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balance_parens.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "balance_parens.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyOzBno/2ixz6BLO0170msVS",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/brockmanmatt/aea4fc4a962188f85d83db761bf0ac50/balance_parens.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Rih86XVVkoqf",
"colab_type": "text"
},
"source": [
"# Notebook: GPT bad at parentheses by themselves without additional tweaking/priming\n",
"\n",
"## However, it can fix f(f(x)) type stuff"
]
},
{
"cell_type": "code",
"metadata": {
"id": "J7wnsgT2kPut",
"colab_type": "code",
"colab": {
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": 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",
"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 89
},
"outputId": "aa3509ef-7828-44c4-ba5b-2ca9d6012cd9"
},
"source": [
"from google.colab import files\n",
"uploaded = files.upload()\n",
"print(\"done\")"
],
"execution_count": 1,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-9542d2d3-32a0-4ba5-950e-f8caf6c3d2f7\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-9542d2d3-32a0-4ba5-950e-f8caf6c3d2f7\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Saving key.json to key.json\n",
"done\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WHPHrUnhpKnI",
"colab_type": "text"
},
"source": [
"I'll install the API"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zq0ltp2xn4yt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 292
},
"outputId": "e96b614e-da02-4fa9-fa0a-08657d7b321b"
},
"source": [
"!pip install openai\n",
"import openai, json, pandas as pd"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting openai\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a8/65/c7461f4c87984534683f480ea5742777bc39bbf5721123194c2d0347dc1f/openai-0.2.4.tar.gz (157kB)\n",
"\r\u001b[K |██ | 10kB 13.8MB/s eta 0:00:01\r\u001b[K |████▏ | 20kB 2.9MB/s eta 0:00:01\r\u001b[K |██████▎ | 30kB 3.5MB/s eta 0:00:01\r\u001b[K |████████▍ | 40kB 4.0MB/s eta 0:00:01\r\u001b[K |██████████▍ | 51kB 3.3MB/s eta 0:00:01\r\u001b[K |████████████▌ | 61kB 3.6MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 71kB 3.6MB/s eta 0:00:01\r\u001b[K |████████████████▊ | 81kB 4.0MB/s eta 0:00:01\r\u001b[K |██████████████████▊ | 92kB 4.4MB/s eta 0:00:01\r\u001b[K |████████████████████▉ | 102kB 4.5MB/s eta 0:00:01\r\u001b[K |███████████████████████ | 112kB 4.5MB/s eta 0:00:01\r\u001b[K |█████████████████████████ | 122kB 4.5MB/s eta 0:00:01\r\u001b[K |███████████████████████████ | 133kB 4.5MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▏ | 143kB 4.5MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▎| 153kB 4.5MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 163kB 4.5MB/s \n",
"\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2020.6.20)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
"Building wheels for collected packages: openai\n",
" Building wheel for openai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for openai: filename=openai-0.2.4-cp36-none-any.whl size=170709 sha256=d1b35bdeae855b8cdf8a811f5d80d659a3a8e38f12c11be20c1b2398d0e028c5\n",
" Stored in directory: /root/.cache/pip/wheels/74/96/c8/c6e170929c276b836613e1b9985343b501fe455e53d85e7d48\n",
"Successfully built openai\n",
"Installing collected packages: openai\n",
"Successfully installed openai-0.2.4\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q2yE0jcnpMEV",
"colab_type": "text"
},
"source": [
"Loading in key.json that I uploaded; I do this so I don't need to worry about accidently leaking creds if I share the colab (which I'm 99% sure is just a json file that won't expose them)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bwNXXwHen5x9",
"colab_type": "code",
"colab": {}
},
"source": [
"openai.api_key = json.load(open(\"key.json\", \"r\"))[\"key\"]"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "k67w5H0fpTkT",
"colab_type": "text"
},
"source": [
"Default keyword arguments to pass the aPI"
]
},
{
"cell_type": "code",
"metadata": {
"id": "e1EwpqqJkTYh",
"colab_type": "code",
"colab": {}
},
"source": [
"#arguments to send the API\n",
"kwargs = {\n",
"\"engine\":\"davinci\",\n",
"\"temperature\":0,\n",
"\"max_tokens\":150,\n",
"\"stop\":\"\\n\\n\",\n",
"}"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zZubgPoOpWDH",
"colab_type": "text"
},
"source": [
"Quick wrapper to automatically save prompts and responses sent for later analysis if needed"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sXTDJx0An9Bl",
