sec6-co_occurrence_failures.ipynb

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   "cell_type": "code",
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   "id": "424c58c9832985a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-02T23:26:23.542769Z",
     "start_time": "2024-06-02T23:26:23.539653Z"
    },
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   },
   "outputs": [],
   "source": [
    "import ast\n",
    "from itertools import combinations\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from util.plotting import *\n",
    "from datetime import date, datetime, time, timedelta\n",
    "import json\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import ListedColormap, BoundaryNorm\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "import warnings\n",
    "from scipy.stats import gmean\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5fdcb95",
   "metadata": {},
   "source": [
    "## Co-occurrence of outages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24fa2638",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load outage data\n",
    "path = f'data/clean/outage/2024-08-31/outage_unexploded.csv'\n",
    "df = pd.read_csv(path, parse_dates=['Date'])\n",
    "# rename Service\n",
    "for service in df['Service'].unique():\n",
    "    if service == \"api\":\n",
    "        df.loc[df['Service'] == service, 'Service'] = \"API-OpenAI\"\n",
    "    elif service == \"chatgpt\":\n",
    "        df.loc[df['Service'] == service, 'Service'] = \"ChatGPT\"\n",
    "    elif service == \"labs\":\n",
    "        df.loc[df['Service'] == service, 'Service'] = \"DALL·E\"\n",
    "    elif service == \"playground\":\n",
    "        df.loc[df['Service'] == service, 'Service'] = \"Playground\"\n",
    "    elif service == \"api.anthropic.com\":\n",
    "        df.loc[df['Service'] == service, 'Service'] = \"API-Anthropic\"\n",
    "    elif service == \"claude.ai\":\n",
    "        df.loc[df['Service'] == service, 'Service'] = \"Claude\"\n",
    "    elif service == \"console.anthropic.com\":\n",
    "        df.loc[df['Service'] == service, 'Service'] = \"Console\"\n",
    "    elif service == \"character.ai\":\n",
    "        df.loc[df['Service'] == service, 'Service'] = \"Character.AI\"\n",
    "\n",
    "service_lst = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground', 'API-Anthropic', 'Claude', 'Console', 'Character.AI']\n",
    "dfs = {}\n",
    "# filter by service\n",
    "for service in service_lst:\n",
    "    dfs[service] = df[df['Service'] == service]\n",
    "    dfs[service].reset_index(drop=True, inplace=True)\n",
    "display(dfs['API-OpenAI'].head())\n",
    "\n",
    "provider_lst = ['openai', 'anthropic', 'characterai']\n",
    "dfs_provider = {}\n",
    "# filter by provider\n",
    "for provider in provider_lst:\n",
    "    dfs_provider[provider] = df[df['provider'] == provider]\n",
    "    dfs_provider[provider].reset_index(drop=True, inplace=True)\n",
    "display(dfs_provider['openai'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efb45cb2",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>API-OpenAI</th>\n",
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       "      <th>DALL·E</th>\n",
       "      <th>Playground</th>\n",
       "      <th>API-Anthropic</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-02-11</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>2021-02-13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-02-14</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>2024-08-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1295</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>2024-08-30</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>2024-08-31</td>\n",
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       "<p>1298 rows × 9 columns</p>\n",
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      "text/plain": [
       "           Date  API-OpenAI  ChatGPT  DALL·E  Playground  API-Anthropic  \\\n",
       "0    2021-02-11           0      NaN     NaN         NaN            NaN   \n",
       "1    2021-02-12           0      NaN     NaN         NaN            NaN   \n",
       "2    2021-02-13           0      NaN     NaN         NaN            NaN   \n",
       "3    2021-02-14           0      NaN     NaN         NaN            NaN   \n",
       "4    2021-02-15           0      NaN     NaN         NaN            NaN   \n",
       "...         ...         ...      ...     ...         ...            ...   \n",
       "1293 2024-08-27           0      0.0     0.0         0.0            0.0   \n",
       "1294 2024-08-28           1      1.0     0.0         0.0            0.0   \n",
       "1295 2024-08-29           0      0.0     0.0         0.0            0.0   \n",
       "1296 2024-08-30           0      0.0     0.0         0.0            0.0   \n",
       "1297 2024-08-31           0      0.0     0.0         0.0            0.0   \n",
       "\n",
       "      Claude  Console  Character.AI  \n",
       "0        NaN      NaN           NaN  \n",
       "1        NaN      NaN           NaN  \n",
       "2        NaN      NaN           NaN  \n",
       "3        NaN      NaN           NaN  \n",
       "4        NaN      NaN           NaN  \n",
