dj_Benchmark_12trip.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Бенчмарк для задачи OneTwoTrip Contest\n",
    "\n",
    "https://boosters.pro/championship/onetwotrip_challenge/overview\n",
    "    \n",
    "    \n",
    "для студентов ВМК МГУ\n",
    "\n",
    "2019, Александр Дьяконов www.dyakonov.org/ag/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# подгружаем все нужные пакеты\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# для встроенных картинок\n",
    "%pylab inline\n",
    "# отключить предупреждения\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# прогресс-бар\n",
    "from tqdm import tqdm, tqdm_notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "размеры: (196056, 43) (455011, 37)\n"
     ]
    }
   ],
   "source": [
    "data_train = pd.read_csv('onetwotrip_challenge_train.csv')\n",
    "data_test = pd.read_csv('onetwotrip_challenge_test.csv')\n",
    "print ('размеры:', data_train.shape, data_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## посмотрим на данные\n",
    "\n",
    "обратите внимание, как выводятся дата-фреймы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "   orderid                                             userid  field0  \\\n",
       "0        0  10d654494cbe97bbb25d51ead2600679aff9e097924add...       0   \n",
       "1        1  4aafc0391f72bbcf60537aece62923baf9ce644b64ac36...     144   \n",
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       "\n",
       "     field1  field2  field3  field4  field5  field6  field7  ...  \\\n",
       "0 -0.626508      11      12       1       1       0       1  ...   \n",
       "1 -0.393794       5       7       2       0       0       2  ...   \n",
       "2 -0.548937       2       3       2       0       0       1  ...   \n",
       "3 -0.238651      10      11       1       1       3       2  ...   \n",
       "4 -0.704079       8      11       1       1       0       1  ...   \n",
       "\n",
       "   indicator_goal22  indicator_goal23  indicator_goal24  indicator_goal25  \\\n",
       "0                 1                 0                 1                 1   \n",
       "1                 1                 0                 1                 0   \n",
       "2                 1                 0                 1                 1   \n",
       "3                 1                 0                 1                 1   \n",
       "4                 1                 0                 0                 1   \n",
       "\n",
       "   goal21  goal22  goal23  goal24  goal25  goal1  \n",
       "0       0       1       0       0       0      0  \n",
       "1       0       0       0       0       0      0  \n",
       "2       0       0       0       0       0      0  \n",
       "3       0       0       0       0       0      0  \n",
       "4       0       0       0       0       0      0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
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       "   orderid                                             userid  field0  \\\n",
       "0        0  3a6a6af55e097d3f92705936a7ea3ca8aef651f5966832...       0   \n",
       "1        1  2df10f61960163da94a4294773ed9c865296e37c330304...      82   \n",
       "2        2  20dc3fec5b5eb42fbfe08119063c3a0010a73c7ec94abb...       0   \n",
       "3        3  ed75b3496977bac207eccb59dc91fe9a8d6a27777a6422...       6   \n",
       "4        4  a346d08351c5fd0bda82984ed7c8b12b6395829da5b857...     115   \n",
       "\n",
       "     field1  field2  field3  field4  field5  field6  field7  ...  field25  \\\n",
       "0 -0.548937      10      10       1       1       0       1  ...        1   \n",
       "1 -0.626508       3       4       3       0       0       1  ...        1   \n",
       "2 -0.548937       6       8       1       1       0       2  ...       35   \n",
       "3  0.304348       7       7       2       0      10       1  ...        1   \n",
       "4 -0.471365       3       3       2       0       0       1  ...        1   \n",
       "\n",
       "   field26  field27  field28  field29  indicator_goal21  indicator_goal22  \\\n",
       "0        1        1        0        4                 1                 1   \n",
       "1        1        1        0        1                 1                 1   \n",
       "2        1        1        0        2                 1                 0   \n",
       "3        3        1        0        3                 1                 1   \n",
       "4        1        1        0        1                 1                 1   \n",
       "\n",
       "   indicator_goal23  indicator_goal24  indicator_goal25  \n",
       "0                 0                 0                 1  \n",
       "1                 0                 1                 1  \n",
       "2                 0                 1                 1  \n",
       "3                 0                 1                 1  \n",
       "4                 0                 1                 1  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## получаем таблички для обучения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# удаляем ненужные признаки\n",
    "ids = data_test.pop('orderid') # сохраняем id для теста\n",
    "data_test.drop(['userid'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['field0', 'field1', 'field2', 'field3', 'field4', 'field5', 'field6',\n",
       "       'field7', 'field8', 'field9', 'field10', 'field11', 'field12',\n",
       "       'field13', 'field14', 'field15', 'field16', 'field17', 'field18',\n",
       "       'field19', 'field20', 'field21', 'field22', 'field23', 'field24',\n",
       "       'field25', 'field26', 'field27', 'field28', 'field29',\n",
       "       'indicator_goal21', 'indicator_goal22', 'indicator_goal23',\n",
       "       'indicator_goal24', 'indicator_goal25'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = data_test.columns # значимые колонки\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data_train.pop('goal1') # целевая переменная для первой задачи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.values # мне так спокойней - в numpy.array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data_train[cols] # оставить только нужные колонки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>field0</th>\n",
       "      <th>field1</th>\n",
       "      <th>field2</th>\n",
       "      <th>field3</th>\n",
       "      <th>field4</th>\n",
       "      <th>field5</th>\n",
       "      <th>field6</th>\n",
       "      <th>field7</th>\n",
       "      <th>field8</th>\n",
       "      <th>field9</th>\n",
