anki_predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "sns.set(style=\"white\")\n",
    "sns.set(font='SimHei')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('momo_sm_202012280955.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>user_id</th>\n",
       "      <th>voc_id</th>\n",
       "      <th>first_response</th>\n",
       "      <th>repeat_long</th>\n",
       "      <th>used_interval</th>\n",
       "      <th>first_grade</th>\n",
       "      <th>pp</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2566</td>\n",
       "      <td>180</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.868940</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37454</td>\n",
       "      <td>488</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>37454</td>\n",
       "      <td>488</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.927505</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>37454</td>\n",
       "      <td>488</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.948683</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>37454</td>\n",
       "      <td>488</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.920186</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  voc_id  first_response  repeat_long  used_interval  first_grade  \\\n",
       "0     2566     180               4            3              4          4.0   \n",
       "1    37454     488               4            3              3          4.0   \n",
       "2    37454     488               4            4              5          4.0   \n",
       "3    37454     488               4            5              7          4.0   \n",
       "4    37454     488               4            6             15          4.0   \n",
       "\n",
       "         pp  p  \n",
       "0  0.868940  1  \n",
       "1  0.900000  1  \n",
       "2  0.927505  1  \n",
       "3  0.948683  1  \n",
       "4  0.920186  1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[df['type'] == 0].index, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['ease'], bins=np.arange(6)-0.5)\n",
    "plt.ylabel('复习记录(条)')\n",
    "plt.xlabel('反馈')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['p'], bins=2)\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_major_locator(MultipleLocator(1))\n",
    "plt.ylabel('复习记录(条)')\n",
    "plt.xlabel('遗忘/认识')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([x for x in df['used_interval']], bins=20)\n",
    "plt.xlabel('真实间隔(天)')\n",
    "plt.ylabel('复习记录(条)')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1HxIREXWru1gsAeYDSJoN3N2rYVkUXg1c0rH5JmDkPMXOwPJ6YkZExFjq7oa6HPigpIXAbsAZko4CHrB9wai2c4Gf2u68COFjwGclXQq8Dtil5rwREdFFrcWiPLE9HzgIuNj2tWO0/Q6w36ht35V0IEWhOd32r+rMGxER3dV+nYXth4BFG/D424DbJi5RRESMV67gjoiISikWERFRKcUiIiIqpVhERESlFIuIiKiUYhEREZVSLCIiolKKRUREVEqxiIiISikWERFRKcUiIiIqpVhERESlFIuIiKiUYhEREZVSLCIiolKKRUREVEqxiIiISikWERFRqfZlVSUdAxwK3AUssH3nGG33At4H7G97WNIewLk8uqzqsbaX1Bw5IiJGqbVYSJoHHAzMBfYATgCO7tF2G+BM4ADbw+Xm3YB32V5cZ86IiBhb3UcW+wIX2l4t6Upg4RhtPwX8AjhE0iLbPwd2Bw6U9H7gFuBI2w/1u/OZM7fegOj1mTVretMRKg1CRkjOiZacE2tjyll3sZgOLAUou5WmdWtUdjftBOwPTAMulbQ7cBbwVdsPSzoHOAw4r9+dr1x5P2vWFAcpbXrRVqy4r+d9bck5VkZIzvHaGHK2JSMk50QbyTllylDPD9l1F4tVFG/+I2b0aDcXuMD2zwAk3Qz8OXCp7YfLNsuAOXUFjYiI3uoeDbUEmA8gaTZwd492PwSeU7bbiuIo4xfA9ZK2lLQ58ErghprzRkREF3UXi8uBOZIWAhcBZ0g6StLho9r9B3CnpCUU3Vaftn0zcBrFEcWNwBLbX6o5b0REdFFrN1R5Yns+cBBwse1re7QbBt7eZfv5wPl1ZoyIiGq1X2dRjl5aVPd+IiKiPrmCOyIiKqVYREREpRSLiIiolGIRERGVUiwiIqJSikVERFRKsYiIiEopFhERUSnFIiIiKqVYREREpRSLiIiolGIRERGVUiwiIqJSikVERFRKsYiIiEopFhERUSnFIiIiKqVYREREpdqXVZV0DHAocBewwPadY7TdC3gfsL/tYUkzgMXAlsBVto+rO29ERKyr1mIhaR5wMDAX2AM4ATi6R9ttgDOBA2wPl5tPBRbb/oSk8yXNs31dH7veDGDKlKG1Nj5hm2nr9XNMtNG5RmtDzqqMkJzjsbHkbENGSM6JNpKzI+9mo9sMDQ8Pj942YSR9CLjb9sclDQHft/1nPdpeBDwO+DqwyPbPJf0MeKHtVZL+CniO7X/qY9cvA66ZmJ8iImKTsxvw7c4NdXdDTQeWApTdSl3LqKQ9gJ2A/YFpwKWSdgfW2F5VNrsH2K7P/X6X4oe9A1i9/vEjIjYpmwFPpngPXUvdxWIVxZv/iBk92s0FLrD9MwBJNwN/DjwiaajslpoOVB/LFx5iVFWMiIi+/LTbxrpHQy0B5gNImg3c3aPdD4HnlO22ojjK+AlwEzCvbLMzsLzOsBER0V3d5yw2A64GbqDoFjqb4lP/A7Yv6Gg3BJwOvBjYBviM7VMlzQU+C1wKvA7YxfYvagscERFd1VosACRNBQ4C7rB97Xo8/ukUheYK27+a6HwREVGt9mIRERGDL1dwR0REpRSLiIiolGIRERGVUiwiIqJSikVERFRKsYiIiEq1T1Ee9ZC0HdWv3x+avjZF0ouBLSqaPWj7O5ORp5tBeS4HxSC85jA4OdsixWIUST8Efj1GkyHgSbbnTFKkXi6juDJ+rPmyXgTsODlxevoU8P8YO+crKaZzacpAPJeSTgGeUNHsbtvvnYw8YxiE1xwGJGdbXvcUi3XdaXv+WA0kXTlZYcaw0vbfjNWgJTnvrZpWXtKek5Sll0F5Ll8K/PUY9w8B509SlrEMwmsOg5OzFa97isW6+jnkbMNh6SUT1KZun56gNnUalOdyte3bxmogqQ1T8g/Caw6Dk7MVr3um+6hQTnI4i6J6r7C9puFIXUn6U4r1PoaAW8davrYp5Txhc3k05/I29ge39bmUtI3t325om8k0QK95a3O25XVPseihnFJ9IbAt8ADFehrbUsyIe2yT2TpJ2oVixt4VwL0UOXekmB7+8I4lahsl6ZXAB4D/4tGcO1OMyNvL9kMNxgMG57kEkHQYsA/FQjVDFNP3n2X7e40G6zAIrzkMTk5o9nVPN1RvZwFH2/7JyAZJ04GvSNrH9jebi7aWk4H9bN8zskHSFOBLFOuff6WpYKO8F9jV9sOdGyV9huIk4sWNpFrbQDyXkj5NsQrkpyhWkJxOcQL+Ekl7jSwi1gKD8JrDgORs+nVPsehtG2CttTNs3ydpObB5M5G62ppReWyvkQTFL1RbPBZ4FsVCVwBI2gLYnh4rczVgUJ7LObaPHrVtqaQdgJcAbSkWg/Caw+DkbPR1T7Ho7aPAjZL+HbidYnnY+cA9ti9tNNna3gdcL+lGipxbU/zifMP2fzaabG1vAi6W9Hsezbkd8CnbNzaa7FGD8lz+StK/UIyAGfnd/AuKo5+PNhlslEF4zWFwcjb6uuecxRgkbQ/sQXG4twpYYvvHjYbqQtLWFIejIzlvsn1fs6m6Kxezmg6ssn1703lGG4TnslyB8j3AfhQ576U4r/Lplj6nrX7NR7Q9Z9Ove4pFRERUytxQERFRKcUiIiIqpViMUznOufUkvaPpDP0oT9i12gA9l3/SdIZ+DMJrDgOVc1Je9xSL8duq6QB9uqXpAH1qxRj2CoPyXLZhaop+DMJrDoOTc1Je95zg3ghJ+l+2z246Ry+StgSeRjGlwsNV7SeLpEOBL7cp08agrdOnjDYoOZuSYtGDpNOBfYFzgV8Cp1AMVfsb29c3GG0tko7vsvkI4NyqGTUnk6Qv2/5LSQuADwG/AZ5EMY3G1Y2GK0n6JXArcAZwcZum9+gk6SW2/0vS5sBhwBzgBtv/1nC0tQzK9CmDkrNp6YbqovzleSLwYopPwG8Hnk1RPP5Pg9G62Qp4N/AH4Lby30MUb3pt8rjy/7cBO9t+EcUcN6c0F2kdt1CMYZ9DcXHeuyU9peFM3Yz8Dp4L7An8CnirpNZ8OCiNTJ/yCtuvt30w8AyKK6YPbjbaWgYlZ6NSLLrbEviJ7fsp3sxOtf07278o72sN2++jeIN7JcWqXucBv7bdhnUNOk0tZ/D93cjsmLZvprhati2Gbd9v+4PA3sDvgEWSftBwrl6ebfso22cCL6d9b2xdp08pb7Zp+pSByClpuaS7Jf2s499ySZMyvUum++juGuAdko6wfS7FpfVIuoBiZspWsb2kXKTlFEkHAVMbjtSNKZ67Z5dZrwOOB/6nyVCj/HHFtPKq7U8Cn5T0xOYidbW9pFcBD0t6jO0/UHzwq1oidLINyvQpg5JzV4qjyYNtPzjZO885izFI+tPyaGLk67+wfXmTmapI2hs4xPZbm87SjaQnA2uAuyhW/7rQ9iPNpipIem55tNNqknYHdgN2p5iB9D+A64GP2P58k9lGGzV9yr3AsrZNnwJdc95U9iy0iqRnUazwt2Ky951iEbERkLRFE582xyLpxRTnVL4MPEhx7m8lcLrtBxqM1lU599JZwP4UR8LvsP3fzabqTtIzKZ7bW2xfMxn7zDmLiI1ACwvFU4Gzgd8D5wCLgB9RHFWe02C0dUg6vLx5KPA9YDbFucrzGgvVRTn6DRVz5n+RYtDNSZJOm4z955xFxICR9MZe