       "...      ...      ...           ...  \n",
       "1293     0.0      0.0           0.0  \n",
       "1294     0.0      0.0           0.0  \n",
       "1295     0.0      0.0           0.0  \n",
       "1296     0.0      0.0           0.0  \n",
       "1297     0.0      0.0           0.0  \n",
       "\n",
       "[1298 rows x 9 columns]"
      ]
     },
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    }
   ],
   "source": [
    "# data preparation\n",
    "# preapre data for heatmap\n",
    "start_date = date(2021, 2, 11)\n",
    "end_date = date(2024, 8, 31)\n",
    "delta = timedelta(days=1)\n",
    "dates = []\n",
    "while start_date <= end_date:\n",
    "    dates.append(start_date)\n",
    "    start_date += delta\n",
    "\n",
    "dates = pd.DataFrame(dates, columns=['Date'])\n",
    "dates['Date'] = pd.to_datetime(dates['Date'])\n",
    "dates\n",
    "\n",
    "# dfs[service] left join dates on 'Date' with only 'daily_availability' column\n",
    "for service in service_lst:\n",
    "    df = dfs[service]\n",
    "    df = pd.merge(dates, df, on='Date', how='left')\n",
    "    df = df[['Date', 'outage_flag']]\n",
    "    dfs[service] = df\n",
    "\n",
    "# prepare data for plotting\n",
    "data = {\n",
    "    'Date': dfs['API-OpenAI']['Date'],\n",
    "    'API-OpenAI': dfs['API-OpenAI']['outage_flag'],\n",
    "    'ChatGPT': dfs['ChatGPT']['outage_flag'],\n",
    "    'DALL·E': dfs['DALL·E']['outage_flag'],\n",
    "    'Playground': dfs['Playground']['outage_flag'],\n",
    "    'API-Anthropic': dfs['API-Anthropic']['outage_flag'],\n",
    "    'Claude': dfs['Claude']['outage_flag'],\n",
    "    'Console': dfs['Console']['outage_flag'],\n",
    "    'Character.AI': dfs['Character.AI']['outage_flag']\n",
    "}\n",
    "\n",
    "df_outage_flag = pd.DataFrame(data)\n",
    "df_outage_flag"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c1565b",
   "metadata": {},
   "source": [
    "### fig. concurrent outage counts matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c3511ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>API-OpenAI</th>\n",
       "      <th>ChatGPT</th>\n",
       "      <th>DALL·E</th>\n",
       "      <th>Playground</th>\n",
       "      <th>API-Anthropic</th>\n",
       "      <th>Claude</th>\n",
       "      <th>Console</th>\n",
       "      <th>Character.AI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Service</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>API-OpenAI</th>\n",
       "      <td>95</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChatGPT</th>\n",
       "      <td>31</td>\n",
       "      <td>63</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DALL·E</th>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Playground</th>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>API-Anthropic</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Claude</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Console</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Character.AI</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               API-OpenAI  ChatGPT  DALL·E  Playground  API-Anthropic  Claude  \\\n",
       "Service                                                                         \n",
       "API-OpenAI             95       31      18          21              1       3   \n",
       "ChatGPT                31       63       9           9              5       7   \n",
       "DALL·E                 18        9      23           8              0       0   \n",
       "Playground             21        9       8          24              0       0   \n",
       "API-Anthropic           1        5       0           0             25      24   \n",
       "Claude                  3        7       0           0             24      29   \n",
       "Console                 3        7       0           0             22      26   \n",
       "Character.AI            2        1       0           1              0       0   \n",
       "\n",
       "               Console  Character.AI  \n",
       "Service                               \n",
       "API-OpenAI           3             2  \n",
       "ChatGPT              7             1  \n",
       "DALL·E               0             0  \n",
       "Playground           0             1  \n",
       "API-Anthropic       22             0  \n",
       "Claude              26             0  \n",
       "Console             27             0  \n",
       "Character.AI         0            29  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "service_lst = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground', 'API-Anthropic', 'Claude', 'Console', 'Character.AI']\n",
    "outage_count = {}\n",
    "for service in service_lst:\n",
    "    coocurrence = []\n",
    "    for service2 in service_lst:\n",
    "        coocurrence.append(df_outage_flag[(df_outage_flag[service] > 0) & (df_outage_flag[service2] > 0)].shape[0])\n",
    "    outage_count[service] = coocurrence\n",
    "\n",
    "df_outage_count = pd.DataFrame(outage_count)\n",
    "df_outage_count['Service'] = service_lst\n",
    "df_outage_count = df_outage_count[['Service', 'API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground', 'API-Anthropic', 'Claude', 'Console', 'Character.AI']]\n",
