2021_04/04_02/Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Regression",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNOBGBL011uBK4DTADt6i8B"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "mM6cVyWutOcp",
        "outputId": "4030b7e7-a013-44f6-fb4c-fe652eb9ca9d"
      },
      "source": [
        "# k-최근접 이웃 회귀 \n",
        "\n",
        "# 데이터 준비\n",
        "\n",
        "import numpy as np\n",
        "perch_length = np.array([8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0, 21.0,\n",
        "       21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5, 22.5, 22.7,\n",
        "       23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5, 27.3, 27.5, 27.5,\n",
        "       27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0, 36.5, 36.0, 37.0, 37.0,\n",
        "       39.0, 39.0, 39.0, 40.0, 40.0, 40.0, 40.0, 42.0, 43.0, 43.0, 43.5,\n",
        "       44.0])\n",
        "perch_weight = np.array([5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0, 110.0,\n",
        "       115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0, 130.0,\n",
        "       150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0, 197.0,\n",
        "       218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0, 514.0,\n",
        "       556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0, 820.0,\n",
        "       850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0, 1000.0,\n",
        "       1000.0])\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(perch_length, perch_weight)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()\n",
        "\n",
        "# 훈련 세트와 테스트 세트로 나누기\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_input, test_input, train_target, test_target = train_test_split(perch_length, perch_weight, random_state=42)\n",
        "\n",
        "# 배열 크기 바꾸기\n",
        "test_array = np.array([1,2,3,4])\n",
        "print(test_array.shape)\n",
        "test_array = test_array.reshape(2,2) # reshape() 함수로 배열의 크기를 바꿀 수 있다. 한번에 n차원 배열로 변경이 가능하다는 소리다.\n",
        "print(test_array.shape)\n",
        "\n",
        "# 실사용 배열 크기 변경하기\n",
        "train_input = train_input.reshape(-1,1) # 크기에 -1을 지정하면 나머지 원소 개수로 모두 채우라는 의미이다.\n",
        "test_input = test_input.reshape(-1,1)\n",
        "print(train_input.shape, test_input.shape)\n",
        "\n",
        "# 결정계수(R^2)\n",
        "\n",
        "from sklearn.neighbors import KNeighborsRegressor # k-최근접 이웃 회귀 알고리즘\n",
        "knr = KNeighborsRegressor()\n",
        "# k-최근접 이웃 회귀 모델을 훈련합니다.\n",
        "knr.fit(train_input, train_target)\n",
        "\n",
        "print(\"테스트 세트 :\", knr.score(test_input, test_target)) # 회귀에서는 정확한 숫자를 맞힌다는 것은 거의 불가능 하다. 예측하는 값이나 타깃 모두 임의의 수치이기 때문이다.\n",
        "\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error # 타깃과 예측의 절댓값 오차를 평균하여 변환\n",
        "\n",
        "# 테스트 세트에 대한 예측을 만든다.\n",
        "test_prediction = knr.predict(test_input)\n",
        "\n",
        "# 테스트 세트에 대한 평균 절댓값 오차를 계산\n",
        "mae = mean_absolute_error(test_target, test_prediction)\n",
        "print(mae)\n",
        "\n",
        "# 과대적합 vs 과소적합\n",
        "\n",
        "print(\"훈련 세트 :\",knr.score(train_input, train_target))\n",
        "\n",
        "knr.n_neighbors = 3\n",
        "\n",
        "# 모델을 다시 훈련시킵니다.\n",
        "knr.fit(train_input, train_target)\n",
        "print(knr.score(train_input, train_target))\n",
        "\n",
        "print(knr.score(test_input, test_target))\n",
        "\n",
        "knr = KNeighborsRegressor()\n",
        "x = np.arange(5,45).reshape(-1,1)\n",
        "\n",
        "# n = 1,5,10일 때 예측 결과를 그래프로 그립니다\n",
        "for n in [1,5,10]:\n",
        "  knr.n_neighbors = n\n",
        "  knr.fit(train_input, train_target)\n",
        "  # 지정한 범위 x에 대한 예측을 구합니다.\n",
        "  prediction = knr.predict(x)\n",
        "\n",
        "plt.scatter(train_input, train_target)\n",
        "plt.plot(x, prediction)\n",
        "plt.title('n_neighbors = {}'.format(n))\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(4,)\n",
            "(2, 2)\n",
            "(42, 1) (14, 1)\n",
            "테스트 세트 : 0.9928094061010639\n",
            "19.157142857142862\n",
            "훈련 세트 : 0.9698823289099255\n",
            "0.9804899950518966\n",
            "0.974645996398761\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}