h2o-world-2017/nlp/AmazonReviews.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "## Predicting Good Amazon Reviews\n",
    "\n",
    "For this demo, we will be using the [Amazon Fine Food Reviews Data](https://www.kaggle.com/snap/amazon-fine-food-reviews).  The Amazon Fine Food Reviews dataset consists of 568,454 food reviews Amazon users left up to October 2012.\n",
    "\n",
    "This script is based off of the [Craigslist Word2Vec Demo](https://github.com/h2oai/h2o-3/blob/master/h2o-py/demos/word2vec_craigslistjobtitles.ipynb).\n",
    "\n",
    "## Import Data\n",
    "\n",
    "We will begin by importing our review data into our H2O cluster.  In this case, I will be starting up an H2O cluster on my local computer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: java version \"1.7.0_80\"; Java(TM) SE Runtime Environment (build 1.7.0_80-b15); Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)\n",
      "  Starting server from /Users/megankurka/anaconda3/envs/py27/lib/python2.7/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /var/folders/gv/w2f3zs_d33l3dt5j67k9nhhr0000gn/T/tmpmt_yBb\n",
      "  JVM stdout: /var/folders/gv/w2f3zs_d33l3dt5j67k9nhhr0000gn/T/tmpmt_yBb/h2o_megankurka_started_from_python.out\n",
      "  JVM stderr: /var/folders/gv/w2f3zs_d33l3dt5j67k9nhhr0000gn/T/tmpmt_yBb/h2o_megankurka_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>02 secs</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.16.0.2</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>7 days, 16 hours and 40 minutes </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_megankurka_xhw01z</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>3.556 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>8</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://127.0.0.1:54321</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>AutoML, XGBoost, Algos, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>2.7.13 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------------\n",
       "H2O cluster uptime:         02 secs\n",
       "H2O cluster version:        3.16.0.2\n",
       "H2O cluster version age:    7 days, 16 hours and 40 minutes\n",
       "H2O cluster name:           H2O_from_python_megankurka_xhw01z\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    3.556 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         AutoML, XGBoost, Algos, Core V3, Core V4\n",
       "Python version:             2.7.13 final\n",
       "--------------------------  ----------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init(max_mem_size=\"4G\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# Use local data file or download from GitHub\n",
    "import os\n",
    "docker_data_path = \"/home/h2o/data/nlp/AmazonReviews.csv\"\n",
    "if os.path.isfile(docker_data_path):\n",
    "  data_path = docker_data_path\n",
    "else:\n",
    "  data_path = \"https://s3.amazonaws.com/tomk/h2o-world/megan/AmazonReviews.csv\"\n",
    "\n",
    "\n",
    "# Load data into H2O\n",
    "reviews = h2o.import_file(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#' Add Target Column: \"PositiveReview\"\n",
    "reviews[\"PositiveReview\"] = (reviews[\"Score\"] >= 4).ifelse(\"1\", \"0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>ProductId  </th><th>UserId        </th><th>Summary                               </th><th style=\"text-align: right;\">  Score</th><th style=\"text-align: right;\">  HelpfulnessDenominator</th><th style=\"text-align: right;\">    Id</th><th>ProfileName                                     </th><th style=\"text-align: right;\">  HelpfulnessNumerator</th><th style=\"text-align: right;\">       Time</th><th>Text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </th><th style=\"text-align: right;\">  PositiveReview</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>B00141QYSQ </td><td>A1YS02UZZGRDCT</td><td>Do Not Buy                            </td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">                       2</td><td style=\"text-align: right;\"> 41471</td><td>Evan Eberhardt                                  </td><td style=\"text-align: right;\">                     2</td><td style=\"text-align: right;\">1.34836e+09</td><td>These are made in China (do not buy ANY pet food from China).  Dogswell has been using propylene glycol to soften their treats (what are they thinkng?).  Do not purchase or support this company in any way until they clean up their act.  And for whatever reason Amazon doesn't allow returns of this item, so I had to toss mine out.  Bad business all around on this one.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           </td><td style=\"text-align: right;\">               0</td></tr>\n",
       "<tr><td>B0089SPEO2 </td><td>A3JOYNYL458QHP</td><td>Less lemon and less zing              </td><td style=\"text-align: right;\">      3</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\"> 28582</td><td>coleridge                                       </td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">1.32391e+09</td><td>Everything is ok, except it just isn't as good as it is in the bags.  Just considerably more bland -- less lemon and less zing.  Boring.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   </td><td style=\"text-align: right;\">               0</td></tr>\n",
       "<tr><td>B001PMCDK2 </td><td>A14TTMM0Z03Y2W</td><td>my cat goes crazy for these!          </td><td style=\"text-align: right;\">      5</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">389965</td><td>Lindsay S. Bradford                             </td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">1.3106e+09 </td><td>Best cat treat ever. There isn't anything comparable to the love my cat has for these treats, he snubs away any other kind now.<br />I know he likes to manipulate me with his cattiness but these treats are my way of manipulating him to come sit on my lap and have some chill time. :)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                </td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td>B002Q8JOSI </td><td>A17UQD2RSSQH5X</td><td>My dogs tell me these treats are YUMMY</td><td style=\"text-align: right;\">      5</td><td style=\"text-align: right;\">                       1</td><td style=\"text-align: right;\">212536</td><td>in the dark                                     </td><td style=\"text-align: right;\">                     1</td><td style=\"text-align: right;\">1.31613e+09</td><td>My two Corgis were thoroughly spoiled by my late husband (I spent a year and a half dieting them down a combined total of 25 pounds!)<br /><br />They are accustomed to the finest of fare, and they absolutely love the Wellness brand of treats.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         </td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td>B00176G870 </td><td>A2F2MZW8EOGH5J</td><td>Yummy to the tummy                    </td><td style=\"text-align: right;\">      5</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">115971</td><td>daemoncycler \"\"When you arrive at a fork in th..</td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">1.33479e+09</td><td>We used to have drive down to the specialty pet food store for this product.  So glad we discovered Amazon.  As far as I can tell it is no more expensive & in some cases less - Prime membership is awesome.  