{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c7695254",
   "metadata": {},
   "source": [
    "启用spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e02bcd7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark import SparkConf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8273d047",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
      "26/06/04 05:34:03 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
      "26/06/04 05:34:11 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - in-memory</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://master:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v3.5.8</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>yarn</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>TravelDemand</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x7f67f4ddac90>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf = SparkConf().setAppName('TravelDemand').setMaster('yarn')\n",
    "spark = SparkSession.builder.config(conf=conf).getOrCreate()\n",
    "spark"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9e42f9e",
   "metadata": {},
   "source": [
    "数据准备\n",
    "从原始游记 CSV 抽取建模字段，存为 CSV 再上传 HDFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1dbd80e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "os.makedirs('data', exist_ok=True)\n",
    "raw = pd.read_csv('Travel_tips.csv')\n",
    "sub = pd.DataFrame()\n",
    "sub['days_raw'] = raw['出行天数'].fillna('').astype(str)\n",
    "sub['cost_raw'] = raw['人均费用'].fillna('').astype(str)\n",
    "sub['companion'] = raw['人物'].fillna('').astype(str).str.strip()\n",
    "sub['year'] = raw['年份']\n",
    "sub['text_len'] = raw['正文'].fillna('').astype(str).str.len()\n",
    "sub['aspect_cnt'] = raw['aspect'].fillna('').astype(str).apply(lambda s: 0 if s == '' else len(s.split(',')))\n",
    "sub['senti'] = raw['情感分数']\n",
    "sub.to_csv('data/travel.csv', index=False, encoding='utf-8')\n",
    "len(sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aaeb2fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "!hdfs dfs -mkdir -p /user/mqmrx/data\n",
    "!hdfs dfs -put -f data/travel.csv /user/mqmrx/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1a83f92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataFrame[days_raw: string, cost_raw: string, companion: string, year: string, text_len: string, aspect_cnt: string, senti: string]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = '/user/mqmrx/data/travel.csv'\n",
    "df = spark.read.csv(file, header=True, encoding='utf-8')\n",
    "df.createOrReplaceTempView('travelTable')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6de9addf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "data": {
      "text/plain": [
       "294"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24377e81",
   "metadata": {},
   "source": [
    "数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f472c61c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[days: int, cost: double, companion_code: int, year: int, text_len: int, aspect_cnt: int, label: int]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = '''\n",
    "select\n",