"colab_type": "code",
"colab": {}
},
"source": [
"import datetime\n",
"def query(prompt, myKwargs = kwargs):\n",
" \"\"\"\n",
" wrapper for the API to save the prompt and the result\n",
" \"\"\"\n",
"\n",
" r = openai.Completion.create(prompt=prompt, **myKwargs)[\"choices\"][0][\"text\"].strip()\n",
" with open(\"{}.json\".format(datetime.datetime.now().strftime(\"%Y%m%d%s\")), \"w\") as fh:\n",
" json.dump({\"prompt\":prompt, \"response\":r}, fh, indent=4)\n",
" return r"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "EdFXafcJpZ3Q",
"colab_type": "text"
},
"source": [
"Test to make sure my query works"
]
},
{
"cell_type": "code",
"metadata": {
"id": "4SlyKgjyopPn",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "1a32dc96-ed83-46e6-db15-d950b6e99132"
},
"source": [
"newKwargs = kwargs.copy()\n",
"newKwargs[\"stop\"] = \"\\n\"\n",
"query(\"q: what is 1+1?\\na:\", newKwargs)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'2'"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "P4S7CFEqh818",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"\"\"q: is () balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is (() balanced or unbalanced\n",
"a: unbalanced\n",
"\n",
"q: is (()) balanced or unbalanced\n",
"a:\"\"\""
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9LFUKZtGiMhn",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "d5749c52-2cba-44d6-e072-f51ce964e922"
},
"source": [
"query(prompt, newKwargs)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'unbalanced'"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9M00Q2SGiPBd",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"\"\"A set of parentheses is balanced if it has a closing parenthese for each opening parenthese.\n",
"\n",
"q: is ) balanced or unbalanced\n",
"a: unbalanced; missing an opening parethese\n",
"\n",
"q: is () balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is (()) balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is (() balanced or unbalanced\n",
"a: unbalanced; it has an unclosed parethese\n",
"\n",
"q: is (()) balanced or unbalanced\n",
"a:\"\"\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tBAOgCnvifor",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "05850ea5-66a6-42f6-85de-1e5d224706cd"
},
"source": [
"query(prompt, newKwargs)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'unbalanced; it has an unclosed parethese'"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vby_QThzigR1",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "2116c71a-cfc2-4d48-f2ca-994fcf69ff8b"
},
"source": [
"prompt = \"\"\"A set of parentheses is balanced if it has a closing parenthese for each opening parenthese.\n",
"\n",
"q: is ' ) ' balanced or unbalanced\n",
"a: unbalanced; missing an opening parethese\n",
"\n",
"q: is ' ( ) ' balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is ' ( ( ) ) ' balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is ' ( ( ) ' balanced or unbalanced\n",
"a: unbalanced; it has an unclosed parethese\n",
"\n",
"q: is ' ( ( ) ) ' balanced or unbalanced\n",
"a:\"\"\"\n",
"query(prompt, newKwargs)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'balanced'"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7_7ahuZhi9Nf",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "28dd6524-16dc-4167-8cd7-a9ad6c02ea47"
},
"source": [
"prompt = \"\"\"A set of parentheses is balanced if it has a closing parenthese for each opening parenthese.\n",
"\n",
"q: is ' ) ' balanced or unbalanced\n",
"a: unbalanced; missing an opening parethese\n",
"\n",
"q: is ' ( ) ' balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is ' ( ( ) ) ' balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is ' ( ( ) ' balanced or unbalanced\n",
"a: unbalanced; it has an unclosed parethese\n",
"\n",
"q: is ' ( ( ( ) ) ' balanced or unbalanced\n",
"a:\"\"\"\n",
"query(prompt, newKwargs)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'balanced'"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KV8_evEIjAYp",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "7f3280a5-fbb7-4e87-b6a5-1f57acc5f3e5"
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"source": [
"prompt = \"\"\"A set of parentheses is balanced if it has a closing parenthese for each opening parenthese.\n",
"\n",
"q: is ' ) ' balanced or unbalanced\n",
"a: unbalanced; missing an opening parethese\n",
"\n",
