LLM_picking_countries_at_random.ipynb

LLM_generating_random_countries.ipynb

{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:03:31.062066Z",
          "end_time": "2023-07-16T20:03:31.083918Z"
        },
        "trusted": true
      },
      "id": "43eb3af8",
      "cell_type": "code",
      "source": "%load_ext autoreload\n%autoreload 2",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:03:32.502218Z",
          "end_time": "2023-07-16T20:03:32.530644Z"
        },
        "trusted": true
      },
      "id": "8f128ebc",
      "cell_type": "code",
      "source": "# get environment variable: OPENAI_API_KEY\nfrom dotenv import load_dotenv, find_dotenv\nload_dotenv(find_dotenv())",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "True"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:03:33.401680Z",
          "end_time": "2023-07-16T20:03:33.495545Z"
        },
        "trusted": true
      },
      "id": "72017eb0",
      "cell_type": "code",
      "source": "import openai\nfrom retry import retry",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:03:34.935395Z",
          "end_time": "2023-07-16T20:03:34.957562Z"
        },
        "trusted": true
      },
      "id": "b80caa94",
      "cell_type": "code",
      "source": "@retry(tries=10, delay=3)\ndef country(text: str) -> str:\n    functions = [{\n        \"name\": \"print_country\",\n        \"description\": \"A function that prints the given country\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"country\": {\n                    \"type\": \"string\",\n                    \"enum\": ['Canada', 'France', 'Germany', 'Italy', 'Japan', 'United Kingdom', 'United States'],\n                    \"description\": \"The country.\",\n                },\n            },\n            \"required\": [\"country\"],\n        }\n    }]\n    response = openai.ChatCompletion.create(\n        model = 'gpt-3.5-turbo',\n        temperature = 1,\n        messages = [{\n            'role': 'user',\n            'content': text\n        }],\n        functions = functions,\n        function_call = {'name': 'print_country'},\n    )\n    function_call = response.choices[0].message[\"function_call\"]\n    argument = json.loads(function_call[\"arguments\"])\n    return argument",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:03:36.422400Z",
          "end_time": "2023-07-16T20:03:36.440729Z"
        },
        "trusted": true
      },
      "id": "40d43381",
      "cell_type": "code",
      "source": "prompt = \"Pick a random country from the G7 countries\"",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:03:45.736129Z",
          "end_time": "2023-07-16T20:35:10.862063Z"
        },
        "trusted": true
      },
      "id": "499f9359",
      "cell_type": "code",
      "source": "dictionaries = [country(prompt) for i in range(1100)]",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:35:20.358280Z",
          "end_time": "2023-07-16T20:35:20.378970Z"
        },
        "trusted": true
      },
      "id": "a1e96672",
      "cell_type": "code",
      "source": "countries = [dictionaries[i]['country'] if 'country' in list(dictionaries[i].keys()) else np.nan for i in range(len(dictionaries))]",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:35:22.078253Z",
          "end_time": "2023-07-16T20:35:22.095790Z"
        },
        "trusted": true
      },
      "id": "a2447091",
      "cell_type": "code",
      "source": "from collections import Counter",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:35:24.470639Z",
          "end_time": "2023-07-16T20:35:24.616816Z"
        },
        "trusted": true
      },
      "id": "a3455bf0",
      "cell_type": "code",
      "source": "import pandas as pd\n\ndf_random_countries = pd.DataFrame()\ndf_random_countries[\"random_countries\"] = countries",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:35:39.591546Z",
          "end_time": "2023-07-16T20:35:39.619284Z"
        },
        "trusted": true
      },
      "id": "b405acbb",
      "cell_type": "code",
      "source": "df_random_countries.to_csv(\"random_countries.csv\", index=False)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:35:51.132567Z",
          "end_time": "2023-07-16T20:35:51.154557Z"
        },
        "trusted": true
