Forecast_codes/Gr_Boosting_Model_ex1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of using Gradient Boosting Model\n",
    "Demonstrate Gradient Boosting on the Boston housing dataset.\n",
    "\n",
    "This example fits a Gradient Boosting model with least squares loss and 500 regression trees of depth 4.\n",
    "\n",
    "https://scikit-learn.org/stable/modules/ensemble.html\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\n",
    "\n",
    "https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py\n",
    "\n",
    "https://stats.stackexchange.com/questions/277399/gradient-boosting-regression-boston-housing-data-example\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(__doc__)\n",
    "# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>\n",
    "#\n",
    "# License: BSD 3 clause\n",
    "\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import ensemble\n",
    "from sklearn import datasets\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################\n",
    "# Load data\n",
    "boston = datasets.load_boston()\n",
    "X, y = shuffle(boston.data, boston.target, random_state=13)\n",
    "X = X.astype(np.float32)\n",
    "offset = int(X.shape[0] * 0.9)\n",
    "X_train, y_train = X[:offset], y[:offset]\n",
    "X_test, y_test = X[offset:], y[offset:]\n",
    "\n",
    "\n",
    "boston\n",
    "\n",
    "df_d=pd.DataFrame(boston.data)\n",
    "df_t=pd.DataFrame(boston.target)\n",
    "df_fs=pd.DataFrame(boston.feature_names)\n",
    "df_d.head()\n",
    "df_t.head()\n",
    "df_fs.head()\n",
    "\n",
    "# df_d.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################\n",
    "# Fit regression model\n",
    "params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2,\n",
    "          'learning_rate': 0.01, 'loss': 'ls'}\n",
    "clf = ensemble.GradientBoostingRegressor(**params)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "mse = mean_squared_error(y_test, clf.predict(X_test))\n",
    "print(\"MSE: %.4f\" % mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################\n",
    "# Plot training deviance\n",
    "\n",
    "# compute test set deviance\n",
    "test_score = np.zeros((params['n_estimators'],), dtype=np.float64)\n",
    "\n",
    "for i, y_pred in enumerate(clf.staged_predict(X_test)):\n",
    "    test_score[i] = clf.loss_(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('Deviance')\n",
    "plt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-',\n",
    "         label='Training Set Deviance')\n",
    "plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',\n",
    "         label='Test Set Deviance')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('Boosting Iterations')\n",
    "plt.ylabel('Deviance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################\n",
    "# Plot feature importance\n",
    "feature_importance = clf.feature_importances_\n",
    "# make importances relative to max importance\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, boston.feature_names[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another simpler and straightforward example of using GBoosting in Regression\n",
    "https://scikit-learn.org/stable/modules/ensemble.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.009154859960321"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.datasets import make_friedman1\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "X, y = make_friedman1(n_samples=1200, random_state=0, noise=1.0)\n",
    "X_train, X_test = X[:200], X[200:]\n",
    "y_train, y_test = y[:200], y[200:]\n",
    "est = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1,\n",
    "                                max_depth=1, random_state=0, loss='ls').fit(X_train, y_train)\n",
    "mean_squared_error(y_test, est.predict(X_test))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       13.09353595, 14.42735362, 17.77339352,  4.75543135,  4.20821348,\n",
       "        9.41512374, 12.12736635, 18.04621834, 11.17692257, 18.0358748 ,\n",
       "       19.54668933, 16.1796038 , 12.7896533 ,  8.70680243, 11.68408194,\n",
       "       11.45751424, 13.61261803, 19.45467878, 12.54996039,  9.48489573,\n",
       "       16.33219252,  9.51836261, 16.04657652,  9.2231391 , 10.21781907,\n",
       "        8.89951764, 14.3435796 ,  9.77872719, 10.12540765, 17.74263247,\n",
