DS_Lecture9.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Trees"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Motivation**\n",
    "\n",
    "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n",
    "\n",
    "The most commonly used binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes. The trick, of course, comes in deciding which questions to ask at each step. In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features. Let's now look at an example of this.\n",
    "\n",
    "Consider the following two-dimensional data, which has one of four class labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=300, centers=4,\n",
    "                  random_state=0, cluster_std=1.0)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Side-Note:**\n",
    "* From [GeeksforGeeks](https://www.geeksforgeeks.org/sys-path-in-python): \"*When a module is imported within a Python file, the interpreter first searches for the specified module among its built-in modules. If not found it looks through the list of directories defined by `sys.path`.*\" \n",
    "    * **`import sys`**\n",
    "    * `sys.path`\n",
    "* Add your own module directory to Python with:\n",
    "    * `sys.path.append()`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Tree Levels**\n",
    "<img src=\"figures/05.08-decision-tree-levels.png\" width=1000>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Node Impurity in trees\n",
    "\n",
    "A classification tree is built by the following process: first the single variable\n",
    "is found which best splits the data into two groups (`best` will be defined\n",
    "shortly).  The data is separated, and then this process is applied\n",
    "*separately* to each sub-group, and so on recursively until the subgroups either reach a minimum size or until no improvement can be made.\n",
    "\n",
    "The partitioning method can be applied to many different kinds of\n",
    "data.  We will start by looking at the classification problem,\n",
    "which is one of the more instructive cases (but also has the\n",
    "most complex equations).\n",
    "The sample population consists of $n$ observations from $K$ classes.  A given\n",
    "model will break these observations into $k$ terminal groups;\n",
    "to each of these groups is assigned a predicted class.\n",
    "\n",
    "Most tree algorithms use one of several measures of impurity, or diversity, of a node.  Let us denote $\\hat{p}_{mk}$ as the proportion of training observations in the $m$th region that are from the $k$th class. We then define the *Gini index* as\n",
    "$$\n",
    "G_m = \\sum_{i=1}^K { \\hat{p}_{mk}(1 - \\hat{p}_{mk} ) }\n",
    "$$\n",
    "Remembering $p (1-p)$ to be the variance of the Bernoulli distribution, the Gini index measures the total variance across the $K$ classes. For nearly \"pure\" regions (all $\\hat{p}_{mk}$ being close to 0 or 1), it takes on a very small value.\n",
    "\n",
    "An alternative measure is given by the information index or *cross entropy* \n",
    "$$\n",
    "D_m = - \\sum_{i=1}^K { \\hat{p}_{mk} \\log{ \\hat{p}_{mk} } }\n",
    "$$\n",
    "\n",
    "NOTE: The expressions above are general enough for multiple category classification and hence more complicated than the version discussed in class.For binary outcomes $(y \\in 0,1)$ the Gini index reduces to simply\n",
    "$$\n",
    "G_m = 2 \\hat{p}_m (1 - \\hat{p}_m ) \n",
    "$$\n",
    "\n",
    "The two impurity functions are plotted in the figure below,\n",
    "along with a rescaled version of the Gini measure.\n",
    "For the two class problem the measures differ only slightly, and\n",
    "will nearly always choose the same split point.\n",
    "\n",
    "<img src=\"figures/Gini1.png\" width=400>\n",
    "\n",
    "For example, in a two-class problem with 400 observations in each class (denote this by (400, 400)), suppose one split created nodes (300, 100) and (100, 300), while the other created nodes (200, 400) and (200, 0). Compute the misclassification rate and write it down formally."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanations and examples at:**\n",
    "\n",
    "https://scikit-learn.org/stable/modules/tree.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydot\n",
    "from IPython.display import Image\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from six import StringIO  \n",
    "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier, export_graphviz\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, BaggingRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error,confusion_matrix, classification_report\n",
    "\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Source.gv.png'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn import tree\n",
    "from graphviz import Source\n",
    "import graphviz\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"]=3,3\n",
