{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8fb1c460",
   "metadata": {},
   "source": [
    "# Implementing the Classifier\n",
    "We are now ready to implement a $k$-nearest neighbor classifier based on multiple attributes. We have used only two attributes so far, for ease of visualization. But usually predictions will be based on many attributes. Here is an example that shows how multiple attributes can be better than pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f90a4983",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:53.177390Z",
     "iopub.status.busy": "2026-08-31T06:02:53.177125Z",
     "iopub.status.idle": "2026-08-31T06:02:53.926021Z",
     "shell.execute_reply": "2026-08-31T06:02:53.925771Z"
    },
    "tags": [
     "hide-input",
     "thebe-init",
     "runnable"
    ]
   },
   "outputs": [],
   "source": [
    "path_data = '../../data/'\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "plt.style.use('fivethirtyeight')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd9382c",
   "metadata": {},
   "source": [
    "```{contents}\n",
    ":local:\n",
    ":depth: 2\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f84950f2",
   "metadata": {},
   "source": [
    "## Banknote authentication\n",
    "\n",
    "This time we'll look at predicting whether a banknote (e.g., a \\$20 bill) is counterfeit or legitimate.  Researchers have put together a data set for us, based on photographs of many individual banknotes: some counterfeit, some legitimate.  They computed a few numbers from each image, using techniques that we won't worry about for this course.  So, for each banknote, we know a few numbers that were computed from a photograph of it as well as its class (whether it is counterfeit or not).  Let's load it into a table and take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3580859",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:53.927379Z",
     "iopub.status.busy": "2026-08-31T06:02:53.927272Z",
     "iopub.status.idle": "2026-08-31T06:02:53.937235Z",
     "shell.execute_reply": "2026-08-31T06:02:53.937032Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WaveletVar</th>\n",
       "      <th>WaveletSkew</th>\n",
       "      <th>WaveletCurt</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.62160</td>\n",
       "      <td>8.66610</td>\n",
       "      <td>-2.8073</td>\n",
       "      <td>-0.44699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.54590</td>\n",
       "      <td>8.16740</td>\n",
       "      <td>-2.4586</td>\n",
       "      <td>-1.46210</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.86600</td>\n",
       "      <td>-2.63830</td>\n",
       "      <td>1.9242</td>\n",
       "      <td>0.10645</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.45660</td>\n",
       "      <td>9.52280</td>\n",
       "      <td>-4.0112</td>\n",
       "      <td>-3.59440</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32924</td>\n",
       "      <td>-4.45520</td>\n",
       "      <td>4.5718</td>\n",
       "      <td>-0.98880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>0.40614</td>\n",
       "      <td>1.34920</td>\n",
       "      <td>-1.4501</td>\n",
       "      <td>-0.55949</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>-1.38870</td>\n",
       "      <td>-4.87730</td>\n",
       "      <td>6.4774</td>\n",
       "      <td>0.34179</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>-3.75030</td>\n",
       "      <td>-13.45860</td>\n",
       "      <td>17.5932</td>\n",
       "      <td>-2.77710</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>-3.56370</td>\n",
       "      <td>-8.38270</td>\n",
       "      <td>12.3930</td>\n",
       "      <td>-1.28230</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>-2.54190</td>\n",
       "      <td>-0.65804</td>\n",
       "      <td>2.6842</td>\n",
       "      <td>1.19520</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1372 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      WaveletVar  WaveletSkew  WaveletCurt  Entropy  Class\n",
       "0        3.62160      8.66610      -2.8073 -0.44699      0\n",
       "1        4.54590      8.16740      -2.4586 -1.46210      0\n",
       "2        3.86600     -2.63830       1.9242  0.10645      0\n",
       "3        3.45660      9.52280      -4.0112 -3.59440      0\n",
       "4        0.32924     -4.45520       4.5718 -0.98880      0\n",
       "...          ...          ...          ...      ...    ...\n",
       "1367     0.40614      1.34920      -1.4501 -0.55949      1\n",
       "1368    -1.38870     -4.87730       6.4774  0.34179      1\n",
       "1369    -3.75030    -13.45860      17.5932 -2.77710      1\n",
       "1370    -3.56370     -8.38270      12.3930 -1.28230      1\n",
       "1371    -2.54190     -0.65804       2.6842  1.19520      1\n",
       "\n",
       "[1372 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banknotes = pd.read_csv(path_data + 'banknote.csv')\n",
    "banknotes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9326709",
   "metadata": {},
   "source": [
    "Let's look at whether the first two numbers tell us anything about whether the banknote is counterfeit or not.  Here's a scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4b69e64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:53.938361Z",
     "iopub.status.busy": "2026-08-31T06:02:53.938300Z",
     "iopub.status.idle": "2026-08-31T06:02:53.939726Z",
     "shell.execute_reply": "2026-08-31T06:02:53.939563Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "color_table = pd.DataFrame({\n",