       "       14.23377523, 14.71041064,  8.41568039, 16.77416333, 24.55145884,\n",
       "       21.31435176,  8.87001599, 13.64998774,  9.42378606,  9.58816769,\n",
       "       11.42512214,  3.9387699 , 14.30367612, 14.23606308, 17.07430002,\n",
       "       15.10081552, 24.44645095,  9.32364117, 14.08902635, 11.08718908,\n",
       "       23.14194772, 18.5809369 ,  8.77811402,  8.06529133, 10.72304818,\n",
       "       23.33577583, 21.98924267, 13.55790004, 24.08186252, 10.27725945,\n",
       "       11.61230973, 19.16915924, 13.11998892, 18.32581033, 12.7456088 ,\n",
       "       19.13670767, 16.44259451, 17.87550316, 13.4647684 , 17.26483473,\n",
       "       17.6824196 ,  4.9283306 , 21.25914311, 12.51824174,  9.11898876,\n",
       "       13.28349955,  7.64468203, 24.96740919, 25.24122087, 14.48037724,\n",
       "        4.39766878,  7.26178327, 21.15301582, 20.1658278 , 20.81546931,\n",
       "       16.61568114, 17.11176182, 10.69730149,  9.61918609, 20.49905217,\n",
       "       13.84243631,  7.56381713, 19.0888195 , 19.4626417 , 19.86500111,\n",
       "       20.96732702, 15.62695585, 13.64811536,  5.33810135, 13.58840519,\n",
       "       15.24364174, 18.53481294, 13.45865892, 17.3981996 , 12.06392928,\n",
       "       13.69186452, 11.1388767 , 16.0872288 , 15.01702271, 16.29907881,\n",
       "       19.37092256, 15.42294637, 13.29321084, 13.6615075 ,  5.85398493,\n",
       "       11.47027494,  8.29200017, 13.94759147,  6.13771036, 10.38657114,\n",
       "       13.32050028,  8.84274113, 19.24160937, 18.5727319 , 14.03832669,\n",
       "       15.9097472 , 15.03960572, 22.90269131, 20.86804469, 26.11754656,\n",
       "       11.35170111, 10.63498163, 20.86065198, 11.42936046, 12.6708422 ,\n",
       "       10.1385038 , 12.69039154, 13.33648939, 15.93263815, 15.87494107,\n",
       "       13.21609085,  7.84969271,  5.74592294, 14.22422544, 13.7979302 ,\n",
       "        7.88868895,  8.3500868 , 21.72369119,  9.28725909,  8.19163689,\n",
       "        8.90627255, 17.31482092, 19.9701591 , 13.60848656, 13.4502633 ,\n",
       "        6.88047376,  9.9636888 , 20.41013082, 20.03625878, 15.07835264,\n",
       "       18.78795036, 16.6828522 ,  9.12949504, 18.55845538,  9.19198241,\n",
       "        6.1967684 , 17.74097814, 19.36400889,  3.86936716, 18.59005921,\n",
       "       14.63193204, 20.06821219, 17.07219914,  8.25516809, 12.42202486,\n",
       "       18.08292745, 11.03979817, 17.92937242, 11.800586  , 13.29445259,\n",
       "        6.27530253, 15.17481202, 18.28091804, 10.02162098, 14.88407908,\n",
       "       16.1174218 , 19.64697796, 13.74800347, 15.34687154, 23.97884905,\n",
       "       12.47160944,  8.60961744, 14.75147636, 18.33160037, 17.41761363,\n",
       "        9.63165926, 11.78342246, 13.10919688, 11.27495176,  8.13970456,\n",
       "       15.49437399, 19.29402814, 20.63856227, 19.28176592,  7.81776045,\n",
       "        4.76654553, 25.39898131, 13.33155928, 20.79246266,  7.46920988,\n",
       "       13.61397555, 23.8491373 , 10.33498574,  5.61595443, 16.49204848,\n",
       "       10.46209147, 16.29140144, 11.62497694, 12.98850883, 20.31077024,\n",
       "       16.1688662 , 16.62792778, 12.52222053, 13.58747453, 12.9324559 ,\n",
       "        6.0886434 , 12.94727478, 12.36698196,  5.27048914,  9.70956387,\n",
       "       20.74460574, 13.64376631, 14.07631682, 18.05196182,  8.27683482,\n",
       "       17.2160413 , 18.07401856, 11.44779023,  7.26172542, 16.29802115,\n",
       "       16.90227933, 20.59068891,  9.73114087,  4.14314454, 14.04097436,\n",
       "       16.48707366, 18.90261071, 15.11469159, 15.95121007, 18.2916779 ,\n",
       "       16.05951508,  8.8950326 ,  8.88467571, 11.96932577,  8.85026719,\n",
       "       12.40612575, 13.02461977, 10.96502773, 14.23236268, 11.84058306,\n",
       "       15.47304532,  7.39606199, 18.35407234, 18.32546314,  9.04951328,\n",
       "        8.9710367 , 15.25448408, 18.84211021, 12.17252338,  8.51059488,\n",
       "       17.75777732, 22.94649734, 11.67462978, 11.31558119, 15.72722186,\n",
       "        6.09760706, 21.46092688, 11.46321759,  6.49856896, 20.32981295])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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