      },
      "id": "9381a1fe",
      "cell_type": "code",
      "source": "df_random_countries = df_random_countries.dropna()",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:36:00.072460Z",
          "end_time": "2023-07-16T20:36:00.095509Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_random_countries.shape",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "(1069, 1)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:36:02.688685Z",
          "end_time": "2023-07-16T20:36:02.707762Z"
        },
        "trusted": true
      },
      "id": "e08fe3c4",
      "cell_type": "code",
      "source": "df_random_countries = df_random_countries[:1000]",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:36:05.815504Z",
          "end_time": "2023-07-16T20:36:05.836395Z"
        },
        "trusted": true
      },
      "id": "2528648d",
      "cell_type": "code",
      "source": "from collections import Counter\n\nserie_rc = pd.Series(Counter(list(df_random_countries[\"random_countries\"])))\nserie_rc_pct = 100*serie_rc/1000",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:36:36.251212Z",
          "end_time": "2023-07-16T20:36:36.277785Z"
        },
        "trusted": true
      },
      "id": "df841acb",
      "cell_type": "code",
      "source": "df = pd.DataFrame()\ndf['country'] = serie_rc.index\ndf['counter'] = serie_rc.values\ndf = df.set_index(\"country\")",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:37:08.649170Z",
          "end_time": "2023-07-16T20:37:08.684791Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_final = pd.DataFrame()\ndf_final[\"country\"] = ['Canada', 'France', 'Germany', 'Italy', 'Japan', 'United Kingdom', 'United States']\ndf_final = df_final.set_index(\"country\")\ndf_final[\"counters\"] = df[\"counter\"]\ndf_final = df_final.fillna(0)\ndf_final[\"counters\"] = df_final[\"counters\"].astype(int)\ndf_final[\"counters_percentage\"] = 100*df_final[\"counters\"]/(df_final[\"counters\"].sum())\ndf_final",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "                counters  counters_percentage\ncountry                                      \nCanada               642                 64.2\nFrance                12                  1.2\nGermany               42                  4.2\nItaly                  2                  0.2\nJapan                  3                  0.3\nUnited Kingdom        41                  4.1\nUnited States        258                 25.8",
            "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>counters</th>\n      <th>counters_percentage</th>\n    </tr>\n    <tr>\n      <th>country</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Canada</th>\n      <td>642</td>\n      <td>64.2</td>\n    </tr>\n    <tr>\n      <th>France</th>\n      <td>12</td>\n      <td>1.2</td>\n    </tr>\n    <tr>\n      <th>Germany</th>\n      <td>42</td>\n      <td>4.2</td>\n    </tr>\n    <tr>\n      <th>Italy</th>\n      <td>2</td>\n      <td>0.2</td>\n    </tr>\n    <tr>\n      <th>Japan</th>\n      <td>3</td>\n      <td>0.3</td>\n    </tr>\n    <tr>\n      <th>United Kingdom</th>\n      <td>41</td>\n      <td>4.1</td>\n    </tr>\n    <tr>\n      <th>United States</th>\n      <td>258</td>\n      <td>25.8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-07-16T20:38:13.989436Z",
          "end_time": "2023-07-16T20:38:14.943986Z"
        },
        "trusted": true
      },
      "id": "dac0af52",
      "cell_type": "code",
      "source": "import numpy as np\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots()\nx = [i for i in range(len(df_final.index))]\ny = df_final['counters_percentage']\nax.bar(x, y)\nfor i, val in enumerate(df_final[\"counters_percentage\"]):\n    ax.text(i-.4, val+1, f\" {val:.1f}%\")\nax.set_title(f\"{prompt}\\n(n=1000, model='gpt-3.5-turbo', temp=1)\")\nax.set_xticks([i for i in range(len(df_final.index))])\nax.set_xticklabels(df_final.index, rotation=45)\nax.set_yticks([10*i for i in range(8)])\nax.set_yticklabels([f\"{10*i}%\" for i in range(8)])\nax.set_ylabel(\"% of responses\")\nplt.savefig(f\"countries.png\", dpi=700, bbox_inches='tight', format='png')\n# plt.show()",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "_draft": {
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