4_1_regression.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/minyoung347/python101/blob/main/4_1_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BteJ4w_gohoL"
      },
      "source": [
        "## Gradient Descent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XTDcj-JohoP"
      },
      "source": [
        "**실제값을 Y=4X+6 시뮬레이션하는 데이터 값 생성**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "355NzAfAohoQ",
        "outputId": "96cc6418-818f-4e88-a3a8-505c07fa0537",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ac8540cfc70>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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lU4gaxKXH8GGpbVNTkyKRiPr165f05y0tLYrFYglfAPITn2LdFYSmW24skw3a0mN4XHB66tQpLV26VPPnz09Z+bpy5UpVVVV5OQwAARG0T7EmL3UNwnJlNwuMw9JELl9ktdQ2Eolo06ZNmjt3bpefnT59Wn/zN3+j9957T9XV1SmDj5aWFrW0tLR/H4vFVFFRwVJbII8F4cZp8hi9WK7sRaDFMtlwyflS29OnT+u6667T22+/rW3btnU7iKKiIhUVFXkxDAAB5den2ExvqKlu7vFUQS5v7l4sV/Yq0KLAOH+5HnzEA48DBw7oxRdf1IABRKsAnHO7H0Rnmd5Q093cJen/btqrKy8YrMKe9srq3Ly5O1mubOf99SLQiqPAOH85Ljg9fvy46urqVFdXJ0lqaGhQXV2d3nnnHZ0+fVrXXnutdu/erUcffVStra1qbGxUY2OjPvnkE7fHDgAZyabIMd3NXZKOnfhEn135vK1iSbf3JXFzNsFOoFX11L6MC1kpMM5fjoOP3bt3a/z48Ro/frwkacmSJRo/frzuuusuHTp0SL/+9a/13nvvady4cSorK2v/euWVV1wfPAA4le0N1e7N/cMTp9MGD17c3N2cTfC66ZvXG87BXI7TLldccYW6q1E1bKsYAEiQbVrCaQqgu/oKu2PZWX9MBQURW/Ugbm5f70dNRnyZbOe0U6nNFBirW4KJvV0A5JVsb6jpbu4dpQtk7I7l5sdq9dHHp9u/T1cPMu+SCt37/IEujzudTfCrJiOTAmOTVxshPc+bjAGASbK9oXZMFdiVKsiwO5aOgYeUuh5k694jmrJ6W9LAQ3LedMvPmox4gfGccedo8vABaQMPN+tk4D+CDwB5xY0bajxV0L9Poa3XTBVkpBtLKsnqQVLdkOO+PW2Ediy90tGsgIk1GV4XwcIfBB8A8opbN9SZo8u0887Pq3+fXimPSRfIdDeWdDqmdLq7Icef+/Hfv+PwFc4wrXU5Ox+HAzUfAPJONkWOHRX2LNC/fmmMFm+olZRZO/hUY+nXu1eXdEsyR5tPud7bI9kYTWldTmOycCD4AJCRoK80cOuG6kYgk2wsbZalG/7fq2l/d1DfYl9uyF43fbOLxmThQPABwDG/Vxp4Fei4dUN1I5DpPJbWNsv2klm7KYYw3JDdXEqM3CH4AOCIl+22U71eEJZUuhXIdAy05l0yRGuefyvtDr/5dEMO2s7HSC6rXW294GRXPAD+am2zNGX1tpT1BfGb3I6lV7ryH38vdmc1WbJAq9+nzhS0fnSy+z4f8fdKSn5Dzof3qvP7EvTUYNDkfFdbAOHkdWFjR17szmqyVIFW08nTsiR9e9pndN7APilvom4V0QZFulRXUGbM8hXBBwDb/Fxp4Gegk2t2Aq3Hf/9u2hklk1al+CFVqsvv1CCcI/gAYJufKw1MXFLp1TS+m4GWKatSciXfZsyCiuADCCGvbpJ+FjaatqTSy2l8EwOtoMqnGbMgI/gAQsbLm6SfKw1MWsHh9TS+3QDqg+YWbak7FPp0SjYI5IKB9upAiPix4ZZf7bZN2VfEj71E7OzxUhCRVjz9pr71eJ3mP7BTU1ZvYwO1JEybMUNyBB9ASPi54dbM0WXasfRKbfzaZ/WjeeO08Wufdbxpmd3XyfW+In7sJWJnj5fOl40dXJPzcydeZI60CxASfue6/SpszPUKDr+m8VMtlS2IdA08JIonU6EJWTAQfAAhEeZcdy5XcPg5jd850PqguUUrnn4z5fEUTyaXbz1PgojgAwgJct3e8LvwtWOgtaXukK3fyTSgDHMH0FzPmKF7BB9ASJi0OiRMcjmN72VAaXIHULeConzveWIyCk6BkDBldUgY5arw1aviST9WRWVq694jmrJ6m+Y/sJOVPSHGxnJAyJj8iTbocpGmcHvDOL83B3Qi3zYSDBsn92+CDyCEwpzLz0duBpQ19cc0/4GdaY/b+LXP+pqyMDkogj3sagvkOXLd4eJm8aSpq6Joi55fCD6APMBMSPC5FVCauirK1KAI3iD4AEKOGhB0ZOqqKFODIniD1S5AiJm8qgG5YeqqKNqi5xeCDyCEWtssvXzwA93xqzd82esFZ97zmvpj2lJ3SDX1x4x+X03YM6czU4MieIPVLkDIJEuzdMfvVQ255FXtS1BTWybWAgX1vQSrXYC8lapPQnfypYDPq5taqvc8ntoyuTeFiauiaIueH0i7ACHR2map6ql9jgIPSfqguSUQqYJseFX70t17Tmorc/GgaM64czR5+AACjxBi5gMIiXR9EpIpiChh19QwTm+nCxCy2Zae3hRAZpj5AEIik/RJ5w/kYVwF4yRAcMqv3hRBKmYF7GDmAwgJJ/0PCiJdAw8p+5kAE3kZIPjRm4ICTIQRMx9ASKTrkyBJ/T7VS//3CxckDTzispkJMJGXAYLXvSno04KwIvgAQiJdn4SIpFVfHqNBJfZusmFZBeNlgOBlbwqKWRFmBB9AiNhpHpVvbay9bl7lVcMuL2tVgFyj5gMImXR9Ekzd28NL8QChc+1EqUu1E170pmCjNYQZwQcQQt01j4rPBCzeUKuIlBCAhLmNtdfNq9xu2JVvM1TIL6RdgDxk4t4efghS8yo2WkOYMfMB5CnaWJstX2eokB/YWA4ADEafDwQFG8sBQEgwQ4UwIvgAAMOZuPsskA0KTgEAgK8IPgAAgK8IPgAAgK8IPgAAgK8oOAUM1tpmGbnKwdRxAQgGgg/AUKb2dzB1XACCg7QLYKCte49o8YbaLruaNjad0uINtdq69wjjAhBYBB+AYVrbLFU9tS/pjrPxx6qe2qfWNneaE7e2WaqpP6YtdYdUU38s5fP6PS4A4UXaBTDMroYPu8wsdGRJOtJ0SrsaPsy68ZSTFIqf4zIZ9S5A9hzPfGzfvl2zZ89WeXm5IpGINm/enPBzy7J01113qaysTL1799a0adN04MABt8YLdGH3k3tQHG1OfYPP5LhUnKZQ/BqXybbuPaIpq7dp/gM79a3H6zT/gZ2asnob6SbAIcfBx4kTJzR27FitXbs26c9/+MMf6sc//rHuu+8+vfrqq+rTp49mzJihU6fC+x8k5C4ACOPNYFDf4vQHOTgumUxSKH6My2TUuwDucZx2mTVrlmbNmpX0Z5Zlac2aNfrnf/5nzZkzR5L0s5/9TIMHD9bmzZs1b9687EYLI+Vq9UP8ZtD5Bhq/GaxbMCGQqy8mDuuvsmixGptOJQ0OIpJKo2em+zOVSQrFj3GZKl2wFtGZYG16ZSkpGMAGVwtOGxoa1NjYqGnTprU/Fo1GNWnSJNXU1CT9nZaWFsVisYQvBEeuPg2Gqfix86yRJC2fXSnpzE2to/j3y2dXZnWTyySF0qMg4vm4TJBsFs9JsAYgPVcLThsbGyVJgwcPTnh88ODB7T/rbOXKlaqqqnJzGPBJLj8NhqX4sbtZo3ULJnT5WalLM0qZplBmji7zdFy5lup6zBpdauv3w1zvArgp56td7rzzTi1ZsqT9+1gspoqKihyOCHblMgAIQ/GjnbTRjqVXerKyIpsUyszRZZpeWRq6FR/dXY+HXv6TrecIa70L4DZXg4/S0jOfDt5//32Vlf3lE9D777+vcePGJf2doqIiFRUVuTkM+CSXAUDQix+dzBp5MXMTT6Es3lCriJQwDjsplB4FEaNnlLqTbKmspLTXIxKRUmXxwlzvAnjB1eBj2LBhKi0t1QsvvNAebMRiMb366qtavHixmy8FA+QyAAh68aMJaaOwp1CSSZVWmXdJRdrrYf3vH1omwRqARI6Dj+PHj+vgwYPt3zc0NKiurk79+/fXkCFDdNttt+n73/++PvOZz2jYsGFatmyZysvLNXfuXDfHDQPkMgDI9pN7rpmSNgprCiWZ7tIq9z5vrxfRVy87T8/sbcybYA3wiuPgY/fu3Zo6dWr79/F6jUWLFmn9+vX63ve+pxMnTujrX/+6PvroI02ZMkVbt25VcbGZ09/IXK4DgCB/cjcpbRTkFIpddlZH2TGtslT/dHVlXgRrgJcilmUZtRYxFospGo2qqalJJSUluR4ObMj1LqdBbHfd2mZpyuptaWeNdiy90vhzCYKa+mOa/8DOjH+f6wGk5+T+nfPVLgi+XE/dB/GTe65njfKNk/QV1wPwHrvawhXxAGDOuHM0efgA/iNtQzxtVBpNTK2URosD253VVHbTV9+eNoLrAfiAmQ8gh3I9a5Qv7BZH33Llp3XLlZ/megAeI/gAciyIaaOgcZrm4noA3iLtAiAvkOYCzMHMB2BDEFfUoCvSXIAZCD6ANHK9lBjuIs0F5B5pF6Ab8a6YnVtvxzd/27r3SI5GBgDBRfCBvNDaZqmm/pi21B1STf0xtabaIazT76Triln11D5bzwUA+AvSLgi9TNMmJmz+BgBhxMwHQi2btInXm79lMhuD7PCeA2Zg5gOhlS5tEtGZtMn0ytKkqx283PyNIlb/8Z4D5mDmA6HlJG2STLwrZqpFmBGduXlNHNbf0bieef2IvkkRq68oHAbMQvCB0Mo2bRLviimpSwCS6WZjz7x+WLdsrE36M4pYs5MqpULhMGAe0i4ILTfSJtMrS3XbtBF6+OUGffTx6fbHSzOYrt+694j+4bE93R5DEWsiu83dukupRHsXUjgMGIbgA6FldzOxVGmTZDe0fr176cbLztMtV37G0YxH/NO3XZkWsYaJ3RqNeEql8zWOp1T+/rLzbL0e7zngH9IuCK1s0iapagSaPj6tNc8f0HP7Gh2NJV39SWeZFLGGid0aDTsplU11h2y9Zr6/54CfCD4QaplsJuZFjYCTT9WZFLGGiZP3305R8YcnTqt/n0LXC4cBZI60C0LP6WZiXjQXc/Kp2mkRa9g4ef/tBnVzx5Xr4Zf/pIiUENRkWjgMIDsEH8gLTjYT86K5WLr6E0kqiEg/nc/W7k7ef7tB3fTKUk0c1r9LDUkmhcMAskfwAXTiRXOxeP3J4g21XT59x/10/nh94SJugk7efydFxT0KIo5mwAB4h5oPhIKbbbO9ai6Wqv6kLFqs+xZM0BcuKs9swCHj5P13WlQcnwGbM+4cTR4+gMADyJGIZVlGddaJxWKKRqNqampSSUlJroeDAPCibXZ8tYWUvEYgVbGqHXZ7V+Qzp+8/rdOB3HNy/yb4QKCl6vHgRpDADS23nL7/BHVAbhF85Kl8+49va5ulKau3pVwZEc/371h6ZcbvQ769p6bh/QeCw8n9m4LTkMjHT+leLIntzMkqGbiP9x8IJwpOQyBfd+z0YkksgsnNgmMA3mPmI+DSdYOM6Ew3yOmVpaGbrvZiSSy6Mj31kY+zfkDQEXwEnB+pB1Nlu3Ec0jP9xp5uU7lsCo4BeIe0S8Dlc+ohm43jkJ7p6Twv9uAB4A+Cj4DL99RDJhvHIb0g3NidzPoBMAtpl4Aj9eB84zikF4R0Xj7P+gFBR/ARcN3tGZJPqQeWZLorCDf2fJ/1A4KMtEsIkHqA24JwY/dqDx4A3mPmIyRIPcBNQUjnMesHBBczHyHCjp1wS1BWEjHrBwQTe7sASMn0Ph9xpjdCA/IBG8sBcA03dgB2sLEcANewkgiA2wg+gDzmZFaDGRAAbiH4APKUk3qOoNR+AAgGVrsAecjJvi2m7/ECIHgIPoA842TfliDs8QIgeAg+gDzjZN8WNm8D4AVqPoA848W+LWzeBsAJgg8gz3ixbwubtwFwguADyDNO920xfY8XAMFDzQccaW2zVFN/TFvqDqmm/hiFhgHkZN+WoOzxAiBYaK8O2+j1EC70+QDgJvZ2gevivR46/7HEP++yg2gw0eEUgFvY2wWuStfrIaIzvR6mV5ZyMwoYJ/u2sMcLALdQ84G06PVArQsAuImZD6TlRV+IIKHeAQDc5frMR2trq5YtW6Zhw4apd+/eGj58uFasWCHDSkvQSXef7L3oCxEU7GsCAO5zfeZj9erVWrdunR555BGNGjVKu3fv1o033qhoNKpbb73V7ZeDC9J9snfaFyIsqHUBAG+4PvPxyiuvaM6cObr66qt13nnn6dprr9VVV12lXbt2uf1SSMJpbYKdT/b52uuBWhcA8IbrMx+XXnqp7r//fr311lsaMWKE/uu//ks7duzQPffck/T4lpYWtbS0tH8fi8XcHlKgZLOc0WltgpNP9jNHl2ndggldnr80xLUP+V7rAgBecT34uOOOOxSLxXTBBReoR48eam1t1Q9+8APdcMMNSY9fuXKlqqqq3B5GIGVT2JiqD0d8BiNZHw4nn+wnDx+gmaPLNL2yNG96PeRzrQsAeMn1tMsTTzyhRx99VI899phqa2v1yCOP6O6779YjjzyS9Pg777xTTU1N7V/vvvuu20PyTTbLMbMpbEw3gyGdmcHoPJ5MPtnHez3MGXeOJg8f4Hvg4eeS13itS6ozjOhMcBi2WhcA8JrrMx+333677rjjDs2bN0+SNGbMGL399ttauXKlFi1a1OX4oqIiFRUVuT0M32Uza5FtYaPTGYy4oH2y93vJa7zWZfGGWkWkhOsT5loXAPCa6zMfJ0+eVEFB4tP26NFDbW1tbr+UMbJdjpltYaPdGYzGWOJxQfpkn6slr/Fal9JoYgBWGi2mpTwAZMj1mY/Zs2frBz/4gYYMGaJRo0Zpz549uueee/T3f//3br+UEdxYjpltYaPdmYkVv/mDevcqaL9hBuWTfa6XvOZbrQsAeM31mY+f/OQnuvbaa/UP//APuvDCC/Xd735X3/jGN7RixQq3X8oIbizHzDb9kW4GI+7DE6e7zBIE4ZO9CUtec13rAgBh4vrMR9++fbVmzRqtWbPG7ac2khvLMbNt4tXdDEYynWcJTP9kz5JXAAgXNpbLkhtFm2408YrPYJzVp7DbcaSaJTD5k33QCmMBAN0j+MiSW0WbbqQ/Zo4u07KrL7Q17iDNEgSpMBYAkB672mbJzaJNN9IfpdHeto4L0ixBUApjAQD2MPPhAjeLNrNNf4R1liAIhbEAAHsilmF73cdiMUWjUTU1NamkpCTXw3Ekm31Z3BTviSElnyUI8s3alPcYAJDIyf2b4MMDJtwg/e4GCgDIb07u39R8uMyUm77py2cBAPmLmQ8XpdpZtrt0hwmzJAAAZIuZjxzIpAW4KbMkAAD4idUuLnHaAjxXG6UBAJBrBB8ucdICPN0siXRmlqS17S9HtLZZqqk/pi11h1RTfyzhZwAABAlpF5c4aQHuZJZk8vABpGdcRp0NAOQWwYdLnGwO95vXD9t6zqPNp1IWscbTM6b37DDtRk8gBwC5R/DhEictwO3OkgzsU6Tv/vK/HBWxmsS0G32qQO5I0yl9c0Otvj1thG658tNGvpcAECbUfLjIbgtwuy3QFZGjIlaTmFZQ212dTdy9z7+ly1a9QLEvAHiMmQ+X2WnuZXeW5IPjLbZe07QdajNZduy1dHU2cY2xlkCkswAgyJj58ICdzeHszJI4KWI1idNlx35wGqB1Xm0EAHAPMx85lG6WxEkRq0mcLDv2i5MArfNqIwCAu5j5yLHuZkni6RlJXepDOhexmsTEGZt0dTbJmJbOAoCwIPgwnN0iVpPYLaj1c8amYyBnl2npLAAIC9IuARC0HWrjN/pvbqhN+nNLuZmxiQdy//LrfWqMpZ7VMDWdBQBhQfAREPH0DLITD+R+uu2A7n3+QJefm5zOAoCwIO0C18WX2qYSX2qbq9UkPQoi+ta0EbpvwYQz/VQ6MDmdBQBhwcyHx0xrL+4Hp3vX5ErQ0lkAEBYEHx4yrb24X0xcapsK6SwA8B9pF4+40V68tc1STf0xbak7pJr6Y4FpeuXmUtugvgcAgNSY+fCAG+3Fgzxr4lZztCC/BwCA1Jj58EC27cVN25TNKTeaowX9PQAApEbw4YFsah7SzZpIwdh3JJvmaGF5DwAAyZF28UA2NQ9erRTJxaqbTFeTBGW1DAAgMwQfHsim5sGLlSK5rJ3IZDVJkFbLAACcI+3igWxqHtzelC2ItRMmbkwHAHAPwYdHMq15cHNTtqDWTpi4MR0AwD2kXTyUSc1DfNZk8YZaRaSEwMHpviNBrZ1w8z0AAJiHmQ+PxWse5ow7R5OHD7B1w8xmpUhHQa6dcOs9AACYh5kPQ7mx70jQayfYewUAwongw2DdrRSxs3TWrU6jucTeKwAQPgQfAWR36Sy1EwAAE1HzETBOl85SOwEAMA0zHwGS6YZ11E4AAExC8BEg2SydpXYCAGAK0i4BEuSlswAAxDHzYZjuVrEEfeksAAASwYdR0q1iCcPSWQAASLsYws4qlmw2rAMAwBQEHwZwsgEcS2cBAEFH2sUATlexsHQWABBkBB+y16rcS5msYmHpLAAgqPI++LDbqtxLrGIBAOSTvK75cNqq3CvxVSyp5loiOhMQsYoFABAGeRN8tLZZqqk/pi11h1RTf0yf/E+b7SJPr7GKBQCQT/Ii7ZIstdK/T6E+PPFJyt/prlW5F+KrWDqPs9TnFBAAAF4LffART610nr/oLvDoyM9W5axiAQDkA0/SLocOHdKCBQs0YMAA9e7dW2PGjNHu3bu9eKluddc/wy6/izzjq1jmjDtHk4cPIPAAAISO6zMff/7zn3XZZZdp6tSp+u1vf6uzzz5bBw4c0FlnneX2S6WVrn9Gd2hVDgCAN1wPPlavXq2Kigo9/PDD7Y8NGzbM7ZexJdOUCUWeAAB4x/W0y69//WtdfPHF+spXvqJBgwZp/PjxeuCBB1Ie39LSolgslvDlFrspk77FPRK+D0qr8s4rePxYmQMAQLZcn/n44x//qHXr1mnJkiX6p3/6J/3+97/XrbfeqsLCQi1atKjL8StXrlRVVZXbw5CktLvASlJBRGo+1dr+ff8+hVp2tfmrS0xojgYAQCYilmW5+nG5sLBQF198sV555ZX2x2699Vb9/ve/V01NTZfjW1pa1NLS0v59LBZTRUWFmpqaVFJSkvV44qtdJNkqPI0nWUye+Ui1gicIYwcAhFMsFlM0GrV1/3Y97VJWVqbKysqExy688EK98847SY8vKipSSUlJwpebUu0Cm6qUw+8GY0452QHXyXOSvgEA+MX1tMtll12m/fv3Jzz21ltvaejQoW6/lG2d+2d80NyiFU+/mfJ4vxuMOeF0B9x0SN8AAPzm+szHt7/9be3cuVP/+q//qoMHD+qxxx7T/fffr5tvvtntl