Линейная регрессия.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Линейная регрессия"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Итак, для построения линейной регрессии нам нужно пройти следующие темы:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Loss function  \n",
    "2) Optimizer (gradient descent)  \n",
    "3) Train_test_split  \n",
    "4) Optimization  \n",
    "5) Regularization (L1, L2)  \n",
    "6) Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Давайте решим задачу линейной регрессии"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Будем разбирать задачу [Boston Housing](https://www.kaggle.com/c/boston-housing). Где нужно определить цену дома."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
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       "      <td>78.9</td>\n",
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       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
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       "      <td>61.1</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston = load_boston()\n",
    "df = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Отлично, мы добавили наш датасет, теперь давайте добавим нашу \"таргет переменную\" и посмотрим как это выглядит."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = boston['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
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       "      <td>5.64</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
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       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  target  \n",
       "0       15.3  396.90   4.98    24.0  \n",
       "1       17.8  396.90   9.14    21.6  \n",
       "2       17.8  392.83   4.03    34.7  \n",
       "3       18.7  394.63   2.94    33.4  \n",
       "4       18.7  396.90   5.33    36.2  \n",
       "..       ...     ...    ...     ...  \n",
       "501     21.0  391.99   9.67    22.4  \n",
       "502     21.0  396.90   9.08    20.6  \n",
       "503     21.0  396.90   5.64    23.9  \n",
       "504     21.0  393.45   6.48    22.0  \n",
       "505     21.0  396.90   7.88    11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Давайте разделим нашу выборку на train и test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = boston.data, boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((404, 13), (404,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Давайте попробуем создать модель без настройки параметров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_model = SGDRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.90513239e+13, 7.07493606e+13, 9.02659614e+13, 6.55779143e+13,\n",
       "       8.39172410e+13, 5.13779824e+13, 8.71454375e+13, 7.23732357e+13,\n",
       "       1.47585667e+14, 1.53905837e+14, 1.48076020e+14, 9.05183335e+13,\n",
       "       1.47710930e+14, 1.48875767e+14, 7.06200786e+13, 5.29782333e+13,\n",
       "       6.87630744e+13, 4.73570287e+13, 6.28354306e+13, 6.75831454e+13,\n",
       "       6.58022827e+13, 9.34005761e+13, 1.48501029e+14, 5.09685451e+13,\n",
       "       1.48627007e+14, 5.49611384e+13, 1.50325667e+14, 6.81672534e+13,\n",
       "       1.47737410e+14, 6.84077661e+13, 9.35376557e+13, 1.46017974e+14,\n",
       "       7.29412092e+13, 5.16498752e+13, 5.17734613e+13, 6.88343912e+13,\n",
       "       5.56081154e+13, 5.61203733e+13, 1.42634581e+14, 8.75620747e+13,\n",
       "       7.78309860e+13, 6.08137109e+13, 9.44446472e+13, 6.12581859e+13,\n",
       "       1.46573018e+14, 4.45876479e+13, 1.47508539e+14, 1.48223433e+14,\n",
       "       1.47683172e+14, 7.41914854e+13, 6.52024875e+13, 6.29136866e+13,\n",
       "       1.46457672e+14, 1.48416818e+14, 4.88782359e+13, 4.71851420e+13,\n",
       "       9.56102623e+13, 8.73856090e+13, 7.72132670e+13, 9.26533611e+13,\n",
       "       1.49795748e+14, 7.00424880e+13, 6.07589177e+13, 3.87254760e+13,\n",
       "       1.47039137e+14, 5.16768808e+13, 1.48347158e+14, 1.47505958e+14,\n",
       "       1.47957402e+14, 6.25823477e+13, 1.49231369e+14, 3.89434944e+13,\n",
       "       8.37889136e+13, 6.96822019e+13, 6.67335776e+13, 4.63474804e+13,\n",
       "       1.46946915e+14, 4.69844371e+13, 7.05748378e+13, 4.47438058e+13,\n",
       "       8.67327415e+13, 5.49080748e+13, 1.52866995e+14, 1.51818745e+14,\n",
       "       1.47834358e+14, 6.91324941e+13, 6.97587707e+13, 6.60508824e+13,\n",
       "       5.32043679e+13, 6.61905068e+13, 8.41013006e+13, 1.48321228e+14,\n",
       "       8.78604273e+13, 1.45814382e+14, 6.49378207e+13, 1.48175901e+14,\n",
       "       6.30896168e+13, 5.16485055e+13, 4.52163101e+13, 8.45412776e+13,\n",
       "       7.80282747e+13, 5.20039659e+13])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3)Создадим функцию для отображения графиков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_graphics(y_test, y_pred):\n",
    "    print(\"Metrics: \")\n",
    "    MSE = mean_squared_error(y_test, y_pred)\n",
    "    print(\"MSE: \", MSE)\n",
    "    \n",
    "    print(\"\\n\\nGraphic: \")\n",
    "    x_ax = range(len(y_test))\n",
    "    plt.plot(x_ax, y_test, label='original')\n",
    "    plt.plot(x_ax, y_pred, label='predicted')\n",
    "    plt.title(\"Boston test and predicted data\")\n",
    "    plt.xlabel(\"X-axis\")\n",
    "    plt.ylabel(\"Y-axis\")\n",
    "    plt.legend(loc='best', fancybox=True, shadow=True)\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics: \n",
      "MSE:  9.77245872734623e+27\n",
      "\n",
      "\n",
      "Graphic: \n"
     ]
    },
    {
     "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": [
    "show_graphics(y_test, simple_model.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим, погрешность невероятно большая MSE ~ 10^28. Давайте попробуем это исправить.  \n",
    "Для начала попробуем посмотреть на максимальные значения в наших признаках."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 88.9762, 100.    ,  27.74  ,   1.    ,   0.871 ,   8.78  ,\n",
       "       100.    ,  12.1265,  24.    , 711.    ,  22.    , 396.9   ,\n",
