find best fit distribution-electric-1-TVOC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution Fitting with Sum of Square Error (SSE)\n",
    "## find best fit by computing the least/minimum residual sum of squares "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "#import statsmodels as sm\n",
    "import statsmodels.api as sm\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams['figure.figsize'] = (16.0, 12.0)\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "# Create models from data\n",
    "def best_fit_distribution(data, bins=200, ax=None):\n",
    "    \"\"\"Model data by finding best fit distribution to data\"\"\"\n",
    "    # Get histogram of original data\n",
    "    y, x = np.histogram(data, bins=bins, density=True)\n",
    "    x = (x + np.roll(x, -1))[:-1] / 2.0\n",
    "\n",
    "    # Distributions to check\n",
    "    DISTRIBUTIONS = [\n",
    "        st.norm,\n",
    "        st.powerlaw,\n",
    "        st.powerlognorm,\n",
    "        st.powernorm,\n",
    "        st.weibull_min,\n",
    "        st.weibull_max,\n",
    "        st.dweibull,\n",
    "        st.erlang,\n",
    "        st.expon,\n",
    "        st.exponnorm,\n",
    "        st.exponweib,\n",
    "        st.logistic,\n",
    "        st.loggamma,\n",
    "        st.loglaplace,\n",
    "        #st.johnsonsb,\n",
    "        #st.johnsonsu,\n",
    "        st.skewnorm,\n",
    "        st.pareto,\n",
    "        st.pearson3,\n",
    "        st.beta,\n",
    "        st.lognorm,\n",
    "        st.gengamma\n",
    "        \n",
    "        \n",
    "    ]\n",
    "\n",
    "    # Best holders\n",
    "    best_distribution = st.norm\n",
    "    best_params = (0.0, 1.0)\n",
    "    best_sse = np.inf\n",
    "\n",
    "    # Estimate distribution parameters from data\n",
    "    for distribution in DISTRIBUTIONS:\n",
    "\n",
    "        # Try to fit the distribution\n",
    "        try:\n",
    "            # Ignore warnings from data that can't be fit\n",
    "            with warnings.catch_warnings():\n",
    "                warnings.filterwarnings('ignore')\n",
    "\n",
    "                # fit dist to data\n",
    "                params = distribution.fit(data)\n",
    "\n",
    "                # Separate parts of parameters\n",
    "                arg = params[:-2]\n",
    "                loc = params[-2]\n",
    "                scale = params[-1]\n",
    "\n",
    "                # Calculate fitted PDF and error with fit in distribution\n",
    "                pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n",
    "                sse = np.sum(np.power(y - pdf, 2.0))\n",
    "\n",
    "                # if axis pass in add to plot\n",
    "                try:\n",
    "                    if ax:\n",
    "                        pd.Series(pdf, x).plot(ax=ax)\n",
    "                    end\n",
    "                except Exception:\n",
    "                    pass\n",
    "\n",
    "                # identify if this distribution is better\n",
    "                if best_sse > sse > 0:\n",
    "                    best_distribution = distribution\n",
    "                    best_params = params\n",
    "                    best_sse = sse\n",
    "                    print(\"distribution: \" +str(distribution))\n",
    "                    print(\"best_params: \" +str(best_params))\n",
    "                    print(\"best_sse: \" +str(best_sse))\n",
    "\n",
    "        except Exception:\n",
    "            pass\n",
    "\n",
    "    return (best_distribution.name, best_params)\n",
    "\n",
    "def make_pdf(dist, params, size=10000):\n",
    "    \"\"\"Generate distributions's Probability Distribution Function \"\"\"\n",
    "\n",
    "    # Separate parts of parameters\n",
    "    arg = params[:-2]\n",
    "    loc = params[-2]\n",
    "    scale = params[-1]\n",
    "\n",
