H2O_AutoML_IPYNB/WA_Cannabis_Sales_Daily.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Washington State Cannabis Sale Forecasts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In November 2012, Washington State voters approved to legalize marijuana through [Washington Initiative 502 (I-502)](https://sos.wa.gov/_assets/elections/initiatives/i502.pdf) by a margin of approximately [56 to 44](https://results.vote.wa.gov/results/20121106/Initiative-Measure-No-502-Concerns-marijuana_ByCounty.html). As part of this initiative, Washington State created a new agency, the [Washington State Liquor and Cannabis Board (WSLCB)](https://lcb.wa.gov), for licensing and regulating liquor and marijuana. As part of its mandate the WSLCB provides public access to [data](https://data.lcb.wa.gov) on how Washington States’s marijuana market is performing.\n",
    "\n",
    "In this exercise, we will analyze daily sales data from 2015-11-01 to 2017-03-04 in an attempt to answer the following questions:\n",
    "\n",
    "1. How many previous weeks influence cannabis sales?\n",
    "1. What days exhibit unusual sales?\n",
    "1. Do stores in counties that voted to legalize marijuana behave differently that those that didn't?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "This analysis will use H2O's Sparkling Water to analyze cannabis sales data, where Spark will be used for data management and manipulation and H2O will be use for data analysis and python will be used as the client. This Jupyter notebook assumes that it was launched with a PySparkling context."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create H2O Context inside Spark Cluster\n",
    "\n",
    "When using PySparkling, the first step is to create a Sparkling Water context within the Spark context, `spark`, so data can be passed back and forth between Spark and H2O."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting to H2O server at http://172.17.0.2:54321... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>08 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.20.0.4</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>13 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>sparkling-water-h2o_local-1534208179743</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>7.052 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://172.17.0.2:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>2.7.15 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------------\n",
       "H2O cluster uptime:         08 secs\n",
       "H2O cluster timezone:       Etc/UTC\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.20.0.4\n",
       "H2O cluster version age:    13 days\n",
       "H2O cluster name:           sparkling-water-h2o_local-1534208179743\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    7.052 Gb\n",
       "H2O cluster total cores:    4\n",
       "H2O cluster allowed cores:  4\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://172.17.0.2:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             2.7.15 final\n",
       "--------------------------  ----------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sparkling Water Context:\n",
      " * H2O name: sparkling-water-h2o_local-1534208179743\n",
      " * cluster size: 1\n",
      " * list of used nodes:\n",
      "  (executorId, host, port)\n",
      "  ------------------------\n",
      "  (driver,d59dd40eeeda,54321)\n",
      "  ------------------------\n",
      "\n",
      "  Open H2O Flow in browser: http://172.17.0.2:54321 (CMD + click in Mac OSX)\n",
      "\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "from pysparkling import *\n",
    "hc = H2OContext.getOrCreate(spark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation using Spark\n",
    "\n",
    "The next step is to use Spark to prepare the data for analysis by H2O. The particular data manipulations are:\n",
    "\n",
    "1. Read daily sales transactions\n",
    "1. Create daily sales aggregates by store\n",
    "1. Find unusual days\n",
    "1. Join county voting results for I-502 with aggregated data\n",
    "1. Collapse infrequent counties and cities for analysis, i.e. manage high cardinality categorical columsn\n",
    "1. Create lagged predictors\n",
    "1. Create train / test splits for modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Daily Sales Transactions for Different Stores\n",
    "\n",
    "Reading the data involve a straight-forward use of the `spark.read.csv` function with the schema metadata. The data file contains six columns:\n",
    "\n",
    "|     | Column Name | Description |\n",
    "| --- | ----------- | ----------- |\n",
    "|  1  | SalesDate | Date of sale|\n",
    "|  2  | Organization | Organization that owns the store |\n",
    "|  3  | County | County of store location|\n",
    "|  4  | City | City of store location |\n",
    "|  5  | Sales Price | Price of line item |\n",
    "|  6  | Freq | Number of occurrences |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "schema = StructType([StructField('SalesDate', DateType(), metadata = {'desc': 'Date of sale'}),\n",
    "                     StructField('Organization', StringType(), metadata = {'desc': 'Organization that owns the store'}),\n",
    "                     StructField('County', StringType(), metadata = {'desc': 'County of store location'}),\n",
    "                     StructField('City', StringType(), metadata = {'desc': 'City of store location'}),\n",
    "                     StructField('SalesPrice', DoubleType(), metadata = {'desc': 'Price of line item'}),\n",
    "                     StructField('Freq', IntegerType(), metadata = {'desc': 'Number of occurrences'})\n",
    "                    ])\n",
    "\n",
    "# https://s3-us-west-2.amazonaws.com/h2o-tutorials/data/topics/time_series/wa_cannabis/WA_Cannabis_Sales_Daily.csv\n",
    "raw_sales = spark.read.csv('../../data/topics/time_series/wa_cannabis/WA_Cannabis_Sales_Daily.csv',\n",
    "                           header = True, schema = schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------------+-------+--------+-----------------+------------------+\n",
      "|summary| Organization| County|    City|       SalesPrice|              Freq|\n",
      "+-------+-------------+-------+--------+-----------------+------------------+\n",
      "|  count|      3765407|3765407| 3765407|          3765407|           3765407|\n",
      "|   mean|         null|   null|    null|26.74136408893552|  8.11807701000184|\n",
      "| stddev|         null|   null|    null| 35.7532082540557|20.264077828026984|\n",
      "|    min|     #HASHTAG|  ADAMS|ABERDEEN|          -651.78|                 1|\n",
      "|    max|WORLD OF WEED| YAKIMA|    YELM|          3104.92|              2175|\n",
      "+-------+-------------+-------+--------+-----------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Describe numeric and string columns\n",
    "raw_sales.describe().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+--------------+--------------+----------------------------+\n",