    "# set the index to 'Service'\n",
    "df_outage_count.set_index('Service', inplace=True)\n",
    "\n",
    "display(df_outage_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "967caa3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig. outage count co-occurrence\n",
    "set_one_column_figure_font(height_multiplier=1.0)\n",
    "\n",
    "# Create the heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "# give 5 grey colors\n",
    "custom_colors = sns.color_palette('Greys', 5)\n",
    "cmap = ListedColormap(custom_colors)\n",
    "custom_bins = [0, 20, 40, 60, 80, 100]\n",
    "norm = BoundaryNorm(custom_bins, cmap.N)\n",
    "# mask = np.triu(np.ones_like(df_outage_count, dtype=bool), k=1)\n",
    "\n",
    "\n",
    "sns.heatmap(df_outage_count, cmap=cmap, norm=norm, annot=True, cbar_kws={'label': r'\\textbf{Co-occurrence Outages in Days Count}'}, annot_kws={'fontsize': 20})\n",
    "\n",
    "# rotate the x-axis labels\n",
    "plt.xticks(rotation=30)\n",
    "tick_labels = [r'\\textbf{API-OpenAI}', r'\\textbf{ChatGPT}', r'\\textbf{DALL·E}', r'\\textbf{Playground}', r'\\textbf{API-Anthropic}', r'\\textbf{Claude}', r'\\textbf{Console}', r'\\textbf{Character.AI}']\n",
    "plt.gca().set_xticklabels(tick_labels, fontsize=14)\n",
    "plt.gca().set_yticklabels(tick_labels, fontsize=14)\n",
    "# remove y-axis label\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.savefig('plot/sec6-co_occurrence_failures/fig-outage_co-ocurrence_count.pdf', bbox_inches='tight')\n",
    "plt.savefig('plot/sec6-co_occurrence_failures/fig-outage_co-ocurrence_count.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7afc18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18947368421052632"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "18/95"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573b0db2",
   "metadata": {},
   "source": [
    "### fig. outage probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e10a704",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               API-OpenAI  ChatGPT  DALL·E  Playground  API-Anthropic  Claude  \\\n",
      "API-OpenAI         100.00    49.21   78.26       87.50            4.0   10.34   \n",
      "ChatGPT             32.63   100.00   39.13       37.50           20.0   24.14   \n",
      "DALL·E              18.95    14.29  100.00       33.33            0.0    0.00   \n",
      "Playground          22.11    14.29   34.78      100.00            0.0    0.00   \n",
      "API-Anthropic        1.05     7.94    0.00        0.00          100.0   82.76   \n",
      "Claude               3.16    11.11    0.00        0.00           96.0  100.00   \n",
      "Console              3.16    11.11    0.00        0.00           88.0   89.66   \n",
      "Character.AI         2.11     1.59    0.00        4.17            0.0    0.00   \n",
      "\n",
      "               Console  Character.AI  \n",
      "API-OpenAI       11.11          6.90  \n",
      "ChatGPT          25.93          3.45  \n",
      "DALL·E            0.00          0.00  \n",
      "Playground        0.00          3.45  \n",
      "API-Anthropic    81.48          0.00  \n",
      "Claude           96.30          0.00  \n",
      "Console         100.00          0.00  \n",
      "Character.AI      0.00        100.00  \n"
     ]
    }
   ],
   "source": [
    "# Define the list of services\n",
    "services = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground', 'API-Anthropic', 'Claude', 'Console', 'Character.AI']\n",
    "\n",
    "outage_probability = {}\n",
    "for service_ref in services:\n",
    "    proportions = []\n",
    "    for service_cmp in services:\n",
    "        p = df_outage_flag[(df_outage_flag[service_cmp] > 0) & (df_outage_flag[service_ref] > 0)].shape[0] / df_outage_flag[df_outage_flag[service_cmp] > 0].shape[0]\n",
    "        proportions.append(round(p * 100, 2))\n",
    "    outage_probability[service_ref] = proportions\n",
    "\n",
    "df_outage_probability = pd.DataFrame(outage_probability, index=services)\n",
    "df_outage_probability = df_outage_probability.T\n",
    "# df_outage_probability = df_outage_probability.astype(str) + '\\%'\n",
    "\n",
    "print(df_outage_probability)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "003a71ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllllll}\n",
      "\\toprule\n",
      " & API-OpenAI & ChatGPT & DALL·E & Playground & API-Anthropic & Claude & Console & Character.AI \\\\\n",
      "\\midrule\n",
      "API-OpenAI & 100.0\\% & 49.21\\% & 78.26\\% & 87.5\\% & 4.0\\% & 10.34\\% & 11.11\\% & 6.9\\% \\\\\n",
      "ChatGPT & 32.63\\% & 100.0\\% & 39.13\\% & 37.5\\% & 20.0\\% & 24.14\\% & 25.93\\% & 3.45\\% \\\\\n",
      "DALL·E & 18.95\\% & 14.29\\% & 100.0\\% & 33.33\\% & 0.0\\% & 0.0\\% & 0.0\\% & 0.0\\% \\\\\n",
      "Playground & 22.11\\% & 14.29\\% & 34.78\\% & 100.0\\% & 0.0\\% & 0.0\\% & 0.0\\% & 3.45\\% \\\\\n",
      "API-Anthropic & 1.05\\% & 7.94\\% & 0.0\\% & 0.0\\% & 100.0\\% & 82.76\\% & 81.48\\% & 0.0\\% \\\\\n",
      "Claude & 3.16\\% & 11.11\\% & 0.0\\% & 0.0\\% & 96.0\\% & 100.0\\% & 96.3\\% & 0.0\\% \\\\\n",
      "Console & 3.16\\% & 11.11\\% & 0.0\\% & 0.0\\% & 88.0\\% & 89.66\\% & 100.0\\% & 0.0\\% \\\\\n",
