{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cyberosa/.pyenv/versions/3.12.2/envs/hf_dashboards/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import gradio as gr\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "div_data = pd.read_parquet(\"../data/closed_markets_div.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>currentAnswer</th>\n",
       "      <th>id</th>\n",
       "      <th>openingTimestamp</th>\n",
       "      <th>market_creator</th>\n",
       "      <th>opening_datetime</th>\n",
       "      <th>first_outcome_prob</th>\n",
       "      <th>second_outcome_prob</th>\n",
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       "      <th>off_by_perc</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.999944</td>\n",
       "      <td>63.21</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yes</td>\n",
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       "      <td>0.6632</td>\n",
       "      <td>1.088266</td>\n",
       "      <td>66.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yes</td>\n",
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       "      <td>1731542400</td>\n",
       "      <td>quickstart</td>\n",
       "      <td>2024-11-14 01:00:00</td>\n",
       "      <td>0.6804</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.385074</td>\n",
       "      <td>31.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>no</td>\n",
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       "      <td>2024-11-15 01:00:00</td>\n",
       "      <td>0.2358</td>\n",
       "      <td>0.7642</td>\n",
       "      <td>0.268926</td>\n",
       "      <td>23.58</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  currentAnswer                                          id openingTimestamp  \\\n",
       "0           yes  0x5cae41dcc8a30d76a31b126c6f3b358b8d0131ca       1731542400   \n",
       "1           yes  0xf8442bd26cd80d8447bb6203ededc44a77cd2a12       1731542400   \n",
       "2           yes  0xde3a4b0d527013165b1b6b8aae051d223f8b770e       1731542400   \n",
       "3           yes  0xccadef7757659ce271b209d647c2a51fabd88c77       1731542400   \n",
       "4            no  0x78d0fc5884e74d87b0529e40da2b9490db60e731       1731628800   \n",
       "\n",
       "  market_creator    opening_datetime  first_outcome_prob  second_outcome_prob  \\\n",
       "0     quickstart 2024-11-14 01:00:00              0.3679               0.6321   \n",
       "1     quickstart 2024-11-14 01:00:00              0.3368               0.6632   \n",
       "2     quickstart 2024-11-14 01:00:00              0.4468               0.5532   \n",
       "3     quickstart 2024-11-14 01:00:00              0.6804               0.3196   \n",
       "4     quickstart 2024-11-15 01:00:00              0.2358               0.7642   \n",
       "\n",
       "   kl_divergence  off_by_perc  \n",
       "0       0.999944        63.21  \n",
       "1       1.088266        66.32  \n",
       "2       0.805644        55.32  \n",
       "3       0.385074        31.96  \n",
       "4       0.268926        23.58  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "div_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2024-12-28 01:00:00')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(div_data.opening_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.099820</td>\n",
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       "    <tr>\n",
       "      <th>194</th>\n",
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       "      <td>0.9107</td>\n",
       "      <td>0.093542</td>\n",
       "      <td>8.93</td>\n",
