{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Decision tree learning & Impurity (entropy/Gini) examples\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SciPy Entropy Examples\n",
    "Below are a few simple examples of calculating entropy using the built in function from scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# entropy of a fair coin, i.e. 50% chance of heads, 50% chance of tails\n",
    "from scipy.stats import entropy\n",
    "entropy([1/2, 1/2], base=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9709505944546688"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# entropy of a biased coin, e.g. 60% chance of heads, 40% chance of tails\n",
    "entropy([60/100, 40/100], base=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# entropy of a coin with both sides the same, e.g. 100% chance of heads\n",
    "entropy([1], base=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-learn decision tree examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   calorific_value  nitrogen  turbidity style   alcohol  sugars  bitterness  \\\n",
      "0        45.305310  0.459548   1.917273   ale  4.227692   16.67   12.568947   \n",
      "1        43.889381  0.548977   3.186364   ale  4.289231   16.73   14.974000   \n",
      "2        41.588496  0.542847   1.568182   ale  4.344615   16.48   11.848789   \n",
      "3        44.553097  0.480301   1.871818   ale  4.424615   18.59   13.879632   \n",
      "4        41.013274  0.441860   2.345455   ale  4.264615   16.35   12.186053   \n",
      "\n",
      "   beer_id  colour  degree_of_fermentation  \n",
      "0      167   11.04               62.178571  \n",
      "1      128   13.44               63.032857  \n",
      "2       88   14.04               63.468571  \n",
      "3      147   12.48               63.531429  \n",
      "4       74   12.12               63.747143  \n",
      "(124, 10)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# details for beer dataset\n",
    "training_file = \"beer_training.csv\"\n",
    "test_file = \"beer_test.csv\"\n",
    "independent_cols = [\"calorific_value\", \"nitrogen\", \"turbidity\", \"alcohol\", \"sugars\", \"bitterness\", \"beer_id\", \n",
    "                    \"colour\", \"degree_of_fermentation\"]\n",
    "dependent_col = \"style\"\n",
    "\n",
    "# Here we load our training dataset in from the training file using the pandas library\n",
    "df_training = pd.read_csv(training_file)\n",
    "print(df_training.head())\n",
    "print(df_training.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   calorific_value  nitrogen  turbidity   alcohol  sugars  bitterness  \\\n",
      "0        45.305310  0.459548   1.917273  4.227692   16.67   12.568947   \n",
      "1        43.889381  0.548977   3.186364  4.289231   16.73   14.974000   \n",
      "2        41.588496  0.542847   1.568182  4.344615   16.48   11.848789   \n",
      "3        44.553097  0.480301   1.871818  4.424615   18.59   13.879632   \n",
      "4        41.013274  0.441860   2.345455  4.264615   16.35   12.186053   \n",
      "\n",
      "   beer_id  colour  degree_of_fermentation  \n",
      "0      167   11.04               62.178571  \n",
      "1      128   13.44               63.032857  \n",
      "2       88   14.04               63.468571  \n",
      "3      147   12.48               63.531429  \n",
      "4       74   12.12               63.747143  \n",
      "(124, 9)\n"
     ]
    }
   ],
   "source": [
    "# set up a matrix X containing the independent variables from the training data\n",
    "X_training = df_training.loc[:,independent_cols]\n",
    "print(X_training.head())\n",
    "print(X_training.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    ale\n",
      "1    ale\n",
      "2    ale\n",
      "3    ale\n",
      "4    ale\n",
      "Name: style, dtype: object\n",
      "(124,)\n"
     ]
    }
   ],
   "source": [
    "# Set up a vector y containing the dependent variable / target attribute for the training data\n",
    "y_training = df_training.loc[:,dependent_col]\n",
    "print(y_training.head())\n",
    "print(y_training.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value counts\n",
      "lager    44\n",
      "ale      42\n",
      "stout    38\n",
      "Name: style, dtype: int64\n",
      "\n",
      "Normalised value counts (i.e. probabilities)\n",
      "lager    0.354839\n",
      "ale      0.338710\n",
      "stout    0.306452\n",
      "Name: style, dtype: float64\n",
      "\n",
      "Training set entropy: 1.5823126935513492\n"
     ]
    }
   ],
   "source": [
    "# compute the entropy of the training dataset\n",
    "print(\"Value counts\")\n",
    "print(df_training[\"style\"].value_counts())\n",
    "print(\"\\nNormalised value counts (i.e. probabilities)\")\n",
    "print(df_training[\"style\"].value_counts(normalize=True))\n",
    "\n",
    "training_entropy = entropy(df_training[\"style\"].value_counts(normalize=True), base=2)\n",
