llm-random-numbers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LLM Random Number Picker\n",
    "\n",
    "Inspired by this [Information is Beautiful viz on what random number ChatGPT guesses](https://twitter.com/infobeautiful/status/1778059112250589561), I tried the same with 3 LLMs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import httpx\n",
    "\n",
    "\n",
    "# If we have prior cached data, load it\n",
    "data = {}\n",
    "if os.path.exists(\"llm-random-numbers.json\"):\n",
    "    with open(\"llm-random-numbers.json\", \"r\") as f:\n",
    "        data = json.load(f)\n",
    "\n",
    "\n",
    "def claude_haiku(temperature):\n",
    "    r = httpx.post(\n",
    "        \"https://api.anthropic.com/v1/messages\",\n",
    "        headers={\n",
    "            \"x-api-key\": os.environ[\"ANTHROPIC_API_KEY\"],\n",
    "            \"anthropic-version\": \"2023-06-01\",\n",
    "            \"content-type\": \"application/json\",\n",
    "        },\n",
    "        json={\n",
    "            \"model\": \"claude-3-haiku-20240307\",\n",
    "            \"max_tokens\": 2,\n",
    "            \"temperature\": temperature,\n",
    "            \"messages\": [\n",
    "                {\n",
    "                    \"role\": \"user\",\n",
    "                    \"content\": \"Pick a random number from 0 - 100. Write ONLY the number NOTHING ELSE\",\n",
    "                }\n",
    "            ],\n",
    "        },\n",
    "    )\n",
    "    r.raise_for_status()\n",
    "    return r.json()[\"content\"][0][\"text\"]\n",
    "\n",
    "\n",
    "def openai_gpt_35(temperature):\n",
    "    r = httpx.post(\n",
    "        \"https://api.openai.com/v1/chat/completions\",\n",
    "        headers={\n",
    "            \"Authorization\": f\"Bearer {os.environ['OPENAI_API_KEY']}\",\n",
    "            \"content-type\": \"application/json\",\n",
    "        },\n",
    "        json={\n",
    "            \"model\": \"gpt-3.5-turbo\",\n",
    "            \"max_tokens\": 2,\n",
    "            \"temperature\": temperature,\n",
    "            \"messages\": [\n",
    "                {\n",
    "                    \"role\": \"user\",\n",
    "                    \"content\": \"Pick a random number from 0 - 100. Write ONLY the number NOTHING ELSE\",\n",
    "                }\n",
    "            ],\n",
    "        },\n",
    "    )\n",
    "    r.raise_for_status()\n",
    "    return r.json()[\"choices\"][0][\"message\"][\"content\"]\n",
    "\n",
    "\n",
    "def google_gemini(temperature):\n",
    "    r = httpx.post(\n",
    "        \"https://europe-west2-aiplatform.googleapis.com/v1/projects/{GOOGLE_PROJECT}/locations/europe-west2/publishers/google/models/gemini-1.0-pro-001:generateContent\".format(**os.environ),\n",
    "        headers={\n",
    "            \"Authorization\": f\"Bearer {os.environ['GOOGLE_API_KEY']}\",\n",
    "            \"content-type\": \"application/json\",\n",
    "        },\n",
    "        json={\n",
    "            \"contents\": [\n",
    "                {\n",
    "                    \"role\": \"user\",\n",
    "                    \"parts\": [\n",
    "                        {\n",
    "                            \"text\": \"Pick a random number from 0 - 100. Write ONLY the number NOTHING ELSE\",\n",
    "                        }\n",
    "                    ]\n",
    "                }\n",
    "            ],\n",
    "            \"generationConfig\": {\n",
    "                \"maxOutputTokens\": 2,\n",
    "                \"temperature\": temperature,\n",
    "            },\n",
    "        },\n",
    "    )\n",
    "    r.raise_for_status()\n",
    "    return r.json()[\"candidates\"][0][\"content\"][\"parts\"][0][\"text\"]\n",
    "\n",
    "\n",
    "# Get 200 random numbers\n",
    "for attempt in range(0, 200):\n",
    "    # At each temperature from 1.0 to 0.0\n",
    "    for t in range(100, -1, -10):\n",
    "        temperature = t / 100\n",
    "        # For each provider\n",
    "        for provider, method in [(\"O\", openai_gpt_35), (\"C\", claude_haiku), (\"G\", google_gemini)]:\n",
