sklearn/sklearn_dbscan_distance_matrix_pre_computed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. normal dbscan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "298 µs ± 4.41 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "from sklearn.cluster import DBSCAN\n",
    "import numpy as np\n",
    "X = np.array([[1, 2], [2, 2], [2, 3],[8, 7], [8, 8], [25, 80]])\n",
    "clustering = DBSCAN(eps=3, min_samples=2).fit(X)\n",
    "clustering.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  0  0  1  1 -1]\n"
     ]
    }
   ],
   "source": [
    "print(clustering.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. dbscan with precomputed metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import euclidean_distances\n",
    "X = np.array([[1, 2], [2, 2], [2, 3],[8, 7], [8, 8], [25, 80]])\n",
    "M  = euclidean_distances(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.        ,  1.41421356,  8.60232527,  9.21954446,\n",
       "        81.60882305],\n",
       "       [ 1.        ,  0.        ,  1.        ,  7.81024968,  8.48528137,\n",
       "        81.32035416],\n",
       "       [ 1.41421356,  1.        ,  0.        ,  7.21110255,  7.81024968,\n",
       "        80.36168241],\n",
       "       [ 8.60232527,  7.81024968,  7.21110255,  0.        ,  1.        ,\n",
       "        74.95331881],\n",
       "       [ 9.21954446,  8.48528137,  7.81024968,  1.        ,  0.        ,\n",
       "        73.97972695],\n",
       "       [81.60882305, 81.32035416, 80.36168241, 74.95331881, 73.97972695,\n",
       "         0.        ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "267 µs ± 7.25 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "from sklearn.cluster import DBSCAN\n",
    "import numpy as np\n",
    "X = np.array([[1, 2], [2, 2], [2, 3],[8, 7], [8, 8], [25, 80]])\n",
    "clustering = DBSCAN(eps=3, min_samples=2, metric=\"precomputed\").fit(M)\n",
    "clustering.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  0  0  1  1 -1]\n"
     ]
    }
   ],
   "source": [
    "print(clustering.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# same result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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