ClusterinKmeans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "596b86fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import LabelEncoder,MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "04a5305c",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>index</th>\n",
       "      <th>source</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>values</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>14889</td>\n",
       "      <td>heart_rate</td>\n",
       "      <td>49:01.9</td>\n",
       "      <td>['65.0', '1']</td>\n",
       "      <td>Video games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>14890</td>\n",
       "      <td>heart_rate</td>\n",
       "      <td>49:01.9</td>\n",
       "      <td>['64.0', '1']</td>\n",
       "      <td>Video games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>14892</td>\n",
       "      <td>heart_rate</td>\n",
       "      <td>49:01.9</td>\n",
       "      <td>['65.0', '0']</td>\n",
       "      <td>Video games</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>['64.0', '0']</td>\n",
       "      <td>Video games</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>56046</th>\n",
       "      <td>56046</td>\n",
       "      <td>137190</td>\n",
       "      <td>heart_rate</td>\n",
       "      <td>13:17.3</td>\n",
       "      <td>['53.0', '1']</td>\n",
       "      <td>Video games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56047</th>\n",
       "      <td>56047</td>\n",
       "      <td>137191</td>\n",
       "      <td>heart_rate</td>\n",
       "      <td>13:17.3</td>\n",
       "      <td>['52.0', '1']</td>\n",
       "      <td>Video games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56048</th>\n",
       "      <td>56048</td>\n",
       "      <td>137192</td>\n",
       "      <td>heart_rate</td>\n",
       "      <td>13:17.3</td>\n",
       "      <td>['53.0', '1']</td>\n",
       "      <td>Video games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56049</th>\n",
       "      <td>56049</td>\n",
       "      <td>137193</td>\n",
       "      <td>heart_rate</td>\n",
       "      <td>13:17.3</td>\n",
       "      <td>['54.0', '1']</td>\n",
       "      <td>Video games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56050</th>\n",
       "      <td>56050</td>\n",
       "      <td>137194</td>\n",
       "      <td>heart_rate</td>\n",
       "      <td>13:19.6</td>\n",
       "      <td>['53.0', '1']</td>\n",
       "      <td>Video games</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>56051 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0   index      source timestamp         values     activity\n",
       "0               0   14889  heart_rate   49:01.9  ['65.0', '1']  Video games\n",
       "1               1   14890  heart_rate   49:01.9  ['64.0', '1']  Video games\n",
       "2               2   14891  heart_rate   49:01.9  ['64.0', '0']  Video games\n",
       "3               3   14892  heart_rate   49:01.9  ['65.0', '0']  Video games\n",
       "4               4   14893  heart_rate   49:01.9  ['64.0', '0']  Video games\n",
       "...           ...     ...         ...       ...            ...          ...\n",
       "56046       56046  137190  heart_rate   13:17.3  ['53.0', '1']  Video games\n",
       "56047       56047  137191  heart_rate   13:17.3  ['52.0', '1']  Video games\n",
       "56048       56048  137192  heart_rate   13:17.3  ['53.0', '1']  Video games\n",
       "56049       56049  137193  heart_rate   13:17.3  ['54.0', '1']  Video games\n",
       "56050       56050  137194  heart_rate   13:19.6  ['53.0', '1']  Video games\n",
       "\n",
       "[56051 rows x 6 columns]"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"activityData.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "bc3803ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_10448\\3399605942.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"value\"][i] = df[\"values\"][i][2:f_occur]\n"
     ]
    }
   ],
   "source": [
    "df[\"value\"] = 0\n",
    "# for i in range(len(df[\"values\"])):\n",
    "#     df[\"value\"][i] = 0\n",
    "    \n",
    "\n",
    "for i in range(len(df[\"values\"])):\n",
    "    f_occur= df[\"values\"][i][2:].find(\"'\") + 2\n",
    "    df[\"value\"][i] = df[\"values\"][i][2:f_occur]\n",
    "\n",
    "# df[\"values\"][0][2:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "e1656dbf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity</th>\n",
       "      <th>encoded_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20853</th>\n",
       "      <td>Cooking</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8486</th>\n",
       "      <td>Eat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36840</th>\n",
       "      <td>Football</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20489</th>\n",
       "      <td>In bus</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4093</th>\n",
       "      <td>In computer</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>In vehicle</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19854</th>\n",
       "      <td>Meeting</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26441</th>\n",
       "      <td>Movie</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30540</th>\n",
       "      <td>Pause</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10240</th>\n",
       "      <td>Phone was out of the pocket (forgot)</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1403</th>\n",
       "      <td>Picnic</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42553</th>\n",
       "      <td>Shop</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46219</th>\n",
       "      <td>Shopping&amp; wearing</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13767</th>\n",
       "      <td>Train</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Video games</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8728</th>\n",
       "      <td>Walk</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>Watching TV</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   activity  encoded_y\n",
       "20853                               Cooking          0\n",
       "8486                                    Eat          1\n",
       "36840                              Football          2\n",
       "20489                                In bus          3\n",
       "4093                            In computer          4\n",
       "183                              In vehicle          5\n",
       "19854                               Meeting          6\n",