Loving Pets treats are some of the best according to my dog.  They do not develop that nasty smell like some dog treats do.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 </td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td>B001CHFUGY </td><td>A2M8VROSDPU4JT</td><td>Very good coffee                      </td><td style=\"text-align: right;\">      5</td><td style=\"text-align: right;\">                       1</td><td style=\"text-align: right;\">434484</td><td>Officefan \"\"Officefankt                         </td><td style=\"text-align: right;\">                     1</td><td style=\"text-align: right;\">1.27725e+09</td><td>I really liked this coffee, it was just as good as everyone claimed it was.  Strong, bold and flavorful!  I would recommend!                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               </td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td>B0041CIR62 </td><td>A16I6WJUEBJ1C3</td><td>okay but not as healthy as it appears </td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">                       1</td><td style=\"text-align: right;\">138997</td><td>doctorsirena \"\"doctorsirena                     </td><td style=\"text-align: right;\">                     1</td><td style=\"text-align: right;\">1.34369e+09</td><td>I am always looking for healthier, whole grain versions of foods I enjoy.  Unfortunately, these Peacock brand noodles are yet another food masquerading as healthy.  The product title in big letters on the front says \"\"Brown Rice Vermicelli\"\", making the consumer think \"\"this is made with brown rice, so it should be a healthy choice\"\".  But the first indication that it is not is when looking at the fiber content on the nutrition facts - only 0.6g per 2oz serving.  Then onto the ingredients list to see why so low... contains brown rice, sago starch and water.  The sago starch comes from palms and must not have much (if any) fiber.<br /><br />The Annie Chun's Maifun Brown Rice Noodles (sold on Amazon and in my local healthy grocer) has become one of my staples and is my frame of reference when comparing to the Peacock brand.  The Annie Chun's product is made with 100% whole grain, with ingredients brown rice flour and water.  Per 2oz serving, it has 4g fiber and pretty much the same calories and other nutrients as the Peacock brand.<br /><br />If you do try this Peacock brand noodles and have not used rice noodles before, you will need to seek guidance elsewhere on preparation.  As others have pointed out, the Peacock package gives almost no directions on how to prepare the product, aside from a brief mention in the recipes (in the header text it does say that they are \"\"easy-to-cook\"\" but does not say how).  It also contains a very strange recipe for rice noodles: Aglio Olio style - this is an Italian recipe for noodles with olive oil/garlic/sprinkled with grated cheese that I think would not be very tasty.  The second recipe appears to be for a soup with veggie strips.  Neither recipe gives amounts or much direction.  In comparison, the Annie Chun's package gives clear, specific directions on rice noodle preparation and two recipes.<br /><br />I use rice noodles = maifun = rice sticks = sometimes called vermicelli for making the Vietnamese salad \"\"bun tofu\"\", to serve with stir-fried veggies or in lettuce rolls.  They can also be used in spring rolls/egg rolls.  When cooking with thin rice noodles, be careful not to oversoak/overcook/overmix or they tend to disintegrate.  Asian rice noodle vermicelli (maifun) are not the same as Italian vermicelli and are not readily interchangeable.  If making an Italian recipe, the best results would be expected from Italian pasta and not maifun.<br /><br />A few final notes...  Both Peacock and Annie Chun's brown rice noodles are gluten free.  The Peacock is made in Singapore and the Annie Chun's in Thailand.  The Peacock noodles do taste fine (kind of bland), but so do the Annie Chun's.  At this time, they are both approximately the same price.  Peacock come in an plastic bag with some noodle crushage upon shipping; Annie Chun's are perfect upon removal from their cellophane bag in a box.  Overall, I highly recommend the Annie Chun's Maifun as a healthier option over the Peacock brand.  On a related note, the Annie Chun's soba and brown rice pad thai noodles are also excellent.<br /><br />Rating for this product: 2.5 stars rounded down to</td><td style=\"text-align: right;\">               0</td></tr>\n",
       "<tr><td>B001R3BQFW </td><td>AM50E42AFUVNL </td><td>Taste great.                          </td><td style=\"text-align: right;\">      5</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">126555</td><td>T. Higley \"\"Tina                                </td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">1.32356e+09</td><td>I have tried many different drink mix, this is the best tasting by far. It does not have the after taste of the sweetener and I really like it, it is pretty strong, so I use a big water bottle (20 oz) for one tube, it still a little stronger than I like, but it is just my taste.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    </td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td>B005HGAV8I </td><td>A2I5KDNOESGJ1H</td><td>variety galore                        </td><td style=\"text-align: right;\">      5</td><td style=\"text-align: right;\">                       1</td><td style=\"text-align: right;\">438837</td><td>TJ                                              </td><td style=\"text-align: right;\">                     1</td><td style=\"text-align: right;\">1.33402e+09</td><td>This is my favorite item to order for my Keurig. There are so many flavors, my finicky palate never gets bored!  The only downside is there are probably 5-6 decaf varieties.  I don't drink decaf (I REQUIRE copious amounts of caffeine), so they sit on the shelf...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    </td><td style=\"text-align: right;\">               1</td></tr>\n",
       "<tr><td>B000GFYRHQ </td><td>A3A7YUR6FS6ZCI</td><td>Bigelow Earl Grey Green Tea           </td><td style=\"text-align: right;\">      5</td><td style=\"text-align: right;\">                       0</td><td style=\"text-align: right;\">245379</td><td>Tea Lover                                       </td><td style=\"text-align: right;\">                     0</td><td style=\"text-align: right;\">1.17841e+09</td><td>Tastes like Earl Grey, but it's green tea so it's healthier.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               </td><td style=\"text-align: right;\">               1</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  PositiveReview</th><th style=\"text-align: right;\">  Count</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">               0</td><td style=\"text-align: right;\">  21791</td></tr>\n",
       "<tr><td style=\"text-align: right;\">               1</td><td style=\"text-align: right;\">  78209</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"PositiveReview\"].table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Baseline Model\n",
    "\n",
    "We will start by training a baseline model that does not use the review and instead uses other attributes in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Find the 80th quantile of time in the dataset\n",
    "time_split = reviews[\"Time\"].quantile(prob = [0.8])[1]\n",
