SVM.ipynb

{
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  "metadata": {
    "colab": {
      "name": "SVM.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPGUNo4CO1x9thbIIc6y+Bv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jacqueline45/EE559_Project/blob/main/SVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/EE559_Project-main.zip\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0yp0e1yzfeUt",
        "outputId": "2274e865-6d06-41f3-88a8-6de025e6a780"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/EE559_Project-main.zip\n",
            "cd4e0a51e0b87e7bb5bd032f2efedcc9a0da1845\n",
            "   creating: EE559_Project-main/\n",
            "  inflating: EE559_Project-main/.DS_Store  \n",
            " extracting: EE559_Project-main/.gitignore  \n",
            "  inflating: EE559_Project-main/Feature_Selection.py  \n",
            "  inflating: EE559_Project-main/MSE.py  \n",
            "  inflating: EE559_Project-main/README.md  \n",
            "  inflating: EE559_Project-main/baseline.py  \n",
            "   creating: EE559_Project-main/cf_matrix_plots/\n",
            "  inflating: EE559_Project-main/cf_matrix_plots/MSE.png  \n",
            "  inflating: EE559_Project-main/cf_matrix_plots/MSE_val.png  \n",
            "  inflating: EE559_Project-main/cf_matrix_plots/baseline_system.png  \n",
            "  inflating: EE559_Project-main/cf_matrix_plots/baseline_system_val.png  \n",
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            "  inflating: EE559_Project-main/cf_matrix_plots/p_feat_reduct_val.png  \n",
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            "  inflating: EE559_Project-main/cf_matrix_plots/trivial_system_val.png  \n",
            "  inflating: EE559_Project-main/data_analysis.py  \n",
            "   creating: EE559_Project-main/datasets/\n",
            "  inflating: EE559_Project-main/datasets/algerian_fires_test.csv  \n",
            "  inflating: EE559_Project-main/datasets/algerian_fires_train.csv  \n",
            "   creating: EE559_Project-main/feat_KDE/\n",
            "  inflating: EE559_Project-main/feat_KDE/BUI.png  \n",
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            "  inflating: EE559_Project-main/feat_KDE/FFMC.png  \n",
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            "   creating: EE559_Project-main/feat_KDE_test/\n",
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            "  inflating: EE559_Project-main/kNN.py  \n",
            "  inflating: EE559_Project-main/perceptron.py  \n",
            "  inflating: EE559_Project-main/trivial.py  \n",
            "   creating: EE559_Project-main/utils/\n",
            "  inflating: EE559_Project-main/utils/Add_feat.py  \n",
            "  inflating: EE559_Project-main/utils/metrics.py  \n",
            "  inflating: EE559_Project-main/utils/read_data.py  \n"
          ]
        }
      ]
    },
    {
      "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": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Generates all performance measures\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "from statistics import mean\n",
        "import pandas as pd\n",
        "\n",
        "def Add_feat(X_tr, X_test):\n",
        "    mean_ISI_tr = [0,0,0,0]\n",
        "    mean_ISI_test = [0,0,0,0]\n",
        "    for i in range(4, X_tr.shape[0]-4):\n",
        "        mean_ISI_tr.append(mean(X_tr['ISI'][i-4:i]))\n",
        "\n",
        "    for i in range(4, X_test.shape[0]):\n",
        "        mean_ISI_test.append(mean(X_test['ISI'][i-4:i]))\n",