    "    'Class':np.array([1, 0]),\n",
    "    'Color':np.array(['darkblue', 'gold'])\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7448eac1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:53.940604Z",
     "iopub.status.busy": "2026-08-31T06:02:53.940549Z",
     "iopub.status.idle": "2026-08-31T06:02:53.944530Z",
     "shell.execute_reply": "2026-08-31T06:02:53.944356Z"
    },
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>WaveletVar</th>\n",
       "      <th>WaveletSkew</th>\n",
       "      <th>WaveletCurt</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Class</th>\n",
       "      <th>Color</th>\n",
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       "      <th>0</th>\n",
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       "      <td>-2.8073</td>\n",
       "      <td>-0.44699</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.54590</td>\n",
       "      <td>8.16740</td>\n",
       "      <td>-2.4586</td>\n",
       "      <td>-1.46210</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-2.63830</td>\n",
       "      <td>1.9242</td>\n",
       "      <td>0.10645</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
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       "      <td>-3.59440</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.32924</td>\n",
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       "      <td>4.5718</td>\n",
       "      <td>-0.98880</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>-1.4501</td>\n",
       "      <td>-0.55949</td>\n",
       "      <td>1</td>\n",
       "      <td>darkblue</td>\n",
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       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>-1.38870</td>\n",
       "      <td>-4.87730</td>\n",
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       "      <td>-13.45860</td>\n",
       "      <td>17.5932</td>\n",
       "      <td>-2.77710</td>\n",
       "      <td>1</td>\n",
       "      <td>darkblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>-3.56370</td>\n",
       "      <td>-8.38270</td>\n",
       "      <td>12.3930</td>\n",
       "      <td>-1.28230</td>\n",
       "      <td>1</td>\n",
       "      <td>darkblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>-2.54190</td>\n",
       "      <td>-0.65804</td>\n",
       "      <td>2.6842</td>\n",
       "      <td>1.19520</td>\n",
       "      <td>1</td>\n",
       "      <td>darkblue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1372 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      WaveletVar  WaveletSkew  WaveletCurt  Entropy  Class     Color\n",
       "0        3.62160      8.66610      -2.8073 -0.44699      0      gold\n",
       "1        4.54590      8.16740      -2.4586 -1.46210      0      gold\n",
       "2        3.86600     -2.63830       1.9242  0.10645      0      gold\n",
       "3        3.45660      9.52280      -4.0112 -3.59440      0      gold\n",
       "4        0.32924     -4.45520       4.5718 -0.98880      0      gold\n",
       "...          ...          ...          ...      ...    ...       ...\n",
       "1367     0.40614      1.34920      -1.4501 -0.55949      1  darkblue\n",
       "1368    -1.38870     -4.87730       6.4774  0.34179      1  darkblue\n",
       "1369    -3.75030    -13.45860      17.5932 -2.77710      1  darkblue\n",
       "1370    -3.56370     -8.38270      12.3930 -1.28230      1  darkblue\n",
       "1371    -2.54190     -0.65804       2.6842  1.19520      1  darkblue\n",
       "\n",
       "[1372 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banknotes = pd.merge(banknotes, color_table, on='Class')\n",
    "banknotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36ff7df5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:53.945497Z",
     "iopub.status.busy": "2026-08-31T06:02:53.945425Z",
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     "shell.execute_reply": "2026-08-31T06:02:53.984723Z"
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "banknotes_darkblue = banknotes[banknotes['Color'] == 'darkblue']\n",
    "banknotes_gold = banknotes[banknotes['Color'] == 'gold']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "\n",
    "ax.scatter(banknotes_darkblue['WaveletVar'], \n",
    "           banknotes_darkblue['WaveletCurt'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue', s=10)\n",
    "\n",
    "ax.scatter(banknotes_gold['WaveletVar'], \n",
    "           banknotes_gold['WaveletCurt'],  \n",
    "           label='Color=gold', \n",
    "           color='gold', s=10)\n",
    "\n",
    "\n",
    "x_label = 'WaveletVar'\n",
    "y_label = 'WaveletCurt'\n",
    "y_vals = ax.get_yticks()\n",
    "plt.ylabel(y_label)\n",
    "plt.xlabel(x_label)\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.xlim(-7.5, 7.5)\n",
    "plt.ylim(-6, 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457422bf",
   "metadata": {},
   "source": [
    "Pretty interesting!  Those two measurements do seem helpful for predicting whether the banknote is counterfeit or not.  However, in this example you can now see that there is some overlap between the blue cluster and the gold cluster.  This indicates that there will be some images where it's hard to tell whether the banknote is legitimate based on just these two numbers.  Still, you could use a $k$-nearest neighbor classifier to predict the legitimacy of a banknote.\n",
    "\n",
    "Take a minute and think it through: Suppose we used $k=11$ (say).  What parts of the plot would the classifier get right, and what parts would it make errors on?  What would the decision boundary look like?\n",