97VoYMOOwHm2Py7p+8Bfjlz7I+nKZqOtYx9Jh1AcTZxcHvV8vcew9CZ9W9JbgL2Av7K9XNJjgGXAu+reeYpFxOB5GfAC4N/pOCkPtKlPeSnwAUmX2L5a0nUAkvanZT0ato+U9FqKAQ1TJS2kGD7dtq6yIyhOcA8DXy+3rQYm5RxQzllEDCBJ5wMfaOO6CyPK7pIX2r6oY9v5wAdtL28uWXeStgNOBOYCPwDe36IlaoE/dkWdCLyBomDMBr5m++S6951iERExYCQ9A9gJ+OFkjeBLN1RExADoMrpsF4rrlm6djNFlreo7jIiIdbVhdFmKRTRK0hPLaUBaaxAyxkbvj6PLgOOA62yfXZ6r2HYyAqRYjJOkb0k6XtK0prOMZVByAh8Fjm15zkHIGBu3pcBrJM0uZ2l+D0zu6LKc4I6IGABNjy5LsehB0hSK4WkzgC+1eSGUctGWPXl04ZblFMPpft9krtEkvZRiNtfOnJ+3/atGg3UYhIwRTUg3VG8fp1i/4skUM1L+k6S2zeqJpLcCn6XI+VvgEYqpqpdKetxYj51Mkk4A3kpxleyXgW9SFOIlkialz7XKIGSMaEqGzvb2PNt7AJRXn/4jcIOkd9r+ZrPR1vIG27uO3liu9HcAcOHkR+pqT9u7jdq2uLzIaD5wQQOZRhuEjBGNyJFFb6slPb68PYtidbIDgLc0F6mrIUn7dW4oV3ebR3FSrC0elPTmcipoACTNo1i0Z1JmzezDIGQceJKOkfT6pnNUGZSckyXnLHqQNJ9irYD7KNaLfnm5yEyrlOcrzqaYK+iXFAvgrAROs31Jk9k6SZoOnEqxlOpqiu6ymyimq76hyWwjBiHjWCR9C7ia4rX/XdN5einXXPkJxYqOrZott9Og5JwsKRZjkPRY4AmDcHKzPCG/FfD7jiUhIxpXXqMyi2LAwIq2/n4OSs6mpFj0IOllwOeAacDngZNsr2w2VURhEEbASZoNLKS4aOwBilXotgU+Y/vYJrN1GpScTcs5i95OohgN9TSKNXkt6ehmI0UMzgg4ilXn/sH2zrZ3tb0j8EzgJZL2aThbp0HJ2aiMhuptte0fA0j6LMXJ4hdI+s+RUVIRDRmUEXDbAL/o3GD7PknLgc2bidTVoORsVIpFbz+WdBKwBPh74M22PyXphQ3n6oukc20f0XSOKpIus71/0znG0sKMQ5L2sz2yAE7nCLhPNhdrHR8FbpT078DtFF2684F7bF/aaLK1DUrOriQdA/zC9hfq3E/OWfRQntx+C/B8iiu423RtRSVJ2w3Kifm2n0hsW8ZBGQEHIGl7YA+K8wCrgCUjR+xtMig5u5msUVspFj2Uo4v+muKXp7XTfZTXBMzh0Wsq9gN+Y3tJc6n6I2mR7dc0nWOEpKfbvq28vRvF83pjW4fNZgTcpqmpUVspFj1I+gTFVA+3AYdTjIz65zaNty6vC7geuBl4ErCCYr77xwPX2/6nBuOtRdKPgMeWX45M970tcIftZzSTam2SrrA9v+z7fx7wfYri+6+229S9E5ugpkdtZTRUb8+z/QbbxwF/B+xKMd1Hm0ZH7ApcUH46PxN4sMx8AMVkeG3yWuAO4CO2d7C9A0VBa0WhGOVltvex/S7gpUBGwUUbNDpqK8Wit0GY7uPnwFxJjwG+SrEoCpKeCGzZZLDRbP83RZ/wdpK+Wk7M17bD2m3LAQz3dmz7PUVXT0wQSYc1naEfLczZddQWxTU2tY/aymio3k6kmG12FcVY9pHpPg5pNtajbP9A0iLgXeWKWSNz2n8DOKa5ZN2Vz98HJL0E+DeKw+g2+RfgbcBTJL2WogBfBnym0VR9GpQRcAxO8W1bzkZHbeWcxRjKEVGzbP+y6SzrQ9KQ7Va+wOV077vavrzlOZ9u+7Y2ZxwxKCPgYv01OWorxWJAlatmvR842vZDXe4/BHiO7ZMmPdzaOUZyvrnb4IA25Owj46uB5zb9XI6Q9EngTNs/bDrLWCR90PaHJb2IYoLG5wI3AH9r++fNpnuUpFkUPQm7UHxan0ExWGSh7bOazNYmKRYDTNJOwAeAK4BrKfrXnwW8BrjZ9qkNxvujQcg5CBlHSPoB8GOKAQMn27612UTddYwu+zZwLLCMYqDDIbYPbDbdoyRdCpxq+8qObbOBLwJ/NwjD0CdDisVGQNIuwJ9TnNS+Ffi67XvHfFADBiHngGS80vZe5TT6xwAPU7yxfatN1wN1FItrOheVkvQ92y9oMlsnSTfaflGX7acDV9te3ECs1kmxGKcWTv0Qm5iRN+GOr58LLABeYfv5zSVbW8cR0AuA/W27vNr4w93mtmpKOV3GKyjm1Bo5cfwXFN1m820/0mC8SpIOs/3FuveT0VDjd0DTAWKTt9abl+2bgfeV/1rD9vPKOat2B6aWgxoOB17XbLK12T5Z0tUUs0w/g2Lo9JeAywfkyvhJGbWVI4sxDMKaARGdBuFUOOAAAATVSURBVGHUVtuMmjJniOKq/ZU5V7G2FIseyjUDXk1xzcI9FEPVdqboz35J2/qxY+M3KCPgBsmATZnT6KitdEP1NihrBsQmouzzPx24QFKvUVutKRSSLuPR+cBGDAHDnedcGjYyZc5Jkv4KeJXtNwCUXVOtKRbAeRSjtt48smFk1Jak79d9JJRi0dugrBkQmxDbNwGHlKO2duPRUVvvbOHR7ikUR0JHNR1kDD8H3tIxZc53oZ1T5gBP6hzeC2D7x5KuoVg1sVbphuphkNYMiGgrSa8Cltq+veksvUh6A/CUcsqckW3LgGM6Pyw2relRWykWFbqtGZCTiBEbZlD+htqWszyi3JfiHOq9FCt5XjEZo7bSDTXK6Kkfyhfh/o77X01RyVvTNxzRJv1O8ULDf0P9DhigRX/r5XmJJeXR0Mj1Xt+hmCeqVjmy6GKQpn6IaKNB+RsalJwAkp5l+yeS9qI4D/QFivNWO0/GhcIpFmMYhKkfItpsUP6GBiGnpO8C51OcPz3H9spy+3dsv7ju/adYREQMgPIK+NMoJmP8W9uLJM0Evmp7Xt37T7GIiBggkl4OfJzimpXVwN/bvqzu/aZYREQMIEkzO7qiah+1lTW4IyJaTIXzym6oP+ooFIdQjOqqVY4sIiJarg2jtlIsIiIGRJOjtlIsIiKiUs5ZREREpRSLiIiolGIRERGVUiwiIqJSikXEOEi6VdL2fbRbKOnQSYi0QSQdIencpnNE+2WK8oga2H57v20l/QlwhO3Ta4wUsUFyZBHRvD8B3tF0iIix5MgiNlqSjgD2tH1E+fVVwIeAvYA3UUzEdoLtT5T3Hwn8b4oLno63/X/LlRL/FXgVxdWzm/e573OBq2yf25Fln/Lu/YGrKK6+/UK5/fGSfg0sG1mboFuejp/j48AbgBm250s6EDjS9mvKNp8BvmX7QklvoZgOYjPgjM7lQyP6lSOL2NQ8HjgWeB7wfIo1jJH0POAfKa6OfSHwIUlPonhD3xnYAVgMbLcB+z4E+CLwdGBX4IW2DwfmAj+3vW1HoeiVZ8Q/A+cAf1l+/U1grqSRD4B7Al8r5xP6a+ClwGzg3ZK23oCfITZRObKITckQxbrFBj4GXAYsKO+bDzwD+GH59ZaAgHnAonJ50H+TdM8G7P8G218FkGTgcWO07ZXnzvLrc2xfMtLY9sOSrgbmSboX+B/b95X7eiNFwdiNolg+gY6lgiP6kSOL2JQ8hWL+/7nAImAP4CZJj6UoJOeXn+63Bf4UuL7c3jknzpoN2P9PO25XzbPTK8+I67s8ZhHw8vLfIgBJzwSuBn4DvAv4+fpFj01djixiY7YKeCqApAOAZwLPBk4C9qZ4E/0lMJPifMQ/StoW+B3wPeDVwHeAt0k6k+Jcw+M3IE+vArESmClpq452vfIsG+P7Xwa8G3iQovsMYCeKCefOAQ6kKDoR45Yji9iYXQZMLU8IHwB8G7gFuAZYXt7+uO07bP8PcAKwhKLr519tL6M4x3AzcDvwN8CvJzpk2V10MsWRx3LgyWPkGev7PFTmfLBjJtJvlf/fCRxWfv9nT/TPEBu/zDobERGV0g0VsR7KYa6j3WV7x0kPEzEJcmQRERGVcs4iIiIqpVhERESlFIuIiKiUYhEREZVSLCIiotL/B1ZD++vAByVMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def _compute_bin_p_recall(group):\n",
    "    return pd.DataFrame([\n",
    "        {'p': group['p'].mean()}\n",
    "    ])\n",
    "global_forgetting = df.groupby(\n",
    "    pd.qcut(df['used_interval'], 20, duplicates=\"drop\")\n",
    ").apply(_compute_bin_p_recall).reset_index().drop(['level_1'], axis=1)\n",
    "sns.barplot(x='used_interval', y='p', data=global_forgetting, color=sns.color_palette()[0])\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_repeat_long = df.groupby(\n",
    "    df['repeat_long']\n",
    ").apply(_compute_bin_p_recall).reset_index().drop(['level_1'], axis=1)[:15]\n",
    "sns.barplot(x='repeat_long', y='p', data=global_repeat_long, color=sns.color_palette()[0])\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_repeat_long = df.groupby(\n",
    "    df['con_rep']\n",
    ").apply(_compute_bin_p_recall).reset_index().drop(['level_1'], axis=1)[:10]\n",
    "sns.barplot(x='con_rep', y='p', data=global_repeat_long, color=sns.color_palette()[0])\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "traces = df[df.repeat_long == 3]\n",
    "global_lu = traces.groupby(\n",
    "    df['used_interval']\n",
    ").apply(_compute_bin_p_recall).reset_index().drop(['level_1'], axis=1)[:15]\n",
    "sns.barplot(x='used_interval', y='p', data=global_lu, color=sns.color_palette()[0])\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'ease'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-a2f6386bcd12>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtraces\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtraces\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtraces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mease\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtraces\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'used_interval'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'真实间隔(天)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'复习记录(条)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5177\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5178\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5179\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5181\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'ease'"