      "|  nrows|min(SalesDate)|max(SalesDate)|count(DISTINCT Organization)|\n",
      "+-------+--------------+--------------+----------------------------+\n",
      "|3765407|    2015-11-01|    2017-03-04|                         222|\n",
      "+-------+--------------+--------------+----------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Additional summaries\n",
    "raw_sales.select([count('*').alias('nrows'), min('SalesDate'), max('SalesDate'), countDistinct('Organization')]).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Daily Sales Aggregates by Store\n",
    "\n",
    "In order to analyze aggreate sales demand, the transactional sales data are aggregated three ways, each of which uses the `log(x + 1)` function to manage their inherent skewness:\n",
    "\n",
    "1. `Log1pDemandInThou = log1p(sum(store sales)/1000)`\n",
    "1. `Log1pOtherDemandInThou = log1p(sum(citywise sales)/1000) - log1p(sum(store sales)/1000)`, demand from the rest of the organizations\n",
    "1. `Log1pNumSales = log1p(sum(I(store sales > 0)))`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Number of Organization-Days: ', 87279)\n"
     ]
    }
   ],
   "source": [
    "demand = raw_sales.groupBy('SalesDate', 'Organization', 'County', 'City') \\\n",
    "    .agg(log1p(sum(col('Freq') * col('SalesPrice')) / 1000).alias('Log1pDemandInThou'),\n",
    "         log1p(sum(when(col('SalesPrice') > 0, col('Freq')).otherwise(0))).alias('Log1pNumSales')) \\\n",
    "    .alias('demand')\n",
    "print(\"Number of Organization-Days: \", demand.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------------+------+--------+--------------------+------------------+\n",
      "|summary| Organization|County|    City|   Log1pDemandInThou|     Log1pNumSales|\n",
      "+-------+-------------+------+--------+--------------------+------------------+\n",
      "|  count|        87279| 87279|   87279|               87278|             87279|\n",
      "|   mean|         null|  null|    null|  1.5845741187313314|  5.43356257230457|\n",
      "| stddev|         null|  null|    null|  0.7088442952488704|1.0017507127933845|\n",
      "|    min|     #HASHTAG| ADAMS|ABERDEEN|-0.02223538405071...|               0.0|\n",
      "|    max|WORLD OF WEED|YAKIMA|    YELM|  3.9183588912456506| 8.152774052744075|\n",
      "+-------+-------------+------+--------+--------------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "demand.describe().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Number of Days: ', 490)\n"
     ]
    }
   ],
   "source": [
    "daily_demand = raw_sales.groupBy('SalesDate') \\\n",
    "    .agg(log1p(sum(col('Freq') * col('SalesPrice')) / 1000).alias('Log1pCitywideDemandInThou')) \\\n",
    "    .alias('daily_demand')\n",
    "print(\"Number of Days: \", daily_demand.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Number of Organization-Days: ', 87279)\n"
     ]
    }
   ],
   "source": [
    "demand = demand.join(daily_demand, demand.SalesDate == daily_demand.SalesDate, how = \"left_outer\") \\\n",
    "    .select('demand.*', 'daily_demand.Log1pCitywideDemandInThou')\n",
    "print(\"Number of Organization-Days: \", demand.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand = demand.select('SalesDate', 'Organization', 'County', 'City', 'Log1pDemandInThou',\n",
    "                       (col('Log1pCitywideDemandInThou') - col('Log1pDemandInThou')).alias('Log1pOtherDemandInThou'),\n",
    "                       'Log1pNumSales') \\\n",
    "         .alias('demand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+--------------------+----------------------+-----------------+\n",
      "|summary|   Log1pDemandInThou|Log1pOtherDemandInThou|    Log1pNumSales|\n",
      "+-------+--------------------+----------------------+-----------------+\n",
      "|  count|               87278|                 87278|            87279|\n",
      "|   mean|  1.5845741187313314|    5.2779171104516465|5.433562572304564|\n",
      "| stddev|  0.7088442952488708|    0.7240699016354217|1.001750712793385|\n",
      "|    min|-0.02223538405071...|     2.646763824081182|              0.0|\n",
      "|    max|  3.9183588912456506|     7.300001605928917|8.152774052744075|\n",
      "+-------+--------------------+----------------------+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "demand.describe(['Log1pDemandInThou', 'Log1pOtherDemandInThou', 'Log1pNumSales']).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find Unusual Days\n",
    "\n",
    "Fifteen unusual days are discovered by examining the week-over-week ratios in `Log1pCitywideDemandInThou`. Not surprisingly, these days are at or around [420](https://en.wikipedia.org/wiki/420_%28cannabis_culture%29) and the holidays Fourth of July, Thanksgiving, Christmas, and New Year's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.window import Window\n",
    "\n",
    "w = Window().orderBy(col('SalesDate'))\n",
    "plot_data = \\\n",
    "  daily_demand.select('SalesDate', 'Log1pCitywideDemandInThou',\n",
    "                      round(col('Log1pCitywideDemandInThou') / lag('Log1pCitywideDemandInThou', count = 7).over(w), 4).alias('WoW'),\n",
    "                      round(abs(col('Log1pCitywideDemandInThou') / lag('Log1pCitywideDemandInThou', count = 7).over(w) - 1), 4).alias('AbsWoWDiff')) \\\n",
    "                      .orderBy('AbsWoWDiff', ascending = False).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3eab3cac50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plot_data['WoW'].plot.hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesDate</th>\n",
       "      <th>Log1pCitywideDemandInThou</th>\n",
       "      <th>WoW</th>\n",
       "      <th>AbsWoWDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>6.584401</td>\n",
       "      <td>1.1465</td>\n",
       "      <td>0.1465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>5.742801</td>\n",
       "      <td>0.8641</td>\n",
       "      <td>0.1359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-25</td>\n",
       "      <td>6.332216</td>\n",
       "      <td>0.9057</td>\n",
       "      <td>0.0943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-20</td>\n",
       "      <td>7.177266</td>\n",
       "      <td>1.0871</td>\n",
       "      <td>0.0871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>6.973423</td>\n",
       "      <td>1.0812</td>\n",
       "      <td>0.0812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>6.602294</td>\n",
       "      <td>0.9199</td>\n",
       "      <td>0.0801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>6.829579</td>\n",
       "      <td>1.0785</td>\n",
       "      <td>0.0785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>6.725603</td>\n",
       "      <td>1.0734</td>\n",
       "      <td>0.0734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>6.463089</td>\n",
       "      <td>0.9276</td>\n",
       "      <td>0.0724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-12-26</td>\n",
