{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Calibrating Binary Classifier Prediction Interval\n\nThis example requires full licence, and the program will break if you use the trial licence.\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 required modules\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nfrom matplotlib import pylab as plt\nfrom modeva import DataSet\nfrom modeva.models import MoXGBClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Build a model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ds = DataSet()\nds.load(name=\"TaiwanCredit\")\nds.set_random_split()\n\nmodel = MoXGBClassifier(max_depth=2)\nmodel.fit(ds.train_x, ds.train_y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Calibrate the model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "model.calibrate_interval(X=ds.test_x, y=ds.test_y, alpha=0.1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Get prediction interval\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "model.predict_interval(ds.test_x[:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Rest calibration when needed\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "model.reset_calibrate_interval()"
      ]
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "codemirror_mode": {
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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