     ]
    }
   ],
   "source": [
    "traces = traces[traces.ease == 3]\n",
    "plt.hist([x for x in traces['used_interval'] if x <= 10], bins=np.arange(10+1)-0.5)\n",
    "plt.xlabel('真实间隔(天)')\n",
    "plt.ylabel('复习记录(条)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_repeat_long = df.groupby(\n",
    "    df['acc_lapses']\n",
    ").apply(_compute_bin_p_recall).reset_index().drop(['level_1'], axis=1)[:10]\n",
    "sns.barplot(x='acc_lapses', y='p', data=global_repeat_long, color=sns.color_palette()[0])\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylim(0.6, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_repeat_long = df.groupby(\n",
    "    df['con_lapses']\n",
    ").apply(_compute_bin_p_recall).reset_index().drop(['level_1'], axis=1)[:10]\n",
    "sns.barplot(x='con_lapses', y='p', data=global_repeat_long, color=sns.color_palette()[0])\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylim(0.5, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "Index(['cid'], dtype='object')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-4528c26d2f6d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'cid'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'last'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'acc_lapses'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_major_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMultipleLocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'累计遗忘(次)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop_duplicates\u001b[1;34m(self, subset, keep, inplace)\u001b[0m\n\u001b[0;32m   4892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4893\u001b[0m         \u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"inplace\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4894\u001b[1;33m         \u001b[0mduplicated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkeep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4895\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4896\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mduplicated\u001b[1;34m(self, subset, keep)\u001b[0m\n\u001b[0;32m   4949\u001b[0m         \u001b[0mdiff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdifference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4950\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdiff\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4951\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4952\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4953\u001b[0m         \u001b[0mvals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msubset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: Index(['cid'], dtype='object')"
     ]
    }
   ],
   "source": [
    "plt.hist([x for x in df.drop_duplicates(['cid'], keep='last')['acc_lapses'] if x <= 10], bins=10)\n",
    "plt.xlim(0, 9)\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_major_locator(MultipleLocator(1))\n",
    "plt.xlabel('累计遗忘(次)')\n",
    "plt.ylabel('复习记录(条)')\n",
    "# plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([\n",
    "    x for x in df.drop_duplicates(['cid'], keep='last')['repeat_long']\n",
    "    if x <= 18\n",
    "], bins=np.arange(18+1)-0.5)\n",
    "plt.xlim(1, 18)\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_major_locator(MultipleLocator(1))\n",
    "plt.xlabel('累计重复(次)')\n",
    "plt.ylabel('复习记录(条)')\n",
    "# plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pp'] = 0.8029878575651462"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    # correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    plt.figure()\n",
    "    plt.plot((0, 1), (0, 1), label='Optimal average observation')\n",
    "    plt.plot(brier['detail']['bin_prediction_means'], brier['detail']['bin_correct_means'],  '*', label='Average observation',)\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(brier['detail']['bin_counts'])\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Prediction')\n",
    "    plt.ylabel('Observeation')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of predictions')\n",
    "    plt.bar(bins, counts, width=(0.5 / bin_count), alpha=0.5, label='Number of predictions')\n",
    "    plt.legend(loc='lower center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.6566361214554547\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fpr, tpr, threshold = roc_curve(df['p'], df['pp'])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print('AUC:', roc_auc)\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)  # 假正率为横坐标，真正率为纵坐标做曲线\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cHeuV6jw1qc8V5ck+P/l9N5aSpvRbmxkWkJLTswn5NZJrt1No2bAOM0Z54FbP8Kn2VZ1RpDoSIzWhWnBza2CUgFbTODk588UX/6nqZghGptXpOBoWx67j0aCDaUNbM6hzQ6QmkA3F2ERQEwRBqETxjxRsOhhBVGw67Zo74je8DU51a+d9wqoggpogCEIlUGu0/Hr+HntPxWBhJuW10W3p3b6+SeSqrEwiqAmCIBjZ3YeZbDwYzr2ELLq0cWb60NbUsbWo6mbVSiKoCYIgGIlKreHn0zEcPHsPW2sz5oxrT1cPkd7PmERQEwRBMIKo2DQ2HojgYYqSvh3cmDy4JTaWIl+jsYmFEEKJtMo0lD+vQatMq/BzT5w4lj/+OFXh560M8fEP8PefVdXNAPKz3hdlxYqlldwSAUCj0bL18E0+2hKGSq1l0eSOvDq6rQholUQENaFEeRf3onkYRd7FvRV63lu3ov6X4eN4hZ7XFIms99VHYmo2l6KSCQ2LZXCXRgS83p32zUu3xlAoH3H5UShS5oaZoPk7r54q/Ciq8KMgM8PutW/Lff5Tp44zadJUjhz5DZ1Oh0aj4eWXJxMSsgO5XM7atR8yfPgovLy8+fjjNcTFxQKwfPlK6td348CBfdy+Hc2jR8koFArWr/8UtVrN2rUrefjwISqVikWL3sbDoy3HjoXy3/9+i0wmo0mTpnTo0JEhQ0YUmU2/KEqlgjVrPiQtLRULC0t9BpTU1BSWLFlIYmICEydOYfTosaSlpbFixVI0mvwckytWrMLZ2YXQ0N/54YfvUalUvPjiZEaO9AHyM9P7+s5g9+6dvP9+AE5OTkVm/3dwcCiU0T8+/gEbNnyjz3rv7t6ShQuX6Nv9dNb7EyeOsWXLJnQ6HaNHj2HcuBfZsOEbcnJyiIqK5OHDeGbOnMPgwUPL/fmamjy1luu3H3E/SYGnuxPvTO9Mq0YiAXFVECM1oUg2Uz9G7t4TZP/bwVdmjrxlT2ymlrz3maFOnjzOiBE+uLk1ICLiBnK5nG7devDXX2FotVqiom7i7d2Zn3/+CalUSmBgEGPHjic4+O+ciIcPH+Sll17Wb3uSlpaKl1cnAgODGDZsBPv3548uDxzYz5tvvsXcuQtQKBSMG/ciISEbaN3agy+//IaOHb3Zs2dXsW3dvHkTbdp4EBgYxKhRPgQG5r/fo0fJLF++kn//O4hvv/0apVLB5cthWFtbExgYxNy5b5KZmUlGRjqBgZ/yf//3BYGB+WVzc3P150hNTWHTpu9xd29JnTp19dn/MzIy9Nn/H2f0/+yzr2jXrgNHjhymU6cuBAYG4ehYj8DAoAIB7WkZGRl8+eWnfPJJIF999R179+7m7t0YIH8roPXrP+P99wP45ZeKHZGbggfJCkLDYrmfpKBVozp4t3QSAa0KiZGaUCSpdV0k5lb5ozWZGWhUSMyskFqX/x9rYmICMTG3+eSTdSQmJnDy5HHatm3HsGGjOHz4AHK5nK5