XGBoost.ipynb

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  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNL/a6zqWeZYScdnq+4EFS+",
      "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/Armin-Abdollahi/Iris-Flower-Classification/blob/main/XGBoost.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Goy9kEHKlzkV"
      },
      "outputs": [],
      "source": [
        "#Importing dataset from sklearn\n",
        "from sklearn import datasets\n",
        "from sklearn import metrics\n",
        "\n",
        "iris = datasets.load_iris() #dataset loading\n",
        "X = iris.data               #Features stored in X \n",
        "y = iris.target             #Class variable"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting dataset into Training (80%) and testing data (20%) using train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "QzD-4v6hl-HH"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create an XGB classifier and instance of the same\n",
        "from sklearn import svm\n",
        "from xgboost import XGBClassifier\n",
        "clf = XGBClassifier()\n",
        "clf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqLQsaSJmCFr",
        "outputId": "9dad21da-4447-4293-f056-8eedc99f424e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48WK-7OumFa6",
        "outputId": "3247b114-83c7-40f4-bb0e-bdd7306e7680"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(objective='multi:softprob')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "K_qCmOIvmI9z"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#classification accuracy\n",
        "from sklearn import metrics\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRcrylbPmLsb",
        "outputId": "15514954-ee53-4861-c1f7-b4c56a348974"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing library and segregation of data as train and test using DMatrix Data structure\n",
        "import xgboost as xgb\n",
        "\n",
        "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
        "dtest = xgb.DMatrix(X_test, label=y_test)"
      ],
      "metadata": {
        "id": "9GSctSKymO2t"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#paramaters \n",
        "param = {\n",
        "    'max_depth': 3,  # the maximum depth of each tree\n",
        "    'eta': 0.3,  # the training step for each iteration\n",
        "    'silent': 1,  # logging mode - quiet\n",
        "    'objective': 'multi:softprob',  # error evaluation for multiclass training\n",
        "    'num_class': 3}  # the number of classes that exist in this datset\n",
        "num_round = 5  # the number of training iterations"
      ],
      "metadata": {
        "id": "5FbNRP2cmSIf"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model builing using training data\n",
        "bst = xgb.train(param, dtrain, num_round)"
      ],
      "metadata": {
        "id": "HAlkVtI8mVeg"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#To calculate predication using test data\n",
        "y_predict = bst.predict(dtest)\n",
        "#print(y_predict)"
      ],
      "metadata": {
        "id": "tkUfWXPomY1H"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bst.dump_model('dump.raw.txt')"
      ],
      "metadata": {
        "id": "5pLnF7d4mbwn"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediction using test data\n",
        "preds = bst.predict(dtest)"
      ],
      "metadata": {
        "id": "1Rq8R85emedd"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating prediction accuracy\n",
        "import numpy as np\n",
        "best_preds = np.asarray([np.argmax(line) for line in preds])\n",
        "from sklearn.metrics import precision_score\n",
        "print (precision_score(y_test, best_preds, average='macro'))\n",
        "# >> 1.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTWee9ThmoB8",
        "outputId": "69ca4f2b-b5fb-4246-baae-55557b699fb2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Saving the model in pkl file\n",
        "import sklearn.externals\n",
        "import joblib\n",
        "joblib.dump(bst, 'bst_model.pkl', compress=True)\n",
        "# bst = joblib.load('bst_model.pkl') # load it later"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SynHvP10mrvb",
        "outputId": "aefd77f8-2602-4e28-c763-edf5283f91a9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['bst_model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "xgb.plot_tree(bst, num_trees=1)\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(150, 100)\n",
        "fig.savefig('treeIris.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "GmdJU8m6mvOF",
        "outputId": "60e06ecd-b088-4565-81b1-57e0a65ef4ac"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 10800x7200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature importance\n",
        "from xgboost import plot_importance\n",
        "from matplotlib import pyplot\n",
        "plot_importance(bst)\n",
        "pyplot.show()\n",
        "\n",
        "#f1 - sepal length in cm\n",
        "#f2 - sepal width in cm\n",
        "#f3 - petal length in cm\n",
        "#f4 - petal width in cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "H5CtydtJm0Aa",
        "outputId": "7a901135-2ef2-46f7-947f-57507bc3fab5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}