4-2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "4-2 확률적 경사 하강법.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5ALSPQSJp-n"
      },
      "source": [
        "# 확률적 경사 하강법"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hnCwTs5KJp-t"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/4-2.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": "3x4OwaSIR50l"
      },
      "source": [
        "## SGDClassifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3z-zKXoRmWB"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "fish = pd.read_csv('https://bit.ly/fish_csv_data')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAiJVY9nR1fF"
      },
      "source": [
        "fish_input = fish[['Weight','Length','Diagonal','Height','Width']].to_numpy()\n",
        "fish_target = fish['Species'].to_numpy()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AW6LMW_URpto"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    fish_input, fish_target, random_state=42)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RTAwK_DRutj"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "ss = StandardScaler()\n",
        "ss.fit(train_input)\n",
        "train_scaled = ss.transform(train_input)\n",
        "test_scaled = ss.transform(test_input)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSyujXY7sli6"
      },
      "source": [
        "from sklearn.linear_model import SGDClassifier"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KofoXhbwR9yu",
        "outputId": "1d6a0eac-9a2d-4008-a7fa-044c2e39907d"
      },
      "source": [
        "sc = SGDClassifier(loss='log', max_iter=10, random_state=42)\n",
        "sc.fit(train_scaled, train_target)\n",
        "\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.773109243697479\n",
            "0.775\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
            "  ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "duwA4N3eSUk5",
        "outputId": "f26d3a96-dca9-46b0-8201-4e12b15d57aa"
      },
      "source": [
        "sc.partial_fit(train_scaled, train_target)\n",
        "\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8151260504201681\n",
            "0.825\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEtfnUQhzKO2"
      },
      "source": [
        "## 에포크와 과대/과소적합"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pt7BHZVZ-dWT"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "sc = SGDClassifier(loss='log', random_state=42)\n",
        "\n",
        "train_score = []\n",
        "test_score = []\n",
        "\n",
        "classes = np.unique(train_target)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-65Gz13tVOP7"
      },
      "source": [
        "for _ in range(0, 300):\n",
        "    sc.partial_fit(train_scaled, train_target, classes=classes)\n",
        "    \n",
        "    train_score.append(sc.score(train_scaled, train_target))\n",
        "    test_score.append(sc.score(test_scaled, test_target))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "V19SzZJ5ZjSI",
        "outputId": "de31130f-e860-4143-99ec-014654191160"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(train_score)\n",
        "plt.plot(test_score)\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdp2Ykst1K_I",
        "outputId": "6214bd83-00c8-4a16-9931-ad7746ddff9f"
      },
      "source": [
        "sc = SGDClassifier(loss='log', max_iter=100, tol=None, random_state=42)\n",
        "sc.fit(train_scaled, train_target)\n",
        "\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.957983193277311\n",
            "0.925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OL7-y1kgIP4S",
        "outputId": "554597de-bec8-45d1-92e6-a0f5a7ebe519"
      },
      "source": [
        "sc = SGDClassifier(loss='hinge', max_iter=100, tol=None, random_state=42)\n",
        "sc.fit(train_scaled, train_target)\n",
        "\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9495798319327731\n",
            "0.925\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}