3KkY/+MgX2LbP2Onw3G7Hp+X6Ot4+yM3ZS9bQAA+cX14OOSSy7Rpk2btHHjRo0ePVorVqzQmjVrdMMNN7j9UhkL6i6yrW2WNtUdsnVsurF7kb4BAMAOT9qrf/GLX9QXv/hFL57aFelWwSRrMNbaZuW87fmuhg/14YnTaY8b0KcwbXM0t9M3AADYFfq9XZKJ7yK7eEOtIkpcBZOswZgpdRF200BzxpWnDYzsPpeJqScAQLB5srdLEKRaBdO5wZhJdRF200DTK0tdey7TUk8AgODLy5mPuHS7yKari4joTF3E9MpSX1IwdpqmldncjyaT1BMAAG7I25mPuO52kXVSF+GHeLpI+kt6KC7yv19296NJ91xy8FwAADiR98FHd0ysi7CbLvL7uQAAsCuv0y5xqVaymFoXkS5dlKvnAgDAjrwPPrpbyTK9stTYuoh4usi05wIAIJ28TrukW8ny3L5G6iIAAHBZ3gYfdjt8Tq8spS4CAAAX5W3axclKFuoiAABwT94GH05XslAXAQCAO/I27WLqShYAAMIub4OPeIfPVImTiOx3CwUAAPblbfBhYofP1jZLNfXHtKXukGrqj7GdPQAglPK25kP6S4fPzn0+SnOwY60pO+cCAOC1iGVZRn28jsViikajampqUklJiS+vmarDqV/i/UY6X4j4CFjSCwAwnZP7d17PfMTlciWLaTvnAgDgtbyt+TCFaTvnAgDgNYKPHDNx51wAALxE8JFj9BsBAOQbgo8co98IACDfEHzkmIn9RgAA8BLBhwHi/UbYORcAkA9YamsIds4FAOQLgg+DsHMuACAfkHYBAAC+IvgAAAC+IvgAAAC+IvgAAAC+IvgAAAC+IvgAAAC+IvgAAAC+IvgAAAC+IvgAAAC+Mq7DqWVZkqRYLJbjkQAAALvi9+34fbw7xgUfzc3NkqSKioocjwQAADjV3NysaDTa7TERy06I4qO2tjYdPnxYffv2VSTizqZqsVhMFRUVevfdd1VSUuLKc5qGcwyPfDhPzjE88uE8OUd7LMtSc3OzysvLVVDQfVWHcTMfBQUFOvfccz157pKSktD+4cRxjuGRD+fJOYZHPpwn55heuhmPOApOAQCArwg+AACAr/Ii+CgqKtLy5ctVVFSU66F4hnMMj3w4T84xPPLhPDlH9xlXcAoAAMItL2Y+AACAOQg+AACArwg+AACArwg+AACArwIZfKxdu1bnnXeeiouLNWnSJO3atavb43/xi1/oggsuUHFxscaMGaNnnnkm4eeWZemuu+5SWVmZevfurWnTpunAgQNenoItTs7zgQce0Oc+9zmdddZZOuusszRt2rQux//d3/2dIpFIwtfMmTO9Po1uOTnH9evXdxl/cXFxwjEmXksn53jFFVd0OcdIJKKrr766/RjTruP27ds1e/ZslZeXKxKJaPPmzWl/p7q6WhMmTFBRUZE+/elPa/369V2Ocfrv3GtOz/PJJ5/U9OnTdfbZZ6ukpESTJ0/W7373u4Rj/uVf/qXLtbzgggs8PIvuOT3H6urqpH+vjY2NCceZdC2dnmOyf2+RSESjRo1qP8a067hy5Updcskl6tu3rwYNGqS5c+dq//79aX/Pz3tl4IKP//iP/9CSJUu0fPly1dbWauzYsZoxY4aOHj2a9PhXXnlF8+fP11e/+lXt2bNHc+fO1dy5c7V37972Y374wx/qxz/+se677z69+uqr6tOnj2bMmKFTp075dVpdOD3P6upqzZ8/Xy+++KJqampUUVGhq666SocOHUo4bubMmTpy5Ej718aNG/04naScnqN0pvtex/G//fbbCT837Vo6Pccnn3wy4fz27t2rHj166Ctf+UrCcSZdxxMnTmjs2LFau3atreMbGhp09dVXa+rUqaqrq9Ntt92mm266KeHGnMnfhtecnuf27ds1ffp0PfPMM3rttdc0depUzZ49W3v27Ek4btSoUQnXcseOHV4M3xan5xi3f//+hHMYNGhQ+89Mu5ZOz/FHP/pRwrm9++676t+/f5d/kyZdx5deekk333yzdu7cqeeee06nT5/WVVddpRMnTqT8Hd/vlVbATJw40br55pvbv29tbbXKy8utlStXJj3+uuuus66++uqExyZNmmR94xvfsCzLstra2qzS0lLr3/7t39p//tFHH1lFRUXWxo0bPTgDe5yeZ2f/8z//Y/Xt29d65JFH2h9btGiRNWfOHLeHmjGn5/jwww9b0Wg05fOZeC2zvY733nuv1bdvX+v48ePtj5l2HTuSZG3atKnbY773ve9Zo0aNSnjs+uuvt2bMmNH+fbbvm9fsnGcylZWVVlVVVfv3y5cvt8aOHevewFxk5xxffPFFS5L15z//OeUxJl/LTK7jpk2brEgkYv3pT39qf8zk62hZlnX06FFLkvXSSy+lPMbve2WgZj4++eQTvfbaa5o2bVr7YwUFBZo2bZpqamqS/k5NTU3C8ZI0Y8aM9uMbGhrU2NiYcEw0GtWkSZNSPqfXMjnPzk6ePKnTp0+rf//+CY9XV1dr0KBBGjlypBYvXqxjx465Ona7Mj3H48ePa+jQoaqoqNCcOXP0hz/8of1npl1LN67jgw8+qHnz5qlPnz4Jj5tyHTOR7t+kG++bidra2tTc3Nzl3+SBAwdUXl6u888/XzfccIPeeeedHI0wc+PGjVNZWZmmT5+ul19+uf3xMF7LBx98UNOmTdPQoUMTHjf5OjY1NUlSl7+9jvy+VwYq+Pjggw/U2tqqwYMHJzw+ePDgLjnGuMbGxm6Pj/+vk+f0Wibn2dnSpUtVXl6e8Icyc+ZM/exnP9MLL7yg1atX66WXXtKsWbPU2trq6vjtyOQcR44cqYceekhbtmzRhg0b1NbWpksvvVTvvfeeJPOuZbbXcdeuXdq7d69uuummhMdNuo6ZSPVvMhaL6eOPP3bl799Ed999t44fP67rrruu/bFJkyZp/fr12rp1q9atW6eGhgZ97nOfU3Nzcw5Hal9ZWZnuu+8+/epXv9KvfvUrVVRU6IorrlBtba0kd/5bZpLDhw/rt7/9bZd/kyZfx7a2Nt1222267LLLNHr06JTH+X2vNG5XW2Rv1apVevzxx1VdXZ1QkDlv3rz2/z9mzBhddNFFGj58uKqrq/X5z38+F0N1ZPLkyZo8eXL795deeqkuvPBC/fu//7tWrFiRw5F548EHH9SYMWM0ceLEhMeDfh3z0WOPPaaqqipt2bIloR5i1qxZ7f//oosu0qRJkzR06FA98cQT+upXv5qLoToycuRIjRw5sv37Sy+9VPX19br33nv185//PIcj88Yjjzyifv36ae7cuQmPm3wdb775Zu3duzenNSjJBGrmY+DAgerRo4fef//9hMfff/99lZaWJv2d0tLSbo+P/6+T5/RaJucZd/fdd2vVqlV69tlnddFFF3V77Pnnn6+BAwfq4MGDWY/ZqWzOMa5Xr14aP358+/hNu5bZnOOJEyf0+OOP2/oPVy6vYyZS/ZssKSlR7969XfnbMMnjjz+um266SU888USXae3O+vXrpxEjRgTmWiYzceLE9vGH6VpalqWHHnpICxcuVGFhYbfHmnIdb7nlFv3mN7/Riy++qHPPPbfbY/2+VwYq+CgsLNRf/dVf6YUXXmh/rK2tTS+88ELCJ+KOJk+enHC8JD333HPtxw8bNkylpaUJx8RiMb366qspn9NrmZyndKYSecWKFdq6dasuvvjitK/z3nvv6dixYyorK3Nl3E5keo4dtba26o033mgfv2nXMptz/MUvfqGWlhYtWLAg7evk8jpmIt2/STf+NkyxceNG3Xjjjdq4cWPCculUjh8/rvr6+sBcy2Tq6uraxx+ma/nSSy/p4MGDtj4Q5Po6WpalW265RZs2bdK2bds0bNiwtL/j+73ScYlqjj3++ONWUVGRtX79emvfvn3W17/+datfv35WY2OjZVmWtXDhQuuOO+5oP/7ll1+2evbsad19993Wm2++aS1fvtzq1auX9cYbb7Qfs2rVKqtfv37Wli1brNdff92aM2eONWzYMOvjjz/2/fzinJ7nqlWrrMLCQuuXv/yldeTIkfav5uZmy7Isq7m52frud79r1dTUWA0NDdbzzz9vTZgwwfrMZz5jnTp1KhDnWFVVZf3ud7+z6uvrrddee82aN2+eVVxcbP3hD39oP8a0a+n0HOOmTJliXX/99V0eN/E6Njc3W3v27LH27NljSbLuuecea8+ePdbbb79tWZZl3XHHHdbChQvbj//jH/9ofepTn7Juv/12680337TWrl1r9ejRw9q6dWv7Menet1xwep6PPvqo1bNnT2vt2rUJ/yY/+uij9mO+853vWNXV1VZDQ4P18ssvW9OmTbMGDhxoHT161Pfzsyzn53jvvfdamzdvtg4cOGC98cYb1re+9S2roKDAev7559uPMe1aOj3HuAULFliTJk1K+pymXcfFixdb0WjUqq6uTvjbO3nyZPsxub5XBi74sCzL+slPfmINGTLEKiwstCZOnGjt3Lmz/WeXX365tWjRooTjn3jiCWvEiBFWYWGhNWrUKOvpp59O+HlbW5u1bNkya/DgwVZRUZH1+c9/3tq/f78fp9ItJ+c5dOhQS1KXr+XLl1uWZVknT560rrrqKuvss8+2evXqZQ0dOtT62te+ltP/mFuWs3O87bbb2o8dPHiw9YUvfMGqra1NeD4Tr6XTv9f//u//tiRZzz77bJfnMvE6xpdbdv6Kn9eiRYusyy+/vMvvjBs3ziosLLTOP/986+GHH+7yvN29b7ng9Dwvv/zybo+3rDNLjMvKyqzCwkLrnHPOsa6//nrr4MGD/p5YB07PcfXq1dbw4cOt4uJiq3///tYVV1xhbdu2rcvzmnQtM/l7/eijj6zevXtb999/f9LnNO06Jjs/SQn/znJ9r4z870ABAAB8EaiaDwAAEHwEHwAAwFcEHwAAwFcEHwAAwFcEHwAAwFcEHwAAwFcEHwAAwFcEHwAAwFcEHwAAwFcEHwAAwFcEHwAAwFcEHwAAwFf/H9Tcrt0L9TXRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "np.random.seed(0)\n",
        "# y = 4X + 6 식을 근사(w1=4, w0=6). random 값은 Noise를 위해 만듬\n",
        "X = 2 * np.random.rand(100,1)\n",
        "y = 6 +4 * X+ np.random.randn(100,1)\n",
        "\n",
        "# X, y 데이터 셋 scatter plot으로 시각화\n",
        "plt.scatter(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "kjWcb_lAohoR",
        "outputId": "a6d6fff7-a66e-4c12-b327-616b890b7eab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((100, 1), (100, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "X.shape, y.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8dpDJWqohoS"
      },
      "source": [
        "**w0과 w1의 값을 최소화 할 수 있도록 업데이트 수행하는 함수 생성**\n",
        "\n",
        "* 예측 배열 y_pred는 np.dot(X, w1.T) + w0 임\n",
        "100개의 데이터 X(1,2,...,100)이 있다면 예측값은 w0 + X(1)*w1 + X(2)*w1 +..+ X(100)*w1이며, 이는 입력 배열 X와 w1 배열의 내적임.\n",
        "* 새로운 w1과 w0를 update함\n",
        "![](https://github.com/minyoung347/python101/blob/main/image01.png?raw=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "AGDYsOtaohoS"
      },
      "outputs": [],
      "source": [
        "# w1 과 w0 를 업데이트 할 w1_update, w0_update를 반환.\n",
        "def get_weight_updates(w1, w0, X, y, learning_rate=0.01):\n",
        "    N = len(y)\n",
        "    # 먼저 w1_update, w0_update를 각각 w1, w0의 shape와 동일한 크기를 가진 0 값으로 초기화\n",
        "    w1_update = np.zeros_like(w1)\n",
        "    w0_update = np.zeros_like(w0)\n",
        "    # 예측 배열 계산하고 예측과 실제 값의 차이 계산\n",
        "    y_pred = np.dot(X, w1.T) + w0\n",
        "    diff = y-y_pred\n",
        "\n",
        "    # w0_update를 dot 행렬 연산으로 구하기 위해 모두 1값을 가진 행렬 생성\n",
        "    w0_factors = np.ones((N,1))\n",
        "\n",
        "    # w1과 w0을 업데이트할 w1_update와 w0_update 계산\n",
        "    w1_update = -(2/N)*learning_rate*(np.dot(X.T, diff))\n",
        "    w0_update = -(2/N)*learning_rate*(np.dot(w0_factors.T, diff))\n",
        "\n",
        "    return w1_update, w0_update"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "t-r6ef7HohoT",
        "outputId": "4b1286e6-8335-4a86-cd55-12b621480e21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 1)\n",
            "(1, 1) (1, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[0.]]), array([[0.]]))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "w0 = np.zeros((1,1))\n",
        "w1 = np.zeros((1,1))\n",
        "y_pred = np.dot(X, w1.T) + w0\n",
        "diff = y-y_pred\n",
        "print(diff.shape)\n",
        "w0_factors = np.ones((100,1))\n",
        "w1_update = -(2/100)*0.01*(np.dot(X.T, diff))\n",
        "w0_update = -(2/100)*0.01*(np.dot(w0_factors.T, diff))\n",
        "print(w1_update.shape, w0_update.shape)\n",
        "w1, w0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktpF7PoeohoT"
      },
      "source": [
        "**반복적으로 경사 하강법을 이용하여 get_weigth_updates()를 호출하여 w1과 w0를 업데이트 하는 함수 생성**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "iazpuIH_ohoU"
      },
      "outputs": [],
      "source": [
        "# 입력 인자 iters로 주어진 횟수만큼 반복적으로 w1과 w0를 업데이트 적용함.\n",
        "def gradient_descent_steps(X, y, iters=10000):\n",
        "    # w0와 w1을 모두 0으로 초기화.\n",
        "    w0 = np.zeros((1,1))\n",
        "    w1 = np.zeros((1,1))\n",
        "\n",
        "    # 인자로 주어진 iters 만큼 반복적으로 get_weight_updates() 호출하여 w1, w0 업데이트 수행.\n",
        "    for ind in range(iters):\n",
        "        w1_update, w0_update = get_weight_updates(w1, w0, X, y, learning_rate=0.01)\n",
        "        w1 = w1 - w1_update\n",
        "        w0 = w0 - w0_update\n",
        "\n",
        "    return w1, w0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpUVe1GTohoU"
      },
      "source": [
        "**예측 오차 비용을 계산을 수행하는 함수 생성 및 경사 하강법 수행**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "_NPYhIAfohoV",
        "outputId": "bdc02827-0dab-4eda-eac5-48868bbb4120",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "w1:4.022 w0:6.162\n",
            "Gradient Descent Total Cost:0.9935\n"
          ]
        }
      ],
      "source": [
        "def get_cost(y, y_pred):\n",
        "    N = len(y)\n",
        "    cost = np.sum(np.square(y - y_pred))/N\n",
        "    return cost\n",
        "\n",
        "w1, w0 = gradient_descent_steps(X, y, iters=1000)\n",
        "print(\"w1:{0:.3f} w0:{1:.3f}\".format(w1[0,0], w0[0,0]))\n",
        "y_pred = w1[0,0] * X + w0\n",
        "print('Gradient Descent Total Cost:{0:.4f}'.format(get_cost(y, y_pred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "scrolled": true,
        "id": "kM8tK6ncohoV",
        "outputId": "662b9d84-4713-4dc8-9461-fc8f4b2e59ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ac85416ac50>]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(X, y)\n",