       "26441                                 Movie          7\n",
       "30540                                 Pause          8\n",
       "10240  Phone was out of the pocket (forgot)          9\n",
       "1403                                Picnic          10\n",
       "42553                                  Shop         11\n",
       "46219                     Shopping& wearing         12\n",
       "13767                                 Train         13\n",
       "0                               Video games         14\n",
       "8728                                   Walk         15\n",
       "5567                            Watching TV         16"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "encoder.fit(df[\"activity\"])\n",
    "encoded_y = encoder.transform(df[\"activity\"])\n",
    "\n",
    "df[\"encoded_y\"] = encoded_y\n",
    "\n",
    "target = pd.DataFrame({'activity':df['activity'].tolist(),'encoded_y':encoded_y}).drop_duplicates().sort_values(['encoded_y'],ascending=True)\n",
    "target\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "853db30f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_10448\\111900370.py:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1[\"val2\"] = 1\n"
     ]
    }
   ],
   "source": [
    "df1 = df[[\"value\"]]\n",
    "\n",
    "df1[\"val2\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "e6a6ae3b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "b7e94388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 59.99876577,   1.        ],\n",
       "       [ 71.01894532,   1.        ],\n",
       "       [117.41315225,   1.        ],\n",
       "       [ 85.39927703,   1.        ]])"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "ff06e56d",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = kmeans.predict(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "3974d8f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "k_labels = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "01d8762a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "centroids = kmeans.cluster_centers_\n",
    "\n",
    "df1 = np.array(df1)\n",
    "u_labels = np.unique(k_labels)\n",
    " \n",
    "#plotting the results:\n",
    " \n",
    "for i in u_labels:\n",
    "    plt.scatter(df1[k_labels == i , 0] , df1[k_labels == i , 1] , label = i)\n",
    "plt.scatter(centroids[:,0] , centroids[:,1] , s = 50, color = 'k')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "1162a4fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['65.0' 1]\n",
      " ['64.0' 1]\n",
      " ['64.0' 1]\n",
      " ...\n",
      " ['53.0' 1]\n",
      " ['54.0' 1]\n",
      " ['53.0' 1]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u_labels = np.unique(k_labels)\n",
    "\n",
    "df1 = np.array(df1)\n",
    "\n",
    "print(df1)\n",
    " \n",
    "#plotting the results:\n",
    " \n",
    "for i in u_labels:\n",
    "    plt.scatter(df1[k_labels == i , 0] , df1[k_labels == i , 1] , label = i)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "8c7900c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[\"k_means\"] = k_labels \n",
    "\n",
    "actt = np.unique(df[\"encoded_y\"])\n",
    "\n",
    "u_act = [{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0},{0: 0,1 :0, 2: 0, 3:0}] \n",
    "\n",
    "for i in range(len(actt)):\n",
    "    u_act[i] = {0: 0,1 :0, 2: 0, 3:0}\n",
    "\n",
    "for i in range(len(df[\"encoded_y\"])):\n",
    "    u_act[df[\"encoded_y\"][i]][df[\"k_means\"][i]] =  u_act[df[\"encoded_y\"][i]][df[\"k_means\"][i]] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "d283bcd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{0: 1778, 1: 1424, 2: 61, 3: 784}, {0: 1107, 1: 1577, 2: 40, 3: 548}, {0: 185, 1: 208, 2: 213, 3: 371}, {0: 1907, 1: 2123, 2: 71, 3: 960}, {0: 4068, 1: 4325, 2: 68, 3: 1547}, {0: 884, 1: 1044, 2: 630, 3: 766}, {0: 687, 1: 507, 2: 0, 3: 99}, {0: 2507, 1: 2796, 2: 7, 3: 595}, {0: 332, 1: 804, 2: 2, 3: 388}, {0: 362, 1: 197, 2: 10, 3: 55}, {0: 1063, 1: 1103, 2: 0, 3: 127}, {0: 56, 1: 37, 2: 0, 3: 0}, {0: 535, 1: 485, 2: 0, 3: 85}, {0: 1521, 1: 1856, 2: 10, 3: 743}, {0: 1207, 1: 1357, 2: 243, 3: 385}, {0: 3163, 1: 3570, 2: 44, 3: 1670}, {0: 514, 1: 234, 2: 0, 3: 6}]\n"
     ]
    }
   ],
   "source": [
    "print(u_act)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "51673123",
   "metadata": {},
   "outputs": [],
   "source": [
    "ui = []\n",
    "for i in range(len(u_act)):\n",
    "     ui.append(list(u_act[i].keys())[list(u_act[i].values()).index(max(u_act[i].values()))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "27d354bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 3, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "print(ui)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "907b28fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "target[\"clusterLabel\"] = ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "8e37f22b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   activity  encoded_y  clusterLabel\n",
      "20853                               Cooking          0             0\n",
      "8486                                    Eat          1             1\n",
      "36840                              Football          2             3\n",
      "20489                                In bus          3             1\n",
      "4093                            In computer          4             1\n",
      "183                              In vehicle          5             1\n",
      "19854                               Meeting          6             0\n",
      "26441                                 Movie          7             1\n",
      "30540                                 Pause          8             1\n",
      "10240  Phone was out of the pocket (forgot)          9             0\n",
      "1403                                Picnic          10             1\n",
      "42553                                  Shop         11             0\n",
      "46219                     Shopping& wearing         12             0\n",
      "13767                                 Train         13             1\n",
      "0                               Video games         14             1\n",
      "8728                                   Walk         15             1\n",
      "5567                            Watching TV         16             0\n"
     ]
    }
   ],
   "source": [
    "print(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f45b9e51",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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