3-3_Feature_Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XjTc5n2flYUu"
   },
   "source": [
    "# 특성 공학과 규제"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B8YOr2hElYUv"
   },
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/3-3.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fZwhQU2l8tI6"
   },
   "source": [
    "## 데이터 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "3kjaTfOqEVwY"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8qmTS1RzKRKT",
    "outputId": "25aa3409-3306-4751-fba7-bd3bcc9e3cad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8.4   2.11  1.41]\n",
      " [13.7   3.53  2.  ]\n",
      " [15.    3.82  2.43]\n",
      " [16.2   4.59  2.63]\n",
      " [17.4   4.59  2.94]\n",
      " [18.    5.22  3.32]\n",
      " [18.7   5.2   3.12]\n",
      " [19.    5.64  3.05]\n",
      " [19.6   5.14  3.04]\n",
      " [20.    5.08  2.77]\n",
      " [21.    5.69  3.56]\n",
      " [21.    5.92  3.31]\n",
      " [21.    5.69  3.67]\n",
      " [21.3   6.38  3.53]\n",
      " [22.    6.11  3.41]\n",
      " [22.    5.64  3.52]\n",
      " [22.    6.11  3.52]\n",
      " [22.    5.88  3.52]\n",
      " [22.    5.52  4.  ]\n",
      " [22.5   5.86  3.62]\n",
      " [22.5   6.79  3.62]\n",
      " [22.7   5.95  3.63]\n",
      " [23.    5.22  3.63]\n",
      " [23.5   6.28  3.72]\n",
      " [24.    7.29  3.72]\n",
      " [24.    6.38  3.82]\n",
      " [24.6   6.73  4.17]\n",
      " [25.    6.44  3.68]\n",
      " [25.6   6.56  4.24]\n",
      " [26.5   7.17  4.14]\n",
      " [27.3   8.32  5.14]\n",
      " [27.5   7.17  4.34]\n",
      " [27.5   7.05  4.34]\n",
      " [27.5   7.28  4.57]\n",
      " [28.    7.82  4.2 ]\n",
      " [28.7   7.59  4.64]\n",
      " [30.    7.62  4.77]\n",
      " [32.8  10.03  6.02]\n",
      " [34.5  10.26  6.39]\n",
      " [35.   11.49  7.8 ]\n",
      " [36.5  10.88  6.86]\n",
      " [36.   10.61  6.74]\n",
      " [37.   10.84  6.26]\n",
      " [37.   10.57  6.37]\n",
      " [39.   11.14  7.49]\n",
      " [39.   11.14  6.  ]\n",
      " [39.   12.43  7.35]\n",
      " [40.   11.93  7.11]\n",
      " [40.   11.73  7.22]\n",
      " [40.   12.38  7.46]\n",
      " [40.   11.14  6.63]\n",
      " [42.   12.8   6.87]\n",
      " [43.   11.93  7.28]\n",
      " [43.   12.51  7.42]\n",
      " [43.5  12.6   8.14]\n",
      " [44.   12.49  7.6 ]]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('https://bit.ly/perch_csv_data')\n",
    "perch_full = df.to_numpy()\n",
    "print(perch_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "PsRC7rvE9SbL"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "perch_weight = np.array(\n",
    "    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0, \n",
    "     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0, \n",
    "     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0, \n",
    "     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0, \n",
