{ "id": 223466, "title": "Brief summary of \"How to calculate Levenshtein distance\"🔰", "url": "/competitions/bms-molecular-translation/discussion/223466", "author_name": "", "post_date": "2021-03-04T01:49:17.246407500Z", "votes": 36, "comment_count": 12, "views": 0, "content": "

In this competition, Levenshtein distance is used for evaluation.
\nSo, I briefly summarized it.

\n

The Levenshtein distance is one of the methods to calculate the similarity between two strings.
\nLevenshtein distance is calculated by the number of operations;

\n\n

when converting one string to the other.

\n

For example, Levenshtein distance between
\n'Rievenstein' and 'Levenshtein' is 3.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
stringoperation
Rievenstein
ievensteindelete R
Levensteinreplace i with L
Levenshteininsert h at position t
\n

If you use Python, Levenshtein distance is calculated by just running like below!!
\n(For the first time, Perhaps you should run !pip install python-Levenshtein)

\n
import Levenshtein\n\nstr1 = 'Rievenstein'\nstr2 = 'Levenshtein'\n\nprint(Levenshtein.distance(str1, str2))\n# 3\n
\n

If you want to know the operarions(how to convert one to the other), use editops.

\n
print(Levenshtein.editops(str1, str2))\n# [('delete', 0, 0), ('replace', 1, 0), ('insert', 7, 6)]\n
\n

If you want to get the similarity value, you can use ratio.

\n
print(Levenshtein.ratio(str1, str2))\n# 0.8181818181818182\nprint(Levenshtein.ratio(str1, str1))\n# 1.0\nprint(Levenshtein.ratio(str1, ''))\n# 0.0\n
\n

If you want to select the average string in the list of several strings, you should use to median.

\n
print(Levenshtein.median([\n                    'Rievenstein',\n                    'Levenshtein',\n                    'Revenshtein',\n                    'Lievenstein',\n                    'Levenshtain',\n                    'Levennshtein'\n                    ]))\n# Levenshtein\n
\n
\n

That's it!
\nPlease try your best and enjoy this competition😊

\n

If you want to know more detail, please refer to Wikipedia(described in the Overview of this competition)

", "messages": [ { "id": "1225833", "postDate": "03/04/2021 01:49:17", "content": "

In this competition, Levenshtein distance is used for evaluation.
\nSo, I briefly summarized it.

\n

The Levenshtein distance is one of the methods to calculate the similarity between two strings.
\nLevenshtein distance is calculated by the number of operations;

\n\n

when converting one string to the other.

\n

For example, Levenshtein distance between
\n'Rievenstein' and 'Levenshtein' is 3.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
stringoperation
Rievenstein
ievensteindelete R
Levensteinreplace i with L
Levenshteininsert h at position t
\n

If you use Python, Levenshtein distance is calculated by just running like below!!
\n(For the first time, Perhaps you should run !pip install python-Levenshtein)

\n
import Levenshtein\n\nstr1 = 'Rievenstein'\nstr2 = 'Levenshtein'\n\nprint(Levenshtein.distance(str1, str2))\n# 3\n
\n

If you want to know the operarions(how to convert one to the other), use editops.

\n
print(Levenshtein.editops(str1, str2))\n# [('delete', 0, 0), ('replace', 1, 0), ('insert', 7, 6)]\n
\n

If you want to get the similarity value, you can use ratio.

\n
print(Levenshtein.ratio(str1, str2))\n# 0.8181818181818182\nprint(Levenshtein.ratio(str1, str1))\n# 1.0\nprint(Levenshtein.ratio(str1, ''))\n# 0.0\n
\n

If you want to select the average string in the list of several strings, you should use to median.

\n
print(Levenshtein.median([\n                    'Rievenstein',\n                    'Levenshtein',\n                    'Revenshtein',\n                    'Lievenstein',\n                    'Levenshtain',\n                    'Levennshtein'\n                    ]))\n# Levenshtein\n
\n
\n

That's it!
\nPlease try your best and enjoy this competition😊

\n

If you want to know more detail, please refer to Wikipedia(described in the Overview of this competition)