    "iris = load_iris()\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(iris.data, iris.target)\n",
    "\n",
    "dot_data = tree.export_graphviz(clf, out_file=None,\n",
    "                                feature_names=iris.feature_names, \n",
    "                                precision = 1,\n",
    "                    filled=True, rounded=True, )  \n",
    "graph = graphviz.Source(dot_data)  \n",
    "graph#.render(format=\"png\",view=True) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function creates images of tree models using pydot\n",
    "def print_tree(estimator, features, class_names=None, filled=True):\n",
    "    tree = estimator\n",
    "    names = features\n",
    "    color = filled\n",
    "    classn = class_names\n",
    "    \n",
    "    dot_data = StringIO()\n",
    "    export_graphviz(estimator, out_file=dot_data, feature_names=features, class_names=classn, filled=filled)\n",
    "    graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "    return(graph)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression Trees"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In R, I exported the dataset from package 'ISLR' to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 263 entries, 1 to 321\n",
      "Data columns (total 21 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  263 non-null    object \n",
      " 1   AtBat       263 non-null    int64  \n",
      " 2   Hits        263 non-null    int64  \n",
      " 3   HmRun       263 non-null    int64  \n",
      " 4   Runs        263 non-null    int64  \n",
      " 5   RBI         263 non-null    int64  \n",
      " 6   Walks       263 non-null    int64  \n",
      " 7   Years       263 non-null    int64  \n",
      " 8   CAtBat      263 non-null    int64  \n",
      " 9   CHits       263 non-null    int64  \n",
      " 10  CHmRun      263 non-null    int64  \n",
      " 11  CRuns       263 non-null    int64  \n",
      " 12  CRBI        263 non-null    int64  \n",
      " 13  CWalks      263 non-null    int64  \n",
      " 14  League      263 non-null    object \n",
      " 15  Division    263 non-null    object \n",
      " 16  PutOuts     263 non-null    int64  \n",
      " 17  Assists     263 non-null    int64  \n",
      " 18  Errors      263 non-null    int64  \n",
      " 19  Salary      263 non-null    float64\n",
      " 20  NewLeague   263 non-null    object \n",
      "dtypes: float64(1), int64(16), object(4)\n",
      "memory usage: 45.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/Hitters.csv').dropna()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df[['Years', 'Hits']].values\n",
    "y = np.log(df.Salary.values)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(11,4))\n",
    "ax1.hist(df.Salary.values)\n",
    "ax1.set_xlabel('Salary')\n",
    "ax2.hist(y)\n",
    "ax2.set_xlabel('Log(Salary)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_leaf_nodes=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_leaf_nodes=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_leaf_nodes=3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = DecisionTreeRegressor(max_leaf_nodes=3)\n",
    "regr.fit(X, y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure 8.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = tree.export_graphviz(regr, out_file=None,\n",
    "                                feature_names=['Years', 'Hits'], \n",
    "                                precision = 1,\n",
    "                    filled=True, rounded=True)  \n",
    "graph = graphviz.Source(dot_data)  \n",
    "graph "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**FIGURE 8.1** For the Hitters data, a regression tree for predicting the log\n",
    "salary of a baseball player, based on the number of years that he has played in\n",
    "the major leagues and the number of hits that he made in the previous year. At a\n",
    "given internal node, the label (of the form Xj < tk) indicates the left-hand branch\n",
    "emanating from that split, and the right-hand branch corresponds to Xj ≥ tk.\n",
    "For instance, the split at the top of the tree results in two large branches. The\n",
    "left-hand branch corresponds to Years<4.5, and the right-hand branch corresponds\n",
    "to Years>=4.5. The tree has two internal nodes and three terminal nodes, or\n",
    "leaves. The number in each leaf is the mean of the response for the observations\n",
    "that fall there."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Figure 8.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('Years', 'Hits', kind='scatter', color='orange', figsize=(7,6))\n",
    "plt.xlim(0,25)\n",
    "plt.ylim(ymin=-5)\n",
    "plt.xticks([1, 4.5, 24])\n",
    "plt.yticks([1, 117.5, 238])\n",
    "plt.vlines(4.5, ymin=-5, ymax=250)\n",
    "plt.hlines(117.5, xmin=4.5, xmax=25)\n",