    "print(\"\\nTraining set entropy:\",training_entropy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data: 1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(243.4909090909091, 587.0880000000001, 'X[7] <= 9.42\\nentropy = 1.582\\nsamples = 124\\nvalue = [42, 44, 38]'),\n",
       " Text(121.74545454545455, 456.624, 'X[5] <= 9.667\\nentropy = 0.316\\nsamples = 35\\nvalue = [0, 2, 33]'),\n",
       " Text(60.872727272727275, 326.1600000000001, 'entropy = 0.0\\nsamples = 33\\nvalue = [0, 0, 33]'),\n",
       " Text(182.61818181818182, 326.1600000000001, 'entropy = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
       " Text(365.23636363636365, 456.624, 'X[3] <= 3.934\\nentropy = 1.256\\nsamples = 89\\nvalue = [42, 42, 5]'),\n",
       " Text(304.3636363636364, 326.1600000000001, 'entropy = 0.0\\nsamples = 38\\nvalue = [0, 38, 0]'),\n",
       " Text(426.1090909090909, 326.1600000000001, 'X[5] <= 8.226\\nentropy = 0.847\\nsamples = 51\\nvalue = [42, 4, 5]'),\n",
       " Text(304.3636363636364, 195.69600000000003, 'X[4] <= 17.46\\nentropy = 0.863\\nsamples = 7\\nvalue = [0, 2, 5]'),\n",
       " Text(243.4909090909091, 65.23200000000008, 'entropy = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
       " Text(365.23636363636365, 65.23200000000008, 'entropy = 0.0\\nsamples = 5\\nvalue = [0, 0, 5]'),\n",
       " Text(547.8545454545455, 195.69600000000003, 'X[7] <= 15.54\\nentropy = 0.267\\nsamples = 44\\nvalue = [42, 2, 0]'),\n",
       " Text(486.9818181818182, 65.23200000000008, 'entropy = 0.0\\nsamples = 42\\nvalue = [42, 0, 0]'),\n",
       " Text(608.7272727272727, 65.23200000000008, 'entropy = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]')]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# create a model using the default settings (using Gini index)\n",
    "model = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "model.fit(X_training, y_training)\n",
    "\n",
    "# compute the accuracy on the training predictions\n",
    "predictions_training = model.predict(X_training)\n",
    "accuracy_training = metrics.accuracy_score(y_training, predictions_training)\n",
    "print(\"Accuracy on training data:\",accuracy_training)\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "tree.plot_tree(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see above that the netropy at the root node is the same as the overall entropy of the training set, i.e. 1.582"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data: 1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(362.70000000000005, 597.96, 'X[1] <= 0.36\\ngini = 0.665\\nsamples = 124\\nvalue = [42, 44, 38]'),\n",
       " Text(223.20000000000002, 489.24, 'X[7] <= 9.42\\ngini = 0.552\\nsamples = 84\\nvalue = [5, 42, 37]'),\n",
       " Text(111.60000000000001, 380.52000000000004, 'X[5] <= 9.667\\ngini = 0.111\\nsamples = 34\\nvalue = [0, 2, 32]'),\n",
       " Text(55.800000000000004, 271.8, 'gini = 0.0\\nsamples = 32\\nvalue = [0, 0, 32]'),\n",
       " Text(167.4, 271.8, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
       " Text(334.8, 380.52000000000004, 'X[3] <= 3.934\\ngini = 0.34\\nsamples = 50\\nvalue = [5, 40, 5]'),\n",
       " Text(279.0, 271.8, 'gini = 0.0\\nsamples = 37\\nvalue = [0, 37, 0]'),\n",
       " Text(390.6, 271.8, 'X[5] <= 8.714\\ngini = 0.651\\nsamples = 13\\nvalue = [5, 3, 5]'),\n",
       " Text(279.0, 163.07999999999998, 'X[2] <= 1.615\\ngini = 0.408\\nsamples = 7\\nvalue = [0, 2, 5]'),\n",
       " Text(223.20000000000002, 54.360000000000014, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
       " Text(334.8, 54.360000000000014, 'gini = 0.0\\nsamples = 5\\nvalue = [0, 0, 5]'),\n",
       " Text(502.20000000000005, 163.07999999999998, 'X[7] <= 14.94\\ngini = 0.278\\nsamples = 6\\nvalue = [5, 1, 0]'),\n",
       " Text(446.40000000000003, 54.360000000000014, 'gini = 0.0\\nsamples = 5\\nvalue = [5, 0, 0]'),\n",
       " Text(558.0, 54.360000000000014, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(502.20000000000005, 489.24, 'X[2] <= 0.969\\ngini = 0.141\\nsamples = 40\\nvalue = [37, 2, 1]'),\n",
       " Text(446.40000000000003, 380.52000000000004, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
       " Text(558.0, 380.52000000000004, 'X[7] <= 9.06\\ngini = 0.051\\nsamples = 38\\nvalue = [37, 0, 1]'),\n",
       " Text(502.20000000000005, 271.8, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(613.8000000000001, 271.8, 'gini = 0.0\\nsamples = 37\\nvalue = [37, 0, 0]')]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# create a model using the default settings (using Gini index)\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_training, y_training)\n",
    "\n",
    "# compute the accuracy on the training predictions\n",
    "predictions_training = model.predict(X_training)\n",
    "accuracy_training = metrics.accuracy_score(y_training, predictions_training)\n",
    "print(\"Accuracy on training data:\",accuracy_training)\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "tree.plot_tree(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ct4101",
   "language": "python",
   "name": "ct4101"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}