    "reviews[\"Train\"] = (reviews[\"Time\"] < time_split).ifelse(\"Yes\", \"No\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = reviews[reviews[\"Train\"] == \"Yes\"]\n",
    "test = reviews[reviews[\"Train\"] == \"No\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "from h2o.estimators import H2OGradientBoostingEstimator\n",
    "\n",
    "predictors = ['ProductId', 'UserId', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Time']\n",
    "response = 'PositiveReview'\n",
    "\n",
    "gbm_baseline = H2OGradientBoostingEstimator(stopping_metric = \"AUC\", stopping_tolerance = 0.001,\n",
    "                                            stopping_rounds = 5, score_tree_interval = 10,\n",
    "                                            model_id = \"gbm_baseline.hex\"\n",
    "                                           )\n",
    "gbm_baseline.train(x = predictors, y = response, \n",
    "                   training_frame = train, validation_frame = test\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC on Validation Data: 0.616\n"
     ]
    }
   ],
   "source": [
    "print(\"AUC on Validation Data: \" + str(round(gbm_baseline.auc(valid = True), 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a big room for improvement.  Our error is 22%.  To improve our model, we will train word embeddings for the review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.459316258112: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>384.0</td>\n",
       "<td>4276.0</td>\n",
       "<td>0.9176</td>\n",
       "<td> (4276.0/4660.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>128.0</td>\n",
       "<td>15220.0</td>\n",
       "<td>0.0083</td>\n",
       "<td> (128.0/15348.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>512.0</td>\n",
       "<td>19496.0</td>\n",
       "<td>0.2201</td>\n",
       "<td> (4404.0/20008.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0    1      Error    Rate\n",
       "-----  ---  -----  -------  ----------------\n",
       "0      384  4276   0.9176   (4276.0/4660.0)\n",
       "1      128  15220  0.0083   (128.0/15348.0)\n",
       "Total  512  19496  0.2201   (4404.0/20008.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_baseline.confusion_matrix(valid = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable importance plot below shows us that the most important variable is `HelpfulnessNumerator`.  Looking at the partial dependency plot for that variable, we see that the more people who find the review helpful, the more likely it is a good review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W2tEZvl7jeUleW1V7J/l+hu+OfWCGz3OxazMX1Vq7YAyfn0jy3qr6bIYbKW2ZITzdI8MU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\nSZLUzU/QHv7bNltY1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116587b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_baseline.varimp_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "image/png": 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xNup70aJssNjrXpd3RZKkoihg8E/gkENg6tRR2mB7ezYvYsstR2mDzXfOOfDI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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103685f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "pdp_helpfulness = gbm_baseline.partial_plot(train, cols = [\"HelpfulnessNumerator\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenize Words in Review\n",
    "\n",
    "Our first step will be to tokenize the words in the review column. We will do this by creating a function called `tokenize`.  This will split the reviews into words and remove any stop words, small words, or words with numbers in them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set Stop Words\n",
    "# The STOP WORDS we are importing are from the nltk package\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# Use local data file or download from GitHub\n",
    "docker_data_path = \"/home/h2o/data/nlp/stopwords.csv\"\n",
    "if os.path.isfile(docker_data_path):\n",
    "  data_path = docker_data_path\n",
    "else:\n",
    "  data_path = \"https://raw.githubusercontent.com/h2oai/h2o-tutorials/master/h2o-world-2017/nlp/stopwords.csv\"\n",
    "\n",
    "STOP_WORDS = pd.read_csv(data_path, header=0)\n",
    "STOP_WORDS = list(STOP_WORDS['STOP_WORD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tokenize(sentences, stop_word = STOP_WORDS):\n",
    "    tokenized = sentences.tokenize(\"\\\\W+\")\n",
    "    tokenized_lower = tokenized.tolower()\n",
    "    tokenized_filtered = tokenized_lower[(tokenized_lower.nchar() >= 2) | (tokenized_lower.isna()),:]\n",
    "    tokenized_words = tokenized_filtered[tokenized_filtered.grep(\"[0-9]\",invert=True,output_logical=True),:]\n",
    "    tokenized_words = tokenized_words[(tokenized_words.isna()) | (~ tokenized_words.isin(STOP_WORDS)),:]\n",
    "    return tokenized_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Break reviews into sequence of words\n",
    "words = tokenize(reviews[\"Text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>C1       </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>made     </td></tr>\n",
       "<tr><td>china    </td></tr>\n",
       "<tr><td>buy      </td></tr>\n",
       "<tr><td>pet      </td></tr>\n",
       "<tr><td>food     </td></tr>\n",
       "<tr><td>china    </td></tr>\n",
       "<tr><td>dogswell </td></tr>\n",
       "<tr><td>using    </td></tr>\n",
       "<tr><td>propylene</td></tr>\n",
       "<tr><td>glycol   </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Word2Vec Model\n",
    "\n",
    "Now that we've tokenized our words, we can train a word2vec model.  We can use the `find_synonms` function to sanity check our word2vec model after training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Train Word2Vec Model\n",
    "from h2o.estimators.word2vec import H2OWord2vecEstimator\n",
    "\n",
    "# This takes time to run - left commented out\n",
    "#w2v_model = H2OWord2vecEstimator(vec_size = 100, model_id = \"w2v.hex\")\n",
    "#w2v_model.train(training_frame=words)\n",
    "\n",
    "# Pre-trained model available on s3: https://s3.amazonaws.com/tomk/h2o-world/megan/w2v.hex\n",
    "w2v_model = h2o.load_model(\"/home/h2o/data/megan/w2v.hex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(u'coffe', 0.8426127433776855),\n",
       "             (u'coffees', 0.7995628714561462),\n",
       "             (u'starbucks', 0.7715724110603333),\n",
       "             (u'espresso', 0.7529839873313904),\n",
       "             (u'expresso', 0.7438507080078125)])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check - find synonyms for the word 'coffee'\n",
    "w2v_model.find_synonyms(\"coffee\", count = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(u'rancid', 0.6790934801101685),\n",
       "             (u'unedible', 0.6622459888458252),\n",
       "             (u'moldy', 0.6498295664787292),\n",
       "             (u'expired', 0.6377609968185425),\n",
       "             (u'experation', 0.6255136132240295)])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check - find synonyms for the word 'stale'\n",
    "w2v_model.find_synonyms(\"stale\", count = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a word embedding for each word in our vocabulary, we will aggregate the words for each review using the `transform` function.  This will give us one aggregated word embedding for each review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate a vector for each review\n",