        "plt.plot(X,y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ay6gq9DpohoV"
      },
      "source": [
        "**미니 배치 확률적 경사 하강법을 이용한 최적 비용함수 도출**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "3syQ1CWVohoV"
      },
      "outputs": [],
      "source": [
        "def stochastic_gradient_descent_steps(X, y, batch_size=10, iters=1000):\n",
        "    w0 = np.zeros((1,1))\n",
        "    w1 = np.zeros((1,1))\n",
        "    prev_cost = 100000\n",
        "    iter_index =0\n",
        "\n",
        "    for ind in range(iters):\n",
        "        np.random.seed(ind)\n",
        "        # 전체 X, y 데이터에서 랜덤하게 batch_size만큼 데이터 추출하여 sample_X, sample_y로 저장\n",
        "        stochastic_random_index = np.random.permutation(X.shape[0])\n",
        "        sample_X = X[stochastic_random_index[0:batch_size]]\n",
        "        sample_y = y[stochastic_random_index[0:batch_size]]\n",
        "        # 랜덤하게 batch_size만큼 추출된 데이터 기반으로 w1_update, w0_update 계산 후 업데이트\n",
        "        w1_update, w0_update = get_weight_updates(w1, w0, sample_X, sample_y, learning_rate=0.01)\n",
        "        w1 = w1 - w1_update\n",
        "        w0 = w0 - w0_update\n",
        "\n",
        "    return w1, w0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x4dQMimEohoW",
        "outputId": "feeeb45a-76c1-4ef6-d26b-48236c5689fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "w1: 4.028 w0: 6.156\n",
            "Stochastic Gradient Descent Total Cost:0.9937\n"
          ]
        }
      ],
      "source": [
        "w1, w0 = stochastic_gradient_descent_steps(X, y, iters=1000)\n",
        "print(\"w1:\",round(w1[0,0],3),\"w0:\",round(w0[0,0],3))\n",
        "y_pred = w1[0,0] * X + w0\n",
        "print('Stochastic Gradient Descent Total Cost:{0:.4f}'.format(get_cost(y, y_pred)))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waWVBSgcohoW"
      },
      "source": [
        "## 사이킷런 LinearRegression을 이용한 보스턴 주택 가격 예측"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -r python101/\n",
        "!git clone https://github.com/minyoung347/python101.git"
      ],
      "metadata": {
        "id": "drlQoKO7uAi2",
        "outputId": "0d59499f-1930-49b9-9302-fe4a2c25143c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'python101/': No such file or directory\n",
            "Cloning into 'python101'...\n",
            "remote: Enumerating objects: 918, done.\u001b[K\n",
            "remote: Counting objects: 100% (918/918), done.\u001b[K\n",
            "remote: Compressing objects: 100% (493/493), done.\u001b[K\n",
            "remote: Total 918 (delta 471), reused 811 (delta 421), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (918/918), 104.06 MiB | 7.94 MiB/s, done.\n",
            "Resolving deltas: 100% (471/471), done.\n",
            "Updating files: 100% (66/66), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "bostonDF = pd.read_csv('/content/python101/input/BostonHousing.csv')\n",
        "bostonDF.columns = ['CRIM', 'ZN', 'INDUS',\t'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'PRICE']"
      ],
      "metadata": {
        "id": "-WZJGjgJpR7X"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bostonDF"
      ],
      "metadata": {
        "id": "E31oOaogp_sg",
        "outputId": "193495a4-7afc-484f-b5fb-24f7e7bef9d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  \\\n",
              "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
              "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
              "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
              "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
              "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
              "..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
              "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   \n",
              "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   \n",
              "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   \n",
              "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   \n",
              "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   \n",
              "\n",
              "     PTRATIO       B  LSTAT  PRICE  \n",
              "0       15.3  396.90   4.98   24.0  \n",
              "1       17.8  396.90   9.14   21.6  \n",
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              "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]"
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              "<p>506 rows × 14 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "bostonDF",
              "summary": "{\n  \"name\": \"bostonDF\",\n  \"rows\": 506,\n  \"fields\": [\n    {\n      \"column\": \"CRIM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.60154510533249,\n        \"min\": 0.00632,\n        \"max\": 88.9762,\n        \"num_unique_values\": 504,\n        \"samples\": [\n          0.09178,\n          0.05644,\n          0.10574\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ZN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.32245299451514,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          25.0,\n          30.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDUS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.860352940897585,\n        \"min\": 0.46,\n        \"max\": 27.74,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          8.14,\n          1.47,\n          1.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHAS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NOX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11587767566755595,\n        \"min\": 0.385,\n        \"max\": 0.871,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          0.401,\n          0.538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RM\",\n      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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 24,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 187,\n        \"max\": 711,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          370,\n          666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PTRATIO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1649455237144406,\n        \"min\": 12.6,\n        \"max\": 22.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          19.6,\n          15.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 91.29486438415783,\n        \"min\": 0.32,\n        \"max\": 396.9,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          396.24,\n          395.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LSTAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.141061511348571,\n        \"min\": 1.73,\n        \"max\": 37.97,\n        \"num_unique_values\": 455,\n        \"samples\": [\n          6.15,\n          4.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PRICE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.197104087379818,\n        \"min\": 5.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          14.1,\n          22.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "mAgMi2V1ohoW",
        "outputId": "67879903-e8be-4323-8814-b09c2b2de082",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Boston 데이타셋 크기 : (506, 14)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
              "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
              "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
              "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
              "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
              "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
              "\n",
              "        B  LSTAT  PRICE  \n",
              "0  396.90   4.98   24.0  \n",
              "1  396.90   9.14   21.6  \n",
              "2  392.83   4.03   34.7  \n",
              "3  394.63   2.94   33.4  \n",
              "4  396.90   5.33   36.2  "
            ],
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              "type": "dataframe",
              "variable_name": "bostonDF",
              "summary": "{\n  \"name\": \"bostonDF\",\n  \"rows\": 506,\n  \"fields\": [\n    {\n      \"column\": \"CRIM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.60154510533249,\n        \"min\": 0.00632,\n        \"max\": 88.9762,\n        \"num_unique_values\": 504,\n        \"samples\": [\n          0.09178,\n          0.05644,\n          0.10574\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ZN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.32245299451514,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          25.0,\n          30.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDUS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.860352940897585,\n        \"min\": 0.46,\n        \"max\": 27.74,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          8.14,\n          1.47,\n          1.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHAS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NOX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11587767566755595,\n        \"min\": 0.385,\n        \"max\": 0.871,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          0.401,\n          0.538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7026171434153233,\n        \"min\": 3.561,\n        \"max\": 8.78,\n        \"num_unique_values\": 446,\n        \"samples\": [\n          6.849,\n          4.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.148861406903617,\n        \"min\": 2.9,\n        \"max\": 100.0,\n        \"num_unique_values\": 356,\n        \"samples\": [\n          51.8,\n          33.