    "     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0, \n",
    "     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0, \n",
    "     1000.0, 1000.0]\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "cRKkoWoZ9J0m"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_input, test_input, train_target, test_target = train_test_split(perch_full, perch_weight, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y5uMFE_8V1tx"
   },
   "source": [
    "## 사이킷런의 변환기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "EclugdXmSs-L"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V5is7cZhKbPU",
    "outputId": "51fa6feb-30d4-4c7e-b89d-a2ec8033834b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 2. 3. 4. 6. 9.]]\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures()\n",
    "poly.fit([[2, 3]])\n",
    "print(poly.transform([[2, 3]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bKXkK0oJc4nG",
    "outputId": "298cbbc5-33f8-4957-a7b5-d8eb2b44b2b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2. 3. 4. 6. 9.]]\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(include_bias=False)\n",
    "poly.fit([[2, 3]])\n",
    "print(poly.transform([[2, 3]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "__kE6eJdNZfm"
   },
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(include_bias=False)\n",
    "\n",
    "poly.fit(train_input)\n",
    "train_poly = poly.transform(train_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2a_lmkKle4kF",
    "outputId": "e277109c-dc79-49c3-bca2-bd32a19d2ebb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42, 9)\n"
     ]
    }
   ],
   "source": [
    "print(train_poly.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X6GUwfXTfKbl",
    "outputId": "d733cfd1-e13e-4652-ec50-48ffd0f90034"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0', 'x1', 'x2', 'x0^2', 'x0 x1', 'x0 x2', 'x1^2', 'x1 x2',\n",
       "       'x2^2'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "DJMPxe2mgbOo"
   },
   "outputs": [],
   "source": [
    "test_poly = poly.transform(test_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PdDAslHzNk3H"
   },
   "source": [
    "## 다중 회귀 모델 훈련하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E9ygy-8WOvIP",
    "outputId": "5c2bdda2-f603-4c50-e94a-2f196ab7b793"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9903183436982124\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(train_poly, train_target)\n",
    "print(lr.score(train_poly, train_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GKKyfFcAd7zm",
    "outputId": "4ee7e6ea-7126-4941-ac5f-c39575ac8642"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9714559911594134\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(test_poly, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "2fDt5mrReMwU"
   },
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=5, include_bias=False)\n",
    "\n",
    "poly.fit(train_input)\n",
