symspell.py

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]
    lenseq2 = len(seq2) + 1
    thisrow = list(range(1, lenseq2))
    thisrow.append(0)
    for x in range(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 range(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))
    # outlist = sorted(as_list, key=lambda (freq, dist): dist, -freq)
    outlist = sorted(as_list, key=lambda tfd: (tfd[1][0], -tfd[1][1]))

    if (verbose==0 and outlist != []):
        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 {}, the word < {} > was not found (no suggested correction)".format(i, doc_word))
                    unknown_word_count += 1
                elif suggestion[0]!=doc_word:
                    if printlist:
                        print ("In line {}, {}: suggested correction is < {} >".format(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 = 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 (" ")