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DIS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.105710126627611,\n        \"min\": 1.1296,\n        \"max\": 12.1265,\n        \"num_unique_values\": 412,\n        \"samples\": [\n          2.2955,\n          4.2515\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 24,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 187,\n        \"max\": 711,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          370,\n          666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PTRATIO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1649455237144406,\n        \"min\": 12.6,\n        \"max\": 22.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          19.6,\n          15.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 91.29486438415783,\n        \"min\": 0.32,\n        \"max\": 396.9,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          396.24,\n          395.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LSTAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.141061511348571,\n        \"min\": 1.73,\n        \"max\": 37.97,\n        \"num_unique_values\": 455,\n        \"samples\": [\n          6.15,\n          4.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PRICE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.197104087379818,\n        \"min\": 5.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          14.1,\n          22.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "# from sklearn.datasets import load_boston\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')  #사이킷런 1.2 부터는 보스턴 주택가격 데이터가 없어진다는 warning 메시지 출력 제거\n",
        "%matplotlib inline\n",
        "\n",
        "# boston 데이타셋 로드\n",
        "# boston = load_boston()\n",
        "\n",
        "# boston 데이타셋 DataFrame 변환\n",
        "# bostonDF = pd.DataFrame(boston.data , columns = boston.feature_names)\n",
        "\n",
        "# boston dataset의 target array는 주택 가격임. 이를 PRICE 컬럼으로 DataFrame에 추가함.\n",
        "# bostonDF['PRICE'] = boston.target\n",
        "print('Boston 데이타셋 크기 :',bostonDF.shape)\n",
        "bostonDF.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc_9iNKPohoW"
      },
      "source": [
        "* CRIM: 지역별 범죄 발생률  \n",
        "* ZN: 25,000평방피트를 초과하는 거주 지역의 비율\n",
        "* NDUS: 비상업 지역 넓이 비율\n",
        "* CHAS: 찰스강에 대한 더미 변수(강의 경계에 위치한 경우는 1, 아니면 0)\n",
        "* NOX: 일산화질소 농도\n",
        "* RM: 거주할 수 있는 방 개수\n",
        "* AGE: 1940년 이전에 건축된 소유 주택의 비율\n",
        "* DIS: 5개 주요 고용센터까지의 가중 거리\n",
        "* RAD: 고속도로 접근 용이도\n",
        "* TAX: 10,000달러당 재산세율\n",
        "* PTRATIO: 지역의 교사와 학생 수 비율\n",
        "* B: 지역의 흑인 거주 비율\n",
        "* LSTAT: 하위 계층의 비율\n",
        "* MEDV: 본인 소유의 주택 가격(중앙값)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjuobPVpohoX"
      },
      "source": [
        "* 각 컬럼별로 주택가격에 미치는 영향도를 조사"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Yw6wmWFMohoX",
        "outputId": "6e32395c-96bc-4124-c9f0-c9b8d0679eca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 2개의 행과 4개의 열을 가진 subplots를 이용. axs는 4x2개의 ax를 가짐.\n",
        "fig, axs = plt.subplots(figsize=(16,8) , ncols=4 , nrows=2)\n",
        "lm_features = ['RM','ZN','INDUS','NOX','AGE','PTRATIO','LSTAT','RAD']\n",
        "for i , feature in enumerate(lm_features):\n",
        "    row = int(i/4)\n",
        "    col = i%4\n",
        "    # 시본의 regplot을 이용해 산점도와 선형 회귀 직선을 함께 표현\n",
        "    sns.regplot(x=feature , y='PRICE',data=bostonDF , ax=axs[row][col])\n",
        "\n",
        "fig1 = plt.gcf()\n",
        "fig1.savefig('p322_boston.tif', format='tif', dpi=300, bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tr_unv_MohoX"
      },
      "source": [
        "**학습과 테스트 데이터 세트로 분리하고 학습/예측/평가 수행**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "NwsWOm68ohoX",
        "outputId": "f2f7304e-b6c9-480d-d53b-64566fabab3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE : 17.297 , RMSE : 4.159\n",
            "Variance score : 0.757\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "y_target = bostonDF['PRICE']\n",
        "X_data = bostonDF.drop(['PRICE'],axis=1,inplace=False)\n",
        "\n",
        "X_train , X_test , y_train , y_test = train_test_split(X_data , y_target ,test_size=0.3, random_state=156)\n",
        "\n",
        "# Linear Regression OLS로 학습/예측/평가 수행.\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train ,y_train )\n",
        "y_preds = lr.predict(X_test)\n",
        "mse = mean_squared_error(y_test, y_preds)\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "print('MSE : {0:.3f} , RMSE : {1:.3F}'.format(mse , rmse))\n",
        "print('Variance score : {0:.3f}'.format(r2_score(y_test, y_preds)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "RA1oW9r7ohoX",
        "outputId": "bad5bdcd-26b1-48aa-9959-c3d46d8aa073",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "절편 값: 40.995595172164826\n",
            "회귀 계수값: [ -0.1   0.1   0.    3.  -19.8   3.4   0.   -1.7   0.4  -0.   -0.9   0.\n",
            "  -0.6]\n"
          ]
        }
      ],
      "source": [
        "print('절편 값:',lr.intercept_)\n",
        "print('회귀 계수값:', np.round(lr.coef_, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "MNBFyFRuohoX",
        "outputId": "835fb820-3d85-41b0-b58d-281104468bf1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RM          3.4\n",
              "CHAS        3.0\n",
              "RAD         0.4\n",
              "ZN          0.1\n",
              "INDUS       0.0\n",
              "AGE         0.0\n",
              "TAX        -0.0\n",
              "B           0.0\n",
              "CRIM       -0.1\n",
              "LSTAT      -0.6\n",
              "PTRATIO    -0.9\n",
              "DIS        -1.7\n",
              "NOX       -19.8\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# 회귀 계수를 큰 값 순으로 정렬하기 위해 Series로 생성. index가 칼럼명에 유의\n",
        "coeff = pd.Series(data=np.round(lr.coef_, 1), index=X_data.columns )\n",
        "coeff.sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "-lwRXd-vohoX",
        "outputId": "34fa5ad2-fddc-4cde-c077-7b2e69cedf3d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 5 folds 의 개별 Negative MSE scores:  [-12.46 -26.05 -33.07 -80.76 -33.31]\n",
            " 5 folds 의 개별 RMSE scores :  [3.53 5.1  5.75 8.99 5.77]\n",
            " 5 folds 의 평균 RMSE : 5.829 \n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "y_target = bostonDF['PRICE']\n",
        "X_data = bostonDF.drop(['PRICE'],axis=1,inplace=False)\n",
        "lr = LinearRegression()\n",
        "\n",
        "# cross_val_score( )로 5 Fold 셋으로 MSE 를 구한 뒤 이를 기반으로 다시  RMSE 구함.\n",
        "neg_mse_scores = cross_val_score(lr, X_data, y_target, scoring=\"neg_mean_squared_error\", cv = 5)\n",
        "rmse_scores  = np.sqrt(-1 * neg_mse_scores)\n",
        "avg_rmse = np.mean(rmse_scores)\n",
        "\n",
        "# cross_val_score(scoring=\"neg_mean_squared_error\")로 반환된 값은 모두 음수\n",
        "print(' 5 folds 의 개별 Negative MSE scores: ', np.round(neg_mse_scores, 2))\n",
        "print(' 5 folds 의 개별 RMSE scores : ', np.round(rmse_scores, 2))\n",
        "print(' 5 folds 의 평균 RMSE : {0:.3f} '.format(avg_rmse))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZz049qVohoY"
      },
      "source": [
        "## Polynomial Regression과 오버피팅/언더피팅 이해\n",
        "### Polynomial Regression 이해"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7By-ECo5ohoY"
      },
      "source": [
        "PolynomialFeatures 클래스로 다항식 변환\n",
        "\n",
        "![](https://github.com/minyoung347/python101/blob/main/image02.png?raw=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "6M29btghohoY",
        "outputId": "723c54ca-aed9-47bb-f546-6965e95b08a0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "일차 단항식 계수 feature:\n",
            " [[0 1]\n",
            " [2 3]]\n",
            "변환된 2차 다항식 계수 feature:\n",
            " [[1. 0. 1. 0. 0. 1.]\n",
            " [1. 2. 3. 4. 6. 9.]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "import numpy as np\n",
        "\n",
        "# 다항식으로 변환한 단항식 생성, [[0,1],[2,3]]의 2X2 행렬 생성\n",
        "X = np.arange(4).reshape(2,2)\n",
        "print('일차 단항식 계수 feature:\\n',X )\n",
        "\n",
        "# degree = 2 인 2차 다항식으로 변환하기 위해 PolynomialFeatures를 이용하여 변환\n",
        "poly = PolynomialFeatures(degree=2)\n",
        "poly.fit(X)\n",
        "poly_ftr = poly.transform(X)\n",
        "print('변환된 2차 다항식 계수 feature:\\n', poly_ftr)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAI73FUqohoY"
      },
      "source": [
        "3차 다항식 결정값을 구하는 함수 polynomial_func(X) 생성. 즉 회귀식은 결정값 y = 1+ 2x_1 + 3x_1^2 + 4x_2^3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Yg4GkN4fohoY",
        "outputId": "33ba48f7-022c-4e00-ce7a-d0411460c49e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "일차 단항식 계수 feature: \n",
            " [[0 1]\n",
            " [2 3]]\n",
            "[0 2]\n",
            "[1 3]\n",
            "삼차 다항식 결정값: \n",
            " [  5 125]\n"
          ]
        }
      ],
      "source": [
        "def polynomial_func(X):\n",
        "    y = 1 + 2*X[:,0] + 3*X[:,0]**2 + 4*X[:,1]**3\n",
        "    print(X[:, 0])\n",
        "    print(X[:, 1])\n",
        "    return y\n",
        "\n",
        "X = np.arange(0,4).reshape(2,2)\n",
        "\n",
        "print('일차 단항식 계수 feature: \\n' ,X)\n",
        "y = polynomial_func(X)\n",
        "print('삼차 다항식 결정값: \\n', y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FlX4F2__ohoY"
      },
      "source": [
        "3차 다항식 계수의 피처값과 3차 다항식 결정값으로 학습"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "C4gRCvIiohoY",
        "outputId": "4e6f184a-29ae-486c-ed77-e86208574c75",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3차 다항식 계수 feature: \n",
            " [[ 1.  0.  1.  0.  0.  1.  0.  0.  0.  1.]\n",
            " [ 1.  2.  3.  4.  6.  9.  8. 12. 18. 27.]]\n",
            "Polynomial 회귀 계수\n",