    "    coalesce(cast(regexp_extract(days_raw, '([0-9]+)', 1) as int), 2) as days,\n",
    "    coalesce(cast(regexp_extract(cost_raw, '([0-9]+)', 1) as double), 300.0) as cost,\n",
    "    case companion when '和朋友' then 0 when '家庭出游' then 1 when '一个人' then 2 when '情侣/夫妻' then 3 when '和同学' then 4 when '带孩子' then 5 else 6 end as companion_code,\n",
    "    coalesce(cast(year as int), 2018) as year,\n",
    "    coalesce(cast(text_len as int), 0) as text_len,\n",
    "    coalesce(cast(aspect_cnt as int), 0) as aspect_cnt,\n",
    "    case when cast(senti as int) > 0 then 1 else 0 end as label\n",
    "from travelTable where senti is not null and senti <> '' '''\n",
    "df1 = spark.sql(sql)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea3ca056",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    }
   ],
   "source": [
    "path = '/user/mqmrx/data/TravelHDFS'\n",
    "df1.write.parquet(path, mode='overwrite')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8041906",
   "metadata": {},
   "source": [
    "读入HDFS已清洗数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ee18c38c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[days: int, cost: double, companion_code: int, year: int, text_len: int, aspect_cnt: int, label: int]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = spark.read.parquet(path)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3c2944c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Row(days=2, cost=300.0, companion_code=6, year=2018, text_len=266, aspect_cnt=3, label=1),\n",
       " Row(days=2, cost=300.0, companion_code=6, year=2018, text_len=1335, aspect_cnt=3, label=1),\n",
       " Row(days=3, cost=400.0, companion_code=2, year=2018, text_len=3134, aspect_cnt=3, label=0),\n",
       " Row(days=1, cost=50.0, companion_code=0, year=2018, text_len=839, aspect_cnt=1, label=1),\n",
       " Row(days=2, cost=800.0, companion_code=2, year=2018, text_len=5544, aspect_cnt=5, label=1),\n",
       " Row(days=1, cost=88.0, companion_code=0, year=2018, text_len=74, aspect_cnt=0, label=0),\n",
       " Row(days=365, cost=138000.0, companion_code=0, year=2019, text_len=4328, aspect_cnt=2, label=1),\n",
       " Row(days=1, cost=300.0, companion_code=1, year=2019, text_len=1930, aspect_cnt=5, label=1),\n",
       " Row(days=18, cost=7000.0, companion_code=1, year=2019, text_len=3631, aspect_cnt=3, label=1),\n",
       " Row(days=31, cost=8888.0, companion_code=0, year=2019, text_len=1293, aspect_cnt=0, label=0)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ee207f",
   "metadata": {},
   "source": [
    "数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78d25aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "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>label</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  cnt\n",
       "0      0   89\n",
       "1      1  205"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.createOrReplaceTempView('travelTable')\n",
    "sql = 'select label, count(*) as cnt from travelTable group by label order by label'\n",