       "        36.98  ])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.max(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Давайте попробуем улучшить нашу модель, добавив нормализацию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как мы видим, наши данные очень различные. В одном максимальное значение будет 0.871, а где-то 711. И хотя правильным будет обработать каждый признак в отдельности, с применением One-Hot Encoding, нормализацией. Однако это будет достаточно долго и мутарно, поэтому мы можем воспользоваться функциями из библиотек."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте попробуем нормализовать данные и посмотреть на максимальные их значения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11308137, 0.20764371, 0.06124919, 0.00237101, 0.00206515,\n",
       "       0.01815583, 0.23762592, 0.02762361, 0.03579802, 0.99339501,\n",
       "       0.05309732, 0.88980798, 0.07006318])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize(X_train).max(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Это уже лучше, попробуем построить модель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model_with_norm = SGDRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_norm.fit(normalize(X_train), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics: \n",
      "MSE:  50.89061483199003\n",
      "\n",
      "\n",
      "Graphic: \n"
     ]
    },
    {
     "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": [
    "show_graphics(y_test, model_with_norm.predict(normalize(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как мы можем видеть, модель уже хоть как-то научилась предсказывать цену, однако как мы видим из графиков, делает она это достаточно посредственно. Однако это уже куда лучше, чем было раньше."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.00431953e+00,  1.48681806e+01, -2.66509072e+00,  1.19229596e-01,\n",
       "        5.93144361e-04,  1.63474365e+00, -5.86152379e+00, -2.36421689e-03,\n",
       "        6.71189315e-01, -3.19064659e+00, -1.38294461e+00,  1.69143060e+01,\n",
       "       -9.14968090e+00])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_norm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.15846384])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_norm.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на наши веса, чтобы выявить переобучение."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Давайте попробуем тоже самое, но используя scale**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разница между normalize и scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.18895583e-03, 2.36704370e-02, 1.89041930e-02, 1.36009579e-04,\n",
       "       9.90091936e-04, 1.14971287e-02, 1.20494752e-01, 7.27815401e-03,\n",
       "       1.48931131e-02, 6.92846549e-01, 3.31895315e-02, 6.56790646e-01,\n",
       "       2.21376208e-02])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize(X_train).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.59388454e-17, -2.66563449e-17,  2.11162221e-15,  6.56790354e-17,\n",
       "       -3.80993367e-15,  4.59698286e-15, -4.28699980e-16, -1.20365764e-15,\n",
       "        4.17707673e-17,  5.71599973e-17,  2.10491690e-14,  7.44440263e-15,\n",
       "        2.55021526e-16])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale(X_train).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.19469958e-02, 4.79296611e-02, 1.05814187e-02, 4.89466200e-04,\n",
       "       1.99502729e-04, 2.62985985e-03, 4.87109239e-02, 4.66672768e-03,\n",
       "       1.06512236e-02, 1.48205262e-01, 5.85889738e-03, 2.09589855e-01,\n",
       "       1.15065497e-02])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize(X_train).std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale(X_train).std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если хотите, можете попробовать разобрать, что тут написано про scale:\n",
    "\n",
    "Центр к среднему значению и компонентная шкала к единичной дисперсии.\n",
    "Это означает, что среднее значение вдоль оси вычитается из X, а полученное значение делится на std вдоль оси.  \n",
    "\n",
    "Это что-то сложное из теории вероятности, что понять будет достаточно сложно, а как работает normalize мы уже поговорили. В общем случае, можно использовать normalize. Качество модели будет изменяться не настолько сильно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_with_scale = SGDRegressor().fit(scale(X_train), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics: \n",
      "MSE:  17.143444411035883\n",
      "\n",
      "\n",
      "Graphic: \n"
     ]
    },
    {
     "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": [
    "show_graphics(y_test, model_with_scale.predict(scale(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Посмотрим на наши веса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.82782896,  1.01635041,  0.25539838,  0.85059205, -1.97132574,\n",
       "        2.70107411, -0.0090655 , -2.9166867 ,  2.11493288, -1.29143541,\n",
       "       -2.08643593,  0.88460288, -3.91359636])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_scale.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.43200722])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_scale.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2638f562a60>]"
      ]
     },
     "execution_count": 78,
     "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": [
    "plt.plot(model_with_scale.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как мы видим, веса находятся в районе $[-4;3]$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А так как наши значения являются достаточно маленькими, можем это проверить."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.59388454e-17, -2.66563449e-17,  2.11162221e-15,  6.56790354e-17,\n",
       "       -3.80993367e-15,  4.59698286e-15, -4.28699980e-16, -1.20365764e-15,\n",
       "        4.17707673e-17,  5.71599973e-17,  2.10491690e-14,  7.44440263e-15,\n",
       "        2.55021526e-16])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale(X_train).mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "То можно попробовать добавить регуляризацию, вдруг переобучение и правда было."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "alpha=0.0001, penalty='elasticnet', eta0=0.01, epsilon=0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Регуляризация"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для этого давайте добавим пару параметров в нашу модель, такие как penalty и alpha.  \n",
    "penalty - выбор между L1, L2 и elasticnet(совмещение двух)  \n",
