Code-Example-024-DBSCAN-with-Scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DBSCAN with Scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up and generate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.28171457  0.91044406]\n",
      " [ 0.8389241  -0.53005338]\n",
      " [ 0.40915474  0.80944352]\n",
      " [-0.98415213  0.13142155]\n",
      " [ 1.15919021  0.4910425 ]]\n"
     ]
    }
   ],
   "source": [
    "X, label = make_moons(n_samples=200, noise=0.1, random_state=19)\n",
    "print(X[:5,])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBSCAN(eps=0.25, min_samples=12)\n"
     ]
    }
   ],
   "source": [
    "# model = DBSCAN?\n",
    "model = DBSCAN(eps=0.25, min_samples=12).fit(X)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access the cluster labels and core points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  0,  0,  0,  0,  1,  0,  1,  1,  0,  0,  0,  1,  0,  1,  0,\n",
       "        1,  1,  1,  1,  0,  0,  1,  1,  0,  0,  1,  1,  1,  0,  0,  0,  0,\n",
       "        0,  1,  0,  1,  0,  1,  1,  0,  0,  1,  0,  1,  0,  1,  0,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  0,  0,  1,  1,  0,  1,  0,  0,  0,  0,\n",
       "        0,  0,  1,  0,  0,  1, -1,  1,  0,  1,  1,  1,  0,  1,  0,  0,  1,\n",
       "        1,  0,  0,  1,  0,  0,  0,  0,  0,  0,  1,  0,  1,  1,  0,  1,  1,\n",
       "        0,  0,  0,  1,  1,  1,  1,  0,  0,  0,  1,  1,  1,  1,  1,  0,  0,\n",
       "        0,  1,  0,  1,  1,  1,  0,  0,  0,  0,  0,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  0,  0,  1,  1,  0,  0,  1,  1,  0,  0,  1,  1,\n",
       "        1,  1,  1,  1,  0,  1,  1,  1,  0,  0,  0,  1,  0,  1,  1,  0,  0,\n",
       "        0,  0,  1,  0,  1,  0,  0,  0,  0,  0,  0,  1,  1,  0,  0,  0,  0,\n",
       "        1,  1,  0,  1,  0,  1,  0,  0,  0,  1,  0,  1,  0])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   7,   9,  10,  12,  13,  14,  18,  20,  21,  22,\n",
       "        23,  24,  26,  28,  29,  31,  32,  33,  34,  35,  37,  38,  39,\n",
       "        40,  41,  42,  44,  45,  46,  47,  49,  50,  51,  55,  56,  57,\n",
       "        58,  59,  60,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71,\n",
       "        73,  75,  76,  77,  78,  79,  81,  83,  84,  86,  87,  88,  89,\n",
       "        91,  92,  93,  94,  96,  98, 101, 102, 104, 105, 106, 107, 108,\n",
       "       109, 110, 112, 113, 114, 115, 117, 118, 119, 121, 122, 123, 124,\n",
       "       125, 126, 127, 128, 129, 130, 131, 134, 136, 137, 138, 139, 140,\n",
       "       142, 143, 144, 145, 147, 148, 149, 150, 154, 155, 157, 158, 159,\n",
       "       160, 162, 163, 164, 165, 166, 170, 172, 173, 175, 177, 180, 182,\n",
       "       183, 184, 187, 188, 189, 190, 191, 195, 197])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.core_sample_indices_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the clusters in feature space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df = pd.DataFrame(dict(feature_1=X[:,0], feature_2=X[:,1], label=label))\n",
    "\n",
    "\n",
    "cluster_name = set(label)\n",
    "colors = dict(zip(cluster_name, cm.rainbow(np.linspace(0, 1, len(cluster_name)))))\n",
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "grouped = df.groupby('label')\n",
    "\n",
    "for key, group in grouped:\n",
    "    group.plot(ax=ax, kind='scatter', x='feature_1', y='feature_2', color=colors[key].reshape(1,-1))\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}