      "Character.AI & 2.11\\% & 1.59\\% & 0.0\\% & 4.17\\% & 0.0\\% & 0.0\\% & 0.0\\% & 100.0\\% \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# save the table\n",
    "df_outage_probability.to_csv('plot/backup_table_csv/table-outage_probability.csv', index=False)\n",
    "# to latex\n",
    "print(df_outage_probability.to_latex(index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "795644a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig. outage probability\n",
    "set_one_column_figure_font(height_multiplier=1.0)\n",
    "\n",
    "# Create the heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "# give 5 grey colors\n",
    "custom_colors = sns.color_palette('Greys', 5)\n",
    "cmap = ListedColormap(custom_colors)\n",
    "custom_bins = [0, 20, 40, 60, 80, 100]\n",
    "norm = BoundaryNorm(custom_bins, cmap.N)\n",
    "\n",
    "sns.heatmap(df_outage_probability, cmap=cmap, norm=norm, annot=True, fmt=\".2f\", cbar_kws={'label': r'\\textbf{Co-occurrence Outage Probability (\\%)}'})\n",
    "\n",
    "# rotate the x-axis labels\n",
    "plt.xticks(rotation=30)\n",
    "tick_labels = [r'\\textbf{API-OpenAI}', r'\\textbf{ChatGPT}', r'\\textbf{DALL·E}', r'\\textbf{Playground}', r'\\textbf{API-Anthropic}', r'\\textbf{Claude}', r'\\textbf{Console}', r'\\textbf{Character.AI}']\n",
    "plt.gca().set_xticklabels(tick_labels, fontsize=14)\n",
    "plt.gca().set_yticklabels(tick_labels, fontsize=14)\n",
    "\n",
    "plt.savefig('plot/sec6-co_occurrence_failures/fig-outage_probability.pdf', bbox_inches='tight')\n",
    "plt.savefig('plot/sec6-co_occurrence_failures/fig-outage_probability.png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f8ad668",
   "metadata": {},
   "source": [
    "### (for observation) table. percent of outage days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3ba0985",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:8: SyntaxWarning: invalid escape sequence '\\%'\n",
      "<>:8: SyntaxWarning: invalid escape sequence '\\%'\n",
      "/var/folders/y1/bc3yybl90rxfhtj2gdqnbwg80000gn/T/ipykernel_84014/3856341291.py:8: SyntaxWarning: invalid escape sequence '\\%'\n",
      "  outage_days_percentage.append(str(p_service)+'\\%')\n"
     ]
    },
    {
     "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>Service</th>\n",
       "      <th>API-OpenAI</th>\n",
       "      <th>ChatGPT</th>\n",
       "      <th>DALL·E</th>\n",
       "      <th>Playground</th>\n",
       "      <th>API-Anthropic</th>\n",
       "      <th>Claude</th>\n",
       "      <th>Console</th>\n",
       "      <th>Character.AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outage Days (%)</th>\n",
       "      <td>7.32\\%</td>\n",
       "      <td>11.15\\%</td>\n",
       "      <td>4.12\\%</td>\n",
       "      <td>1.92\\%</td>\n",
       "      <td>5.98\\%</td>\n",
       "      <td>6.94\\%</td>\n",
       "      <td>6.46\\%</td>\n",
       "      <td>9.12\\%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Service         API-OpenAI  ChatGPT  DALL·E Playground API-Anthropic  Claude  \\\n",
       "Outage Days (%)     7.32\\%  11.15\\%  4.12\\%     1.92\\%        5.98\\%  6.94\\%   \n",
       "\n",
       "Service         Console Character.AI  \n",
       "Outage Days (%)  6.46\\%       9.12\\%  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calcute the number of days with outage\n",
    "# select only the rows with outage_flag > 0\n",
    "outage_days_percentage = []\n",
    "for service in service_lst:\n",
    "    df = dfs[service]\n",
    "    p_service = df[df['outage_flag'] > 0].shape[0] / df.dropna().shape[0] * 100\n",
    "    p_service = round(p_service, 2)\n",
    "    outage_days_percentage.append(str(p_service)+'\\%')\n",
    "\n",
    "data = {\n",
    "    'Service': service_lst,\n",
    "    'Outage Days (%)': outage_days_percentage\n",
    "}\n",
    "\n",
    "df_outage_days_percent = pd.DataFrame(data).T\n",
    "df_outage_days_percent.columns = df_outage_days_percent.iloc[0]\n",
    "df_outage_days_percent = df_outage_days_percent[1:]\n",
    "df_outage_days_percent\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fc58ad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llllllll}\n",
      "\\toprule\n",
      "API-OpenAI & ChatGPT & DALL·E & Playground & API-Anthropic & Claude & Console & Character.AI \\\\\n",
      "\\midrule\n",
      "7.32\\% & 11.15\\% & 4.12\\% & 1.92\\% & 5.98\\% & 6.94\\% & 6.46\\% & 9.12\\% \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# save the table\n",
    "df_outage_days_percent.to_csv('plot/backup_table_csv/table-outage_days_percent.csv', index=False)\n",
    "# to latex\n",
    "print(df_outage_days_percent.to_latex(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5157684",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "321969ee",
   "metadata": {},
   "source": [
    "## Impact Range of Incidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f78bf6a",
   "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>incident_id</th>\n",
       "      <th>Incident_Title</th>\n",
       "      <th>incident_impact_level</th>\n",
       "      <th>Incident_color</th>\n",
       "      <th>provider</th>\n",
       "      <th>Playground</th>\n",
