{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14144752",
   "metadata": {},
   "source": [
    "# Xgboost\n",
    "\n",
    "XGBoost initially started as a research project by Tianqi Chen as part of the Distributed (Deep) Machine Learning Community (DMLC) group. Initially, it began as a terminal application which could be configured using a libsvm configuration file. It became well known in the ML competition circles after its use in the winning solution of the Higgs Machine Learning Challenge. Soon after, the Python and R packages were built, and XGBoost now has package implementations for Java, Scala, Julia, Perl, and other languages. This brought the library to more developers and contributed to its popularity among the Kaggle community, where it has been used for a large number of competitions.\n",
    "\n",
    "It was soon integrated with a number of other packages making it easier to use in their respective communities. It has now been integrated with scikit-learn for Python users and with the caret package for R users. It can also be integrated into Data Flow frameworks like Apache Spark, Apache Hadoop, and Apache Flink using the abstracted Rabit and XGBoost4J. XGBoost is also available on OpenCL for FPGAs. An efficient, scalable implementation of XGBoost has been published by Tianqi Chen and Carlos Guestrin.\n",
    "\n",
    "While the XGBoost model often achieves higher accuracy than a single decision tree, it sacrifices the intrinsic interpretability of decision trees.  For example, following the path that a decision tree takes to make its decision is trivial and self-explained, but following the paths of hundreds or thousands of trees is much harder. To achieve both performance and interpretability, some model compression techniques allow transforming an XGBoost into a single \"born-again\" decision tree that approximates the same decision function. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1362e9f8",
   "metadata": {},
   "source": [
    "## The algorithm\n",
    "\n",
    "XGBoost works as Newton-Raphson in function space unlike gradient boosting that works as gradient descent in function space, a second order Taylor approximation is used in the loss function to make the connection to Newton Raphson method.\n",
    "\n",
    "A generic unregularized XGBoost algorithm is:\n",
    "\n",
    "Input: training set $\\{(x_{i},y_{i})\\}_{i=1}^{N}$, a differentiable loss function $L(y, F(x))$, a number of weak learners M and a learning rate $\\alpha$.\n",
    "\n",
    "Algorithm:\n",
    "\n",
    "1. Initialize model with a constant value:\n",
    "    $$\n",
    "    \\hat{f}_0(x) = arg_{\\theta}\\quad min \\sum_{i=1}^N L(y_i,\\theta)\n",
    "    $$\n",
    "\n",
    "2. For m = 1 to M:\n",
    "\n",
    "    2.1 Compute the 'gradients' and 'hessians':\n",
    "        $$\n",
    "        \\begin{aligned}\n",
    "        \\hat{g}_m(x_i) &= \\left[\\frac{\\partial L(y_i,f(x_i))}{\\partial f(x_i)} \\right]_{f(x)=\\hat{f}_{m-1}(x)}\\\\\n",
    "        \\hat{h}_m(x_i) &= \\left[\\frac{\\partial^2 L(y_i,f(x_i))}{\\partial^2 f(x_i)} \\right]_{f(x)=\\hat{f}_{m-1}(x)}\\\\\n",
    "        \\end{aligned}\n",
    "        $$\n",
    "    \n",
    "    2.2 Fit a base learner (or weak learner, e.g. tree) using the training set $\\{ x_i, -\\frac{\\hat{g}_m(x_i)}{\\hat{h}_m(x_i)} \\}_{i=1}^N$ , by solving the optimization problem below:\n",
    "        $$\n",
    "        \\begin{aligned}\n",
    "        \\hat{\\phi}_m &= arg_{\\theta}\\quad min \\sum_{i=1}^N \\frac{1}{2}\\hat{h}_m(x_i) \\left[ \\phi(x_i) -\\frac{\\hat{g}_m(x_i)}{\\hat{h}_m(x_i)} \\right]^2 \\\\\n",
    "        \\hat{f}_m(x) &= \\alpha \\hat{\\phi}_m(x)\n",
    "        \\end{aligned}\n",
    "        $$\n",
    "        \n",
    "    2.3 Update the model:\n",
    "        $$\n",
    "        \\hat{f}_{(m)}(x) = \\hat{f}_{(m-1)}(x) + \\hat{f}_m(x) \n",
    "        $$\n",
    "    \n",
    "3. Output: \n",
    "    $$\n",
    "    \\hat{f}(x) = \\hat{f}_{(M)}(x) = \\sum_{m=0}^M \\hat{f}_{m}(x)\n",
    "    $$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1a0a8f",
   "metadata": {},
   "source": [
    "## Build Model\n",
    "\n",
    "To build the model with mlita package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9671f7ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(209, 13)\n",
      "(209, 12)\n",
      "(209,)\n"
     ]
    }
   ],
   "source": [
    "from mlita.ml import MachineLearning\n",
    "import numpy as np\n",
    "\n",
    "model = MachineLearning.from_csv('./data/nmr_O.csv')\n",
    "print(model.data.shape)\n",
    "print(model.x_data.shape)\n",
    "print(model.y_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "628d2e82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30.750021], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 超参调整\n",
    "params={'max_depth':8,\n",
    "    'n_estimators':29,\n",
    "    'min_child_weight':8,\n",
    "    'subsample':0.9,\n",
    "    'colsample_bytree':0.6,\n",
    "    'reg_alpha':0.1,\n",
    "    'reg_lambda':0.7\n",
    "    }\n",
    "model_xgboost = model.xgboost(params=params)\n",
    "x_test = np.random.rand(1,12)\n",
    "model_xgboost.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565a911d",
   "metadata": {},
   "source": [
    "## Save Data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ad4c2ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.to_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "134e05df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mse': 8.575921681875219,\n",
       " 'mae': 1.7102975633621214,\n",
       " 'r2': 0.9003680781166987,\n",
       " 'r': 0.9543486060650683}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = model.make_score()\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b29122",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d9dec227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.save_picture()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79177f03",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef889462",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd52c015",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "3b4dd1ce6c8ed31661adb8e55d1f9c30ec54ed472785ff10576acd13e5f2c743"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}