        "\n",
        "    mean_ISI_tr.extend([0,0,0,0])\n",
        "\n",
        "    X_test = X_test.reset_index(drop=True)\n",
        "    mean_ISI_test[0] = sum(X_tr['ISI'].iloc[-4:])/4\n",
        "    mean_ISI_test[1] = (sum(X_tr['ISI'].iloc[-3:])+X_test['ISI'].iloc[0])/4\n",
        "    mean_ISI_test[2] = (sum(X_tr['ISI'].iloc[-2:])+sum(X_test['ISI'].iloc[:2]))/4\n",
        "    mean_ISI_test[3] = (X_tr['ISI'].iloc[X_tr.shape[0]-1]+sum(X_test['ISI'].iloc[:3]))/4\n",
        "    \n",
        "    X_tr = X_tr.assign(mean_ISI = mean_ISI_tr)\n",
        "    X_test = X_test.assign(mean_ISI = mean_ISI_test)\n",
        "    \n",
        "    X_tr = X_tr.drop(X_tr.index[:4])\n",
        "    X_tr = X_tr.drop(X_tr.index[-4:])\n",
        "\n",
        "    X_tr = X_tr.reset_index(drop=True)\n",
        "\n",
        "    return X_tr, X_test\n",
        "\n",
        "def read_data(file):\n",
        "    \"\"\"Reads a CSV file and returns a data matrix and the labels separatelly\"\"\"\n",
        "    data = pd.read_csv(file)\n",
        "    return data.iloc[:, :-1], data.iloc[:,-1]\n",
        "\n",
        "def metrics(true_labels, pred_labels, plot_title, work='test'):\n",
        "    cf_matrix = confusion_matrix(true_labels, pred_labels)\n",
        "    TP, TN, FP, FN = cf_matrix[1][1], cf_matrix[0][0], cf_matrix[0][1], cf_matrix[1][0]\n",
        "    Recall = TP/(TP+FN)\n",
        "    Precision = TP/(TP+FP)\n",
        "    F1_score = 2*Recall*Precision/(Recall+Precision)\n",
        "    Accuracy = (TP+TN)/true_labels.shape[0]\n",
        "    \n",
        "    if work == 'test':\n",
        "        ax = sns.heatmap(cf_matrix, annot=True, cmap='Blues')\n",
        "\n",
        "        ax.set_title(plot_title)\n",
        "        ax.set_xlabel('Predicted Values')\n",
        "        ax.set_ylabel('Actual Values ')\n",
        "\n",
        "        ax.xaxis.set_ticklabels(['0','1'])\n",
        "        ax.yaxis.set_ticklabels(['0','1'])\n",
        "\n",
        "        plt.savefig('/content/EE559_Project-main/cf_matrix_plots/'+plot_title+'.png')\n",
        "\n",
        "        return F1_score, Accuracy\n",
        "\n",
        "    else:\n",
        "        return F1_score, Accuracy, TP, TN, FP, FN\n",
        "\n",
        "def plot_val_cf_matrix(true_labels, pred_labels, plot_title, TP, TN, FP, FN):\n",
        "    cf_matrix = confusion_matrix(true_labels, pred_labels)\n",
        "    cf_matrix[1][1], cf_matrix[0][0], cf_matrix[0][1], cf_matrix[1][0] = TP, TN, FP, FN\n",
        "    \n",
        "    ax = sns.heatmap(cf_matrix, annot=True, cmap='Blues')\n",
        "\n",
        "    ax.set_title(plot_title)\n",
        "    ax.set_xlabel('Predicted Values')\n",
        "    ax.set_ylabel('Actual Values ')\n",
        "\n",
        "    ## Ticket labels - List must be in alphabetical order\n",
        "    ax.xaxis.set_ticklabels(['0','1'])\n",
        "    ax.yaxis.set_ticklabels(['0','1'])\n",
        "\n",
        "    plt.savefig('/content/EE559_Project-main/cf_matrix_plot/'+plot_title+'_val.png')"
      ],
      "metadata": {
        "id": "ePsCx9Own0DN"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "rFjH9PL2mnpf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5e8000e-d605-41c1-cb99-6536d5436f8d"
      },
      "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_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "PyYu6VJj_qpA",
        "outputId": "0f375c2e-3797-4112-f64e-b40a774b9480"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Temperature          RH         Ws       Rain       FFMC        DMC  \\\n",