       "      <th>API-OpenAI</th>\n",
       "      <th>DALL·E</th>\n",
       "      <th>ChatGPT</th>\n",
       "      <th>API-Anthropic</th>\n",
       "      <th>Claude</th>\n",
       "      <th>Console</th>\n",
       "      <th>Character.AI</th>\n",
       "      <th>investigating_flag</th>\n",
       "      <th>investigating_timestamp</th>\n",
       "      <th>investigating_description</th>\n",
       "      <th>identified_flag</th>\n",
       "      <th>identified_timestamp</th>\n",
       "      <th>identified_description</th>\n",
       "      <th>monitoring_flag</th>\n",
       "      <th>monitoring_timestamp</th>\n",
       "      <th>monitoring_description</th>\n",
       "      <th>resolved_flag</th>\n",
       "      <th>resolved_timestamp</th>\n",
       "      <th>resolved_description</th>\n",
       "      <th>postmortem_flag</th>\n",
       "      <th>postmortem_timestamp</th>\n",
       "      <th>postmortem_description</th>\n",
       "      <th>start_timestamp</th>\n",
       "      <th>close_timestamp</th>\n",
       "      <th>time_span</th>\n",
       "      <th>over_one_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zm9ssldbs7ds</td>\n",
       "      <td>Increased queue wait times for GPT-3.5-Turbo f...</td>\n",
       "      <td>1</td>\n",
       "      <td>#f4ac36</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-29 20:41:00+00:00</td>\n",
       "      <td>We are seeing a spike in demand for 3.5 turbo ...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-03-01 02:16:00+00:00</td>\n",
       "      <td>The queue length is settling and wait times fo...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-03-01 04:13:00+00:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-29 20:41:00+00:00</td>\n",
       "      <td>2024-03-01 04:13:00+00:00</td>\n",
       "      <td>0 days 07:32:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62wkltqhbrmk</td>\n",
       "      <td>Elevated error rate impacting API services (in...</td>\n",
       "      <td>2</td>\n",
       "      <td>#e86c09</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 15:06:00+00:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 18:38:00+00:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 20:40:00+00:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-14 15:06:00+00:00</td>\n",
       "      <td>2024-02-14 20:40:00+00:00</td>\n",
       "      <td>0 days 05:34:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70p519qgngrw</td>\n",
       "      <td>Elevated errors on GPT-4V for ChatGPT and API</td>\n",
       "      <td>2</td>\n",
       "      <td>#e86c09</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2024-02-13 15:03:00+00:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 15:36:00+00:00</td>\n",
       "      <td>The issue has been identified and a fix is bei...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 18:39:00+00:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 20:40:00+00:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-13 15:03:00+00:00</td>\n",
       "      <td>2024-02-14 20:40:00+00:00</td>\n",
       "      <td>1 days 05:37:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mswp9vt22gm2</td>\n",
       "      <td>Elevated errors across multiple API endpoints ...</td>\n",
       "      <td>2</td>\n",
       "      <td>#e86c09</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 20:11:00+00:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 20:38:00+00:00</td>\n",
       "      <td>We have identified this issue and a fix is bei...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 21:46:00+00:00</td>\n",
       "      <td>A fix has been implemented, and we are monitor...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 23:38:00+00:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-13 20:11:00+00:00</td>\n",
       "      <td>2024-02-13 23:38:00+00:00</td>\n",
       "      <td>0 days 03:27:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shqybftjc7mc</td>\n",
       "      <td>Elevated errors on GPT-4V for ChatGPT and API</td>\n",
       "      <td>1</td>\n",
       "      <td>#f4ac36</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 17:19:00+00:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 18:19:00+00:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 18:39:00+00:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-12 17:19:00+00:00</td>\n",
       "      <td>2024-02-12 18:39:00+00:00</td>\n",
       "      <td>0 days 01:20:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    incident_id                                     Incident_Title  \\\n",
       "0  zm9ssldbs7ds  Increased queue wait times for GPT-3.5-Turbo f...   \n",
       "1  62wkltqhbrmk  Elevated error rate impacting API services (in...   \n",
       "2  70p519qgngrw      Elevated errors on GPT-4V for ChatGPT and API   \n",
       "3  mswp9vt22gm2  Elevated errors across multiple API endpoints ...   \n",
       "4  shqybftjc7mc      Elevated errors on GPT-4V for ChatGPT and API   \n",
       "\n",
       "   incident_impact_level Incident_color provider  Playground  API-OpenAI  \\\n",
       "0                      1        #f4ac36   openai           0           1   \n",
       "1                      2        #e86c09   openai           0           1   \n",