    "df3 = spark.sql(sql).toPandas()\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2671fbcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Micro Hei', 'Microsoft YaHei', 'Noto Sans CJK SC', 'sans-serif']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "os.makedirs('fig', exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20f21fb1",
   "metadata": {},
   "source": [
    "正向/非正向 占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4737929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4), dpi=200)\n",
    "labels = df3['label'].apply(lambda x: '非正向' if x == 0 else '正向')\n",
    "plt.pie(df3['cnt'], labels=labels, autopct='%2.2f%%')\n",
    "plt.savefig('fig/travel_label.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ff6fa0",
   "metadata": {},
   "source": [
    "出行天数与人均费用分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42fae607",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wQJ06dZJhGPr222/l5eWlKVOm2B1n6+GelFIdpWrVqoqKipKUcs3NmjUz15v6tpifn5/dN1wAAAAAAPbt378/wz+kHz9+XFu2bDG/rlu3rry8vDRr1iwzXDFy5Ei98847Dp2nT58+5hYd9u6xZ8+erSFDhtid69y5c6pSpUqGbadPn7bZdi+63+/ZN23aZG5J9OSTT2b4YtHQoUPNLWTs+eOPP9SxY0eHzvvzzz/r559/ttsns5eILBaLWrdurblz56ar6vrNN99ISgmUZPYCT1alDYPcSZXbtC8zjR8/3ioIIqWEjebMmaNdu3YpPDxcS5cu1ZgxY/Twww9b9fv222/NEE3Dhg0zrCDTqVMnDRw4UF999ZWuX7+uiRMn6qOPPrLqEx0dra+//lpSSmhn0aJFVkEQKaVCzMKFCxUYGCjDMPTpp5+qV69ecnW1/jNlq1atVLNmTf3555/asGGD9u/fr8DAwKx9gwAApvzz1xMAAHDX9e7dW0WLFlWxYsX0448/6qWXXjLbPvjgA9WqVUt9+vTJdC/auy0pKck8zqkwyLhx42QYhtW/RYsWWfUZOHBguj6GYejPP//UtWvXtGvXLkkpN8rHjh1L98/d3V2S1K5dOx07dsxmmVQpJfDy8ssvm/vU+vr6au7cuVm+rg4dOmjUqFHm11OnTtUbb7yR5XlSvf322+ZDpdatW5sPlaSUN6KaN2+u5s2ba9asWdk+BwAAAADcq65fvy4pZYuWOzV48GDzHqt58+bm57Nnz7b6/NChQ4qPjzf/QOzq6mp1f5+ZtH84t1cZJPWtf3DPLklfffWVeTxgwIA7nu9ua9u2rZ5++ul0W9t06NBBpUuX1siRI3P8nBcvXpSU8kKWrW1bMnPhwgWzmskDDzxg83e9aNGi5vZFycnJGVavTVv9dtCgQTbPmTYEdvuzMimlUkl0dLSklC2jbG35U7duXTVq1EiS9M8//5jP0W7Xv39/83jy5Mk21wUAyByVQQAAuI9VqVJFCxcuVNWqVa0qgBw+fFgTJ05UUlKSQkJCVKRIkTxcZXqJiYnm8e1vEOSUHTt2qE+fPuk+NwxDS5Ys0eHDhzV27Fjz81WrVikuLk6S1Llz53Rv2Jw/f17x8fGSpIcffjjTN3AGDhxo3qgXKVJEq1evzrAMpyM++ugjHT582Cyj+tVXXykxMVFfffVVlvZE/uWXXzRv3jxJKd/3zz//PFvrAQAAAABnlJycbIZAIiMjFRMTo8KFC9+Vc//3v//VmTNnJEnPP/+8uUXMzZs39ffff+uxxx6zOTZtGMTePWJqGKR169bptumoX7++fvvtN1WtWlV//vmnVVvTpk31008/qVKlSmYVjYxk9f7+1KlTZrVTW1KvLSQkJNO+9rYzud39fs9+4cIFbdy4UVLK9dvalmXfvn1W1V2PHz+us2fPKjAw0HzWFBERoe3bt+v69et65ZVXbJ7zmWee0e7du/XII4/o999/v+Nr6NixY4bVSPr06aMXX3xRBQsWvONz3C61MkjJkiWz/XLT1q1bzeNmzZrJw8PDZt/27dvrww8/lJQS2BgzZozZlpCQoJ07d0pKCYG1atXK5jz+/v6qVauWQkJCFBYWpiNHjlgFPtKuqXXr1nbX3759e/3000/mmho3bpyuT7du3TRkyBAlJiZq5cqVmjp1qjw9Pe3OCwDIGGEQAADymdDQUG3YsMFme+obHjExMXb7HTt2zKHz3X7DHh8fr169eikhIUGSNHHiRFksFv3555+Z7t/qqNGjR9/R+NTQhSSz2kZO2rJli9q3b6+4uDjVrVtXFovFLH26e/duvfTSSzIMQxUqVDD3N019COTu7p7hjW9YWJh5bK/MaHx8vPr27auFCxdKSnkQt3TpUtWtWzfb1+Pi4qKlS5eqTZs2ZinhadOm6ejRo1q2bJm8vb0znSMuLk6vvvqq+SDtrbfeypdbCAEAAABAXomMjLT6w/fu3bvVtGnTbM83b948q0qdqVslDBo0SD179jQ/d3V11QcffCBJKliwoN5//31J0saNG/XKK6/o0qVL2rRpkxo2bJjheRwNg4SGhkqSKlWqZBXciIqKMgMgt7dJ1tVGcvKFjuPHj1tV1bDnwIEDOnDgwB2fk3v2FGvXrjVf1GnatKnNl4huf2bzwQcfaMWKFapSpYr+/vtvWSwWTZo0SZMmTZLFYtHjjz+uJ598MsO5UrdVuXTpUrqgxrVr1/TAAw84tPbg4GDzdyTV0aNHJUnz58+32oKpcOHCZnAh1ZYtW1S8ePFMzxMQEJDu+U9qGOROtohJG4SpU6eO3b41a9aUu7u74uPj9fvvvyspKcn8HT969Khu3rwpKeX3tlixYnbnqlu3rkJCQiSlbGGVNgySlTWl/V3Zv39/hn18fHz0r3/9Szt37lRMTIw2b96sTp062Z0XAJAxwiAAAOQz06ZN07Rp0zLtFxERYTe1n13Dhg3Tvn37JElBQUHmTfjBgwcz3Ds0O24Pg2zevFkXLlxweHzqTbokHTp0KMMSlfY8+eSTqlixYoZt//3vf/X2228rMTFRpUuX1ooVK6xKbj755JN69913NXbsWA0dOlTPPvusChUqpP/973+SUh4UFCpUKN28ad+MslUVJCoqSh07drR60DB58mS1adMmS9eXEQ8PD/3www9q0aKFfvnlF0kpDzACAwN14MCBTB+avPPOO1YP9/7zn//c8ZoAAAAAwJncHjbYsmXLHYVBbG214Ovrq8cff9z8+u2339atW7ckSSNHjlSlSpUkpdwHRkREKCEhQW3atNH27dsVEBCQbr60W7HaCmtcuHBBkZGRklIqV6TdIiStdevW2dxq5sSJE3a3oUnVuHFjbd++PdN+dxv37P9n8+bN5nGLFi0cGnP69GmtWrVKUsqzp9SfhVGjRumbb75RTEyMxo4dq3Xr1mU4PjVAERUVZRVqkKSGDRsqKSlJI0aMsApKZeTy5cv67bffMmw7f/68zp8/b36dGnJJ+zvSo0ePzC5VkjR37lwFBQWZXxuGocuXL1tdS3akfdnIz8/Pbl93d3eVLVtWYWFhio+PV1hYmPm/D1mZ5/Y+aZ/LZXUue/Ok1aJFC7NyyaZNmwiDAEA2EQYBAACmxYsXa+rUqZJSAgu2Hu7ktI8//tjc7zSrZs2aleW9bufOnZsuDHLkyBENGjTILG1ZvHhxbdy4McOb2Pfee0+LFy/WuXPntHPnToWHh5tvxFy9elUbNmxQu3btrMakvj0hpWwTk5HJkydbPVT6/PPP1atXrxzZa1qS3NzctG7dOnXu3Nm8zkceeSTTh0rbtm3Tf//7X/PrVq1aae3aten6xcTEmMd///23VqxYYXfeggUL5shDMwAAAADID37++Werr7/77jt98sknDgUg7sTYsWN1/PhxnTlzRu+88475eZMmTTRt2jT169dP165dU4cOHfTHH3+kqwDgSBgkdYuYu6F06dIO9WvatKlVFYeMuLq6KikpST179jS3ULHFz89Pp06dstnOPfv/SX2JSJKeeOIJu/OkGjVqlPmz9tprr+m1115L12f9+vUZ/r6EhoaaPxeGYejixYsqU6aMpJQAR2oQxpGtfho0aKBvvvnG/HrdunVmSKVVq1ZWoQM3NzdJsqr4c/sWL8nJyWbFDy8vL7Mayu0vCl29etV8dnTo0CE9+uijOnPmjKKjo+Xm5qZSpUopMDBQnTp1UteuXc1z3+7cuXPmsSO/K2XKlDHDGqdOnTLDINmZJ1Xa35O4uDgz5CJJpUqVsjtP2iDMuXPn0gV7UqX9uUr78wYAyBrCIAAA5AOVK1e2WRrxdq+++qoOHDigcuXKmTerGZkzZ46mT5/u8BrWrVunXr16SUq52V2yZInVfpwvvfSSVYUMe9566y19+eWXklJKRWb05lF+c/HiRTOQ4uvrq/Xr16tWrVoZ9nV3d9fs2bNVtmxZValSxbyRTrVw4cJ0YZA9e/ZISqkccnv/VCNHjtTq1at18OBBTZ06Va+99ppGjx6tjz/++E4vT5L09NNP63//+5/Wr1+v/v37a9GiRZo4caLdMZcvX1bPnj2tygZPmTJFU6ZMsTvuhx9+yHRbodKlS1u9cQMAAAAA97LUipGpTp48qZ07d+qpp57K1fO6u7tr+fLlunjxYro/IPft21cHDhzQ9OnTderUKQ0aNEgLFiyw6pP2D922tmJ9/PHHzW1iUm3cuFHDhw9XQkKCvLy8tGLFCpUtWzbd2Jdffln79+/XQw89pI0bN9q8jhYtWujMmTMOVSnIC9yzp7h+/br++ecfSSnb5NSsWdPuPJK0a9cuffvtt5n2syftliunT582wwlpf6ZatmyZ6TxVqlRRlSpVzK/Tfq98fHzUt2/fdGNSt1KWUoJRPj4+5tfnz5831/L999+rSZMmGZ43NTAipVT7jYiIML9OSkrS6dOndfr0aa1cuVIffPCBFi1aZG4NlVbaraNsbc+TVto+acfm1DypW81IKf/7YSvEksrDw0Ourq5KTEyUYRi6efNmhlvUpH2WePjwYZuhEQCAfYRBAADIBwoWLGhV4tWe1ICGh4eH3TFVq1Y13whKm97PyI4dO9SlSxfz5vbpp592eD05Yfny5YqLi3O4/8iRI7V48WJJUo0aNbRhw4YsnS+j/XafeuopffnllwoODtaCBQvMNxX27t2ryZMnq0KFClbjnn76aUkpD1BS37BIvZldvXq1Lly4YL5ZcfPmTbNccEBAgNUbJGl5eHho2bJl2rdvn1588cUsXZMjUt+ucXNz0+zZs/XKK69YPQC5XVJSkrp166YzZ87k+FoAAAAAwJkcPnzY3N4jMDBQv//+uxITE/XNN9/kehhESvmjvK17//Hjxys4OFhnzpzRqlWr9M8//8jf399sj4+Pl5Ryz2grDOLp6WluWxMZGan//Oc/+vrrr5WcnCxvb2+tX79e9erVy3Bs4cKFJaXci96+9c3p06dVsGBBRUREKDw8XJIyDRekPruwdW+dW7hnTxEWFmaGT8qWLauCBQva7X/16lWrl4vmzJmjf/3rX+bXMTExqlu3riRp+vTpGYYpKlasaBWeOHPmjFk5IjXU4u/vb7MSqy3btm3ToUOHzK8XLlyoRx55xKrCjiSzoodkOzCVmSpVqmjWrFmaMmWK/Pz8VKlSJZUsWVJSSjjk559/NrevOXr0qJo2bart27frscces5ondUsoKeVnMjNp//ukDXHk1Typc6XOcePGjQzDICVLllTRokV148YNxcXFKSIiwioQBABwDGEQAACclKenp1VlD1t++OEHvfDCC1Y3b7ldwvZ2qTe/jkp9A0WS/vrrL/3zzz9q1KjRHa/j9ddf1+DBg83rv3btmpo2baqbN29q8ODBGW6bk3qj7u7urokTJ2rAgAFKSEjQrFmz9O6770pKCdukPjho0KCB3TXc/oZK+/btM7zZHTx4sBITE1WxYkUNHz7c5nxRUVHmOm7/75pZKde333473Zttku2HM9evXzcf/vXr10/Dhg3LcN602/EAAAAAgDNIW5nzzTff1JIlS7R69WotXbpUn3zyicqXL59naytatKimT5+uWbNmafLkyenuMVPDFZn9kTs8PFyTJ0/W