find best fit distribution-electric-1-humidity.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       38.2\n",
       "1       38.2\n",
       "2       38.2\n",
       "3       38.2\n",
       "4       38.3\n",
       "        ... \n",
       "1135    41.3\n",
       "1136    41.3\n",
       "1137    41.3\n",
       "1138    41.3\n",
       "1139    41.3\n",
       "Name: Humidity , Length: 1140, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TVOC\n",
    "data = series.iloc[:,1]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1140.000000\n",
       "mean       39.906316\n",
       "std         0.956359\n",
       "min        38.200000\n",
       "25%        39.200000\n",
       "50%        39.300000\n",
       "75%        40.900000\n",
       "max        41.700000\n",
       "Name: Humidity , 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([ 31.,  46.,  18., 120., 409.,   2.,   5.,  11.,  13.,  16.,  72.,\n",
       "       194., 123.,  51.,  29.])"
      ]
     },
     "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": [
    {
     "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([38.2       , 38.43333333, 38.66666667, 38.9       , 39.13333333,\n",
       "       39.36666667, 39.6       , 39.83333333, 40.06666667, 40.3       ,\n",
       "       40.53333333, 40.76666667, 41.        , 41.23333333, 41.46666667,\n",
       "       41.7       ])"
      ]
     },
     "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 0x00000224ADE62970>\n",
      "best_params: (39.90631578947369, 0.9559392030490902)\n",
      "best_sse: 383.99676023430925\n",
      "distribution: <scipy.stats._continuous_distns.powerlaw_gen object at 0x00000224ADF1FAF0>\n",
      "best_params: (1.0601215123523344, 38.198930107916475, 3.5010699299477457)\n",
      "best_sse: 381.28679847360695\n",
      "distribution: <scipy.stats._continuous_distns.dweibull_gen object at 0x00000224ADEA2100>\n",
      "best_params: (3.311147439579882, 40.04348741529556, 1.0205442528311273)\n",
      "best_sse: 366.03915174693213\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
}