    "    # Get sane start and end points of distribution\n",
    "    start = dist.ppf(0.01, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)\n",
    "    end = dist.ppf(0.99, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99, loc=loc, scale=scale)\n",
    "\n",
    "    # Build PDF and turn into pandas Series\n",
    "    x = np.linspace(start, end, size)\n",
    "    y = dist.pdf(x, loc=loc, scale=scale, *arg)\n",
    "    pdf = pd.Series(y, x)\n",
    "\n",
    "    return pdf\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      23.11\n",
       "1      24.20\n",
       "2      25.37\n",
       "3      23.86\n",
       "4      23.03\n",
       "       ...  \n",
       "727    19.49\n",
       "728    19.28\n",
       "729    19.73\n",
       "730    20.44\n",
       "731    22.07\n",
       "Length: 732, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Load data from statsmodels datasets\n",
    "data = pd.Series(sm.datasets.elnino.load_pandas().data.set_index('YEAR').values.ravel())\n",
    "data\n",
    "\n",
    "#location = os.getcwd()+'/Covid-19_Data/Raw Data/'\n",
    "#owid_location = 'owid-covid-data.csv'\n",
    "\n",
    "#data = pd.read_csv(location+owid_location)\n",
    "#data = pd.read_csv(owid_location)\n",
    "#data = data[(data.location != 'International') & (data.location != 'World')]\n",
    "#data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Humidity    Temperature     MQ139    TVOC   eCO2   Detector\n",
      "Time                                                                \n",
      "0:00:00       38.2            21.9      69       0     400       OFF\n",
      "0:00:02       38.2            21.9      69       0     400       OFF\n",
      "0:00:04       38.2            21.9      69       0     400       OFF\n",
      "0:00:06       38.2            21.9      68       0     400       OFF\n",
      "0:00:08       38.3            21.9      68       0     400       OFF\n"
     ]
    }
   ],
   "source": [
    "series = pd.read_csv('electrical_1.csv', header=0, index_col=0, squeeze=True)\n",
    "series = series.drop('Reading ID', axis=1)\n",
    "series = series.drop('Status', axis=1)\n",
    "print(series.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = series.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TVOC\n",
    "#series.iloc[:,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "new_cases_per_million = data['new_cases_per_million']\n",
    "new_cases_per_million = new_cases_per_million.dropna()\n",
    "new_cases_per_million\n",
    "new_cases_per_million = new_cases_per_million[ new_cases_per_million >= 0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          0\n",
       "        ... \n",
       "1135    7020\n",
       "1136    7115\n",
       "1137    7261\n",
       "1138    7110\n",
       "1139    7144\n",
       "Name:   TVOC, Length: 1140, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TVOC\n",
    "data = series.iloc[:,4]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1140.000000\n",
       "mean      447.824561\n",
       "std      1094.403007\n",
       "min         0.000000\n",
       "25%        96.000000\n",
       "50%       137.000000\n",
       "75%       182.000000\n",
       "max      7261.000000\n",
       "Name:   TVOC, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([969.,  60.,  40.,  20.,   4.,   3.,   3.,   3.,   4.,   2.,  10.,\n",
       "         6.,   0.,   3.,  13.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist, bins, _ = plt.hist(data, bins = 15)\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-f70ba7f6d185>:6: RuntimeWarning: divide by zero encountered in log10\n",
      "  logbins = np.logspace(np.log10(bins[0]),np.log10(bins[-1]),len(bins))\n",
      "D:\\Users\\Z10903\\Anaconda3\\lib\\site-packages\\numpy\\core\\function_base.py:144: RuntimeWarning: invalid value encountered in multiply\n",