              "0      18.952399   43.855865  12.292536  -0.340306  73.063752  -2.371083   \n",
              "1      34.498610   46.230441  14.838211   0.939652  56.027977   0.441002   \n",
              "2      15.258665   57.084279  10.968696   1.397913  33.114404   3.389994   \n",
              "3      24.847936   99.910000  17.924025   3.958666  26.148986   5.179630   \n",
              "4      19.381227  104.398680  23.740540  13.394723  28.658020  -2.247164   \n",
              "..           ...         ...        ...        ...        ...        ...   \n",
              "179    28.449213   57.127011  15.195444  -0.161958  71.450733  57.128627   \n",
              "180    26.680911   73.619431  17.054505   0.127765  61.231373  18.986399   \n",
              "181    44.750966   58.428753  21.687939   0.995222  62.921683  27.594857   \n",
              "182    33.298270   79.558329  20.464269  17.019373  47.242905   6.039570   \n",
              "183    32.762295   84.322685  21.653932  -0.213253  67.294270  33.599199   \n",
              "\n",
              "             DC        ISI        BUI  Classes  \n",
              "0     28.543573   0.487246   6.225461        0  \n",
              "1    -10.007636  -1.472158   2.268104        0  \n",
              "2    -13.774588   1.338737  -3.327908        0  \n",
              "3     -1.392789  -0.755090   0.131303        0  \n",
              "4     -3.432689   0.558249  -1.928471        0  \n",
              "..          ...        ...        ...      ...  \n",
              "179  239.290670  11.267031  68.680725        1  \n",
              "180  170.968390   9.496533  39.281865        1  \n",
              "181  176.118476  -0.271474  30.340267        0  \n",
              "182  -11.298033   0.035798   4.744741        0  \n",
              "183  165.945211  10.440299  35.986552        1  \n",
              "\n",
              "[184 rows x 10 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>RH</th>\n",
              "      <th>Ws</th>\n",
              "      <th>Rain</th>\n",
              "      <th>FFMC</th>\n",
              "      <th>DMC</th>\n",
              "      <th>DC</th>\n",
              "      <th>ISI</th>\n",
              "      <th>BUI</th>\n",
              "      <th>Classes</th>\n",
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              "      <td>1.397913</td>\n",
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              "      <td>3.389994</td>\n",
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              "      <td>1.338737</td>\n",
              "      <td>-3.327908</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>24.847936</td>\n",
              "      <td>99.910000</td>\n",
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              "      <td>-0.161958</td>\n",
              "      <td>71.450733</td>\n",
              "      <td>57.128627</td>\n",
              "      <td>239.290670</td>\n",
              "      <td>11.267031</td>\n",
              "      <td>68.680725</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>26.680911</td>\n",
              "      <td>73.619431</td>\n",
              "      <td>17.054505</td>\n",
              "      <td>0.127765</td>\n",
              "      <td>61.231373</td>\n",
              "      <td>18.986399</td>\n",
              "      <td>170.968390</td>\n",
              "      <td>9.496533</td>\n",
              "      <td>39.281865</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>44.750966</td>\n",