", "rawMarkdown": "In this competition, ***Levenshtein distance*** is used for evaluation.\nSo, I briefly summarized it.\n\nThe Levenshtein distance is one of the methods to calculate the similarity between two strings.\nLevenshtein distance is calculated by the number of operations;\n- ***insert***\n- ***delete***\n- ***replace***\n\nwhen converting one string to the other.\n\nFor example, Levenshtein distance between\n***'Rievenstein'*** and ***'Levenshtein'*** is **3**.\n\n|string|operation|\n|---|---|\n|**R**ievenstein||\n|**i**evenstein|delete **R**|\n|**L**evenstein|replace **i** with **L**|\n|Levens**h**tein|insert **h** at position **t**|\n\nIf you use *Python*, Levenshtein distance is calculated by just running like below!!\n(*For the first time, Perhaps you should run* `!pip install python-Levenshtein`)\n\n```\nimport Levenshtein\n\nstr1 = 'Rievenstein'\nstr2 = 'Levenshtein'\n\nprint(Levenshtein.distance(str1, str2))\n# 3\n```\n\nIf you want to know the *operarions*(how to convert one to the other), use `editops`.\n\n```\nprint(Levenshtein.editops(str1, str2))\n# [('delete', 0, 0), ('replace', 1, 0), ('insert', 7, 6)]\n```\n\nIf you want to get the *similarity value*, you can use `ratio`.\n\n```\nprint(Levenshtein.ratio(str1, str2))\n# 0.8181818181818182\nprint(Levenshtein.ratio(str1, str1))\n# 1.0\nprint(Levenshtein.ratio(str1, ''))\n# 0.0\n```\n\nIf you want to select the *average string* in the list of several strings, you should use to `median`.\n\n```\nprint(Levenshtein.median([\n 'Rievenstein',\n 'Levenshtein',\n 'Revenshtein',\n 'Lievenstein',\n 'Levenshtain',\n 'Levennshtein'\n ]))\n# Levenshtein\n```\n\n----\nThat's it!\nPlease try your best and enjoy this competition😊\n\n\n*If you want to know more detail, please refer to Wikipedia(described in the Overview of this competition)*", "votes": null }, { "id": "1228051", "postDate": "03/06/2021 04:35:32", "content": "

Thank you very much. This is very helpful!

", "rawMarkdown": "Thank you very much. This is very helpful!", "votes": null }, { "id": "1229594", "postDate": "03/07/2021 13:20:15", "content": "

@matsumuranaoki
\nThanks!
\nI hope it will help you somewhat.

", "rawMarkdown": "matsumuranaoki \nThanks!\nI hope it will help you somewhat.", "votes": null }, { "id": "1230921", "postDate": "03/08/2021 14:44:03", "content": "

edlib is a highly efficient implementation

\n

and is also as simple as:
\nlevenshtein_distance = edlib.align(str1, str2)['editDistance']

", "rawMarkdown": "[edlib](https://pypi.org/project/edlib/) is a highly efficient implementation\n\nand is also as simple as:\n`levenshtein_distance = edlib.align(str1, str2)['editDistance']`", "votes": null }, { "id": "1232865", "postDate": "03/10/2021 03:02:32", "content": "

@yhirakawa Good post on how to calculate levenshtein_distance using Python . While many posts focusses on theory and sharing videos on what levenshtein_distance is , its good you had also shared the implementation

", "rawMarkdown": "yhirakawa Good post on how to calculate levenshtein_distance using Python . While many posts focusses on theory and sharing videos on what levenshtein_distance is , its good you had also shared the implementation", "votes": null }, { "id": "1232868", "postDate": "03/10/2021 03:03:15", "content": "

@nofreewill Thanks for sharing about edlib library . I am hearing about it for the first time

", "rawMarkdown": "nofreewill Thanks for sharing about edlib library . I am hearing about it for the first time", "votes": null }, { "id": "1233086", "postDate": "03/10/2021 06:07:45", "content": "

You're welcome! :)

", "rawMarkdown": "You're welcome! :)", "votes": null }, { "id": "1257787", "postDate": "03/31/2021 05:18:20", "content": "

@usharengaraju
\nThank you for your comment☺️

\n

If you want to know how to implement more in detail, please check the official page here.
\nNot only many other usages in levenshtein but also hamming and jaro are shown there😊

", "rawMarkdown": "usharengaraju \nThank you for your comment☺️\n\nIf you want to know how to implement more in detail, please check the official page [here](https://rawgit.com/ztane/python-Levenshtein/master/docs/Levenshtein.html).\nNot only many other usages in levenshtein but also hamming and jaro are shown there😊", "votes": null }, { "id": "1257791", "postDate": "03/31/2021 05:25:03", "content": "

@nofreewill
\nThank you😊
\nI did not know edlib and I've found this is useful in calculating levenshtein distance.