       "      <td>6.328705</td>\n",
       "      <td>0.9305</td>\n",
       "      <td>0.0695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-12-02</td>\n",
       "      <td>6.397731</td>\n",
       "      <td>0.9513</td>\n",
       "      <td>0.0487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>6.896208</td>\n",
       "      <td>0.9515</td>\n",
       "      <td>0.0485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-12-23</td>\n",
       "      <td>6.728288</td>\n",
       "      <td>1.0459</td>\n",
       "      <td>0.0459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>7.320255</td>\n",
       "      <td>1.0438</td>\n",
       "      <td>0.0438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>7.247409</td>\n",
       "      <td>1.0429</td>\n",
       "      <td>0.0429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-01-09</td>\n",
       "      <td>6.783079</td>\n",
       "      <td>1.0382</td>\n",
       "      <td>0.0382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>7.258878</td>\n",
       "      <td>1.0359</td>\n",
       "      <td>0.0359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-09-02</td>\n",
       "      <td>7.421344</td>\n",
       "      <td>1.0351</td>\n",
       "      <td>0.0351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>7.060536</td>\n",
       "      <td>1.0345</td>\n",
       "      <td>0.0345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>7.010243</td>\n",
       "      <td>0.9657</td>\n",
       "      <td>0.0343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SalesDate  Log1pCitywideDemandInThou     WoW  AbsWoWDiff\n",
       "0   2016-01-01                   6.584401  1.1465      0.1465\n",
       "1   2015-12-25                   5.742801  0.8641      0.1359\n",
       "2   2016-12-25                   6.332216  0.9057      0.0943\n",
       "3   2016-04-20                   7.177266  1.0871      0.0871\n",
       "4   2015-12-24                   6.973423  1.0812      0.0812\n",
       "5   2016-04-27                   6.602294  0.9199      0.0801\n",
       "6   2017-01-01                   6.829579  1.0785      0.0785\n",
       "7   2015-11-25                   6.725603  1.0734      0.0734\n",
       "8   2016-01-07                   6.463089  0.9276      0.0724\n",
       "9   2015-12-26                   6.328705  0.9305      0.0695\n",
       "10  2015-12-02                   6.397731  0.9513      0.0487\n",
       "11  2016-11-30                   6.896208  0.9515      0.0485\n",
       "12  2015-12-23                   6.728288  1.0459      0.0459\n",
       "13  2016-07-01                   7.320255  1.0438      0.0438\n",
       "14  2016-11-23                   7.247409  1.0429      0.0429\n",
       "15  2016-01-09                   6.783079  1.0382      0.0382\n",
       "16  2016-09-01                   7.258878  1.0359      0.0359\n",
       "17  2016-09-02                   7.421344  1.0351      0.0351\n",
       "18  2016-04-01                   7.060536  1.0345      0.0345\n",
       "19  2016-09-08                   7.010243  0.9657      0.0343"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (20, 10))\n",
    "for dt in ['2015-11-25', '2015-12-02', '2015-12-23', '2015-12-24', '2015-12-25', '2015-12-26', '2016-01-01',\n",
    "           '2016-01-07', '2016-04-20', '2016-04-27', '2016-07-01',\n",
    "           '2016-11-23', '2016-11-30',                             '2016-12-25',               '2017-01-01']:\n",
    "    plt.axvline(x = datetime.datetime.strptime(dt, '%Y-%m-%d'), color = 'orange', linestyle='--')\n",
    "plot_data.plot(x = 'SalesDate', y = 'Log1pCitywideDemandInThou', ax = ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand = demand.select('*',\n",
    "                       when(col('SalesDate') == '2016-04-20', 'FourTwenty')\n",
    "            .otherwise(when(col('SalesDate') == '2016-04-27', 'FourTwentySeven')\n",
    "            .otherwise(when(col('SalesDate') == '2016-07-01', 'PreJuly4th')\n",
    "            .otherwise(when(col('SalesDate') == '2015-11-25', 'ThanksgivingMinusOne')\n",
    "            .otherwise(when(col('SalesDate') == '2016-11-23', 'ThanksgivingMinusOne')\n",
    "            .otherwise(when(col('SalesDate') == '2015-12-02', 'ThanksgivingPlusSix')\n",
    "            .otherwise(when(col('SalesDate') == '2016-11-30', 'ThanksgivingPlusSix')\n",
    "            .otherwise(when(col('SalesDate') == '2015-12-23', 'ChristmasMinusTwo')\n",
    "            .otherwise(when(col('SalesDate') == '2015-12-24', 'ChristmasMinusOne')\n",
    "            .otherwise(when(col('SalesDate') == '2015-12-25', 'Christmas')\n",
    "            .otherwise(when(col('SalesDate') == '2016-12-25', 'Christmas')\n",
    "            .otherwise(when(col('SalesDate') == '2015-12-26', 'ChristmasPlusOne')\n",
    "            .otherwise(when(col('SalesDate') == '2016-01-01', 'NewYearsDay')\n",
    "            .otherwise(when(col('SalesDate') == '2017-01-01', 'NewYearsDay')\n",
    "            .otherwise(when(col('SalesDate') == '2016-01-07', 'NewYearsDayPlusSix')\n",
    "            .otherwise('N/A'))))))))))))))).alias('DayOfInterest')).alias('demand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-----+\n",
      "|       DayOfInterest|count|\n",
      "+--------------------+-----+\n",
      "|                 N/A|84886|\n",
      "|         NewYearsDay|  347|\n",
      "| ThanksgivingPlusSix|  345|\n",
      "|ThanksgivingMinusOne|  344|\n",
      "|           Christmas|  306|\n",
      "|          PreJuly4th|  185|\n",
      "|     FourTwentySeven|  170|\n",
      "|          FourTwenty|  165|\n",
      "|  NewYearsDayPlusSix|  136|\n",
      "|   ChristmasMinusTwo|  133|\n",
      "|   ChristmasMinusOne|  133|\n",
      "|    ChristmasPlusOne|  129|\n",
      "+--------------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "demand.groupBy('DayOfInterest').count().sort('count', ascending = False).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join Sales Data with Washington Initiative 502 Vote to Legalize Cannabis\n",
    "\n",
    "Generally speaking counties in Western Washington voted to legalize cannabis sales, while those in Eastern Washington did not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "schema = StructType([StructField('County', StringType(), metadata = {'desc': 'County'}),\n",
    "                     StructField('LegalizationVote', DoubleType(), metadata = {'desc': 'Fraction voting to legalize'})\n",
    "                    ])\n",
    "\n",
    "# https://s3-us-west-2.amazonaws.com/h2o-tutorials/data/topics/time_series/wa_cannabis/Initiative-Measure-No-502-Concerns-marijuana_ByCounty.csv\n",
    "legalization = spark.read.csv('../../data/topics/time_series/wa_cannabis/Initiative-Measure-No-502-Concerns-marijuana_ByCounty.csv',\n",
    "                              header = True, schema = schema).alias('legalization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------+-------------------+\n",
