenshtein('ba', 'abc')
2
>>> dameraulevenshtein('fee', 'deed')
2
It works with arbitrary sequences too:
>>> dameraulevenshtein('abcd', ['b', 'a', 'c', 'd', 'e'])
2
"""
# codesnippet:D0DE4716-B6E6-4161-9219-2903BF8F547F
# Conceptually, this is based on a len(seq1) + 1 * len(seq2) + 1 matrix.
# However, only the current and two previous rows are needed at once,
# so we only store those.
oneago = None
thisrow = range(1, len(seq2) + 1) + [0]
for x in xrange(len(seq1)):
# Python lists wrap around for negative indices, so put the
# leftmost column at the *end* of the list. This matches with
# the zero-indexed strings and saves extra calculation.
twoago, oneago, thisrow = oneago, thisrow, [0] * len(seq2) + [x + 1]
for y in xrange(len(seq2)):
delcost = oneago[y] + 1
addcost = thisrow[y - 1] + 1
subcost = oneago[y - 1] + (seq1[x] != seq2[y])
thisrow[y] = min(delcost, addcost, subcost)
# This block deals with transpositions
if (x > 0 and y > 0 and seq1[x] == seq2[y - 1]
and seq1[x-1] == seq2[y] and seq1[x] != seq2[y]):
thisrow[y] = min(thisrow[y], twoago[y - 2] + 1)
return thisrow[len(seq2) - 1]
def get_suggestions(string, silent=False):
'''return list of suggested corrections for potentially incorrectly
spelled word'''
if (len(string) - longest_word_length) > max_edit_distance:
if not silent:
print "no items in dictionary within maximum edit distance"
return []
global verbose
suggest_dict = {}
min_suggest_len = float('inf')
queue = [string]
q_dictionary = {} # items other than string that we've checked
while len(queue)>0:
q_item = queue[0] # pop
queue = queue[1:]
# early exit
if ((verbose<2) and (len(suggest_dict)>0) and
((len(string)-len(q_item))>min_suggest_len)):
break
# process queue item
if (q_item in dictionary) and (q_item not in suggest_dict):
if (dictionary[q_item][1]>0):
# word is in dictionary, and is a word from the corpus, and
# not already in suggestion list so add to suggestion
# dictionary, indexed by the word with value (frequency in
# corpus, edit distance)
# note q_items that are not the input string are shorter
# than input string since only deletes are added (unless
# manual dictionary corrections are added)
assert len(string)>=len(q_item)
suggest_dict[q_item] = (dictionary[q_item][1],
len(string) - len(q_item))
# early exit
if ((verbose<2) and (len(string)==len(q_item))):
break
elif (len(string) - len(q_item)) < min_suggest_len:
min_suggest_len = len(string) - len(q_item)
# the suggested corrections for q_item as stored in
# dictionary (whether or not q_item itself is a valid word
# or merely a delete) can be valid corrections
for sc_item in dictionary[q_item][0]:
if (sc_item not in suggest_dict):
# compute edit distance
# suggested items should always be longer
# (unless manual corrections are added)
assert len(sc_item)>len(q_item)
# q_items that are not input should be shorter
# than original string
# (unless manual corrections added)
assert len(q_item)<=len(string)
if len(q_item)==len(string):
assert q_item==string
item_dist = len(sc_item) - len(q_item)
# item in suggestions list should not be the same as
# the string itself
assert sc_item!=string
# calculate edit distance using, for example,
# Damerau-Levenshtein distance
item_dist = dameraulevenshtein(sc_item, string)
# do not add words with greater edit distance if
# verbose setting not on
if ((verbose<2) and (item_dist>min_suggest_len)):
pass
elif item_dist<=max_edit_distance:
assert sc_item in dictionary # should already be in dictionary if in suggestion list
suggest_dict[sc_item] = (dictionary[sc_item][1], item_dist)
if item_dist < min_suggest_len:
min_suggest_len = item_dist
# depending on order words are processed, some words
# with different edit distances may be entered into
# suggestions; trim suggestion dictionary if verbose
# setting not on
if verbose<2:
suggest_dict = {k:v for k, v in suggest_dict.items() if v[1]<=min_suggest_len}
# now generate deletes (e.g. a substring of string or of a delete)
# from the queue item
# as additional items to check -- add to end of queue
assert len(string)>=len(q_item)
# do not add words with greater edit distance if verbose setting
# is not on
if ((verbose<2) and ((len(string)-len(q_item))>min_suggest_len)):
pass
elif (len(string)-len(q_item))<max_edit_distance and len(q_item)>1:
for c in range(len(q_item)): # character index
word_minus_c = q_item[:c] + q_item[c+1:]
if word_minus_c not in q_dictionary:
queue.append(word_minus_c)
q_dictionary[word_minus_c] = None # arbitrary value, just to identify we checked this
# queue is now empty: convert suggestions in dictionary to
# list for output
if not silent and verbose!=0:
print "number of possible corrections: %i" %len(suggest_dict)
print " edit distance for deletions: %i" % max_edit_distance
# output option 1
# sort results by ascending order of edit distance and descending
# order of frequency
# and return list of suggested word corrections only:
# return sorted(suggest_dict, key = lambda x:
# (suggest_dict[x][1], -suggest_dict[x][0]))
# output option 2
# return list of suggestions with (correction,
# (frequency in corpus, edit distance)):
as_list = suggest_dict.items()
outlist = sorted(as_list, key=lambda(term, (freq, dist)): (dist, -freq))
if verbose==0:
return outlist[0]
else:
return outlist
'''
Option 1:
['file', 'five', 'fire', 'fine', ...]
Option 2:
[('file', (5, 0)),
('five', (67, 1)),
('fire', (54, 1)),
('fine', (17, 1))...]
'''
def best_word(s, silent=False):
try:
return get_suggestions(s, silent)[0]
except:
return None
def correct_document(fname, printlist=True):
# correct an entire document
with open(fname) as file:
doc_word_count = 0
corrected_word_count = 0
unknown_word_count = 0
print "Finding misspelled words in your document..."
for i, line in enumerate(file):
# separate by words by non-alphabetical characters
doc_words = re.findall('[a-z]+', line.lower())
for doc_word in doc_words:
doc_word_count += 1
suggestion = best_word(doc_word, silent=True)
if suggestion is None:
if printlist:
print "In line %i, the word < %s > was not found (no suggested correction)" % (i, doc_word)
unknown_word_count += 1
elif suggestion[0]!=doc_word:
if printlist:
print "In line %i, %s: suggested correction is < %s >" % (i, doc_word, suggestion[0])
corrected_word_count += 1
print "-----"
print "total words checked: %i" % doc_word_count
print "total unknown words: %i" % unknown_word_count
print "total potential errors found: %i" % corrected_word_count
return
## main
import time
if __name__ == "__main__":
print "Please wait..."
time.sleep(2)
start_time = time.time()
try:
create_dictionary("big.txt")
except:
create_dictionary("testdata/big.txt")
run_time = time.time() - start_time
print '-----'
print '%.2f seconds to run' % run_time
print '-----'
print " "
print "Word correction"
print "---------------"
while True:
word_in = raw_input('Enter your input (or enter to exit): ')
if len(word_in)==0:
print "goodbye"
break
start_time = time.time()
print get_suggestions(word_in)
run_time = time.time() - start_time
print '-----'
print '%.5f seconds to run' % run_time
print '-----'
print " "