            " [0.   0.18 0.18 0.36 0.54 0.72 0.72 1.08 1.62 2.34]\n",
            "Polynomial 회귀 Shape : (10,)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# 3 차 다항식 변환\n",
        "poly_ftr = PolynomialFeatures(degree=3).fit_transform(X)\n",
        "print('3차 다항식 계수 feature: \\n',poly_ftr)\n",
        "\n",
        "# Linear Regression에 3차 다항식 계수 feature와 3차 다항식 결정값으로 학습 후 회귀 계수 확인\n",
        "model = LinearRegression()\n",
        "model.fit(poly_ftr,y)\n",
        "print('Polynomial 회귀 계수\\n' , np.round(model.coef_, 2))\n",
        "print('Polynomial 회귀 Shape :', model.coef_.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJpoH5V_ohoZ"
      },
      "source": [
        "**사이킷런 파이프라인(Pipeline)을 이용하여 3차 다항회귀 학습**  \n",
        "\n",
        "사이킷런의 Pipeline 객체는 Feature 엔지니어링 변환과 모델 학습/예측을 순차적으로 결합"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "4hJfl2dlohoZ",
        "outputId": "240b9498-d413-49ac-c984-4ed8ae6e79ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polynomial 회귀 계수\n",
            " [0.   0.18 0.18 0.36 0.54 0.72 0.72 1.08 1.62 2.34]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "import numpy as np\n",
        "\n",
        "def polynomial_func(X):\n",
        "    y = 1 + 2*X[:,0] + 3*X[:,0]**2 + 4*X[:,1]**3\n",
        "    return y\n",
        "\n",
        "# Pipeline 객체로 Streamline 하게 Polynomial Feature변환과 Linear Regression을 연결\n",
        "model = Pipeline([('poly', PolynomialFeatures(degree=3)),\n",
        "                  ('linear', LinearRegression())])\n",
        "X = np.arange(4).reshape(2,2)\n",
        "y = polynomial_func(X)\n",
        "\n",
        "model = model.fit(X, y)\n",
        "print('Polynomial 회귀 계수\\n', np.round(model.named_steps['linear'].coef_, 2))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2KMz_uaUohoZ"
      },
      "source": [
        "### 다항 회귀를 이용한 과소적합 및 과적합 이해"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7cmAUMWYohoZ"
      },
      "source": [
        "**cosine 곡선에 약간의 Noise 변동값을 더하여 실제값 곡선을 만듬**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "xyGUveibohoZ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import cross_val_score\n",
        "%matplotlib inline\n",
        "\n",
        "# 임의의 값으로 구성된 X값에 대해 코사인 변환 값을 반환.\n",
        "def true_fun(X):\n",
        "    return np.cos(1.5 * np.pi * X)\n",
        "\n",
        "# X는 0부터 1까지 30개의 임의의 값을 순서대로 샘플링한 데이터입니다.\n",
        "np.random.seed(0)\n",
        "n_samples = 30\n",
        "X = np.sort(np.random.rand(n_samples))\n",
        "\n",
        "# y 값은 코사인 기반의 true_fun()에서 약간의 노이즈 변동 값을 더한 값입니다.\n",
        "y = true_fun(X) + np.random.randn(n_samples) * 0.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "btbncxgMohoZ",
        "outputId": "e785122e-22f9-45c1-c578-5e6ea9c19c45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ac81b5f0790>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "uGAUhfirohoZ",
        "outputId": "fa307589-fb5e-4c12-85bb-eb6f56f88c13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Degree 1 회귀 계수는 [-1.61] 입니다.\n",
            "Degree 1 MSE 는 0.4077289625098685 입니다.\n",
            "\n",
            "Degree 4 회귀 계수는 [  0.47 -17.79  23.59  -7.26] 입니다.\n",
            "Degree 4 MSE 는 0.04320874987232064 입니다.\n",
            "\n",
            "Degree 15 회귀 계수는 [-2.98293000e+03  1.03899390e+05 -1.87416123e+06  2.03716219e+07\n",
            " -1.44873283e+08  7.09315363e+08 -2.47065792e+09  6.24561050e+09\n",
            " -1.15676510e+10  1.56894936e+10 -1.54006023e+10  1.06457264e+10\n",
            " -4.91377530e+09  1.35919645e+09 -1.70380786e+08] 입니다.\n",
            "Degree 15 MSE 는 181238256.56423894 입니다.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(14, 5))\n",
        "degrees = [1, 4, 15]\n",
        "\n",
        "# 다항 회귀의 차수(degree)를 1, 4, 15로 각각 변화시키면서 비교합니다.\n",
        "for i in range(len(degrees)):\n",
        "    ax = plt.subplot(1, len(degrees), i + 1)\n",
        "    plt.setp(ax, xticks=(), yticks=())\n",
        "\n",
        "    # 개별 degree별로 Polynomial 변환합니다.\n",
        "    polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)\n",
        "    linear_regression = LinearRegression()\n",
        "    pipeline = Pipeline([(\"polynomial_features\", polynomial_features),\n",
        "                         (\"linear_regression\", linear_regression)])\n",
        "    pipeline.fit(X.reshape(-1, 1), y)\n",
        "\n",
        "    # 교차 검증으로 다항 회귀를 평가합니다.\n",
        "    scores = cross_val_score(pipeline, X.reshape(-1, 1), y, scoring=\"neg_mean_squared_error\", cv=10)\n",
        "    # Pipeline을 구성하는 세부 객체를 접근하는 named_steps['객체명']을 이용해 회귀계수 추출\n",
        "    coefficients = pipeline.named_steps['linear_regression'].coef_\n",
        "    print('\\nDegree {0} 회귀 계수는 {1} 입니다.'.format(degrees[i], np.round(coefficients, 2)))\n",
        "    print('Degree {0} MSE 는 {1} 입니다.'.format(degrees[i], -1*np.mean(scores)))\n",
        "\n",
        "    # 0 부터 1까지 테스트 데이터 세트를 100개로 나눠 예측을 수행합니다.\n",
        "    # 테스트 데이터 세트에 회귀 예측을 수행하고 예측 곡선과 실제 곡선을 그려서 비교합니다.\n",
        "    X_test = np.linspace(0, 1, 100)\n",
        "    # 예측값 곡선\n",
        "    plt.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label=\"Model\")\n",
        "    # 실제 값 곡선\n",
        "    plt.plot(X_test, true_fun(X_test), '--', label=\"True function\")\n",
        "    plt.scatter(X, y, edgecolor='b', s=20, label=\"Samples\")\n",
        "    plt.xlabel(\"x\"); plt.ylabel(\"y\"); plt.xlim((0, 1)); plt.ylim((-2, 2)); plt.legend(loc=\"best\")\n",
        "    plt.title(\"Degree {}\\nMSE = {:.2e}(+/- {:.2e})\".format(degrees[i], -scores.mean(), scores.std()))\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PptvzZx6ohol"
      },
      "source": [
        "## Regularized Linear Models – Ridge, Lasso\n",
        "### Regularized Linear Model - Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "bostonDF = pd.read_csv('/content/python101/input/BostonHousing.csv')\n",
        "bostonDF.columns = ['CRIM', 'ZN', 'INDUS',\t'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'PRICE']"
      ],
      "metadata": {
        "id": "dTl1MvDQvPPt"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "VnNvyOq7ohom",
        "outputId": "1c1f31cd-67f5-4887-8991-7631b48f1e41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 5 folds 의 개별 Negative MSE scores:  [-11.422 -24.294 -28.144 -74.599 -28.517]\n",
            " 5 folds 의 개별 RMSE scores :  [3.38  4.929 5.305 8.637 5.34 ]\n",
            " 5 folds 의 평균 RMSE : 5.518 \n"
          ]
        }
      ],
      "source": [
        "# 앞의 LinearRegression예제에서 분할한 feature 데이터 셋인 X_data과 Target 데이터 셋인 Y_target 데이터셋을 그대로 이용\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# boston 데이타셋 로드\n",
        "# boston = load_boston()\n",
        "\n",
        "# boston 데이타셋 DataFrame 변환\n",
        "# bostonDF = pd.DataFrame(boston.data , columns = boston.feature_names)\n",
        "\n",
        "# boston dataset의 target array는 주택 가격임. 이를 PRICE 컬럼으로 DataFrame에 추가함.\n",
        "# bostonDF['PRICE'] = boston.target\n",
        "\n",
        "y_target = bostonDF['PRICE']\n",
        "X_data = bostonDF.drop(['PRICE'],axis=1,inplace=False)\n",
        "\n",
        "\n",
        "ridge = Ridge(alpha = 10)\n",
        "neg_mse_scores = cross_val_score(ridge, X_data, y_target, scoring=\"neg_mean_squared_error\", cv = 5)\n",
        "rmse_scores  = np.sqrt(-1 * neg_mse_scores)\n",
        "avg_rmse = np.mean(rmse_scores)\n",
        "print(' 5 folds 의 개별 Negative MSE scores: ', np.round(neg_mse_scores, 3))\n",
        "print(' 5 folds 의 개별 RMSE scores : ', np.round(rmse_scores,3))\n",
        "print(' 5 folds 의 평균 RMSE : {0:.3f} '.format(avg_rmse))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rhel7FRyohom"
      },
      "source": [
        "**alpha값을 0 , 0.1 , 1 , 10 , 100 으로 변경하면서 RMSE 측정**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "4JCZWpyeohom",
        "outputId": "b27978b6-a1db-4886-8d9a-bc7661765a35",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 0 일 때 5 folds 의 평균 RMSE : 5.829 \n",
            "alpha 0.1 일 때 5 folds 의 평균 RMSE : 5.788 \n",
            "alpha 1 일 때 5 folds 의 평균 RMSE : 5.653 \n",
            "alpha 10 일 때 5 folds 의 평균 RMSE : 5.518 \n",
            "alpha 100 일 때 5 folds 의 평균 RMSE : 5.330 \n"
          ]
        }
      ],
      "source": [
        "# 릿지에 사용될 alpha 파라미터의 값을 정의\n",
        "alphas = [0, 0.1, 1, 10, 100]\n",
        "\n",
        "# alphas list 값을 반복하면서 alpha에 따른 평균 rmse를 구함.\n",
        "for alpha in alphas :\n",
        "    ridge = Ridge(alpha = alpha)\n",
        "\n",
        "    # cross_val_score를 이용해 5 폴드의 평균 RMSE를 계산\n",
        "    neg_mse_scores = cross_val_score(ridge, X_data, y_target, scoring=\"neg_mean_squared_error\", cv = 5)\n",
        "    avg_rmse = np.mean(np.sqrt(-1 * neg_mse_scores))\n",
        "    print('alpha {0} 일 때 5 folds 의 평균 RMSE : {1:.3f} '.format(alpha, avg_rmse))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vyP1M8V9ohom"
      },
      "source": [
        "**각 alpha에 따른 회귀 계수 값을 시각화. 각 alpha값 별로 plt.subplots로 맷플롯립 축 생성**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "jueP8fRWohon",
        "outputId": "902d77dc-2adc-49ca-882a-c53fa9dff313",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 5 Axes>"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# 각 alpha에 따른 회귀 계수 값을 시각화하기 위해 5개의 열로 된 맷플롯립 축 생성\n",
        "fig , axs = plt.subplots(figsize=(18,6) , nrows=1 , ncols=5)\n",
        "# 각 alpha에 따른 회귀 계수 값을 데이터로 저장하기 위한 DataFrame 생성\n",
        "coeff_df = pd.DataFrame()\n",
        "\n",
        "# alphas 리스트 값을 차례로 입력해 회귀 계수 값 시각화 및 데이터 저장. pos는 axis의 위치 지정\n",
        "for pos , alpha in enumerate(alphas) :\n",
        "    ridge = Ridge(alpha = alpha)\n",
        "    ridge.fit(X_data , y_target)\n",