      "|summary|County|   LegalizationVote|\n",
      "+-------+------+-------------------+\n",
      "|  count|    39|                 39|\n",
      "|   mean|  null| 0.5062769230769228|\n",
      "| stddev|  null|0.06759592973396542|\n",
      "|    min| ADAMS|             0.3783|\n",
      "|    max|YAKIMA|             0.6829|\n",
      "+-------+------+-------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "legalization.describe().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+----------------+\n",
      "|   County|LegalizationVote|\n",
      "+---------+----------------+\n",
      "| SAN JUAN|          0.6829|\n",
      "|JEFFERSON|          0.6523|\n",
      "|     KING|          0.6349|\n",
      "|  WHATCOM|          0.5685|\n",
      "|   KITSAP|          0.5598|\n",
      "+---------+----------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "legalization.sort('LegalizationVote', ascending = False).show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+----------------+\n",
      "|  County|LegalizationVote|\n",
      "+--------+----------------+\n",
      "|GARFIELD|          0.3783|\n",
      "|FRANKLIN|          0.3894|\n",
      "|   ADAMS|          0.3938|\n",
      "| LINCOLN|          0.4186|\n",
      "|  YAKIMA|          0.4219|\n",
      "+--------+----------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "legalization.sort('LegalizationVote').show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand = demand.join(legalization, demand.County == legalization.County, how = \"left_outer\") \\\n",
    "    .select('demand.*', 'legalization.LegalizationVote')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- SalesDate: date (nullable = true)\n",
      " |-- Organization: string (nullable = true)\n",
      " |-- County: string (nullable = true)\n",
      " |-- City: string (nullable = true)\n",
      " |-- Log1pDemandInThou: double (nullable = true)\n",
      " |-- Log1pOtherDemandInThou: double (nullable = true)\n",
      " |-- Log1pNumSales: double (nullable = true)\n",
      " |-- DayOfInterest: string (nullable = false)\n",
      " |-- LegalizationVote: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "demand.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collapse Infrequent Counties and Cities\n",
    "\n",
    "In order to correct for high cardinality county and city features, the infrequent locations are collapsed into an `OTHER` category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+----+\n",
      "|      County|Freq|\n",
      "+------------+----+\n",
      "|        KING|  51|\n",
      "|     SPOKANE|  21|\n",
      "|   SNOHOMISH|  20|\n",
      "|      PIERCE|  19|\n",
      "|      KITSAP|  11|\n",
      "|     WHATCOM|  10|\n",
      "|    THURSTON|  10|\n",
      "|       CLARK|   8|\n",
      "|       MASON|   6|\n",
      "|     CLALLAM|   6|\n",
      "|GRAYS HARBOR|   6|\n",
      "|      SKAGIT|   6|\n",
      "|     COWLITZ|   6|\n",
      "|    KITTITAS|   4|\n",
      "|      CHELAN|   4|\n",
      "|      BENTON|   4|\n",
      "|       GRANT|   3|\n",
      "|      YAKIMA|   3|\n",
      "|      ISLAND|   3|\n",
      "|       LEWIS|   3|\n",
      "+------------+----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "demand.select('Organization', 'County').distinct().groupBy('County') \\\n",
    "    .agg(count('*').alias('Freq')).orderBy('Freq', ascending = False).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand = demand.withColumn('County',\n",
    "                           when(col('County') == 'KING', 'KING')\n",
    "                .otherwise(when(col('County') == 'SPOKANE', 'SPOKANE')\n",
    "                .otherwise(when(col('County') == 'SNOHOMISH', 'SNOHOMISH')\n",
    "                .otherwise(when(col('County') == 'PIERCE', 'PIERCE')\n",
    "                .otherwise(when(col('County') == 'KITSAP', 'KITSAP')\n",
    "                .otherwise(when(col('County') == 'THURSTON', 'THURSTON')\n",
    "                .otherwise(when(col('County') == 'WHATCOM', 'WHATCOM')\n",
    "                .otherwise(when(col('County') == 'CLARK', 'CLARK')\n",
    "                .otherwise('OTHER')))))))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+----+\n",
      "|   County|Freq|\n",
      "+---------+----+\n",
      "|    OTHER|  72|\n",
      "|     KING|  51|\n",
      "|  SPOKANE|  21|\n",
      "|SNOHOMISH|  20|\n",
      "|   PIERCE|  19|\n",
      "|   KITSAP|  11|\n",
      "| THURSTON|  10|\n",
      "|  WHATCOM|  10|\n",
      "|    CLARK|   8|\n",
      "+---------+----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "demand.select('Organization', 'County').distinct().groupBy('County') \\\n",
    "    .agg(count('*').alias('Freq')).orderBy('Freq', ascending = False).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+----+\n",
      "|         City|Freq|\n",
      "+-------------+----+\n",
      "|      SEATTLE|  37|\n",
      "|      SPOKANE|  16|\n",
      "|       TACOMA|  12|\n",
      "|    VANCOUVER|   7|\n",
      "|      EVERETT|   6|\n",
      "|      OLYMPIA|   5|\n",
      "|     LONGVIEW|   5|\n",
      "|   BELLINGHAM|   5|\n",
      "| PORT ORCHARD|   4|\n",
      "|    BREMERTON|   3|\n",
      "|     ABERDEEN|   3|\n",
      "| MOUNT VERNON|   3|\n",
      "|   ELLENSBURG|   3|\n",
      "|     LYNNWOOD|   3|\n",
      "|     KIRKLAND|   3|\n",
      "|     BELLEVUE|   3|\n",
      "| PORT ANGELES|   3|\n",
      "|      BELFAIR|   3|\n",
      "|    CLARKSTON|   2|\n",
      "|SEDRO WOOLLEY|   2|\n",
      "+-------------+----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "demand.select('Organization', 'City').distinct().groupBy('City') \\\n",
    "    .agg(count('*').alias('Freq')).orderBy('Freq', ascending = False).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand = demand.withColumn('City',\n",
    "                           when(col('City') == 'SEATTLE', 'SEATTLE')\n",
    "                .otherwise(when(col('City') == 'SPOKANE', 'SPOKANE')\n",
    "                .otherwise(when(col('City') == 'TACOMA', 'TACOMA')\n",
    "                .otherwise('OTHER'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----+\n",
      "|   City|Freq|\n",
      "+-------+----+\n",
      "|  OTHER| 157|\n",
      "|SEATTLE|  37|\n",
      "|SPOKANE|  16|\n",
      "| TACOMA|  12|\n",
      "+-------+----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "demand.select('Organization', 'City').distinct().groupBy('City') \\\n",
    "    .agg(count('*').alias('Freq')).orderBy('Freq', ascending = False).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Lagged Predictors\n",
    "\n",
    "Up to seven weeks of sales will be considered as features in the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.window import Window\n",
    "\n",
    "w = Window().partitionBy([col(x) for x in ['Organization']]).orderBy(col('SalesDate'))\n",
    "demand = demand.select('SalesDate', 'DayOfInterest', 'Organization', 'County', 'City', 'LegalizationVote',\n",
    "                       'Log1pDemandInThou',\n",