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      "text/plain": [
       "    currentAnswer                                          id  \\\n",
       "120           yes  0x5feab3ad41a2575461b06c5e911d67d3616ee17e   \n",
       "148           yes  0x99a7e8be86a78cf2096a15887e9add122ff113d2   \n",
       "194            no  0x34acf849dd645b559ddbf09059df39a1d9b10f18   \n",
       "\n",
       "    openingTimestamp market_creator    opening_datetime  first_outcome_prob  \\\n",
       "120       1723420800     quickstart 2024-08-12 02:00:00              0.9034   \n",
       "148       1723593600     quickstart 2024-08-14 02:00:00              0.9050   \n",
       "194       1723939200     quickstart 2024-08-18 02:00:00              0.0893   \n",
       "\n",
       "     second_outcome_prob  kl_divergence  off_by_perc  \n",
       "120               0.0966       0.101590         9.66  \n",
       "148               0.0950       0.099820         9.50  \n",
       "194               0.9107       0.093542         8.93  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "div_data.loc[div_data[\"off_by_perc\"]<=10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.840439</td>\n",
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       "      <th>323</th>\n",
       "      <td>yes</td>\n",
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       "      <td>0.0499</td>\n",
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       "    currentAnswer                                          id  \\\n",
       "52             no  0x927beda324bfd4514a7b64ab5594451fdaf4796e   \n",
       "293           yes  0x90bb15982f2b5a5f044ad8ff49fe20daddfb8ca7   \n",
       "315            no  0x29462bf8c8f24772cd6da03878a4aee5c5813474   \n",
       "323           yes  0x0ad9d4edb0a401ec9a5b4f2ccf7942d28c29d4e3   \n",
       "\n",
       "    openingTimestamp market_creator    opening_datetime  first_outcome_prob  \\\n",
       "52        1722816000     quickstart 2024-08-05 02:00:00              0.8792   \n",
       "293       1724803200     quickstart 2024-08-28 02:00:00              0.1166   \n",
       "315       1724976000          pearl 2024-08-30 02:00:00              0.9416   \n",
       "323       1724976000     quickstart 2024-08-30 02:00:00              0.0499   \n",
       "\n",
       "     second_outcome_prob  kl_divergence  off_by_perc  \n",
       "52                0.1208       2.113619        87.92  \n",
       "293               0.8834       2.149006        88.34  \n",
       "315               0.0584       2.840439        94.16  \n",
       "323               0.9501       2.997734        95.01  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "div_data.loc[div_data[\"kl_divergence\"]>=2.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "       1.02443289, 1.41510544, 1.57939379, 0.32185952, 0.58770667,\n",
       "       1.27511083, 1.04782412, 0.17316362, 0.22602269, 0.3400988 ,\n",
       "       0.46124212, 0.78350962, 0.7001718 , 0.42113728, 0.38038233,\n",
       "       1.75446368, 0.82393947, 0.34052042, 0.21294573, 0.1468772 ,\n",
       "       0.28581715, 1.75388582, 0.42205191, 1.09123953, 0.15911297,\n",
       "       0.13673652, 0.67963883, 0.16652695, 0.26905661, 0.5352891 ,\n",
       "       0.17876788, 0.73147229, 0.37483891, 1.67825223, 0.43834994,\n",
       "       0.37062899, 0.31580724, 0.43695578, 0.8672625 , 0.14306238,\n",
       "       0.58788667, 0.60972557, 1.37753286, 0.12069979, 0.13845779,\n",
       "       0.93700377, 0.34743208, 0.12658415, 0.23610215, 0.14421685,\n",
       "       0.56352341, 1.29681935, 0.18248157, 2.14900601, 0.45728486,\n",