       "      <th>...</th>\n",
       "      <th>field25</th>\n",
       "      <th>field26</th>\n",
       "      <th>field27</th>\n",
       "      <th>field28</th>\n",
       "      <th>field29</th>\n",
       "      <th>indicator_goal21</th>\n",
       "      <th>indicator_goal22</th>\n",
       "      <th>indicator_goal23</th>\n",
       "      <th>indicator_goal24</th>\n",
       "      <th>indicator_goal25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.626508</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>144</td>\n",
       "      <td>-0.393794</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>134</td>\n",
       "      <td>-0.548937</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.238651</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.704079</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   field0    field1  field2  field3  field4  field5  field6  field7  field8  \\\n",
       "0       0 -0.626508      11      12       1       1       0       1       1   \n",
       "1     144 -0.393794       5       7       2       0       0       2       1   \n",
       "2     134 -0.548937       2       3       2       0       0       1       1   \n",
       "3       0 -0.238651      10      11       1       1       3       2       1   \n",
       "4       0 -0.704079       8      11       1       1       0       1       1   \n",
       "\n",
       "   field9  ...  field25  field26  field27  field28  field29  indicator_goal21  \\\n",
       "0       0  ...        1        2        1        0        4                 1   \n",
       "1       0  ...       41        3        1        0        2                 1   \n",
       "2       0  ...        1       11        7        0        1                 1   \n",
       "3       0  ...       18        1        1        0        4                 1   \n",
       "4       0  ...        1        1        1        0        3                 1   \n",
       "\n",
       "   indicator_goal22  indicator_goal23  indicator_goal24  indicator_goal25  \n",
       "0                 1                 0                 1                 1  \n",
       "1                 1                 0                 1                 0  \n",
       "2                 1                 0                 1                 1  \n",
       "3                 1                 0                 1                 1  \n",
       "4                 1                 0                 0                 1  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# что получилось\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Эксперименты\n",
    "\n",
    "сначала делим выборку на обучение и тест\n",
    "\n",
    "не самое лучшее решение, но для быстроты экспериментов сгодится"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_train, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Случайный лес\n",
    "\n",
    "\n",
    "строим по одному дереву и вычисляем метрику качества (ROC AUC)\n",
    "\n",
    "обратите внимание на прогресс-бар"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2be4ed53aa84261896b78ba8c029131",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fce3be62f60>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "model = RandomForestClassifier(max_features=1, n_estimators=1, oob_score=False, warm_start=True, random_state=1)\n",
    "\n",
    "aucs = []\n",
    "for t in tqdm_notebook(list(range(1, 101))):\n",
    "    model.set_params(n_estimators=t)\n",
    "    model.fit(X_train, y_train)\n",
    "    a = model.predict_proba(X_test)[:, 1]\n",
    "    q = roc_auc_score(y_test, a)\n",
    "    aucs.append(q)\n",
    "    \n",
    "plt.plot(range(1, 101), aucs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08b2382018ab4201b3b2c5904899732f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4a4fa8a5f8>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# аналогичные эксперименты с max_features=2\n",
    "\n",
    "model = RandomForestClassifier(max_features=2, n_estimators=1, oob_score=False, warm_start=True, random_state=1)\n",
    "\n",
    "aucs = []\n",
    "for t in tqdm_notebook(list(range(1, 101))):\n",
    "    model.set_params(n_estimators=t)\n",
    "    model.fit(X_train, y_train)\n",
    "    a = model.predict_proba(X_test)[:, 1]\n",
    "    q = roc_auc_score(y_test, a)\n",
    "    aucs.append(q)\n",
    "    \n",
    "plt.plot(range(1, 101), aucs)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Козырь\n",
    "\n",
    "более хорошая модель из другой библиотеки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "model = lgb.LGBMClassifier(num_leaves=6, learning_rate=0.1, n_estimators=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\tvalid_0's auc: 0.68674\tvalid_1's auc: 0.665981\n",
      "[100]\tvalid_0's auc: 0.71142\tvalid_1's auc: 0.675922\n",
      "[150]\tvalid_0's auc: 0.727589\tvalid_1's auc: 0.676426\n",
      "[200]\tvalid_0's auc: 0.739755\tvalid_1's auc: 0.678309\n",
      "[250]\tvalid_0's auc: 0.748912\tvalid_1's auc: 0.678582\n",
      "[300]\tvalid_0's auc: 0.757664\tvalid_1's auc: 0.678602\n",
      "[350]\tvalid_0's auc: 0.764987\tvalid_1's auc: 0.677316\n",
      "[400]\tvalid_0's auc: 0.771289\tvalid_1's auc: 0.676125\n",
      "[450]\tvalid_0's auc: 0.77833\tvalid_1's auc: 0.675192\n",
      "[500]\tvalid_0's auc: 0.784456\tvalid_1's auc: 0.674098\n"
     ]
    }
   ],
   "source": [
    "param = {'num_leaves': 6, 'objective': 'binary', 'learning_rate': 0.1}\n",
    "param['metric'] = 'auc'\n",
    "\n",
    "q = lgb.train(param, train_set=lgb.Dataset(X_train, y_train), num_boost_round=500,\n",
    "              valid_sets=[lgb.Dataset(X_train, y_train), lgb.Dataset(X_test, y_test)],\n",
    "             verbose_eval=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "тут сразу качество лучше"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обучение и формирование ответа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "        learning_rate=0.1, max_depth=-1, min_child_samples=20,\n",
       "        min_child_weight=0.001, min_split_gain=0.0, n_estimators=300,\n",
       "        n_jobs=-1, num_leaves=6, objective=None, random_state=None,\n",
       "        reg_alpha=0.0, reg_lambda=0.0, silent=True, subsample=1.0,\n",
       "        subsample_for_bin=200000, subsample_freq=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = model.predict_proba(data_test)[:, 1] # вероятности за 1й класс"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(a,  columns=['proba'], index=ids.values).to_csv('dj1_01_.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}