        "    # alpha에 따른 피처별 회귀 계수를 Series로 변환하고 이를 DataFrame의 컬럼으로 추가.\n",
        "    coeff = pd.Series(data=ridge.coef_ , index=X_data.columns )\n",
        "    colname='alpha:'+str(alpha)\n",
        "    coeff_df[colname] = coeff\n",
        "    # 막대 그래프로 각 alpha 값에서의 회귀 계수를 시각화. 회귀 계수값이 높은 순으로 표현\n",
        "    coeff = coeff.sort_values(ascending=False)\n",
        "    axs[pos].set_title(colname)\n",
        "    axs[pos].set_xlim(-3,6)\n",
        "    sns.barplot(x=coeff.values , y=coeff.index, ax=axs[pos])\n",
        "\n",
        "# for 문 바깥에서 맷플롯립의 show 호출 및 alpha에 따른 피처별 회귀 계수를 DataFrame으로 표시\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ccfpl4Czohon"
      },
      "source": [
        "**alpha 값에 따른 컬럼별 회귀계수 출력**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "s0wwlq_rohon",
        "outputId": "6ec67a10-4780-4fd7-d763-7e87941d8044",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           alpha:0  alpha:0.1    alpha:1  alpha:10  alpha:100\n",
              "RM        3.809865   3.818233   3.854000  3.702272   2.334536\n",
              "CHAS      2.686734   2.670019   2.552393  1.952021   0.638335\n",
              "RAD       0.306049   0.303515   0.290142  0.279596   0.315358\n",
              "ZN        0.046420   0.046572   0.047443  0.049579   0.054496\n",
              "INDUS     0.020559   0.015999  -0.008805 -0.042962  -0.052826\n",
              "B         0.009312   0.009368   0.009673  0.010037   0.009393\n",
              "AGE       0.000692  -0.000269  -0.005415 -0.010707   0.001212\n",
              "TAX      -0.012335  -0.012421  -0.012912 -0.013993  -0.015856\n",
              "CRIM     -0.108011  -0.107474  -0.104595 -0.101435  -0.102202\n",
              "LSTAT    -0.524758  -0.525966  -0.533343 -0.559366  -0.660764\n",
              "PTRATIO  -0.952747  -0.940759  -0.876074 -0.797945  -0.829218\n",
              "DIS      -1.475567  -1.459626  -1.372654 -1.248808  -1.153390\n",
              "NOX     -17.766611 -16.684645 -10.777015 -2.371619  -0.262847"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-31245c5b-6c34-4c5a-97c8-b34f86f43da0\" class=\"colab-df-container\">\n",
              "    <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>alpha:0</th>\n",
              "      <th>alpha:0.1</th>\n",
              "      <th>alpha:1</th>\n",
              "      <th>alpha:10</th>\n",
              "      <th>alpha:100</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RM</th>\n",
              "      <td>3.809865</td>\n",
              "      <td>3.818233</td>\n",
              "      <td>3.854000</td>\n",
              "      <td>3.702272</td>\n",
              "      <td>2.334536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHAS</th>\n",
              "      <td>2.686734</td>\n",
              "      <td>2.670019</td>\n",
              "      <td>2.552393</td>\n",
              "      <td>1.952021</td>\n",
              "      <td>0.638335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAD</th>\n",
              "      <td>0.306049</td>\n",
              "      <td>0.303515</td>\n",
              "      <td>0.290142</td>\n",
              "      <td>0.279596</td>\n",
              "      <td>0.315358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZN</th>\n",
              "      <td>0.046420</td>\n",
              "      <td>0.046572</td>\n",
              "      <td>0.047443</td>\n",
              "      <td>0.049579</td>\n",
              "      <td>0.054496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INDUS</th>\n",
              "      <td>0.020559</td>\n",
              "      <td>0.015999</td>\n",
              "      <td>-0.008805</td>\n",
              "      <td>-0.042962</td>\n",
              "      <td>-0.052826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.009312</td>\n",
              "      <td>0.009368</td>\n",
              "      <td>0.009673</td>\n",
              "      <td>0.010037</td>\n",
              "      <td>0.009393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>0.000692</td>\n",
              "      <td>-0.000269</td>\n",
              "      <td>-0.005415</td>\n",
              "      <td>-0.010707</td>\n",
              "      <td>0.001212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAX</th>\n",
              "      <td>-0.012335</td>\n",
              "      <td>-0.012421</td>\n",
              "      <td>-0.012912</td>\n",
              "      <td>-0.013993</td>\n",
              "      <td>-0.015856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CRIM</th>\n",
              "      <td>-0.108011</td>\n",
              "      <td>-0.107474</td>\n",
              "      <td>-0.104595</td>\n",
              "      <td>-0.101435</td>\n",
              "      <td>-0.102202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LSTAT</th>\n",
              "      <td>-0.524758</td>\n",
              "      <td>-0.525966</td>\n",
              "      <td>-0.533343</td>\n",
              "      <td>-0.559366</td>\n",
              "      <td>-0.660764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PTRATIO</th>\n",
              "      <td>-0.952747</td>\n",
              "      <td>-0.940759</td>\n",
              "      <td>-0.876074</td>\n",
              "      <td>-0.797945</td>\n",
              "      <td>-0.829218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DIS</th>\n",
              "      <td>-1.475567</td>\n",
              "      <td>-1.459626</td>\n",
              "      <td>-1.372654</td>\n",
              "      <td>-1.248808</td>\n",
              "      <td>-1.153390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NOX</th>\n",
              "      <td>-17.766611</td>\n",
              "      <td>-16.684645</td>\n",
              "      <td>-10.777015</td>\n",
              "      <td>-2.371619</td>\n",
              "      <td>-0.262847</td>\n",
              "    </tr>\n",
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              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"coeff_df\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"alpha:0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.211069023334257,\n        \"min\": -17.766611228300096,\n        \"max\": 3.809865206809224,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -1.4755668456002526,\n          -0.5247583778554896,\n          3.809865206809224\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:0.1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.922292368718134,\n        \"min\": -16.68464523185645,\n        \"max\": 3.818233217307196,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -1.4596255697013627,\n          -0.5259662030656257,\n          3.818233217307196\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.3769326762417964,\n        \"min\": -10.77701464816452,\n        \"max\": 3.8540001982986767,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -1.3726535250455092,\n          -0.5333432253389818,\n          3.8540001982986767\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4631286418448768,\n        \"min\": -2.3716189615754737,\n        \"max\": 3.7022720695011744,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -1.2488082128636517,\n          -0.5593664222657841,\n          3.7022720695011744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:100\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8411928260367437,\n        \"min\": -1.1533898902377873,\n        \"max\": 2.334535785331564,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -1.1533898902377873,\n          -0.6607638276989454,\n          2.334535785331564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "ridge_alphas = [0 , 0.1 , 1 , 10 , 100]\n",
        "sort_column = 'alpha:'+str(ridge_alphas[0])\n",
        "coeff_df.sort_values(by=sort_column, ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEI1gnILohon"
      },
      "source": [
        "### 라쏘 회귀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "uF_lXQTbohon"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import Lasso, ElasticNet\n",
        "\n",
        "# alpha값에 따른 회귀 모델의 폴드 평균 RMSE를 출력하고 회귀 계수값들을 DataFrame으로 반환\n",
        "def get_linear_reg_eval(model_name, params=None, X_data_n=None, y_target_n=None,\n",
        "                        verbose=True, return_coeff=True):\n",
        "    coeff_df = pd.DataFrame()\n",
        "    if verbose : print('####### ', model_name , '#######')\n",
        "    for param in params:\n",
        "        if model_name =='Ridge': model = Ridge(alpha=param)\n",
        "        elif model_name =='Lasso': model = Lasso(alpha=param)\n",
        "        elif model_name =='ElasticNet': model = ElasticNet(alpha=param, l1_ratio=0.7)\n",
        "        neg_mse_scores = cross_val_score(model, X_data_n,\n",
        "                                             y_target_n, scoring=\"neg_mean_squared_error\", cv = 5)\n",
        "        avg_rmse = np.mean(np.sqrt(-1 * neg_mse_scores))\n",
        "        print('alpha {0}일 때 5 폴드 세트의 평균 RMSE: {1:.3f} '.format(param, avg_rmse))\n",
        "        # cross_val_score는 evaluation metric만 반환하므로 모델을 다시 학습하여 회귀 계수 추출\n",
        "\n",
        "        model.fit(X_data_n , y_target_n)\n",
        "        if return_coeff:\n",
        "            # alpha에 따른 피처별 회귀 계수를 Series로 변환하고 이를 DataFrame의 컬럼으로 추가.\n",
        "            coeff = pd.Series(data=model.coef_ , index=X_data_n.columns )\n",
        "            colname='alpha:'+str(param)\n",
        "            coeff_df[colname] = coeff\n",
        "\n",
        "    return coeff_df\n",
        "# end of get_linear_regre_eval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "7adWw3nmohoo",
        "outputId": "f4d4b967-d84f-4505-ede2-713ac9226a49",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#######  Lasso #######\n",
            "alpha 0.07일 때 5 폴드 세트의 평균 RMSE: 5.612 \n",
            "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 5.615 \n",
            "alpha 0.5일 때 5 폴드 세트의 평균 RMSE: 5.669 \n",
            "alpha 1일 때 5 폴드 세트의 평균 RMSE: 5.776 \n",
            "alpha 3일 때 5 폴드 세트의 평균 RMSE: 6.189 \n"
          ]
        }
      ],
      "source": [
        "# 라쏘에 사용될 alpha 파라미터의 값들을 정의하고 get_linear_reg_eval() 함수 호출\n",
        "lasso_alphas = [ 0.07, 0.1, 0.5, 1, 3]\n",
        "coeff_lasso_df =get_linear_reg_eval('Lasso', params=lasso_alphas, X_data_n=X_data, y_target_n=y_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "tyFiG4kZohoo",
        "outputId": "7a47da20-9ade-434e-9861-28a98665597f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         alpha:0.07  alpha:0.1  alpha:0.5   alpha:1   alpha:3\n",
              "RM         3.789725   3.703202   2.498212  0.949811  0.000000\n",
              "CHAS       1.434343   0.955190   0.000000  0.000000  0.000000\n",
              "RAD        0.270936   0.274707   0.277451  0.264206  0.061864\n",
              "ZN         0.049059   0.049211   0.049544  0.049165  0.037231\n",
              "B          0.010248   0.010249   0.009469  0.008247  0.006510\n",
              "NOX       -0.000000  -0.000000  -0.000000 -0.000000  0.000000\n",
              "AGE       -0.011706  -0.010037   0.003604  0.020910  0.042495\n",
              "TAX       -0.014290  -0.014570  -0.015442 -0.015212 -0.008602\n",
              "INDUS     -0.042120  -0.036619  -0.005253 -0.000000 -0.000000\n",
              "CRIM      -0.098193  -0.097894  -0.083289 -0.063437 -0.000000\n",
              "LSTAT     -0.560431  -0.568769  -0.656290 -0.761115 -0.807679\n",
              "PTRATIO   -0.765107  -0.770654  -0.758752 -0.722966 -0.265072\n",
              "DIS       -1.176583  -1.160538  -0.936605 -0.668790 -0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d4862a8f-408b-4136-9bb5-b193429cbff3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>alpha:0.07</th>\n",
              "      <th>alpha:0.1</th>\n",
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              "      <th>alpha:3</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RM</th>\n",
              "      <td>3.789725</td>\n",
              "      <td>3.703202</td>\n",
              "      <td>2.498212</td>\n",
              "      <td>0.949811</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHAS</th>\n",
              "      <td>1.434343</td>\n",
              "      <td>0.955190</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAD</th>\n",
              "      <td>0.270936</td>\n",
              "      <td>0.274707</td>\n",
              "      <td>0.277451</td>\n",
              "      <td>0.264206</td>\n",
              "      <td>0.061864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZN</th>\n",
              "      <td>0.049059</td>\n",
              "      <td>0.049211</td>\n",
              "      <td>0.049544</td>\n",
              "      <td>0.049165</td>\n",
              "      <td>0.037231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.010248</td>\n",
              "      <td>0.010249</td>\n",
              "      <td>0.009469</td>\n",
              "      <td>0.008247</td>\n",
              "      <td>0.006510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NOX</th>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>-0.011706</td>\n",
              "      <td>-0.010037</td>\n",
              "      <td>0.003604</td>\n",
              "      <td>0.020910</td>\n",
              "      <td>0.042495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAX</th>\n",
              "      <td>-0.014290</td>\n",
              "      <td>-0.014570</td>\n",
              "      <td>-0.015442</td>\n",
              "      <td>-0.015212</td>\n",
              "      <td>-0.008602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INDUS</th>\n",
              "      <td>-0.042120</td>\n",
              "      <td>-0.036619</td>\n",
              "      <td>-0.005253</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CRIM</th>\n",
              "      <td>-0.098193</td>\n",
              "      <td>-0.097894</td>\n",
              "      <td>-0.083289</td>\n",
              "      <td>-0.063437</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LSTAT</th>\n",
              "      <td>-0.560431</td>\n",
              "      <td>-0.568769</td>\n",
              "      <td>-0.656290</td>\n",
              "      <td>-0.761115</td>\n",
              "      <td>-0.807679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PTRATIO</th>\n",
              "      <td>-0.765107</td>\n",
              "      <td>-0.770654</td>\n",
              "      <td>-0.758752</td>\n",
              "      <td>-0.722966</td>\n",
              "      <td>-0.265072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DIS</th>\n",
              "      <td>-1.176583</td>\n",
              "      <td>-1.160538</td>\n",
              "      <td>-0.936605</td>\n",
              "      <td>-0.668790</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"coeff_lasso_df\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"alpha:0.07\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.229844606769961,\n        \"min\": -1.1765825829224554,\n        \"max\": 3.7897251555829268,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -0.7651074244119963,\n          -0.09819264586897299,\n          3.7897251555829268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:0.1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1747595375888247,\n        \"min\": -1.1605383416913873,\n        \"max\": 3.7032017520246283,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -0.7706543386974573,\n          -0.09789363113925925,\n          3.7032017520246283\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:0.5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.826649408520379,\n        \"min\": -0.9366046900382905,\n        \"max\": 2.498212457664958,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.7587521092097119,\n          -0.6562895014598762,\n          2.498212457664958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.45281787434806936,\n        \"min\": -0.7611145367697878,\n        \"max\": 0.9498106999845147,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.020909514944737532,\n          0.9498106999845147,\n          -0.7229663585199505\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23500838354440554,\n        \"min\": -0.8076791217691309,\n        \"max\": 0.06186425775925852,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.06186425775925852,\n          -0.008601700859546353,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# 반환된 coeff_lasso_df를 첫번째 컬럼순으로 내림차순 정렬하여 회귀계수 DataFrame출력\n",
        "sort_column = 'alpha:'+str(lasso_alphas[0])\n",
        "coeff_lasso_df.sort_values(by=sort_column, ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVtLqRmZohoo"
      },
      "source": [
        "### 엘라스틱넷 회귀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "KresKBXaohoo",
        "outputId": "b9bed7e3-3dd8-496a-f608-0c1c81b3a9e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#######  ElasticNet #######\n",
            "alpha 0.07일 때 5 폴드 세트의 평균 RMSE: 5.542 \n",
            "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 5.526 \n",
            "alpha 0.5일 때 5 폴드 세트의 평균 RMSE: 5.467 \n",
            "alpha 1일 때 5 폴드 세트의 평균 RMSE: 5.597 \n",
            "alpha 3일 때 5 폴드 세트의 평균 RMSE: 6.068 \n"
          ]
        }
      ],
      "source": [
        "# 엘라스틱넷에 사용될 alpha 파라미터의 값들을 정의하고 get_linear_reg_eval() 함수 호출\n",
        "# l1_ratio는 0.7로 고정\n",
        "elastic_alphas = [ 0.07, 0.1, 0.5, 1, 3]\n",
        "coeff_elastic_df =get_linear_reg_eval('ElasticNet', params=elastic_alphas,\n",
        "                                      X_data_n=X_data, y_target_n=y_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "SVH9-eqRohoo",
        "outputId": "88e3e807-35c2-4acb-b4aa-2041ecc04e97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         alpha:0.07  alpha:0.1  alpha:0.5   alpha:1   alpha:3\n",
              "RM         3.574162   3.414154   1.918419  0.938789  0.000000\n",
              "CHAS       1.330724   0.979706   0.000000  0.000000  0.000000\n",
              "RAD        0.278880   0.283443   0.300761  0.289299  0.146846\n",
              "ZN         0.050107   0.050617   0.052878  0.052136  0.038268\n",
              "B          0.010122   0.010067   0.009114  0.008320  0.007020\n",
              "AGE       -0.010116  -0.008276   0.007760  0.020348  0.043446\n",
              "TAX       -0.014522  -0.014814  -0.016046 -0.016218 -0.011417\n",
              "INDUS     -0.044855  -0.042719  -0.023252 -0.000000 -0.000000\n",
              "CRIM      -0.099468  -0.099213  -0.089070 -0.073577 -0.019058\n",
              "NOX       -0.175072  -0.000000  -0.000000 -0.000000 -0.000000\n",
              "LSTAT     -0.574822  -0.587702  -0.693861 -0.760457 -0.800368\n",
              "PTRATIO   -0.779498  -0.784725  -0.790969 -0.738672 -0.423065\n",
              "DIS       -1.189438  -1.173647  -0.975902 -0.725174 -0.031208"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-68ffbb97-27e9-4812-b92a-577f5318e5be\" class=\"colab-df-container\">\n",
              "    <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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alpha:0.07</th>\n",
              "      <th>alpha:0.1</th>\n",
              "      <th>alpha:0.5</th>\n",
              "      <th>alpha:1</th>\n",
              "      <th>alpha:3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RM</th>\n",
              "      <td>3.574162</td>\n",
