XGBM and LGBM.ipynb

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  "metadata": {
    "colab": {
      "name": "XGBM.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rd8feRsNAN0Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# First XGBoost model for Pima Indians dataset\n",
        "from numpy import loadtxt\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfZj0gYKASj-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load data\n",
        "dataset = loadtxt('/content/pima-indians-diabetes.data.csv', delimiter=\",\")\n",
        "# split data into X and y\n",
        "X = dataset[:,0:8]\n",
        "Y = dataset[:,8]\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AltDpRc9AUqB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# split data into train and test sets\n",
        "seed = 7\n",
        "test_size = 0.33\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uasE5oYfAWFn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "4e44f36f-092d-43ef-adf2-fc47ea151fe3"
      },
      "source": [
        "# fit model no training data\n",
        "model = XGBClassifier()\n",
        "model.fit(X_train, y_train)\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlMNBgyzAZNe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# make predictions for test data\n",
        "y_pred = model.predict(X_test)\n",
        "predictions = [round(value) for value in y_pred]\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-qAVXM3AbZi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4370608c-6db1-412c-c00f-0b1e163a1e02"
      },
      "source": [
        "# evaluate predictions\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 77.95%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXj4KChol4Zk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WepxshEIA5qy",
        "colab_type": "text"
      },
      "source": [
        "***Light GBM***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_aZcd2dBAL_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hX6Cf0qJBCHh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the dataset\n",
        "dataset = pd.read_csv('/content/pima-indians-diabetes.data.csv')\n",
        "# split data into X and y\n",
        "X = dataset.iloc[:,0:8]\n",
        "Y = dataset.iloc[:,8]\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RW0QEt1DBD2l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)\n"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tcjgTk-BFxH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import lightgbm as lgb\n",
        "d_train = lgb.Dataset(x_train, label=y_train)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEa8q77rBRFr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "params = {}\n",
        "params['learning_rate'] = 0.003\n",
        "params['boosting_type'] = 'gbdt'\n",
        "params['objective'] = 'binary'\n",
        "params['metric'] = 'binary_logloss'\n",
        "params['sub_feature'] = 0.5\n",
        "params['num_leaves'] = 10\n",
        "params['min_data'] = 50\n",
        "params['max_depth'] = 10"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ssLa99wBh_r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = lgb.train(params, d_train, 100)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYYGYM9XBmlG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Prediction\n",
        "y_pred=clf.predict(x_test)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TCcf6cqDIqh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = [round(value) for value in y_pred]"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8JXv7WNC2zn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "accuracy = accuracy_score(y_test, predictions)\n"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XdXO7gVCol-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "24d472f1-03d5-4eeb-b947-7f76ff315f4f"
      },
      "source": [
        "accuracy"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6822916666666666"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_ZCw6_iCqK6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}