duyOVSrl9O5qrVy/j7z8LtVqNk5Oz/jxDhw6nVavW+udyuRl//RXG6dMnkMvN9AmEW7duw7fffo1Op2PWrLlAydn0n3br1k2mT89P1NuxY2c2bvwOgKZNm+tHd66u9UlISKBnz9789dcl3nxzDnXr1sXffwGxsffJzs7m7bcXAWBubkFqagr167thY2OLr++MAmuVisr+X1xGf0PFxd2nQYOG+l0P2rZtR3T0rf+93wjMzc1xcHAkLy+vVOc1ZXkqDX+GJxKfoqSOjTm92jlRx8YcqVSsO6tKIqgJxdJlp2PmOQiztgNRhR9DV0GTRU6dOsGoUWP55z/f5vbtW6xcuZxZs+bQrl17goK+4vjxo/j4PA/kZ713cnLilVdeJy4ulosX/9Sf5+mM8YcO7cfFxZXZs1exa9d2IiMjADh//hyBgUFIpX9fmCgpm/7TWrZszbVrl+nY0ft/We9bAXD//j0UiiykUhkJCQ9xdnbhypXL9O7dlzff/CebNuWPiCZOnIKra30+++wr5HI533+/Wb+fnKWlJTJZwW1Fisr+X9xOAZCfWFmpVGJtbV1s5vuGDRsTHx+HQpGFhYUlERHhTJ48jTt3orGysi75AxMK0Ol0nL76EJVOx8NUJW2bOtCyob1YRF1NiKAmFMtq2Hz9Y1nflyvsvKdOHWfChMkAtGjRkszMTB48iKNBg4Z4e3fi7NkzvPnmPwEYM2YcH3+8hnnz3iAnJ1s/0ipK1649+PDD5Vy9ehlnZ2fS0vKDcIMGDXj9dV/s7evg4uLKrFlzS5VN39f3Fdas+RB//1n/u6f2HiqVCnv7OqxYsYzk5GRmzJiJra0tzZo1Z9WqFWza9B15eXm8/fZy6tSpy/Tpfsyf/w/y8vLw9u6MvX2dYt+vqOz/xe0UADB1qi8LF85Fq9Wyfv2nODg4Fjqnvb09c+cuYOFCf3Q6HWPHjqNp02bFf0hCkZLTsgk+FMH1mFTeGNeBQZ0aYmtlnFmNWmUaOb9/heWQORVyhcRUiCz9Ikt/rZaXl8c77yxCp9Mhl8vRaLTMn7+IZs2aV3XTjMqUPuPHjJmlX6vVcSQslt3Hb4MEJg50p0cHN347e7dQ2eKy9Jc2SOWcDEYVfgyztgOx7OdXZBmRpb8wMVITajVzc3O++OIrk/uBFyrOg+T8BMS34tJp38IRv+Ee1KtjiVJVuu/Uk8tjigtSYPyZx7WdCGqCIAhFUGu0HDx3j32n72BhJuN1n7b0alf6BMSlDVI2Uz8m94/tqGPCQJOXP/O4eWcsek4pd59MgQhqJqS4SQSCUJtUxB2Vuw8z+e+BcO4nZtHNw4WXhramjo25/nWtMg31wxh0KnMkZpYlnqu0QcqYM49NgQhqJkIuN0ehyMDGpugFxoJQG+h0OhSKDORy82cXLkKeSsPe03f49dx97GzM8H+hA51bOxcud3EvOsfmaOIfIW9S/HIQKFuQMtbMY1MggpqJcHBwJjU1iaysNKRSKVqt6dxjMrX+gmn3WS43x8GhcCB6lsh7qWw6GEFCajb9vNyY/FxLrJ/K11jgUmKf5miTYshLigGpFPNOY4o9d2mDlLFmHpsCEdRMhEwmx8nJDTCdGZ+PmVp/QfS5NLJz1ew6Hs3RsDic6ljy1hRvPJsVXhYBT11KBJDKkNZ1Q9aoXYnvIYJU5RFBTRAEk3Ul+hEhv0aQmpHL0K6NeaF/CyzMZcWWL3ApUSIFrQZk8mfeVxMqjwhqgiCYnKxsFdt+j+KP6w9p4GTDMt/2uDesk7/V0qGS15I9vpSoaeKF1DkZVDmV3HqhJCKoCYJgMnQ6HX9GJLL1t5soc9SM7dOM0b2aYSbPXyxtyFqyx5cSlSot8iZeldZ2wTAiqAmCYBJSM3PZcjiSS1HJNKtvx1tT2tLYJT/Bs1jwXHuIoCYIQq2m0+k4eSWeHaG3UGu0TBrUkqHdGiF7IsG1WPBce4igJghCrZWYlk3wwQjC76bSpnFdXhnlgatD4V0JxILn4iUkJLBs2TL9tkTr168nLS2Nd999FzMzM2bOnMmQIUMACAoK4uDBgzg6OrJu3TqcnJwqvb0iqAmCUOtotTp+vxjL7hPRSCUSXh7ehv7eDZCWkFFHLHguWnBwMGPGjGHcuHHs37+foKAgzp8/zyeffELz5s2ZMmUKffv25caNG4SGhrJr1y7Onz/P559/TkBAQKW3VwQ1QRBqlbikLDYejOD2gwy83Ovx8vA2ONpb5s9sLCFLvimuJYuPj0ej0RQ4Zm9vX2Bro3r16nHz5k2ys7O5fPkyLVq04PLly7Rp0wYALy8vrl69ytmzZ/Hx8UEmk9GzZ0/WrFlTqX15zOhBzZDhqFKpZMmSJaSnp6NSqVixYgVt27Y1+D3Ku4WCs7NduerXRKbWZ1PrL5hun52d7fjc062IV+3gtbVF1stU5KHMVRc6bm0hx86m6JRbCSlKbGwsCh23tDTD2bHyNl4tz+c8bdo04uLiChzz9/dn3rx5+ucjR47k3XffZfPmzSQlJTFhwgTOnTunf93e3p7ExEQUCgWenp4ASCQSlEplmdv1mFqtJi0trVSXMY0a1MLCwgwajv7000+0bduWuXPncunSJT799FOCgoIKlMnIyCAjI6PAMXNzc1xcXMR+aqVkan02tf6C6fX5TnwGmw/fJCY+gx6erkwd0gp76/xg9PTMRr0nZjYqVVoOn40pVGRYz2bkKHOLfM8clRaFovBrOTmqSvt/X9791LZu3VrkSO1JgYGBvP3223h4eJCRkcG4ceNo3vzv/QgVCgU6nQ5bW1uys7P1x7OyskrdLoBt27bxxx9/sG7dOsaNG0dycjLTp09n4cKFhvWtTO9qoNOnTxs0HK1Xrx4XL15EqVRy6dIlWrRoUahMcHAwgYGBBY517tyZbdu2iZFaGZhan02tv2Aafc7JU/P9r5HsPX4LB3tL3nu1B93b1S9QxmHu1zw6Eowy8hw6dR4SuTnWbXpQb4gfctv8/0dlGXXVhpGam1tRI9qCsrKyuH79Oh4eHpw9e5b69euTkJBAdnY2VlZWXL9+nTFjxlC3bl0OHTqEj48PMTExODg4lKlNX331Ffv27ePo0aP06tWLt956ixEjRlSPoGbocLRPnz7s3LmTkJAQoqKi8PMrvOjRz8+P8ePHFzhmbp7/l5gYqZWOqfXZ1PoLta/PGiD3qU0507NyuXb7ET8du8UA7wbMftEbZVZOEf2Wk6eVo1Pnz2zUqVXkaeWkZsshO79sWUZdtWGkZog5c+awbNkyPvzwQxo0aMCqVasIDw/njTfeoGHDhmi1Wry8vNDpdAQGBrJq1SouXryIr69vqdv1d/vknDx5khEjRuh/5w2uW+Z3NYChw9GQkBCmTp3KkCFD0Gq1PP/88+zbt69AmadvXgqCYDpyn7g8qFJruRGTwt2ELGb4eLJ4aifaNnXAxsoMZVbRKavEzMay8/DwYPfu3QWOdenSBS8vL+7fv8+KFSuQ/m/NX0hICKGhoYwcOZIuXbqU6f0mTpxIz549adGiBStXrixyQFMSowY1b29vg4ajWVlZ3LhxgyFDhnDt2jUUCoUxmyUIQg2VkKLkcvQjcvI0uDewp1MrZ2wtik9A/Jgpzmw0Ni8vL7y8CqYJMzc3Z8SIEeU67/z58/Hz88POzg6pVMratWtp1qyZwfWNGtR69epVaDi6c+dOLC0tGTPm772HfH19Wbx4Md7e3jg6OrJ06VJjNksQhBpGpdZwMTKJuGQFdtZmdPNwwcHOAqlU7OReG8XHx3Pz5k3986SkJLp162ZQXaMGNZlMZtBwtEGDBmzdutWYTREEoQbS6XScC08gW6XjQbKCNo3r0qpxnRIXUQs12/z58wkLCysww1IikRASEmJQfaOvU6uI4aggCKYnJSOHzb9Gcjn6ETOfb8+ATg300/SF2uvcuXMcO3YMKyurMtWXPruIIAhC5dHqdBz7K473Npwj/G4qU55riZd7PRHQTES/fv04f/58meuLNFmCIFQbCalKgg9GEHEvDY8mdXllpAcuDtYoVWVbsiPUTPPnz6d9+/Y0adJEf2zt2qKzwTxNBDVBEKqcVqvj8J/32XPyNjKZhFdGetDPyw2JuHdmcvr06UOfPn3KXF8ENUEQqlRsYhYbD4ZzJz4T75ZO+A5vg4Nd4UwdgmkYP348UVFRnDp1ColEQp8+fWjVqpXB9cU9NUEQqoRKrWXPydus3PQnyek5/OP5dsyb0EEENBP3008/MWvWLOLj43nw4AFvvPEGe/bsMbi+GKkJgonQKtPIKWHrlcoU/SCdTQciiEtW0KudK1MGt8JOTAQRyE+gvGPHDlxcXAB4/fXXmTp1KuPGjTOovhipCUI1o1Wmofx5DdoKTuWUd3EvmodR5F3cW6HnLY3cPA3bj0SxJuQiylw1b77oxcwx7URAEwrQ6XRlritGaoJQzTwZfCz7FU7uXVpPb72iCj+KKvxoga1XKkN4TAqbDkWQlJbDoE4NeXGgO1YW4idIKGj+/PlMmjSJQYMGIZFIOHbsGIsWLTK4vvhGCUI1YazgYzP1Y3L/2I46Jgw0eSAzR968MxY9p1REs59JmaPih6O3OHE5HhcHK95+qRNtmpRtWxKh9nv++efp0MibBX4AACAASURBVKEDZ86cAfLTKBa1HVlxRFAThGrCWMFHal0XiblVfsCUmYFGhcTMqlLuq12KSmLzr5GkK/IY2aMJz/dtjrnZsxMQC6atRYsWpQpkTxJBTRCqCWMGn8reeiVDkcf3v9/kfHgijZxtmTfBi+ZuFbd1lE6Vg/r2BXRdG4CZZYWdV6j5RFAThGrEWMGnsrZe0el0nL2ewPe/3yRXpWF8v+aM7NkUuaxi56Rp4iPRZT1C++geNGpdoecWKt/GjRuZMWMGkD/7sSj+/v4GnUsENUGoRqrTvl+lXQKQkpFDyK+RXIl+hHsDe14Z1ZaGTobtrmyovEv7QPt3yixt+kMyD6wBmRnO72yv0PcSKk95Zjs+TQQ1QRCKZOgsTK1Ox/FLcew8Fo1Wp2Pq4FYM7tLIKHudmbUfiib2Otq0eNBqQCJF3rJnpU16EYzj1Vdf1T82dERWHBHUBEEooDSzMBNSlGw8GMHN+2l4NnPAb4QHznXLtmWIISRmliCT5wc0qRR02kqb9CLUDCKoCYJQgCGzMDVaLYfP32fPqTvIZVJmjPSgb2UlIFblInVujsy5KdI6tuji/jL+ewpVJisri8jIyGI3mX6aCGqCIBTwrFmY9xIy2XgggrsJmXRq5cT0YZWbgFju3l3/WObaDKtG80soLdQ0Y8aMYd++ffrnlpaW+Pv788cffxhUXwQ1odSqUw5BwTiKmoWpUmvZdyaGg2fvYmMpZ8649nRp4yy2hxEqVGpqaoHniYmJmJsbnkZNBDWh1Co6jZNQ/Tw9C/NWXDobN54n/pGS3u3rM2VwK2ytzKqwhUJtExgYSGBgIBKJhLZt2wL5syIdHBxYvHixwecxOKjl5eVx4sQJsrKyChw3NHOyUPNVlxyCQuXJyVOz+8RtjlyIxdHegoWTOtKhRb2qbpZQC/n7++Pv70/fvn05depUmc9jcFB77bXXyMzMxMPDo8DlBhHUTEdV5xAUKtf1OykEH4ogOT2H5zo3ZMIAkYBYML7yxhSDv6FRUVGcPn0amUzkbTNVVZlDUKg8ihwVO0JvcepKPK6O1rwzrTOtG4vPWKgcb731FjExMTRr1oyYmBiys7P1lyMNYXDumvHjx7Njx44yNVKoPR5PILAe9x5mnoPQZadXdZOECnQxMonl357jzNWHjO7VlA9f7SYCmlCpNmzYwMsv52fTiYuLY8GCBYSEhBhc3+CR2rVr19iyZQvfffcdDRs21F+CLM2bCTVfdUrjJFSc9Kxctv52kwuRSTRxsWXBxI40rW9n1PcUs2iFomzatEk/pb9Pnz78+OOPjB07Vh/onsXgoDZv3ryytVAQhGpLp9Nx5tpDth+JIlelZcKAFgzv3qTCExAXRcyiFYpiYWFBXl6e/nl2dnaplo0YHNS6d+/OiRMnOHHiBAD9+/enf//+pWiqIAjVSXJ6NiGHIrl2J4WWDeswY5QHbvUqNgFxUcQsWqEkT+58DXD8+HHefPNNg+sbHNSCgoL45ZdfGD9+PBKJhE8//ZSIiAhmzZpV+lYLglBltDodR8Pi2HU8GnQwbWhrBnVuiLSSFlGLWbRCScaOHUu7du30GUSmT5+Ou7u7wfUNDmrbtm1j37592NraAjBhwgTGjBkjgpog