{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# BlocPower data: Buildings in New York City\n", "\n", "The four main sections of this notebook are:\n", "1. [Setup](#Setup)\n", "2. [Model energy usage in kWh of buildings](#2.-Model-energy-usage-in-kWh-of-buildings)\n", "3. [Detect buildings that consume energy inefficiently](#3.-Detect-buildings-that-consume-energy-inefficiently)\n", "4. [Export data and models to RStudio](#4.-Export-data-and-models-to-RStudio)\n", "\n", "A lot of our findings were based on [this article](http://www.informs-sim.org/wsc11papers/082.pdf) by a group of researchers from \n", "+ IBM T.J. Watson Research Center, \n", "+ CUNY Institute for Urban Systems and \n", "+ McMaster University. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Setup\n", "## Load data \n", "+ BlocPower data\n", "+ Public heating and cooling data in New York\n", "\n", "\n", "First click on the 1001 icon in the top right, to open the data panel:\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Click in empty cell below and insert BlocPower_T.csv as Spark SQL DataFrame\n", "\n", "\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "--- \n", "\n", "### ^^ Make sure the dataset at the end is called df_data_1\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "\n", "\n", "\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "--- \n", "\n", "### ^^ Make sure the dataset at the end is called df_data_2\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "\n", "\n", "\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "--- \n", "\n", "### ^^ Make sure the dataset at the end is called df_data_3\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Now we can rename the data frames to be more meaningful:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df2 = df_data_1 #BlocPower_T.csv\n", "dfHDD = df_data_2 #HDD-Features.csv\n", "dfCH = df_data_3 #CDD-HDD-Features.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import Spark and Python Libraries" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from pyspark.sql.functions import udf\n", "from pyspark.sql.types import DoubleType" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Model energy usage in kWh of buildings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspect the first few rows of the BlocPower data\n", "+ UTSUM_Electricity_Usage .- annual energy usage of each building in measure in kWh\n", "+ INFO_Year of Construction .- year when building was constructed\n", "+ INFO_Number of Stories .- number of stories of each building\n", "+ INFO_Total Square Feet .- square feet of each building\n", "+ PLEI_1_Quantity .- number of plugged equipment (e.g., microwaves, computers, ...)\n", "+ PLEI_3_Quantity .- same as PLEI_1 \n", "## Data Preparation\n", "+ On the first column (electricity usage), we need to reformat the values to get an actual number (e.g., 117,870 kWh to 117870)\n", "+ On the 4th column (square feet), we need to get rid of the commas (e.g., 14,600 to 14600)\n", "+ Notice that there are missing values" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-----------------------+-------------------------+----------------------+----------------------+---------------+---------------+\n", "|UTSUM_Electricity_Usage|INFO_Year of Construction|INFO_Number of Stories|INFO_Total Square Feet|PLEI_1_Quantity|PLEI_3_Quantity|\n", "+-----------------------+-------------------------+----------------------+----------------------+---------------+---------------+\n", "| 117,870 kWh| 1955| 4| 14,600| 1| 2|\n", "| 16,207 kWh| 1940| 1| 600| 1| |\n", "| 15,564 kWh| 1920| 2| 6,000| 2| 1|\n", "| 25,851 kWh| 1965| 1| 5,000| 6| 1|\n", "| 32,343 kWh| 1930| 3| 4,196| 1| 1|\n", "+-----------------------+-------------------------+----------------------+----------------------+---------------+---------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "df2.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Functions To Clean and Prepare Data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# define cleaning functions\n", "def energy(v): # reformat the values to get an actual number (e.g., 117,870 kWh to 117870)\n", " v = v.encode('ascii','ignore').split(' ')[0].replace(',','')\n", " return np.nan if(v=='') else float(v)\n", "def age(v): # computes the age of a buildings, given the year of construction\n", " v = v.encode('ascii','ignore')\n", " return 2016.0-float(v) if(len(v)==4) else np.nan\n", "def stories(v):\n", " return float(v)\n", "def sqFeet(v): # get rid of commas \n", " v = v.encode('ascii','ignore').replace(',','')\n", " return np.nan if(v=='') else float(v) \n", "def plei(v): # in the PLEI columns, missing values can be interpeted as 0 plugged equipment\n", " try:\n", " vv = float(v)\n", " except:\n", " vv = 0.0\n", " return vv \n", "# Define udf's to apply the defined function to the Spark dataframe\n", "udfEnergy = udf(energy, DoubleType())\n", "udfAge = udf(age, DoubleType())\n", "udfStories = udf(stories, DoubleType())\n", "udfSqFeet = udf(sqFeet, DoubleType())\n", "udfPlei = udf(plei, DoubleType())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Apply Data Preparation Functionalities to Dataframe" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dfN = df2.withColumn(\"UTSUM_Electricity_Usage\", udfEnergy(\"UTSUM_Electricity_Usage\")) \\\n", " .withColumn(\"INFO_Year of Construction\", udfAge(\"INFO_Year of Construction\")) \\\n", " .withColumn(\"INFO_Number of Stories\", udfStories(\"INFO_Number of