{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Performance Metrics (Regression)\n\nEvaluate model performance and residuals.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Installation\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# To install the required package, use the following command:\n# !pip install modeva"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Authentication\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# To get authentication, use the following command: (To get full access please replace the token to your own token)\n# from modeva.utils.authenticate import authenticate\n# authenticate(auth_code='eaaa4301-b140-484c-8e93-f9f633c8bacb')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Import modeva modules\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from modeva import DataSet\nfrom modeva import TestSuite\nfrom modeva.models import MoLGBMRegressor\nfrom modeva.models import MoXGBRegressor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Load BikeSharing Dataset\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ds = DataSet()\nds.load(name=\"BikeSharing\")\nds.set_random_split()\nds.set_target(\"cnt\")\n\nds.scale_numerical(features=(\"cnt\",), method=\"log1p\")\nds.preprocess()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Fit a XGBoost model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "model1 = MoXGBRegressor()\nmodel1.fit(ds.train_x, ds.train_y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Fit a LGBM model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "model2 = MoLGBMRegressor(max_depth=2, verbose=-1, random_state=0)\nmodel2.fit(ds.train_x, ds.train_y.ravel())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Basic accuracy analysis\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ts = TestSuite(ds, model1)\nresults = ts.diagnose_accuracy_table(train_dataset=\"train\", test_dataset=\"test\",\n                                     metric=(\"MAE\", \"MSE\", \"R2\"))\nresults.table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Compare the XGBoost model with LGBM model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "tsc = TestSuite(ds, models=[model1, model2])\nresults = tsc.compare_accuracy_table(train_dataset=\"train\", test_dataset=\"test\",\n                                     metric=(\"MAE\", \"MSE\", \"R2\"))\nresults.plot(\"MAE\")"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.11"
    }
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