       "       0.47658524, 1.09213332, 0.30924625, 0.22064667, 0.14294701,\n",
       "       0.47401165, 0.42587192, 0.76614807, 0.36456312, 0.14629826,\n",
       "       0.80140077, 1.50103204, 0.13478933, 0.75077629, 0.36831384,\n",
       "       0.19759764, 0.39363536, 0.4807815 , 0.34814004, 0.3831656 ,\n",
       "       2.84043939, 0.55112692, 0.20028188, 0.94674994, 0.50236155,\n",
       "       0.3738211 , 0.25000101, 1.25141388, 2.99773428, 0.84793139,\n",
       "       0.16759059, 0.13433171, 0.42510675, 0.69314718, 0.69555007,\n",
       "       0.53290083, 0.46092497, 0.53734073, 0.31320503, 0.67393296,\n",
       "       1.59652169, 0.58950815, 0.22991644, 0.69615169, 1.20932041,\n",
       "       0.17709524, 0.16877374, 0.71764481, 0.2342045 , 0.34206786,\n",
       "       1.6419611 , 0.5519949 , 0.46314719, 0.32365473, 0.16463882,\n",
       "       0.43371027, 0.43417327, 0.1782897 , 0.71192234, 0.37833644,\n",
       "       0.32158362, 0.34347671, 0.18608864, 0.95815494, 0.57075257,\n",
       "       1.28989387, 0.22765371, 0.81238127, 0.38404614, 0.47658524,\n",
       "       0.30734071, 1.81584726, 0.98029606, 0.69314718, 0.66689481,\n",
       "       1.0782216 , 0.20028188, 0.65277329, 0.28701563, 0.69314718,\n",
       "       0.69314718, 0.69314718, 0.69314718, 0.2826282 , 0.3569607 ,\n",
       "       0.18693217, 0.93700377, 0.66009933, 0.33561261, 1.35595916,\n",
       "       0.5808905 , 0.52915933, 0.95295472, 0.3093825 , 0.46601161,\n",
       "       0.57128359, 1.32463498, 0.69314718, 0.69314718, 0.72815279,\n",
       "       0.69314718, 1.51458238, 0.53631439, 0.94623461, 0.7870184 ,\n",
       "       0.95815494, 0.79053953, 0.36701392, 0.35140315, 0.94494744,\n",
       "       0.32379296, 0.69314718, 0.84350506, 0.35738948, 0.62735944,\n",
       "       0.69314718, 0.69314718, 0.62418098, 0.69314718, 0.69314718,\n",
       "       0.60330648, 0.69314718, 1.29062061, 1.15740734, 0.60239282,\n",
       "       0.57252374, 0.95139991, 0.85896567, 0.83263916, 0.69314718,\n",
       "       0.52746326, 0.75395932, 0.57057563, 1.38949949, 0.69314718,\n",
       "       0.98349948, 0.25721753, 1.67450991, 0.23117572, 0.27878841,\n",
       "       0.69314718, 0.69314718, 0.69314718, 0.78679874, 0.34488755,\n",
       "       0.37033931, 0.27984619, 0.29383429, 0.91180083, 0.69314718,\n",
       "       0.69314718, 0.6953496 , 0.67924428, 0.69314718, 0.69314718,\n",
       "       0.6909496 , 0.43479094, 0.69314718, 0.69314718, 0.58536959,\n",
       "       0.8187104 , 0.6953496 , 0.83263916, 0.66904011, 0.69314718,\n",
       "       0.69314718, 0.43155244, 0.85496048, 0.69314718, 0.69314718,\n",
       "       0.69314718, 0.6953496 , 0.37994357, 0.67688021, 0.65027928,\n",
       "       0.63468961, 0.90362133, 0.69314718, 0.90312775, 0.41143288,\n",
       "       0.69314718, 0.91804226, 0.93521874, 0.45744285, 0.69314718,\n",
       "       0.38243241, 0.36528332, 0.69314718, 0.69314718, 0.69314718,\n",
       "       0.69314718, 0.69314718, 0.75502258, 0.69314718, 0.34644177,\n",
       "       0.43216849, 1.2265593 , 0.69314718, 1.30195321, 0.67119001,\n",
       "       0.35524739, 0.69314718, 0.67119001, 0.44613086, 0.71559731,\n",
       "       0.69314718, 0.76314055, 0.26565969, 0.69314718, 0.57075257,\n",
       "       0.86132917, 0.57075257, 0.55025969, 1.25421402, 0.35610368,\n",
       "       0.69314718, 0.69314718, 0.39245017, 0.69314718, 0.54731673,\n",
       "       0.25128586, 1.50688132, 0.31512179, 0.29948478, 0.37687765,\n",
       "       0.4764242 , 0.69314718, 0.95659203, 0.31704223, 0.15712176,\n",