Stories\")) \\\n", " .withColumn(\"INFO_Total Square Feet\", udfSqFeet(\"INFO_Total Square Feet\")) \\\n", " .withColumn(\"PLEI_1_Quantity\", udfPlei(\"PLEI_1_Quantity\")) \\\n", " .withColumn(\"PLEI_3_Quantity\", udfPlei(\"PLEI_3_Quantity\")).cache()\n", "dfN = dfN.withColumnRenamed(\"UTSUM_Electricity_Usage\",\"energy\") \\\n", " .withColumnRenamed(\"INFO_Year of Construction\",\"age\") \\\n", " .withColumnRenamed(\"INFO_Number of Stories\",\"number_stories\") \\\n", " .withColumnRenamed(\"INFO_Total Square Feet\",\"square_feet\") \\\n", " .withColumnRenamed(\"PLEI_1_Quantity\",\"plei_1\") \\\n", " .withColumnRenamed(\"PLEI_3_Quantity\",\"plei_3\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fill in the missing values with an average" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# compute avergae of non-missing energy and age\n", "energy_mean = np.nanmean(np.asarray(dfN.select(\"energy\").rdd.map(lambda r: r[0]).collect()))\n", "age_mean = np.nanmean(np.asarray(dfN.select(\"age\").rdd.map(lambda r: r[0]).collect()))\n", "# fill missing values with the computed average\n", "dfN = dfN.na.fill({\"energy\": energy_mean, \"age\": age_mean})" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# define spark dataframe to be written to our objetc store\n", "dfOut = dfN.select('energy', 'age', 'number_stories','square_feet','plei_1','plei_3')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Form a feature matrix and scale the columns" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# use the .toPandas() function to map Spark dataframes to pandas dataframes\n", "dfNp = dfN.toPandas()\n", "dfHDDp = dfHDD.toPandas()\n", "# concatenate two pandas dataframes\n", "feat = pd.concat([dfNp, dfHDDp], axis=1)\n", "# get the column names of the concatenated dataframe\n", "cols = feat.columns\n", "# scale data to prepare for regression model \n", "from sklearn import preprocessing\n", "scaler = preprocessing.MaxAbsScaler() \n", "feat = scaler.fit_transform(feat)\n", "# define a new dataframe with the scaled data\n", "dfScaled = pd.DataFrame(feat,columns=cols)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the correlations" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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bl7WSP/Sv2EiaUstto9UPSrGsxlSucnqhijlw4ACPP/44P/vZzzCbzdx3330A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\nR0Nm8SyzXksgmqKvx8ZUKEq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cheZMmc3mootcxBNJTg2M4o8mQZVZYjUQSxGIh9jQugad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JhULR0ane3l7sdnvcdlVVVVx//fVYrVYAbrnlFnbv3p10v4cPH+bIkSNxr9XU1LBlyxYsFkvC\nDpvIvdgRSCGEEEIIIUpJ0XWmLBYLDQ0NNDU1sWnTJpqbm6msrIyb4gdw880385vf/AaPx4PBYOCd\nd96hrq4u6X4bGxtpbGxM+J7L5cLv92f1e4jpMRgMaLXaQidDCCGEEEKIlBVdZwpg69at7Nu3j4MH\nD2I0GtmxYwcATzzxBGvWrGH16tXY7XbuueceHn74YZRKJRUVFTz00EMFTrkQQgghhBBitijKzlRt\nbS27du2a8Pq2bdvi/t6wYQMbNmzIV7KEEEIIIYQQIkpuWBFCCCGEEEKINEhnqsTIMhnFR8pEpCud\n2JF4Kx1SVkJIPRDxZmI8SGeqhLQ6/Rz60EmrUxbLKBZSJiJd6cSOxFvpkLISQuqBiDdT4yGvnSmL\nxcIPf/hDrrnmmnwedkYIA2cdwzg9fs46hmdkz77USJmIdKUTOxJvpUPKSgipByLeTI6HlBagePbZ\nZwHYvHkzSqUy+vdU7rvvPmD0mUJVVVUpJrGwwoCi0IlgNA3XVpk46xjm2ipTUaRJwPJqE6e7pUyy\noVjqWj5Mpz6Pzw9pA/Iv3ZiUshIzRSbtstQDEasU4iHdDl5KnakDBw4AcPfdd6NUKqN/TyXSmSo1\nrU5/tNAbrJpCJ4cGq4Z6q7UoA3C2iY2NTy2VMslUsdW1fJisPifLD2kD8ifTmJSyEqUuG+2y1AMR\nq5jjIRLvqxfYSHXcJ6XO1DPPPDPp3zPJ+OHIYin8YkjDbJcoNkT6irWu5UOyEanJ8mO25E0hZSsm\npaxEqcpmuyz1QMQqxniIjfcL/R5uSPHzsgBFEpHhSKtBU7TDkaIwJDayS/IznuRH4UkZiNlO6oCY\nTWLjvd5mSPnzWXlob19fH6dPn8blcrFu3ToqKysJhUK43W6MRiNKZWn22Yp5OFIUlsRGdkl+xpP8\nKDwpAzHbSR0Qs0kk3rWa1LtGGXWmwuEwTz75JIcOHSIUCgGwcOFCKisr8Xq97Nixg89//vP84R/+\nYSaHKShpREQyEhvZJfkZT/Kj8KQMxGwndUDMJunGc8r92gAAIABJREFUe0ZDRr/4xS94/vnn+exn\nP8vDDz8c957RaGTt2rUcPXo05f12dnbyyCOP8PWvf51vf/vbXLp0adLtd+7cyZe+9KWUjyOEEEII\nIYQQ6cqoM/XKK6+wceNG/uiP/oj6+voJ79fV1XHlypWU97t//35uv/12fvCDH3DXXXexd+/epNv+\n6le/Yu7cuSkfQwghhBBCCCEykVFnqre3l2XLliV9X6fT4Xa7U9qny+Xi/PnzNDY2ArB+/Xp6e3vp\n6uqasG17ezvHjh3jc5/7XGoJF0IIIYQQQogMZdSZslgs9Pb2Jn3//Pnz2O32lPbZ09ODzWaLW7TC\nbrfT09MTt10wGGT//v185StfKdkFLoQQQgghhBClK6MFKNatW8eLL77Ipk2bMBqNce+9++67/Pa3\nv83KqFE4PPGZxAcOHGDdunXU1tbS3d095T4OHz7MkSNH4l6rqalhy5YtWCyWhMcQuScdYSGEEEII\nUaoy6kx9/vOf59SpU3zrW9/i2muvBeC5557jmWee4YMPPqChoYF77rknpX3a7Xb6+/sJhULRC+3e\n3t4JI1xnzpyhp6eHQ4cOEQwGcbvdfPWrX2XPnj2YzeYJ+21sbIxOHRzP5XLh9/tTSqfIDoPBgFar\nLXQyhBBCCCGESFlGnSmj0cju3bv55S9/SXNzM1qtltOnTzNnzhzuv/9+7rrrrpQvlC0WCw0NDTQ1\nNbFp0yaam5uprKykpqYmbrudO3dG/+1wOPjWt77FD3/4w0y+jhBCCCGEEEJMW8YP7dVqtdx7773c\ne++92UgPAFu3bmXfvn0cPHgQo9HIjh07AHjiiSdYs2YNq1evztqxhBBCCCGEECIdGXemcqG2tpZd\nu3ZNeH3btm0Jt6+qquKnP/1prpNFmMkf6DXV+/lMi8hMJH+znc9SbsWpUOUSYvqrAGUrjanuR2J2\n+ibLq0Llo5SfSFc2zoMSf7kVm7/TzWspk+xLqTO1b9++lA+gUCj4sz/7s5Q/V2xanX7OOoa5tspE\ng1WT8vv5TIvITKvTT0ufm6oyPZcHPFnLZym34lSocnm328cbF53cXGdlfZ1h0m2zlcZU9yMxO32T\n5VWh8lHKT6QrEju15QYcQ14WVxhTjiGJv9yKzV+lAk53T53XUia5kVJn6tSpUykfQKEo/f5vGDjr\nGMbpGQ3Ceqs1rlc/1fv5TIvITCR/IcwbF51YdMqs5LOUW3EqVLmEgDcuOmlzegEna+usOU9jqvuR\nmJ2+yfKqUPko5SfSFYmdfo+fi04fdVZtyjEk8Zdbsfl7xjGMRaeaMq+lTHInpc7U3r17c5WOoqYA\nrq0yRXvz44NvqvfzmRaRmUj+tvS5ubnOGh2ZyjSfpdyKU6HKRQncXGcFRkemJpvql600profidnp\nmyyvCpWPUn4iXbGxc/2csZGpVGJI4i+3xuevUgGDvuCkeS1lkjuKcBYesNTW1sbx48dxOBwAVFdX\nc8MNN7Bw4cKME5gPDodjWkujyz1T2WcwGLDZbNMug3yZTfdMFWsZ5FOh75maThnIPVO5lY16IPdM\npS82/8POPkYe/RphZ2/S7dW33IHif3yNUCiUx1TObOPrgNwzlX+ptkNyz1T2aTQaqqqqUvpMRgtQ\n+P1+9u/fT1NTEzA2pS8cDvP000+zYcMGtm3bhlpdlOtcpGw6v+Tmi1SE3FKM+3+29yuKS6HKJZVH\nVmcrjanuR2J2+ibLq0Llo5SfSFc2zoMSf7k1fhQ81c+I7Miol/P000