3!-DBSCAN-density.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import DBSCAN\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('last.txt')\n",
    "data.columns =(\"a\",\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = data.head(10000)\n",
    "df = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x3013beb8d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x307d3f6e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1 = df['a'].values\n",
    "f2 = df['b'].values\n",
    "X = np.array(list(zip(f1,f2)))\n",
    "plt.scatter(f1,f2, c='black' , s=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DBSCAN(eps =30 , min_samples= 3, algorithm='ball_tree').fit(X)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#db = DBSCAN(eps=2/6371., min_samples=5, algorithm='ball_tree', metric='haversine').fit(np.radians(X))\n",
    "#db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_df = pd.DataFrame(X)\n",
    "print(Counter(model.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2).fit(X)\n",
    "pca_2d = pca.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0, pca_2d.shape[0]):\n",
    "    if model.labels_[i] == 0:\n",
    "        c1 = plt.scatter(pca_2d[i,0],pca_2d[i,1],c='r',marker='+')\n",
    "    elif model.labels_[i] == 1:\n",
    "         c2 = plt.scatter(pca_2d[i,0],pca_2d[i,1],c='g',marker='o')\n",
    "    elif model.labels_[i] == -1:\n",
    "         c3 = plt.scatter(pca_2d[i,0],pca_2d[i,1],c='b',marker='*')\n",
    "            \n",
    "plt.legend([c1, c2, c3], ['Cluster 1', 'Cluster 2','Noise'])\n",
    "plt.title('DBSCAN ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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