1CDxjxRsOhhBVGw67Zo74je8DU5GTEBcFDGLVngWd3f3UgWyJxkc1CwtLYmPj6dVq1YAPHz4EEtLseOsINQEao2WX8/fY++pGCzMpLw2ui2929evshRXlb0Tt2A6DA5q7777LjNmzNCvF4iIiGDdunVGa5ggCBXj7sNMNh4M515CFl3bODNtaGvq2FZeAuKiiFm0wpOqZOfrvn37sn//fi5dugRAp06dqFv32ZcLgoKCOHjwII6Ojqxbtw4nJ6diy549e5Zvv/2W7777TiRJFWq06jBdXaXW8PPpGA6evYettRlzx7enSxuXKmmLIJSkyna+rlu3rn5GiiHCwsIIDQ1l165dnD9/ns8//5yAgIAiy6anp7Nq1SqCgoJEQBNqvKqern7jziM+/T6MhylK+nZwY/LglthYigTEQtnt3r2bixcvsnr1auLj41mwYAGQP7Fj2rRpAOzZs4eNGzdib2/PypUradGihUHnrjE7X58+fRofHx9kMhk9e/ZkzZo1xZZdsWIFrq6uHD58mOHDh+Pm5lbg9YyMDDIyMgocMzc3x8VF/OUpVB9VPV09O1fN7uO3Cb0Ui6OdJYsmd6R9c5GAWChafHw8Go2mwDF7e3vs7e0LHLt//z4bN25k27ZtACxfvpw5c+bQv39/ZsyYwaBBg9BoNHzzzTf8+OOPJCYmsmrVKr777rtK68tjJQa1NWvWsGzZMgCWLl1aZJm1a9cWW1+hUODp6QmARCJBqVQWWe78+fOEh4fz3XffoVQqmTVrFlu2bKFOnTr6MsHBwYWutXbu3Jlt27bh4FC+tTXOzsbNnFAdmVqfK6u/zu9sr5T3KcmCaV1YMM2wXYJrioQUJTY2he8DWlqa4exorX9e1s/Z0POXt44xlOe7PW3aNOLi4goc8/f3L5BsQ6vVsnjxYlq2bMmePXsYPXo04eHhDBgwAMjP+nH+/Hm0Wi0DBw7E2tqaZs2akZSUhEajMShf8J9//vnMMt26dTOoTyUGNW9vb/3j7t27l1CyaLa2tmRnZ+ufP71tzWNXr17Fx8eHxo0bA9CiRQuuXbtGnz599GX8/PwYP358gXqPN45LTVWgVmtL3T7I/0IkJWWWqW5NZWp9ruz+5pwMRhV+DGRy0Kgxazuwwi5BFnWvLitbxfYjUZy59hC3etbMGNmWXp0a1arPOEelRaHILXw8R6XvZ3k+Z0POXxF1KlpZ+yyXS3FwsGHr1q1FjtSetHfvXiQSCe+88w73799n4sSJuLq6FiifmJiIlZVVgStsVlZWpKSk4Ozs/Mz2fPHFF/rHCoWCiIgIWrdujVQqJTIykpYtW7J3717D+lbSi6NGjdI/fjqgGMLb25tDhw7h4+NDTEwMDg4ORZZ7/BcA5KdECQ8Pp0mTJgXKFDUkFipGdZjUUJsYc7r60/fqLkQksuVwJIocNT69mzKmdzPM5GInDcEwT9/mKcrVq1f1gczV1ZU6deoQHR2tf12hUKDT6bC1tSUxMbHQcUNs3rxZ/3jmzJns2LGDDh06AHD9+nU++ugjQ7tk3HtqvXr1IjAwkFWrVnHx4kV8fX3ZuXMnlpaWjBkzRl+uf//+nDx5ksmTJ5Oens6kSZP0ozbB+Kp6UkNtY4zp6sXdq2uhk+Fg9w8WTfagiatpXVIWKoe7u7s+iCUnJ5OQkIC7uzvx8fG4ublx/fp1nnvuOTw9PQkICOCNN94gKyuLxMREHB0dS/1+4eHhNG/eXP+8adOm3Llzx+D6Bge13NxcLCz+vn6cnp7O6dOnC4zmniaTyQgJCSE0NJSRI0fSpUvR1/klEgnLly83uNFCxajqSQ2C4Z5OLZWnk3FV1QRVxwks7+OJTGr8BMSCaZowYQLLly9nypQppKWlsXjxYmxsbPD396dTp05cu3aNDz/8EBsbG2xtbVm6dCmxsbFMmDABubz046YpU6bw/PPPM3DgQCQSCSdOnGDChAkG15foDBwfFrXFds+ePTl79mzpWmwE4p5a6Tzus1aZVmwOvtp0GbK2fMapv29Advskap0MuUSDukU/HIe8VmTZ2tLnx5QqLYfPxhQ6PqxnM6zN8gN6efpsyPkrok5FK+89tfKIjo7m+vXrDBgwQD+pT6vVcvz4ceRyOf369Svzua9cucKlS5fQ6XR4eXnRuXNng+s+M4xmZWWRnp6ORqMhPj5ef400Li4OGxvjZ/QWjEfk4KsZtFodR8JisYm8S6auDfbeQ/DWhWOWLVJLCVWnqPyMUqm0VGuZi2Nubo6joyPDhg3j9OnTpar7zKD2448/EhwcTEZGBtOnT9cHNUdHR95///2ytVioNkQOvurtQbKCjQfDiY7LoEOLCbw8vA316lgChk1vFoSaJjAwkEOHDpGUlMTQoUMJCgriwoULLFmyxKD6zwxqfn5++Pn50bdvX44cOVLuBgvVi8jBVz2pNVoOnrvHvtN3sDCTMdPHk57tXEW2HaHW2759O7/++ivDhw/H0tKSkJAQhgwZUnFB7bHZs2eXuZGCIBgu5mEG//0lgtikLLp5uDBtaGvsbcyrulmCUCns7OzIzPz7PmFcXBzW1oYvZjc4qE2bNo3o6GhSUlL0x2JiYpg4caLBbyYIQvHyVBr2nr7Dr+fuY2djhv8LHejc+tkLVwWhNnnnnXfw9fUlIyODWbNmcePGDVavXm1wfYOD2qpVqzh37hz37t3Dy8uL69ev06ZNGxHUBKECRN5LZdPBCBJSs+nn5cbk51piLRIQCyZowIABdOjQgcuXL6PT6fD29i7VejeDg9rhw4c5evQoixYtYvbs2WRnZ/P111+XqdGCIOTLzlWz61g0Ry/F4VTHkremeOPZrPQLVgWhNnF0dCzzLEqDF1NYWloSFRVF165d+fPPP+nQoQNXrlwp05sKggBXopN5b8M5jl2KY1i3xgS81kMENMHkvfLKK8XmCTaEwUFtyZIlfPLJJ4wYMYJvv/2WYcOG0alTpzK/sSCYqkxlHt/uu85nO69gaS5nmW8XpgxuhYW5yNkoCDY2Nly4cKHM9Q2+/DhkyBCGDBkCwC+//MLdu3fx8PAo8xsLgqnR6XT8GZHI1t9uosxRM7ZPM0b3aoaZXKS4EoTHxo4dy0cffcStW7fo2LGj/niFbD3ztN9//53o6Gj8/Py4dOmSCGqCYKDUzFy2HI7kUlQyzerb8daUtjR2sa3qZglCtbNlyxacnZ05fvw4x48fB/LzA4eEhBhU3+Cg9v777xMbG8v169eZMWMGFy5cKPWWAELZie1haiadTsfJK/HsCL2FWqNl0qCWDO3WSCQgFoRiPLkNTVkYHNSOHDnC0aNHee655zA3N+fTTz/V73wqGJ/YHqbmSUzLJvhgBOF3U2nTuC6vjPLA1aHydkQWhJoqNzeXO3fuIJVKadasmX5DaEMYHNRcXFwK7GkTFhZGvXr1StdSodTE9jA1j1ar4/cL99l94jZSqYSXh7ehv3cDpCLFlSA809GjR1m2bBlOTk5otVrS09NZs2YN/fv3N6i+wUFt5cqVzJ8/n7S0NEaPHk12djafffZZmRsuGObpfbSe3B5GqH7ikrLYeDCC2w8y8HKvx8vD2+Bob1nVzRKEGiMgIICNGzfq52zcuHEDf39/QkNDDapvcFDz8vJi//793LlzB51OR4sWLTAzExkPjE1sD1MzqDVaDvxxl31nYrCykDNrrCc92ooExIJQWpaWljRq1Ej/vFGjRsbJ/RgUFMTIkSNp3bp16VoolFtt2h6mNk54uROfwcYD4cQmKejh6crUIa2wtza9BMS18bMVKl+TJk0YP348Q4YMQafT8fvvv+Pp6UlgYCAA/v7+JdY3OKip1WoWL16MSqViyJAhDB8+nBYtWpSv9YJBatP2MNVpwkt5f4RzVRr2nrzDr3/eo66tBfMneOHdyskILa0ZqtNnK9Rc7du3p3379vrn48aNK1V9g4PanDlzmDNnDunp6Zw+fZr//Oc/REZGsnfv3lK9oWCaKmPCS2mDVHl+hCPu5icgTkzLZoB3AyYObIm1ZamWfdYaYjKTUJGeNRJ7llL9K0xJSeHkyZOcOHGC2NhYBg4cWK43F0xHZUx4MTRIledHWJmjZuexWxz/6wEuda1YPLUTbZs6VFgfaiIxmUmoTgwOahMmTCAvL4+hQ4cyc+ZMkU1EKBVjTngpbZAq64/wX7eS2fxrJGlZuYzo3oTn+zXHwkzkaxSTmYTqxOCgtn79etzd3Y3ZFqGWM9aEl9IGqdL+CGco89j2exTnbiTQ0NmGueM70KKBfYW0vbaoTZOZhJrN4KAmAppQXsaa8FKWkYIhP8I6nY5z4Ql8/1sU2blqxvVtzqheTZHLRIqrp9WmyUxCzWZwUBs+fDg7d+7E3l78hSpUP6UdKTzrRzglI4fNv0ZyOfoRzd3smTHKg0bOIgGxIFR3Bge1Ll268NtvvzFhwgRjtkcQyqSiRgpanY4Tfz3gh6O30Gp1THmuJUO6NkYqFYuoBcFYwsLCaNOmDTY2NuU+l8FBrUOHDvz73//m6tWreHt764+Xdg2BIFRXCalKgg9GEHEvjbZNHfAb0QYXkYBYEIxu/vz5+m1mxowZw759+8p8LoOD2pUrV+jRowe5ubmcO3dOf1wENaGm02i1/PZnLD+dvI1cJuGVkR7083ITKa4EoZLIZDKSk5NxdXUlNTW1XOcyOKitXbu2XG8kCNVRbGIWGw+Gcyc+E++WTvgOb4ODnUVVN0sQTMrcuXMZM2YMmZmZALRt21b/mk6nQyKREB4ebtC5SrX4OiIigqioKIYPH86pU6d47rnnSlNdEKoNlVrLL3/E8Msfd7G2lPOP59vRzcNFjM4EoQpMmjSJSZMmAdC3b19OnTpV5nMZHNQCAwM5dOgQSUlJDB06lKCgIC5cuMCSJUvK/OaCUBWiH6Sz6UAEcckKerVzZcrgVtiZYAJiQaiOyntLy+AFN9u3b2fHjh2YmZlhaWlJSEgI+/fvL9ebV0daZRrKn9egFYtHa53cPA3bj0SxJuQiylw1b77oxcwx7QwOaOK7IQjG99Zbb5GSksLhw4f57bffSElJKVV9g4OanZ2d/nonQFxcnEF73AQFBTF+/Hhee+01kpOTn1n+X//6F19++aWhzapwT+YPFGqPGzEpvLfhHIf/vM/ATg1Z9XoPOrYsXUZ98d0QBOM7ceIEY8eOZe/evezZs4fnn3++VJcjDb78+M477+Dr60tGRgazZs3ixo0brF69usQ6YWFhhIaGsmvXLs6fP8/nn39OQEBAseUvXbrE1q1befXVVwu9lpGRQUZGRoFj5ubmuLi4GNqFEolM47WTMkfFlz/8xeFzd3F1sOLtlzrRpknpEhCL74YgVJ61a9cSHBysz2IVHR3N3LlzOXTokEH1DQ5qAwYMoEOHDly+fBmdToe3tzeOjo4l1jl9+jQ+Pj7IZDJ69uzJmjVrii2bnZ3N6tWrmTlzJhqNptDrwcHB+k3iHuvcuTPbtm3DwaF8C/acne1wmPs1j44Eo4w8h06dh0RujnWbHtQb4ofc1q5c56+OnJ2rb5/Umakk7vkEl/GLkNuWPQP+2WvxfP3jZdIyc5kwqCVTh3uUKQFxTf1uVOfPuLQSUpTY2BSelWppaYaz499XjMraZ0PPX946xlCbPmeAnJwcXF1d9c9dXFzIzc01uH6pZj9aWloyaNAgUlNTuXfvHg4ODiXOFlMoFHh6egIgkUhQKpXFlv3444955ZVXyMvLIy4urtDrfn5+jB8/vsAxc/P8eyGpqQrUam1puqLn7GxHUlImICdPK0enzs8fqFOryNPKSc2WQ3bmM89Tk/zd5+op5+RWVPfCiT+8tUybTWYo8vj+95ucD0+kkbMty1/tQV1LORlpxX//SlbzvhvV/TMurRyVFoWi8A9bTo5K38/y9NmQ81dEnYpW1j7L5dIyDQYWLlxIv379GDZsGP7+/uTk5NCjRw8WLlwIwKlTp/jXv/6FlZUVixcvpnPnzqV+j2nTpjF16lTGjh2LRCJh3759+Pr6Glzf4KD2+eefc+fOHT744APGjBmDjY0NXbt2LfESpK2tLdnZ2frnWVlZRZY7c+YM6enp+Pj4sHv37iLL2NvbGz3vpMg0XrXKe5lPp9Nx9noC3/9+k1yVhvH9mjOyZ1Pc6tcp94+M+G4Ipu7AgQOEhobSr18/1q9fz9ChQ5k2bRpLliwhLCyM1q1bs2LFCrZv3465uTmvvfYau3btKvX7vP7667Rr144TJ04A+be+evXqZXB9g4PaDz