ValukovAlex2018Problem21/main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Загрузим  данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(517, 13)\n"
     ]
    },
    {
     "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>sat</td>\n",
       "      <td>90.6</td>\n",
       "      <td>43.7</td>\n",
       "      <td>686.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y month  day  FFMC   DMC     DC  ISI  temp  RH  wind  rain  area\n",
       "0  7  5   mar  fri  86.2  26.2   94.3  5.1   8.2  51   6.7   0.0   0.0\n",
       "1  7  4   oct  tue  90.6  35.4  669.1  6.7  18.0  33   0.9   0.0   0.0\n",
       "2  7  4   oct  sat  90.6  43.7  686.9  6.7  14.6  33   1.3   0.0   0.0\n",
       "3  8  6   mar  fri  91.7  33.3   77.5  9.0   8.3  97   4.0   0.2   0.0\n",
       "4  8  6   mar  sun  89.3  51.3  102.2  9.6  11.4  99   1.8   0.0   0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('forestfires.csv')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Распределение по area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.hist(df['area'], bins = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Расположение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(df['X'], df['Y'], 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Работа с данными"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сделаем из категориальных признаков бинарные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "df['month'] = le.fit_transform(df['month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "df['day'] = le.fit_transform(df['day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>day_0</th>\n",
       "      <th>day_1</th>\n",
       "      <th>day_2</th>\n",
       "      <th>day_3</th>\n",
       "      <th>day_4</th>\n",
       "      <th>day_5</th>\n",
       "      <th>day_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day_0  day_1  day_2  day_3  day_4  day_5  day_6\n",
       "0    1.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "1    0.0    0.0    0.0    0.0    0.0    1.0    0.0\n",
       "2    0.0    0.0    1.0    0.0    0.0    0.0    0.0\n",
       "3    1.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "4    0.0    0.0    0.0    1.0    0.0    0.0    0.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder(sparse=False)\n",
    "day_ohe = ohe.fit_transform(df['day'].values.reshape(-1, 1))\n",
    "day_ohe_df = pd.DataFrame(day_ohe, columns=['day_{}'.format(i) for i in range(day_ohe.shape[1])])\n",
    "day_ohe_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_0</th>\n",
       "      <th>month_1</th>\n",
       "      <th>month_2</th>\n",
       "      <th>month_3</th>\n",
       "      <th>month_4</th>\n",
       "      <th>month_5</th>\n",
       "      <th>month_6</th>\n",
       "      <th>month_7</th>\n",
       "      <th>month_8</th>\n",
       "      <th>month_9</th>\n",
       "      <th>month_10</th>\n",
       "      <th>month_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month_0  month_1  month_2  month_3  month_4  month_5  month_6  month_7  \\\n",
       "0      0.0      0.0      0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "1      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "2      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "3      0.0      0.0      0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "4      0.0      0.0      0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "\n",
       "   month_8  month_9  month_10  month_11  \n",
       "0      0.0      0.0       0.0       0.0  \n",
       "1      0.0      0.0       1.0       0.0  \n",
       "2      0.0      0.0       1.0       0.0  \n",
       "3      0.0      0.0       0.0       0.0  \n",
       "4      0.0      0.0       0.0       0.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder(sparse=False)\n",
    "month_ohe = ohe.fit_transform(df['month'].values.reshape(-1, 1))\n",
    "month_ohe_df = pd.DataFrame(month_ohe, columns=['month_{}'.format(i) for i in range(month_ohe.shape[1])])\n",
    "month_ohe_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(517, 30)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>...</th>\n",
       "      <th>month_9</th>\n",
       "      <th>month_10</th>\n",
       "      <th>month_11</th>\n",
       "      <th>day_0</th>\n",
       "      <th>day_1</th>\n",
       "      <th>day_2</th>\n",
       "      <th>day_3</th>\n",
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       "      <td>7</td>\n",
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       "      <td>86.2</td>\n",
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       "      <td>7</td>\n",
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       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>0.9</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y  FFMC   DMC     DC  ISI  temp  RH  wind  rain  ...    month_9  \\\n",
       "0  7  5  86.2  26.2   94.3  5.1   8.2  51   6.7   0.0  ...        0.0   \n",
       "1  7  4  90.6  35.4  669.1  6.7  18.0  33   0.9   0.0  ...        0.0   \n",
       "2  7  4  90.6  43.7  686.9  6.7  14.6  33   1.3   0.0  ...        0.0   \n",
       "3  8  6  91.7  33.3   77.5  9.0   8.3  97   4.0   0.2  ...        0.0   \n",
       "4  8  6  89.3  51.3  102.2  9.6  11.4  99   1.8   0.0  ...        0.0   \n",
       "\n",
       "   month_10  month_11  day_0  day_1  day_2  day_3  day_4  day_5  day_6  \n",
       "0       0.0       0.0    1.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "1       1.0       0.0    0.0    0.0    0.0    0.0    0.0    1.0    0.0  \n",
       "2       1.0       0.0    0.0    0.0    1.0    0.0    0.0    0.0    0.0  \n",
       "3       0.0       0.0    1.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "4       0.0       0.0    0.0    0.0    0.0    1.0    0.0    0.0    0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df.drop(['day', 'month'], axis = 1), month_ohe_df, day_ohe_df], axis = 1)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_col = [col for col in df.columns if col != 'area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[X_col]\n",
    "y = df['area']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Логистическая регрессия с L2-регуляризацией"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "alfa = np.arange(0.005, 10, 0.005)\n",
    "scores = []\n",
    "for a in alfa:\n",
    "    lr = Ridge(a)\n",
    "    lr.fit(X_train, y_train)\n",
    "    pred = lr.predict(X_test)\n",
    "    scores.append(mean_squared_error(pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Зависимость ошибки от коэфициента регуляризации:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(alfa, scores)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Зафисимость ошибки от логарифма коэфициента регуляризации:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(np.log(alfa), scores)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}