{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYmmUAcJGuFB"
      },
      "source": [
        "# 마켓과 머신러닝"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jqs5_59hGuFE"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/1-3.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJp1cvmpGuFF"
      },
      "source": [
        "## 생선 분류 문제"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QjvcIpHmGuFF"
      },
      "source": [
        "### 도미 데이터 준비하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "AhUYjyga-mAw"
      },
      "outputs": [],
      "source": [
        "bream_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0, 31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0, 35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0]\n",
        "bream_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0, 500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0, 700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "mSj-Xx1gENO_",
        "outputId": "9549ce45-1fe1-4b8a-d2b8-c268a543bae8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(bream_length, bream_weight)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8LOWA7jQGuFH"
      },
      "source": [
        "### 빙어 데이터 준비하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Hj_L_F9RH5B0"
      },
      "outputs": [],
      "source": [
        "smelt_length = [9.8, 10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]\n",
        "smelt_weight = [6.7, 7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "z0ywO_s4S98S",
        "outputId": "dd053605-5019-41ea-fc26-c4d0f4b6d640"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(bream_length, bream_weight)\n",
        "plt.scatter(smelt_length, smelt_weight)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RU-Qs13GuFI"
      },
      "source": [
        "## 첫 번째 머신러닝 프로그램"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "djyDIbjmTDag"
      },
      "outputs": [],
      "source": [
        "length = bream_length+smelt_length\n",
        "weight = bream_weight+smelt_weight"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMNfgqb62z7W",
        "outputId": "67e2e47d-1f1a-48f1-f888-663d5d0df066"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[25.4, 242.0], [26.3, 290.0], [26.5, 340.0], [29.0, 363.0], [29.0, 430.0], [29.7, 450.0], [29.7, 500.0], [30.0, 390.0], [30.0, 450.0], [30.7, 500.0], [31.0, 475.0], [31.0, 500.0], [31.5, 500.0], [32.0, 340.0], [32.0, 600.0], [32.0, 600.0], [33.0, 700.0], [33.0, 700.0], [33.5, 610.0], [33.5, 650.0], [34.0, 575.0], [34.0, 685.0], [34.5, 620.0], [35.0, 680.0], [35.0, 700.0], [35.0, 725.0], [35.0, 720.0], [36.0, 714.0], [36.0, 850.0], [37.0, 1000.0], [38.5, 920.0], [38.5, 955.0], [39.5, 925.0], [41.0, 975.0], [41.0, 950.0], [9.8, 6.7], [10.5, 7.5], [10.6, 7.0], [11.0, 9.7], [11.2, 9.8], [11.3, 8.7], [11.8, 10.0], [11.8, 9.9], [12.0, 9.8], [12.2, 12.2], [12.4, 13.4], [13.0, 12.2], [14.3, 19.7], [15.0, 19.9]]\n"
          ]
        }
      ],
      "source": [
        "fish_data = [[l, w] for l, w in zip(length, weight)]\n",
        "\n",
        "print(fish_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSSIOoXz3Nm9",
        "outputId": "a9203bfa-7e04-4bdb-c7ca-a8640d40e5cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ],
      "source": [
        "fish_target = [1]*35 + [0]*14\n",
        "print(fish_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "MU_19M2Elp65"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "PCYut6Q2lpgQ"
      },
      "outputs": [],
      "source": [
        "kn = KNeighborsClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "s1Ip0gXK35SK",
        "outputId": "385067c6-eb47-413c-f2fc-49ee4e2c8415"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "kn.fit(fish_data, fish_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Nqdxnci3-yw",
        "outputId": "1a15f4b1-8379-4fd2-8cd9-964d8c2f0a29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "kn.score(fish_data, fish_target)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZXPiKvRGuFL"
      },
      "source": [
        "### k-최근접 이웃 알고리즘"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "mVZ6Ikqj4D7q",