    "review_vecs = w2v_model.transform(words, aggregate_method = \"AVERAGE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">        C1</th><th style=\"text-align: right;\">        C2</th><th style=\"text-align: right;\">        C3</th><th style=\"text-align: right;\">        C4</th><th style=\"text-align: right;\">       C5</th><th style=\"text-align: right;\">         C6</th><th style=\"text-align: right;\">        C7</th><th style=\"text-align: right;\">        C8</th><th style=\"text-align: right;\">        C9</th><th style=\"text-align: right;\">        C10</th><th style=\"text-align: right;\">       C11</th><th style=\"text-align: right;\">       C12</th><th style=\"text-align: right;\">       C13</th><th style=\"text-align: right;\">        C14</th><th style=\"text-align: right;\">       C15</th><th style=\"text-align: right;\">       C16</th><th style=\"text-align: right;\">        C17</th><th style=\"text-align: right;\">       C18</th><th style=\"text-align: right;\">     C19</th><th style=\"text-align: right;\">       C20</th><th style=\"text-align: right;\">         C21</th><th style=\"text-align: right;\">       C22</th><th style=\"text-align: right;\">      C23</th><th style=\"text-align: right;\">        C24</th><th style=\"text-align: right;\">       C25</th><th style=\"text-align: right;\">        C26</th><th style=\"text-align: right;\">        C27</th><th style=\"text-align: right;\">        C28</th><th style=\"text-align: right;\">        C29</th><th style=\"text-align: right;\">         C30</th><th style=\"text-align: right;\">         C31</th><th style=\"text-align: right;\">         C32</th><th style=\"text-align: right;\">        C33</th><th style=\"text-align: right;\">       C34</th><th style=\"text-align: right;\">       C35</th><th style=\"text-align: right;\">        C36</th><th style=\"text-align: right;\">        C37</th><th style=\"text-align: right;\">       C38</th><th style=\"text-align: right;\">       C39</th><th style=\"text-align: right;\">       C40</th><th style=\"text-align: right;\">       C41</th><th style=\"text-align: right;\">       C42</th><th style=\"text-align: right;\">        C43</th><th style=\"text-align: right;\">        C44</th><th style=\"text-align: right;\">        C45</th><th style=\"text-align: right;\">        C46</th><th style=\"text-align: right;\">        C47</th><th style=\"text-align: right;\">       C48</th><th style=\"text-align: right;\">       C49</th><th style=\"text-align: right;\">       C50</th><th style=\"text-align: right;\">       C51</th><th style=\"text-align: right;\">         C52</th><th style=\"text-align: right;\">       C53</th><th style=\"text-align: right;\">        C54</th><th style=\"text-align: right;\">       C55</th><th style=\"text-align: right;\">      C56</th><th style=\"text-align: right;\">        C57</th><th style=\"text-align: right;\">       C58</th><th style=\"text-align: right;\">      C59</th><th style=\"text-align: right;\">        C60</th><th style=\"text-align: right;\">       C61</th><th style=\"text-align: right;\">        C62</th><th style=\"text-align: right;\">        C63</th><th style=\"text-align: right;\">        C64</th><th style=\"text-align: right;\">       C65</th><th style=\"text-align: right;\">       C66</th><th style=\"text-align: right;\">       C67</th><th style=\"text-align: right;\">        C68</th><th style=\"text-align: right;\">        C69</th><th style=\"text-align: right;\">       C70</th><th style=\"text-align: right;\">       C71</th><th style=\"text-align: right;\">       C72</th><th style=\"text-align: right;\">         C73</th><th style=\"text-align: right;\">         C74</th><th style=\"text-align: right;\">      C75</th><th style=\"text-align: right;\">       C76</th><th style=\"text-align: right;\">      C77</th><th style=\"text-align: right;\">       C78</th><th style=\"text-align: right;\">       C79</th><th style=\"text-align: right;\">        C80</th><th style=\"text-align: right;\">        C81</th><th style=\"text-align: right;\">        C82</th><th style=\"text-align: right;\">        C83</th><th style=\"text-align: right;\">      C84</th><th style=\"text-align: right;\">       C85</th><th style=\"text-align: right;\">        C86</th><th style=\"text-align: right;\">        C87</th><th style=\"text-align: right;\">       C88</th><th style=\"text-align: right;\">         C89</th><th style=\"text-align: right;\">        C90</th><th style=\"text-align: right;\">       C91</th><th style=\"text-align: right;\">        C92</th><th style=\"text-align: right;\">         C93</th><th style=\"text-align: right;\">        C94</th><th style=\"text-align: right;\">       C95</th><th style=\"text-align: right;\">       C96</th><th style=\"text-align: right;\">       C97</th><th style=\"text-align: right;\">        C98</th><th style=\"text-align: right;\">        C99</th><th style=\"text-align: right;\">     C100</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
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       "<tr><td style=\"text-align: right;\">-0.150943 </td><td style=\"text-align: right;\">-0.18015  </td><td style=\"text-align: right;\"> 0.0778386</td><td style=\"text-align: right;\"> 0.0872104</td><td style=\"text-align: right;\">0.0563445</td><td style=\"text-align: right;\">-0.24971   </td><td style=\"text-align: right;\"> 0.0175048</td><td style=\"text-align: right;\"> 0.0280696</td><td style=\"text-align: right;\">-0.145466 </td><td style=\"text-align: right;\">-0.135046  </td><td style=\"text-align: right;\">-0.0253416</td><td style=\"text-align: right;\">-0.121022 </td><td style=\"text-align: right;\"> 0.0212608</td><td style=\"text-align: right;\">-0.0836051 </td><td style=\"text-align: right;\">-0.0258977</td><td style=\"text-align: right;\">-0.186726 </td><td style=\"text-align: right;\"> 0.0887503 </td><td style=\"text-align: right;\"> 0.092384 </td><td style=\"text-align: right;\">0.190242</td><td style=\"text-align: right;\"> 0.184348 </td><td style=\"text-align: right;\"> 0.00708532 </td><td style=\"text-align: right;\"> 0.0642552</td><td style=\"text-align: right;\">0.0441702</td><td style=\"text-align: right;\">-0.00202942</td><td style=\"text-align: right;\"> 0.0220385</td><td style=\"text-align: right;\">-0.00437946</td><td style=\"text-align: right;\">-0.0803709 </td><td style=\"text-align: right;\"> 0.122429  </td><td style=\"text-align: right;\">-0.101544  </td><td style=\"text-align: right;\">-0.0196541  </td><td style=\"text-align: right;\">-0.0171053  </td><td style=\"text-align: right;\">-0.105091   </td><td style=\"text-align: right;\"> 0.0395368 </td><td style=\"text-align: right;\">-0.0367961</td><td style=\"text-align: right;\"> 0.0533833</td><td style=\"text-align: right;\">-0.00683283</td><td style=\"text-align: right;\">-0.0268374 </td><td style=\"text-align: right;\">-0.128483 </td><td style=\"text-align: right;\"> 0.0172005</td><td style=\"text-align: right;\">-0.154467 </td><td style=\"text-align: right;\">-0.270173 </td><td style=\"text-align: right;\">-0.0830348</td><td style=\"text-align: right;\">-0.137496  </td><td style=\"text-align: right;\">-0.0550283 </td><td style=\"text-align: right;\">-0.0101595 </td><td style=\"text-align: right;\">-0.012558  </td><td style=\"text-align: right;\"> 0.00959401</td><td style=\"text-align: right;\">-0.214333 </td><td style=\"text-align: right;\">-0.0928339</td><td style=\"text-align: right;\">-0.0827343</td><td style=\"text-align: right;\">-0.0985919</td><td style=\"text-align: right;\"> 0.0518963  </td><td style=\"text-align: right;\"> 0.0749577</td><td style=\"text-align: right;\"> 0.150248  </td><td style=\"text-align: right;\"> 0.0510552</td><td style=\"text-align: right;\">0.188824 </td><td style=\"text-align: right;\">-0.00526999</td><td style=\"text-align: right;\"> 0.0881286</td><td style=\"text-align: right;\">0.153188 </td><td style=\"text-align: right;\"> 0.0382074 </td><td style=\"text-align: right;\">-0.0933986</td><td style=\"text-align: right;\">-0.032291  </td><td style=\"text-align: right;\"> 0.0610677 </td><td style=\"text-align: right;\"> 0.0871974 </td><td style=\"text-align: right;\">-0.0214434</td><td style=\"text-align: right;\">-0.155718 </td><td style=\"text-align: right;\">-0.134393 </td><td style=\"text-align: right;\"> 0.0726165 </td><td style=\"text-align: right;\">-0.121445  </td><td style=\"text-align: right;\"> 0.0336814</td><td style=\"text-align: right;\">-0.0385515</td><td style=\"text-align: right;\"> 0.0968749</td><td style=\"text-align: right;\"> 0.0911647  </td><td style=\"text-align: right;\">-0.0175451  </td><td style=\"text-align: right;\">0.196648 </td><td style=\"text-align: right;\">-0.118069 </td><td style=\"text-align: right;\">0.14769  </td><td style=\"text-align: right;\"> 0.174018 </td><td style=\"text-align: right;\"> 0.176979 </td><td style=\"text-align: right;\">-0.00566942</td><td style=\"text-align: right;\"> 0.0909446 </td><td style=\"text-align: right;\">-0.0588353 </td><td style=\"text-align: right;\">-0.00699213</td><td style=\"text-align: right;\">0.121181 </td><td style=\"text-align: right;\">-0.0203108</td><td style=\"text-align: right;\">-0.00200737</td><td style=\"text-align: right;\">-0.00281995</td><td style=\"text-align: right;\">-0.165172 </td><td style=\"text-align: right;\">-0.0193035  </td><td style=\"text-align: right;\">-0.0236158 </td><td style=\"text-align: right;\"> 0.156242 </td><td style=\"text-align: right;\">-0.0956303 </td><td style=\"text-align: right;\">-0.0634159  </td><td style=\"text-align: right;\"> 0.00229758</td><td style=\"text-align: right;\"> 0.0288154</td><td style=\"text-align: right;\">-0.0148597</td><td style=\"text-align: right;\"> 0.0351692</td><td style=\"text-align: right;\"> 0.0786452 </td><td style=\"text-align: right;\">-0.0978148 </td><td style=\"text-align: right;\">-0.214739</td></tr>\n",
       "<tr><td style=\"text-align: right;\">-0.0784339</td><td style=\"text-align: right;\">-0.177443 </td><td style=\"text-align: right;\"> 0.142632 </td><td style=\"text-align: right;\">-0.0650279</td><td style=\"text-align: right;\">0.0404134</td><td style=\"text-align: right;\">-0.125338  </td><td style=\"text-align: right;\"> 0.0700381</td><td style=\"text-align: right;\">-0.0735972</td><td style=\"text-align: right;\">-0.107172 </td><td style=\"text-align: right;\">-0.00103557</td><td style=\"text-align: right;\">-0.0938815</td><td style=\"text-align: right;\">-0.0259808</td><td style=\"text-align: right;\">-0.0738917</td><td style=\"text-align: right;\">-0.0284076 </td><td style=\"text-align: right;\">-0.168088 </td><td style=\"text-align: right;\">-0.0821494</td><td style=\"text-align: right;\">-0.0301608 </td><td style=\"text-align: right;\">-0.0434049</td><td style=\"text-align: right;\">0.147666</td><td style=\"text-align: right;\">-0.106854 </td><td style=\"text-align: right;\">-0.0290663  </td><td style=\"text-align: right;\"> 0.0301064</td><td style=\"text-align: right;\">0.296466 </td><td style=\"text-align: right;\">-0.0221055 </td><td style=\"text-align: right;\">-0.0097013</td><td style=\"text-align: right;\">-0.0430679 </td><td style=\"text-align: right;\"> 0.0405026 </td><td style=\"text-align: right;\"> 0.0911821 </td><td style=\"text-align: right;\">-0.0533509 </td><td style=\"text-align: right;\"> 0.132035   </td><td style=\"text-align: right;\"> 0.0559074  </td><td style=\"text-align: right;\">-0.0556636  </td><td style=\"text-align: right;\">-0.0278374 </td><td style=\"text-align: right;\">-0.0757753</td><td style=\"text-align: right;\"> 0.0602816</td><td style=\"text-align: right;\">-0.0362208 </td><td style=\"text-align: right;\">-0.0349378 </td><td style=\"text-align: right;\">-0.118651 </td><td style=\"text-align: right;\"> 0.0400976</td><td style=\"text-align: right;\"> 0.0448786</td><td style=\"text-align: right;\">-0.177866 </td><td style=\"text-align: right;\">-0.0899878</td><td style=\"text-align: right;\"> 0.0200344 </td><td style=\"text-align: right;\"> 0.0137393 </td><td style=\"text-align: right;\">-0.055251  </td><td style=\"text-align: right;\"> 0.00808215</td><td style=\"text-align: right;\">-0.244367  </td><td style=\"text-align: right;\">-0.0610968</td><td style=\"text-align: right;\"> 0.114113 </td><td style=\"text-align: right;\">-0.0938133</td><td style=\"text-align: right;\">-0.0838846</td><td style=\"text-align: right;\"> 0.086218   </td><td style=\"text-align: right;\"> 0.0763978</td><td style=\"text-align: right;\">-0.055101  </td><td style=\"text-align: right;\"> 0.161861 </td><td style=\"text-align: right;\">0.0677596</td><td style=\"text-align: right;\">-0.163391  </td><td style=\"text-align: right;\">-0.0369254</td><td style=\"text-align: right;\">0.161951 </td><td style=\"text-align: right;\"> 0.147807  </td><td style=\"text-align: right;\">-0.0508916</td><td style=\"text-align: right;\">-0.0593187 </td><td style=\"text-align: right;\"> 0.0267307 </td><td style=\"text-align: right;\"> 0.0513137 </td><td style=\"text-align: right;\">-0.0530928</td><td style=\"text-align: right;\">-0.150629 </td><td style=\"text-align: right;\">-0.114022 </td><td style=\"text-align: right;\">-0.0121485 </td><td style=\"text-align: right;\">-0.106058  </td><td style=\"text-align: right;\">-0.0341939</td><td style=\"text-align: right;\">-0.140576 </td><td style=\"text-align: right;\"> 0.0854251</td><td style=\"text-align: right;\"> 0.0648086  </td><td style=\"text-align: right;\"> 0.0184547  </td><td style=\"text-align: right;\">0.187935 </td><td style=\"text-align: right;\">-0.0841014</td><td style=\"text-align: right;\">0.0374503</td><td style=\"text-align: right;\"> 0.146254 </td><td style=\"text-align: right;\"> 0.024031 </td><td style=\"text-align: right;\">-0.14471   </td><td style=\"text-align: right;\">-0.0462424 </td><td style=\"text-align: right;\">-0.0357163 </td><td style=\"text-align: right;\"> 0.130185  </td><td style=\"text-align: right;\">0.137596 </td><td style=\"text-align: right;\"> 0.0922682</td><td style=\"text-align: right;\"> 0.00303045</td><td style=\"text-align: right;\">-0.0148133 </td><td style=\"text-align: right;\">-0.0671439</td><td style=\"text-align: right;\">-0.131944   </td><td style=\"text-align: right;\">-0.0581845 </td><td style=\"text-align: right;\"> 0.177339 </td><td style=\"text-align: right;\">-0.0196272 </td><td style=\"text-align: right;\">-0.0119319  </td><td style=\"text-align: right;\"> 0.0924081 </td><td style=\"text-align: right;\">-0.0405273</td><td style=\"text-align: right;\">-0.153547 </td><td style=\"text-align: right;\">-0.0561063</td><td style=\"text-align: right;\">-0.013661  </td><td style=\"text-align: right;\"> 0.0313455 </td><td style=\"text-align: right;\">-0.173018</td></tr>\n",