    "                       lag('Log1pDemandInThou', count = 7).over(w).alias('Log1pDemandInThou_L7'),\n",
    "                       lag('Log1pDemandInThou', count = 14).over(w).alias('Log1pDemandInThou_L14'),\n",
    "                       lag('Log1pDemandInThou', count = 21).over(w).alias('Log1pDemandInThou_L21'),\n",
    "                       lag('Log1pDemandInThou', count = 28).over(w).alias('Log1pDemandInThou_L28'),\n",
    "                       lag('Log1pDemandInThou', count = 35).over(w).alias('Log1pDemandInThou_L35'),\n",
    "                       lag('Log1pOtherDemandInThou', count = 7).over(w).alias('Log1pOtherDemandInThou_L7'),\n",
    "                       lag('Log1pNumSales', count = 7).over(w).alias('Log1pNumSales_L7'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- SalesDate: date (nullable = true)\n",
      " |-- DayOfInterest: string (nullable = false)\n",
      " |-- Organization: string (nullable = true)\n",
      " |-- County: string (nullable = false)\n",
      " |-- City: string (nullable = false)\n",
      " |-- LegalizationVote: double (nullable = true)\n",
      " |-- Log1pDemandInThou: double (nullable = true)\n",
      " |-- Log1pDemandInThou_L7: double (nullable = true)\n",
      " |-- Log1pDemandInThou_L14: double (nullable = true)\n",
      " |-- Log1pDemandInThou_L21: double (nullable = true)\n",
      " |-- Log1pDemandInThou_L28: double (nullable = true)\n",
      " |-- Log1pDemandInThou_L35: double (nullable = true)\n",
      " |-- Log1pOtherDemandInThou_L7: double (nullable = true)\n",
      " |-- Log1pNumSales_L7: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "demand.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Train / Test Splits for Modeling\n",
    "\n",
    "Given the time series nature of this exercise, the train and test splits are based on time, where everything up to 2017-02-25 is in the training set and everything from 2017-02-26 onwards in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = demand.filter(demand.SalesDate <= '2017-02-25')\n",
    "test = demand.filter(demand.SalesDate >= '2017-02-26')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------------------------+\n",
      "|Number of Dates in Training Data Set|\n",
      "+------------------------------------+\n",
      "|                                 483|\n",
      "+------------------------------------+\n",
      "\n",
      "+-----------------------------------+\n",
      "|Number of Dates in Testing Data Set|\n",
      "+-----------------------------------+\n",
      "|                                  7|\n",
      "+-----------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train.select(countDistinct('SalesDate').alias('Number of Dates in Training Data Set')).show()\n",
    "test.select(countDistinct('SalesDate').alias('Number of Dates in Testing Data Set')).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Data in H2O\n",
    "\n",
    "The steps for analyzing the data in H2O are as follows:\n",
    "1. Copy data from Spark to H2O\n",
    "1. Segment organizations into folds for cross-validation\n",
    "1. Run automatic machine learning to experiment with generalized linear models, random forests, extreme random trees, and gradient boosting machines.\n",
    "1. Answer questions using leading model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copy Data from Spark to H2O\n",
    "\n",
    "First copy the training and test data inside the H2O context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h2o\n",
    "train_hf = hc.as_h2o_frame(train, \"train\")\n",
    "test_hf = hc.as_h2o_frame(train, \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in ['Organization', 'County', 'City', 'DayOfInterest']:\n",
    "    train_hf[j] = train_hf[j].asfactor()\n",
    "    test_hf[j] = test_hf[j].asfactor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment Organizations into Folds for Cross-Validation\n",
    "\n",
    "Then segment the training data into folds for cross-validation using the organization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "organizations = train_hf['Organization'].unique().sort(0).as_data_frame()\n",
    "organizations = organizations.rename(columns = {'C1': 'Organization'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(2307)\n",
    "organizations = organizations.assign(Fold = np.random.randint(1,6, size = organizations.count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Organization\n",
      "Fold              \n",
      "1               45\n",
      "2               42\n",
      "3               43\n",
      "4               50\n",
      "5               42\n"
     ]
    }
   ],
   "source": [
    "print(organizations.groupby(['Fold']).count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/spark-dcfe757b-aeed-4b79-a3fd-6b4dcdc2adb3/userFiles-bad4ce1a-89c4-457e-96a4-22c7ae38bf4e/h2o_pysparkling_2.3-2.3.10.zip/h2o/utils/shared_utils.py:177: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "organizations_hf = h2o.H2OFrame(organizations, 'Organization')\n",
    "organizations_hf['Organization'] = organizations_hf['Organization'].asfactor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_hf = train_hf.merge(organizations_hf, all_x = True, all_y = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:85793\n",
      "Cols:15\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>       </th><th>Organization  </th><th>SalesDate          </th><th>DayOfInterest  </th><th>County  </th><th>City   </th><th>LegalizationVote  </th><th>Log1pDemandInThou  </th><th>Log1pDemandInThou_L7  </th><th>Log1pDemandInThou_L14  </th><th>Log1pDemandInThou_L21  </th><th>Log1pDemandInThou_L28  </th><th>Log1pDemandInThou_L35  </th><th>Log1pOtherDemandInThou_L7  </th><th>Log1pNumSales_L7  </th><th>Fold         </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>enum          </td><td>time               </td><td>enum           </td><td>enum    </td><td>enum   </td><td>real              </td><td>real               </td><td>real                  </td><td>real                   </td><td>real                   </td><td>real                   </td><td>real                   </td><td>real                       </td><td>real              </td><td>int          </td></tr>\n",
       "<tr><td>mins   </td><td>              </td><td>1.446336e+12       </td><td>               </td><td>        </td><td>       </td><td>0.3938            </td><td>-0.0222353840507   </td><td>-0.0222353840507      </td><td>-0.0222353840507       </td><td>-0.0222353840507       </td><td>-0.0222353840507       </td><td>-0.0222353840507       </td><td>2.64676382408              </td><td>0.0               </td><td>1.0          </td></tr>\n",
       "<tr><td>mean   </td><td>              </td><td>1.46928358138e+12  </td><td>               </td><td>        </td><td>       </td><td>0.554445003672    </td><td>1.58369975647      </td><td>1.58363105437         </td><td>1.58388835568          </td><td>1.5841901778           </td><td>1.58430579739          </td><td>1.5856692825           </td><td>5.27126169731              </td><td>5.42964729386     </td><td>3.02130709965</td></tr>\n",
       "<tr><td>maxs   </td><td>              </td><td>1.4879808e+12      </td><td>               </td><td>        </td><td>       </td><td>0.6829            </td><td>3.91835889125      </td><td>3.91835889125         </td><td>3.91835889125          </td><td>3.91835889125          </td><td>3.91835889125          </td><td>3.91835889125          </td><td>7.30000160593              </td><td>8.15277405274     </td><td>5.0          </td></tr>\n",
       "<tr><td>sigma  </td><td>              </td><td>11722469709.0      </td><td>               </td><td>        </td><td>       </td><td>0.0585488707153   </td><td>0.708972583256     </td><td>0.708876894453        </td><td>0.709234331211         </td><td>0.709448174114         </td><td>0.709652159537         </td><td>0.709986675053         </td><td>0.724108967611             </td><td>1.00193621221     </td><td>1.39611784332</td></tr>\n",
       "<tr><td>zeros  </td><td>              </td><td>0                  </td><td>               </td><td>        </td><td>       </td><td>0                 </td><td>4                  </td><td>4                     </td><td>4                      </td><td>4                      </td><td>4                      </td><td>4                      </td><td>0                          </td><td>3                 </td><td>0            </td></tr>\n",
       "<tr><td>missing</td><td>0             </td><td>0                  </td><td>0              </td><td>0       </td><td>0      </td><td>0                 </td><td>1                  </td><td>1555                  </td><td>3106                   </td><td>4653                   </td><td>6200                   </td><td>7747                   </td><td>1555                       </td><td>1554              </td><td>0            </td></tr>\n",
       "<tr><td>0      </td><td>#HASHTAG      </td><td>2015-11-01 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>2.10887533611      </td><td>nan                   </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                        </td><td>nan               </td><td>3.0          </td></tr>\n",
       "<tr><td>1      </td><td>#HASHTAG      </td><td>2015-11-02 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>1.76296382571      </td><td>nan                   </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                        </td><td>nan               </td><td>3.0          </td></tr>\n",
       "<tr><td>2      </td><td>#HASHTAG      </td><td>2015-11-03 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>1.85991487903      </td><td>nan                   </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                        </td><td>nan               </td><td>3.0          </td></tr>\n",
       "<tr><td>3      </td><td>#HASHTAG      </td><td>2015-11-04 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>1.96823340642      </td><td>nan                   </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                        </td><td>nan               </td><td>3.0          </td></tr>\n",
       "<tr><td>4      </td><td>#HASHTAG      </td><td>2015-11-05 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>1.89333483022      </td><td>nan                   </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                        </td><td>nan               </td><td>3.0          </td></tr>\n",
       "<tr><td>5      </td><td>#HASHTAG      </td><td>2015-11-06 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>2.18242899385      </td><td>nan                   </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                        </td><td>nan               </td><td>3.0          </td></tr>\n",
       "<tr><td>6      </td><td>#HASHTAG      </td><td>2015-11-07 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>2.3391484323       </td><td>nan                   </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                        </td><td>nan               </td><td>3.0          </td></tr>\n",
       "<tr><td>7      </td><td>#HASHTAG      </td><td>2015-11-08 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>2.03316982855      </td><td>2.10887533611         </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>4.19975778939              </td><td>6.12905021006     </td><td>3.0          </td></tr>\n",
       "<tr><td>8      </td><td>#HASHTAG      </td><td>2015-11-09 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>1.91237404784      </td><td>1.76296382571         </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>4.4639213204               </td><td>5.84932477995     </td><td>3.0          </td></tr>\n",
       "<tr><td>9      </td><td>#HASHTAG      </td><td>2015-11-10 00:00:00</td><td>N/A            </td><td>KING    </td><td>SEATTLE</td><td>0.6349            </td><td>1.99188137777      </td><td>1.85991487903         </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>nan                    </td><td>4.56406017977              </td><td>5.89989735358     </td><td>3.0          </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_hf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Automatic Machine Learning\n",
    "\n",
    "With the training and test data, we use H2O's Automatic Machine Learning to explore models of daily sales data based on generalized linear models, random forest, extreme random trees, and gradient boosting machines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Predictors\n",
    "predictors = ['DayOfInterest', 'County', 'City', 'LegalizationVote',\n",
    "              'Log1pDemandInThou_L7', 'Log1pDemandInThou_L14', 'Log1pDemandInThou_L21',\n",
    "              'Log1pDemandInThou_L28', 'Log1pDemandInThou_L35',\n",
    "              'Log1pOtherDemandInThou_L7', 'Log1pNumSales_L7']\n",
    "response = 'Log1pDemandInThou'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "from h2o.automl import H2OAutoML\n",
    "aml = H2OAutoML(max_models = 6, exclude_algos = ['DeepLearning'])\n",
    "aml.train(x = predictors, y = response,\n",
    "          training_frame = train_hf,\n",
    "          leaderboard_frame = test_hf,\n",
    "          fold_column = 'Fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                             </th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">    rmse</th><th style=\"text-align: right;\">      mse</th><th style=\"text-align: right;\">      mae</th><th style=\"text-align: right;\">    rmsle</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>DRF_0_AutoML_20180814_010158                         </td><td style=\"text-align: right;\">               0.0180551</td><td style=\"text-align: right;\">0.134369</td><td style=\"text-align: right;\">0.0180551</td><td style=\"text-align: right;\">0.0728116</td><td style=\"text-align: right;\">0.0644834</td></tr>\n",
       "<tr><td>XRT_0_AutoML_20180814_010158                         </td><td style=\"text-align: right;\">               0.0193647</td><td style=\"text-align: right;\">0.139157</td><td style=\"text-align: right;\">0.0193647</td><td style=\"text-align: right;\">0.0776709</td><td style=\"text-align: right;\">0.0656589</td></tr>\n",
       "<tr><td>StackedEnsemble_BestOfFamily_0_AutoML_20180814_010158</td><td style=\"text-align: right;\">               0.0240886</td><td style=\"text-align: right;\">0.155205</td><td style=\"text-align: right;\">0.0240886</td><td style=\"text-align: right;\">0.0971759</td><td style=\"text-align: right;\">0.0740517</td></tr>\n",
       "<tr><td>StackedEnsemble_AllModels_0_AutoML_20180814_010158   </td><td style=\"text-align: right;\">               0.0251443</td><td style=\"text-align: right;\">0.15857 </td><td style=\"text-align: right;\">0.0251443</td><td style=\"text-align: right;\">0.100462 </td><td style=\"text-align: right;\">0.0754529</td></tr>\n",
       "<tr><td>GBM_grid_0_AutoML_20180814_010158_model_2            </td><td style=\"text-align: right;\">               0.0271504</td><td style=\"text-align: right;\">0.164774</td><td style=\"text-align: right;\">0.0271504</td><td style=\"text-align: right;\">0.106466 </td><td style=\"text-align: right;\">0.0776994</td></tr>\n",
       "<tr><td>GBM_grid_0_AutoML_20180814_010158_model_1            </td><td style=\"text-align: right;\">               0.0278626</td><td style=\"text-align: right;\">0.166921</td><td style=\"text-align: right;\">0.0278626</td><td style=\"text-align: right;\">0.108198 </td><td style=\"text-align: right;\">0.0786982</td></tr>\n",
       "<tr><td>GBM_grid_0_AutoML_20180814_010158_model_0            </td><td style=\"text-align: right;\">               0.0282   </td><td style=\"text-align: right;\">0.167929</td><td style=\"text-align: right;\">0.0282   </td><td style=\"text-align: right;\">0.109385 </td><td style=\"text-align: right;\">0.0791152</td></tr>\n",
       "<tr><td>GLM_grid_0_AutoML_20180814_010158_model_0            </td><td style=\"text-align: right;\">               0.0390183</td><td style=\"text-align: right;\">0.197531</td><td style=\"text-align: right;\">0.0390183</td><td style=\"text-align: right;\">0.122757 </td><td style=\"text-align: right;\">0.0960737</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(aml.leaderboard)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer Questions using Leading Model\n",
    "\n",
    "We can now return to the questions that motivated this analysis:\n",
    "\n",
    "1. How many previous weeks influence cannabis sales?\n",
    "1. What days exhibit unusual sales?\n",
    "1. Do stores in counties that voted to legalize marijuana behave differently that those that didn't?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = h2o.get_model(aml.leaderboard[0,'model_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: train = 0.9368, valid = 0.9417, xval = 0.9333\n"
     ]
    }
   ],
   "source": [
    "print(\"R^2: train = {:.4f}, valid = {:.4f}, xval = {:.4f}\" \\\n",
    "      .format(best_model.r2(train = True), best_model.r2(valid = True), best_model.r2(xval = True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model.varimp_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Examining demand lags\n",
    "\n",
    "The variable importance plot of the leading model as well as the partial dependency plots show that five week's worth of sales should be sufficient for forecasting cannabis sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_demand = best_model.partial_plot(data = train_hf,\n",
    "                                     cols = ['Log1pDemandInThou_L7', 'Log1pDemandInThou_L14',\n",
    "                                             'Log1pDemandInThou_L21', 'Log1pDemandInThou_L28',\n",
    "                                             'Log1pDemandInThou_L35'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_other_lagged = best_model.partial_plot(data = train_hf,\n",
    "                                           cols = ['Log1pOtherDemandInThou_L7', 'Log1pNumSales_L7'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "glm = h2o.get_model(aml.leaderboard[7, 'model_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: train = 0.9214, valid = 0.9264, xval = 0.9209\n"
     ]
    }
   ],
   "source": [
    "print(\"R^2: train = {:.4f}, valid = {:.4f}, xval = {:.4f}\" \\\n",
    "      .format(glm.r2(train = True), glm.r2(valid = True), glm.r2(xval = True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log1pDemandInThou_L7: 0.5418\n",
      "Log1pDemandInThou_L14: 0.2107\n",
      "Log1pDemandInThou_L21: 0.0666\n",
      "Log1pDemandInThou_L28: 0.0566\n",
      "Log1pDemandInThou_L35: 0.0217\n"
     ]
    }
   ],
   "source": [
    "for j in ['Log1pDemandInThou_L7', 'Log1pDemandInThou_L14', 'Log1pDemandInThou_L21', 'Log1pDemandInThou_L28',\n",
    "          'Log1pDemandInThou_L35']:\n",
    "    print(j + \": {:.4f}\".format(glm.coef()[j]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Effects of unusual days\n",
    "\n",
    "As the time series plot suggested, the effects of unusual days, such as 420 and Christmas, are factor into the trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "pdp_doi = best_model.partial_plot(data = train_hf, cols = ['DayOfInterest'], plot = False)[0].as_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dayofinterest</th>\n",
       "      <th>mean_response</th>\n",
       "      <th>stddev_response</th>\n",
       "      <th>std_error_mean_response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FourTwenty</td>\n",
       "      <td>1.671158</td>\n",
       "      <td>0.709334</td>\n",
       "      <td>0.002422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ChristmasMinusOne</td>\n",
       "      <td>1.642617</td>\n",
       "      <td>0.710394</td>\n",
       "      <td>0.002425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ThanksgivingMinusOne</td>\n",
       "      <td>1.638590</td>\n",
       "      <td>0.712813</td>\n",
       "      <td>0.002434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PreJuly4th</td>\n",
       "      <td>1.628304</td>\n",
       "      <td>0.715839</td>\n",
       "      <td>0.002444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ChristmasMinusTwo</td>\n",
       "      <td>1.600179</td>\n",
       "      <td>0.703979</td>\n",
       "      <td>0.002403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>N/A</td>\n",
       "      <td>1.583246</td>\n",
       "      <td>0.688998</td>\n",
       "      <td>0.002352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FourTwentySeven</td>\n",
       "      <td>1.549095</td>\n",
       "      <td>0.705651</td>\n",
       "      <td>0.002409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NewYearsDay</td>\n",
       "      <td>1.548752</td>\n",
       "      <td>0.695942</td>\n",
       "      <td>0.002376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ThanksgivingPlusSix</td>\n",
       "      <td>1.546976</td>\n",
       "      <td>0.701255</td>\n",
       "      <td>0.002394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NewYearsDayPlusSix</td>\n",
