Z07_D_random_forest_regressor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import sklearn.linear_model as lm\n",
    "from sklearn import datasets, linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ?? RandomForestRegressor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proszę narysować wykres dowolnej funkcji na odcinku $[0,3]$.\n",
    "\n",
    "Będzie to model, który będziemy chcieli znaleźć."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = lambda x: ((x*2-1)*(x**2-2)*(x-2)+3)\n",
    "x_tr = np.linspace(0, 3, 200)\n",
    "y_tr = f(x_tr)\n",
    "x = stats.uniform(0,3).rvs(100)\n",
    "y = f(x) + stats.norm(0,0.2).rvs(len(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Zadanie\n",
    "wykonaj regresję z różnymi parametrami"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.vstack(x)\n",
    "x_plot = np.vstack(np.linspace(0, 10, 1000))\n",
    "regr_rf = RandomForestRegressor(max_depth=5, n_estimators =5 ,random_state=2)\n",
    "y_rbf = regr_rf.fit(x, y)\n",
    "# Plot outputs\n",
    "plt.figure(figsize=(6,6));\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([0,3])\n",
    "axes.set_ylim([0,8])\n",
    "plt.scatter(x, y,  color='black')\n",
    "plt.plot(x_plot, regr_rf.predict(x_plot), color='blue',linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BC7jrLvjhD2HPPZO/5q9/nXz7T34C55wD114Ln/40LFiQ45uSkuQ9HwOT49uff/5B/4qS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\nH4MdqM5tjkFnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.vstack(x)\n",
    "x_plot = np.vstack(np.linspace(0, 10, 1000))\n",
    "regr_rf = RandomForestRegressor(max_depth=10, max_features=1, n_estimators=10, random_state=3)\n",
    "y_rbf = regr_rf.fit(x, y)\n",
    "# Plot outputs\n",
    "plt.figure(figsize=(6,6));\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([0,3])\n",
    "axes.set_ylim([0,8])\n",
    "plt.scatter(x, y,  color='black')\n",
    "plt.plot(x_plot, regr_rf.predict(x_plot), color='blue',linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Zad\n",
    "Wyznacz optymalne parametry w modelu RandomForestRegressor za pomocą GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zad\n",
    "\n",
    "Wyznacz optymalne parametry dla modelu RandomForestRegressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales\n",
       "1  230.1   37.8       69.2   22.1\n",
       "2   44.5   39.3       45.1   10.4\n",
       "3   17.2   45.9       69.3    9.3\n",
       "4  151.5   41.3       58.5   18.5\n",
       "5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_adv = pd.read_csv('Advertising.csv', index_col=0)\n",
    "X = df_adv[['TV', 'radio','newspaper']]\n",
    "y = df_adv['sales']\n",
    "df_adv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
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