       "2                      2        #e86c09   openai           0           1   \n",
       "3                      2        #e86c09   openai           0           1   \n",
       "4                      1        #f4ac36   openai           0           1   \n",
       "\n",
       "   DALL·E  ChatGPT  API-Anthropic  Claude  Console  Character.AI  \\\n",
       "0       0        0              0       0        0             0   \n",
       "1       0        0              0       0        0             0   \n",
       "2       0        1              0       0        0             0   \n",
       "3       0        1              0       0        0             0   \n",
       "4       0        1              0       0        0             0   \n",
       "\n",
       "   investigating_flag   investigating_timestamp  \\\n",
       "0                   0                       NaT   \n",
       "1                   1 2024-02-14 15:06:00+00:00   \n",
       "2                   1 2024-02-13 15:03:00+00:00   \n",
       "3                   1 2024-02-13 20:11:00+00:00   \n",
       "4                   0                       NaT   \n",
       "\n",
       "                    investigating_description  identified_flag  \\\n",
       "0                                         NaN                1   \n",
       "1  We are currently investigating this issue.                0   \n",
       "2  We are currently investigating this issue.                1   \n",
       "3  We are currently investigating this issue.                1   \n",
       "4                                         NaN                1   \n",
       "\n",
       "       identified_timestamp  \\\n",
       "0 2024-02-29 20:41:00+00:00   \n",
       "1                       NaT   \n",
       "2 2024-02-13 15:36:00+00:00   \n",
       "3 2024-02-13 20:38:00+00:00   \n",
       "4 2024-02-12 17:19:00+00:00   \n",
       "\n",
       "                              identified_description  monitoring_flag  \\\n",
       "0  We are seeing a spike in demand for 3.5 turbo ...                1   \n",
       "1                                                NaN                1   \n",
       "2  The issue has been identified and a fix is bei...                1   \n",
       "3  We have identified this issue and a fix is bei...                1   \n",
       "4         We are currently investigating this issue.                1   \n",
       "\n",
       "       monitoring_timestamp  \\\n",
       "0 2024-03-01 02:16:00+00:00   \n",
       "1 2024-02-14 18:38:00+00:00   \n",
       "2 2024-02-14 18:39:00+00:00   \n",
       "3 2024-02-13 21:46:00+00:00   \n",
       "4 2024-02-12 18:19:00+00:00   \n",
       "\n",
       "                              monitoring_description  resolved_flag  \\\n",
       "0  The queue length is settling and wait times fo...              1   \n",
       "1  A fix has been implemented and we are monitori...              1   \n",
       "2  A fix has been implemented and we are monitori...              1   \n",
       "3  A fix has been implemented, and we are monitor...              1   \n",
       "4  A fix has been implemented and we are monitori...              1   \n",
       "\n",
       "         resolved_timestamp              resolved_description  \\\n",
       "0 2024-03-01 04:13:00+00:00  This incident has been resolved.   \n",
       "1 2024-02-14 20:40:00+00:00  This incident has been resolved.   \n",
       "2 2024-02-14 20:40:00+00:00  This incident has been resolved.   \n",
       "3 2024-02-13 23:38:00+00:00  This incident has been resolved.   \n",
       "4 2024-02-12 18:39:00+00:00  This incident has been resolved.   \n",
       "\n",
       "   postmortem_flag postmortem_timestamp postmortem_description  \\\n",
       "0                0                  NaT                    NaN   \n",
       "1                0                  NaT                    NaN   \n",
       "2                0                  NaT                    NaN   \n",
       "3                0                  NaT                    NaN   \n",
       "4                0                  NaT                    NaN   \n",
       "\n",
       "            start_timestamp            close_timestamp        time_span  \\\n",
       "0 2024-02-29 20:41:00+00:00  2024-03-01 04:13:00+00:00  0 days 07:32:00   \n",
       "1 2024-02-14 15:06:00+00:00  2024-02-14 20:40:00+00:00  0 days 05:34:00   \n",
       "2 2024-02-13 15:03:00+00:00  2024-02-14 20:40:00+00:00  1 days 05:37:00   \n",
       "3 2024-02-13 20:11:00+00:00  2024-02-13 23:38:00+00:00  0 days 03:27:00   \n",
       "4 2024-02-12 17:19:00+00:00  2024-02-12 18:39:00+00:00  0 days 01:20:00   \n",
       "\n",
       "   over_one_day  \n",
       "0         False  \n",
       "1         False  \n",
       "2          True  \n",
       "3         False  \n",
       "4         False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load incident data\n",
    "path = f'data/clean/incident/2024-08-31/incident_stages.csv'\n",
    "df = pd.read_csv(path)\n",
    "\n",
    "status_lst = ['investigating', 'identified', 'monitoring', 'resolved', 'postmortem', 'start']\n",
    "for status in status_lst:\n",