    "alpha - величина, с которой мы будем штрафовать нашу модель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_with_reg = SGDRegressor(alpha=0.0001, penalty='l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor()"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_reg.fit(scale(X_train), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics: \n",
      "MSE:  17.326820550451608\n",
      "\n",
      "\n",
      "Graphic: \n"
     ]
    },
    {
     "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": [
    "show_graphics(y_test, model_with_reg.predict(scale(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Подбор гиперпараметров"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Модель стала хуже работать, ведь действительно, такие параметры как alpha и тд. должны подбираться вручную, это называется гиперпараметрами."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Однако, нам может помочь еще одна библиотека, которая занимается перебором заданных гиперпараметров и поиском лучшего."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_with_params = GridSearchCV(estimator=SGDRegressor(), \n",
    "                                param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1], 'penalty': ('l1', 'l2', 'elasticnet')})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SGDRegressor(),\n",
       "             param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1],\n",
       "                         'penalty': ('l1', 'l2', 'elasticnet')})"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_params.fit(scale(X_train), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics: \n",
      "MSE:  17.103911962521114\n",
      "\n",
      "\n",
      "Graphic: \n"
     ]
    },
    {
     "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": [
    "show_graphics(y_test, model_with_params.predict(scale(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(alpha=0.001, penalty='l1')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_params.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы можем посмотреть параметры, которые были отобраны как лучшие"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) Давайте попробуем предсказать)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У нас есть уже готовая модель с подобранными весами, давайте просто попробуем подать на нее какие-либо данные и посмотрим, что она выдаст."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1) Давайте возьмем уже какие-нибудь готовые данные, которые она видела"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.45484342])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_params.predict([scale(X_train[40])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на реальный ответ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[40]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Довольно близко к реальному ответу))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2) Ну и наконец самое интересное, мы сгенерируем признаки сами и подадим на модель"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для начала посмотрим как выглядят наши данные вообще"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.2802e-01, 0.0000e+00, 8.5600e+00, 0.0000e+00, 5.2000e-01,\n",
       "       6.4740e+00, 9.7100e+01, 2.4329e+00, 5.0000e+00, 3.8400e+02,\n",
       "       2.0900e+01, 3.9524e+02, 1.2270e+01])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>0.12802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.474</td>\n",
       "      <td>97.1</td>\n",
       "      <td>2.4329</td>\n",
       "      <td>5.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>395.24</td>\n",
       "      <td>12.27</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM   ZN  INDUS  CHAS   NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "108  0.12802  0.0   8.56   0.0  0.52  6.474  97.1  2.4329  5.0  384.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  target  \n",
       "108     20.9  395.24  12.27    19.8  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['CRIM'] == 1.2802e-01]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы нашли реальный пример, а теперь давайте на его основе составим свой."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM         3.613524\n",
       "ZN          11.363636\n",
       "INDUS       11.136779\n",
       "CHAS         0.069170\n",
       "NOX          0.554695\n",
       "RM           6.284634\n",
       "AGE         68.574901\n",
       "DIS          3.795043\n",
       "RAD          9.549407\n",
       "TAX        408.237154\n",
       "PTRATIO     18.455534\n",
       "B          356.674032\n",
       "LSTAT       12.653063\n",
       "target      22.532806\n",
       "dtype: float64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_sample = df.drop('target', axis=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM         3.613524\n",
       "ZN          11.363636\n",
       "INDUS       11.136779\n",
       "CHAS         0.069170\n",
       "NOX          0.554695\n",
       "RM           6.284634\n",
       "AGE         68.574901\n",
       "DIS          3.795043\n",
       "RAD          9.549407\n",
       "TAX        408.237154\n",
       "PTRATIO     18.455534\n",
       "B          356.674032\n",
       "LSTAT       12.653063\n",
       "dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "our_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-331.73854939])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_params.predict([our_sample])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Не стоит забывать, что мы тренировали нашу модель, используя scale. Поэтому подаем значения тоже вместе со scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.59105199])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_params.predict([scale(our_sample)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы получили среднюю цену дома в этом датасете)) А теперь давайте возьмем максимальные признаки и посмотрим, что выйдет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21.39939831])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_with_params.predict([scale(df.drop('target', axis=1).max())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Можно сказать, что мы построили первую простейшую модель"
   ]
  }
 ],
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