              "      <td>58.428753</td>\n",
              "      <td>21.687939</td>\n",
              "      <td>0.995222</td>\n",
              "      <td>62.921683</td>\n",
              "      <td>27.594857</td>\n",
              "      <td>176.118476</td>\n",
              "      <td>-0.271474</td>\n",
              "      <td>30.340267</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>33.298270</td>\n",
              "      <td>79.558329</td>\n",
              "      <td>20.464269</td>\n",
              "      <td>17.019373</td>\n",
              "      <td>47.242905</td>\n",
              "      <td>6.039570</td>\n",
              "      <td>-11.298033</td>\n",
              "      <td>0.035798</td>\n",
              "      <td>4.744741</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>32.762295</td>\n",
              "      <td>84.322685</td>\n",
              "      <td>21.653932</td>\n",
              "      <td>-0.213253</td>\n",
              "      <td>67.294270</td>\n",
              "      <td>33.599199</td>\n",
              "      <td>165.945211</td>\n",
              "      <td>10.440299</td>\n",
              "      <td>35.986552</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>184 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f95e5dab-f6d9-4e5a-8c2c-dab738709901')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f95e5dab-f6d9-4e5a-8c2c-dab738709901 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f95e5dab-f6d9-4e5a-8c2c-dab738709901');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "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_train.drop(['Classes'], axis = 1).columns.values)\n",
        "\n",
        "trainedsvm = svm.LinearSVC().fit(fires, target)\n",
        "feature_plot(trainedsvm, fires_train.drop(['Classes'], axis = 1).columns.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "1somGHUA9xhH",
        "outputId": "6a1c9dc2-963a-48d3-ec08-993c95c1e5e1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Temperature' 'RH' 'Ws' 'Rain' 'FFMC' 'DMC' 'DC' 'ISI' '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 import svm, datasets\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "C_range = [0.01, 0.1, 1, 10,100]  \n",
        "gamma_range = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
        "svcModel = SVC(kernel='rbf',max_iter = 1000)\n",
        "parameters = {'C':C_range, 'gamma':gamma_range}\n",
        "clf1 = GridSearchCV(svcModel, parameters, cv=10, n_jobs=-1)\n",
        "clf1.fit(fires, target)\n",
        "\n",
        "c1 = clf1.best_params_['C']\n",
        "gamma1 = clf1.best_params_['gamma']\n",
        "\n",
        "print('c1 value is', c1, 'gamma1 value is', gamma1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1K-fcHrB6eE4",
        "outputId": "cba0e298-6748-4f26-e8d5-9a1936bc99e2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "c1 value is 100 gamma1 value is 0.1\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svcModel1 = SVC(C=c1, gamma=gamma1,kernel='rbf')\n",
        "svc_family = svcModel1.fit(fires, target)\n",
        "pred = svc_family.predict(firestst)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFeKtwAd7dfZ",
        "outputId": "503daad8-a8eb-4c00-ce7c-83f5642b52d9"
      },
      "execution_count": 10,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "q = accuracy_score(targettst,pred)\n",
        "q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9DHEQp738qCg",