119/bW6NUr16da1Zs8ZmBczMjBgxQkuXLjW/dnV1VbNmzeyOSQ2vOPLMI6dxz269vUhmf5xPTEzUSy+9ZL5EI0nly5e3CgWlDRaUK1cuXWAoVdrtflOfC8XExJiVQdq2bWt3LRlJ/d6nvtyT+pmLi4tGjhxp9kv7zCyz8IstFotFffr0UZ8+fWz22bFjh1588UWdO3dO169fV58+ffTbb79ZBZ/S/pykrQhji60+eTVPVvr5+vrq77//lpTyc0cYBACyjjAIAAB5LPWtACllq5aMSqpm161bt8y3LV588cV0DyC++uorDR06VMnJybJYLCpatKiuX7+eY+fPDbGxsVZvbUgp+yPbKzXriPDwcEVHR1t99t1335nlLp955hkdOXLEqt3NzU0fffSRfHx89Ndff+nVV1/Vl19+qRMnTmjKlCl666235OHhoe+//94ck1pRxFGBgYHpyoL+888/5kOKgIAA9e/f3+b4Y8eOmQ83slJOc/78+ZowYYKklH1uBw8erPHjx0uy/XAm7T7J3t7eNh/gFC1a1OF1AAAAAEB+d/bsWc2dO1dSyr1Q+/btVaxYMa1evVqJiYmaMGGC/vvf/+bpGlu3bq3WrVtn2JYaBilUqFCGbd9//70WLVqkjRs3Wm2XIaVURKlcubJDazhx4oTdl0/c3Nz02WefZVrdNPXePaNqAnfb/XjPnva5kb1ATlJSkl566SWtXbvW6vPt27fr8uXL5tdpK8Xu3LnTKhwipVSKqVmzpsqXLy93d3fFx8fr6NGjkqSVK1eaQY3u3bvbXfftli9frt27dysgIECGYejQoUPy9PTU9evXNWrUKHl7e+uVV16R9H9boRQoUCDblUEc0bhxY61fv16PPfaYEhMTdfDgQW3fvl3PPPOM2Sftdi2pwSh70n5/U6v05OU89ua6Xdqfr/z+vBIA8ivCIAAA5LHz58+b4QZHb5oclZSUZM6d0VshW7duVXJyslxdXTVnzhzNnj1bO3bsyNE15LS1a9eaN+EWi0WGYWjTpk1as2aN2rVrl+15hw0bpmXLltls79ixY7rPKlSooLCwML3xxhsyDEMWi0WDBw/WkCFDdP78ec2dO1e9e/fW6tWrJaXc4GY1DJKR1GokqWuwJzY21jzObN/WVOvWrTP3yHVxcbnjfX0BAAAAwJm9+eab5n3q0KFDVbBgQbVs2VIBAQE6ePCgZs6cqeHDh+foyx/ZlZiYqPXr1+vq1avq0aOHpP+repD2j7qpLBaL3nnnHZ08edL8rEKFCjp16lSOrem1117ToEGD5OvrqwceeMBu3xs3bpjPTubNm6d58+Y5dI758+dr/vz5d7pUhzj7PXvaNdvbFiQhIcGsCPLII4+YL9h8/PHHNsekBlrSGjNmjGrWrCkXFxdVrVpVISEh5lyp/039/f1Vv359h6/h8uXLGjx4sCTpvffe04cffigppWLKr7/+qp9++kkDBw7UQw89pJYtW5pBhLtRjaZWrVrq0KGDVqxYIUnatGmTVRgk7RpuD85kJG2ftEGfnJqnSJEi5vO5+Ph4JSQk2P1ZjouLs9p2J6P/3UmVNqCWtjoLAMBxd3dTPQAAkK8sWLBAgYGBWrdunfkQKL9LfdtKkj766CPzBnPIkCF5+pZA6ttNvXv3lpeXl7m+RYsWmW+8tG/fPtvlRNPasGGDeZzZXsqpDyQlxx4s7dmzR127dlViYqIsFoumT5+uDh06ZHutAAAAAODMNm/erO+++05SSrWF119/XVLKPeL7778vKWUrixEjRuTVEpWcnKydO3dq8ODB8vX1Vbt27TR58mSzPfWP+xm9oe/q6qoxY8aYX7/88svatGmT+fXzzz+vS5cuZflfcHCwOUepUqVUvXr1TIMgknTmzJlsfQ/uJme/Z0+7TnsvNRUsWFBr1qxR//79NWPGjBw5d0BAgCTp0KFDOnz4sLlNTlaeaRmGob59++rChQuqUaOG1cs/bm5uWrFihR566CElJiaqf//+Sk5OVlRUlCQ59DOaE1Kr/ErW2/JI1gGjixcvZjrX+fPnzeO026zk1Dyurq7y9fV1eK6085QuXTrT4Eiq3KzIAgDOjMogAADcxx544AHt3bvXbpnW/GTXrl1medGiRYtq6NChCg8P17Rp0/TPP//olVde0ZIlS7I199KlS819ivfs2aMGDRooOTlZb731lj7//HOz3+HDh/Xoo48qMTHR6s2MVJ6ennr77bc1atQohYeHa+DAgWZbr169srW2tBITE60emjVq1Mhu/7RvTth7YyfVtWvXlJSUJFdXV82bN08vvvhi9hcLAAAAAE7s9OnTeuGFF8yvx40bZ7V1Sfv27fXUU0/pp59+0qJFi9SpU6cMq05m188//6ydO3dq1KhR6dpiYmK0detW/fjjj/rxxx+t/gArSVeuXDErXMbExEiyve3KSy+9pFWrVum1115TixYtdPbsWbPNw8NDPj4+WV57dv+ofuLECfO4Z8+eNrc6SfXuu+8qOTlZjz32mLp27Wq372effWa1lUp23A/37GkrOWRWUaJUqVKaPn269uzZY362efNmNWvWzGqO1CoVP/74o9q0aWNzvjp16mjhwoW6fv26BgwYIMMw5O7ubncrntuNHj1aq1evlsVi0YQJE9I9EytZsqR++OEHNWjQQOPGjZOLi4suXbokSSpRooTD57kTaat2JCcnW7U9/PDD5nFq5RVb4uPjFR4eLikloObv75+teW7vU7FixXRrSv3fhbCwMKtwSFbmuV3asJS9CiIAANsIgwAAcJ+7V4Ig8fHxeuONN8yvX3/9dRUuXFhjx47V999/r/Pnz2vp0qUKDAzUsGHDsn2ehIQE9evXT8nJyfL19dV//vMfq/ahQ4cqMTFR7u7uGj16dIZzvP766/rqq68UERFhvmFVq1YtNW/ePNvrSrV8+XJduHBBklS+fHlVqVLFbv+01VIy2v/5ds8++6x++OEHJSYm2n0AAwAAAAD3s1u3bqljx45mJcjGjRubW3ekNXPmTD366KOKi4tT7969Vb16dVWtWvWOzr1x40YtXLhQhw8fVvHixc0wyJEjR7R69Wpt3rxZu3btstrOI9VTTz2lwYMHq2PHjubzgOjoaElS8eLFMzyfi4uLVq1alaU1dunSRUeOHNEDDzygXbt2ZWmsPb///rt5/Oabb6pWrVp2+6fet9eqVUsjR4602/frr7++4zDI/XDPXqZMGfP49qoVue2JJ54wj1O3Oe7WrZvVmuxJTk42twgeNGiQWrRokWG/OnXq6JdffjErkaRe54MPPpjdpWdJavhESgnUpFWvXj3zOO2WRBn5888/zeot1apVswoclS9fXmXKlFFERIROnDiha9eu2Q1ppT1XnTp10q0ptUrLb7/9pgYNGmRrntulDZ45+t8YAGCNbWIAAMA94fXXXzdvGEuWLKm3335bkuTl5aWZM2ea/d5666072gf4vffe059//ikXFxctWLDA6m2MpUuXmuVwhw0bZvMNhsKFC+udd96x+mzs2LHZXlOqxMREffrpp+bXQUFBmY5J+2DJ0bcoWrZsSRAEAAAAAGy4deuWOnTooAMHDkhKuUdduHBhhi9bVK1aVR9++KEk6erVq3ruueey/Af0v/76Sx9//LH59fbt23X48GFJKQGO1OoM69ev18iRI7V161arIIi/v7/effddHTt2TDt27FCXLl1UoEABsz01DJK65WlO2LRpk/76669MK0dk1d69eyVJBQoUsKpskB/cL/fsabcXOXfunBISErI0vnnz5rJYLOa/tM9d2rZta9WWdrslKSV0kLa/q6truucv9qQGmx5//HF99tlndvumBkEMw9DJkyclWW+Pkpt++ukn8zgwMNCqrXHjxmZwaOvWrVZbqdxu9erV5nFG1W1btmwpKeUa169fb3Oef/75RyEhIZJSqvo89thjVu2tWrUyj9etW2dzHkfWlOrq1au6du2apJT/bnfrew8AzoYwCAAAyPfGjBljtb/snDlzrN5WaNu2rXnzbxiG+vTpo2nTpmX5PNu2bTO3hElOTtakSZPMh3shISHmW16PPPKI3nvvPZvzGIZhtUewJPPBwZ2YMGGCefPt7u6uPn36ZDom7VtNaR+Y5CXDMPJ6CQAAAACQLTExMWrTpo35ooCrq6u+++47lS9f3uaY4cOHq0OHDpJS7g2bNGmiU6dO2T3PtWvXFBQUpIceekg1a9a0qkxpsVjUsmVLrVmzRidOnFDRokUlpVRIcHFJeeRfvXp1jRw5Uvv379fJkyc1duxYVa5cOd15kpKSzD+4li5d2vFvhB3btm0zQw41a9bMkTklKS4uTtu3b5eUErJxZFuVu+l+uWcvWbKkWSEjISHBDCbdDa6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      "text/plain": [
       "<Figure size 2200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edf = df2.select('days', 'cost').toPandas()\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4), dpi=200)\n",
    "axes[0].hist(edf['days'].clip(upper=15), bins=15, color='#3b6fb6')\n",
    "axes[0].set_title('出行天数分布')\n",
    "axes[0].set_xlabel('天数')\n",
    "axes[1].hist(edf['cost'].clip(upper=5000), bins=30, color='#E1654A')\n",
    "axes[1].set_title('人均费用分布(截断5000)')\n",
    "axes[1].set_xlabel('人均费用')\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/travel_feature_dist.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78c03e1",
   "metadata": {},
   "source": [
    "各出行人群正向率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e16e9806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rate = spark.sql('select companion_code, avg(label) as rate, count(*) as cnt from travelTable group by companion_code order by companion_code').toPandas()\n",
    "names = {0: '和朋友', 1: '家庭出游', 2: '一个人', 3: '情侣/夫妻', 4: '和同学', 5: '带孩子', 6: '其他'}\n",
    "rate['人群'] = rate['companion_code'].map(names)\n",
    "plt.figure(figsize=(7, 4), dpi=200)\n",
    "plt.barh(rate['人群'], rate['rate'], color='#4C9F70')\n",
    "plt.xlabel('正向率')\n",
    "plt.title('各出行人群正向率')\n",
    "plt.savefig('fig/travel_companion_rate.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b64ed892",
   "metadata": {},
   "source": [
    "特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b9b4b7d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler\n",
    "feature_cols = ['days', 'cost', 'companion_code', 'year', 'text_len', 'aspect_cnt']\n",
    "assembler = VectorAssembler(inputCols=feature_cols, outputCol='features')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0faf2fe",
   "metadata": {},
   "source": [
    "拆分样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8177cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = df2.randomSplit([0.7, 0.3], seed=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0035b9c7",
