[Tutorial] Distance.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# String Distance Problems and Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='fables/string2string-logo.png')\n",
    "# Image(url='https://raw.githubusercontent.com/stanfordnlp/string2string/main/fables/string2string-logo.png?token=GHSAT0AAAAAAB5KHQETSF3UOOWXYPJHECZAZAXW5HA')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial provides an introduction to the **distance** module in the **string2string** package, and presents a few examples to showcase its usage. While it is not an exhaustive overview of the module, it offers insights into its key functionalities. For a more comprehensive understanding of the module, please consult the official documentation."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "String distance refers to the problem of quantifying the extent to which two strings (or list of strings) are dissimilar to each other based on a distance function.\n",
    "\n",
    "The string2string package currently offers the following distance functions:\n",
    "\n",
    "* Levenshtein edit distance\n",
    "* Damerau-Levenshtein edit distance\n",
    "* Hamming distance\n",
    "* Jaccard distance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we begin, please make sure that you have installed the string2string packag via *pip*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install string2string"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Levenshtein Edit Distance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Levenshtein edit distance metric, also known as the edit distance, refers to the minimum number of insertion, deletion, or substitution operations required to transform one string into another. \n",
    "\n",
    "We can compute the Levenshtein edit distance between two strings using our library as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edit distance between 'The quick brown fox jumps over the lazy dog' and 'The kuack brown box jumps over the lazy dog' is 3.0\n",
      "Time taken to compute the edit distance: 0.0020427703857421875 seconds\n"
     ]
    }
   ],
   "source": [
    "# For measuring computation time\n",
    "import time\n",
    "# Import the LevenshteinEditDistance class\n",
    "from string2string.distance import LevenshteinEditDistance\n",
    "\n",
    "# Create a LevenshteinEditDistance class instance with the default (unit cost) weights\n",
    "edit_dist = LevenshteinEditDistance()\n",
    "\n",
    "# Create two strings\n",
    "str1 = \"The quick brown fox jumps over the lazy dog\"\n",
    "str2 = \"The kuack brown box jumps over the lazy dog\"\n",
    "\n",
    "# Let's compute the edit distance between the two strings and measure the computation time\n",
    "start = time.time()\n",
    "edit_dist_score  = edit_dist.compute(str1, str2)\n",
    "end = time.time()\n",
    "\n",
    "print(f\"Edit distance between '{str1}' and '{str2}' is {edit_dist_score}\")\n",
    "print(f\"Time taken to compute the edit distance: {end - start} seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LevenshteinEditDistance class provides a compute function that, by default, efficiently calculates the edit distance between two strings or lists of strings using the Wagner-Fisher algorithm, which is based on dynamic programming principles. However, it is also possible to obtain the same result using recursive or memoized versions of the algorithm, but these methods are not as efficient as the Wagner-Fisher algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These code snippets demonstrate different ways to use the compute method of the LevenshteinEditDistance class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's use a shorter string for the next example\n",
    "str1 = \"wikiepdia\"\n",
    "str2 = \"wikipedia\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recursive Method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edit distance between 'wikiepdia' and 'wikipedia' is 2.0, and the time taken to compute the edit distance is 0.752446174621582 seconds\n"
     ]
    }
   ],
   "source": [
    "# Recursive method\n",
    "start = time.time()\n",
    "edit_dist_score  = edit_dist.compute(str1, str2, method='recursive')\n",
    "end = time.time()\n",
    "\n",
    "print(f\"Edit distance between '{str1}' and '{str2}' is {edit_dist_score}, and the time taken to compute the edit distance is {end - start} seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recursive Memoization Method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edit distance between 'wikiepdia' and 'wikipedia' is 2.0, and the time taken to compute the edit distance is 0.00018095970153808594 seconds\n"
     ]
    }
   ],
   "source": [
    "# Recursive memoization method\n",
    "start = time.time()\n",