    "plt.annotate('R1', xy=(2,117.5), fontsize='xx-large')\n",
    "plt.annotate('R2', xy=(11,60), fontsize='xx-large')\n",
    "plt.annotate('R3', xy=(11,170), fontsize='xx-large');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pruning\n",
    "This is currently not supported in scikit-learn. See first point under 'disadvantages of decision trees in the <A href='http://scikit-learn.github.io/dev/modules/tree.html#'>documentation</A>. Implementation has been <A href='https://github.com/scikit-learn/scikit-learn/pull/941'>discussed</A> but Random Forests have better predictive qualities than a single pruned tree anyway if I understand correctly.\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Trees"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset available on http://www-bcf.usc.edu/~gareth/ISL/data.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 297 entries, 0 to 301\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Age        297 non-null    int64  \n",
      " 1   Sex        297 non-null    int64  \n",
      " 2   ChestPain  297 non-null    object \n",
      " 3   RestBP     297 non-null    int64  \n",
      " 4   Chol       297 non-null    int64  \n",
      " 5   Fbs        297 non-null    int64  \n",
      " 6   RestECG    297 non-null    int64  \n",
      " 7   MaxHR      297 non-null    int64  \n",
      " 8   ExAng      297 non-null    int64  \n",
      " 9   Oldpeak    297 non-null    float64\n",
      " 10  Slope      297 non-null    int64  \n",
      " 11  Ca         297 non-null    float64\n",
      " 12  Thal       297 non-null    object \n",
      " 13  AHD        297 non-null    object \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 34.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_csv('data/Heart.csv').drop('Unnamed: 0', axis=1).dropna()\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.ChestPain = pd.factorize(df2.ChestPain)[0]\n",
    "df2.Thal = pd.factorize(df2.Thal)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = df2.drop('AHD', axis=1)\n",
    "y2 = pd.factorize(df2.AHD)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_features=3, max_leaf_nodes=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_features=3, max_leaf_nodes=6)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_features=3, max_leaf_nodes=6)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=None, max_leaf_nodes=6, max_features=3)\n",
    "clf.fit(X2,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8047138047138047"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X2,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph2, = print_tree(clf, features=X2.columns, class_names=['No', 'Yes'])\n",
    "Image(graph2.create_png())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dtreeviz\n",
    "\n",
    "We are using the wonderful tree visualization library `dtreeviz` :\n",
    "https://github.com/parrt/dtreeviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1 = DecisionTreeClassifier(max_depth=3)  # limit depth of tree\n",
    "iris = load_iris()\n",
    "clf1.fit(iris.data, iris.target)         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:ns2=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"337.0\" height=\"384.0\" viewBox=\"0.0 0.0 336.5 383.75\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1.0 1.0) rotate(0) translate(4 379.75)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-379.75 332.5,-379.75 332.5,4 -4,4\" />\n",
       "<g id=\"clust5\" class=\"cluster\">\n",
       "<title>cluster_legend</title>\n",
       "</g>\n",
       "\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>node3</title>\n",
       "<svg width=\"150px\" height=\"57px\" viewBox=\"0 0 200.0375 75.912054\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"4\" y=\"-152\">\n",
       " <metadata>\n",
       "  <rdf:RDF>\n",
       "   <ns2:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
       "    <dc:date>2023-07-02T11:10:28.484135</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <ns2:Agent>\n",
       "      <dc:title>Matplotlib v3.5.3, https://matplotlib.org/</dc:title>\n",
       "     </ns2:Agent>\n",
       "    </dc:creator>\n",
       "   </ns2:Work>\n",
       "  </rdf:RDF>\n",
       " </metadata>\n",
       " <defs>\n",
       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
       " </defs>\n",
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      "text/plain": [
       "<dtreeviz.utils.DTreeVizRender at 0x7fa9bc0972b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "#!pip install dtreeviz\n",
    "#from dtreeviz.trees \n",
    "import dtreeviz\n",
    "\n",
    "viz=dtreeviz.model(clf1, \n",
    "   iris.data, \n",
    "   iris.target,\n",
    "   target_name='variety',\n",
    "   feature_names=iris.feature_names, \n",
    "   class_names=[\"setosa\", \"versicolor\", \"virginica\"]  # need class_names for classifier\n",
    "  )  \n",
    "\n",