    "\n",
    "The patterns that show up in the data can get pretty wild.  For instance, here's what we'd get if used a different pair of measurements from the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3826d8f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:53.986013Z",
     "iopub.status.busy": "2026-08-31T06:02:53.985939Z",
     "iopub.status.idle": "2026-08-31T06:02:54.017967Z",
     "shell.execute_reply": "2026-08-31T06:02:54.017747Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "banknotes_darkblue = banknotes[banknotes['Color'] == 'darkblue']\n",
    "banknotes_gold = banknotes[banknotes['Color'] == 'gold']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "\n",
    "ax.scatter(banknotes_darkblue['WaveletSkew'], \n",
    "           banknotes_darkblue['Entropy'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue', s=10)\n",
    "\n",
    "ax.scatter(banknotes_gold['WaveletSkew'], \n",
    "           banknotes_gold['Entropy'],  \n",
    "           label='Color=gold', \n",
    "           color='gold', s=10)\n",
    "\n",
    "x_label = 'WaveletSkew'\n",
    "y_label = 'Entropy'\n",
    "y_vals = ax.get_yticks()\n",
    "plt.ylabel(y_label)\n",
    "plt.xlabel(x_label)\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.xlim(-16, 16)\n",
    "plt.ylim(-10, 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d7643dd",
   "metadata": {},
   "source": [
    "There does seem to be a pattern, but it's a pretty complex one.  Nonetheless, the $k$-nearest neighbors classifier can still be used and will effectively \"discover\" patterns out of this.  This illustrates how powerful machine learning can be: it can effectively take advantage of even patterns that we would not have anticipated, or that we would have thought to \"program into\" the computer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ffe00db",
   "metadata": {},
   "source": [
    "## Multiple attributes\n",
    "\n",
    "So far I've been assuming that we have exactly 2 attributes that we can use to help us make our prediction.  What if we have more than 2?  For instance, what if we have 3 attributes?\n",
    "\n",
    "Here's the cool part: you can use the same ideas for this case, too.  All you have to do is make a 3-dimensional scatterplot, instead of a 2-dimensional plot.  You can still use the $k$-nearest neighbors classifier, but now computing distances in 3 dimensions instead of just 2.  It just works.  Very cool!\n",
    "\n",
    "In fact, there's nothing special about 2 or 3.  If you have 4 attributes, you can use the $k$-nearest neighbors classifier in 4 dimensions.  5 attributes?  Work in 5-dimensional space.  And no need to stop there!  This all works for arbitrarily many attributes; you just work in a very high dimensional space.  It gets wicked-impossible to visualize, but that's OK.  The computer algorithm generalizes very nicely: all you need is the ability to compute the distance, and that's not hard.  Mind-blowing stuff!\n",
    "\n",
    "For instance, let's see what happens if we try to predict whether a banknote is counterfeit or not using 3 of the measurements, instead of just 2.  Here's what you get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "306deb4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.019161Z",
     "iopub.status.busy": "2026-08-31T06:02:54.019089Z",
     "iopub.status.idle": "2026-08-31T06:02:54.085254Z",
     "shell.execute_reply": "2026-08-31T06:02:54.085041Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure(figsize=(6,6)).add_subplot(111, projection='3d')\n",
    "ax.scatter(banknotes['WaveletSkew'], \n",
    "           banknotes['WaveletVar'], \n",
    "           banknotes['WaveletCurt'], \n",
    "           c=banknotes['Color'], s=5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6f6100",
   "metadata": {},
   "source": [
    "Awesome!  With just 2 attributes, there was some overlap between the two clusters (which means that the classifier was bound to make some mistakes for pointers in the overlap).  But when we use these 3 attributes, the two clusters have almost no overlap.  In other words, a classifier that uses these 3 attributes will be more accurate than one that only uses the 2 attributes.\n",
    "\n",
    "This is a general phenomenom in classification.  Each attribute can potentially give you new information, so more attributes sometimes helps you build a better classifier.  Of course, the cost is that now we have to gather more information to measure the value of each attribute, but this cost may be well worth it if it significantly improves the accuracy of our classifier.\n",
    "\n",
    "To sum up: you now know how to use $k$-nearest neighbor classification to predict the answer to a yes/no question, based on the values of some attributes, assuming you have a training set with examples where the correct prediction is known.  The general roadmap is this:\n",
    "\n",
    "1. identify some attributes that you think might help you predict the answer to the question.\n",
    "2. Gather a training set of examples where you know the values of the attributes as well as the correct prediction.\n",
    "3. To make predictions in the future, measure the value of the attributes and then use $k$-nearest neighbor classification to predict the answer to the question."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a6b866",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "## Distance in Multiple Dimensions\n",