/T1NTEHXfcwac//WnmzJkD\nQGdnJ88//zwvvfQSZWVlbNmyJRtpFUIIIYQQQoiikcoPoxO8/vrrbNiwgT/90z+ltrYWpVKJUqmk\ntraWL3/5y2zYsIHXX389W2nNi8icx1KdOJBszmapfp9cyWZ+JMvzRK9nPKc2jXTkex/pChTw2IWS\nSuwkey/bZZZof7O1/cjm+SDb5TRby0TkT4jkcRvMZ0JmuUzbjtl4bs23jDpTgUCAZcuWJX1/2bJl\nBIOlU+VanX5eOT/AW1c8/J+3u3m321foJKWk1enn0IdOWp3x82zf7fax/62ukvs+uZLN/EiW54le\nT7ZtNmRj3x2DgZylbypHL3v4XlMHRy978n7sQkkldpK9l+2YSrS/2dp+RPLirSse/t/jjoy+f7bL\nabaWicif93tG+PUHTv7tZO+EuH3riod/OXyZt67Mnva6UDJtO97o8PCd1zp4o0PKKpcy6kytWrWK\nEydOJH3/xIkTrFy5MpND5E1kyciRYIjXWvq54hp9Bkyp/Po3fsnL2F9UI0swl9L3yZVs5keyPE/0\nerJtsyFb+77Q78lJ+qYSAF4918fJK0O8eq5vVvyKlkrsJPtMaJJts5Wm2dp+xC4N/VpLP8O+QNrf\nP9t1f7aWicifEHC2a4hjlwY50THImZi4DQKvtfRz1uHmtZZ+GaHKoUzbjgDwyrk+jncM8sosObcW\niurRRx99NN0P19XV8corr3Dy5EnMZjNKpRK3201LSwtPPvkkDoeDrVu3olAoGBkZif6n1Won3W9n\nZyff/e53ee6553j99de55pprsFgscdu8//77fP/73+f555/nxRdfpKenJ+2Om9vtJhQKoVap8ASC\nXFtjZsgXYN1CK3NNpbF4hgJQq1QMjYwujWnTq6KvazQaBn0Bbq4rvu+j0WgwGAzRMsi1bOZHJM8H\nR4JcNy7Px5dFsvLJhkz3HSkDr9dLv3sk6+mbihIw6rSYtEo+Xm9jfllxxWgujC+zarMuWg8UKBKW\nZeQzvmCIFTVllOuUWY2pZHFbzO1HNo1vi9QqFcMjQa6fa0YJrJpnocaYeh5nu+7P1DKJzX+8HoK/\nfQG8yX9NV9YtRnHDOrKwILG4KlIGHrebQFiBVa+izmZgRbUJ69W4VQIajZphX5CNi23MN8uDX7Mp\nUTuUbtuhvLo/TyDEbUsqqLNoZCW/aVCpVJhMppQ+k9HS6A888EBan3vmmWcmff+xxx5j06ZN3HLL\nLTQ3N/Pcc8+xZ8+euG0uXLiA0WikurqaQCDAY489xm233cbGjRtTTk/sEpStTj8tfW6uqzIxz1x6\nJ6lkFSWyBHOxKdSy3NnKj4sDftoHvCwo11NXHn9SSVQWuWzI0t13bBmM+P0FaWhbnX5OO4ZZPsue\nyh4ps2RLEicy/gn2+Vi+v1jbj2xK1BaFgbYBP6e6h7kuw9jMdjnNtDKRpdELb3wdaHWOPhA2UewH\ngfz95DZ7JGuHMmk7AoyuNjf+3CESy/vS6Pfee290OfRscblcnD9/nocffhiA9evX85Of/ISuri5q\namqi29XX10f/rVarqa+vjz7nKl2xQ6r+YJhac+k9HTpZemfSSTcbspEfYeB092i8DPqCLCyPj5dE\nZZHrBzoXwz5SFal3rln4VPbJHq6YSD6eYJ9of7O5/TjdPcxAFvI72+U0m8tE5F6krUkW+9KRyp9M\n2w41+Tl3zGYZdaY+//nPZysdUT09PdhsNpTKsVOF3W6np6cnrjMVy+l00tzczN/8zd9kdGx5OrRI\nhcRLdkg+Tp/kVX5JfovZSmJ/ZpHyzK2SmMc22UxEt9vNd7/7Xe6++24WLVqUdLvDhw9z5MiRuNdq\namrYsmULFoslegyrNcTyeRXoNKq4Dp3IjVLO4zDQYNXILzxZ0GDVUGe1yq/tV002rUNiLr/SyW+5\nL0HMBIliX2I7v7KZ33LuyJ2i60zZ7Xb6+/sJhULRC+3e3l7sdvuEbb1eL3v27GHNmjXceeedk+63\nsbGRxsbGhO+5XK64e6ZkTmn+GAyGKRckKUYSJ9kl+TlmOnkhJ8P8SiW/JZbFTBIb+xLb+ZWL/JZz\nR24U3Q/BFouFhoYGmpqaAGhubqaysnLCFD+v18vu3btZtWoVmzdvzsqxc7l8tZg5JE6yS/JzjORF\naZPyEzOVxHZ+SX6XlqIbmQLYunUr+/bt4+DBgxiNRnbs2AHAE088wZo1a1i9ejXPP/88LS0tjIyM\n8Oabb6JQKFi/fj333HNP2seNzCmNrOanYPaubCWSi517vKLGVJQr9KWy/0JTACvnmBgcCWLWqgq2\nCEYx/GIXG1vLq0eXZp0sbcneK5bvU+oi+Ribn5PlbbbvS5ByFIUWOUcogOXVJq4M+lho1Utc5lii\n69FYhWobsnXcmda2FWVnqra2ll27dk14fdu2bdF/b968OWsjUrFGh1KNnHEM0+PRc3nAEzfE+m73\n6MN8b66zsqpal/Xji9IQmXt8stvHr3/flZN4yPWUisj+GxdrsNmyvvuUXB4K8FpLPxsX25iX5+eW\nFNvUlUhstQ34ee3iEF2DI6ydb56QtmTpLrbvU6oi+VhbbsAx5GVppZFQmCnzNlv3JUg5ikK7OOCn\n1emja3CE9QvMAHiC8PoFV8I2SWRX5Hr0ZOcQI8GxdqBQbUO2jjsT2zYZYBknMrTqD4Z446KT/pgh\nVnnyvIgVJnfxkOsh/tj9X3b5srz31ASB11r6Oetw81pLP8E8HruYp1K0D3g5eWWQ3zuGOTMubcnS\nXczfp5RE8rHf4+eNi078wRDtA17OTDNvszEiJeUoCilMfBvUNuClbcDLe0naJJF9idqBQrUN2Tru\nTG3binJkqpBih1ZvrrNGR6YUV9+7uc4KjI5MSU90dlOSu3jI9TKmsfuvtRR2hFUFbFw8OjS2cbEt\nr88vKdblYhXAgnI9gbCCrsGRCdM8kqW7WL9PqYnNx+vnjI5MLSjXx41M5fqZcVKOopDGt0ELy/UA\nBJO0SSL7krUDhWgbstUmzdS2TRGebN3xWSLRk6Yj90XFzhce/16hzYQ5p4me9p0P2cy7yNPFcyEf\n90wZY8pgxO8vWEzlMh+nEqQwD6GMlO/4ehB7rw4kv19n/D09ie7xGb+tSCxRWxRp62Pb/Oncwyb3\nFaRmfDsUdvYx8ujXCDt7k35GfcsdKP7H1wiFZI5ItoyvA5Gcjf2xJvaCcTbEZr4lOhfAxHY/TOLr\n0Fy3/bOhbdNoNFRVVaX0GRmZitHq9NPS56aqbOxeKaUCTnePzZtfXGEsijmeM3HOab5kM++OXvbw\n6rk+bl1SwbpaQ5ZSOCbXjU3s/jsGA7zd7ixITBUyngt1H2Tsd15uMCR8fap7oRRMvLcnURsl7UXq\nEp0PGqyapHUy2faZKNaLjWwqpns3xZh3u328dr6fj863UK5X09bvjsa0tCf5kSifY+9jG3/fWqLt\ns11W2WqTZlrbVgwDLEUh0b1SZxzDtA944+bNF8Mcz5k65zQfspl3AeDVc32cvDLEq+f6CGQpjYVy\nod9TkJgqZDwX6j7IVO95mur1ydooaS9SN9m9s9nYXozK9N5N1fAgmr7uyf/zDGc/4TNcpF083TXM\nsUsDnOsZjsZ0CGlP8iHZ/VLJ7qUtpvurZiMZmbpKAXyszsKgL0i9LURrv4frro5MDfqC0XnziyuM\ncVNuIp9NZKopH8mm7Ey2TeR4M3HOaSLZHgoen3fJjjWdqZxq4NYlFSgVCjYttsVVpthh+NDV/U4W\nC4WaOhrbuC6uMKBWhKgyJv/1PRcUwIoaEw63P+/Hjtz3Fs7zfZCRODyd4J6n9QstdLh8VBo0ca/H\nbh8bX+Pv7YltoyLGtxfFPMWiGCiAdQtMqFRqrhsOxJVTZEpobJ2NlM+5BPfaiuQyvnfT48b711+e\ndBP9d38MBtOk24h4Y/cDw9qF5ejVSlr7RkemlIw+ysI1EsRSoEdZzFTjp1Amus4bfy9t5HNhRs+j\n73fFb5+srGKn1afz+I1sK/VzkurRRx99tNCJKDS3283RjmH+76keNCo1erWKGrMOvRoWWDQYtWou\nD3qZZzXS4fKgUCjp94Z48dwAPZ4gCoUCqz7+jotWp5+3Lw8x6Fdw/PIQapUKW8w2rU4/ze2D0dfH\n/51om1g2vYollfoJr5cajUaDwWDA7XZPmPs+2ffPRCTvXL4Qb1zdf+Tfeo2KCwMBnjvdi0ajYY5p\n8t8b+r1hbHoV5QYNNaaxcvvVB046hwIMjIT5z/d7CISVDAfh3StDKBTx3+fdbt+0j5dNkfytLDNg\nt5h4/7KT58/2UGHSscCS36kbp3tGeLWlvyDH7hoOEgyFsBrym//nnX5aet2UG7QstBkwGAwca+vn\nuVMOUCjp9wZwBxVUG0dj5cKAn4t9How6DS+1OBn2h+jxhDja7uLa6jIu9LtZZDexxBY/7aO5fZDq\nMi2ra01J2xoxsS060TnCv7/bTXWZDrNWiVqt4sKAn/840Y1CpebNS4P4w8pozHQMBelw+phbrmf9\nfJPk7TRF2mOrURfNf7wegr99AbyepJ9T1i1GuWwFgZd/Men+1bffRUg6U9MSWwf6vQHsJg1vtrmw\nGTQYtEpCKJhjUnOqZ4QXzvYVpL2eqcafj91uNxYtGLVqPuh1R68brHoVOrUSFGHUajUX+n2c6x/h\nF2f6KNNpaKjQ09I3tn2isjp62cPTx7vRajSEw/C7tsTng3ydK4rtnKRSqTCZUmszZGSK0V8am1qd\nHLs0yIA3yNr5ZVQaNRg0SuaaNZzsHBqd9tftoc6qpX3Ai8sX5PeOYbqGfKgVYRaWj/2KHBlahTBv\nXHRi0Sk56xiOPntk/NBrndUa93e9dfQXofGvJRqhmqnG51E2ntsy3unu0f2fcQxj0alwevz0uP0c\nbR+kzekFnHykuibpaEUAeO60g+Mdg9w4z8x1G+ehAs44hjnRMUidTUeXy8fprmE8/hA31ZYRCIXi\nvk/sNLOpjpdN46fXXAMcbXPR3DaIPwhr5s3LW+MQAF7+sI+3Owpz7OfP9nDyyhAr55ZxfXV+jh0A\nXvygl5NXhuga8rO+YfRmkfeuDPJOx2j8XV9tpN/tZ7m9gjDwyod9tPZ7aRsYocKg4t2OIcw6FR/0\nuOkcHGGRTcdvzvWxdE1NdPGcSBmf7h5mYbk1L/VqJggAL5/r41jHIAD3r6zkw54hLvT5OOtw4w2G\nWTPPxBsXR+sswOHWftqcXnrcfq6pqJF8TYHkVXEJAhf6PBy5OMCHPR563AGurTIwOOLm+uoqXjnX\nx/GOQULkt72eqRKdjyOvn+wcwunx4w+Go9eGJ66MXpNqVSOU69U0t7lo7fcSDIX5SI0Jx/AI/mCY\nBVbrhLKCsdsTVAoFn1hqTXg+yNe5Yqack2RkCvC43ahUCtRKBXcss7G4woDNqGauWY1OpUSrVoEC\nrqspw6RRcm21CZNGjS8YZqndyJIKA2bt6CVwZKhSrVIx4AtwTXUZgWCIa6tGf6mMfX9oJMi1VSYq\n9Kq4v2161YRtiqG3ngvJRqam+/3THRoOA5qr+7+uykSFQY3LF6TepqeqTIc3EGLdwvK4kYrIUHrs\n1CurUccyu56PN9ioNqgIAVqVCn8Y6m0G6ioM0X3VWXWUG9XUlesouzrkrgBMBh1zyjSsnm/BpldO\na0popmLz9/q5FuwWE36/D58/yB8ssbEwjw/OVQJzyw1sqLdwQ62ZKkP+Yl0J2M0GVs8rY2WthZo8\nHVsJmHRaKgxq1tdZqb86MuX1jRAIhrhxnpmGSgNLK/SYr7Yb5QYtFp2KxgYb7kCIm+abqa8wolLA\nbYsrcPuDrJhrodo4FltqlQpfMMSKmjIsutHYirRnSyqNE0bUofSnW6Qrti0iFMKo01JhULFpsY0K\nnZpqk5aa8tG2fv3CcoLhECvmWLAbVCivfn7QF+DmOitzr7YbkSm++VaKZRiX/1kcmdJ8+l6Uw4Oo\nPMPJ/wNCGm2Wv1HpiZSBx+1GEYbacj0GtZI/WGyjwqBmid2I3aCiTK/FrFWxocHK/LL4rlRszJdi\nHObKZHmR6HwcuSZSXX39IzUmzLr4a8Prasow69VUlWkJA+sj1xkGNUsrDVi0Skx6LWadilsW2agp\nU6MC7GV6yvVqPl5vZU6ZhmH/6DVq+dVrz0TXqVZ9dqZ0js+HYrzWlZGpNHUOBVCh4IZaMyc7hzHr\n1FQZNVh0ajwBHwO+IF2DPowaNSNBBY//7jK3L60YvbdhwMfrF1ysW2COPoOkttyA0+PjuioT88xq\n7AYVZxzDuEb0cattxfbAx/+d7LXZ4t1uH+90uPhYvTXpCjTprlITWb1tQ4ONVXPLON09zOIKI59a\nenW0KOxnqV1PuW5sjOjigJ/f93pp6/eydsFop8cbCBEIhXH74Z1Lgzg9Bo5ccLJxkY3VtWW0Ob2U\n61R8df0cFMDRDg+/Pd/PxgYbp7rd0TjwB0Kc6/XiDYTpHFRRa9ZSVz75Cj3ZEIkvo2G0GdAolSyp\n1KNV5f/urd7hAC+f6+MTSyrAlt9pI053gFdb+rl1cQVU5O/YIcK4fEFCMTPlrTol11TpMes0vHFx\ngFVzzQwFoWvQx0gwjEmr5p3Lg6ysKcPlDdAx4GZRpYFDv+/lowvK0SgVHPpwbEXG0XgxcrJziJHg\n6GuhMKPHTXA3sqzSFSuMWqnE6w/xsw96+YMlFYACx7CfRZVhfIEwTk+AQx+O5pfdoGTdAjMV+tH6\nUwyrREoZAj4v3m8/NOkmcl/VRF3uAO39Hm6oNXOmy818q44z3S4cQ3r0GhUKRZjxq9JHYv5jdVbK\ndUpOd0scwvTq5PjzMYxOxztywcmnr63E4Q5w8HQvN82zMNesobZcz5XBEd67MsTtSyv4wg1VfNjr\nweEO8MbFAW6aZ0F5tYyUCggGw/x/7/WyqMLI6a4hFlj1ePwhmtvc3FhbRigMhz50TlgVtt5qpW3A\nH3deyXY+zIRr3aLsTHV2drJ3715cLhcmk4nt27czf/78Cdu9+uqrPPfcc4TDYVasWMGXv/xllMrU\nLwT7vQHe6XDR6w5wwenFqBmdw73QqsOoVXGk1cmAN0BDhY6m8y7e7hgkFIbNKyr5zXknwVCIuRYN\ng74gTo+fi04fdVYtJzuHmGu2RqcJnuv1UmfVpjRtr5SDK12xU998gVB02lKsdIeGY/d9tG2ApXY9\njqGR6BB6mLHpf0O+IAvLR4fV2wa8HL04QK/Hj0IBN9WaaKg08NpJB+91upln0dLm9NI56OPFD/tY\nO8/MCx/0sXLOCItsVQSBlz7s43yvB38wzOp5Rs46hllotXLskov3u4a40O/hhrllBEOh6LTRXA+B\nx+7r9VYnB9/v4qPzzHykJs/T/GKmVK2ZX5hjh8L5O3YAeOmD0akWTm+AmxsqADjf56HN6eV8n4t+\nTwCnJ8Anl9k40z2MeyRIz7CfQBhGAiGMGgUf9Hi4Muije8jPa+f7WbvAHF11K9F04ciU4oECTuso\nBQHg0O/7CIVDXOj30uce4UzXIAPeEK+2OHF6g3z2ugp+eaaPRRXa6FThdqcXq0FDrcVa8Om7s70M\nRfoCcHVVygBnut0Ew2Fa+jyoFKBVKbjo9HLkoosOl58ba0fbzNhzq1blYqldL3FIanVy/AIRr54b\nnf5eb9XTMTh2y8DGhnJ6h0d4s2OIdqcPlRLWLzDjD4Z586KTDxxuPP4QS+01HPqgmzPdw1waGGHd\ngjKOXOjnyuAIbU4viyr0eAMh2ga8E65fY88hkWuiTMpyqnwo9fhIe5rfe++9x9GjR7nmmmuir736\n6qv84z/+I8899xx9fX2sXLkShSL1LPrnf/5nbrvtNrZt24bJZOKpp57iE5/4RNw23d3dPP744+zZ\ns4e7776bV155BbfbzeLFi1M+nmvIjUKhwKxXY9WrWGo3UmczUGUa/TWx0qSlTKtinllHnc2ILxDi\n1iU2jBo1ZToVJq2K66tN2I1ahkeCLK8x4w0EWWYfm8I3/vViGMosBomm+SlIPGUmVrpDw7H7Xrew\nnBqThsEppleORrASvXZ02ufaBRYqDWpc3gBWgw6zXkWZTsVN8ywMeANsXGRDoxqdaLVmgYU5JjVK\nQKvWMOgPcuviCkLhUDQ+VCo1/mCY6+eUUV2mYb5FF51+lY8h8EgZjPh8OD1+PrGkgro83lQcyRtv\nIDRrjq0EtBoNbn+QW5dUsKji6jQ/rxene4TlNWZcPj+N9VbM+tH7Ny06NQutBsw6FatqLZg0KrQq\nJSvnmnF6AtxcV051mQ6vP3k8J5pSHFGM0y3yafw0P61aQ4/bzy0NNvo9ftYutFJbrmN4ZDRWrri8\nfGSOGQVhrqsyYdWro21JhV41ZRuWC6Vchjmb5nfbZwm88qtJt5FFKkZFysDrdhNGwUggxKp5ZpQK\nBSvnlgEKFlr11NuMuP3xbeZU59bZKtU6GVsGKpUSjz/EmgUWFljHbj+wl2kJh2GBVY9CARsabFQa\n1PR5/Cyxm/Bd3a7KoMRq0DE8EuS2JRVcHvSxaq6FUBiW15iYV24gFA5zbZUx4fVrNm85KaW2KZ1p\nfopwOJzW0vN/93d/h91u52tf+xoAbW1t/PVf/zULFy5kzpw5NDc38+CDD3L33XentF+Xy8XXvvY1\nfvKTn0RHmb7yla/w93//99TU1ES3+8UvfkF3dzdf/vLosqjHjx/n5z//OTt37kz5uzgcDkb8foLj\nXo994j2MLW8dWRoXxpbHHT9HePy80Mh2Moc4XuzTvkf8/oR5NplU8jN229hlQRMdJ/b92M+HGCv7\nyP4iz5dSMxYbyZ5QHtnv+HQHiV9meXyacxk3sWXg8fsLNlydKM9n+rEjxx1fBpEyHy8SA5GluSN/\nx7ZD03nEQjEshVtsEtWDSPnExsf412LzK1m7P13ZyvtSLMPY/A87+xh59GuEnb1Jt1ffcgfqP3wA\n71//6eT7/Yf/B880pvn5K6rTSvdMElsGfr8/7tmJkcd8qBg77yVqM2Ovj6YTh8UUq7lMy3QffZOo\nDCJ5Pv4xK5FzhHLc/iLXspFyiG2zVOP2AxPPI4ke+5ONc8ZUjxMqFhqNhqqqqpQ+k/b1Q0dHB+vW\nrYv+3dTUhMFg4LHHHkOn07F//36amppS7kz19PRgs9nipuvZ7XZ6enriOlM9PT3Y7fbo31VVVfT0\n9KT7dXin08ub7S7qrHqqTFre7hhgVa2FjgFy6/tlAAAgAElEQVQP88sNDI0E6B32xz1xOtGc+PHB\nCTKHfTo6BgO83R4/J3c6FyHTrZSRMlhRY6LPG4qWW4VeGX0uQ+S4b13x8FpLPxsX21gz1xDdx4Vx\n5agAOgb9XBzw83qrk1uXjG0f21CNT8P4OEj19Vw52eXl2ZOdfGJJBTfPN0z9gSx645Ines9Uvo8N\nhevEjT/u+w4vr37Yy7xyHVVGDTq1kjcuDrB2oYU1cw3RmIjcl2k16KLPNIrEyHSmC09Wb4r9RJdr\nJ7u8PHeqi40NNo5fdnFjrSUuJtXj/j/ZVJVUOlLZrO+zvQxTpVBr0PR1T72hwYR/loxgRa5vbpxn\n4fKAl9pyPSevDHJLg40+T4CXP5zYXo+P4anisJiujXKdluleD0y2TWx7kugaQ3H1M4fbXFj1KpbZ\nTTi9o2V16+IKVIogeo2GM93D1Ji1NFh10fuzI8daXm2K3v8/1Xllut8jle1KVdrXEF6vF4NhrBKd\nOHGCG264AZ1utFOxZMkSXn/99bT27XQ62bFjBz09PfzjP/4jyQbPWlpa+PrXv044HKa+vj7pdgCH\nDx/myJEjca/V1NSwZcsW1Do9b7Z38E7HID3DfuaaNQSCYV4910eFUcPwyDA9bj/eQJhKs56VC+0E\ngiHeutTDlaEAb10aZH2DHYNuYoD4A0Fa2zrxoabVFWDlQjsadfEOb+ZbpNN8od+Ts/nVsXN1xy99\nvm6BOe64IeC1ln7OOtwA3DTXEB1pSrR8/eBIkN+09HO6axiVYmz7ydIw2TL5U72eS0fbXQW/bynf\nxy4273UOceySi/N9GlbWGKkwqOka8vF6q5Mb5xo46xim/+q89hU1xoSPXhCZOdruoqXXjT8Yplyv\n5OVzfTmPSbnXqcCmsUgFzJ6FKmLvf+r3BFg738yr5/rwB0N0DXn53cVB3h7XXqcaw8UU8/lOy3SO\nl06awow9mqVMp6Jcr+LtS0O8e2WIUBju+0glb1x0cWnAG/dYH2KOFXn0T6L7atP5Hul+l1KT9vnB\nbrfT0tLCrbfeSmdnJ+3t7XzmM5+Jvj80NIRGk3rv0263EwgE2LlzJ5HbuXp7e+NGoQD0ej0nTpxg\n7969WCwW/vZv/xa1OvnXaWxspLGxMeF7KsLcs3IuH5lnxW7UYNGrae1zU2cz0Of2U2EcvadiyBdk\ncYWegH8EgLtWzOHDnmGW2k0QCuDxBBLuf818C5ddPmotOgL+EQL+lLNlxtJoNGi1WlYvtFFu0FJr\n0cWtZpMtjYs1XHb5mF+uY16FJVpuJo0Co14fd9w/+uh8znS5ua7GSJlBN2EfsdvWVWr44hojZx1u\nrh+3fbI0jP+Oqb6ebZF6unllDTUW/egSrJN8j1z40toFfLRruCDHLgaRMrjjuirmWAxUGDVYtCrU\nKgUWk5HFdgNmgy4aEzajFrffz7I5NvrdIzmPkdkgth7Msxq4rspIa7+HBpshLzGZr/perCL5r9Pp\nCJeZ0dy5mbDHnXR7RcM1qMqtKDZ+ctL9aq02