    "    df[f'{status}_timestamp'] = pd.to_datetime(df[f'{status}_timestamp'])\n",
    "\n",
    "# rename columns\n",
    "df.rename(columns={'API':'API-OpenAI', 'ChatGPT':'ChatGPT', 'Labs':'DALL·E', 'Playground':'Playground', 'api.anthropic.com':'API-Anthropic', 'claude.ai':'Claude', 'console.anthropic.com':'Console', 'Character.AI':'Character.AI'}, inplace=True)\n",
    "\n",
    "service_lst = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground', 'API-Anthropic', 'Claude', 'Console', 'Character.AI']\n",
    "dfs = {}\n",
    "for service in service_lst:\n",
    "    dfs[service] = df[df[service] == 1]\n",
    "    dfs[service].reset_index(drop=True, inplace=True)\n",
    "\n",
    "provider_lst = ['openai', 'anthropic', 'character']\n",
    "dfs_provider = {}\n",
    "for provider in provider_lst:\n",
    "    for service in service_lst:\n",
    "        if provider in dfs[service]['provider'].unique():\n",
    "            dfs_provider[provider] = dfs_provider.get(provider, pd.DataFrame())\n",
    "            dfs_provider[provider] = pd.concat([dfs_provider[provider], dfs[service][dfs[service]['provider'] == provider]], axis=0)\n",
    "\n",
    "# display(dfs['API-OpenAI'])\n",
    "display(dfs_provider['openai'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecf640ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "service_lst = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground', 'API-Anthropic', 'Claude', 'Console', 'Character.AI']\n",
    "service_df = df[service_lst]\n",
    "\n",
    "display(service_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "80f385cf",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>542 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     API-OpenAI  ChatGPT  DALL·E  Playground  API-Anthropic  Claude  Console  \\\n",
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       "\n",
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       "\n",
       "[542 rows x 8 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "service_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "87eaf000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "impact_range\n",
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       "3     12\n",
       "4     36\n",
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       "    API-OpenAI  ChatGPT  DALL·E  Playground  impact_range  \\\n",
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       "    API-Anthropic  Claude  Console  impact_range  \\\n",
       "0               1       1        1             3   \n",
       "1               1       1        1             3   \n",
       "2               1       1        1             3   \n",
       "3               1       1        1             3   \n",
       "4               1       1        1             3   \n",
       "..            ...     ...      ...           ...   \n",
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       "\n",
       "                   impacted_services  \n",
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   ],
   "source": [
    "openai_lst = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground']\n",
    "anthropic_lst = ['API-Anthropic', 'Claude', 'Console']\n",
    "\n",
    "openai_df = dfs_provider['openai'][openai_lst]\n",
    "anthropic_df = dfs_provider['anthropic'][anthropic_lst]\n",
    "\n",
    "openai_df['impact_range'] = openai_df.sum(axis=1)\n",
    "openai_impact_counts = openai_df['impact_range'].value_counts().sort_index()\n",
    "display(openai_impact_counts)\n",
    "openai_df['impacted_services'] = openai_df.apply(lambda row: [service for service in openai_df.columns[:-1] if row[service] == 1], axis=1)\n",
    "display(openai_df)\n",
    "\n",
    "anthropic_df['impact_range'] = anthropic_df.sum(axis=1)\n",
    "anthropic_impact_counts = anthropic_df['impact_range'].value_counts().sort_index()\n",
    "display(anthropic_impact_counts)\n",
    "anthropic_df['impacted_services'] = anthropic_df.apply(lambda row: [service for service in anthropic_df.columns[:-1] if row[service] == 1], axis=1)\n",
    "display(anthropic_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ca0dd5b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>impact_range</th>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>OpenAI</th>\n",
       "      <td>57.63</td>\n",
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       "      <td>2.73</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anthropic</th>\n",
       "      <td>12.82</td>\n",
       "      <td>15.38</td>\n",
       "      <td>71.79</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "impact_range      1      2      3    4\n",
       "OpenAI        57.63  31.44   2.73  8.2\n",
       "Anthropic     12.82  15.38  71.79  NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "impact_range & 1 & 2 & 3 & 4 \\\\\n",
      "\\midrule\n",
      "OpenAI & 57.630000 & 31.440000 & 2.730000 & 8.200000 \\\\\n",
      "Anthropic & 12.820000 & 15.380000 & 71.790000 & NaN \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create a impact range dataframe\n",
    "impact_range_df = pd.DataFrame()\n",
    "impact_range_df['OpenAI'] = openai_impact_counts\n",
    "impact_range_df['Anthropic'] = anthropic_impact_counts\n",
    "impact_range_df.T\n",
