{
  "cells": [
    {
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      "metadata": {},
      "source": [
        "\n# Tree Ensemble Models (Classification)\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": [
        "from modeva import DataSet\nfrom modeva import TestSuite\nfrom modeva.models import (MoLGBMClassifier,\n                           MoXGBClassifier,\n                           MoCatBoostClassifier,\n                           MoGradientBoostingClassifier,\n                           MoRandomForestClassifier)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Load and prepare dataset\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ds = DataSet()\nds.load(name=\"TaiwanCredit\")\nds.set_random_split()\nds.set_target(\"FlagDefault\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train model\nYou may replace the model by anyone of the following, including `MoGradientBoostingClassifier`, `MoRandomForestClassifier`, `MoXGBClassifier`, `MoCatBoostClassifier`\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "model = MoLGBMClassifier(max_depth=2, verbose=-1, random_state=0)\nmodel.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, model)\nresults = ts.diagnose_accuracy_table()\nresults.table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Feature importance analysis\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "results = ts.interpret_fi()\nresults.plot(n_bars=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Effect importance analysis\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "results = ts.interpret_ei()\nresults.plot(n_bars=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Local feature importance analysis\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "results = ts.interpret_local_fi(dataset='train', sample_index=1, centered=True)\nresults.plot(n_bars=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Local effect importance analysis\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "results = ts.interpret_local_ei(dataset='train', sample_index=1)\nresults.plot(n_bars=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Main effect plot\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For numerical feature\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "results = ts.interpret_effects(features=\"PAY_1\")\nresults.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Extract the detail information of the effect, e.g., split points and values.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "results.value[\"Details\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Main effect plot for categorical feature\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For categorical feature\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "results = ts.interpret_effects(features=\"EDUCATION\")\nresults.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For 2 features\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "results = ts.interpret_effects(features=(\"PAY_1\", \"PAY_2\"))\nresults.plot()"
      ]
    }
  ],
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