    "train_poly = poly.transform(train_input)\n",
    "test_poly = poly.transform(test_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hcM8R4VHSzR8",
    "outputId": "e00c0862-0252-4969-98e5-7f71c540aaa4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42, 55)\n"
     ]
    }
   ],
   "source": [
    "print(train_poly.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UffVFVTGP8xj",
    "outputId": "1ed2680b-460d-4e94-c2a4-9dfa498e9127"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999991097\n"
     ]
    }
   ],
   "source": [
    "lr.fit(train_poly, train_target)\n",
    "print(lr.score(train_poly, train_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GtITdlYFg7AY",
    "outputId": "32e51885-a743-4c29-b1fb-aa55ae744fc1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-144.40579242684848\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(test_poly, test_target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K2YMPSelQBpO"
   },
   "source": [
    "## 규제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "hCC7wKy3QQrE"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss = StandardScaler()\n",
    "ss.fit(train_poly)\n",
    "\n",
    "train_scaled = ss.transform(train_poly)\n",
    "test_scaled = ss.transform(test_poly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qyLI7JQsJ7RQ"
   },
   "source": [
    "## 릿지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LdNuDNQGQipv",
    "outputId": "7fd4652c-5c7b-40de-bd84-c4f97319da28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9896101671037343\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge = Ridge()\n",
    "ridge.fit(train_scaled, train_target)\n",
    "print(ridge.score(train_scaled, train_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p5mXLecwhdnF",
    "outputId": "d7c988da-0421-4b7c-e8a0-90e107a0be53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9790693977615391\n"
     ]
    }
   ],
   "source": [
    "print(ridge.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "wXd3_Kq6hlbM"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "train_score = []\n",
    "test_score = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "9MvIvQOrhfqC"
   },
   "outputs": [],
   "source": [
    "alpha_list = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "for alpha in alpha_list:\n",
    "    # 릿지 모델을 만듭니다\n",
    "    ridge = Ridge(alpha=alpha)\n",
    "    # 릿지 모델을 훈련합니다\n",
    "    ridge.fit(train_scaled, train_target)\n",
    "    # 훈련 점수와 테스트 점수를 저장합니다\n",
    "    train_score.append(ridge.score(train_scaled, train_target))\n",
    "    test_score.append(ridge.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "95DjrJxlhiow",
