find best fit distribution-cartoon-1-mq139.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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Humidity  Temperature  MQ139  TVOC  eCO2 Detector\n",
      "Time                                                      \n",
      "0:00:00      43.5         21.3     98     0   400      OFF\n",
      "0:00:01      43.5         21.3     97     0   400      OFF\n",
      "0:00:02      43.6         21.4     97     0   400      OFF\n",
      "0:00:03      43.6         21.4     97     0   400      OFF\n",
      "0:00:04      43.7         21.4     98     0   400      OFF\n"
     ]
    }
   ],
   "source": [
    "series = pd.read_csv('carton.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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = series.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       98\n",
       "1       97\n",
       "2       97\n",
       "3       97\n",
       "4       98\n",
       "      ... \n",
       "325    192\n",
       "326    193\n",
       "327    193\n",
       "328    193\n",
       "329    193\n",
       "Name: MQ139, Length: 330, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.iloc[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       98\n",
       "1       97\n",
       "2       97\n",
       "3       97\n",
       "4       98\n",
       "      ... \n",
       "325    192\n",
       "326    193\n",
       "327    193\n",
       "328    193\n",
       "329    193\n",
       "Name: MQ139, Length: 330, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = series.iloc[:,3]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    330.000000\n",
       "mean     140.800000\n",
       "std       33.873751\n",
       "min       97.000000\n",
       "25%      105.000000\n",
       "50%      145.000000\n",
       "75%      173.000000\n",
       "max      193.000000\n",
       "Name: MQ139, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([74., 30., 13., 12., 10., 14.,  6., 15., 16., 24., 15., 23., 19.,\n",
       "       25., 34.])"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 97. , 103.4, 109.8, 116.2, 122.6, 129. , 135.4, 141.8, 148.2,\n",
       "       154.6, 161. , 167.4, 173.8, 180.2, 186.6, 193. ])"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distribution: <scipy.stats._continuous_distns.norm_gen object at 0x000001FCC7403910>\n",
      "best_params: (140.8, 33.822388138538344)\n",
      "best_sse: 0.12596098564207814\n",
      "distribution: <scipy.stats._continuous_distns.powerlaw_gen object at 0x000001FCC74BFA90>\n",
      "best_params: (0.49512153337024345, 96.99999999999997, 96.85741124635197)\n",
      "best_sse: 0.10289974506654861\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 9,
     "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 Carton 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 Carton 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
}