Knn Anomaly Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afdb90a8-892e-4ec9-9dac-48c227e06179",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e3b163c-f2e0-43a9-936f-093a28552d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv\")\n",
    "df = data[['sepal_length', 'sepal_width']] #taking only 2 columns of intrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11f33bfe-536d-4d83-a385-d226b138e03b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f60b2f9feb0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "\n",
    "plt.scatter(df['sepal_length'], df['sepal_width']) #plot length on x axis and width on y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ccd0133-b7e2-4388-9c90-c437f6437bbb",
   "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>NearestNeighbors(n_neighbors=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\">NearestNeighbors</label><div class=\"sk-toggleable__content\"><pre>NearestNeighbors(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "NearestNeighbors(n_neighbors=3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use knn to identify outliers\n",
    "\n",
    "X = df.values #load in data as X\n",
    "nbrs = NearestNeighbors(n_neighbors=3)\n",
    "nbrs.fit(X) #fit model with X data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "043cd063-9050-4e5f-a317-e9e563c9ed42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f60b0bbe0b0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculate distances and indexes for the k neighbors\n",
    "distances, indexes = nbrs.kneighbors(X)\n",
    "plt.plot(distances.mean(axis=1)) #shows mean disatnce from k-neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7231325b-d027-4748-9574-16ae670d6985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 14,  15,  18,  22,  32,  33,  41,  59,  60, 108, 109, 117, 118,\n",
       "        131]),)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#points above 0.15 are considered anomalies\n",
    "\n",
    "#filter out anomalies\n",
    "outlier_index = np.where(distances.mean(axis=1) >0.15)\n",
    "outlier_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "76238e1f-a78f-492e-8a84-4cdef0de79e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width\n",
       "14            5.8          4.0\n",
       "15            5.7          4.4\n",
       "18            5.7          3.8\n",
       "22            4.6          3.6\n",
       "32            5.2          4.1\n",
       "33            5.5          4.2\n",
       "41            4.5          2.3\n",
       "59            5.2          2.7\n",
       "60            5.0          2.0\n",
       "108           6.7          2.5\n",
       "109           7.2          3.6\n",
       "117           7.7          3.8\n",
       "118           7.7          2.6\n",
       "131           7.9          3.8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find value of these outliers\n",
    "outlier_values = df.iloc[outlier_index] #select values only labled as outliers\n",
    "outlier_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "23c58b34-15ea-48d5-892b-8aaa63c5db43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f60b0ad86d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#highlight outlier data and plot them\n",
    "plt.scatter(df['sepal_length'], df['sepal_width'], color = 'b', s=65)\n",
    "plt.scatter(outlier_values['sepal_length'], outlier_values['sepal_width'], color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9018d7e9-aab2-4948-84f5-ec5b199a838a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 [3.10]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}