        "outputId": "7dc3cf6e-83ee-4a10-f91a-c25594e90f6c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.75"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "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": "275133f5-2cbd-4ba8-e32a-d2ae35fb7b61"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test RBF Accuracy= 0.8333333333333334 Test Linear Accuracy= 0.85\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": "1fbcd41e-9e2d-4f82-b704-88f54c20bf9b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test F1_score= 0.8363636363636363 Test Accuracy= 0.85\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": "ae952d4a-1ab9-45a1-938a-4fe71533cd17"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test RBF F1_score= 0.8214285714285715 Test RBF Accuracy= 0.8333333333333334\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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FcRNtLFjJ5qLNzLqFuhxBA9PyHvcCRpGbhzYz6zbqMqAj4s7855KmAA9XrCIzsxroLpslDSH39VdmZt1Gj2JuOq6yYuag17DpHPQycisLzcy6jRRXEhYzxdG/GoWYmdVSinPQHQ7qJU0vps3MrJ6luNS70H7QvYA+wI6StoeNNwluA+xShdrMzKqmoc7ugz4XOB/YGXiS9wN6NTCxwnWZmVVVglPQBfeDvhq4WtJXIuKaKtZkZlZ1PROchC7mxpINkrZreSJpe0n/XMGazMyqrpxz0JJulLRC0ry8tgGSHpD0x+zP7Tvqp5iAPici3mp5EhErgXOKeJ+ZWd1okIo+inAzcFyrtgnA9IgYAkzPnheuqYgL9VDeEhtJPYCti6nQzKxelHMEHREPAW+2aj4ZaPkqwcnAKR31U8xKwnuB2yX9NHt+btZmZtZtlLKQUNI4YFxeU1NENHXwtsaIWJo9XgY0dnSdYgL6wqyQL2XPHwAmFfE+M7O6UcpKwiyMOwrkQu8PSZvtErpZTUV0tCEirouI0yLiNGA+uY37zcy6jTLPQbdluaRBANmfKzqsqZheJQ2XdIWkRcD3gOc7W6GZWYpUwtFJU4Gx2eOxwG86ekOhlYR7AWOy4w3gdkAR4W9VMbNup5wLVbJtmUeSW4m9GLgIuAy4Q9LZwCvA6R31U2gO+nlgJnBiRLyYXdTfRWhm3VI594OOiDHtnPp4Kf0UmuL4DLAU+L2kSZI+TpdG92Zm6Woo4ahmTW2KiF9HxGhgH+D35PblGCjpWknHVqtAM7NqqMKHhKXX1NELImJtRPwiIk4CdgWexhv2m1k3I6noo1pKGq1HxMqIaIqIkuZRzMxSl+IUR2e+k9DMrNvpLl8aa2bW7aQXzw5oMzMAengEbWaWpgTz2QFtZgagBCc5HNBmZngEbWaWrHr7Vm8zsy2GR9BmZomq5hLuYjmgzcyAhvTy2QFtZga+i8PMLFkJznA4oFP3yMyHuPyy77OheQOjTv0sZ58zruM3Wbezyw59aPryEQzctjcRcNP0F7j2dwv4P6cP44QRg9kQ8Pqqdzjv2odZtnJdrcutSx5BW0mam5v5v9//Hj+ddBONjY2c8bnTGHn0Meyx5561Ls2qbH1z8M1bZ/PMy2/Sr1dPZv7HiTw4989c/V/PcekdcwA477h9mHDqAZx//eM1rrY+pTgHXc2d86xE856dy+DBH2LXwYPZauutOe7TJzDj99NrXZbVwPK31vHMy28C8PY761m4ZBU7D+jDmnXvbXxN3149iahVhfUvxQ37PYJO2Irly/ngoA9ufD6wsZFn586tYUWWgt126sv+Hx7A7BffAOA7nxvOmKP2YPW6dznh4vtqXF39SnAAXf0RtKR/LHBunKTZkmbfMKmpmmWZ1YW+H+jJz/7laCZMnrVx9Py925/mI1/+FXc8/BLjjtunxhXWrxRH0LWY4ri4vRPZt7WMiIgR/jAsN2JetnTZxucrli+nsbGxhhVZLfXsIX52wUjuePglpv7h1c3O3z7zZU4+5EM1qKx7UAlHtVRkikNSe7+HC3DCFGnf/f6WV19dxOLFr9E4sJF77/kt/3HlD2pdltXIT847nIVLVjHxt/M3tu3xwf78adkaAE44aDAvLFlVq/LqX4JzHJWag24EPgWsbNUu4NEKXbPb6dmzJ9/41nf40rgvsmFDM6eMOpU99xxS67KsBj6690DOOGoP5r3yJo9cfhIAF095irOOGcKQnbdhw4bgtTfW8rVJvoOjs7akpd7TgH4RMaf1CUkzKnTNbunIoz7GkUd9rNZlWI09tnAF/T83ebP2++csqUE13VN68VyhgI6IswucO6MS1zQz65IEE9q32ZmZ4ZWEZmbJSnAK2gFtZgZJznA4oM3MAJTgENoBbWaGpzjMzJJVznyWtAhYAzQD6yNiRGf6cUCbmUElJqGPjog3utKBA9rMjDRvs/N+0GZm5Oagiz2KEMD9kp6U1Omd3zyCNjOjtA8Js9DND96miMjfI/mIiFgiaSDwgKTnI+KhUmtyQJuZUdoURxbG7W5aHxFLsj9XSLobOBgoOaA9xWFmRvmmOCT1ldS/5TFwLDCvMzV5BG1mRllv4mgE7s4WvvQEfhER93amIwe0mRmULaEj4iXggHL05YA2M2PL2rDfzKyupBfPDmgzs5wEE9oBbWZGmisJHdBmZng3OzOzZCWYzw5oMzPwhv1mZslKMJ8d0GZm4CkOM7N0JZjQDmgzM3ybnZlZsjwHbWaWqAYHtJlZqtJLaAe0mRme4jAzS1aC+eyANjMDj6DNzJLlpd5mZolKL54d0GZmgKc4zMyS5ZWEZmapSi+fHdBmZpBkPjugzcwAGhKchHZAm5mR5oeEDbUuwMzM2uYRtJkZaY6gHdBmZvg2OzOzZHkEbWaWKAe0mVmiPMVhZpaoFEfQvs3OzIzcSsJijw77ko6TtFDSi5ImdLYmB7SZGZQtoSX1AH4CHA8MBcZIGtqZkjzFYWZGWZd6Hwy8GBEvAUi6DTgZmF9qR8kGdK+eCc7Y14ikcRHRVOs6UrDm9rG1LiEZ/ndRXqVkjqRxwLi8pqa8/xe7AK/lnVsMHNKZmjzFUR/GdfwS2wL530WNRERTRIzIOyryg9IBbWZWXkuAwXnPd83aSuaANjMrr1nAEEkflrQ1MBqY2pmOkp2Dtk14ntHa4n8XCYqI9ZLGA/cBPYAbI+K5zvSliChrcWZmVh6e4jAzS5QD2swsUQ7oxJVryah1H5JulLRC0rxa12KV5YBOWDmXjFq3cjNwXK2LsMpzQKdt45LRiHgXaFkyaluwiHgIeLPWdVjlOaDT1taS0V1qVIuZVZkD2swsUQ7otJVtyaiZ1R8HdNrKtmTUzOqPAzphEbEeaFkyugC4o7NLRq37kDQFeAzYW9JiSWfXuiarDC/1NjNLlEfQZmaJckCbmSXKAW1mligHtJlZohzQZmaJckDbJiQ1S5ojaZ6kX0rq04W+bpZ0Wvb4+kIbPUkaKemwTlxjkaQdW7XdJOncVm2nSPpdMbWapcIBba2ti4hhEbEf8C5wXv5JSZ36mrSI+GJEzC/wkpFAyQHdjinkFvXkG521m9UNB7QVMhPYMxvdzpQ0FZgvqYekKyXNkjS3ZbSqnInZ/tX/HxjY0pGkGZJGZI+Pk/SUpGckTZe0O7kfBF/PRu9HStpJ0p3ZNWZJOjx77w6S7pf0nKTrAbVR93RgH0mDsvf0BT4B/FrSd7L+5klqkrTZ+/NH5ZJGSJrR0k+2F/MfJD0t6eSsfd+sbU729zGkDH/3Zg5oa1s2Uj4eeDZrOhD4WkTsBZwNrIqIg4CDgHMkfRgYBexNbu/qs2hjRCxpJ2AScGpEHAB8NiIWAdcBP8pG7zOBq7PnBwGnAtdnXVwEPBwR+wJ3A7u1vkZENAN3AqdnTScBMyJiNTAxIg7KfkPoDZxYwl/Lt4AHI+Jg4Gjgyiz8zwOujohhwAhyuw6adZm/1dta6y1pTvZ4JnADuaD9Q0S8nLUfC+yfN2e7LTAEOAqYkgXknyU92Eb/hwIPtfQVEe3ta/wJYGjeAHcbSf2ya3wme+9vJa1s5/1TgKvIBf1o4Nas/WhJ/w70AQYAzwH/1U4frR0L/L2kf82e9yL3A+Ix4FuSdgXuiog/FtmfWUEOaGttXTYS3CgLybX5TcBXIuK+Vq/7dBnraAAOjYh32qilGI8CgyQdQO4HzGhJvYD/B4yIiNckfZdcyLa2nvd/u8w/L3Ij/4WtXr9A0hPACcA9ks6NiLZ+OJmVxFMc1hn3AV+StBWApL2yX/UfAj6XzVEPIjcN0NrjwFHZlAiSBmTta4D+ea+7H/hKyxNJLT80HgLOyNqOB7Zvq8DIbTJzOzAZ+F0W9C1h+0Y2Gm/vro1FwN9lj09t9d/9lZZ5a0nDsz//BngpIv4T+A2wfzv9mpXEAW2dcT0wH3gq++LSn5L7bexu4I/ZuVvI/eq/iYh4HRgH3CXpGXIhCrlphlEtHxICXwVGZB+6zef9u0kuJhfwz5Gb6ni1QJ1TgAOyP4mIt8jNf88jF7az2nnfxcDVkmYDzXntlwBbAXOz61+StZ8OzMumhvbL/tvNusy72ZmZJcojaDOzRDmgzcwS5YA2M0uUA9rMLFEOaDOzRDmgzcwS5YA2M0vU/wLOebsBHSXcNQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#After feature Engineering\n",
        "# Droping the 1st Least prominnent Feature \n",
        "# Drop Temperature"
      ],
      "metadata": {
        "id": "c3uDHYydtR4g"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
        "#1drop temperature\n",
        "fires_trainx = fires_train.iloc[:,2:-1]\n",
        "fires_trainy = fires_train.iloc[:,-1]\n",
        "fires_testx = fires_test.iloc[:,2:-1]\n",
        "fires_testy = fires_test.iloc[:,-1]\n",
        "#2drop Temp,RH\n",
        "fires_trainx_2 = fires_train.iloc[:,3:-1]\n",
        "fires_trainy_2 = fires_train.iloc[:,-1]\n",
        "fires_testx_2 = fires_test.iloc[:,3:-1]\n",
        "fires_testy_2 = fires_test.iloc[:,-1]\n",
        "\n",
        "#4drop\n",
        "fires_trainx_4 = fires_train.iloc[:,5:-1]\n",
        "fires_trainy_4 = fires_train.iloc[:,-1]\n",
        "fires_testx_4 = fires_test.iloc[:,5:-1]\n",
        "fires_testy_4 = fires_test.iloc[:,-1]\n",
        "\n",
        "scalar = MinMaxScaler()\n",
        "\n",
        "fires = scalar.fit_transform(fires_trainx)\n",
        "firestst = scalar.fit_transform(fires_testx)\n",
        "#2drop\n",
        "fires_2 = scalar.fit_transform(fires_trainx_2)\n",
        "firestst_2 = scalar.fit_transform(fires_testx_2)\n",
        "#4drop\n",
        "fires_4 = scalar.fit_transform(fires_trainx_4)\n",
        "firestst_4 = scalar.fit_transform(fires_testx_4)\n",
        "\n",
        "\n",
        "#1\n",
        "model_linear = SVC(kernel = \"linear\")\n",
        "model_linear.fit(fires,fires_trainy)\n",
        "pred_test_linear = model_linear.predict(firestst)\n",
        "\n",
        "# kernel = rbf\n",
        "model_rbf = SVC(kernel = \"rbf\")\n",
        "model_rbf.fit(fires,fires_trainy)\n",
        "pred_test_rbf = model_rbf.predict(firestst)\n",
        "\n",
        "#2drop\n",
        "model_linear1 = SVC(kernel = \"linear\")\n",
        "model_linear1.fit(fires_2,fires_trainy_2)\n",
        "pred_test_linear1 = model_linear1.predict(firestst_2)\n",
        "\n",
        "#test rbf\n",