/8wNGjR9m/fz/jx49n3rx5z1yn5u3tzaFDh/Dx8SEmJgYHh6IvJR06dIj79+/j6+tLUlISeXl5uLi4MHnyZIM7UhFEpvGqVZ7NJh+l5xDyayRXbz/CvYE9r4xqS0On8ueRe0x8N4TaKD4+vtDtnqIGEElJSWzYsIGpU6cC+beWHi/nGjhwIGfOnEGlUtGuXTucnZ0BcHBw4MGDBzRo0KDU7erVq1epAtmTDA5qZmZm3Lt3jyNHjuDr60tubi4yWcn3J3r16kVgYCCrVq3i4sWL+Pr6snPnTiwtLRkzZoy+3Icffqh/vHv3buLi4koV0CrinpqpqZ59toOmi8tU09nZjjUtnUt83dTUpj6Le2rFK8/nPG3atEK3e/z9/Zk3b16BYytWrGDp0qX88ccfAEilUmxt83etsLe3JzExEYVCgZubm77O4+NlCWrlYXBQ+8c//sHkyZPp2rUr3bp1w8fHh5kzZ5ZYRyaTERISQmhoKCNHjqRLly7PfJ8XXnjB0CbpVcw9NdNRnfucffgLJNZ1C1zme3KU9KSHKUo2HQjnZmw6ns0c8BvhgXNdq0LlqnN/jaW29VncUytaee+pbd26tciR2pN27tyJu7s7Xbt21Qc1uVyuT1+lUCiAwrebFAoFOp2u1G0rL4OD2pQpU5gy5e/LQD/99BOWlpbPrGdubs6IESPK1jrB5BhymU+j1XL4/H32nLqDmUzKjFEe9O0gEhALQmk9ObIqzu+//05GRga+vr7ExcVhbm5OSkoKYWFhdOnShRs3btCoUSM8PT1ZvXo1Wm3+ACMyMpKGDRsauwuFlGr2Y1JSEteuXUOr1dKuXTvq169vrHYJQpHuJWSy8UAEdxMy6dzamenDWlPXViQgFgRj+eabb/SPv/zySxo2bEjLli1ZtmwZ/fv355dffmHHjh3Y2trSu3dv5s/P/8PUy8urwtYRl4bBQW337t2sX7+ejh07IpFIWL58OQsXLtQnoRQEY1KpNew7E8PBs/ewsZQzZ1x7urRxFqMzQahET95r++abb7hw4QJ+fn76dWVvv/02Z8+eJTMzs8oS3hsc1D777DN2796tv+n38OFDJk6cKIKaYHS3YtPZeDCc+EdKerevz5TBrbC1MqvqZgmCSWvYsGGRlxd79uxZBa35m8G5Hxs0aFDor+JnZRQRhPLIyVPz/W83WbvlInkqDQsndeR1H08R0AShFlu6dGmJiTqepcSR2tKlS/WPra2t8fHxoXfv3kgkEv744w9atWpV5jc2VVplGjm/f4XlkDlIretWdXOqret3Ugg+FEFyeg6DOzfihQEtsLIo1S1gQRBqoLi4OCIjI+nUqVOZ6pf4K9G9e3dyc3NRKpUcOnSI3Nxczpw5w7x58+jTp48+TZVguCczvZclBVRtp8hRsePILU5djae+ozXvTOtM68Yi+AuCqZgzZw4BAQHMnDmTjh076o8but6txKCmVCr54osv6NevH9OnT8fMzIy8vDyuXLnC6dOnWbRoUflab0JEpvdnuxiZyJbDN8lUqhjdqylj+zTDTF76BMSCINRcy5YtA/LzAT8mkUg4cuSIQfVLDGpff/01u3btonHjxoVeu3//PlOnTq30VFY1VXlSQNV26Vm5bPntJhcjk2jiYsuCiR1pWr/2ZMMQBMFwoaGh5apfYlCTSCTFrggvansYoXhS67pIzK3yR2syM9CokJhZmfR9NZ1Ox5lrD9l+JIpclZYJA1owvHsT5DKD5y8JglALaTQaEhMTcXV1JSMjg7p1Df+dLDGozZs3j4kTJ9K3b18aNmyIubk5ubm5xMbGcubMGRYvLluePlMlMr3/LTk9m5BDkVy7k0LLRnWYMdIDt3oVl4BYEISa6ejRo7z33nvk5uYSGhrK8OHDWbFiBaNGjTKofolBbdKkSXTr1o1jx45x//59cnNzsbKyomPHjixYsICmTZtWSCdMhcj0DlqdjqNhcew6Fg3AtKGtGdS5IVKxiFoQBCAgIIDNmzfj6+uLnZ0dP//8M9OmTauYoAbQvHlzmjdvXu6GCkL8IwUbD0ZwKzad9s0deXlEG5zqFE5ALAiC6dLpdAVyUtrY2JTqdpdY+CMYnVqj5dfz99h7KgYLMymvjW5L7/b1RYorQRAKmTJlCv/4xz/Iycnhu+++4+DBg6XKXCWCmmBUdx9msvFAOPcSs+jaxplpQ1tTRyQgFgShGG+88QYeHh6cPXuW5ORk5s+fz4ABAwyuL4KaYBQqtYa9p2I4dO4ettZmzB3fni5tKj9jtyAINc+AAQPo2rUrOp1OvxmpoURQEyrczftpbDwYQUKKkr4d3Jg8uCU2liJfoyAIz3b9+nXeeecdYmNjkUgkuLq68tFHHxXILlISEdSECpOdq+bH49GEhsXhVMeSf072pl1zkfRaEATDLV68mEWLFjFkyBAgfzH222+/zaFDhwyqL1a5ChXi2u1HvL/hHEfD4hjSpREfvtZdBDRBEEpNo9EUuIfWt29f8vLyDK4vRmpCuWRlq9h+JIoz1x7iVs+apdO70LJRnapuliAINciePXv0jzt27MhLL73E2LFjkUgk7N+/Hw8PD4PPJYKaUCY6nY6LkUlsORyJIkeNT++mjOktEhALglB6586d0z+WyWS0bNmSGzduAJR6nbQIauVgqnujpWXlsuXwTcJuJtHU1Y5Fkz1o4ioSEAuCUDZr167VP96zZw/nz58vNu/ws4igVg6mtjeaTqfj1NV4dhy5hUqjZeJAd4Z1b4xMKm7NCoJQMdavX8/cuXOxtrbWHytNogYR1MrAFPdGS0rLJvhQBDdiUmndqA6vjGpLfUfrZ1cUBEEohRdffJGIiAi8vb2RyUp/O0MEtTIwpb3RtFodR8Ji+fF4NBKJBN9hrRnQSSQgFgTBOHbv3k2XLl24cOFCgePjxo0zqL4IamVgKnujxSUr2HQwnOi4DDq0qMfLw9tQr45lVTdLEIRarEePHgwcOFCM1CpbddwbraImrqg1Wg6evcu+MzFYmMmY6eNJz3auIgGxUGE0QK5KW+i4hZkUMX/WkomK8AAAHORJREFUtF26dIm//vpL/1yn0yGRSDhy5IhB9UVQK6PquDdaRUxciXmYwX9/iSA2KYtuHi5MG9oaexvzCm6pYOpyVVoOn40pdHxYz2ZYm4mJR6bso48+Kld9EdRqgYqYuJKn0rD31B0Onb+HvY05/i90oHNrZ2M1WRAEoUhffvklEokEnU5HQkIC9+/fp0uXLmzZssWg+iKo1QLlnbgSeS+VTQcjSEjNpn9HNyYNaom1SEAsCEIV2Lx5c4Hnx48f5+TJkwbXF0GtFijrxJXsXDW7jkVz9FJ+AuK3pnjj2UzkaxQEofoYMGAAGzZsMLi80YNaUFAQBw8exNHRkXXr1uHk5FSojFqtZtmyZTx8+BCFQsHChQvp27evsZtWq5R24sqV6GSCD0WSlpnLsG6NGd+vBRbm4ha9IAhVa+nSpQWeP3z4ELVabXB9owa1sLAwQkND2bVrF+fPn+fzzz8nICCgULnjx4/j4eHB+vXriYyMZMmSJSKolZKhE1cylXmEbL3IsbBYGjjZMGdce9wbigTEgiBUD927dy/w3NbWln79+hlc36hB7fTp0/j4+CCTyejZsydr1qwpstzgwYP1j1NSUnBxKbxDckZGBhkZGQWOmZubF1lWKEyn0/FnRCJbf7uJMkfN2D7NGN2rGWZyMdNMEISq9WSW/qeXDikUCg4dOlQ9Fl8rFAo8PT2B/IYqlcoSy+fm5vLxxx+zcuXKQq8FBwcTGBhY4Fjnzp3Ztm0bDg425Wqns3PtTsb7KD2br3+8wrnrD2nZuC7zJ3nTvIFpjc5q+2dclOrc54QUJTY2FoWOW1qa4VxE+jVDy5e1z6VtT1nrGEN1/pwN9eOPPxZ4/jiwhYeHk5mZiY2NTfUIara2tmRnZ+ufZ2VllVj+vffe44UXXqBDhw6FXvPz82P8+PEFjpmb56+fSk1VoFYXXshpCGdnO5KSMstUt7rT6XScvBLPjtBbqDVaJg1qydBujajvWqfW9rkotfkzLk5173OOSotCkVv4eI6qyHYbUr48fS5te8pap6KVtc9yubTcg4GK9OSMx4yMDPbu3cuOHTtwc3Nj0aJFjB071uBzGTWoeXt7c+jQIXx8fIiJicHBwaHYsmvXrqVevXpMnz69yNft7e2xt7c3VlNrncRUJcGHIgm/m0qbxnV5ZZQHrg4iAbEgCNXT5cuX2b59O6GhoQwcOJCAgAA6depU6vMYNaj16tWLwMBAVq1axcWLF/H19WXnzp1YWloyZswYfbnTp08TEhJCx44dmTp1Kubm5gQHBxuzabWWVqvjtwv3+enEbaRSCS+PaEP/jg1EAmJBEKqtsWPHEhUVha2tLcOHD8fBwYGjR49y9OhRfZlFixYZdC6jBjWZTEZISAihoaGMHDmSLl26FFmuT58+hIeHG7MpJiE2KYuNByK4E5+Bl3t+AmJHe5GAWBCEssvKyuKtt94iLy+P9PR0Vq1aRd26dVmwYAGQH5CmTZsG5E/42LhxI/b29qxcuZL/b+/O46K4sgWO/xoaFEEjKCpoVMAtRpGYRMQlLhg3XMbgOPKUGNdJHHDyBk10nhl9QcckauKCT2U0RmPGyUvEGEM0aniauBuXqIhLxA1EUEGQZu+u9wfSI7J1Q3cDzfl+Pnw+dnHr1rl0dx2r6tYpT09Pg7YxePBgBg8ebJJ4zX6fmr29PUOHDjX3Zuq0Aq2O6KM3+e7IDRzqqZkxqjO+z0kBYiFE1e3cuZNRo0YxfPhwYmJiWL16Nbm5ucycOZNXXnmFyZMnM2DAALRaLevXr2f79u2kpKSwaNEiNmzYYNA2QkJCTBavVBSp5a4nZfDp93Ek3tPQs3Nzxg9qT6MGUoBYCFGxpKQktFptsWVPz18oOgoDePDgAa6uruzbt49+/foBhWfaTpw4gU6no3///jRo0IC2bdty7949tFptpR4fUxVWkdTq8pR+V9eG9PBuWan16pK6Nl6o2WOWKf2mU5X3ecKECSQmJhZbFhISQmhoaIm2qampbNq0iX/84x+cO3dOv7xRo0akpKTg4OCAm5ubfrmDgwOpqam4ulq2MLpVJLW6NqU/7mYam3dfIuVhNv193Bnbvx0N6hv+VtbGMVdFXRsv1Pwxy5R+06jqlP4vvvii1CO1p+Xn5xMWFkZYWBgtWrQgLy9P/zuNRoOiKDg5OZGSklJiuaVJOYlaJCungM17LrF02xkA5gS9wOtDOxmV0IQQooibmxutWrUq9vN0UtNqtYSFheHv74+/vz+2trY888wzJCUlARAbG0urVq3w8fHh+PHjQOHkkpSUFFxcLF8gXfaGtcTZq/fZ8sMl0jV5DO3RmtF9PahnJwWIhSiNPFnbdLZv386BAwe4d+8e0dHRuLu7M2XKFEJCQnjhhRe4cOEC77//Po6Ojjg5OTFv3jwSEhIIDAxErbZ8ipGkVsNlZOWxbf9Vjl9MpqWrIyGveePpLjehC1EeebK26YwbN45x48aVWO7h4UFsbCyhoaE4OhbOa1ixYgUHDx5ErVYbVYTYlCSp1VCKonD8YjL/3H+V7NwCftfHg+F+bVDbyhdSCFH9vLy88PLyKrbMxsaGAQMGVFNEhSSp1UCpGTl8/sNlfr32AE/3Rkwe1omWrk7VHZYQQtR4ktRqEJ2i8NPZO/zv//2GTqcwfmA7Br30LDY2chO1EEIYQpJaDZGcmsVnuy9x+fZDnmvjzKRhnWjW2KG6wxJCiFpFklo10+p07DuZwI6f41HbqnhjWCf6ertJiSshhKgESWrV6HZKJpu+j+PG3Uf4tGtK8JCOODcsWaFACCGEYSSpVYP8Ah3RR28QffQmDeqreXP087zcqZkcnQkhRBVJUrOwa4npbNp9iTv3Nfg935ygQR1wcrCr7rCEEMIqSFKzkNw8LTt+jmffyds0bliPt3/vjbdX0+oOSwghrIokNQu4eCOVz3Zf4n56DgNeaMnY/l441JM/vRBCmJrsWc0oKyefL2N+4+dzSTR3duDd/3iBjq2dqzssIYSwWpLUzOTMlXts2XuZDE0ew3xbM7qPB/ZSgFgIIcxKkpqJpWvy+Oe+K5y8lEIrVydmBXrj4SYFiIXpSAV6IcomSc1EFEXhWGwy/9x/hdx8LWNe8WSYb2spQCxMTirQC1E2SWom8CA9hy0/XOZ8/AO8WjZi8rDncG/qWN1hmZSiKKSl3SMvLwew/NNsqyIlxQadrnJPRq+JdAp4P1vyPXh4/zYZj291rOljLm0MCpCRloSqYWMcHKzr+yMsR5JaFegUhQNnEvnqwDUURSFoUHv8u7eyygLEmZnpqFQqmjdvhUpVu44G1GobCgpq7g7eWFqdQoYmr8TyRo722D7+7NX0MZc2BkVRaGAPGRn3ASSxiUqRpFZJd1Oz+Oz7OK4kpNO5rTOThnbC1YoLEGdnZ+Li0rzWJTRRe6hUKuzs7Wnc2JX09PuS1ESlSFIzklan44cTt/nm5+vYq22YPLwTfbpafwFinU6Lra18XIT52dnZo9UWVHcYopaSvZQRbiU/YtP3l7iZ/IjuHVyZOLgDjZ3qTgFia0/comaQz5moCklqBsgv0LLryA12H7uFo4MdM3/XhZc6NavusKpdWVPLq0qmpgtrJLdiWIYktQr8lpDOpt1xJD3IoneXFvzBv70UIH6srKnlVVXR1PTFixdSUFDAggWLSEq6Q2joH/n6611GbWPs2JFGr1NVy5YtIT7+GnZ2dqxcuVa/XAF0upKzGW1sVFT1mGXjxvW4ubkzfPhIAD7//DN69vSjffuO5a736NEjfv75gH69Ig8e3Gfr1s38+c9hVYys7pFbMSxDkloZcvIK2H4wnphTCbg0qsdfxnWji2eT6g5LPBYTs48pU2agVteej/CRI4eIioousVxnwGxGUwkOfsOgdo8yH/H997sYMnREseUuTZpKQhM1Wu3ZI1jQhesP2Lz7Mg8ycvDv3orX+nlKAeIa5sUXe/D555uYPHm6ftmTR19FRyjff7+LZs2aceHCeXr08OPEiaO8//4HACxZ8j43blynffuOzJ49F4B16yL49dczaLVaZs+eS4cOnTh9+heio3fi6OjEhQvn+fTTrWXGdePGdZYtW4JWq6VtW09mz57LgQM/smPH1zx8mEZIyAxeftmXSZOmlrr+/n3R7N8bjY2NDXm5OYSFvUvnzl1KxLB85UYANm9aS+yFc+i0Bcye81ee69SJX389w6pVy3FyagiAm5u7vv/FixcybNgIund/CYD4+Gt8/PGH5Ofn06JFC+bPf58dO75m//4fuHnzBn/60wwG+A9h8JDCIzZNxn0+WPLfRERE6vvcsGEdJ08eR6VSMXPmLLy9fQgJmYGvby+OHz9CWloqf//7Mtq0acvGjes5fvwoOp2O4SPHMmDgEOPeeCEqIMe8T9Dk5LMx+iIff/krdmob5k7ozoTBHSSh1UDDhgVw7txZ7t5NqrDtqFG/o2tXbzp1eo5XXx3K9evXABgyZDjr128iIeEWJ08e5+jRQ1y+HMfatRsJCXmbtWtX6/v46aeD9O3bv9yEBvDBB+G8+WYIa9duxNbWhu++24m//2AiIiJxcWlCRERkmQmtiK2tmsUfrGbWrDBWrlxeIoZ/bPgcgF9OHOG3q5f5aPlapkwPZf26wng//vhD5s9fyCefrKGgoPxZhB98EM7MmbNYv34THTt25tq13xg3Loi/LViMh2d7lnwUoU9opTlx4hhxcRdZt+5TFi5czKJFC1CUwlOpDx+mEhERyfDhI/npp/8D4Kuv/sVHH61gxcp12NnJaXxherK3fuzU5RS27r3Co6x8AvzaMKp3W+zUcvm2plKr1YwfP4GtWzeX+vvc3Bz9v93dW2FjY4u7e0uSk+/qd7pdungD0KlTZ27fvkV2dhYJCQmEhMxAUZRiCeHll315+WXfCuO6fv2avt9u3V7g3LlfjR5bh47PoVKp6NDxORITE0rEoH18/e3mzeskJSUy750QFEWHohROQkhKSqJ9+w4UFOj0sZTl5s3rdO7cBYCgoIlGx/rbb1fo2tUblUpFixZu2Nra8vDhQwACAkYB4OzsQlLSHQBmz55LePjf0Gq1DB72O6O3J0RFzH6kFhkZyZgxY5g6dSr3798vs90333zD6NGjCQ4OJj4+3txh6aVl5LBmx3nW7LjAM472vDfpJQL7eUlCqwUCAkbrj7oA1Go70tLSKCgo4OjRwxWuHxd3EYCrV6/g7t4ST08vund/kYiISP7+96X06zdA39bR0bAbgT08vLh48QIA5879Srt27Y0ZEgBXLl9EURSuXI7j2WdblxlDmzYeeHt3Z8lHEfz1vSW88kphvM2aNef69XgUReHSpYvlbqt167b6NqtWfcx33+0EoF69euTkZAHo/xNQmnbtOhAbex5FUUhOvotOp6Nx48YAODg0KNY2JyeHW7dusnz5Kt59dz6R61YY8ucQwihmPVI7ffo0MTExfP3115w4cYKVK1cSHh5eot3t27dZv34927dvJyUlhUWLFrFhw4ZibTIyMsjIyCi2zN7enmbNKj+1/ljsXf65/yo5eVoC+3kypIcUIDZGPTsbBvdsa5Z+DWFnZ8eECZPYtq3wdNyYMYGEh/+NFi1a4OnpVeH63367nf/5n5V4enrh6+uHSqXizJlThITMICsri/HjJxgd+9y577Fs2RJ0Oh1t2ngwYsRoo/sAmD/vz+TmZDFnzrwy27zUoxfnz59h3jshZGdnEfQ43v/8zzksWDAfJycntFpthfF+/PGHqFQqmjRpwltvhQLg4tKEdu07MXfOn2jYsBH/9bclpa7fo0dPzp07y5tvTsHGRsVf/7qwzPvM6tevT1ZWFn/842QKtAWMHBVoyJ9CCKOolPL+G1ZFq1evxtnZmYkTJ6IoCqNGjWLXrpLTqKOiorh69SrvvvsuAKNHjyYqKgpbW9tifUVERBRbr3v37mzbtq1SsWm1OoIX7qFVs4aEjvPh2eYNK9VPXREbexF39zbVHYZVyi/Q8ejx7Md9e6NJTk5iYvA0GjraY6cumeCfbP+kstprdUqZtwyUNbvS2G0Yq6L+79y5yfPPd9YvT07N4seTt0q093+5Nc1dGpRYbu72ltqGMJ5Zj9Q0Gg2dOxd+MFUqFVlZWWW2c3Nz0792cHAgNTUVV1dX/bJJkyYxZsyYYuvZ29sDkJamqVTx1g/+6Ecr98Y8eJDJvXuPjF6/tnJ1bWj0eHU6XY0ukFueml7cV6dT9NfJBg4aDvw7EZUW95Ptn15e1P7JMZdXAFkpI6kZso2qqKh/nU5X7DOak69Do8kt0T4nJ1/f7snPtSHtiy03sr2ltlGRynyXofDz4exsnbU1zZrUnJycyM7O1r/OzMwss11KSor+tUajKXEev1GjRjRqZNqHbTrUU1tlRX0hLM2cN5DXVVKBpHLMegHJx8eHY8eOAXDjxg2cnZ3LbHf8+HGgMPGlpKTg4uJiztBEJZjxTLWo5YpuIH/6p7REV5HCWZySCosqkDz9Y47SdNbErEdqfn5+REREsGjRIk6dOkVwcDBfffUV9evXZ+TIf9/74uHhgZOTE/PmzSMhIYHAwMBaVSmiLlCr7dFoMnB0bCQFZ4VZKIqCtiCfDM1D7O3rV3c4opYya+awtbVly5YtxMTEMGzYMF588cUy265YsYKDBw+iVqvp27evOcMSleDs7Epa2j0yMx9WdyhGs7Gp+U+BzskteZN0dqaa0s6OG9L+yTEb27+5YjKkfZ6DPY4NGuLk9EzpgQlRAbMfDtnb2zN06NAK29nY2DBgwIAK24nqYWurpmlTt4ob1kCVvZhexNzXNrLKLHT7bKmFbg1p/+SYje3fXDEZ1r6lFPcVVSLn+Oo4c++wa9rF7vIel1NWTFJdXYjaQ5LaE4zdAdeWHXZ58Zh7h21s/+Z+D8p7XI6pxlzTPhdCVFVkZCS7d+/GxcWFDz/8kKZNm1Z3SGWyiqRmW8UqIOrHN5Pm5Ws5ev5Oid/3faEl9exK7o6MbV+gQF5ByQoP9mpb1KVcdzC2vTHxFI1ZrSg0dLQvsY5ardK3qUpMxvZvrvegovGWF5O5x2Bs/4a2r+x7bM6YzN2+smOuzX+jsuIrT9E+MykpqUTVmadvnzK0MlRNYdaKIkIIIWquoKAgTp8+XWxZSEgIoaGh+teGVoaqKaziSE0IIYRxMjMzWbp0aYnlTxe5MLQyVE0hSU0IIeogJycnnJycDGpnSGWomkKmbgkhhCiToZWhagq5piaEEKJMWq2WCRMm0KVLF06dOsXYsWOZMMH4xzJZiiQ1IYQQ5crLyyMmJgZXV9dyK0PVBJLUhBBCWA25piaEEMJqSFITQghhNSSpCSGEsBqS1IQQQliNOpHUIiMjGTNmDFOnTuX+/ftltvvmm28YPXo0wcHBxMfHWzBC0zNkzAUFBbzzzju8/vrrBAYGcujQIQtHaTqGvsdFli1bxurVqy0QmfkYM+Zjx44xderUWv/0ckPGnJWVRUhICMHBwYwfP564uDgLR2l6+fn5TJ8+nePHj5fbztjvgVVSrNypU6eUP/zhD0pBQYFy5MgRZf78+aW2u3XrljJ06FBFo9Eo169fV6ZOnWrhSE3H0DHv379f2bhxo6IoinLp0iVl1KhRlgzTZAwdb5HTp08rPj4+yqpVqywUoekZM+aHDx8qAQEBSmJiogUjND1Dx7x161YlIiJCUZTC93r69OmWDNPk8vPzlWnTpikBAQHKsWPHymxn7PfAWln9kdrhw4cZMWIEtra29OzZk7Nnz5ba7uTJk/Tv358GDRrQtm1b7t27V6J6dW1h6Jj9/f2ZMmUKAKmpqTRr1sySYZqMoeMFyM7OZvHixUyfPt2CEZqeMWNesGABzZs3Z+/evSQlJVkwStMydMxNmjTh2rVrZGVlcebMGTw9PS0cqemFh4fTpUuXctsY85mwZlaf1DQaDW5uhU9sLq8Y55PtABwcHEhNTbVIjKZm6JiL5ObmsnTpUmbNmmWJ8EzOmPEuXbqUN954gxYtWlgqPLMwdMwnTpwgLi6OhQsX4ufnx4wZM0hPT7dkqCZj6Jh79+5Neno6W7ZsITY2luHDh1syTJNTq9UGfV6N/d5bK6svaGxoMU4nJydSUlL0rzUaTa29/mBsAdL33nuP1157ja5du5o7NLMwdLxHjhwhPT2dESNGEBUVZanwzMLQMZ8/f54RI0bw7LPPAuDp6cmFCxfo3bu3ReI0JUPHvGXLFoKCghg0aBA6nY7Ro0fX6EelmEptKzxsLlZ/pGZoMU4fHx/9RdjMzExSUlJwcXGxWJymZEwB0iVLltCkSRMmTpxoqfBMztDx7tmzh9u3bxMcHExkZCQ7duzgyy+/tGSoJmPomNu1a6ef9JSdnU1cXBytW7e2WJymZOiYMzMzuXjxIgAXLlxAo9FYLMbqVNsKD5uL1ZfJKq0Yp729PfXr12fkyJHF2s6aNQtHR0cSEhLo2rUr77zzTjVFXTWGjvnw4cNMmzaNbt26oVKpsLe3Z/PmzdUYeeUY8x4XiYqKIjExsdjDEGsTQ8esKAqLFy/m/PnzpKenM3bsWKZNm1aNkVeeoWO+c+cOc+bMITY2FhcXF+bNm8err75ajZGbxty5cxkzZgy+vr6sXbsWHx8f/Pz89L+vbYWHzcXqkxoYXoxTp9Nx8OBB1Go1ffv2tWCEplebCpCaQl0bL8iY68qYjSF/nzqS1IQQQtQNVn9NTQghRN0hSU0IIYTVkKQm6oSoqCi8vb3p1asXffr0Yd26dVXuMzg4uFjZoo0bN7Jx48Yq92uqfoSoi6z+PjUhivj7+/PJJ5+QmppKUFAQPXr0oHv37ibrf+rUqUa1X716damzL43tRwjxb3KkJuocFxcXBgwYwC+//FKtcURERFTr9oWwRpLURJ2kKAo2NjYEBwezZ88eZs6cyeuvv67//cGDBxk6dCi9evUqVs0/IiKCPn36MG3atBIVG1avXl2i8v+3336Lv78/ffr0YcOGDUBhqa6iih69e/cmICCgwn527tzJwIEDGThwoL4aSlRUFGFhYYSFheHr60toaGitrYIjhKlIUhN1zt27d4mJiaFnz54AfPLJJwQGBrJmzRqgsLhzeHg4n376KXv37mXPnj1cvHiRc+fOERUVRXR0NLNmzeLSpUvlbufatWssX76cL774gl27dvHZZ58RHx/PnDlzOHz4MFB4A3x0dLTB/Wzbto1Vq1Zx+fJlAPbu3UtAQAAxMTGcPn3aKh6zIkRVyDU1UWf8+OOP9O7dGwcHByZPnqyveh4YGIi/v7++3dmzZ0lOTub3v/89UHhD69WrV0lLS6Nfv34888wzeHt706FDh3K3d+TIEfr3768vRlvZ59UV9VNUrHbQoEEcOnQIZ2dnunTpwsCBAwHw8PDg0aNHldqGENZCkpqoM4omijytW7duxV4rioKvr6/+dGFWVhY2Njb861//QqVS6dvZ2Bh3ouPQoUO4u7tX6lEoT25XpVLpXz9Zx/HJNkLUVXL6UYin+Pj4EBcXR3x8PHl5eUyZMoVDhw7RtWtXfvrpJx49ekRsbKz+FGBZ/Pz8OHjwIMnJyWRmZhIeHl6sinrjxo1JTEwkPz+/3COsXr16ceDAAZKTk0lJSWH//v36a3KSyIQoTo7UhHhKkyZNWLRoEW+99RYajYYRI0YwaNAgAAICAhgyZAgeHh54eXmV20+7du14++23CQoKQqfT8cYbb/D888/rfz9nzhyCgoLIzc1lzZo1vPTSS6X24+XlxV/+8heCgoIACA0NpWPHjsTGxppoxEJYD6n9KIQQwmrI6UchhBBWQ5KaEEIIqyFJTQghhNWQpCaEEMJqSFITQghhNSSpCSGEsBqS1IQQQlgNSWpCCCGsxv8DX1Fkkq3nNaYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_brier(df['pp'], df['p'], 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.8030515016779042\n",