    "#uncomment this ni the notebook (comment necessary to produce html book because of svg issues in latex):\n",
    "#viz.view()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![iris_dtreeviz.svg.png](figures/iris_dtreeviz.svg.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quiz\n",
    "\n",
    "1. Outliers\n",
    "   * Classification Trees are resistant to outliers\n",
    "   * Regression Trees are resistant to outliers\n",
    "2. Is \"diversity\" good or bad for prediction?\n",
    "3. The regions are disjoint and rectangular\n",
    "4. Interactions need to be explicitly be modeled like in regression.\n",
    "5. All rows with missing values in any column need to be removed\n",
    "6. Nature of trees\n",
    "   * Are trees \"local\" or \"global\" models?\n",
    "   * Trees are piecewise constant models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lab"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting Classification Trees"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In R, I exported the dataset from package 'ISLR' to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>CompPrice</th>\n",
       "      <th>Income</th>\n",
       "      <th>Advertising</th>\n",
       "      <th>Population</th>\n",
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       "      <th>US</th>\n",
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       "      <th>0</th>\n",
       "      <td>9.50</td>\n",
       "      <td>138</td>\n",
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       "      <td>11</td>\n",
       "      <td>276</td>\n",
       "      <td>120</td>\n",
       "      <td>Bad</td>\n",
       "      <td>42</td>\n",
       "      <td>17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.22</td>\n",
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       "      <td>Yes</td>\n",
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       "      <th>2</th>\n",
       "      <td>10.06</td>\n",
       "      <td>113</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
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       "      <td>Medium</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.40</td>\n",
       "      <td>117</td>\n",
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       "      <td>97</td>\n",
       "      <td>Medium</td>\n",
       "      <td>55</td>\n",
       "      <td>14</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>4</th>\n",
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       "      <td>340</td>\n",
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       "      <td>Bad</td>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sales  CompPrice  Income  Advertising  Population  Price ShelveLoc  Age  \\\n",
       "0   9.50        138      73           11         276    120       Bad   42   \n",
       "1  11.22        111      48           16         260     83      Good   65   \n",
       "2  10.06        113      35           10         269     80    Medium   59   \n",
       "3   7.40        117     100            4         466     97    Medium   55   \n",
       "4   4.15        141      64            3         340    128       Bad   38   \n",
       "\n",
       "   Education Urban   US  \n",
       "0         17   Yes  Yes  \n",
       "1         10   Yes  Yes  \n",
       "2         12   Yes  Yes  \n",
       "3         14   Yes  Yes  \n",
       "4         13   Yes   No  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.read_csv('data/Carseats.csv').drop('Unnamed: 0', axis=1)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Sales        400 non-null    float64\n",
      " 1   CompPrice    400 non-null    int64  \n",
      " 2   Income       400 non-null    int64  \n",
      " 3   Advertising  400 non-null    int64  \n",
      " 4   Population   400 non-null    int64  \n",
      " 5   Price        400 non-null    int64  \n",
      " 6   ShelveLoc    400 non-null    int64  \n",
      " 7   Age          400 non-null    int64  \n",
      " 8   Education    400 non-null    int64  \n",
      " 9   Urban        400 non-null    int64  \n",
      " 10  US           400 non-null    int64  \n",
      " 11  High         400 non-null    int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 37.6 KB\n"
     ]
    }
   ],
   "source": [
    "df3['High'] = df3.Sales.map(lambda x: 1 if x>8 else 0)\n",
    "df3.ShelveLoc = pd.factorize(df3.ShelveLoc)[0]\n",
    "\n",
    "df3.Urban = df3.Urban.map({'No':0, 'Yes':1})\n",
    "df3.US = df3.US.map({'No':0, 'Yes':1})\n",
    "df3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Sales  CompPrice  Income  Advertising  Population  Price  ShelveLoc  Age  \\\n",
       "0   9.50        138      73           11         276    120          0   42   \n",
       "1  11.22        111      48           16         260     83          1   65   \n",