    "\n",
    "# transform to percentage\n",
    "impact_range_percent = impact_range_df.T.div(impact_range_df.T.sum(axis=1), axis=0) * 100\n",
    "impact_range_percent = impact_range_percent.round(2)\n",
    "display(impact_range_percent)\n",
    "\n",
    "# print to latex\n",
    "print(impact_range_percent.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "587c4fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bar chart of impacted service combinations, OpenAI\n",
    "\n",
    "openai_df['impacted_services_str'] =openai_df['impacted_services'].apply(lambda x: ', '.join(x))\n",
    "impacted_combinations_counts = openai_df['impacted_services_str'].value_counts()\n",
    "\n",
    "service_labels = {\n",
    "    'API-OpenAI': 'O1',\n",
    "    'ChatGPT': 'O2',\n",
    "    'DALL·E': 'O3',\n",
    "    'Playground': 'O4'\n",
    "}\n",
    "\n",
    "# Apply mapping to the impacted services column\n",
    "openai_df['impacted_services_short'] = openai_df['impacted_services'].apply(lambda services: [service_labels[service] for service in services])\n",
    "openai_df['impacted_services_short_str'] = openai_df['impacted_services_short'].apply(lambda x: ', '.join(x))\n",
    "impacted_combinations_counts_short = openai_df['impacted_services_short_str'].value_counts()\n",
    "# rename the NaN to None\n",
    "impacted_combinations_counts_short = impacted_combinations_counts_short.rename(index={'': 'Not Given'})\n",
    "\n",
    "# Plot bar chart of impacted service combinations\n",
    "set_half_column_figure_font(height_multiplier=1.0)\n",
    "plt.figure(figsize=(6, 6))\n",
    "impacted_combinations_counts_short[::-1].plot(kind='barh', color='grey', edgecolor='black')\n",
    "# add the count labels to each bar\n",
    "for i, count in enumerate(impacted_combinations_counts_short[::-1]):\n",
    "    plt.text(count+2, i, str(count), ha='left', va='center', fontsize=25.9, color='black')\n",
    "plt.title(r\"\\textbf{OpenAI}\")\n",
    "plt.ylabel(r\"\\textbf{Impacted Services}\")\n",
    "plt.xlabel(r\"\\textbf{Number of Incidents}\")\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "\n",
    "# Add a text box to show the O1, O2, O3, O4 mapping at the top right corner\n",
    "plt.text(0.99, 0.01, \"O1: API-OpenAI\\nO2: ChatGPT\\nO3: DALL·E\\nO4: Playground\", ha='right', va='bottom', transform=plt.gca().transAxes, fontsize=25.9, bbox=dict(facecolor='white', alpha=0.5))\n",
    "# save\n",
    "plt.savefig('plot/sec6-co_occurrence_failures/fig-incident_impacted_service_combinations_openai.pdf', bbox_inches='tight')\n",
    "plt.savefig('plot/sec6-co_occurrence_failures/fig-incident_impacted_service_combinations_openai.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "6ec4255a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bar chart of impacted service combinations, Anthropic\n",
    "\n",
    "anthropic_df['impacted_services'].value_counts()\n",
    "anthropic_df['impacted_services_str'] =anthropic_df['impacted_services'].apply(lambda x: ', '.join(x))\n",
    "impacted_combinations_counts = anthropic_df['impacted_services_str'].value_counts()\n",
    "\n",
    "service_labels = {\n",
    "    'API-Anthropic': 'A1',\n",
    "    'Claude': 'A2',\n",
    "    'Console': 'A3'\n",
    "}\n",
    "\n",
    "# Apply mapping to the impacted services column\n",
    "anthropic_df['impacted_services_short'] = anthropic_df['impacted_services'].apply(lambda services: [service_labels[service] for service in services])\n",
    "anthropic_df['impacted_services_short_str'] = anthropic_df['impacted_services_short'].apply(lambda x: ', '.join(x))\n",
    "impacted_combinations_counts_short = anthropic_df['impacted_services_short_str'].value_counts()\n",
    "# rename the NaN to None\n",
    "impacted_combinations_counts_short = impacted_combinations_counts_short.rename(index={'': 'Not Given'})\n",
    "\n",
    "# Plot bar chart of impacted service combinations\n",
    "set_half_column_figure_font(height_multiplier=1.0)\n",
    "plt.figure(figsize=(6, 6))\n",
    "impacted_combinations_counts_short[::-1].plot(kind='barh', color='grey', edgecolor='black')\n",
    "# add the count labels to each bar\n",
    "for i, count in enumerate(impacted_combinations_counts_short[::-1]):\n",
    "    plt.text(count+2, i, str(count), ha='left', va='center', fontsize=25.9, color=\"black\")\n",
    "plt.title(r\"\\textbf{Anthropic}\")\n",
    "plt.ylabel(r\"\\textbf{Impacted Services}\")\n",
    "plt.xlabel(r\"\\textbf{Number of Incidents}\")\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "\n",
    "# Add a text box to show the O1, O2, O3, O4 mapping at the top right corner\n",
    "plt.text(0.99, 0.01, \"A1: API-Anthropic\\nA2: Claude\\nA3: Console\", ha='right', va='bottom', transform=plt.gca().transAxes, fontsize=25.9, bbox=dict(facecolor='white', alpha=0.5))\n",
    "# save \n",
    "plt.savefig('plot/sec6-co_occurrence_failures/fig-incident_impacted_service_combinations_anthropic.pdf', bbox_inches='tight')\n",
    "plt.savefig('plot/sec6-co_occurrence_failures/fig-incident_impacted_service_combinations_anthropic.png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0ad6f98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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