Module that implements a levenshtein distance algorithm to check for word similarity

levenshtein_distance.py

import numpy as np

def levenshtein_ratio_and_distance(s, t, ratio_calc = False):
    """ levenshtein_ratio_and_distance:
        Calculates levenshtein distance between two strings.
        If ratio_calc = True, the function computes the
        levenshtein distance ratio of similarity between two strings
        For all i and j, distance[i,j] will contain the Levenshtein
        distance between the first i characters of s and the
        first j characters of t
    """
    # Initialize matrix of zeros
    rows = len(s)+1
    cols = len(t)+1
    distance = np.zeros((rows,cols),dtype = int)

    # Populate matrix of zeros with the indeces of each character of both strings
    for i in range(1, rows):
        for k in range(1,cols):
            distance[i][0] = i
            distance[0][k] = k

    # Iterate over the matrix to compute the cost of deletions,insertions and/or substitutions
    for col in range(1, cols):
        for row in range(1, rows):
            if s[row-1] == t[col-1]:
                cost = 0 # If the characters are the same in the two strings in a given position [i,j] then the cost is 0
            else:
                # In order to align the results with those of the Python Levenshtein package, if we choose to calculate the ratio
                # the cost of a substitution is 2. If we calculate just distance, then the cost of a substitution is 1.
                if ratio_calc == True:
                    cost = 2
                else:
                    cost = 1
            distance[row][col] = min(distance[row-1][col] + 1,      # Cost of deletions
                                 distance[row][col-1] + 1,          # Cost of insertions
                                 distance[row-1][col-1] + cost)     # Cost of substitutions
    if ratio_calc == True:
        # Computation of the Levenshtein Distance Ratio
        Ratio = ((len(s)+len(t)) - distance[row][col]) / (len(s)+len(t))
        return Ratio
    else:
        # print(distance) # Uncomment if you want to see the matrix showing how the algorithm computes the cost of deletions,
        # insertions and/or substitutions
        # This is the minimum number of edits needed to convert string a to string b
        return distance[row][col]
        #return "The strings are {} edits away".format(distance[row][col])

word_similarity.py

import levenshtein_distance as ld

class Dictionary:
    def __init__(self, words):
        self.words = words

    def find_most_similar(self, term):
        best_word = {"name": "", "similarity": 100}
        for word in self.words:
            res = ld.levenshtein_ratio_and_distance(word, term, ratio_calc=False)
            if res < best_word.get("similarity"):
                best_word["name"] = word
                best_word["similarity"] = res
        return best_word.get("name")

def __init__():
    fruits = Dictionary(['cherry', 'pineapple', 'melon', 'strawberry', 'raspberry'])
    print(fruits.find_most_similar('strawbery'))
    print(fruits.find_most_similar('berry'))

    things = Dictionary(['stars', 'mars', 'wars', 'codec', 'codewars'])
    print(things.find_most_similar('coddwars'))

    languages = Dictionary(['javascript', 'java', 'ruby', 'php', 'python', 'coffeescript'])
    print(languages.find_most_similar('heaven'))
    print(languages.find_most_similar('javascript'))

__init__()