        "outputId": "beb7fb7a-de0c-4162-c4f6-efcd325baafd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(bream_length, bream_weight)\n",
        "plt.scatter(smelt_length, smelt_weight)\n",
        "plt.scatter(30, 600, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WL14YIlr86-",
        "outputId": "335310a8-361b-49c9-8b6b-22adc80e24f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "kn.predict([[30, 600]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WJ_METitoY1",
        "outputId": "f0dd2e87-826e-4c43-b84d-25c86828ebf8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  25.4  242. ]\n",
            " [  26.3  290. ]\n",
            " [  26.5  340. ]\n",
            " [  29.   363. ]\n",
            " [  29.   430. ]\n",
            " [  29.7  450. ]\n",
            " [  29.7  500. ]\n",
            " [  30.   390. ]\n",
            " [  30.   450. ]\n",
            " [  30.7  500. ]\n",
            " [  31.   475. ]\n",
            " [  31.   500. ]\n",
            " [  31.5  500. ]\n",
            " [  32.   340. ]\n",
            " [  32.   600. ]\n",
            " [  32.   600. ]\n",
            " [  33.   700. ]\n",
            " [  33.   700. ]\n",
            " [  33.5  610. ]\n",
            " [  33.5  650. ]\n",
            " [  34.   575. ]\n",
            " [  34.   685. ]\n",
            " [  34.5  620. ]\n",
            " [  35.   680. ]\n",
            " [  35.   700. ]\n",
            " [  35.   725. ]\n",
            " [  35.   720. ]\n",
            " [  36.   714. ]\n",
            " [  36.   850. ]\n",
            " [  37.  1000. ]\n",
            " [  38.5  920. ]\n",
            " [  38.5  955. ]\n",
            " [  39.5  925. ]\n",
            " [  41.   975. ]\n",
            " [  41.   950. ]\n",
            " [   9.8    6.7]\n",
            " [  10.5    7.5]\n",
            " [  10.6    7. ]\n",
            " [  11.     9.7]\n",
            " [  11.2    9.8]\n",
            " [  11.3    8.7]\n",
            " [  11.8   10. ]\n",
            " [  11.8    9.9]\n",
            " [  12.     9.8]\n",
            " [  12.2   12.2]\n",
            " [  12.4   13.4]\n",
            " [  13.    12.2]\n",
            " [  14.3   19.7]\n",
            " [  15.    19.9]]\n"
          ]
        }
      ],
      "source": [
        "print(kn._fit_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhERu04-u_Mx",
        "outputId": "0f48d857-f3c2-4cf3-d1fb-4ed23d891ce7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ],
      "source": [
        "print(kn._y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "scTxBP0ZvOs0"
      },
      "outputs": [],
      "source": [
        "kn49 = KNeighborsClassifier(n_neighbors=49)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gErf9HtixCQw",
        "outputId": "5a566d5a-2258-4b9d-e7ab-063a629038c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7142857142857143"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "kn49.fit(fish_data, fish_target)\n",
        "kn49.score(fish_data, fish_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGEvjYGrxGfF",
        "outputId": "bb1e82c9-8b5c-45e7-cb61-4b4e6febcc8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7142857142857143\n"
          ]
        }
      ],
      "source": [
        "print(35/49)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mfasb9WsRoQV"
      },
      "source": [
        "### 확인 문제"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pQ2JVooP-y5",
        "outputId": "704322ea-e26b-41b0-d627-9bba4fae6352"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18 0.9795918367346939\n"
          ]
        }
      ],
      "source": [
        "kn = KNeighborsClassifier()\n",
        "kn.fit(fish_data, fish_target)\n",
        "\n",
        "for n in range(5, 50):\n",
        "    # 최근접 이웃 개수 설정\n",
        "    kn.n_neighbors = n\n",
        "    # 점수 계산\n",
        "    score = kn.score(fish_data, fish_target)\n",
        "    # 100% 정확도에 미치지 못하는 이웃 개수 출력\n",
        "    if score < 1:\n",
        "        print(n, score)\n",
        "        break"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "1-3. 마켓과 머신러닝",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}