yMI2AFpbJbrgyOQbqTVMeZLX6Ajq9FMeL1tUPi/4\nfZNvpNGhvFoGBp2Ozavm8WHPMHUVBpxuP1+otdLu9HJtlYlFVeWsmj+xvU41hosp5vOdlmTHi9QD\nvU5H4+KqlNO0YbGGauvoY3zmmHUsqi5nea2V66tNKBRhPruijA6Xj3KDmmqjOrrf2GukUJhpH3e6\n+VZMZT0VlSr1AY+075k6cOAAzz77LDfddBOXLl1ieHiYxx9/PHrT1ve//30cDge7d+9Oed87d+5k\n48aNHDhwgE996lP87ne/Y8+ePXHb/OxnP+OFF17ghz/8IRaLhYcffhiXy8Xjjz+eztcRQgghhBBC\niJSk3T3cvHkzgUCA48ePY7fb2b59e7QjNTQ0xKlTp7jzzjvT2vfWrVvZt28ffX19/OY3v+Ev//Iv\nAXjiiSdYs2YNq1evxuv1csMNN/Dwww+jUCior6+nv78/reP19/cTCCQeVcpUx2CAC/0e6m0G5pmL\nuzdeCDqdDovFkrAMJO/yI1IGv7/k4EzXoOR3AcTWg4v9Xon7AhjfFkn7k1+TnQtiSbnkznTLQORO\nqmUg9SH71Go1Npsttc+kezCVSsWDDz7Igw8+OOG9srIyfvSjH6W7a2pra9m1axc7duzg61//evQZ\nU9u2bYvbbvHixXzzm98E4NKlSxNGr2JNds+U0Wic9H6rdPkDQd5v6cTlCzLkd7O4eo7cLzVO5J6p\nQCCA3z82NSIMvN3uxOnx0zvkpWrpzJtjWywi02PPdA3S4hiU/C6ASBn4AwHebu+XuC+ASBkEAgFG\n/H5pf/IsNv9jzwWx5LyQW9MpA5FbqZSB1IfiUbLdWLvdTnf32Oo7Dodjwn1VsSa7Z8rlcuWs4Wiw\nqDnr8NFg0THoGsjJMUqZwWBAq9VOeF0BXFtliq7+Ig1E7tXbDPQOeSW/C0jivjhIORQnKRchxkh9\nKB4ZdaZGRkY4evQora2tcQ9djVAoFPzZn/1ZRglMZv369fzd3/0d999/PxaLhZdeeomPfexjWT9O\nps8daLBqZuTKJflQKnlXTM/JyMQ8sxr7UmtKyzmL7IvEvSisBquGOqvUh0JJ1q6WynlBiHxIpz7M\nlGuWYpJ2Z8rhcLBz504cDgcmk4nh4WHKysqinSqz2Yxen95qNfv37+f48eM4nU52796NwWDgBz/4\nQdw9U9XV1Xz+85+P3jN1/fXXc/vtt6f7dRLK1rr4ErTpK/a8m0nPTkj0rC9RGOOfaSbybybV7VIz\nVd4X+3lBiHxKpT5Iu5YbaXemnnrqKdxuN7t376a6upqtW7fyjW98g2uuuYYXXniBQ4cO8bd/+7dp\n7fsrX/lKwtfH3zN16623cuutt6Z1jKnMhnXxRWZmWozk8llfYvpmWlyVIimDwpG8FyI3pG7lTtoz\nGE6dOsUdd9zBkiVLoosIhMNhNBoNd911FytWrOBf//Vfs5XOvIvMRbUaNDIXVSQ002Kk3maYMd+l\nlM20uCpFUgaFI3lfHBThEKpgYIr/goVOpkiB1K3cSXtkyufzUV1dDYwuIgDgdo89YG/ZsmU89dRT\nGSavsGRutpjKTIqReWa1rAZUJGZSXJUqKYPCkbwvPLV7CP//+f7k29z2GRQrVudkNWSRG1K3ciPt\nzpTdbqe3txcYXSa9oqKCDz/8kHXr1gGjS5UnWqWt1GQ74OTGv5lHbvwUYmaK1FWpt/kXm9+S/wUQ\nDhN879ikmyhXfTRPiRHZInUpN9LuTK1YsYJjx45x//33A7Bp0yZ+/vOfMzQ0RDgcpqmpiY0bN2Yt\noTOB3Pg3uxV7+csCFMWj2GNlNpGyKCzJfyGyQ+pS7qR9z9Tdd9/NPffcE30+0z333MPGjRs5evQo\nx44do7GxkS9+8YtZS2ipG3/jnwyKzy6lUP6xC1AUY/pmi1KIldlCyqKwJP+FyA6pS7mV0TS/2Ifk\narVatm3bNmHFPTFKHq42u5VC+ctDe4tDKcTKbCFlUViS/0Jkh9Sl3Mroob0iNXLj3+xW7OUvC1AU\nj2KPldlEyqKwJP+FyA6pS7mTdmfq2WefnXIbrVZLRUUFy5cvp6KiIt1DzSgSxLNbsZd/sadvNpGy\nKB5SFoUl+S9Edkhdyo20O1MHDhyY9rZKpZLbbruNP/mTP4k+k0oIIYQQQgghSlnanan//b//N9/5\nzneor6/n05/+NHPmzEGhUHD58mUOHTrExYsX+cY3voHX6+XXv/41L730EjabjXvvvTeb6RdCCCGE\nEEKIgki7M/XjH/+Y2tpatm/fHvf6okWL2L59O9///vd5+umn+eY3v8mOHTtwuVw0NTVNqzPV2dnJ\n3r17cblcmEwmtm/fzvz58+O2CYfDPPXUU5w4cQKVSoXZbOahhx6ipqYm3a8khBBCCCGEENOW9py7\nU6dOsXz58qTvL1++nJMnT0b/vvHGG+np6ZnWvvfv38/tt9/OD37wA+666y727t07YZtjx47x+9//\nnn/6p3/ie9/7HitWrODf//3fU/8iQgghhBBCCJGGtDtTarWac+fOJX3/gw8+QK0eG/gKBoPo9fop\n9+tyuTh//jyNjY0ArF+/nt7eXrq6uuK2UygUBAIBRkZGCIfDuN1uWeRCCCGEEEIIkTdpT/P7+Mc/\nzn/9139RVlbGHXfcQXV1NQDd3d28+OKLvP7663zyk5+Mbn/q1KkJU/US6enpwWazxS1UYbfb6enp\niZvCt3r1at5//322bt2K0WikoqKCRx99NN2vI4QQQgghhBApSbsz9cd//McMDAzw61//ml//+tfR\nzk8oFAJg3bp1/PEf/zEAfr+fRYsWsWzZsrSOFQ5PfFZzS0sLly5dYv/+/RgMBv7t3/6N/fv38+d/\n/ucJ93H48GGOHDkS91pNTQ1btmzBYrEkPIbIPVndUQghhBBClKq0O1NarZZvfOMbtLa2cuLECRwO\nBwBVVVWsWrWKRYsWRbfVaDTcd99909qv3W6nv7+fUCgUvdDu7e3FbrfHbdfU1MSKFSswGAwAbNq0\nid27dyfdb2NjY3Tq4Hgulwu/3z+t9InsMhgMaLXaQidDCCGEEEKIlKXdmYpoaGigoaFhyu3cbjf/\n+q//yuc+9znmzZuXdDuLxUJDQwNNTU1s2rSJ5uZmKisrJ6zSV11dzYkTJ/jMZz6DWq3m2LFjLFiw\nINOvI4QQQgghhBDTknFnarpGRkZ47bXXuOWWWybtTAFs3bqVffv2cfDgQYxGIzt27ADgiSeeYM2a\nNaxevZpPfepTdHR08Fd/9Veo1WqsVitbt27Nx1cRQgghhBBCiPx1plJRW1vLrl27Jry+bdu26L/V\najUPPfRQPpMlhBBCCCGEEFFy978QQgghhBBCpEE6U0IIIYQQQgiRBulMCSGEEEIIIUQapDMlhBBC\nCCGEEGmQzpQQQgghhBBCpEE6U0IIIYQQQgiRhrSXRg8Gg/h8PoxGY8L33W43Op0OlUoFgNVq5Zln\nnkn3cEUrDCgKnQhRcBIHmZM8HCX5UDykLApH8l6I0iB1NYORqZ/+9Kc88sgjSd9/5JFHePLJJ9Pd\nfUlodfo59KGTVqe/0EkRBSRxkDnJw1EdgwHJhyIhMVk4kvdClAapq6PS7kydOHGCdevWJX1//fr1\nHD9+PN3dF70wcNYxjNPj56xjmHChEyQKQuIgc5KHYy70eyQfioDEZOFI3gtRGqSujkl7ml9/fz8V\nFRVJ37fZbPT19aW1787OTvbu3YvL5cJkMrF9+3bmz58/Ybu2tjZ++tOf4nQ6USgU/Lf/9t9Yu3Zt\nWsdMlQK4tsrEWccw11aZZv0Q52wlcZA5ycMx9TYDvUPeWZ8PhSYxWTiS90KUBqmrY9LuTJWVlXH5\n8uWk73d0dGAwGNLa9/79+7n99tu55ZZbaG5uZu/evezZsydum5GREb73ve/x53/+5yxbtoxwOMzw\n8HBax0tXg1VDvdU6qwNISBxkg+ThqHlmNVVLJR+KgcRk4UjeC1EapK6OSnua3w033MDLL79Ma2vr\nhPfOnz/Pyy+/zI033pjyfl0uF+fPn6exsREYnS7Y29tLV1dX3HaHDx9m6dKlLFu2DACFQkFZWVka\n3yQzsz2AxCiJg8xJHo6SfCgeUhaFI3kvRGmQuprByNQDDzzAiRMn+Pa3v83q1atZsGABAO3t7bz9\n9ttYLBYeeOCBlPfb09ODzWZDqRzr59ntdnp6eqipqYm+dunSJdRqNd/5znfo6+ujrq6OL3zhC1gs\nlnS/khBCCCGEEEJMW9qdqYqKCr7zne/w9NNPc+zYMd566y0ADAYDjY2NPPjgg5PeU5WKcHjibW2B\nQID333+ff/iHf8BqtfKzn/2MH//4x/zlX/5lwn0cPnyYI0eOxL1WU1PDli1bsFgsCY8hci+20yyE\nEEIIIUQpSbszBaOLTHz1q18lHA7jcrkIh8OUl5ejUKQ/6Ge32+nv7ycUCkUvtHt7e7Hb7XHbVVVV\ncf3112O1WgG45ZZb2L17d9L9NjY2RqcOjudyufD7Z/eyjoViMBjQarWFToYQQgghhBApy8qwgEKh\noLy8HKvVmlFHCsBisdDQ0EBTUxMAzc3NVFZWxk3xA7j55ptpaWnB4/EA8M4771BXV5fRsYUQQggh\nhBBiuqY9MvXss88CsHnzZpRKZfTvqdx3330pJ2rr1q3s27ePgwcPYjQa2bFjBwBPPPEEa9asYfXq\n1djtdu655x4efvhhlEolFRUVPPTQQykfSwghhBBCCCHSMe3O1IEDBwC4++67USqV0b+nkk5nqra2\nll27dk14fdu2bXF/b9iwgQ0bNqS8fyGEEEIIIYTI1LQ7U88888ykfwshhBBCCCHEbCJLqQkhhBBC\nCCFEGtLuTD3wwAMcPnw46fu/+93v0nrOlBBCCCGEEEKUgpyNTIVCoYxX9hNCCCGEEEKIYpWTzpTb\n7ebEiROYzeZc7F4IIYQQQgghCi6lh/YeOHAgbkn0xx9/nMcffzzp9p/+9KfTT5kQQgghhBBCFLGU\nOlNLlizhk5/8JOFwmBdffJGVK1cyd+7cCdvp9XoWLVrE2rVrs5ZQIYQQQgghhCgmKXWmbrzxRm68\n8UYAfD4ft99+O0uXLs1JwoQQQgghhBCimKXUmYq1ffv2bKZDCCGEEEIIIUpK2p2p9957j9bWVu66\n667oa6+++ioHDhwgEAjw8Y9/nC9+8YsolfIoKyGEEEIIIcTMk3Zn6sCBA9jt9ujfbW1t/OhHP2Lh\nwoXMmTOHF154AavVyt13353yvjs7O9m7dy8ulwuTycT27duZP39+0u137tzJhQsX+OlPf5rWdxFC\nCCGEEEKIVKU9bNTR0cHixYujfzc1NWEwGHjsscf4xje+wW233UZTU1Na+96/fz+33347