       "2  10.06        113      35           10         269     80          2   59   \n",
       "3   7.40        117     100            4         466     97          2   55   \n",
       "4   4.15        141      64            3         340    128          0   38   \n",
       "\n",
       "   Education  Urban  US  High  \n",
       "0         17      1   1     1  \n",
       "1         10      1   1     1  \n",
       "2         12      1   1     1  \n",
       "3         14      1   1     0  \n",
       "4         13      1   0     0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df3.drop(['Sales', 'High'], axis=1)\n",
    "y = df3.High\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=6)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=6)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=6)\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.99      0.93       236\n",
      "           1       0.98      0.82      0.89       164\n",
      "\n",
      "    accuracy                           0.92       400\n",
      "   macro avg       0.93      0.90      0.91       400\n",
      "weighted avg       0.92      0.92      0.92       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y, clf.predict(X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#graph3, = print_tree(clf, features=X.columns, class_names=['No', 'Yes'])\n",
    "#Image(graph3.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>True</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>99</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "True       No  Yes\n",
       "Predicted         \n",
       "No         99   32\n",
       "Yes        19   50"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = pd.DataFrame(confusion_matrix(y_test, pred).T, index=['No', 'Yes'], columns=['No', 'Yes'])\n",
    "cm.index.name = 'Predicted'\n",
    "cm.columns.name = 'True'\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.84      0.80       118\n",
      "           1       0.72      0.61      0.66        82\n",
      "\n",
      "    accuracy                           0.74       200\n",
      "   macro avg       0.74      0.72      0.73       200\n",
      "weighted avg       0.74      0.74      0.74       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Precision of the model using test data is 74%\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pruning not implemented in scikit-learn."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting Regression Trees"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In R, I exported the dataset from package 'MASS' to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   crim     506 non-null    float64\n",
      " 1   zn       506 non-null    float64\n",
      " 2   indus    506 non-null    float64\n",
      " 3   chas     506 non-null    int64  \n",
      " 4   nox      506 non-null    float64\n",
      " 5   rm       506 non-null    float64\n",
      " 6   age      506 non-null    float64\n",
      " 7   dis      506 non-null    float64\n",
      " 8   rad      506 non-null    int64  \n",
      " 9   tax      506 non-null    int64  \n",
      " 10  ptratio  506 non-null    float64\n",
      " 11  black    506 non-null    float64\n",
      " 12  lstat    506 non-null    float64\n",
      " 13  medv     506 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "boston_df = pd.read_csv('data/Boston.csv')\n",
    "bostonX = boston_df.drop('medv',axis=1)\n",
    "boston_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=3)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import *\n",
    "\n",
    "regr = tree.DecisionTreeRegressor(max_depth=3)\n",
    "regr.fit(bostonX, boston_df[\"medv\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'boston' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[36], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m viz \u001b[39m=\u001b[39m dtreeviz(regr,\n\u001b[1;32m      2\u001b[0m                bostonX,\n\u001b[1;32m      3\u001b[0m                boston_df[\u001b[39m\"\u001b[39m\u001b[39mmedv\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[1;32m      4\u001b[0m                target_name\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mprice\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m----> 5\u001b[0m                feature_names\u001b[39m=\u001b[39mboston\u001b[39m.\u001b[39mfeature_names)\n\u001b[1;32m      7\u001b[0m \u001b[39m#viz.view()    \u001b[39;00m\n\u001b[1;32m      8\u001b[0m viz\n",
      "\u001b[0;31mNameError\u001b[0m: name 'boston' is not defined"
     ]
    }
   ],
   "source": [
    "viz = dtreeviz(regr,\n",
    "               bostonX,\n",
    "               boston_df[\"medv\"],\n",
    "               target_name='price',\n",
    "               feature_names=boston.feature_names)\n",
    "              \n",
    "#viz.view()    \n",
    "viz"
   ]
  }
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