research-to-production/occupancy/Draft.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf3750f4-b08d-4931-b2a0-81bc02a3356f",
   "metadata": {},
   "source": [
    "# Room-Occupancy Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "humanitarian-burke",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dedicated-employee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../datatest.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "quality-peoples",
   "metadata": {},
   "outputs": [],
   "source": [
    "signal = df['HumidityRatio'].values[:-2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "matched-current",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2665 entries, 140 to 2804\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   date           2665 non-null   object \n",
      " 1   Temperature    2665 non-null   float64\n",
      " 2   Humidity       2665 non-null   float64\n",
      " 3   Light          2665 non-null   float64\n",
      " 4   CO2            2665 non-null   float64\n",
      " 5   HumidityRatio  2665 non-null   float64\n",
      " 6   Occupancy      2665 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 166.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "virgin-taylor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df['Humidity']/100)\n",
    "light = df['Light']/np.max(df['Light'])\n",
    "ax.plot(light)\n",
    "ax.plot(df['Temperature']/80)\n",
    "# ax.plot(df['HumidityRatio']*40)\n",
    "ax.plot(df['Occupancy'], ls='--')\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "coordinated-celebrity",
   "metadata": {},
   "outputs": [],
   "source": [
    "occupancy = df.Occupancy.values\n",
    "humidity = df.Humidity.values\n",
    "light = df.Light.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "chicken-teacher",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.45353836, 0.07583515, 0.19437358, 0.28346148, 0.08540657,\n",
       "        0.04564834, 0.03092307, 0.03534065, 0.02282417, 0.01914285]),\n",
       " array([22.1    , 22.90225, 23.7045 , 24.50675, 25.309  , 26.11125,\n",
       "        26.9135 , 27.71575, 28.518  , 29.32025, 30.1225 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(humidity[occupancy == 1], density=True)\n",
    "plt.hist(humidity[occupancy == 0], alpha=0.2, density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "disturbed-bandwidth",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import gaussian_kde as kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "known-prairie",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = len(df) // 2\n",
    "i = np.arange(len(df))\n",
    "np.random.shuffle(i)\n",
    "i = i[:n]\n",
    "h = humidity[i]\n",
    "o = occupancy[i]\n",
    "k1 = kde(h[o == 1])\n",
    "k0 = kde(h[o == 0])\n",
    "plt.scatter(humidity, k1.pdf(humidity))\n",
    "plt.scatter(humidity, k0.pdf(humidity))\n",
    "plt.scatter(humidity, k1.pdf(humidity), label=\"occupied\")\n",
    "plt.scatter(humidity, k0.pdf(humidity), label=\"unoccupied\")\n",
    "plt.title(\"Distribution of Light by Class\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "annual-physics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = len(df) // 2\n",
    "i = np.arange(len(df))\n",
    "np.random.shuffle(i)\n",
    "i = i[:n]\n",
    "l = light[i]\n",
    "o = occupancy[i]\n",
    "k1 = kde(l[o == 1])\n",
    "k0 = kde(l[o == 0])\n",
    "plt.scatter(light, k1.pdf(light), label=\"occupied\")\n",
    "plt.scatter(light, k0.pdf(light), label=\"unoccupied\")\n",
    "plt.title(\"Distribution of Light by Class\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "tough-austria",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.4725"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humidity[np.argmin(np.abs(k0.pdf(humidity) - k1.pdf(humidity)))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ruled-musical",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test a threshold-based classifier based on light\n",
    "light_feature = (light > 0.25)*1\n",
    "occupancy = df.Occupancy.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "medium-speed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "frank-bennett",
   "metadata": {},
   "outputs": [],
   "source": [
    "confuse = confusion_matrix(light_feature, occupancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "controlling-johns",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f76fa83ed00>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(confuse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "referenced-fashion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.60600375, 0.        ],\n",
       "       [0.02926829, 0.36472795]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confuse/len(occupancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "specialized-anchor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)//20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "light-helen",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = len(df)*6//10\n",
    "occupancy = df.Occupancy.values[0:n]\n",
    "errors = []\n",
    "thresholds = np.linspace(0, 1, 100)\n",
    "for thresh in thresholds:\n",
    "    light_feature = (light[0:n] > thresh)*1\n",
    "    humidity_feature = (humidity[0:n] > 25.37)*1\n",
    "#     feature = ((light_feature + humidity_feature) == 2)*1\n",
    "    light_feature = (light[0:n] > 0.2)*1\n",
    "    feature = (light_feature + 0.7*humidity_feature > 2*thresh)*1\n",
    "    confuse = confusion_matrix(feature, occupancy)/n\n",
    "#     det = np.linalg.det(confuse/np.linalg.norm(confuse))\n",
    "    errors.append(confuse[0,0] + confuse[1,1] - confuse[0,1] - confuse[1,1])\n",
    "#     errors = confuse[0,0]\n",
    "del confuse, thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "danish-paint",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f76fa7c5220>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(thresholds, errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "lasting-still",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.50093809 0.        ]\n",
      " [0.13433396 0.36472795]]\n"
     ]
    }
   ],
   "source": [
    "occupancy = df.Occupancy.values\n",
    "thresh = thresholds[np.argmax(errors)]\n",
    "# light_feature = (light.values > thresh)*1\n",
    "# feature = light_feature\n",
    "light_feature = (light > 0.1182)*1\n",
    "humidity_feature = (humidity > 25.37)*1\n",
    "feature = (light_feature + 0.5*humidity_feature > 2*thresh)*1\n",
    "confuse = confusion_matrix(feature, occupancy)/len(occupancy)\n",
    "plt.imshow(confuse)\n",
    "plt.title(f\"thresh={thresh:.4f}\")\n",
    "plt.show()\n",
    "print(confuse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "binding-incident",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.60600375 0.        ]\n",
      " [0.02926829 0.36472795]]\n"
     ]
    }
   ],
   "source": [
    "occupancy = df.Occupancy.values\n",
    "thresh = 0.1182\n",
    "light_feature = (light > thresh)*1\n",
    "feature = light_feature\n",
    "confuse = confusion_matrix(feature, occupancy)/len(occupancy)\n",
    "plt.imshow(confuse)\n",
    "plt.title(f\"thresh={thresh:.4f}\")\n",
    "plt.show()\n",
    "print(confuse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "standing-footage",
   "metadata": {},
   "outputs": [],
   "source": [
    "humidity_feature = (humidity > 25.37)*1\n",
    "occupancy = df.Occupancy.values\n",
    "confuse = confusion_matrix(humidity_feature, occupancy)/len(occupancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "direct-nirvana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.51594747, 0.07879925],\n",
       "       [0.11932458, 0.28592871]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confuse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "passive-index",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "light_feature + humidity_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0cdc07a-9600-4bc3-8f31-627ac3329da7",
   "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": 5
}