    "edit_dist_score  = edit_dist.compute(str1, str2, method='recursive-memoization')\n",
    "end = time.time()\n",
    "\n",
    "print(f\"Edit distance between '{str1}' and '{str2}' is {edit_dist_score}, and the time taken to compute the edit distance is {end - start} seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dynamic Programming Method (Wagner-Fisher Algorithm)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edit distance between 'wikiepdia' and 'wikipedia' is 2.0, and the time taken to compute the edit distance is 0.0001499652862548828 seconds\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "edit_dist_score  = edit_dist.compute(str1, str2, method='dynamic-programming') # default method\n",
    "end = time.time()\n",
    "\n",
    "print(f\"Edit distance between '{str1}' and '{str2}' is {edit_dist_score}, and the time taken to compute the edit distance is {end - start} seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weighted Levenshtein-Edit Distance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can also provide custom weights for the edit operations (insertion, deletion, and substitution), as well as for the match, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted edit distance between 'GATTACA' and 'GCATGCU' is 2.0\n"
     ]
    }
   ],
   "source": [
    "# This time, we will use a weighted edit distance\n",
    "weighted_edit_dist = LevenshteinEditDistance(\n",
    "    match_weight=0,\n",
    "    insert_weight=1,\n",
    "    delete_weight=1,\n",
    "    substitute_weight=0.5,\n",
    ")\n",
    "\n",
    "# Create two strings\n",
    "str1 = \"GATTACA\"\n",
    "str2 = \"GCATGCU\"\n",
    "\n",
    "# Get the distance\n",
    "edit_dist_score  = weighted_edit_dist.compute(str1, str2)\n",
    "\n",
    "# Print the distance\n",
    "print(f\"Weighted edit distance between '{str1}' and '{str2}' is {edit_dist_score}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Damerau-Levenshtein Distance "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Damerau-Levenshtein distance is the minimum number of insertions, deletions, substitutions, and transpositions required to transform one string into the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Damerau-Levenshtein distance between 'wikiepdia' and 'wikipedia' is 1.0\n",
      "Levenshtein distance between 'wikiepdia' and 'wikipedia' is 2.0\n"
     ]
    }
   ],
   "source": [
    "from string2string.distance import DamerauLevenshteinDistance\n",
    "\n",
    "# Create a DamerauLevenshteinDistance class instance\n",
    "damerau_levenshtein_dist = DamerauLevenshteinDistance()\n",
    "\n",
    "# Create two strings (or lists of strings)\n",
    "str1 = 'wikiepdia'\n",
    "str2 = 'wikipedia'\n",
    "\n",
    "# Get the Damerau-Levenshtein distance\n",
    "damerau_dist_score  = damerau_levenshtein_dist.compute(str1, str2)\n",
    "\n",
    "# Get the Levenshtein distance\n",
    "edit_dist_score = edit_dist.compute(str1, str2)\n",
    "\n",
    "# Print the results -- mind the difference in the distance scores\n",
    "print(f\"Damerau-Levenshtein distance between '{str1}' and '{str2}' is {damerau_dist_score}\")\n",
    "print(f\"Levenshtein distance between '{str1}' and '{str2}' is {edit_dist_score}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hamming Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamming distance between 'wikiepdia' and 'wikipedia' is 2.0\n"
     ]
    }
   ],
   "source": [
    "from string2string.distance import HammingDistance\n",
    "\n",
    "# Create a DamerauLevenshteinDistance class instance\n",
    "hamming_distance = HammingDistance()\n",
    "\n",
    "# Create two strings (or lists of strings)\n",
    "str1 = 'wikiepdia'\n",
    "str2 = 'wikipedia'\n",
    "\n",
    "# Get the Hamming distance\n",
    "hamming_dist_score  = hamming_distance.compute(str1, str2)\n",
    "\n",
    "# Print the result\n",
    "print(f\"Hamming distance between '{str1}' and '{str2}' is {hamming_dist_score}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jaccard Index (Distance)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Jaccard index can be used to quantify the similarity between sets of words or tokens, and is commonly used in tasks such as document similarity or topic modeling. For example, the Jaccard index can be used to measure the overlap between the sets of words in two different documents or to identify the most similar topics across a collection of documents. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jaccard distance between doc1 and doc2 is 0.44\n",
      "Jaccard distance between doc1 and doc3 is 0.73\n",
      "Jaccard distance between doc2 and doc3 is 0.67\n"
     ]
    }
   ],
   "source": [
    "from string2string.distance import JaccardIndex\n",
    "\n",
    "# Create a JaccardIndex class instance\n",
    "jaccard_distance = JaccardIndex()\n",