   "metadata": {},
   "source": [
    "初步测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1afc62f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import LogisticRegression\n",
    "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
    "from pyspark.ml import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8dc6a529",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "26/06/04 05:36:07 WARN InstanceBuilder: Failed to load implementation from:dev.ludovic.netlib.blas.JNIBLAS\n",
      "                                                                                "
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(labelCol='label', maxIter=100, regParam=0.01)\n",
    "pipeline = Pipeline(stages=[assembler, lr])\n",
    "model = pipeline.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f7cf7be9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8222222222222222"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.transform(test)\n",
    "score = MulticlassClassificationEvaluator(labelCol='label', metricName='accuracy').evaluate(pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ac19ff",
   "metadata": {},
   "source": [
    "改进点：多模型对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b85aaa52",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import DecisionTreeClassifier, RandomForestClassifier\n",
    "\n",
    "scores = {}\n",
    "\n",
    "def sparkModel(algorithm, name):\n",
    "    pipeline = Pipeline(stages=[assembler, algorithm])\n",
    "    model = pipeline.fit(train)\n",
    "    pred = model.transform(test)\n",
    "    score = MulticlassClassificationEvaluator(labelCol='label', metricName='accuracy').evaluate(pred)\n",
    "    scores[name] = score\n",
    "    print('{}: {}'.format(name, score))\n",
    "    model.write().overwrite().save('/user/mqmrx/model/travel/' + name)\n",
    "    pred.select(['label', 'prediction']).write.parquet('/user/mqmrx/predict/travel/' + name, mode='overwrite')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6aa61744",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 0.8222222222222222\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(labelCol='label', maxIter=100, regParam=0.01)\n",
    "lr_model = sparkModel(lr, 'lr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "35c7410c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt: 0.8222222222222222\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(labelCol='label', maxDepth=5)\n",
    "dt_model = sparkModel(dt, 'dt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a45816ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf: 0.8444444444444444\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(labelCol='label', numTrees=60, maxDepth=5, seed=42)\n",
    "rf_model = sparkModel(rf, 'rf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff20cb9f",
   "metadata": {},
   "source": [
    "模型调优\n",
    "交叉验证 + 网格搜索调参，提升准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a2a720cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "26c76778",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(labelCol='label', seed=42)\n",