       "<tr><td style=\"text-align: right;\"> 0.0207442</td><td style=\"text-align: right;\">-0.121042 </td><td style=\"text-align: right;\"> 0.205818 </td><td style=\"text-align: right;\"> 0.0271886</td><td style=\"text-align: right;\">0.131417 </td><td style=\"text-align: right;\"> 0.00648422</td><td style=\"text-align: right;\"> 0.0235201</td><td style=\"text-align: right;\"> 0.0599329</td><td style=\"text-align: right;\">-0.0792963</td><td style=\"text-align: right;\">-0.033467  </td><td style=\"text-align: right;\">-0.0964103</td><td style=\"text-align: right;\">-0.122847 </td><td style=\"text-align: right;\">-0.0147825</td><td style=\"text-align: right;\">-0.0390897 </td><td style=\"text-align: right;\">-0.209295 </td><td style=\"text-align: right;\">-0.18769  </td><td style=\"text-align: right;\">-0.0368012 </td><td style=\"text-align: right;\"> 0.079366 </td><td style=\"text-align: right;\">0.193885</td><td style=\"text-align: right;\"> 0.0612297</td><td style=\"text-align: right;\">-0.0492691  </td><td style=\"text-align: right;\"> 0.17026  </td><td style=\"text-align: right;\">0.187284 </td><td style=\"text-align: right;\"> 0.0582825 </td><td style=\"text-align: right;\">-0.0580818</td><td style=\"text-align: right;\">-0.0415637 </td><td style=\"text-align: right;\">-0.0146321 </td><td style=\"text-align: right;\"> 0.108514  </td><td style=\"text-align: right;\"> 0.0676035 </td><td style=\"text-align: right;\"> 0.136187   </td><td style=\"text-align: right;\"> 0.032809   </td><td style=\"text-align: right;\"> 0.0304932  </td><td style=\"text-align: right;\"> 0.0228237 </td><td style=\"text-align: right;\"> 0.0245993</td><td style=\"text-align: right;\"> 0.0379505</td><td style=\"text-align: right;\">-0.119352  </td><td style=\"text-align: right;\"> 0.0148827 </td><td style=\"text-align: right;\">-0.0845919</td><td style=\"text-align: right;\"> 0.142099 </td><td style=\"text-align: right;\">-0.0774023</td><td style=\"text-align: right;\">-0.135862 </td><td style=\"text-align: right;\">-0.110586 </td><td style=\"text-align: right;\">-0.0503919 </td><td style=\"text-align: right;\"> 0.035325  </td><td style=\"text-align: right;\">-0.0402904 </td><td style=\"text-align: right;\">-0.0338926 </td><td style=\"text-align: right;\">-0.199188  </td><td style=\"text-align: right;\">-0.0637347</td><td style=\"text-align: right;\"> 0.0351021</td><td style=\"text-align: right;\">-0.071915 </td><td style=\"text-align: right;\">-0.134324 </td><td style=\"text-align: right;\">-0.00396956 </td><td style=\"text-align: right;\"> 0.0429275</td><td style=\"text-align: right;\">-0.00365068</td><td style=\"text-align: right;\"> 0.188821 </td><td style=\"text-align: right;\">0.143321 </td><td style=\"text-align: right;\">-0.19803   </td><td style=\"text-align: right;\">-0.105554 </td><td style=\"text-align: right;\">0.0157162</td><td style=\"text-align: right;\"> 0.00800393</td><td style=\"text-align: right;\">-0.0189451</td><td style=\"text-align: right;\">-0.115075  </td><td style=\"text-align: right;\"> 0.0210362 </td><td style=\"text-align: right;\"> 0.127758  </td><td style=\"text-align: right;\">-0.143485 </td><td style=\"text-align: right;\">-0.181347 </td><td style=\"text-align: right;\">-0.08403  </td><td style=\"text-align: right;\">-0.0366014 </td><td style=\"text-align: right;\">-0.0730024 </td><td style=\"text-align: right;\"> 0.071158 </td><td style=\"text-align: right;\">-0.175502 </td><td style=\"text-align: right;\"> 0.163554 </td><td style=\"text-align: right;\"> 0.0369658  </td><td style=\"text-align: right;\"> 0.0145968  </td><td style=\"text-align: right;\">0.112552 </td><td style=\"text-align: right;\">-0.046148 </td><td style=\"text-align: right;\">0.0350732</td><td style=\"text-align: right;\"> 0.109729 </td><td style=\"text-align: right;\">-0.0259048</td><td style=\"text-align: right;\">-0.107765  </td><td style=\"text-align: right;\"> 0.0487584 </td><td style=\"text-align: right;\">-0.00650312</td><td style=\"text-align: right;\"> 0.0863861 </td><td style=\"text-align: right;\">0.107657 </td><td style=\"text-align: right;\"> 0.014315 </td><td style=\"text-align: right;\">-0.00369959</td><td style=\"text-align: right;\"> 0.0583035 </td><td style=\"text-align: right;\">-0.05895  </td><td style=\"text-align: right;\"> 0.0226024  </td><td style=\"text-align: right;\"> 0.0161003 </td><td style=\"text-align: right;\"> 0.0398932</td><td style=\"text-align: right;\"> 0.0669144 </td><td style=\"text-align: right;\">-0.0038533  </td><td style=\"text-align: right;\"> 0.0188122 </td><td style=\"text-align: right;\">-0.0172771</td><td style=\"text-align: right;\">-0.11414  </td><td style=\"text-align: right;\">-0.123933 </td><td style=\"text-align: right;\">-0.0714784 </td><td style=\"text-align: right;\">-0.0979761 </td><td style=\"text-align: right;\">-0.198314</td></tr>\n",
       "<tr><td style=\"text-align: right;\">-0.124295 </td><td style=\"text-align: right;\">-0.207359 </td><td style=\"text-align: right;\"> 0.0547896</td><td style=\"text-align: right;\">-0.0304305</td><td style=\"text-align: right;\">0.154015 </td><td style=\"text-align: right;\">-0.0205962 </td><td style=\"text-align: right;\"> 0.0842201</td><td style=\"text-align: right;\">-0.0706606</td><td style=\"text-align: right;\">-0.1647   </td><td style=\"text-align: right;\">-0.108152  </td><td style=\"text-align: right;\">-0.166922 </td><td style=\"text-align: right;\">-0.125771 </td><td style=\"text-align: right;\">-0.0260786</td><td style=\"text-align: right;\">-0.116414  </td><td style=\"text-align: right;\">-0.199608 </td><td style=\"text-align: right;\">-0.140634 </td><td style=\"text-align: right;\">-0.0916514 </td><td style=\"text-align: right;\"> 0.0494637</td><td style=\"text-align: right;\">0.169558</td><td style=\"text-align: right;\">-0.0861265</td><td style=\"text-align: right;\"> 0.0832132  </td><td style=\"text-align: right;\"> 0.0333396</td><td style=\"text-align: right;\">0.200909 </td><td style=\"text-align: right;\"> 0.0498794 </td><td style=\"text-align: right;\">-0.0240786</td><td style=\"text-align: right;\">-0.064085  </td><td style=\"text-align: right;\">-0.0565524 </td><td style=\"text-align: right;\"> 0.0912165 </td><td style=\"text-align: right;\">-0.00639987</td><td style=\"text-align: right;\"> 0.123293   </td><td style=\"text-align: right;\">-0.0445109  </td><td style=\"text-align: right;\">-0.00757608 </td><td style=\"text-align: right;\">-0.00151864</td><td style=\"text-align: right;\">-0.128118 </td><td style=\"text-align: right;\"> 0.124782 </td><td style=\"text-align: right;\"> 0.0386947 </td><td style=\"text-align: right;\"> 0.031987  </td><td style=\"text-align: right;\">-0.136752 </td><td style=\"text-align: right;\"> 0.0398239</td><td style=\"text-align: right;\"> 0.120508 </td><td style=\"text-align: right;\">-0.110199 </td><td style=\"text-align: right;\">-0.139788 </td><td style=\"text-align: right;\">-0.0994056 </td><td style=\"text-align: right;\"> 0.0862307 </td><td style=\"text-align: right;\">-0.0789786 </td><td style=\"text-align: right;\">-0.00783531</td><td style=\"text-align: right;\">-0.210449  </td><td style=\"text-align: right;\">-0.0842535</td><td style=\"text-align: right;\"> 0.0282057</td><td style=\"text-align: right;\">-0.0177935</td><td style=\"text-align: right;\">-0.0681635</td><td style=\"text-align: right;\"> 0.0802795  </td><td style=\"text-align: right;\"> 0.0914081</td><td style=\"text-align: right;\">-0.0484351 </td><td style=\"text-align: right;\"> 0.137631 </td><td style=\"text-align: right;\">0.0856186</td><td style=\"text-align: right;\">-0.00482426</td><td style=\"text-align: right;\">-0.0160471</td><td style=\"text-align: right;\">0.0915147</td><td style=\"text-align: right;\"> 0.161145  </td><td style=\"text-align: right;\">-0.0337171</td><td style=\"text-align: right;\">-0.0471576 </td><td style=\"text-align: right;\">-0.0049108 </td><td style=\"text-align: right;\"> 0.0588171 </td><td style=\"text-align: right;\">-0.110549 </td><td style=\"text-align: right;\">-0.10001  </td><td style=\"text-align: right;\">-0.148354 </td><td style=\"text-align: right;\"> 0.126402  </td><td style=\"text-align: right;\"> 0.0349634 </td><td style=\"text-align: right;\"> 0.123471 </td><td style=\"text-align: right;\">-0.12269  </td><td style=\"text-align: right;\"> 0.114756 </td><td style=\"text-align: right;\"> 0.0349392  </td><td style=\"text-align: right;\"> 0.0251235  </td><td style=\"text-align: right;\">0.0982317</td><td style=\"text-align: right;\">-0.0783563</td><td style=\"text-align: right;\">0.0864391</td><td style=\"text-align: right;\"> 0.163656 </td><td style=\"text-align: right;\">-0.0442912</td><td style=\"text-align: right;\">-0.132373  </td><td style=\"text-align: right;\"> 0.00547678</td><td style=\"text-align: right;\">-0.0919847 </td><td style=\"text-align: right;\"> 0.0815413 </td><td style=\"text-align: right;\">0.167716 </td><td style=\"text-align: right;\">-0.0377526</td><td style=\"text-align: right;\">-0.049083  </td><td style=\"text-align: right;\"> 0.026563  </td><td style=\"text-align: right;\">-0.165197 </td><td style=\"text-align: right;\"> 0.0392436  </td><td style=\"text-align: right;\">-0.00345092</td><td style=\"text-align: right;\"> 0.0228621</td><td style=\"text-align: right;\">-0.00681942</td><td style=\"text-align: right;\"> 0.0554516  </td><td style=\"text-align: right;\"> 0.0590008 </td><td style=\"text-align: right;\">-0.0512865</td><td style=\"text-align: right;\">-0.174769 </td><td style=\"text-align: right;\">-0.0755073</td><td style=\"text-align: right;\">-0.120474  </td><td style=\"text-align: right;\">-0.110672  </td><td style=\"text-align: right;\">-0.162697</td></tr>\n",
       "<tr><td style=\"text-align: right;\">-0.0497291</td><td style=\"text-align: right;\">-0.172739 </td><td style=\"text-align: right;\"> 0.0526745</td><td style=\"text-align: right;\">-0.142728 </td><td style=\"text-align: right;\">0.0208205</td><td style=\"text-align: right;\">-0.101139  </td><td style=\"text-align: right;\"> 0.143159 </td><td style=\"text-align: right;\">-0.0821187</td><td style=\"text-align: right;\">-0.179903 </td><td style=\"text-align: right;\">-0.135258  </td><td style=\"text-align: right;\">-0.146455 </td><td style=\"text-align: right;\">-0.0688982</td><td style=\"text-align: right;\">-0.0514576</td><td style=\"text-align: right;\">-0.0955344 </td><td style=\"text-align: right;\">-0.0173929</td><td style=\"text-align: right;\">-0.16224  </td><td style=\"text-align: right;\"> 0.0854388 </td><td style=\"text-align: right;\"> 0.012287 </td><td style=\"text-align: right;\">0.304265</td><td style=\"text-align: right;\"> 0.0418289</td><td style=\"text-align: right;\">-0.000875351</td><td style=\"text-align: right;\"> 0.202401 </td><td style=\"text-align: right;\">0.151498 </td><td style=\"text-align: right;\">-0.0721573 </td><td style=\"text-align: right;\">-0.0903185</td><td style=\"text-align: right;\">-0.0546163 </td><td style=\"text-align: right;\"> 0.0770515 </td><td style=\"text-align: right;\"> 0.0813198 </td><td style=\"text-align: right;\">-0.0730298 </td><td style=\"text-align: right;\"> 0.000916828</td><td style=\"text-align: right;\">-0.0586318  </td><td style=\"text-align: right;\">-0.000736892</td><td style=\"text-align: right;\">-0.0893292 </td><td style=\"text-align: right;\">-0.105226 </td><td style=\"text-align: right;\"> 0.0503958</td><td style=\"text-align: right;\"> 0.0178565 </td><td style=\"text-align: right;\"> 0.0753953 </td><td style=\"text-align: right;\">-0.164545 </td><td style=\"text-align: right;\"> 0.253618 </td><td style=\"text-align: right;\">-0.0652915</td><td style=\"text-align: right;\">-0.136067 </td><td style=\"text-align: right;\">-0.105342 </td><td style=\"text-align: right;\"> 0.0107835 </td><td style=\"text-align: right;\"> 0.0321385 </td><td style=\"text-align: right;\"> 0.175403  </td><td style=\"text-align: right;\"> 0.0523293 </td><td style=\"text-align: right;\">-0.167852  </td><td style=\"text-align: right;\">-0.14299  </td><td style=\"text-align: right;\"> 0.0454488</td><td style=\"text-align: right;\"> 0.0140015</td><td style=\"text-align: right;\"> 0.130307 </td><td style=\"text-align: right;\"> 0.102035   </td><td style=\"text-align: right;\">-0.0396289</td><td style=\"text-align: right;\"> 0.244186  </td><td style=\"text-align: right;\">-0.0310498</td><td style=\"text-align: right;\">0.230121 </td><td style=\"text-align: right;\">-0.0560533 </td><td style=\"text-align: right;\">-0.0312674</td><td style=\"text-align: right;\">0.0940689</td><td style=\"text-align: right;\">-0.14132   </td><td style=\"text-align: right;\"> 0.0553402</td><td style=\"text-align: right;\">-0.0296196 </td><td style=\"text-align: right;\"> 0.0732143 </td><td style=\"text-align: right;\">-0.00124884</td><td style=\"text-align: right;\">-0.0622691</td><td style=\"text-align: right;\">-0.137447 </td><td style=\"text-align: right;\">-0.113088 </td><td style=\"text-align: right;\"> 0.0283403 </td><td style=\"text-align: right;\">-0.00681567</td><td style=\"text-align: right;\"> 0.103245 </td><td style=\"text-align: right;\">-0.0336078</td><td style=\"text-align: right;\"> 0.0141382</td><td style=\"text-align: right;\"> 0.152744   </td><td style=\"text-align: right;\"> 0.0564579  </td><td style=\"text-align: right;\">0.158445 </td><td style=\"text-align: right;\">-0.0893126</td><td style=\"text-align: right;\">0.217361 </td><td style=\"text-align: right;\"> 0.0182976</td><td style=\"text-align: right;\"> 0.160799 </td><td style=\"text-align: right;\">-0.101119  </td><td style=\"text-align: right;\"> 0.12551   </td><td style=\"text-align: right;\"> 0.0114576 </td><td style=\"text-align: right;\"> 0.0421132 </td><td style=\"text-align: right;\">0.14134  </td><td style=\"text-align: right;\"> 0.0318897</td><td style=\"text-align: right;\"> 0.146897  </td><td style=\"text-align: right;\"> 0.00573063</td><td style=\"text-align: right;\">-0.0559328</td><td style=\"text-align: right;\">-0.134932   </td><td style=\"text-align: right;\"> 0.0284711 </td><td style=\"text-align: right;\"> 0.10148  </td><td style=\"text-align: right;\">-0.0223586 </td><td style=\"text-align: right;\">-0.215844   </td><td style=\"text-align: right;\"> 0.0564748 </td><td style=\"text-align: right;\">-0.0546697</td><td style=\"text-align: right;\"> 0.0067921</td><td style=\"text-align: right;\">-0.182821 </td><td style=\"text-align: right;\"> 0.0758764 </td><td style=\"text-align: right;\">-0.0962454 </td><td style=\"text-align: right;\">-0.227877</td></tr>\n",