    "outputId": "f4bd8050-8248-4c0d-c02b-b2f9267b4e81"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qX4lIInA18G4Yr8EYYyJSUkIcV/dsy9U927Ju+16mzN/AS4sKeGPZFrq2bMrIvh0Ydm4uGamhf7AwbC0OVa0ERgMzgFXAC6q6QkTuF5FrvMMuBtaIyFqgFfCAt30a8DmwHNcPslRVXweSgRkisgxYggukf4brGowxpjHo2rIp933jDOb/chB/ur4nTZLi+c3rK+n7+3dZXlAS8t9nDwAaY0wUWl5QwvTFhYz9evcTXhvEZsc1xpgYclZuBmflhmf6ksa/RJUxxpgGZcFhjDEmKBYcxhhjgmLBYYwxJigWHCa27d4MK6ZD4SKIgRGGxoSCjaoysWX3ZvhyLnw5x/3cuf7QvswOcMZQ92pzDkTwkp7G+MmCw0S3kkLY8OGRQZGSAR0vgN7fgfb9oGi1a3l8/AR8+Bdo3gl6DPFC5GwLEWMC2AOAJrrUFxSdBrhXqzMhro7V1Up3wur/uBBZ/wFoFTTvfKgl0vosCxETM2zpWAuO6HSyQXEspTth1euw8hVY/18XIi26wBlDXIi0OtNCxEQ1Cw4LjugQzqA4ln07YPXrriXyxWzQasjqeqgl0rKHhYiJOhYcFhyNk19BcSz7voJVr8GKV1xdWg3Zpx7qE2l5uoWIiQoWHBYcjUNJ4eGjnnZ94banZEDHAQFBcUbDBcWx7C3yQmS6CzithuzTAloi3f2u0JgTZsFhwRGZGltQHMuebS5EVr7qrgWFnNMP9YnknOZ3hcYExYLDgiMyRFNQHMuera5jfcV02PARoK4fpKYlkt3N7wqNqZcFhwWHP2IlKI5l95ZDfSIbPwbU9cnU9Ilkd/W5QGPqZsFhwdEwSgrgyw9jOyiOZfdmWOn1iWya57a1OuvQ7aysLr6WZ0wgCw4LjvCwoDhxJYWuP2TFdChY4La17ulCpMcQCxHjOwsOC47QsKAIj5KCgBBZ6La1Odu1QnoMgRadfS3PxCZfgkNEBgN/AeKBCar6x1r7OwKTgBxgJ3CTqhZ4+/4EXIWbwXcm8CNVVRE5D3gKSAXerNl+rDosOE7CUYMis9ZzFBYUIVO88VCIFC5y29r28vpEhrh5tIxpAA0eHCISD6wFLgcKgIXACFVdGXDMi8AbqjpZRC4FblPVm0XkfOAh4ELv0LnAWFX9QEQWAHcD83HB8VdVfetYtVhwBKF0J3w28ziC4kyIs1n5w27XhkMhsvkTt63tud7orCFuRl9jwuRowRHO2XH7AOtUdb1XwFTgWmBlwDE9gHu897OAV7z3CqQASYAAicA2EWkDNFPVed45nwaGAMcMDnOcqipgwmXu6eyaoOgzyoLCT807wgV3u9euL93IrBXTYeb/ule7vEN9Ipnt/a3VxIxwBkc7YFPA5wKgb61jlgLDcLezhgLpIpKlqh+LyCxgCy44HlfVVSKS550n8Jzt6vrlIjIKGAXQoYP9VXZclr3gQmPoeDjrmxYUkaZ5JxjwY/fa+YWbfHHFdHjnV+6V29vrE7kWMnL9rdVENb//ZRgDXCQii4GLgEKgSkS6AqcDubhguFREBgZzYlUdr6p5qpqXk5MT6rqjT3UVzH3ETRve8wYLjUjXojMM+AncORt++Alc9muoLIMZv4RHz4CJV8DHf3Mjt4wJsXC2OAqBwLZzrrftIFXdjGtxICJNgetUtVhEvgvMU9W93r63gP7Av73zHPWc5gStfAV2rINvPmUT9DU2WV1g4P+4147PXStkxSswY6x7te93qCXSrI3f1ZooEM4/KxcC3USks4gkAcOB1wIPEJFsEampYSxuhBXARlxLJEFEEnGtkVWqugXYLSL9RESAbwOvhvEaYoMqzHnEzfB6+jV+V2NORlYXuHAMfH8ujM6HS34FB/bA2z+HR06HSVfC/H+4p9mNOUFhCw5VrQRGAzOAVcALqrpCRO4XkZp/nS4G1ojIWqAV8IC3fRrwObAc1w+yVFVf9/b9AJgArPOOsY7xk7X2bdj2KQy4x4bURpPsbnDRT+EHH8FdC+GSX0JZMbz1Mxcir/wA9m73u0rTCNkDgLFO1Y2k2lfk7pXHJ/pdkQm37ath8b9dyyMxFS76OfS90/7bmyMcbTiu9YDGuvUfuIfMBvzE/uGIFS27w9cegB/Mg/Z94Z174ckL4PP3/a7MNBIWHLFuzsOQ3gbOGel3JaahZXeFkS/CiOehqhz+PRSmjnTPixhzDBYcsWzjPPeE+Pk/hIRkv6sxfhCB0wa71sdlv3atjif6wqzfQ3mp39WZCGXBEctmj4O0LDjvVr8rMX5LTHHDeUfnQ/er4b8PwhN93LDeGOgHNcGx4IhVm5fAupnQ7weQ1MTvakykyGgH10+EW990Mx6/eAs8fQ1sX+V3ZSaCWHDEqjkPQ3IG9Pmu35WYSNTpAhj1X/j6ONiyzHWev/Vz2F/sd2UmAlhwxKLtq91Spn1Hub8qjalLfIL7w+LuxXDeLW747mPnwqLJUF3td3XGRxYcsWjuI5CYBn2/73clpjFIawFXPwp3/tfNLvD63TDhUti00O/KjE8sOGLNzvWwfBrk3Q5NsvyuxjQmbc6G296CYRNgz1aYOAimfx/2bPO7MtPALDhizdw/Q1wC9B/tdyWmMRKBnt+E0QvdQ6PLX4THzoOPHoPKcr+rMw3EgiOWlBTCkmeh1002S6o5OcnpMOj/4K750LG/Ww/kyfNh3Xt+V2YagAVHLPnoMdBquOBHfldiokVWF/f0+Y0vgFbBM8PguRvdQlMmallwxIq9RbDoKTh7uFuO1JhQOvVr3tPn97n5z57oC+//zp4+j1IWHLFi3hNuhbgBP/G7EhOtEpJh4D3ww3zocQ3Mfgge7w2fvmxPn0eZYwaHiMSLyJ0i8lsRuaDWvl+FtzQTMvt3wYIJcMYQt0aDMeHUrC1cNwFuexvSmsO022DyN2DbCr8rMyFSX4vjH7jV93YAfxWRRwL2DQtbVSa05o+H8j0wcIzflZhY0rG/e/r8qkfcQmF/Hwhv/sz9IWMatfqCo4+q3qiqfwb6Ak1F5GURSQZsYerG4MBemP8knHoltD7T72pMrImLh953uEXC8m6Dhf90w3cXPQXVVX5XZ05QfcGRVPNGVStVdRSwBHgfaBrGukyo5E9yf+FdaK0N46O0FnDVw64Fkn0avP4j+OelsGmB35WZE1BfcOSLyODADap6P/AvoFN9JxeRwSKyRkTWicgv6tjfUUTeE5FlIvKBiOR62y8RkSUBrzIRGeLte0pEvgjYd87xXWoMqtjvhuCecjHkHrH6ozENr01PuO1NuG6iW+984uXw8p3uSXTTaIRtzXERiQfWApcDBcBCYISqrgw45kXgDVWdLCKXArep6s21ztMCWAfkqmqpiDzlfWfa8dYSs2uOL/gnvDkGbnkDOg/0uxpjDndgr5ul+ePHIT4JLvqZmz8tIan+75oGcVJrjnshEKw+wDpVXa+q5cBU4Npax/TA3fYCmFXHfoDrgbdU1QaEB6OyHD78i1tTutMAv6sx5kjJTWHQfe75j04DYOav4cn+8Nm7fldm6lFvcIhIOvDqCZy7HbAp4HOBty3QUg6NzhoKpItI7Zn3hgPP1dr2gHd761Gvo76uukeJSL6I5BcVFZ1A+Y3csuehZBNc+FM3v5AxkSqrC9z4PNz4onveY8p18NwINyGniUj1PcfRBngXGB+m3z8GuEhEFuOG/RYCB4daeL//LGBGwHfGAt2B3kAL4Od1nVhVx6tqnqrm5eTkhKn8CFVdBXMfdbOZdh3kdzXGHJ9Tr3Ctj0G/gS9mwxP94L3fQvk+vysztdTX4pgD/FFVXzuBcxcC7QM+53rbDlLVzao6TFV7Afd624oDDrkBmK6qFQHf2aLOAVwnfZ8TqC26rZgOOz93a0hba8M0JglJMODHbu3zM4bAnHHe0+cv2dPnEaS+4NjFkbeXjtdCoJuIdBaRJNwtp8MCSESyRaSmhrHApFrnGEGt21ReKwQREWAI8OkJ1hedqqtdh2P2adD9G35XY8yJadYGho2H22dAWhZMux2euhq22v/dI0F9wXExcKWI3BXsiVW1EhiNu820CnhBVVeIyP0ick3A+deIyFqgFfBAzfdFpBOuxfLfWqeeIiLLgeVANvC7YGuLamvfgu0rXWsjzqYiM41ch34w6gO4+s/uf9f/GAj/GQOlO/2uLKbVOxzXG1H1D1X9TsOUFHoxMxxX1T1UtX8njF7k1ow2JlqU7oRZv4f8iZCSCZf9L5x7i3s63YTFCQ/HVdWqxhwaMeXz92HzJ3DBjy00TPRJawFXjYM750DLHvDGT2D8xbBxnt+VxZwTupchInEiMjLUxZiTNOdhSG8L59zodyXGhE/rM+HWN+D6SVC6AyZ9DV4eBbu3+F1ZzKhvOG4zERkrIo+LyBXi/BBYjxvxZCLFho9gw4dwwd1uXQRjopkInHmdW/t84Bg3kvDxPJj7Z6g84Hd1Ua++Fse/gdNwHdHfwT3dfT0wRFXresrb+GX2OEjLdvd8jYkVSU1cX8dd86HzhfDuffC3/vDZTL8ri2r1Bccpqnqrqv4DNzS2B/A1VV0S9srM8Sv8BD5/D/rfBUlpfldjTMNrcQqMeA5GvuRaI1Ouh2e/BTs+97uyqFRfcAQ+eFcFFKhqWXhLMkGb8zCkZEBvG8NgYly3QfD9j+Hy38KXc+Fv/eDd37gJFU3I1BccZ4vIbu+1B+hZ815EdjdEgaYe21bC6jegz52Q0szvaozxX0KS6+v74SLXDzL3Eff0+fJp9vR5iBwzOFQ1XlWbea90VU0IeG//SkWCuY9AYhPo932/KzEmsqS3hqF/h9vfgaY58NId8PrdUFXpd2WNnj1a3Jjt+NzN4dP7djfG3RhzpA594buz3GwKnzwNU0fYrauTZMHRmM19FOISof9ovysxJrLFxcNlv4arH4V178Lkq90KhOaEWHA0ViUFsHQqnPtt1yQ3xtQv73YY/ixsXw0TBsFX6/yuqFGy4GisPvwroHDBj/yuxJjG5bQr4db/uHU+Jl4Omxb4XVGjY8HRGO3dDp9Mhp7DIbN9/ccbYw6Xex7c8Q6kZsLkb8CqN/yuqFGx4GiMPn4cqsphwE/8rsSYxiurC9wxE1qdAc/fBAv+6XdFjYYFR2NTuhMWToQzhkJ2V7+rMaZxa5INt7wBpw6GN8fAzPvcYmjmmCw4GpsF46F8rxtaaIw5eUlp8K1nXMf5h3+G6aNsosR62KINjcmBPTDvSTjtKte8NsaERnwCXPUIZLSH934De7a6MEnN9LuyiGQtjsZk4UQoK4YLrbVhTMiJwMB7YOh4tzjUv650w97NEcIaHCIyWETWiMg6EflFHfs7ish7IrJMRD4QkVxv+yUisiTgVSYiQ7x9nUVkvnfO50UkKZzXEDEq9rtO8VMugXbn+V2NMdHr7G/BTdOgeBNMuBy2rfC7oogTtuDw1ip/ArgSNx37CBHpUeuwccDTqtoTuB/4A4CqzlLVc1T1HOBSoBR4x/vOg8CjqtoV2AXcEa5riCifPA37iuDCn/pdiTHR75SL4fa3AIVJg2H9f/2uKKKEs8XRB1inqutVtRyYCtRe/KkH8L73flYd+8EtHPWWqpaKiOCCZJq3bzIwJNSFR5zKcvjwL9ChP3S6wO9qjIkNrc9yw3WbtYVnroNlL/pdUcQIZ3C0AzYFfC7wtgVaCgzz3g8F0kUkq9Yxw4HnvPdZQLGq1kxvWdc5ARCRUSKSLyL5RUVFJ3gJEWLZVNhd6JbINMY0nMz2cPvb0L4vvPwdtzStTc3ue+f4GOAiEVkMXAQUAlU1O0WkDXAWMCPYE6vqeFXNU9W8nJycUNXb8KoqYc4j0OYc6HqZ39UYE3tSm8PNL7u1Pd69D978KVRX1f+9KBbO4biFQOB8GLnetoNUdTNei0NEmgLXqWpxwCE3ANNVtWYlwh1ApogkeK2OI84ZdVZMh11fuKGBIn5XY0xsSkiGYRPcbauPHoM9W+C6CZCY6ndlvghni2Mh0M0bBZWEu+X0WuABIpItIjU1jAUm1TrHCA7dpkJVFdcXcr236Rbg1TDUHhmqq92ysDmnu2c3jDH+iYuDK34Hgx+E1f+BydfAvh1+V+WLsAWH1yIYjbvNtAp4QVVXiMj9InKNd9jFwBoRWQu0Ah6o+b6IdMK1WGoPZ/g5cI+IrMP1eUwM1zX4bs1/oGiVe0o8zu+7isYYAPp9D26YDFuWutl1d37hd0UNTjQGOnry8vI0Pz/f7zKCowrjL4ayEhid755sNcZEjo3z4LnhEJcAN74A7c71u6KQE5FFqppXe7v9GRupPn8PtixxM+BaaBgTeTr0c+uZJ6bCU1fB2nfq/06UsOCIVLPHQbN2cPYIvysxxhxNzqlwx7uQ3c21PhZN9ruiBmHBEYm+/BA2fuxW90uIjRlVjGm00lu5FQW7XAKv3w2zfh/1z3pYcESiOeOgSY5bT9wYE/mS02HEVOh1E/z3QXj1LqiqqP97jZTdPI80hYvg8/dh0G9idoy4MY1SfCJc87ibmv2DP7ip2W+Y7EIlyliLI9LMfhhSMqF3bMzdaExUEYGLfwHXPAbrP4B/fd0FSJSx4Igk21a4Zzf6fi8q/0oxJmac+2248XnY8bmbmr1ojd8VhZQFRySZ8zAkNYW+d/pdiTHmZHW7HG77D1SWwcQrYMPHflcUMhYckWLH525eqt53QFoLv6sxxoRC217wnZlusMvT18LK6JghyYIjUsx9BOKToP9ovysxxoRS805wxzvQ9hx44RaY96TfFZ00C45IULwRlk5190WbtvS7GmNMqKW1gG+/Ct2vgrd/ATPudZOYNlIWHJHgw78CAuff7XclxphwSUyFG56GPnfCx4/DS7dDRZnfVZ0Qe47Db3u2ufXEzx7uVhszxkSvuHi48kHIyIWZ/wt7t8PwKW6xqEbEWhx++/gxqK5wkxkaY6KfCFxwN1w3EQoWwqTBULyp/u9FEAsOP5XuhIWT3JKUWV38rsYY05DOuh5uehl2b4EJg2DLMr8rOm4WHH6a/3eo2AcD7vG7EmOMHzoPhNvfdrew/vV1N91QI2DB4Zey3S44ul8NrXr4XY0xxi+tesB33oXmHWHKN90IywhnweGXhRPc6n4D/8fvSowxfmvWFm57EzpeANPvdOvxRPDU7BYcfigvhY+fgC6XReVyk8aYE5CSASOnwVk3wPu/hf/cA1WVfldVp7AGh4gMFpE1IrJORH5Rx/6OIvKeiCwTkQ9EJDdgXwcReUdEVonIShHp5G1/SkS+EJEl3uuccF5DWHwyGUq/ggvH+F2JMSaSJCTBsPFulGX+JHj+Jijf53dVRwhbcIhIPPAEcCXQAxghIrVv5o8DnlbVnsD9wB8C9j0NPKSqpwN9gO0B+36qqud4ryXhuoawqDzgHvjreAF0PN/vaowxkUYEBv0ffH0cfDYDJn8D9hb5XdVhwtni6AOsU9X1qloOTAWurXVMD6BmGMGsmv1ewCSo6kwAVd2rqqVhrLXhLH0O9my2vg1jzLH1+S586xnYthImXu4mQo0Q4QyOdkDgUy0F3rZAS4Fh3vuhQLqIZAGnAsUi8rKILBaRh7wWTI0HvNtbj4pIcl2/XERGiUi+iOQXFUVIWldVwtxH3YyZXS71uxpjTKTrfhXc8joc2O3CoyDf74oA/zvHxwAXichi4CKgEKjCTYUy0NvfGzgFuNX7zligu7e9BfDzuk6squNVNU9V83JycsJ5Dcfv05dg15dw4U9dc9QYY+rTvjfcMROSm8FTV8PqN/2uKKzBUQgETr6U6207SFU3q+owVe0F3OttK8a1TpZ4t7kqgVeAc739W9Q5APwLd0ss8lVXu6nTW/aAU6/0uxpjTGOS1cWFR8vT4fmRsHCir+WEMzgWAt1EpLOIJAHDgdcCDxCRbBGpqWEsMCngu5kiUtNUuBRY6X2njfdTgCHAp2G8htBZ/QYUrXZ9G3F+N/SMMY1O0xy49Q3odoUbqvvub3x71iNs/4J5LYXRwAxgFfCCqq4QkftF5BrvsIuBNSKyFmgFPOB9twp3m+o9EVkOCPBP7ztTvG3LgWzgd+G6hpBRhdkPQYsucMZQv6sxxjRWSU3gW1PgvFvdHYzp34PK8gYvI6zTqqvqm8Cbtbb9OuD9NGDaUb47E+hZx/bG16u87l3YugyuedzNSWOMMScqPgGu/rObmv3938HerXDDvyGlWYOVYPdMwq2mtdEsF3p+y+9qjDHRQMQNshnyJHw5F/51Jeze3GC/3oIj3L6cC5vmw4Afu6dCjTEmVM65EUa+CLs2wITLYfuqBvm1FhzhNmccNGkJvW7yuxJjTDTqcqmbILG6EiZ9zf2xGmYWHOFUkA/rP4DzR7v1ho0xJhza9ITvzISmreHfQ90zY2FkwRFOs8e5tYTzbve7EmNMtMvsAHfMgHZ5MO12+OixsA3XteAIl62fwtq3oO/3ITnd72qMMbEgtTncPB16DIF3fgVvj4XqqpD/GguOcJnzMCSlQ99RfldijIkliSlw/b+g/2hYMB42Lwn5rwjrcxwx66vPYMV0N5Iqtbnf1RhjYk1cHHztAThnZFiWprYWRzjMfRQSUqDfXX5XYoyJZWEIDbDgCL1dG2DZ83DeLW5uGWOMiTIWHKH24V8AgfPv9rsSY4wJCwuOUNqzFRY/457mzKi9ZpUxxkQHC45Q+ugxqK5wneLGGBOlLDhCZd8OyJ8EZ30TWpzidzXGGBM2FhyhMv9JqCiFAff4XYkxxoSVBUcolJXA/PFw+jegZXe/qzHGmLCy4AiFBf+EAyVuWVhjjIlyFhwnq3wfzPsbdL0c2vbyuxpjjAm7sAaHiAwWkTUisk5EflHH/o4i8p6ILBORD0QkN2BfBxF5R0RWichKEenkbe8sIvO9cz4vIv6ujrRoMpTugAvH+FqGMcY0lLAFh4jEA08AVwI9gBEiUvv593HA06raE7gf+EPAvqeBh1T1dKAPsN3b/iDwqKp2BXYBd4TrGupVeQA++it0HAAd+vlWhjHGNKRwtjj6AOtUdb2qlgNTgWtrHdMDeN97P6tmvxcwCao6E0BV96pqqYgIcCkwzfvOZGBIGK/h2JZMgT1brLVhjIkp4QyOdsCmgM8F3rZAS4Fh3vuhQLqIZAGnAsUi8rKILBaRh7wWTBZQrKqVxzgnACIySkTyRSS/qKgoRJcUoKrCTWbY7jw45eLQn98YYyKU353jY4CLRGQxcBFQCFThpnsf6O3vDZwC3BrMiVV1vKrmqWpeTk4YJhtcPg2KN8LAMSAS+vMbY0yECmdwFALtAz7netsOUtXNqjpMVXsB93rbinEtiSXeba5K4BXgXGAHkCkiCUc7Z4Ooroa5j0CrM+HUwQ3+640xxk/hDI6FQDdvFFQSMBx4LfAAEckWkZoaxgKTAr6bKSI1TYVLgZWqqri+kOu97bcAr4bxGuq26jX4ai0MvMctmGKMMTEkbP/qeS2F0cAMYBXwgqquEJH7ReQa77CLgTUishZoBTzgfbcKd5vqPRFZDgjwT+87PwfuEZF1uD6PieG6hjqpwuxxkNXVretrjDExJqxLx6rqm8Cbtbb9OuD9NA6NkKr93ZlAzzq2r8eN2PLHZ+/AtuVw7d8gLt63Mowxxi92nyUYqjD7IcjoAD1v8LsaY4zxhQVHML6YDQUL4YK7IT7R72qMMcYXFhzBmDMOmraCXjf7XYkxxvjGguN4bVroWhzn/xASU/yuxhhjfGPBcbzmjIPU5nDebX5XYowxvrLgOB5blsHat6HfXZDc1O9qjDHGVxYcx2POw5DcDPp81+9KjDHGdxYc9SlaCytfhd7fgdRMv6sxxhjfWXDUZ+4jkJAC/e/yuxJjjIkIFhzHsutLWPYC5N0GTbL9rsYYYyKCBcexfPgXN63I+T/0uxJjjIkYFhzHktnR3aJq1tbvSowxJmKEdZLDRm/Aj/2uwBhjIo61OIwxxgTFgsMYY0xQLDiMMcYExYLDGGNMUCw4jDHGBMWCwxhjTFAsOIwxxgTFgsMYY0xQRFX9riHsRKQI2HCCX88GvgphOY2BXXNssGuOfid7vR1VNaf2xpgIjpMhIvmqmud3HQ3Jrjk22DVHv3Bdr92qMsYYExQLDmOMMUGx4KjfeL8L8IFdc2ywa45+Yble6+MwxhgTFGtxGGOMCYoFhzHGmKBYcBwHEfmtiCwTkSUi8o6IRP2SgCLykIis9q57uohk+l1TuInIN0VkhYhUi0jUDtkUkcEiskZE1onIL/yuJ9xEZJKIbBeRT/2upaGISHsRmSUiK73/Tf8olOe34Dg+D6lqT1U9B3gD+LXP9TSEmcCZqtoTWAuM9bmehvApMAyY7Xch4SIi8cATwJVAD2CEiPTwt6qwewoY7HcRDawS+B9V7QH0A+4K5X9nC47joKq7Az42AaJ+RIGqvqOqld7HeUCun/U0BFVdpapr/K4jzPoA61R1vaqWA1OBa32uKaxUdTaw0+86GpKqblHVT7z3e4BVQLtQnd/WHD9OIvIA8G2gBLjE53Ia2u3A834XYUKiHbAp4HMB0NenWkwDEJFOQC9gfqjOacHhEZF3gdZ17LpXVV9V1XuBe0VkLDAauK9BCwyD+q7ZO+ZeXLN3SkPWFi7Hc83GRAsRaQq8BPy41p2Tk2LB4VHVQcd56BTgTaIgOOq7ZhG5FbgauEyj5IGfIP47R6tCoH3A51xvm4kyIpKIC40pqvpyKM9tfRzHQUS6BXy8FljtVy0NRUQGAz8DrlHVUr/rMSGzEOgmIp1FJAkYDrzmc00mxEREgInAKlV9JOTnj5I/JMNKRF4CTgOqcdOzf09Vo/qvNBFZByQDO7xN81T1ez6WFHYiMhR4DMgBioElqvo1X4sKAxH5OvBnIB6YpKoP+FtReInIc8DFuCnGtwH3qepEX4sKMxEZAMwBluP+3QL4paq+GZLzW3AYY4wJht2qMsYYExQLDmOMMUGx4DDGGBMUCw5jjDFBseAwxhgTFAsOY8JMRL4UkeyTPcaYSGHBYYwxJigWHMaEkIi8IiKLvDUQRtXa18lb42SKiKwSkWkikhZwyA9F5BMRWS4i3b3v9BGRj0VksYh8JCKnNegFGVMHCw5jQut2VT0PyAPuFpGsWvtPA/6mqqcDu4EfBOz7SlXPBZ4ExnjbVgMDVbUXbh2Y34e1emOOgwWHMaF1t4gsxa1h0h7oVmv/JlX90Hv/DDAgYF/NRHSLgE7e+wzgRW/1ukeBM8JRtDHBsOAwJkRE5GJgENBfVc8GFgMptQ6rPcdP4OcD3s8qDs1c/VtglqqeCXyjjvMZ0+AsOIwJnQxgl6qWen0U/eo4poOI9Pfe3wjMPY5z1kyoeWtIqjTmJFlwGBM6bwMJIrIK+CPudlVta3DrP68CmuP6M47lT8AfRGQxtn6OiRA2O64xDcRbwvMN77aTMY2WtTiMMcYExVocxhhjgmItDmOMMUGx4DDGGBMUCw5jjDFBseAwxhgTFAsOY4wxQfl/CgmLzm7eS1YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.log10(alpha_list), train_score)\n",
    "plt.plot(np.log10(alpha_list), test_score)\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('R^2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5S5vhi-vhjzT",
    "outputId": "42f73306-ab71-45fc-f3c2-8ce4c35c6c53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9903815817570365\n",
      "0.9827976465386884\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge(alpha=0.1)\n",
    "ridge.fit(train_scaled, train_target)\n",