    "We know how to compute distance in 2-dimensional space. If we have a point at coordinates $(x_0,y_0)$ and another at $(x_1,y_1)$, the distance between them is\n",
    "\n",
    "$$D = \\sqrt{(x_0-x_1)^2 + (y_0-y_1)^2}.$$\n",
    "\n",
    "In 3-dimensional space, the points are $(x_0, y_0, z_0)$ and $(x_1, y_1, z_1)$, and the formula for the distance between them is\n",
    "\n",
    "$$\n",
    "D = \\sqrt{(x_0-x_1)^2 + (y_0-y_1)^2 + (z_0-z_1)^2}\n",
    "$$\n",
    "\n",
    "In $n$-dimensional space, things are a bit harder to visualize, but I think you can see how the formula generalized: we sum up the squares of the differences between each individual coordinate, and then take the square root of that.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "508eed83",
   "metadata": {},
   "source": [
    "In the last section, we defined the function `distance` which returned the distance between two points. We used it in two-dimensions, but the great news is that the function doesn't care how many dimensions there are! It just subtracts the two arrays of coordinates (no matter how long the arrays are), squares the differences and adds up, and then takes the square root. To work in multiple dimensions, we don't have to change the code at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02ed74fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.086477Z",
     "iopub.status.busy": "2026-08-31T06:02:54.086405Z",
     "iopub.status.idle": "2026-08-31T06:02:54.087945Z",
     "shell.execute_reply": "2026-08-31T06:02:54.087756Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def distance(point1, point2):\n",
    "    \"\"\"Returns the distance between point1 and point2\n",
    "    where each argument is an array \n",
    "    consisting of the coordinates of the point\"\"\"\n",
    "    return np.sqrt(np.sum((point1 - point2)**2)).item()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f880ce",
   "metadata": {},
   "source": [
    "Let's use this on a [new dataset](https://archive.ics.uci.edu/ml/datasets/Wine). The table `wine` contains the chemical composition of 178 different Italian wines. The classes are the grape species, called cultivars. There are three classes but let's just see whether we can tell Class 1 apart from the other two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d8ecad11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.088961Z",
     "iopub.status.busy": "2026-08-31T06:02:54.088895Z",
     "iopub.status.idle": "2026-08-31T06:02:54.092085Z",
     "shell.execute_reply": "2026-08-31T06:02:54.091842Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "wine = pd.read_csv(path_data + 'wine.csv')\n",
    "\n",
    "# For converting Class to binary\n",
    "\n",
    "def is_one(x):\n",
    "    if x == 1:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "wine['Class1'] = wine['Class'].apply(is_one)\n",
    "\n",
    "# This creates a column 'Class1' - we drop the 'Class' column, rename 'Class1' as 'Class'\n",
    "# then move the column to the first position using 'pop' and 'insert'\n",
    "\n",
    "wine = wine.drop(columns=['Class'])\n",
    "wine = wine.rename(columns={'Class1': 'Class'})\n",
    "class_label = wine.pop('Class')\n",
    "wine.insert(0, 'Class', class_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8640d13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.092987Z",
     "iopub.status.busy": "2026-08-31T06:02:54.092928Z",
     "iopub.status.idle": "2026-08-31T06:02:54.097883Z",
     "shell.execute_reply": "2026-08-31T06:02:54.097690Z"
    },
    "tags": []
   },
   "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>Class</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of Ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color Intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diulted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class  Alcohol  Malic Acid   Ash  Alcalinity of Ash  Magnesium  \\\n",
       "0        1    14.23        1.71  2.43               15.6        127   \n",
       "1        1    13.20        1.78  2.14               11.2        100   \n",
       "2        1    13.16        2.36  2.67               18.6        101   \n",
       "3        1    14.37        1.95  2.50               16.8        113   \n",
       "4        1    13.24        2.59  2.87               21.0        118   \n",
       "..     ...      ...         ...   ...                ...        ...   \n",
       "173      0    13.71        5.65  2.45               20.5         95   \n",
       "174      0    13.40        3.91  2.48               23.0        102   \n",
       "175      0    13.27        4.28  2.26               20.0        120   \n",
       "176      0    13.17        2.59  2.37               20.0        120   \n",
       "177      0    14.13        4.10  2.74               24.5         96   \n",
       "\n",
       "     Total Phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0             2.80        3.06                  0.28             2.29   \n",
       "1             2.65        2.76                  0.26             1.28   \n",
       "2             2.80        3.24                  0.30             2.81   \n",
       "3             3.85        3.49                  0.24             2.18   \n",
       "4             2.80        2.69                  0.39             1.82   \n",
       "..             ...         ...                   ...              ...   \n",
       "173           1.68        0.61                  0.52             1.06   \n",
       "174           1.80        0.75                  0.43             1.41   \n",