", "rawMarkdown": "nofreewill \nThank you😊\nI did not know *edlib* and I've found this is useful in calculating levenshtein distance.", "votes": null }, { "id": "1258246", "postDate": "03/31/2021 13:34:25", "content": "

if you understand Levenshtein distance, you may also want to check this:

\n

Levenshtein Transformer
\nhttps://arxiv.org/abs/1905.11006

\n

\"\"

", "rawMarkdown": "if you understand Levenshtein distance, you may also want to check this:\n\nLevenshtein Transformer\nhttps://arxiv.org/abs/1905.11006\n\n![](https://paperswithcode.com/media/methods/Screen_Shot_2020-08-21_at_4.29.34_PM_gXAQWfx.png)", "votes": null }, { "id": "1258885", "postDate": "04/01/2021 01:27:33", "content": "

@hengck23
\nThanks😍

\n

I've wanted to study Transformer, which is now indispensable in NLP.
\nI've not known that levenshtein is used with Levenshtein as well😲
\nI'll check it out!

", "rawMarkdown": "hengck23 \nThanks😍\n\nI've wanted to study Transformer, which is now indispensable in NLP.\nI've not known that levenshtein is used with Levenshtein as well😲\nI'll check it out!", "votes": null }, { "id": "1264898", "postDate": "04/06/2021 13:45:55", "content": "

Thank you very much. This is very helpful!

", "rawMarkdown": "Thank you very much. This is very helpful!", "votes": null }, { "id": "1265654", "postDate": "04/07/2021 05:13:30", "content": "

Thank you, too😊

", "rawMarkdown": "Thank you, too😊", "votes": null } ], "comments": [ { "id": 1228051, "author_name": "matsumuranaoki", "author_url": "", "post_date": "03/06/2021 04:35:32", "content": "

Thank you very much. This is very helpful!

", "votes": null, "replies": [ { "id": 1229594, "author_name": "yhirakawa", "author_url": "", "post_date": "03/07/2021 13:20:15", "content": "

@matsumuranaoki
\nThanks!
\nI hope it will help you somewhat.

", "votes": null, "replies": [] } ] }, { "id": 1230921, "author_name": "nofreewill", "author_url": "", "post_date": "03/08/2021 14:44:03", "content": "

edlib is a highly efficient implementation

\n

and is also as simple as:
\nlevenshtein_distance = edlib.align(str1, str2)['editDistance']

", "votes": null, "replies": [ { "id": 1232868, "author_name": "usharengaraju", "author_url": "", "post_date": "03/10/2021 03:03:15", "content": "

@nofreewill Thanks for sharing about edlib library . I am hearing about it for the first time

", "votes": null, "replies": [] }, { "id": 1233086, "author_name": "nofreewill", "author_url": "", "post_date": "03/10/2021 06:07:45", "content": "

You're welcome! :)

", "votes": null, "replies": [] }, { "id": 1257791, "author_name": "yhirakawa", "author_url": "", "post_date": "03/31/2021 05:25:03", "content": "

@nofreewill
\nThank you😊
\nI did not know edlib and I've found this is useful in calculating levenshtein distance.

", "votes": null, "replies": [] } ] }, { "id": 1232865, "author_name": "usharengaraju", "author_url": "", "post_date": "03/10/2021 03:02:32", "content": "

@yhirakawa Good post on how to calculate levenshtein_distance using Python . While many posts focusses on theory and sharing videos on what levenshtein_distance is , its good you had also shared the implementation

", "votes": null, "replies": [ { "id": 1257787, "author_name": "yhirakawa", "author_url": "", "post_date": "03/31/2021 05:18:20", "content": "

@usharengaraju
\nThank you for your comment☺️

\n

If you want to know how to implement more in detail, please check the official page here.
\nNot only many other usages in levenshtein but also hamming and jaro are shown there😊

", "votes": null, "replies": [] } ] }, { "id": 1258246, "author_name": "hengck23", "author_url": "", "post_date": "03/31/2021 13:34:25", "content": "

if you understand Levenshtein distance, you may also want to check this:

\n

Levenshtein Transformer
\nhttps://arxiv.org/abs/1905.11006

\n

\"\"

", "votes": null, "replies": [ { "id": 1258885, "author_name": "yhirakawa", "author_url": "", "post_date": "04/01/2021 01:27:33", "content": "

@hengck23
\nThanks😍

\n

I've wanted to study Transformer, which is now indispensable in NLP.
\nI've not known that levenshtein is used with Levenshtein as well😲
\nI'll check it out!

