dj_benchmark_GMSC_01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Бенчмарк\n",
    "\n",
    "задачи для студентов 2го курса ВМК МГУ\n",
    "\n",
    "https://www.kaggle.com/c/msu-iml-2018/\n",
    "\n",
    "2018, Александр Дьяконов https://dyakonov.org/ag/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%pylab inline\n",
    "plt.style.use('seaborn-dark')\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # отключение варнингов\n",
    "pd.set_option('display.max_columns', None) # pd.options.display.max_columns = None \n",
    "# pd.set_option('display.max_rows', None) # не прятать столбцы при выводе дата-фреймов\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "plt.rc('font', size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# загружаем данные\n",
    "\n",
    "не забудьте поменять каталоги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('./data_GMSC/train.csv')\n",
    "test = pd.read_csv('./data_GMSC/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(112500, 11) (37500, 10)\n"
     ]
    }
   ],
   "source": [
    "# размеры данных\n",
    "print(train.shape, test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# смотрим на данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>плохой_клиент</th>\n",
       "      <th>линии</th>\n",
       "      <th>возраст</th>\n",
       "      <th>поведение_30-59_дней</th>\n",
       "      <th>Debt_Ratio</th>\n",
       "      <th>доход</th>\n",
       "      <th>число_кредитов</th>\n",
       "      <th>поведение_90_дней</th>\n",
       "      <th>недвижимость</th>\n",
       "      <th>поведение_60-89_дней</th>\n",
       "      <th>семья</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5522</th>\n",
       "      <td>0</td>\n",
       "      <td>0.111673</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1.329588</td>\n",
       "      <td>800.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.044097</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0.535122</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81586</th>\n",
       "      <td>0</td>\n",
       "      <td>0.047598</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0.169610</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105108</th>\n",
       "      <td>0</td>\n",
       "      <td>0.761149</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>2217.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3543</th>\n",
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       "      <td>0.690684</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0.432552</td>\n",
       "      <td>12416.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        плохой_клиент     линии  возраст  поведение_30-59_дней   Debt_Ratio  \\\n",
       "5522                0  0.111673       46                     0     1.329588   \n",
       "89385               0  0.044097       69                     0     0.535122   \n",
       "81586               0  0.047598       77                     0     0.169610   \n",
       "105108              0  0.761149       58                     1  2217.000000   \n",
       "3543                0  0.690684       55                     0     0.432552   \n",
       "\n",
       "          доход  число_кредитов  поведение_90_дней  недвижимость  \\\n",
       "5522      800.0               8                  0             1   \n",
       "89385    3800.0              10                  0             1   \n",
       "81586    3000.0               7                  0             1   \n",
       "105108      NaN               4                  0             1   \n",
       "3543    12416.0               7                  0             2   \n",
       "\n",
       "        поведение_60-89_дней  семья  \n",
       "5522                       0    2.0  \n",
       "89385                      0    0.0  \n",
       "81586                      0    0.0  \n",
       "105108                     0    0.0  \n",
       "3543                       0    2.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# если хотите работать с numpy-массивом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 3.12582480e-02, 5.70000000e+01, 0.00000000e+00,\n",
       "        3.97520496e-01, 5.00000000e+03, 1.50000000e+01, 0.00000000e+00,\n",
       "        2.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 5.23315890e-02, 6.40000000e+01, 0.00000000e+00,\n",
       "        5.70000000e+01,            nan, 2.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00,            nan]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = train.values\n",
    "X[:2,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# готовим данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((112500, 10), (112500,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = train.pop('плохой_клиент') # целевой вектор\n",
    "train.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# заменить пропуски на -11\n",
    "train.fillna(-11, inplace=True)\n",
    "test.fillna(-11, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Обучаем модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)\n",
    "model.fit(train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = model.predict_proba(test)[:,1] # вероятности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# сохранить решение\n",
    "pd.DataFrame({'id': np.arange(37500), 'a':a}).to_csv('./data_GMSC/solution.csv', index=False)"
   ]
  }
 ],
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