    "            # Store data as a key-value pair. The key is \"Provider,Temperature,Attempt\"\n",
    "            key = f\"{provider},{temperature:.2f},{attempt}\"\n",
    "            if key in data:\n",
    "                continue\n",
    "            print(key)\n",
    "            data[key] = method(temperature)\n",
    "            with open(\"llm-random-numbers.json\", \"w\") as f:\n",
    "                json.dump(data, f, sort_keys=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>m</th>\n",
       "      <th>t</th>\n",
       "      <th>#</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6595</th>\n",
       "      <td>O</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6596</th>\n",
       "      <td>O</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6597</th>\n",
       "      <td>O</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6598</th>\n",
       "      <td>O</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6599</th>\n",
       "      <td>O</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6600 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      m    t    #   n\n",
       "0     C  0.0    0  42\n",
       "1     C  0.0    1  42\n",
       "2     C  0.0   10  42\n",
       "3     C  0.0  100  42\n",
       "4     C  0.0  101  42\n",
       "...  ..  ...  ...  ..\n",
       "6595  O  1.0   95  56\n",
       "6596  O  1.0   96  74\n",
       "6597  O  1.0   97  79\n",
       "6598  O  1.0   98  47\n",
       "6599  O  1.0   99  67\n",
       "\n",
       "[6600 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert data into a Pandas DataFrame with columns \"t\" (for temperature), \"#\" for attempt, and \"n\" for the random number\n",
    "import pandas as pd\n",
    "df = pd.DataFrame([{\"m\": k.split(\",\")[0], \"t\": float(k.split(\",\")[1]), \"#\": int(k.split(\",\")[2]), \"n\": int(v)} for k, v in data.items()])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OpenAI GPT 3.5 Turbo prefers 47\n",
    "\n",
    "- Clearly prefers 47!\n",
    "- No single-digit numbers\n",
    "- Prefers numbers ending with 7: 37, 47, 57, 67, but not 27, 77, 97.\n",
    "- 72, 73 are also popular."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a heatmap of the random numbers. X-axis is the random number n, Y-axis is the temperature t, and the color is the frequency of that random number at that temperature.\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(16, 4))\n",
    "pivot = df[df[\"m\"] == \"O\"].pivot_table(index=\"t\", columns=\"n\", values=\"#\", aggfunc=\"count\").fillna(0)\n",
    "sns.heatmap(pivot, cmap=\"viridis\", cbar_kws={'label': 'Frequency of random number'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anthropic Claude 3 Haiku loves 42\n",
    "\n",
    "- Clearly prefers 42!\n",
    "- Guesses numbers in a much smaller range (23-87) compared to OpenAI.\n",
    "- No single-digit numbers\n",
    "- Prefers numbers ending with 7: 37, 47, 57, 67, also 27, 87, but not 77.\n",
    "- Prefers multiples of 9: 72, 45, 54, 63 are pretty popular.\n",
    "- 72, 73 are also popular.\n",
    "- Avoids multiples of 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 4))\n",
    "pivot = df[df[\"m\"] == \"C\"].pivot_table(index=\"t\", columns=\"n\", values=\"#\", aggfunc=\"count\").fillna(0)\n",
    "sns.heatmap(pivot, cmap=\"viridis\", cbar_kws={'label': 'Frequency of random number'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Google Gemini 1.0 Pro likes 72\n",
    "\n",
    "- Clearly prefers 72!\n",
    "- Guesses numbers in a much smaller range (23-87) compared to OpenAI.\n",
    "- Few single-digit numbers\n",
    "- Prefers numbers ending with 7: 27, 37, 47, 57, 67, 77.\n",
    "- 73 is also popular.\n",
    "- Avoids multiples of 10, with rare exceptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 4))\n",
    "pivot = df[df[\"m\"] == \"G\"].pivot_table(index=\"t\", columns=\"n\", values=\"#\", aggfunc=\"count\").fillna(0)\n",
    "sns.heatmap(pivot, cmap=\"viridis\", cbar_kws={'label': 'Frequency of random number'})\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gramex39",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}