       "175           1.59        0.69                  0.43             1.35   \n",
       "176           1.65        0.68                  0.53             1.46   \n",
       "177           2.05        0.76                  0.56             1.35   \n",
       "\n",
       "     Color Intensity   Hue  OD280/OD315 of diulted wines  Proline  \n",
       "0               5.64  1.04                          3.92     1065  \n",
       "1               4.38  1.05                          3.40     1050  \n",
       "2               5.68  1.03                          3.17     1185  \n",
       "3               7.80  0.86                          3.45     1480  \n",
       "4               4.32  1.04                          2.93      735  \n",
       "..               ...   ...                           ...      ...  \n",
       "173             7.70  0.64                          1.74      740  \n",
       "174             7.30  0.70                          1.56      750  \n",
       "175            10.20  0.59                          1.56      835  \n",
       "176             9.30  0.60                          1.62      840  \n",
       "177             9.20  0.61                          1.60      560  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b86521",
   "metadata": {},
   "source": [
    "The first two wines are both in Class 1. To find the distance between them, we first need a table of just the attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "10c84744",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.098876Z",
     "iopub.status.busy": "2026-08-31T06:02:54.098817Z",
     "iopub.status.idle": "2026-08-31T06:02:54.100392Z",
     "shell.execute_reply": "2026-08-31T06:02:54.100195Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "wine_attributes = wine.copy()\n",
    "\n",
    "wine_attributes = wine_attributes.drop(columns=['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "070fec3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.101402Z",
     "iopub.status.busy": "2026-08-31T06:02:54.101341Z",
     "iopub.status.idle": "2026-08-31T06:02:54.103010Z",
     "shell.execute_reply": "2026-08-31T06:02:54.102850Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.265012394048398"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance(np.array(wine_attributes.iloc[0]), np.array(wine_attributes.iloc[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "441ed3e0",
   "metadata": {},
   "source": [
    "The last wine in the table is of Class 0. Its distance from the first wine is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ade1f517",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.103983Z",
     "iopub.status.busy": "2026-08-31T06:02:54.103912Z",
     "iopub.status.idle": "2026-08-31T06:02:54.105533Z",
     "shell.execute_reply": "2026-08-31T06:02:54.105348Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "506.05936766351834"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance(np.array(wine_attributes.iloc[0]), np.array(wine_attributes.iloc[177]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27fc18c6",
   "metadata": {},
   "source": [
    "That's quite a bit bigger! Let's do some visualization to see if Class 1 really looks different from Class 0. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee81a7d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.106431Z",
     "iopub.status.busy": "2026-08-31T06:02:54.106375Z",
     "iopub.status.idle": "2026-08-31T06:02:54.108185Z",
     "shell.execute_reply": "2026-08-31T06:02:54.108021Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "wine_with_colors = pd.merge(wine, color_table, on='Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3995d853",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.109127Z",
     "iopub.status.busy": "2026-08-31T06:02:54.109071Z",
     "iopub.status.idle": "2026-08-31T06:02:54.139002Z",
     "shell.execute_reply": "2026-08-31T06:02:54.138794Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_darkblue = wine_with_colors[wine_with_colors['Color'] == 'darkblue']\n",
    "wine_gold = wine_with_colors[wine_with_colors['Color'] == 'gold']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "\n",
    "ax.scatter(wine_darkblue['Flavanoids'], \n",
    "           wine_darkblue['Alcohol'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue', s=10)\n",
    "\n",
    "ax.scatter(wine_gold['Flavanoids'], \n",
    "           wine_gold['Alcohol'],  \n",
    "           label='Color=gold', \n",
    "           color='gold', s=10)\n",
    "\n",
    "\n",
    "x_label = 'Flavanoids'\n",
    "y_label = 'Alcohol'\n",
    "plt.ylabel(y_label)\n",
    "plt.xlabel(x_label)\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.xlim(0, 5)\n",
    "plt.ylim(10.8, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b36db38",
   "metadata": {},
   "source": [
    "The blue points (Class 1) are almost entirely separate from the gold ones. That is one indication of why the distance between two Class 1 wines would be smaller than the distance between wines of two different classes. We can see a similar phenomenon with a different pair of attributes too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ecf87bab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.139953Z",
     "iopub.status.busy": "2026-08-31T06:02:54.139897Z",
     "iopub.status.idle": "2026-08-31T06:02:54.172934Z",
     "shell.execute_reply": "2026-08-31T06:02:54.172704Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_darkblue = wine_with_colors[wine_with_colors['Color'] == 'darkblue']\n",
    "wine_gold = wine_with_colors[wine_with_colors['Color'] == 'gold']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "\n",
    "ax.scatter(wine_darkblue['Alcalinity of Ash'], \n",
    "           wine_darkblue['Ash'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue', s=10)\n",
    "\n",
    "ax.scatter(wine_gold['Alcalinity of Ash'], \n",
    "           wine_gold['Ash'],  \n",
    "           label='Color=gold', \n",
    "           color='gold', s=10)\n",
    "\n",
    "\n",
    "x_label = 'Alcalinity of Ash'\n",
    "y_label = 'Ash'\n",
    "plt.ylabel(y_label)\n",
    "plt.xlabel(x_label)\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.xlim(10, 30.2)\n",
    "plt.ylim(1.25, 3.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f09fcc10",
   "metadata": {},
   "source": [
    "But for some pairs the picture is more murky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ddc76610",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.174207Z",
     "iopub.status.busy": "2026-08-31T06:02:54.174136Z",
     "iopub.status.idle": "2026-08-31T06:02:54.211794Z",
     "shell.execute_reply": "2026-08-31T06:02:54.211567Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_darkblue = wine_with_colors[wine_with_colors['Color'] == 'darkblue']\n",
    "wine_gold = wine_with_colors[wine_with_colors['Color'] == 'gold']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4))\n",
    "\n",
    "ax.scatter(wine_darkblue['Magnesium'], \n",
    "           wine_darkblue['Total Phenols'],  \n",
    "           label='Color=darkblue', \n",
    "           color='darkblue', s=10)\n",
    "\n",
    "ax.scatter(wine_gold['Magnesium'], \n",
    "           wine_gold['Total Phenols'],  \n",
    "           label='Color=gold', \n",
    "           color='gold', s=10)\n",
    "\n",
    "\n",
    "x_label = 'Magnesium'\n",
    "y_label = 'Total Phenols'\n",
    "plt.ylabel(y_label)\n",
    "plt.xlabel(x_label)\n",
    "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "plt.xlim(70, 165)\n",
    "plt.ylim(0.9, 4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea4e2e2",
   "metadata": {},
   "source": [
    "Let's see if we can implement a classifier based on all of the attributes. After that, we'll see how accurate it is."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f4b35f",
   "metadata": {},
   "source": [
    "## A Plan for the Implementation\n",
    "It's time to write some code to implement the classifier.  The input is a `point` that we want to classify.  The classifier works by finding the $k$ nearest neighbors of `point` from the training set.  So, our approach will go like this:\n",
    "\n",
    "1. Find the closest $k$ neighbors of `point`, i.e., the $k$ wines from the training set that are most similar to `point`.\n",
    "\n",
    "2. Look at the classes of those $k$ neighbors, and take the majority vote to find the most-common class of wine.  Use that as our predicted class for `point`.\n",
    "\n",
    "So that will guide the structure of our Python code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05223c3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.212899Z",
     "iopub.status.busy": "2026-08-31T06:02:54.212833Z",
     "iopub.status.idle": "2026-08-31T06:02:54.214470Z",
     "shell.execute_reply": "2026-08-31T06:02:54.214266Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def closest(training, p, k):\n",
    "    ...\n",
    "\n",
    "def majority(topkclasses):\n",
    "    ...\n",
    "\n",
    "def classify(training, p, k):\n",
    "    kclosest = closest(training, p, k)\n",
    "    topkclasses = kclosest[['Class']]\n",
    "    return majority(topkclasses)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245d4149",
   "metadata": {},
   "source": [
    "## Implementation Step 1\n",
    "To implement the first step for the kidney disease data, we had to compute the distance from each patient in the training set to `point`, sort them by distance, and take the $k$ closest patients in the training set.  \n",
    "\n",
    "That's what we did in the previous section with the point corresponding to Alice. Let's generalize that code. We'll redefine `distance` here, just for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3f84283",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.215501Z",
     "iopub.status.busy": "2026-08-31T06:02:54.215443Z",
     "iopub.status.idle": "2026-08-31T06:02:54.217507Z",
     "shell.execute_reply": "2026-08-31T06:02:54.217307Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def distance(point1, point2):\n",
    "    \"\"\"Returns the distance between point1 and point2\n",
    "    where each argument is an array \n",
    "    consisting of the coordinates of the point\"\"\"\n",
    "    return np.sqrt(np.sum((point1 - point2)**2))\n",
    "\n",
    "def all_distances(training, point):\n",
    "    \"\"\"The distance between p (an array of numbers) and the numbers in row i of attribute_table.\"\"\"\n",
    "    attributes = training.drop(columns=['Class'])\n",
    "    def distance_from_point(row):\n",
    "        return distance(point, np.array(row))\n",
    "    return attributes.apply(distance_from_point, axis=1)\n",
    "\n",
    "def table_with_distances(training, point):\n",
    "    \"\"\"A copy of the training table with the distance from each row to array p.\"\"\"\n",
    "    training1 = training.copy()\n",
    "    training1['Distance'] = all_distances(training1, point)\n",
    "\n",
    "    return training1\n",
    "\n",
    "def closest(training, new_point, k):\n",
    "    \"\"\"Returns a table of the k rows of the augmented table\n",