    "pipeline = Pipeline(stages=[assembler, rf])\n",
    "grid = ParamGridBuilder().addGrid(rf.numTrees, [40, 80]).addGrid(rf.maxDepth, [4, 6]).build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0649f10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CrossValidator(estimator=pipeline, estimatorParamMaps=grid, evaluator=MulticlassClassificationEvaluator(labelCol='label', metricName='accuracy'), numFolds=3, parallelism=2, seed=42)\n",
    "cv_model = cv.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6dcf65e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "调优后准确率: 0.8333333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    }
   ],
   "source": [
    "best = cv_model.bestModel\n",
    "pred = best.transform(test)\n",
    "score = MulticlassClassificationEvaluator(labelCol='label', metricName='accuracy').evaluate(pred)\n",
    "scores['tuned'] = score\n",
    "print('调优后准确率:', score)\n",
    "best.write().overwrite().save('/user/mqmrx/model/travel/best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b054d023",
   "metadata": {},
   "source": [
    "结果可视化\n",
    "各模型准确率对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4d3e0070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#3b6fb6', '#E1654A', '#4C9F70', '#E9A23B', '#9b59b6']\n",
    "plt.figure(figsize=(6, 4), dpi=200)\n",
    "plt.bar(list(scores.keys()), list(scores.values()), color=colors[:len(scores)])\n",
    "plt.ylim(0, 1)\n",
    "plt.ylabel('准确率')\n",
    "plt.title('各模型准确率对比')\n",
    "for i, v in enumerate(scores.values()):\n",
    "    plt.text(i, v + 0.01, '%.3f' % v, ha='center')\n",
    "plt.savefig('fig/travel_model_compare.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39ce251",
   "metadata": {},
   "source": [
    "特征重要性（随机森林）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f5fe7bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp = best.stages[-1].featureImportances.toArray()\n",
    "order = sorted(range(len(feature_cols)), key=lambda i: imp[i])\n",
    "plt.figure(figsize=(7, 5), dpi=200)\n",
    "plt.barh([feature_cols[i] for i in order], [imp[i] for i in order], color='#3b6fb6')\n",
    "plt.xlabel('重要性')\n",
    "plt.title('特征重要性(随机森林)')\n",
    "plt.savefig('fig/travel_importance.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbbe4ff",
   "metadata": {},
   "source": [
    "混淆矩阵（最优模型）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0df2d27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmp = best.transform(test).select('label', 'prediction').toPandas()\n",
    "cm = pd.crosstab(cmp['label'], cmp['prediction'])\n",
    "plt.figure(figsize=(4.5, 4), dpi=200)\n",
    "plt.imshow(cm.values, cmap='Blues')\n",
    "plt.colorbar()\n",
    "for i in range(cm.shape[0]):\n",
    "    for j in range(cm.shape[1]):\n",
    "        plt.text(j, i, int(cm.values[i, j]), ha='center', va='center')\n",
    "plt.xticks(range(cm.shape[1]), ['非正向', '正向'][:cm.shape[1]])\n",
    "plt.yticks(range(cm.shape[0]), ['非正向', '正向'][:cm.shape[0]])\n",
    "plt.xlabel('预测')\n",
    "plt.ylabel('真实')\n",
    "plt.title('混淆矩阵(最优模型)')\n",
    "plt.savefig('fig/travel_confusion.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}