        "model_rbf1 = SVC(kernel = \"rbf\")\n",
        "model_rbf1.fit(fires_2,fires_trainy_2)\n",
        "pred_test_rbf1 = model_rbf1.predict(firestst_2)\n",
        "\n",
        "# #4drop\n",
        "model_linear4 = SVC(kernel = \"linear\")\n",
        "model_linear4.fit(fires_4,fires_trainy_4)\n",
        "# pred_test_linear4 = model_linear1.predict(firestst_4)\n",
        "\n",
        "#test rbf\n",
        "model_rbf4 = SVC(kernel = \"rbf\")\n",
        "model_rbf4.fit(fires_4,fires_trainy_4)\n",
        "pred_test_rbf4 = model_rbf4.predict(firestst_4)"
      ],
      "metadata": {
        "id": "i1GdCXDVtSLp"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_linear5 = svm.LinearSVC().fit(fires_4, fires_trainy_4)\n",
        "ans4 = model_linear5.predict(firestst_4)"
      ],
      "metadata": {
        "id": "OjETdr-zDEeY"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "F1_score, Accuracy = metrics(fires_testy_4, ans4, \"SVM_4drop_linear\")\n",
        "\n",
        "print(\"Test linear 4 F1_score=\", F1_score, \"Test linear 4  Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "_evX1yPAB2oe",
        "outputId": "d8004a42-663f-4067-a94b-0ce41df1b71b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test linear 4 F1_score= 0.8214285714285715 Test linear 4  Accuracy= 0.8333333333333334\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(fires_testy_4, pred_test_rbf4, \"SVM_4drop_rbf\")\n",
        "\n",
        "print(\"Test rbf 4 F1_score=\", F1_score, \"Test rbf 4  Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "oFXB32nDBwSi",
        "outputId": "0222a108-89d3-43c8-fd3f-fc5dffbe7693"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test rbf 4 F1_score= 0.8070175438596492 Test rbf 4  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(fires_testy, pred_test_linear, \"SVM_1drop_Linear\")\n",
        "print(\"Test Linear F1_score=\", F1_score, \"Test Linear Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "4zHzHtmztSOJ",
        "outputId": "cf8b5a05-3162-426a-ad01-67703d9d9b33"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Linear F1_score= 0.8363636363636363 Test Linear Accuracy= 0.85\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(fires_testy, pred_test_rbf, \"SVM_1drop_rbf\")\n",
        "print(\"Test RBF F1_score=\", F1_score, \"Test RBF Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "ldNg6sVe2l-B",
        "outputId": "6e011df2-d30a-407f-905b-408fc1b4934b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test RBF F1_score= 0.8214285714285715 Test RBF Accuracy= 0.8333333333333334\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(fires_testy_2, pred_test_linear1, \"SVM_2drop_Linear\")\n",
        "print(\"Test Linear 2drop F1_score=\", F1_score, \"Test Linear 2drop Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "n0ocpFuq3zDI",
        "outputId": "55ed94f6-4897-4a78-ef60-bfa3e554d82b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Linear 2drop F1_score= 0.8363636363636363 Test Linear 2drop Accuracy= 0.85\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(fires_testy_2, pred_test_rbf1, \"SVM_2drop_rbf\")\n",
        "print(\"Test RBF 2drop F1_score=\", F1_score, \"Test RBF 2drop Accuracy=\", Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "x29QTsAo31OI",
        "outputId": "89017845-da47-453b-bc56-2b688720a15a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test RBF 2drop F1_score= 0.8214285714285715 Test RBF 2drop Accuracy= 0.8333333333333334\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"
          }
        }
      ]
    }
  ]
}