    "    corresponding to the k smallest distances\"\"\"\n",
    "    with_dists = table_with_distances(training, new_point)\n",
    "    sorted_by_distance = with_dists.sort_values(by=['Distance'])\n",
    "    topk = sorted_by_distance.take(np.arange(k))\n",
    "    return topk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0bea156",
   "metadata": {},
   "source": [
    "Let's see how this works on our `wine` data. We'll just take the first wine and find its five nearest neighbors among all the wines. Remember that since this wine is part of the dataset, it is its own nearest neighbor. So we should expect to see it at the top of the list, followed by four others.\n",
    "\n",
    "First let's extract its attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ecff9ef5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.218521Z",
     "iopub.status.busy": "2026-08-31T06:02:54.218463Z",
     "iopub.status.idle": "2026-08-31T06:02:54.220827Z",
     "shell.execute_reply": "2026-08-31T06:02:54.220635Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alcohol                           14.23\n",
       "Malic Acid                         1.71\n",
       "Ash                                2.43\n",
       "Alcalinity of Ash                 15.60\n",
       "Magnesium                        127.00\n",
       "Total Phenols                      2.80\n",
       "Flavanoids                         3.06\n",
       "Nonflavanoid phenols               0.28\n",
       "Proanthocyanins                    2.29\n",
       "Color Intensity                    5.64\n",
       "Hue                                1.04\n",
       "OD280/OD315 of diulted wines       3.92\n",
       "Proline                         1065.00\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "special_wine = wine.drop(columns=['Class']).iloc[0]\n",
    "special_wine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e400c4bf",
   "metadata": {},
   "source": [
    "And now let's find its 5 nearest neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e1ef71e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.221767Z",
     "iopub.status.busy": "2026-08-31T06:02:54.221713Z",
     "iopub.status.idle": "2026-08-31T06:02:54.232855Z",
     "shell.execute_reply": "2026-08-31T06:02:54.232660Z"
    },
    "tags": []
   },
   "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>Class</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of Ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color Intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diulted wines</th>\n",
       "      <th>Proline</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>13.74</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.25</td>\n",
       "      <td>16.4</td>\n",
       "      <td>118</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.62</td>\n",
       "      <td>5.85</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1060</td>\n",
       "      <td>10.392805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>14.21</td>\n",
       "      <td>4.04</td>\n",
       "      <td>2.44</td>\n",
       "      <td>18.9</td>\n",
       "      <td>111</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.24</td>\n",
       "      <td>0.87</td>\n",
       "      <td>3.33</td>\n",
       "      <td>1080</td>\n",
       "      <td>22.340748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>14.10</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.40</td>\n",
       "      <td>18.8</td>\n",
       "      <td>103</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.38</td>\n",
       "      <td>6.20</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1060</td>\n",
       "      <td>24.760232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>14.38</td>\n",
       "      <td>3.59</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>102</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2.19</td>\n",
       "      <td>4.90</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.44</td>\n",
       "      <td>1065</td>\n",
       "      <td>25.094663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Class  Alcohol  Malic Acid   Ash  Alcalinity of Ash  Magnesium  \\\n",
       "0       1    14.23        1.71  2.43               15.6        127   \n",
       "54      1    13.74        1.67  2.25               16.4        118   \n",
       "45      1    14.21        4.04  2.44               18.9        111   \n",
       "48      1    14.10        2.02  2.40               18.8        103   \n",
       "46      1    14.38        3.59  2.28               16.0        102   \n",
       "\n",
       "    Total Phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0            2.80        3.06                  0.28             2.29   \n",
       "54           2.60        2.90                  0.21             1.62   \n",
       "45           2.85        2.65                  0.30             1.25   \n",
       "48           2.75        2.92                  0.32             2.38   \n",
       "46           3.25        3.17                  0.27             2.19   \n",
       "\n",
       "    Color Intensity   Hue  OD280/OD315 of diulted wines  Proline   Distance  \n",
       "0              5.64  1.04                          3.92     1065   0.000000  \n",
       "54             5.85  0.92                          3.20     1060  10.392805  \n",
       "45             5.24  0.87                          3.33     1080  22.340748  \n",
       "48             6.20  1.07                          2.75     1060  24.760232  \n",
