cas-pml/SL/aufgaben/template/4_WS/Loesungen/WS 10 Loesung.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "# WS 10 Performancevergleiche Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Vergleichen Sie alle Regressoren (ausser `SVR` und `MLPRegressor`) mit folgenden Modifikationen\n",
    "  * die Vergleiche werden ohne und mit Standardisierung der Features durchgeführt\n",
    "  * die Resultate (r2_score) werden in Form einer Heatmap zusammengestellt\n",
    "* informieren Sie sich zum Vorgehen am Code in 3.4 Regression - Modellvergleiche.ipynb\n",
    "* Präsentation der Ergebnisse als \n",
    "  * seaborn heatmap\n",
    "  * alternative Visualisierung: Grouped barplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## for scikit-learn 1.4.2, to silence warnings regarding physical cores\n",
    "import os\n",
    "os.environ['LOKY_MAX_CPU_COUNT'] = '4' ## depending on the hardware used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## prepare env, read and prepare data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "#codepath = '../2_code'\n",
    "#datapath = '../3_data'\n",
    "codepath = '.././2_code'\n",
    "datapath = '../../3_data'\n",
    "from sys import path; path.insert(1, codepath)\n",
    "from os import chdir; chdir(datapath)\n",
    "\n",
    "from bfh_cas_pml import prep_data\n",
    "X_train, X_test, y_train, y_test = prep_data('melb_data_prep.csv', target='Price', seed=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T10:33:02.116059Z",
     "start_time": "2020-04-08T10:33:02.087399Z"
    }
   },
   "outputs": [],
   "source": [
    "## standardize features (lead: train)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_sc = scaler.transform(X_train)\n",
    "X_test_sc = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T10:33:02.294366Z",
     "start_time": "2020-04-08T10:33:02.120049Z"
    }
   },
   "outputs": [],
   "source": [
    "## import trainer classes\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from lightgbm.sklearn import LGBMRegressor\n",
    "\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T10:06:55.137655Z",
     "start_time": "2020-04-08T10:06:55.126100Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## define models\n",
    "models = [\n",
    "    LinearRegression(),\n",
    "    Ridge(),\n",
    "    Lasso(),\n",
    "    KNeighborsRegressor(),\n",
    "    DecisionTreeRegressor(max_depth=8),\n",
    "    RandomForestRegressor(random_state = 1234),\n",
    "    AdaBoostRegressor(DecisionTreeRegressor(max_depth=15), learning_rate=0.07),\n",
    "    GradientBoostingRegressor(),\n",
    "    HistGradientBoostingRegressor(),\n",
    "    CatBoostRegressor(logging_level='Silent'),\n",
    "    LGBMRegressor()\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T10:33:15.141363Z",
     "start_time": "2020-04-08T10:33:02.341448Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regressor                        r2_no       r2_yes\n",
      "=====================================================\n",
      "LinearRegression                 0.560142    0.560142\n",
      "Ridge                            0.560139    0.560142\n",
      "Lasso                            0.560143    0.560142\n",
      "KNeighborsRegressor              0.449957    0.629232\n",
      "DecisionTreeRegressor            0.671175    0.673623\n",
      "RandomForestRegressor            0.777948    0.778405\n",
      "AdaBoostRegressor                0.766560    0.765674\n",
      "GradientBoostingRegressor        0.724983    0.726858\n",
      "HistGradientBoostingRegressor    0.786575    0.784661\n",
      "CatBoostRegressor                0.800349    0.799714\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009829 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1630\n",
      "[LightGBM] [Info] Number of data points in the train set: 12262, number of used features: 23\n",
      "[LightGBM] [Info] Start training from score 1055902.695237\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001633 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1622\n",
      "[LightGBM] [Info] Number of data points in the train set: 12262, number of used features: 23\n",
      "[LightGBM] [Info] Start training from score 1055902.695237\n",
      "LGBMRegressor                    0.788166    0.788287\n"
     ]
    }
   ],
   "source": [
    "## compare models\n",
    "regressors = []\n",
    "r2_nos = []\n",
    "r2_yess = []\n",
    "\n",
    "print('Regressor                        r2_no       r2_yes')\n",
    "print('=====================================================')\n",
    "\n",
    "for model in models:\n",
    "    name = model.__class__.__name__ \n",
    "    regressors.append(name)\n",
    "\n",
    "    ## not scaled\n",
    "    model.fit(X_train, y_train)     \n",
    "    y_pred = model.predict(X_test)  \n",
    "    r2_no = r2_score(y_test, y_pred)   \n",
    "    r2_nos.append(r2_no)\n",
    "    \n",
    "    ## scaled\n",
    "    model.fit(X_train_sc, y_train)     \n",
    "    y_pred = model.predict(X_test_sc)  \n",
    "    r2_yes = r2_score(y_test, y_pred)   \n",
    "    r2_yess.append(r2_yes)\n",
    "\n",
    "    print('%-30s   %0.6f    %0.6f' %(name, r2_no, r2_yes)) ## console output\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-08T10:33:15.742776Z",
     "start_time": "2020-04-08T10:33:15.150619Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## visualize results\n",
    "scores = pd.DataFrame(\n",
    "    {'r2_no': r2_nos, \n",
    "     'r2_yes': r2_yess\n",
    "    }, index=regressors)\n",
    "\n",
    "sns.heatmap(scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 578.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## alternative visualization: grouped catplot\n",
    "scores_df = pd.DataFrame({\n",
    "    'regs' : regressors + regressors,\n",
    "    'scores' : r2_nos + r2_yess,\n",
    "    'stds' : ['no' for i in range(len(regressors))] + ['yes' for i in range(len(regressors))]\n",
    "})\n",
    "\n",
    "sns.catplot(\n",
    "    data=scores_df,\n",
    "    kind='bar',\n",
    "    y=\"regs\", \n",
    "    x=\"scores\", \n",
    "    hue=\"stds\"#,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fazit:**\n",
    "\n",
    "* Unterschiede in der Performance bei Standardisieren zeigen sich bei\n",
    "  * markante Verbesserung bei KNeighborsRegressor\n",
    "  * marginale Verbesserungen bei\n",
    "    * AdaBoostRegressor\n",
    "    * GradientBoostingRegressor\n",
    "    * HistGradientBoostingRegressor\n",
    "* im übrigen kaum Unterschiede"
   ]
  }
 ],
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