      "MAE: 0.29595436307869194\n",
      "RMSE: 0.3847271156262529\n",
      "LL: 0.4672353738596809\n",
      "r2_score: 0.06414294052795344\n"
     ]
    }
   ],
   "source": [
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.8029878575651462\n",
      "MAE: 0.28679109272830905\n",
      "RMSE: 0.3975038796966017\n",
      "LL: 0.5004298481906383\n",
      "r2_score: 0.0011948530719348982\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012280955.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.8029878575651462\n",
      "MAE: 0.2662037520339905\n",
      "RMSE: 0.3980020351051101\n",
      "LL: 0.5048741130633347\n",
      "r2_score: -0.0013101386267642123\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012281834.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.8029878575651462\n",
      "MAE: 0.27736597910940985\n",
      "RMSE: 0.3973913708432532\n",
      "LL: 0.5005818324854053\n",
      "r2_score: 0.0017601734343335451\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012290427.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.3089827693196836\n",
      "RMSE: 0.42406086955285704\n",
      "LL: 0.5613585900973909\n",
      "r2_score: 0.011750084077133849\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012291431.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.31346886850640676\n",
      "RMSE: 0.41789941374651424\n",
      "LL: 0.5384847782112461\n",
      "r2_score: 0.04025930374802267\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012291451.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.3130220832121951\n",
      "RMSE: 0.417728653510952\n",
      "LL: 0.5381085248053739\n",
      "r2_score: 0.041043473617049075\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012291508.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.314371240342692\n",
      "RMSE: 0.4178845898446703\n",
      "LL: 0.5382825319339953\n",
      "r2_score: 0.04032739118281692\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012291522.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.3138161271079119\n",
      "RMSE: 0.41768429400449947\n",
      "LL: 0.5378803953081753\n",
      "r2_score: 0.04124713012572667\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012291539.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.31741134225212114\n",
      "RMSE: 0.4234376358187053\n",
      "LL: 0.559111189632609\n",
      "r2_score: 0.01465277156277267\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012291647.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.8029878575651462\n",
      "MAE: 0.28432030684354154\n",
      "RMSE: 0.39853229399433554\n",
      "LL: 0.503709566830665\n",
      "r2_score: -0.003980010890099583\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012291951.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.31163383190846106\n",
      "RMSE: 0.4274304051298627\n",
      "LL: 0.5604885124061764\n",
      "r2_score: -0.004017335888809814\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012300104.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.8029878575651462\n",
      "MAE: 0.28113648790726725\n",
      "RMSE: 0.3972574026186347\n",
      "LL: 0.5003170717411138\n",
      "r2_score: 0.0024331114256548325\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012302029.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.3146885818598915\n",
      "RMSE: 0.4176549702578504\n",
      "LL: 0.5378728068806113\n",
      "r2_score: 0.04138174490426494\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012311811.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.8029878575651462\n",
      "MAE: 0.27639333011406786\n",
      "RMSE: 0.39693481112844337\n",
      "LL: 0.4999057657490868\n",
      "r2_score: 0.0040525950454458615\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012311956.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.31092235609000424\n",
      "RMSE: 0.42613367270590186\n",
      "LL: 0.557710793218263\n",
      "r2_score: 0.0020653709637764583\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202012312253.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.30864831901534584\n",
      "RMSE: 0.4170525910677065\n",
      "LL: 0.5369913837412281\n",
      "r2_score: 0.04414496001305335\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202101011344.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.8030515016779042\n",
      "MAE: 0.29595436307869194\n",
      "RMSE: 0.3847271156262529\n",
      "LL: 0.4672353738596809\n",
      "r2_score: 0.06414294052795344\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('lr_full.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.8029878575651462\n",
      "MAE: 0.27147911375122596\n",
      "RMSE: 0.3960090193328171\n",
      "LL: 0.49680259118352776\n",
      "r2_score: 0.008692977544570657\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202101011439.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean recall 0.7608337904552934\n",
      "MAE: 0.3110128011010583\n",
      "RMSE: 0.4175391338747475\n",
      "LL: 0.5377447487000322\n",
      "r2_score: 0.04191341568762763\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('momo_sm_202101020212.csv')\n",
    "print('mean recall', np.mean(df['p']))\n",
    "print('MAE:', mean_absolute_error(df['p'], df['pp']))\n",
    "print('RMSE:', np.sqrt(mean_squared_error(df['p'], df['pp'])))\n",
    "print('LL:', log_loss(df['p'], df['pp']))\n",
    "print('r2_score:', r2_score(df['p'], df['pp']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('revlog_202012160921_predict.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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