       "46             4.90  1.04                          3.44     1065  25.094663  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closest(wine, special_wine, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95665946",
   "metadata": {},
   "source": [
    "Bingo! The first row is the nearest neighbor, which is itself – there's a 0 in the `Distance` column as expected. All five nearest neighbors are of Class 1, which is consistent with our earlier observation that Class 1 wines appear to be clumped together in some dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b39ec8",
   "metadata": {},
   "source": [
    "## Implementation Steps 2 and 3\n",
    "Next we need to take a \"majority vote\" of the nearest neighbors and assign our point the same class as the majority."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "77d29761",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.233919Z",
     "iopub.status.busy": "2026-08-31T06:02:54.233861Z",
     "iopub.status.idle": "2026-08-31T06:02:54.235416Z",
     "shell.execute_reply": "2026-08-31T06:02:54.235230Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def majority(topkclasses):\n",
    "    ones = len(topkclasses[topkclasses['Class'] == 1])\n",
    "    zeros = len(topkclasses[topkclasses['Class'] == 0])\n",
    "    if ones > zeros:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def classify(training, new_point, k):\n",
    "    closestk = closest(training, new_point, k)\n",
    "    topkclasses = closestk[['Class']]\n",
    "    return majority(topkclasses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f50af020",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.236290Z",
     "iopub.status.busy": "2026-08-31T06:02:54.236235Z",
     "iopub.status.idle": "2026-08-31T06:02:54.245285Z",
     "shell.execute_reply": "2026-08-31T06:02:54.245094Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify(wine, special_wine, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c22a65",
   "metadata": {},
   "source": [
    "If we change `special_wine` to be the last one in the dataset, is our classifier able to tell that it's in Class 0?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5317e7c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.246305Z",
     "iopub.status.busy": "2026-08-31T06:02:54.246239Z",
     "iopub.status.idle": "2026-08-31T06:02:54.255153Z",
     "shell.execute_reply": "2026-08-31T06:02:54.254967Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "special_wine = wine.drop(columns=['Class']).iloc[177]\n",
    "classify(wine, special_wine, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11b9a44",
   "metadata": {},
   "source": [
    "Yes! The classifier gets this one right too.\n",
    "\n",
    "But we don't yet know how it does with all the other wines, and in any case we know that testing on wines that are already part of the training set might be over-optimistic. In the final section of this chapter, we will separate the wines into a training and test set and then measure the accuracy of our classifier on the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2f73524",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cell-661ab1ce",
   "metadata": {},
   "source": [
    "\n",
    "## Section Practice\n",
    "\n",
    "Use this short exercise to check the main workflow from this section.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cell-fe4dc5d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.256222Z",
     "iopub.status.busy": "2026-08-31T06:02:54.256163Z",
     "iopub.status.idle": "2026-08-31T06:02:54.257488Z",
     "shell.execute_reply": "2026-08-31T06:02:54.257307Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Practice Implementing the Classifier\n",
    "# Put the classifier's steps together on a small banknote training set\n",
    "# (Class 1 = counterfeit, Class 0 = genuine).\n",
    "#   1. Write classify(training, point, k): compute the distance from point to every\n",
    "#      row of training (leave out 'Class'), take the k closest rows, and return\n",
    "#      the most common Class among them.\n",
    "#   2. Classify new_note with k=3.\n",
    "# Print exactly:\n",
    "# Predicted class: 1\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "training = pd.DataFrame({\n",
    "    'WaveletVar': [3.6, 4.5, -1.4, -2.0, 0.3],\n",
    "    'WaveletSkew': [8.7, 8.2, -3.0, 1.0, -0.5],\n",
    "    'Class': [0, 0, 0, 1, 1],\n",
    "})\n",
    "new_note = np.array([0.0, 0.5])\n",
    "### Your code starts here\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cell-64c19f0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:54.258398Z",
     "iopub.status.busy": "2026-08-31T06:02:54.258341Z",
     "iopub.status.idle": "2026-08-31T06:02:54.259925Z",
     "shell.execute_reply": "2026-08-31T06:02:54.259743Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class: 1\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "training = pd.DataFrame({\n",
    "    'WaveletVar': [3.6, 4.5, -1.4, -2.0, 0.3],\n",
    "    'WaveletSkew': [8.7, 8.2, -3.0, 1.0, -0.5],\n",
    "    'Class': [0, 0, 0, 1, 1],\n",
    "})\n",
    "new_note = np.array([0.0, 0.5])\n",
    "\n",
    "def classify(training, point, k):\n",
    "    attributes = training.drop(columns=['Class'])\n",
    "    distances = np.sqrt(((attributes - point) ** 2).sum(axis=1))\n",
    "    closest = training.assign(Distance=distances).sort_values('Distance').head(k)\n",
    "    return closest['Class'].value_counts().idxmax()\n",
    "\n",
    "print('Predicted class:', classify(training, new_note, 3))\n"
   ]
  }
 ],
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