Stochastic_Gradient_Descent.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Stochastic_Gradient_Descent.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOagf936FwDm3R3malIVmV/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/symaeng98/Artificial_Intelligence/blob/main/Stochastic_Gradient_Descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjfJ_q9XXrw-"
      },
      "source": [
        "import pandas as pd\n",
        "fish = pd.read_csv('https://bit.ly/fish_csv_data')\n",
        "fish_target = fish['Species'].to_numpy()\n",
        "fish_input = fish[['Weight', 'Length', 'Diagonal', 'Height', 'Width']].to_numpy()"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9COryunSQTu"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    fish_input, fish_target, random_state = 42)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sdhsgcytSMdU"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "ss = StandardScaler()\n",
        "ss.fit(train_input)\n",
        "train_scaled = ss.transform(train_input)\n",
        "test_scaled = ss.transform(test_input)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIWiwitCSkf0"
      },
      "source": [
        "from sklearn.linear_model import SGDClassifier"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgKafB4zTvdV"
      },
      "source": [
        "sc = SGDClassifier(loss='log', max_iter=10, random_state=42)\n",
        "sc.fit(train_scaled, train_target)\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9ccnSfNVDLF",
        "outputId": "85424565-d42c-410b-d1ea-cdd822c3c67b"
      },
      "source": [
        "sc.partial_fit(train_scaled, train_target)\n",
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.907563025210084\n",
            "0.925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTitE6EBVKod"
      },
      "source": [
        "import numpy as np\n",
        "sc = SGDClassifier(loss='log', random_state=42)\n",
        "train_score = []\n",
        "test_score = []\n",
        "classes = np.unique(train_target)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LcETQh8OaBDb"
      },
      "source": [
        "for _ in range(300):\n",
        "  sc.partial_fit(train_scaled, train_target, classes=classes)\n",
        "  train_score.append(sc.score(train_scaled, train_target))\n",
        "  test_score.append(sc.score(test_scaled, test_target))"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2Bl_fzabEje"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(train_score)\n",
        "plt.plot(test_score)\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('accuracy')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZdXo5pDdiD4"
      },
      "source": [
        "sc = SGDClassifier(loss='log', max_iter=100, tol=None, random_state=42)\n",
        "sc.fit(train_scaled, train_target)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ND_hET-8gwhR",
        "outputId": "1eff3405-5361-43b5-8e69-88048ff545a1"
      },
      "source": [
        "print(sc.score(train_scaled, train_target))\n",
        "print(sc.score(test_scaled, test_target))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.957983193277311\n",
            "0.925\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}