"q: is ' ( ) ' balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is ' ( ( ) ) ' balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is ' ( ( ) ' balanced or unbalanced\n",
"a: unbalanced; it has an unclosed parethese\n",
"\n",
"q: is ' ( ( ( ) ) ) ' balanced or unbalanced\n",
"a:\"\"\"\n",
"query(prompt, newKwargs)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'balanced'"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_o6tbASQjB-B",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "574968d9-8f39-432a-a856-10c0ffd8b7de"
},
"source": [
"prompt = \"\"\"A set of parentheses is balanced if it has a closing parenthese for each opening parenthese.\n",
"\n",
"q: is ' ( ( ) ) ' balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is ' ( ( ) ' balanced or unbalanced\n",
"a: unbalanced; it has an unclosed parethese\n",
"\n",
"q: is ' ( ( ( ) ) ) ' balanced or unbalanced\n",
"a:\"\"\"\n",
"query(prompt, newKwargs)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'balanced'"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BvpyqPORjFJR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "969e12aa-20a5-4c6a-89f8-df35e5fc7bf6"
},
"source": [
"prompt = \"\"\"A set of parentheses is balanced if it has a closing parenthese for each opening parenthese.\n",
"\n",
"q: is ' ( ( ) ) ' balanced or unbalanced\n",
"a: balanced\n",
"\n",
"q: is ' ( ( ) ' balanced or unbalanced\n",
"a: unbalanced; it has an unclosed parethese\n",
"\n",
"q: is ' ( ( ( ) ) ' balanced or unbalanced\n",
"a:\"\"\"\n",
"query(prompt, newKwargs)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'balanced'"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PU1ZJlNqjHMT",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "82e83f30-1394-4bf6-c781-23ca3f6c01d2"
},
"source": [
"prompt = \"\"\"balance the number of open and closed parentheses\n",
"\n",
"input: ' ( ( ) ) '\n",
"output: ' ( ( ) ) '\n",
"\n",
"input: ' ( ( ) '\n",
"output: ' ( ( ) ) '\n",
"\n",
"input: ' ( ( ( '\n",
"output:\"\"\"\n",
"query(prompt, newKwargs)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"\"' ( ( ( ) ) '\""
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "god_6YuXjYtF",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "d3ffecdb-0a74-44be-b9b5-1d01818dbb5c"
},
"source": [
"prompt = \"\"\"fix the following:\n",
"\n",
"input: 'f(x) + f(y'\n",
"output: 'f(x) + f(y)'\n",
"\n",
"input: 'f(f(f(y)'\n",
"output: 'f(f(f(y)))'\n",
"\n",
"input: 'f(f(y'\n",
"output:\"\"\"\n",
"query(prompt, newKwargs)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"\"'f(f(y))'\""
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "sCwkI6jMkWyt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "e38bc3ee-a243-47d7-9e7c-163588b94883"
},
"source": [
"prompt = \"\"\"fix the following:\n",
"\n",
"input: 'f(x) + f(y'\n",
"output: 'f(x) + f(y)'\n",
"\n",
"input: 'f(f(f(y)'\n",
"output: 'f(f(f(y)))'\n",
"\n",
"input: 'f(f(f(f(y'\n",
"output:\"\"\"\n",
"query(prompt, newKwargs)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"\"'f(f(f(f(y)))'\""
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kFEb6XPokZKM",
"colab_type": "code",
"colab": {}
},
"source": [
"tests = {}\n",
"for i in range(1,6):\n",
" tests[i]= {}\n",
" tests[i][\"inputs\"] = []\n",
" newParens = \" \"\n",
" for j in range(i):\n",
" newParens += \" ( \"\n",
" for j in range(i):\n",
" newParens += \" ) \"\n",
" tests[i][\"inputs\"].append(newParens + \" \")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Rj9SvpCYlrIp",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
},
"outputId": "668e60c5-e790-44b8-a257-768892c2b4ce"
},
"source": [
"pd.DataFrame(tests[2])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
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" inputs\n",
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},
{
"cell_type": "markdown",
"metadata": {
"id": "zA8aq5RupEu6",
"colab_type": "text"
},
"source": [
"Look, it just fails miserably on the parentheses"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-Z6iyiX6lNtB",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"outputId": "df34c835-2fcd-4faf-996c-5382e9a6595b"
},
"source": [
"prompt = \"\"\" Parentheses are balanced if there's a closing parenthese for each opening parenthese. Are following parentheses following balanced or unbalanced?\n",
"\n",
"input: ' ( ( ) ) '\n",
"output: balanced\n",
"\n",
"input: ' ( ( ) '\n",
"output: unbalanced\n",
"\n",
"input: ' ( ) ( ) ( ) '\n",
"output: balanced\n",
"\n",
"input: ' ( ) ) '\n",
