SVM_feat16.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SVM.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/EE559_Project-main_Final.zip\n"
      ],
      "metadata": {
        "id": "0yp0e1yzfeUt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "import imblearn\n",
        "import numpy as np \n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "fmtBdtZKlp0z"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "rFjH9PL2mnpf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85ce5def-bf47-4d2b-a933-68fd94745cc5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "fires_train = pd.read_csv(\"/content/EE559_Project-main/datasets/algerian_fires_train.csv\")\n",
        "fires_test = pd.read_csv(\"/content/EE559_Project-main/datasets/algerian_fires_test.csv\")\n",
        "\n",
        "data = fires_train.describe()\n",
        "tstdata = fires_test.describe()\n",
        "\n",
        "fires_train = fires_train.iloc[:,1:]\n",
        "fires_test = fires_test.iloc[:,1:]\n",
        "\n",
        "##Normalising the data as there is scale difference\n",
        "predictors = fires_train.iloc[:,:-1]\n",
        "target = fires_train.iloc[:,-1:]\n",
        "\n",
        "predictorstst = fires_test.iloc[:,:-1]\n",
        "targettst = fires_test.iloc[:,-1:]\n",
        "\n",
        "scalar = MinMaxScaler()\n",
        "\n",
        "fires = scalar.fit_transform(predictors)\n",
        "firestst = scalar.fit_transform(predictorstst)\n",
        "\n",
        " #test Linear\n",
        "model_linear = SVC(kernel = \"linear\")\n",
        "model_linear.fit(fires,target)\n",
        "pred_test_linear = model_linear.predict(firestst)\n",
        "\n",
        " #test RBF\n",
        "\n",
        "# kernel = rbf\n",
        "model_rbf = SVC(kernel = \"rbf\")\n",
        "model_rbf.fit(fires,target)\n",
        "pred_test_rbf = model_rbf.predict(firestst)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fires_train16 = pd.read_csv(\"/content/EE559_Project-main/datasets/firesfeatures.csv\")\n",
        "fires_test16 = pd.read_csv(\"/content/EE559_Project-main/datasets/fireststfeatures.csv\")\n",
        "\n",
        "\n",
        "scalar = MinMaxScaler()\n",
        "\n",
        "fires16 = scalar.fit_transform(fires_train16)\n",
        "firestst16 = scalar.fit_transform(fires_test16)\n",
        "\n",
        " #test Linear\n",
        "model_linear = SVC(kernel = \"linear\")\n",
        "model_linear.fit(fires16,target)\n",
        "pred_test_linear = model_linear.predict(firestst16)\n",
        "\n",
        " #test RBF\n",
        "\n",
        "# kernel = rbf\n",
        "model_rbf = SVC(kernel = \"rbf\")\n",
        "model_rbf.fit(fires16,target)\n",
        "pred_test_rbf = model_rbf.predict(firestst16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyYu6VJj_qpA",
        "outputId": "5819531f-359c-4b60-e31d-da036f8d3777"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "import matplotlib.pyplot as plt\n",
        "def feature_plot(classifier, feature_names, top_features=4):\n",
        " coef = classifier.coef_.ravel()\n",
        " top_positive_coefficients = np.argsort(coef)[-top_features:]\n",
        " top_negative_coefficients = np.argsort(coef)[:top_features]\n",
        " top_coefficients = np.hstack([top_negative_coefficients, top_positive_coefficients])\n",
        " plt.figure(figsize=(18, 7))\n",
        " colors = ['green' if c < 0 else 'blue' for c in coef[top_coefficients]]\n",
        " plt.bar(np.arange(2 * top_features), coef[top_coefficients], color=colors)\n",
        " feature_names = np.array(feature_names)\n",
        " plt.xticks(np.arange(1 + 2 * top_features), feature_names[top_coefficients], rotation=45, ha='right')\n",
        " plt.show()\n",
        "\n",
        "print(fires_train16.columns.values)\n",
        "\n",
        "trainedsvm = svm.LinearSVC().fit(fires16, target)\n",
        "feature_plot(trainedsvm, fires_train16.columns.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "1somGHUA9xhH",
        "outputId": "a1c93d49-e50d-4b1e-8ba5-2bf6b4bf3383"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Temperature' 'Ws' 'RH' 'Rain' 'FFMC' 'DMC' 'ISI' 'Temperature Ws'\n",
            " 'Temperature RH' 'Ws RH' 'Ws FFMC' 'RH FFMC' 'Rain DMC' 'Rain DC'\n",
            " 'Rain ISI' 'Rain BUI']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "q = accuracy_score(targettst,pred_test_rbf)\n",
        "r = accuracy_score(targettst,pred_test_linear)\n",
        "print( \"Test RBF Accuracy=\", q, \"Test Linear Accuracy=\", r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6HISRa9Wj_lk",
        "outputId": "cdf07012-1422-468c-e0c3-ea38bc3ed776"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test RBF Accuracy= 0.7833333333333333 Test Linear Accuracy= 0.8166666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F1_score, Accuracy = metrics(targettst,pred_test_linear, \"SVM_Linear\")\n",
        "print(\"Test F1_score=\", F1_score, \"Test Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "LnRfdMCuoCZN",
        "outputId": "612d0c36-4ce0-4caf-edc1-a49784640ebe"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test F1_score= 0.8070175438596492 Test Accuracy= 0.8166666666666667\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F1_score, Accuracy = metrics(targettst,pred_test_rbf, \"SVM_RBF\")\n",
        "print(\"Test RBF F1_score=\", F1_score, \"Test RBF Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "5Nzqiq8rp5Pt",
        "outputId": "265f67cb-84b4-4fdd-978a-59ed8950d799"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test RBF F1_score= 0.7547169811320754 Test RBF Accuracy= 0.7833333333333333\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}