       "      <td>1.533595</td>\n",
       "      <td>0.693069</td>\n",
       "      <td>0.002366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ChristmasPlusOne</td>\n",
       "      <td>1.491375</td>\n",
       "      <td>0.681491</td>\n",
       "      <td>0.002327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Christmas</td>\n",
       "      <td>1.269792</td>\n",
       "      <td>0.632718</td>\n",
       "      <td>0.002160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dayofinterest  mean_response  stddev_response  \\\n",
       "4             FourTwenty       1.671158         0.709334   \n",
       "1      ChristmasMinusOne       1.642617         0.710394   \n",
       "10  ThanksgivingMinusOne       1.638590         0.712813   \n",
       "9             PreJuly4th       1.628304         0.715839   \n",
       "2      ChristmasMinusTwo       1.600179         0.703979   \n",
       "6                    N/A       1.583246         0.688998   \n",
       "5        FourTwentySeven       1.549095         0.705651   \n",
       "7            NewYearsDay       1.548752         0.695942   \n",
       "11   ThanksgivingPlusSix       1.546976         0.701255   \n",
       "8     NewYearsDayPlusSix       1.533595         0.693069   \n",
       "3       ChristmasPlusOne       1.491375         0.681491   \n",
       "0              Christmas       1.269792         0.632718   \n",
       "\n",
       "    std_error_mean_response  \n",
       "4                  0.002422  \n",
       "1                  0.002425  \n",
       "10                 0.002434  \n",
       "9                  0.002444  \n",
       "2                  0.002403  \n",
       "6                  0.002352  \n",
       "5                  0.002409  \n",
       "7                  0.002376  \n",
       "11                 0.002394  \n",
       "8                  0.002366  \n",
       "3                  0.002327  \n",
       "0                  0.002160  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdp_doi.sort_values('mean_response', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Effect of legalization vote\n",
    "\n",
    "Not too surprisingly, stores in different counties tend to behave similarly despite the differences in their voters' desire to legalize cannabis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_legalization = best_model.partial_plot(data = train_hf, cols = ['LegalizationVote'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "pdp_cats = best_model.partial_plot(data = train_hf, cols = ['County', 'City'], plot = False)\n",
    "pdp_county = pdp_cats[0].as_data_frame()\n",
    "pdp_city = pdp_cats[1].as_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county</th>\n",
       "      <th>mean_response</th>\n",
       "      <th>stddev_response</th>\n",
       "      <th>std_error_mean_response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SPOKANE</td>\n",
       "      <td>1.596291</td>\n",
       "      <td>0.675511</td>\n",
       "      <td>0.002306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PIERCE</td>\n",
       "      <td>1.594987</td>\n",
       "      <td>0.678453</td>\n",
       "      <td>0.002316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KING</td>\n",
       "      <td>1.589492</td>\n",
       "      <td>0.684486</td>\n",
       "      <td>0.002337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SNOHOMISH</td>\n",
       "      <td>1.587058</td>\n",
       "      <td>0.676214</td>\n",
       "      <td>0.002309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CLARK</td>\n",
       "      <td>1.584325</td>\n",
       "      <td>0.679491</td>\n",
       "      <td>0.002320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KITSAP</td>\n",
       "      <td>1.576821</td>\n",
       "      <td>0.676896</td>\n",
       "      <td>0.002311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OTHER</td>\n",
       "      <td>1.573325</td>\n",
       "      <td>0.689574</td>\n",
       "      <td>0.002354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WHATCOM</td>\n",
       "      <td>1.572167</td>\n",
       "      <td>0.678244</td>\n",
       "      <td>0.002316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>THURSTON</td>\n",
       "      <td>1.569665</td>\n",
       "      <td>0.676533</td>\n",
       "      <td>0.002310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      county  mean_response  stddev_response  std_error_mean_response\n",
       "6    SPOKANE       1.596291         0.675511                 0.002306\n",
       "4     PIERCE       1.594987         0.678453                 0.002316\n",
       "1       KING       1.589492         0.684486                 0.002337\n",
       "5  SNOHOMISH       1.587058         0.676214                 0.002309\n",
       "0      CLARK       1.584325         0.679491                 0.002320\n",
       "2     KITSAP       1.576821         0.676896                 0.002311\n",
       "3      OTHER       1.573325         0.689574                 0.002354\n",
       "8    WHATCOM       1.572167         0.678244                 0.002316\n",
       "7   THURSTON       1.569665         0.676533                 0.002310"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdp_county.sort_values('mean_response', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>mean_response</th>\n",
       "      <th>stddev_response</th>\n",
       "      <th>std_error_mean_response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TACOMA</td>\n",
       "      <td>1.587973</td>\n",
       "      <td>0.685097</td>\n",
       "      <td>0.002339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OTHER</td>\n",
       "      <td>1.583931</td>\n",
       "      <td>0.689388</td>\n",
       "      <td>0.002354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SPOKANE</td>\n",
       "      <td>1.582883</td>\n",
       "      <td>0.686807</td>\n",
       "      <td>0.002345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SEATTLE</td>\n",
       "      <td>1.581167</td>\n",
       "      <td>0.688820</td>\n",
       "      <td>0.002352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      city  mean_response  stddev_response  std_error_mean_response\n",
       "3   TACOMA       1.587973         0.685097                 0.002339\n",
       "0    OTHER       1.583931         0.689388                 0.002354\n",
       "2  SPOKANE       1.582883         0.686807                 0.002345\n",
       "1  SEATTLE       1.581167         0.688820                 0.002352"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdp_city.sort_values('mean_response', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shutdown Sparkling Water Services\n",
    "\n",
    "The last step in this script is to be a good cloud citizen and shut down the H2O cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2o.cluster().shutdown()"
   ]
  }
 ],
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