"output: unbalanced\n",
"\n",
"input: ' ( ) ( ) ) '\n",
"output: unbalanced\n",
"\n",
"input: {}\n",
"output:\"\"\"\n",
"query(prompt, newKwargs)\n",
"\n",
"for i in range(1,6):\n",
" print(i)\n",
" tests[i][\"completions\"] = []\n",
" for j in tests[i][\"inputs\"]:\n",
" tests[i][\"completions\"].append(query(prompt.format(j), newKwargs))\n",
" "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"5\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Wnc-Mzl7n2Cb",
"colab_type": "code",
"colab": {}
},
"source": [
"def isBalanced(someParens):\n",
" \"\"\"\n",
" bad way to count balance but works for what I fed it\n",
" \"\"\"\n",
" a = someParens.count(\"(\")\n",
" b = someParens.count(\")\")\n",
" return a==b"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JS09qqaXnAy6",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"outputId": "9514bdb4-7b2a-421a-9b94-c9249853e7ad"
},
"source": [
"for i in range(1,6):\n",
" print(i)\n",
" tests[i][\"actual\"] = []\n",
" for j in tests[i][\"inputs\"]:\n",
" tests[i][\"actual\"].append(isBalanced(j))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"5\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Q9ttnMKApC_h",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"outputId": "a31b8d5b-a4eb-4bb5-aa7a-e4898dc874e7"
},
"source": [
"pd.DataFrame(tests[3])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" inputs completions actual\n",
"0 ( ( ( ) balanced False\n",
"1 ( ( ( ) ) balanced False\n",
"2 ( ( ( ) ) ) balanced True"
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"metadata": {
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},
"execution_count": 71
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]
},
{
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"metadata": {
"id": "S8NJgd_0pBLi",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173
},
"outputId": "60a0d02a-b46f-4eee-d4a7-906d335e2013"
},
"source": [
"pd.DataFrame(tests[4])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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},
"execution_count": 69
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "65pO0pPdlOtn",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "fb244334-307b-4e61-ff98-38c138b99e1b"
},
"source": [
"pd.DataFrame(tests[5])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>inputs</th>\n",
" <th>completions</th>\n",
" <th>actual</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>( ( ( ( ( )</td>\n",
" <td>balanced</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>( ( ( ( ( ) )</td>\n",
" <td>balanced</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>( ( ( ( ( ) ) )</td>\n",
" <td>balanced</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>( ( ( ( ( ) ) ) )</td>\n",
" <td>balanced</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>( ( ( ( ( ) ) ) ) )</td>\n",
" <td>balanced</td>\n",
" <td>True</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" inputs completions actual\n",
"0 ( ( ( ( ( ) balanced False\n",
"1 ( ( ( ( ( ) ) balanced False\n",
"2 ( ( ( ( ( ) ) ) balanced False\n",
"3 ( ( ( ( ( ) ) ) ) balanced False\n",
"4 ( ( ( ( ( ) ) ) ) ) balanced True"
]
},
"metadata": {
"tags": []
},
"execution_count": 70
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "as6Krrjrm54X",
"colab_type": "code",
"colab": {}
},
"source": [
"prompt = \"\"\"Fix the parentheses so there's a closed ) for every open (\n",
"Original: There isn't a good answer (I mean, who knows(right?)\n",
"Fixed: There isn't a good answer (I mean, who knows(right?))\n",
"\n",
"Original: Tim (my friend)) is really tired.\n",
"Fixed: Tim (my friend) is really tired.\n",
"\n",
"Original: {}\n",
"Fixed:\"\"\""
],
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "RyTEMWXAi922",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "4a2c8d7d-b550-4971-fd8a-b93accfadf58"
},
"source": [
"query(prompt.format(\"wait (test (this\"))"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'wait (test (this'"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lvSZA4gwiove",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "c3fc9a0c-847b-4dbc-c219-f59cb0625f1b"
},
"source": [
"query(prompt.format(\"wait (test (this)\"))"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic": {
"type": "string"
},
"text/plain": [
"'wait (test (this))'"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uZ1C1MakitZf",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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