P/jBD7jr\nrrvYu3dv0m1/9atfJVwEQwghhBBCCCFyKe3OlNfrxWAwRP8+ceIEN9xwAzqdDhhd+c/hcKS8X5fL\nxfnz52lsbARg/fr19Pb20tXVNWHb9vZ2jh07xuc+97k0v4UQQgghhBBCpCftzpTdbqelpQUYnZbX\n3t7OypUro+8PDQ2h0WhS3m9PTw82my3uXiu73U5PT0/cdsFgkP379/OVr3xF7ssSQgghhBBC5F3a\n90w1Njby7LPP0tfXx6VLlzCZTKxZsyb6/vnz57M2/S4cDk947cCBA6xbt47a2lq6u7un3Mfhw4c5\ncuRI3Gs1NTVs2bIFi8WS8Bgi96QjLIQQQgghSlXananNmzcTCAQ4fvw4drud7du3YzKZgNFRqVOn\nTnHnnXemvF+73U5/fz+hUCh6od3b2xu32AXAmTNn6Onp4dChQwSDQdxuN1/96lfZs2cPZrN5wn4b\nGxujUwfHc7lc+P3+lNMqMmcwGNBqtYVOhhBCCCGEEClLuzOlUql48MEHefDBBye8V1ZWxo9+9KO0\n9muxWGhoaKCpqYlNmzbR3NxMZWUlNTU1cdvt3Lkz+m+Hw8G3vvUtfvjDH6Z1TCGEEEIIIYRIVdqd\nqVj9/f0MDAwwZ84c9Hp9xvvbunUr+/bt4+DBgxiNRnbs2AHAE088wZo1a1i9enXGxxBCCCGEEEKI\nTGTUmXrrrbd4+umnuXLlCgCPPPIIK1aswOVysWvXLu677z7Wrl2b8n5ra2vZtWvXhNe3bduWcPuq\nqip5xpQQQgghhBAir9K++//YsWP80z/9E2azmfvvvz/uPYvFQkVFBb/97W8zTZ8QQgghhBBCFKW0\nO1P/+Z//yfLly/n7v/97PvnJT054f9myZbS2tmaUOCGEEEIIIYQoVml3ptra2rj55puTvl9eXo7L\n5Up390IIIYQQQghR1NLuTOl0Orxeb9L3u7q6KCsrS3f3QgghhBBCCFHU0u5MXX/99bz22msEg8EJ\n7zmdTl555RVWrVqVUeKEEEIIIYQQolil3Zl68MEH6evr43/+z//JSy+9BMCJEyf4j//4D775zW8C\ncN9992UnlUUsXOgEiKIgcZA5ycMxkhfFQcqhcCTvhUiP1J38S7szVVtby2OPPYbZbOaZZ54B4Je/\n/CUHDx5k4cKF7Ny5k+rq6qwltBi1Ov0c+tBJq9Nf6KSIApI4yJzk4RjJi+Ig5VA4kvdCpEfqTmFk\n9JypBQsW8MgjjzA0NERnZyfhcJiamhosFku20le0wsBZxzBOj5+zjmHqrVYUhU6UyDuJg8xJHo6R\nvCgOUg6FI3kvRHqk7hRO2iNTscrKyliyZAlLly6dFR0pAAVwbZUJq0HDtVUmCdhZSuIgc5KHYyQv\nioOUQ+FI3guRHqk7hZPRyBTA6dOn6e7uZmhoKOH7n/nMZ1LeZ2dnJ3v37sXlcmEymdi+fTvz58+P\n2+b999/nZz/7GT6fD4CbbrqJ//7f/3vqXyADDVaN9PyFxEEWSB6OkbwoDlIOhSN5L0R6pO4URtqd\nqQsXLvAv//IvdHZ2TrpdOp2p/fv3c/vtt3PLLbfQ3NzM3r172bNnT9w2ZWVl/MVf/AXV1dUEAgEe\ne+wxXnvtNTZu3Jjy8TIhAStA4iAbJA/HSF4UBymHwpG8FyI9UnfyL+3O1BNPPIHL5WLr1q0sXboU\no9GYlQS5XC7Onz/Pww8/DMD69ev5yU9+QldXFzU1NdHt6uvro/9Wq9XU19fjcDiykgYhhBBCCCGE\nmEranan29nYeeOABPvGJT2QzPfT09GCz2VAqx27nstvt9PT0xHWmYjmdTpqbm/mbv/mbrKalGIWR\nXx2KjZSJSFc6sSPxVjqkrGYvKXshEpuJdSPtztTcuXOzmY5JhcPJV813u91897vf5e6772bRokVJ\ntzt8+DBHjhyJe62mpoYtW7ZgsVgmPUY4HCYQDKFWKVEoChMC4XCY01cGONM9zHXVJpZWlaFRqwqW\nnmyJ7TSXoosDftoHvCwo11NXrply+2JvRIo9fTNJq9PPGccw11WZWG4wTHh/fFmEgQvO0VWarq0y\n0WCdOt5EdqRTL1qLpKxmQp3O93fI9HjFUvZCJBIb3/muWzO1bqTdmbr//vt58skn+fjHP05lZWXW\nEmS32+nv7ycUCkUvtHt7e7Hb7RO29Xq97NmzhzVr1nDnnXdOut/GxkYaGxsTvudyufD7k6/JXwyF\nHwZOtDtxevw4XB5augeYZ9GXfDAaDAa0Wm2hk5GWMNDq9HHyyhCBsIKF5ZpJG6ViiKPJdAwGeLvd\nWbTpm0nCwJuXBvm9Y5hBX4jr5savgjo+VlqdfjpcXjpcfkKhoCx7m0fp1NtiWaK42Nuc6cj3d8j0\neMVS9kIkEonv5dUmQmHyWrdmct1IuzO1bt06/H4/f/EXf8FHPvIRKioqJowyKBQKvvSlL6W0X4vF\nQkNDA01NTWzatInm5mYqKysnTPHzer3s3r2bVatWsXnz5nS/xpSKpfAjS16ecQxj1KrpGvQx6AvO\nqGAsRV2DIzg9AboGRybdrljiaDIX+j1Fnb6ZpsaspWvIR405/seE8bFSZ7Vy1jGMeyRIjVmHeyQg\ny97mSbr1NtJeRy5UClFWpdDmTCXf3yEbxyuGshcikdj4bh/w4vIFGchj+zCT60bananTp0/zox/9\niJGREd5+++2k26XamQLYunUr+/bt4+DBgxiNRnbs2AGMLnqxZs0aVq9ezfPPP09LSwsjIyO8+eab\nKBQK1q9fzz333JPuV4ob7gwx+hCuYir8yJKXbQN+PHJBlVPJhr5jX1cAa+ebMeuUXHe1LJJ9rpji\nKJnFFQY0ihB24+QjbCJzCmCxTUe5Tpkwv1fUmLjoHJ0+qgRWzinjjGOYBquOheVlUj45FluP0623\nDVYNdVZrdh7mmIZSaHMmEymD5dUm2ga8LCzXl8zFnixPLYrV8moTp7uHWVCujxuZUjB23ZtLM7Vu\npN2Z+slPfoLRaOSb3/wmS5YsydpqfgC1tbXs2rVrwuvbtm2L/nvz5s1ZHZGKDH2uqDHR5w3xxkUn\nN9dZWVWtK6rCVwB15RoWlhdHemaiZNM8Er0eGxtTTQ8ppjhKxDUS4mj7IDfXWVkwO569XVCBEFwZ\nHMGqH4uVjsEAJy8PUFWmx+ULEgqP3pd3uns0rqZzX57ITGS6a225AceQl6WVRj61NPV6WwxT7Iq9\nzUkmdiqSyxfiwx4v5fr8TAfPVp6VWp6LmS22PYptzyKx/m63L+66N5dmYt1IuxPa2dnJZz/7WVau\nXJnVjlQhxA599rj9vHHRSZvTyxsXnYSublNshV9s6Zkpxk/zCE/xOhAdkUr2PuO2LVYnrwxOiHuR\nG5F4GRgXLxf6PfiDoz/mOD2jC1S0D3injCuRPRf6PfR7Rs8D/mCI093DKe9juu1BPhRzm5NIbN61\nDXh565KLc71ufpfHdqnU8kyIyYxvj2JFRqQSXfeK6Uu7M7VgwQLcbnc201IwkaF9q0GD3ajh5jor\nC616bq4r3BQNURixsRA7zSPZ61N9rpSsnGuWuM+TZPFSbzOgUSm5uc6KzaDhuioTC8r1JR1Xpabe\nZsBmGD0PaFTKtPJ9JrQHhRKbdwvL9Xx0vkXaJSEyMFV7pAS57s1Q2tP8vvCFL/C//tf/YtWqVSxd\nujSbaSqI2KH9BRb4SHWNBNQslWyax1TTP0p1Sk3EsgotdWsk7vMlUbzMM6upWlSOAlhZrYu+J9N6\n82eeWU3V1Wkw4ZgySFWptweFND7vVsj5WIiMTNUerarWyXVvBtLuTP3yl7/EYDDw8MMPM3/+fOx2\ne8LV/L71rW9lnMh8iQ0yCajZLVmDM9WFUalfOEnc51eyxUrGv1fqcVVqEpVBJvsRqZPzsRDZNVV7\nJPUsfWl3ptra2oDR50J5vV4uXbo0YZtSe6BsLh9eNhMenJhrsyGPZsN3TFch86ZYyyVRuoo1rTNJ\nogcmS57nVyEfLCpEsct1nZA6l5q0O1N79+7NZjoKrs8bQKFQYlIp0ahHb8hTXX0vfPW/SK89ePW9\n6QZbPld1KtUKkI8Hxk5nufOpJFo6NBv22EAAACAASURBVMBoPCjGbce4bdtdowucVJs01Jrjl8OO\nxFShBcigUcjAxQF/dPnjfK9YV8hjJ4snBeAJhHD7QmjUCqw6VdIVIyOLG2Sr3pdqG5ItHYMBjrU7\nR+9Xs2rw+/0MBpQowiGqjJpo/qSaT9PZfrbnfUSr089pxzDLq0zYjKBSaggE/dh0ozEf206lm2fF\n0uYKkapUrylTPa8Xw0qkpUb16KOPPlroRBTaBYeL091eDp7qQaFS4vKF+a8P+lGp1QTD8PYVN6+2\nDKDXabjo8vPz93sIK1W81zmMWqXCpk/eJIeB5vZBnB4/QyNBllTm7lkZrU4/ze2DU6apmGg0GgwG\nA83nHVwZ8OQsj5LlTSp59m63j+dO96LRaJhjUhMGTnT6eL/Lw5sdw5Tp1Nj0Kt7r9vH87/tpG/AT\nQEm1UUUYeK/bx9E2Fyadhve7xmLnrSse/uNdBxqNmnnm/Ddc0TJo7WP/0Sto1RoWWPKXjjDQ3OHm\ndxddaDUqFlekf59KOsd+87KbI60D6LVqFtnyd+zYeJpv1Y8rAzUXnV6cvhAfODx4QwqG/fDih330\ne4IEQyGWVOppG/BzosvDm5eGMGnVGdf7UmxDsiVSD14528Vb7QMM+cNcdvkYGFHy6rl+DFoNI8HR\n9twdVPB+1zAKReJ8Gn+BP518nc15D2P5P+x2839P9fCbFicqlRK1UsOTb3di1uuYb9HQ3OHhqePd\naDQalAo40jaWZ9MdUSx0m1usImXgdrvB6ybwXwcn3V618qNQvyxPqZsdYssgFAoljOlUrinfuOTh\nyXe6p31ez9c1azH/cKRSqTCZTCl9JuMfoU+fPs0777yDw+EAoKqqiptuuonly5dnuuu8GfYHee18\nP2cdw2iU8LGFZsq0KjoGfLzZ7qNMq+Z8n5tai4bWPh8D3gCvnutjoVU75ZOj8/XgxFJ/2n29zUDv\nkDcneZQsbybLs/EVPXbpUJVyALuhAoNOSYfLy+8uDrCk0siHfW4WWss5fsXF2x2DmHUqAsEQy+0V\nABzvcDHsD/J6q5MF5RrOOoZZYLXyWks/Zx2jK2PeNNcQ92tpPobyI451DHKu14Na2c+a+Ya8jVAF\ngXanl0sDXuaYtQQx5+3YYaCt30uvx09bv5cw5rzUm9h4Aidr66wAHG13cfzyIAoF3L/SzsnLbs50\nD6NRKVErw9TbDFzo93Bt1WhD3z7g5eSVIZyeAGadMi62U/0epd6GZIvdpKHOZqDKpOFs9zDhXh8Q\n4t3Lg9SWaxnw+LnkGmG+RZMwn8b/qjudfJW8HxMEzDoVFr2KapOGdy65OH5lCBQK1s438sq5Po53\nDKIE7llRidPjp6XPDRg54xjmuqv5nuzX9SBM2uYKUSx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WTkhTKAlgJBij1qJFrZKoNavp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oU3vdnettcBBPZdqEQdJLfuV0kIw1QvOCyXKqo2UllnpqjaeemrP+SF2LAgDhdU0Suhdc8D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\nvXv3BqwzFrt372bp0qUsXbrU5WyZN2i1WoxGo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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('ggplot')\n", "ff = pd.tools.plotting.scatter_matrix(dfScaled, diagonal='hist',figsize=(10,10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear regression model \n", "\n", "Hypothesis: energy usage (kWh) can be explained with building characteristics: \n", "+ Age of the building \n", "+ Square feet \n", "+ Number of stories \n", "+ Total plugged equipment, ..." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R-Squared: 0.71750454564\n" ] }, { "data": { "text/html": [ "
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feature_namecoefficient
0Intercept-0.067143
1age-0.023500
2number_stories0.048774
3square_feet0.777122
4plei_10.312308
5plei_30.122954
6domestic_gas0.229171
7heating_gas0.143661
\n", "
" ], "text/plain": [ " feature_name coefficient\n", "0 Intercept -0.067143\n", "1 age -0.023500\n", "2 number_stories 0.048774\n", "3 square_feet 0.777122\n", "4 plei_1 0.312308\n", "5 plei_3 0.122954\n", "6 domestic_gas 0.229171\n", "7 heating_gas 0.143661" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get a list of the features used to explain energy\n", "features = dfScaled.columns.tolist()\n", "response = ['energy']\n", "features.remove(response[0])\n", "# import regression solver\n", "from sklearn import linear_model\n", "# declare a linear regression model \n", "lr = linear_model.LinearRegression(fit_intercept=True)\n", "# define response variable: energy usage\n", "y = np.asarray(dfScaled[response]) \n", "# define features\n", "X = dfScaled[features]\n", "# fit regression model to the data\n", "regr = lr.fit(X,y)\n", "coefs = regr.coef_[0]\n", "# collect regression coefficients\n", "dataRegQ = []\n", "dataRegQ.append(('Intercept', regr.intercept_[0]))\n", "for i in range(len(features)):\n", " dataRegQ.append((features[i],coefs[i]))\n", "# compute energy predictions using our fitted model \n", "yh = regr.predict(X)\n", "# import package to compute the R-squared quality metric\n", "from sklearn.metrics import r2_score\n", "# print results\n", "print 'R-Squared: ', r2_score(y,yh)\n", "pd.DataFrame(dataRegQ,columns=['feature_name','coefficient']) #.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the model accuracy\n", "+ The blue dots represent the observed energy versus the predicted energy by our trained model\n", "+ The black dotted line is at 45 degrees and represents the perfect model\n", "+ The closer the blue dots are to the black dotted line, the better the model fits the data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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qqjBw4EAAdf+K2bRpk/X8IUOGoLS0FB999BF++eUXfPPNN/j888/xxBNPCK6F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\nFDh27BiOHDmC1atXG7xX6vyYpBBpKSsrEyZlGtOnTx+9vxkCwJUrV+D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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(y, yh)\n", "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n", "ax.set_xlabel('Energy Observed',fontsize=20)\n", "ax.set_ylabel('Energy Predicted',fontsize=20)\n", "ax.axis([-0.1, 1.1, -0.1, 1.1])\n", "plt.gcf().set_size_inches( (6, 6) )\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Detect buildings that consume energy inefficiently" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Engineered characteristics of heating and cooling systems for the buildings" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dfCH = dfCH.toPandas()\n", "# get the numerical features\n", "dfR = dfCH[['plug_load_consumption','ac_consumption','domestic_gas','heating_gas']]\n", "# scale features with the max value of each column\n", "dfN = dfR/dfR.max()\n", "# concatenate scaled features and buildings name\n", "dfCH_n = pd.concat((dfN, dfCH['Property Name']),1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Identify the buildings that consume energy inefficiently\n", "+ Air conditioner\n", "+ Plugged-in equipment (microwaves, computers, refrigerators, ...)\n", "+ Gas for domestic usage\n", "+ Gas for heating" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, the following buildings consume AC inefficiently:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Property Nameac_consumption
84CAAABagels0.997882
86PaninicoCafeReport1.000000
87Curran'sSuperiorMeatsReport0.717030
\n", "