    "\n",
    "# Suppose we have three documents\n",
    "doc1 = ['apple', 'banana', 'orange', 'mango', 'grape', 'pineapple', 'strawberry', 'peach']\n",
    "doc2 = ['apple', 'orange', 'mango', 'grape', 'pineapple', 'watermelon']\n",
    "doc3 = ['kiwi', 'apple', 'tomato', 'mandarin', 'grape', 'pineapple']\n",
    "\n",
    "# Get the Jaccard distance between each pair of documents\n",
    "jaccard_dist_score12 = jaccard_distance.compute(doc1, doc2)\n",
    "jaccard_dist_score13 = jaccard_distance.compute(doc1, doc3)\n",
    "jaccard_dist_score23 = jaccard_distance.compute(doc2, doc3)\n",
    "\n",
    "# Print the results with two decimal places\n",
    "print(f\"Jaccard distance between doc1 and doc2 is {jaccard_dist_score12:.2f}\")\n",
    "print(f\"Jaccard distance between doc1 and doc3 is {jaccard_dist_score13:.2f}\")\n",
    "print(f\"Jaccard distance between doc2 and doc3 is {jaccard_dist_score23:.2f}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this simple example, the Jaccard index (or distance) between doc1 and doc2 is 0.44, indicating a moderate degree of similarity between the two documents. The Jaccard distance between doc1 and doc3 is 0.72, indicating a higher degree of similarity between those two documents than between doc1 and doc2. Finally, the Jaccard distance between doc2 and doc3 is 0.67, indicating that those two documents are also moderately similar to each other. Overall, the Jaccard distances can help us understand the degree of overlap between the sets of words in the three documents--and as we can see from this example, it can be pretty useful in downstream text analysis task such as document similarity or topic modeling.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support for List of Strings"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important and useful feature of the distance module in the string2string library is its ability to compute the distance between not only *pairs of strings* but also *lists of strings*. This is particularly useful when one wants to compute the distance of texts at the token or word level.\n",
    "\n",
    "For example, suppose you have two texts represented as lists of words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "text1 = ['the', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']\n",
    "text2 = ['the', 'kuack', 'brown', 'box', 'jumps', 'over', 'the', 'lazy', 'dog']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the distance module to compute the edit distance between these texts at the word level as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edit distance between these two texts at the word level is 2.0\n"
     ]
    }
   ],
   "source": [
    "# Initialize the LevenshteinEditDistance class with the default (unit cost) weights\n",
    "edit_dist = LevenshteinEditDistance()\n",
    "\n",
    "# Get the distance\n",
    "edit_dist_score  = edit_dist.compute(text1, text2)\n",
    "\n",
    "# Print the result\n",
    "print(f\"Edit distance between these two texts at the word level is {edit_dist_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, one can also use our tokenizer to tokenize the text into words and then compute the edit distance between the two tokenized texts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edit distance between these two texts at the character level is 3.0\n",
      "Edit distance between these two texts at the word level is 2.0\n"
     ]
    }
   ],
   "source": [
    "from string2string.misc import Tokenizer\n",
    "\n",
    "# Create a Tokenizer class instance with the default word delimiter (space)\n",
    "tokenizer = Tokenizer(word_delimiter=' ')\n",
    "\n",
    "# Tokenize the two texts\n",
    "text1 = \"The quick brown fox jumps over the lazy dog\"\n",
    "text2 = \"The kuack brown box jumps over the lazy dog\"\n",
    "\n",
    "# Get the distance\n",
    "edit_dist_score  = edit_dist.compute(text1, text2)\n",
    "\n",
    "# Print the result\n",
    "print(f\"Edit distance between these two texts at the character level is {edit_dist_score}\")\n",
    "\n",
    "# Tokenize the two texts\n",
    "text1_tokens = tokenizer.tokenize(text1)\n",
    "text2_tokens = tokenizer.tokenize(text2)\n",
    "\n",
    "# Get the distance\n",
    "edit_dist_score  = edit_dist.compute(text1_tokens, text2_tokens)\n",
    "\n",
    "# Print the result\n",
    "print(f\"Edit distance between these two texts at the word level is {edit_dist_score}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  With this example, we have reached the end of the tutorial. We hope that you have found it useful. If you have any questions or suggestions, please feel free to contact us."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### $\\square$ Q.E.D."
   ]
  }
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