7!-DBSCAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from pylab import rcParams\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sklearn\n",
    "from sklearn.cluster import DBSCAN\n",
    "from collections import Counter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('last.txt')\n",
    "data.columns =(\"a\",\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xcec2f0db70>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcec2e590b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data.head(10000)\n",
    "D= data.head(10000)\n",
    "f1 = D['a'].values\n",
    "f2 = D['b'].values\n",
    "X1 = np.array (list(zip(f1,f2)))\n",
    "plt.scatter(f1,f2, c= 'black' , s=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2287705,      15],\n",
       "       [2287706,      19],\n",
       "       [2287707,      25],\n",
       "       ...,\n",
       "       [2297702,      29],\n",
       "       [2297703,      24],\n",
       "       [2297704,      20]], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MyDBSCAN(D, eps, MinPts):\n",
    "    labels = [0]*len(D)\n",
    "    C = 0\n",
    " \n",
    "    for P in range(0, len(D)):\n",
    "\n",
    "        if not (labels[P] == 0):\n",
    "            continue\n",
    "        \n",
    "        NeighborPts = regionQuery(D, P, eps)\n",
    "\n",
    "        if len(NeighborPts) < MinPts:\n",
    "            labels[P] = -1\n",
    "\n",
    "        else:\n",
    "            C += 1\n",
    "\n",
    "            labels[P] = C\n",
    "           \n",
    "            growCluster(D, labels, P, C, eps, MinPts)\n",
    "    \n",
    "    return labels\n",
    "\n",
    "\n",
    "def growCluster(D, labels, P, C, eps, MinPts):\n",
    "    \n",
    "    SearchQueue = [P]\n",
    "    i = 0\n",
    "    while i < len(SearchQueue):    \n",
    "        \n",
    "        # next point from the queue.        \n",
    "        P = SearchQueue[i]\n",
    "\n",
    "        # all the neighbors of P\n",
    "        NeighborPts = regionQuery(D, P, eps)\n",
    "        \n",
    "        if len(NeighborPts) < MinPts:\n",
    "            i += 1\n",
    "            continue\n",
    "        \n",
    "        for Pn in NeighborPts:\n",
    "           \n",
    "            #noise detection --> -1\n",
    "            if labels[Pn] == -1:\n",
    "                labels[Pn] = C\n",
    " \n",
    "            elif labels[Pn] == 0:\n",
    "                labels[Pn] = C\n",
    "                \n",
    "                SearchQueue.append(Pn)\n",
    "            \n",
    "        i += 1        \n",
    "    \n",
    "\n",
    "def regionQuery(D, P, eps):\n",
    "\n",
    "    neighbors = []\n",
    "    \n",
    "    for Pn in range(0, len(D)):\n",
    "\n",
    "        # threshold\n",
    "        if (np.sqrt((f1[P] - f1[Pn]) ** 2 + (f2[P] - f2[Pn]) ** 2)) < eps:\n",
    "            neighbors.append(Pn)\n",
    "\n",
    "    return neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = MyDBSCAN( X1 , 30 ,2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\irem\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2287705</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2287706</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2287707</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2287708</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2287709</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         a   b  k\n",
       "0  2287705  15  1\n",
       "1  2287706  19  1\n",
       "2  2287707  25  1\n",
       "3  2287708  18  1\n",
       "4  2287709  18  1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D['k'] = k\n",
    "D.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9119</th>\n",
       "      <td>2296824</td>\n",
       "      <td>70</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a   b  k\n",
       "9119  2296824  70 -1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D[(D['b'] >= 60)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'c1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-92a895be38d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mc1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mc4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mc5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'Cluster 1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Cluster 2'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Cluster 3'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Cluster 4'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Cluster 5'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'DBSCAN finds 2 clusters and noise'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'c1' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcec33c7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0, len(D)):\n",
    "    if D['k'][i] == 0:\n",
    "         c1 = plt.scatter(D['a'][i],D['b'][i],c='r',marker='+')\n",
    "    elif D['k'][i] == 1:\n",
    "         c2 = plt.scatter(D['a'][i],D['b'][i],c='g',marker='o')\n",
    "    elif D['k'][i] == -1:\n",
    "         c3 = plt.scatter(D['a'][i],D['b'][i],c='b',marker='*')\n",
    "    elif D['k'][i] == 2:\n",
    "         c4 = plt.scatter(D['a'][i],D['b'][i],c='k',marker='D')\n",
    "    elif D['k'][i] == 3:\n",
    "         c5 = plt.scatter(D['a'][i],D['b'][i],c='y',marker='s')\n",
    "            \n",
    "            \n",
    "plt.legend([c1, c2, c3,c4,c5], ['Cluster 1', 'Cluster 2','Cluster 3','Cluster 4','Cluster 5'])\n",
    "plt.title('DBSCAN finds 2 clusters and noise')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}