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ishaan-jaff 2023-07-29 07:00:13 -07:00
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docs/extras/guides/evaluation/string/string_distance.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "2da95378",
"metadata": {},
"source": [
"# String Distance\n",
"\n",
"One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance. This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
"\n",
"This can be accessed using the `string_distance` evaluator, which uses distance metric's from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
"\n",
"**Note:** The returned scores are _distances_, meaning lower is typically \"better\".\n",
"\n",
"For more information, check out the reference docs for the [StringDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html#langchain.evaluation.string_distance.base.StringDistanceEvalChain) for more info."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8b47b909-3251-4774-9a7d-e436da4f8979",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# %pip install rapidfuzz"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f6790c46",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.evaluation import load_evaluator\n",
"\n",
"evaluator = load_evaluator(\"string_distance\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "49ad9139",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'score': 0.11555555555555552}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluator.evaluate_strings(\n",
" prediction=\"The job is completely done.\",\n",
" reference=\"The job is done\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c06a2296",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'score': 0.0724999999999999}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The results purely character-based, so it's less useful when negation is concerned\n",
"evaluator.evaluate_strings(\n",
" prediction=\"The job is done.\",\n",
" reference=\"The job isn't done\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
"metadata": {},
"source": [
"## Configure the String Distance Metric\n",
"\n",
"By default, the `StringDistanceEvalChain` uses levenshtein distance, but it also supports other string distance algorithms. Configure using the `distance` argument."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a88bc7d7-62d3-408d-b0e0-43abcecf35c8",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[<StringDistance.DAMERAU_LEVENSHTEIN: 'damerau_levenshtein'>,\n",
" <StringDistance.LEVENSHTEIN: 'levenshtein'>,\n",
" <StringDistance.JARO: 'jaro'>,\n",
" <StringDistance.JARO_WINKLER: 'jaro_winkler'>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.evaluation import StringDistance\n",
"\n",
"list(StringDistance)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"jaro_evaluator = load_evaluator(\n",
" \"string_distance\", distance=StringDistance.JARO\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'score': 0.19259259259259254}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jaro_evaluator.evaluate_strings(\n",
" prediction=\"The job is completely done.\",\n",
" reference=\"The job is done\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7020b046-0ef7-40cc-8778-b928e35f3ce1",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'score': 0.12083333333333324}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jaro_evaluator.evaluate_strings(\n",
" prediction=\"The job is done.\",\n",
" reference=\"The job isn't done\",\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}