       "       0.18693217, 0.14629826, 0.69314718, 0.29437106, 0.4405225 ,\n",
       "       0.73022619, 1.55211285, 0.2010152 , 0.16699954, 0.8603831 ,\n",
       "       1.83320666, 0.95633178, 0.83471074, 0.26513811, 0.69314718,\n",
       "       1.07704651, 0.5163408 , 0.35197174, 0.69314718, 0.83263916,\n",
       "       0.30979136, 0.92861638, 0.69314718, 0.69314718, 0.27338477,\n",
       "       0.69314718, 0.69314718, 0.69314718, 0.82211756, 0.69314718,\n",
       "       0.69735603, 0.29557984, 0.81215597, 0.36571569, 0.69314718,\n",
       "       1.24132859, 0.69314718, 0.21815601, 0.30584604, 0.28861584,\n",
       "       0.31717954, 0.55216859, 0.29894526, 0.69314718, 0.69856181,\n",
       "       0.69314718, 1.38909829, 0.35496213, 0.69314718, 0.27680808,\n",
       "       0.69314718, 0.69314718, 0.69314718, 0.67119001, 0.69314718,\n",
       "       0.27931716, 0.69314718, 0.69314718, 0.57075257, 0.69314718,\n",
       "       0.49298571, 0.2182804 , 1.05096564, 0.65932563, 1.03704693,\n",
       "       0.57075257, 0.49036962, 0.53597251, 0.4764242 , 0.73459437,\n",
       "       0.50005055, 0.4764242 , 0.69314718, 0.64588199, 0.37965117,\n",
       "       0.5608916 , 0.69314718, 0.68617157, 1.30822283, 0.93547355,\n",
       "       0.69314718, 0.97021907, 0.65027928, 1.09871229, 0.25077172,\n",
       "       0.69314718, 0.69314718, 0.92558378, 0.69314718, 0.57110655,\n",
       "       0.69314718, 0.69314718, 0.69314718, 0.83263916, 0.69314718,\n",
       "       0.69314718, 0.53682743, 0.69314718, 1.33370191, 1.13072234,\n",
       "       0.28090509, 0.72278199, 0.71559731, 0.69314718, 0.86988436,\n",
       "       0.69314718, 0.61766872, 0.33017212, 0.69314718, 0.69314718,\n",
       "       0.69314718])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "div_data.kl_divergence.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_markets = closed_markets.copy(deep=True)\n",
    "    all_markets[\"market_creator\"] = \"all\"\n",
    "\n",
    "    # merging both dataframes\n",
    "    final_markets = pd.concat([div_data, all_markets], ignore_index=True)\n",
    "    final_markets = final_markets.sort_values(by=\"opening_datetime\", ascending=True)\n",
    "\n",
    "    # Create the main figure and axis\n",
    "    fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "    # Create the boxplot using seaborn\n",
    "    sns.boxplot(\n",
    "        data=closed_markets,\n",
    "        x=\"month_year_week\",\n",
    "        y=\"kl_divergence\",\n",
    "        ax=ax1,\n",
    "        hue=\"market_creator\",\n",
    "        order=[\"pearl\", \"quickstart\", \"all\"],\n",
    "    )\n",
    "\n",
    "    # Set labels and title for the main axis\n",
    "    ax1.set_xlabel(\"Week\")\n",
    "    ax1.set_ylabel(\"KL Divergence\")\n",
    "    ax1.set_title(\"KL Divergence Boxplot with Off-by Percentage\")\n",
    "\n",
    "    # Create a secondary y-axis\n",
    "    ax2 = ax1.twinx()\n",
    "\n",
    "    # Plot the off_by_perc values on the secondary y-axis\n",
    "    for i, week in enumerate(closed_markets[\"month_year_week\"].unique()):\n",
    "        off_by_perc = closed_markets[closed_markets[\"month_year_week\"] == week][\n",
    "            \"off_by_perc\"\n",
    "        ]\n",
    "        ax2.scatter([i] * len(off_by_perc), off_by_perc, color=\"red\", alpha=0.01)\n",
    "\n",
    "    # Set label for the secondary y-axis\n",
    "    ax2.set_ylabel(\"Off-by Percentage\")\n",
    "\n",
    "    # Adjust the layout and display the plot\n",
    "    plt.tight_layout()"
   ]
  }
 ],
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