      "  y *= step\n",
      "D:\\Users\\Z10903\\Anaconda3\\lib\\site-packages\\numpy\\core\\function_base.py:154: RuntimeWarning: invalid value encountered in add\n",
      "  y += start\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_loghist(x, bins):\n",
    "  hist, bins = np.histogram(x, bins=bins)\n",
    "  logbins = np.logspace(np.log10(bins[0]),np.log10(bins[-1]),len(bins))\n",
    "  plt.hist(x, bins=logbins)\n",
    "  plt.xscale('log')\n",
    "\n",
    "plot_loghist(data, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0.        ,  484.06666667,  968.13333333, 1452.2       ,\n",
       "       1936.26666667, 2420.33333333, 2904.4       , 3388.46666667,\n",
       "       3872.53333333, 4356.6       , 4840.66666667, 5324.73333333,\n",
       "       5808.8       , 6292.86666667, 6776.93333333, 7261.        ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### histogram on log scale. \n",
    "#### Use non-equal bin sizes, such that they look equal on log scale.\n",
    "logbins = np.logspace(np.log10(bins[0]),np.log10(bins[-1]),len(bins))\n",
    "#logbins = np.geomspace(data.min(), data.max(), 200) \n",
    "plt.subplot(212)\n",
    "plt.hist(data, bins=logbins)\n",
    "plt.xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distribution: <scipy.stats._continuous_distns.norm_gen object at 0x000001FCBE2C2970>\n",
      "best_params: (447.82456140350877, 1093.9229000832565)\n",
      "best_sse: 0.0001101532012194702\n",
      "distribution: <scipy.stats._continuous_distns.powerlaw_gen object at 0x000001FCBE380AF0>\n",
      "best_params: (0.2717670827123668, -5.360825234245764e-18, 7431.514298026714)\n",
      "best_sse: 9.673646261407894e-05\n",
      "distribution: <scipy.stats._continuous_distns.powerlognorm_gen object at 0x000001FCBE380E20>\n",
      "best_params: (0.0875599777608844, 0.4071409531152761, -21.65745414938464, 48.04082477272044)\n",
      "best_sse: 5.58306657734108e-05\n",
      "distribution: <scipy.stats._continuous_distns.dweibull_gen object at 0x000001FCBE2F2100>\n",
      "best_params: (0.5268122393614327, 135.00000000000006, 166.68691695257735)\n",
      "best_sse: 2.708889556679978e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot for comparison\n",
    "plt.figure(figsize=(12,8))\n",
    "#ax = data.plot(kind='hist', bins=50, normed=True, alpha=0.5) #, color=plt.rcParams['axes.prop_cycle'][1]\n",
    "\n",
    "ax = data.plot(kind='hist', bins=50, density=True, alpha=0.5,  \n",
    "               color=list(matplotlib.rcParams['axes.prop_cycle'])[1]['color'])\n",
    "\n",
    "# Save plot limits\n",
    "dataYLim = ax.get_ylim()\n",
    "\n",
    "# Find best fit distribution\n",
    "best_fit_name, best_fit_params = best_fit_distribution(data, 200, ax)\n",
    "best_dist = getattr(st, best_fit_name)\n",
    "\n",
    "# Update plots\n",
    "ax.set_ylim(dataYLim)\n",
    "ax.set_title(u' eCO2 measurement from an Electrical Device Fire.\\n All Fitted Distributions')\n",
    "ax.set_xlabel(u'eCO2')\n",
    "ax.set_ylabel('Frequency')\n",
    "\n",
    "# Make PDF with best params \n",
    "pdf = make_pdf(best_dist, best_fit_params)\n",
    "\n",
    "# Display\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = pdf.plot(lw=2, label='PDF', legend=True)\n",
    "data.plot(kind='hist', bins=50, density=True, alpha=0.5, label='Data', legend=True, ax=ax)\n",
    "\n",
    "param_names = (best_dist.shapes + ', loc, scale').split(', ') if best_dist.shapes else ['loc', 'scale']\n",
    "param_str = ', '.join(['{}={:0.2f}'.format(k,v) for k,v in zip(param_names, best_fit_params)])\n",
    "dist_str = '{}({})'.format(best_fit_name, param_str)\n",
    "\n",
    "ax.set_title(u'eCO2 measurement from an Electrical Device Fire with best fit distribution \\n' + dist_str)\n",
    "ax.set_xlabel(u'eCO2')\n",
    "ax.set_ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}