" ], "text/plain": [ " Property Name ac_consumption\n", "84 CAAABagels 0.997882\n", "86 PaninicoCafeReport 1.000000\n", "87 Curran'sSuperiorMeatsReport 0.717030" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cname = 'ac_consumption' #'heating_gas'\n", "dfCH_n[dfCH_n[cname]>dfCH_n.quantile(0.95)[0]][['Property Name',cname]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Many dimensions are used to measure energy inefficiencies\n", "Here we are using four inefficiency metrics associated with: \n", "+ Plugged-in equipment\n", "+ Air conditioning\n", "+ Domestic gas \n", "+ Heating gas \n", "\n", "## Can we train a model to consider all of the dimesions to detect inefficient buildings?\n", "\n", "Yes. K-means clustering helps us identify groups of buildings that consume energy inefficiently." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import K-means and PCA library\n", "from sklearn.cluster import KMeans\n", "from sklearn.decomposition import PCA\n", "# declare PCA model with 4 components\n", "pca = PCA(n_components=2)\n", "pca.fit(dfN)\n", "featReduced = pca.fit_transform(dfN)\n", "# declare a K-means model with 4 clusters \n", "kmeans = KMeans(n_clusters=4, tol=0.00001, random_state=1)\n", "# run K-means with our data\n", "kmeans.fit(featReduced)\n", "# get the label for each building using the K-means model\n", "labels = kmeans.predict(featReduced)\n", "buildings = np.asarray(dfCH['Property Name'].values.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize clusters using two out of the four dimensions and the K-means labels to color observations" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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wFBIRERERGAqJiIiICAyFRERERASGQiIiIiICQyERERERgaGQiIiIiMBQSERE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395E9Gor1FxvHNnK/H40kFhtepw6tQc7747QaGwuLI4na5w6VKGvr4oluVy4UKGkydn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\nAweiUCg4c+ZMk49TWVmJo6MjVlbt70dQW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# inefficiency dimensions: ['plug_load_consumption','ac_consumption','domestic_gas','heating_gas']\n", "x = dfCH_n['plug_load_consumption'] \n", "y = dfCH_n['heating_gas'] \n", "plt.scatter(x, y, s=225, c=labels, alpha=0.5)\n", "plt.gcf().set_size_inches( (7, 7) )\n", "plt.xlabel('Plugged Equipment Inefficiency',fontsize=15)\n", "plt.ylabel('Heating Gas Inefficiency',fontsize=15)\n", "tt = 'Buildings Colored by Cluster Labels' \n", "plt.title(tt,fontsize=15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The clustering model helps us identify inefficient groups of buildings \n", "\n", "+ Note that in the above figure, most buildings are part of the purple cluster.\n", "+ Buildings labeled with brown, yellow, and blue are more inefficient than the ones that are part of the purple cluster.\n", "+ Labels are re-coded into a binary variable where 1 is inefficient (brown, yellow or blue clusters) and 0 otherwise (purple cluster)." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# define binary variable to identify inefficient buildings\n", "label_binary = []\n", "for v in labels:\n", " label_binary.append(0 if (v == 0) else 1)\n", "label_binary = np.asarray(label_binary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification model to identify the inefficient buildings" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Accuracy: 0.864077669903\n" ] } ], "source": [ "# declare a logistic regression model \n", "log = linear_model.LogisticRegression(tol = 0.00001, max_iter = 100)\n", "# fit model with our data\n", "log.fit(featReduced, label_binary)\n", "# compute accuracy of the trained model\n", "accuracy = log.score(featReduced, label_binary)\n", "# compute predictions using trained model\n", "y_pred = log.predict(featReduced)\n", "print \"Model Accuracy: \", accuracy" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(2)\n", " plt.xticks(tick_marks, ['efficient','inefficient'], rotation=0)\n", " plt.yticks(tick_marks, ['efficient','inefficient'])\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Confusion matrices allow us to analyze the results of our classifier\n", "\n", "+ The 1st row of the confusion matrix, we notice that our model is very good to predict the efficient buildings\n", "+ The 2nd row tells us that our model is not very accurate to identify inefficient buildings (only 8 out of the 22 inefficient buildings were predicted correctly by our model)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[81 0]\n", " [14 8]]\n" ] }, { "data": { "image/png": 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Yvnw5AECv1yMtLQ1ZWVnQ6/WIjIzElClToNFoLLoOEwQiIiKFWnoVQ/fu3fHK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\nEZE1CDIeMu3atQvDhg3DoEGD0K1bNyQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# import confusion matrix to explore model accuracy\n", "from sklearn.metrics import confusion_matrix\n", "# compute confusion matrix\n", "cm = confusion_matrix(label_binary, y_pred)\n", "np.set_printoptions(precision=2)\n", "print('Confusion matrix, without normalization')\n", "print(cm)\n", "plt.figure()\n", "plot_confusion_matrix(cm)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 4. Export data and models to RStudio\n", "We built a \n", "_[Shiny app](https://ibmdatascience.shinyapps.io/SparkSummitDemo/)_ that summarizes our insights.\n", "\n", "### Add credentials for Object Storage container name\n", "\n", "1. Click inside the empty cell below to activate it\n", "2. On the right hand data panel, click \"Insert to code\" under any of the files shown and select \"Credentials\". \n", "


\n", "3. **Now rename the variable that was inserted to be called `credentials`.** This will be referenced in the last code cell to put your data in the right Object Storage container.\n", "4. Run the code cells below to export to a file in your container called `export_data.csv`\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fileNameOut = 'swift://'+ credentials['container'] + '.' + name + \"/export_data.csv\"\n", "dfOut.write.format('com.databricks.spark.csv').options(header='true').save(fileNameOut)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2 with Spark 1.6", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }