W03_DecisionTree_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision trees solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def image_path(fig_id):\n",
    "    return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
    "\n",
    "def plot_reg_tree(X, x_pred, Y, y_pred, title):\n",
    "    plt.plot(X, y, \"b.\")\n",
    "    plt.plot(x_pred, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.axis([0, 1, -0.2, 1.1])\n",
    "    plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "    plt.ylabel(\"$y$\", fontsize=18, rotation=0)\n",
    "    plt.legend(loc=\"upper center\", fontsize=18)\n",
    "    plt.title(title, fontsize=14)\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if not iris:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    if plot_training:\n",
    "        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"Iris-Virginica\")\n",
    "        plt.axis(axes)\n",
    "    if iris:\n",
    "        plt.xlabel(\"Petal length\", fontsize=14)\n",
    "        plt.ylabel(\"Petal width\", fontsize=14)\n",
    "    else:\n",
    "        plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "        plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
    "    if legend:\n",
    "        plt.legend(loc=\"lower right\", fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clasification studentenset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "with open('data/toelating.pkl','rb') as f:\n",
    "    data=pickle.load(f) \n",
    "X,y = data[:,0:2], data[:, 2]\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "tree_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.05357143, 0.94642857]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict_proba([[100, 100]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict([[100, 100]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decicion Trees en Regressie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimal configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quadratic training set + noise\n",
    "np.random.seed(42)\n",
    "m = 200\n",
    "X = np.random.rand(m, 1)\n",
    "y = 4 * (X - 0.5) ** 2\n",
    "y = y + np.random.randn(m, 1) / 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "tree_reg = DecisionTreeRegressor(max_leaf_nodes=12, random_state=42)\n",
    "tree_reg.fit(X, y)\n",
    "\n",
    "x_pred = np.linspace(0, 1, 500).reshape(-1, 1)\n",
    "y_pred = tree_reg.predict(x_pred)\n",
    "plot_reg_tree(X, x_pred, y, y_pred, 'max_leaf_nodes=12')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}