", "votes": null, "replies": [] } ] }, { "id": 1264898, "author_name": "mohamedbakrey", "author_url": "", "post_date": "04/06/2021 13:45:55", "content": "

Thank you very much. This is very helpful!

", "votes": null, "replies": [ { "id": 1265654, "author_name": "yhirakawa", "author_url": "", "post_date": "04/07/2021 05:13:30", "content": "

Thank you, too😊

", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "1225833": "In this competition, ***Levenshtein distance*** is used for evaluation.\nSo, I briefly summarized it.\n\nThe Levenshtein distance is one of the methods to calculate the similarity between two strings.\nLevenshtein distance is calculated by the number of operations;\n- ***insert***\n- ***delete***\n- ***replace***\n\nwhen converting one string to the other.\n\nFor example, Levenshtein distance between\n***'Rievenstein'*** and ***'Levenshtein'*** is **3**.\n\n|string|operation|\n|---|---|\n|**R**ievenstein||\n|**i**evenstein|delete **R**|\n|**L**evenstein|replace **i** with **L**|\n|Levens**h**tein|insert **h** at position **t**|\n\nIf you use *Python*, Levenshtein distance is calculated by just running like below!!\n(*For the first time, Perhaps you should run* `!pip install python-Levenshtein`)\n\n```\nimport Levenshtein\n\nstr1 = 'Rievenstein'\nstr2 = 'Levenshtein'\n\nprint(Levenshtein.distance(str1, str2))\n# 3\n```\n\nIf you want to know the *operarions*(how to convert one to the other), use `editops`.\n\n```\nprint(Levenshtein.editops(str1, str2))\n# [('delete', 0, 0), ('replace', 1, 0), ('insert', 7, 6)]\n```\n\nIf you want to get the *similarity value*, you can use `ratio`.\n\n```\nprint(Levenshtein.ratio(str1, str2))\n# 0.8181818181818182\nprint(Levenshtein.ratio(str1, str1))\n# 1.0\nprint(Levenshtein.ratio(str1, ''))\n# 0.0\n```\n\nIf you want to select the *average string* in the list of several strings, you should use to `median`.\n\n```\nprint(Levenshtein.median([\n 'Rievenstein',\n 'Levenshtein',\n 'Revenshtein',\n 'Lievenstein',\n 'Levenshtain',\n 'Levennshtein'\n ]))\n# Levenshtein\n```\n\n----\nThat's it!\nPlease try your best and enjoy this competition😊\n\n\n*If you want to know more detail, please refer to Wikipedia(described in the Overview of this competition)*", "1228051": "Thank you very much. This is very helpful!", "1229594": "matsumuranaoki \nThanks!\nI hope it will help you somewhat.", "1230921": "[edlib](https://pypi.org/project/edlib/) is a highly efficient implementation\n\nand is also as simple as:\n`levenshtein_distance = edlib.align(str1, str2)['editDistance']`", "1232865": "yhirakawa Good post on how to calculate levenshtein_distance using Python . While many posts focusses on theory and sharing videos on what levenshtein_distance is , its good you had also shared the implementation", "1232868": "nofreewill Thanks for sharing about edlib library . I am hearing about it for the first time", "1233086": "You're welcome! :)", "1257787": "usharengaraju \nThank you for your comment☺️\n\nIf you want to know how to implement more in detail, please check the official page [here](https://rawgit.com/ztane/python-Levenshtein/master/docs/Levenshtein.html).\nNot only many other usages in levenshtein but also hamming and jaro are shown there😊", "1257791": "nofreewill \nThank you😊\nI did not know *edlib* and I've found this is useful in calculating levenshtein distance.", "1258246": "if you understand Levenshtein distance, you may also want to check this:\n\nLevenshtein Transformer\nhttps://arxiv.org/abs/1905.11006\n\n![](https://paperswithcode.com/media/methods/Screen_Shot_2020-08-21_at_4.29.34_PM_gXAQWfx.png)", "1258885": "hengck23 \nThanks😍\n\nI've wanted to study Transformer, which is now indispensable in NLP.\nI've not known that levenshtein is used with Levenshtein as well😲\nI'll check it out!", "1264898": "Thank you very much. This is very helpful!", "1265654": "Thank you, too😊" }, "source": "meta" }