    "\n",
    "print(ridge.score(train_scaled, train_target))\n",
    "print(ridge.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jUph9pH_KA9_"
   },
   "source": [
    "## 라쏘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ymu-jmekh0IK",
    "outputId": "4d1ceb8d-0bb0-434e-b588-e36884289a48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.989789897208096\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "lasso = Lasso()\n",
    "lasso.fit(train_scaled, train_target)\n",
    "print(lasso.score(train_scaled, train_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t3jO37UMh2iI",
    "outputId": "7ed9d19c-b9d8-4621-fc22-85ef53c5ef8c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9800593698421883\n"
     ]
    }
   ],
   "source": [
    "print(lasso.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uoL2oJ6Ih4Jw",
    "outputId": "773a265f-f1c0-49d8-baf6-9e090d17b739",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/disks/sdb/github/hg-mldl/.env/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+04, tolerance: 5.183e+02\n",
      "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random, positive\n",
      "/mnt/disks/sdb/github/hg-mldl/.env/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.297e+04, tolerance: 5.183e+02\n",
      "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random, positive\n"
     ]
    }
   ],
   "source": [
    "train_score = []\n",
    "test_score = []\n",
    "\n",
    "alpha_list = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "for alpha in alpha_list:\n",
    "    # 라쏘 모델을 만듭니다\n",
    "    lasso = Lasso(alpha=alpha, max_iter=10000)\n",
    "    # 라쏘 모델을 훈련합니다\n",
    "    lasso.fit(train_scaled, train_target)\n",
    "    # 훈련 점수와 테스트 점수를 저장합니다\n",
    "    train_score.append(lasso.score(train_scaled, train_target))\n",
    "    test_score.append(lasso.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "7rkH8Dvzh9UI",
    "outputId": "94fbac16-ff5e-4c40-c902-484109eaa862"
   },
   "outputs": [
    {
     "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(np.log10(alpha_list), train_score)\n",
    "plt.plot(np.log10(alpha_list), test_score)\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('R^2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t4uFD9Flh_Dw",
    "outputId": "6b33f58a-e2eb-49a5-8c31-2cfba5c787c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9888067471131867\n",
      "0.9824470598706695\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(alpha=10)\n",
    "lasso.fit(train_scaled, train_target)\n",
    "\n",
    "print(lasso.score(train_scaled, train_target))\n",
    "print(lasso.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z_bQc3s8Uoai",
    "outputId": "41abe8f0-1bfa-4fe9-b398-4164f46c0852"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(lasso.coef_ == 0))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "3-3 특성 공학과 규제.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}