              "      <td>3.414154</td>\n",
              "      <td>1.918419</td>\n",
              "      <td>0.938789</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHAS</th>\n",
              "      <td>1.330724</td>\n",
              "      <td>0.979706</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAD</th>\n",
              "      <td>0.278880</td>\n",
              "      <td>0.283443</td>\n",
              "      <td>0.300761</td>\n",
              "      <td>0.289299</td>\n",
              "      <td>0.146846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZN</th>\n",
              "      <td>0.050107</td>\n",
              "      <td>0.050617</td>\n",
              "      <td>0.052878</td>\n",
              "      <td>0.052136</td>\n",
              "      <td>0.038268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.010122</td>\n",
              "      <td>0.010067</td>\n",
              "      <td>0.009114</td>\n",
              "      <td>0.008320</td>\n",
              "      <td>0.007020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>-0.010116</td>\n",
              "      <td>-0.008276</td>\n",
              "      <td>0.007760</td>\n",
              "      <td>0.020348</td>\n",
              "      <td>0.043446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAX</th>\n",
              "      <td>-0.014522</td>\n",
              "      <td>-0.014814</td>\n",
              "      <td>-0.016046</td>\n",
              "      <td>-0.016218</td>\n",
              "      <td>-0.011417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INDUS</th>\n",
              "      <td>-0.044855</td>\n",
              "      <td>-0.042719</td>\n",
              "      <td>-0.023252</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CRIM</th>\n",
              "      <td>-0.099468</td>\n",
              "      <td>-0.099213</td>\n",
              "      <td>-0.089070</td>\n",
              "      <td>-0.073577</td>\n",
              "      <td>-0.019058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NOX</th>\n",
              "      <td>-0.175072</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LSTAT</th>\n",
              "      <td>-0.574822</td>\n",
              "      <td>-0.587702</td>\n",
              "      <td>-0.693861</td>\n",
              "      <td>-0.760457</td>\n",
              "      <td>-0.800368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PTRATIO</th>\n",
              "      <td>-0.779498</td>\n",
              "      <td>-0.784725</td>\n",
              "      <td>-0.790969</td>\n",
              "      <td>-0.738672</td>\n",
              "      <td>-0.423065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DIS</th>\n",
              "      <td>-1.189438</td>\n",
              "      <td>-1.173647</td>\n",
              "      <td>-0.975902</td>\n",
              "      <td>-0.725174</td>\n",
              "      <td>-0.031208</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-68ffbb97-27e9-4812-b92a-577f5318e5be')\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-68ffbb97-27e9-4812-b92a-577f5318e5be');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-726fae2a-4556-4932-b51c-55614fb4a4d5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"coeff_elastic_df\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"alpha:0.07\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1759556514346328,\n        \"min\": -1.1894379085143687,\n        \"max\": 3.5741616405035397,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -0.77949809994763,\n          -0.17507249542368375,\n          3.5741616405035397\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:0.1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.108070495194573,\n        \"min\": -1.1736473579005449,\n        \"max\": 3.4141536372357364,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -0.7847253771714974,\n          -0.0,\n          3.4141536372357364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:0.5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.697315548997861,\n        \"min\": -0.9759021217564295,\n        \"max\": 1.9184186343708631,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.790969447083394,\n          -0.6938608863624438,\n          1.9184186343708631\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.46061369780289224,\n        \"min\": -0.760456724624936,\n        \"max\": 0.9387893104313978,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.02034843326771733,\n          0.9387893104313978,\n          -0.7386724219240649\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alpha:3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25187186794860716,\n        \"min\": -0.8003681741012921,\n        \"max\": 0.1468462441808773,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.4230653517652658,\n          0.1468462441808773,\n          -0.011417289936540443\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# 반환된 coeff_elastic_df를 첫번째 컬럼순으로 내림차순 정렬하여 회귀계수 DataFrame출력\n",
        "sort_column = 'alpha:'+str(elastic_alphas[0])\n",
        "coeff_elastic_df.sort_values(by=sort_column, ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odQvGpj2ohoo"
      },
      "source": [
        "### 선형 회귀 모델을 위한 데이터 변환"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "MoX-PfEoohop"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures\n",
        "\n",
        "# method는 표준 정규 분포 변환(Standard), 최대값/최소값 정규화(MinMax), 로그변환(Log) 결정\n",
        "# p_degree는 다향식 특성을 추가할 때 적용. p_degree는 2이상 부여하지 않음.\n",
        "def get_scaled_data(method='None', p_degree=None, input_data=None):\n",
        "    if method == 'Standard':\n",
        "        scaled_data = StandardScaler().fit_transform(input_data)\n",
        "    elif method == 'MinMax':\n",
        "        scaled_data = MinMaxScaler().fit_transform(input_data)\n",
        "    elif method == 'Log':\n",
        "        scaled_data = np.log1p(input_data)\n",
        "    else:\n",
        "        scaled_data = input_data\n",
        "\n",
        "    if p_degree != None:\n",
        "        scaled_data = PolynomialFeatures(degree=p_degree,\n",
        "                                         include_bias=False).fit_transform(scaled_data)\n",
        "\n",
        "    return scaled_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "86aytKJJohop",
        "outputId": "7980d0d4-e8c1-4e72-9020-fc4df0371492",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(506, 13) (506, 13)\n",
            "\n",
            "## 변환 유형:None, Polynomial Degree:None\n",
            "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 5.788 \n",
            "alpha 1일 때 5 폴드 세트의 평균 RMSE: 5.653 \n",
            "alpha 10일 때 5 폴드 세트의 평균 RMSE: 5.518 \n",
            "alpha 100일 때 5 폴드 세트의 평균 RMSE: 5.330 \n",
            "(506, 13) (506, 13)\n",
            "\n",
            "## 변환 유형:Standard, Polynomial Degree:None\n",
            "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 5.826 \n",
            "alpha 1일 때 5 폴드 세트의 평균 RMSE: 5.803 \n",
            "alpha 10일 때 5 폴드 세트의 평균 RMSE: 5.637 \n",
            "alpha 100일 때 5 폴드 세트의 평균 RMSE: 5.421 \n",
            "(506, 104) (506, 13)\n",
            "\n",
            "## 변환 유형:Standard, Polynomial Degree:2\n",
            "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 8.827 \n",
            "alpha 1일 때 5 폴드 세트의 평균 RMSE: 6.871 \n",
            "alpha 10일 때 5 폴드 세트의 평균 RMSE: 5.485 \n",
            "alpha 100일 때 5 폴드 세트의 평균 RMSE: 4.634 \n",
            "(506, 13) (506, 13)\n",
            "\n",
            "## 변환 유형:MinMax, Polynomial Degree:None\n",
            "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 5.764 \n",
            "alpha 1일 때 5 폴드 세트의 평균 RMSE: 5.465 \n",
            "alpha 10일 때 5 폴드 세트의 평균 RMSE: 5.754 \n",
            "alpha 100일 때 5 폴드 세트의 평균 RMSE: 7.635 \n",
            "(506, 104) (506, 13)\n",
            "\n",
            "## 변환 유형:MinMax, Polynomial Degree:2\n",
            "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 5.298 \n",
            "alpha 1일 때 5 폴드 세트의 평균 RMSE: 4.323 \n",
            "alpha 10일 때 5 폴드 세트의 평균 RMSE: 5.185 \n",
            "alpha 100일 때 5 폴드 세트의 평균 RMSE: 6.538 \n",
            "(506, 13) (506, 13)\n",
            "\n",
            "## 변환 유형:Log, Polynomial Degree:None\n",
            "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 4.770 \n",
            "alpha 1일 때 5 폴드 세트의 평균 RMSE: 4.676 \n",
            "alpha 10일 때 5 폴드 세트의 평균 RMSE: 4.836 \n",
            "alpha 100일 때 5 폴드 세트의 평균 RMSE: 6.241 \n"
          ]
        }
      ],
      "source": [
        "# Ridge의 alpha값을 다르게 적용하고 다양한 데이터 변환방법에 따른 RMSE 추출.\n",
        "alphas = [0.1, 1, 10, 100]\n",
        "#변환 방법은 모두 6개, 원본 그대로, 표준정규분포, 표준정규분포+다항식 특성\n",
        "# 최대/최소 정규화, 최대/최소 정규화+다항식 특성, 로그변환\n",
        "scale_methods=[(None, None), ('Standard', None), ('Standard', 2),\n",
        "               ('MinMax', None), ('MinMax', 2), ('Log', None)]\n",
        "for scale_method in scale_methods:\n",
        "    X_data_scaled = get_scaled_data(method=scale_method[0], p_degree=scale_method[1],\n",
        "                                    input_data=X_data)\n",
        "    print(X_data_scaled.shape, X_data.shape)\n",
        "    print('\\n## 변환 유형:{0}, Polynomial Degree:{1}'.format(scale_method[0], scale_method[1]))\n",
        "    get_linear_reg_eval('Ridge', params=alphas, X_data_n=X_data_scaled,\n",
        "                        y_target_n=y_target, verbose=False, return_coeff=False)"
      ]
    },
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        "id": "8S6qdxCUohop"
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fpC1gOfgohop"
      },
      "outputs": [],
      "source": []
    }
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