       "<tr><td style=\"text-align: right;\">-0.0568149</td><td style=\"text-align: right;\">-0.0629231</td><td style=\"text-align: right;\"> 0.0777229</td><td style=\"text-align: right;\">-0.0256429</td><td style=\"text-align: right;\">0.101506 </td><td style=\"text-align: right;\">-0.0389447 </td><td style=\"text-align: right;\">-0.0793635</td><td style=\"text-align: right;\">-0.0593537</td><td style=\"text-align: right;\">-0.182863 </td><td style=\"text-align: right;\">-0.0729923 </td><td style=\"text-align: right;\">-0.097567 </td><td style=\"text-align: right;\">-0.016082 </td><td style=\"text-align: right;\"> 0.0663444</td><td style=\"text-align: right;\"> 0.0590753 </td><td style=\"text-align: right;\"> 0.0231217</td><td style=\"text-align: right;\">-0.142591 </td><td style=\"text-align: right;\">-0.0415118 </td><td style=\"text-align: right;\">-0.102916 </td><td style=\"text-align: right;\">0.127864</td><td style=\"text-align: right;\"> 0.130538 </td><td style=\"text-align: right;\">-0.055481   </td><td style=\"text-align: right;\">-0.180058 </td><td style=\"text-align: right;\">0.0150547</td><td style=\"text-align: right;\"> 0.0358391 </td><td style=\"text-align: right;\">-0.071178 </td><td style=\"text-align: right;\"> 0.0049319 </td><td style=\"text-align: right;\">-0.116692  </td><td style=\"text-align: right;\">-0.0971365 </td><td style=\"text-align: right;\">-0.0252073 </td><td style=\"text-align: right;\"> 0.226713   </td><td style=\"text-align: right;\"> 0.0642868  </td><td style=\"text-align: right;\"> 0.051884   </td><td style=\"text-align: right;\"> 0.10772   </td><td style=\"text-align: right;\">-0.0272336</td><td style=\"text-align: right;\">-0.0262592</td><td style=\"text-align: right;\">-0.00905795</td><td style=\"text-align: right;\"> 0.00874825</td><td style=\"text-align: right;\">-0.0462225</td><td style=\"text-align: right;\"> 0.0685229</td><td style=\"text-align: right;\"> 0.0395826</td><td style=\"text-align: right;\">-0.118837 </td><td style=\"text-align: right;\">-0.0460167</td><td style=\"text-align: right;\">-0.0951681 </td><td style=\"text-align: right;\">-0.0622147 </td><td style=\"text-align: right;\"> 0.0620888 </td><td style=\"text-align: right;\"> 0.0548181 </td><td style=\"text-align: right;\">-0.0196732 </td><td style=\"text-align: right;\">-0.138331 </td><td style=\"text-align: right;\">-0.079592 </td><td style=\"text-align: right;\">-0.0194914</td><td style=\"text-align: right;\">-0.15345  </td><td style=\"text-align: right;\"> 0.114051   </td><td style=\"text-align: right;\">-0.0405353</td><td style=\"text-align: right;\"> 0.127597  </td><td style=\"text-align: right;\"> 0.164525 </td><td style=\"text-align: right;\">0.0668319</td><td style=\"text-align: right;\">-0.0204733 </td><td style=\"text-align: right;\">-0.0646356</td><td style=\"text-align: right;\">0.181865 </td><td style=\"text-align: right;\"> 0.0436541 </td><td style=\"text-align: right;\">-0.072891 </td><td style=\"text-align: right;\">-0.00280418</td><td style=\"text-align: right;\">-0.0996457 </td><td style=\"text-align: right;\">-0.0440742 </td><td style=\"text-align: right;\">-0.107828 </td><td style=\"text-align: right;\">-0.216291 </td><td style=\"text-align: right;\">-0.0736526</td><td style=\"text-align: right;\">-0.00610611</td><td style=\"text-align: right;\">-0.077607  </td><td style=\"text-align: right;\">-0.040397 </td><td style=\"text-align: right;\">-0.0932192</td><td style=\"text-align: right;\"> 0.19004  </td><td style=\"text-align: right;\"> 0.212368   </td><td style=\"text-align: right;\">-0.0100296  </td><td style=\"text-align: right;\">0.0669379</td><td style=\"text-align: right;\">-0.0960082</td><td style=\"text-align: right;\">0.0811439</td><td style=\"text-align: right;\"> 0.152575 </td><td style=\"text-align: right;\"> 0.193627 </td><td style=\"text-align: right;\">-0.142891  </td><td style=\"text-align: right;\"> 0.119491  </td><td style=\"text-align: right;\"> 0.0180099 </td><td style=\"text-align: right;\"> 0.0768278 </td><td style=\"text-align: right;\">0.224927 </td><td style=\"text-align: right;\">-0.17332  </td><td style=\"text-align: right;\"> 0.114557  </td><td style=\"text-align: right;\">-0.0118137 </td><td style=\"text-align: right;\">-0.152121 </td><td style=\"text-align: right;\">-0.000920415</td><td style=\"text-align: right;\">-0.0246056 </td><td style=\"text-align: right;\"> 0.19368  </td><td style=\"text-align: right;\"> 0.0926417 </td><td style=\"text-align: right;\">-0.0566742  </td><td style=\"text-align: right;\"> 0.0758347 </td><td style=\"text-align: right;\"> 0.0157395</td><td style=\"text-align: right;\">-0.119214 </td><td style=\"text-align: right;\">-0.112142 </td><td style=\"text-align: right;\">-0.00329384</td><td style=\"text-align: right;\">-0.0954762 </td><td style=\"text-align: right;\">-0.299782</td></tr>\n",
       "<tr><td style=\"text-align: right;\">-0.0797212</td><td style=\"text-align: right;\">-0.173617 </td><td style=\"text-align: right;\"> 0.108163 </td><td style=\"text-align: right;\">-0.107503 </td><td style=\"text-align: right;\">0.0216342</td><td style=\"text-align: right;\">-0.140633  </td><td style=\"text-align: right;\"> 0.0506238</td><td style=\"text-align: right;\">-0.0568952</td><td style=\"text-align: right;\">-0.150429 </td><td style=\"text-align: right;\">-0.121181  </td><td style=\"text-align: right;\">-0.0734269</td><td style=\"text-align: right;\">-0.0138049</td><td style=\"text-align: right;\">-0.0656658</td><td style=\"text-align: right;\">-0.0808302 </td><td style=\"text-align: right;\">-0.029532 </td><td style=\"text-align: right;\">-0.0875219</td><td style=\"text-align: right;\"> 0.0553002 </td><td style=\"text-align: right;\"> 0.0603757</td><td style=\"text-align: right;\">0.170599</td><td style=\"text-align: right;\"> 0.103466 </td><td style=\"text-align: right;\"> 0.0334554  </td><td style=\"text-align: right;\"> 0.0903015</td><td style=\"text-align: right;\">0.076366 </td><td style=\"text-align: right;\">-0.00445736</td><td style=\"text-align: right;\">-0.0411916</td><td style=\"text-align: right;\">-0.00386698</td><td style=\"text-align: right;\">-0.0204134 </td><td style=\"text-align: right;\"> 0.094304  </td><td style=\"text-align: right;\">-0.00568415</td><td style=\"text-align: right;\">-0.0182749  </td><td style=\"text-align: right;\"> 0.0240352  </td><td style=\"text-align: right;\">-0.115381   </td><td style=\"text-align: right;\">-0.00511071</td><td style=\"text-align: right;\">-0.0473832</td><td style=\"text-align: right;\"> 0.0573445</td><td style=\"text-align: right;\"> 0.0520178 </td><td style=\"text-align: right;\"> 0.0527058 </td><td style=\"text-align: right;\">-0.0603055</td><td style=\"text-align: right;\"> 0.0703867</td><td style=\"text-align: right;\">-0.140389 </td><td style=\"text-align: right;\">-0.211046 </td><td style=\"text-align: right;\">-0.0921957</td><td style=\"text-align: right;\">-0.0302414 </td><td style=\"text-align: right;\">-0.0169181 </td><td style=\"text-align: right;\"> 0.0409367 </td><td style=\"text-align: right;\">-0.0538958 </td><td style=\"text-align: right;\">-0.145829  </td><td style=\"text-align: right;\">-0.180445 </td><td style=\"text-align: right;\"> 0.0426627</td><td style=\"text-align: right;\">-0.146734 </td><td style=\"text-align: right;\">-0.0118336</td><td style=\"text-align: right;\"> 0.166017   </td><td style=\"text-align: right;\"> 0.0466829</td><td style=\"text-align: right;\"> 0.0953235 </td><td style=\"text-align: right;\"> 0.0662368</td><td style=\"text-align: right;\">0.172648 </td><td style=\"text-align: right;\">-0.0419875 </td><td style=\"text-align: right;\"> 0.0646048</td><td style=\"text-align: right;\">0.0287446</td><td style=\"text-align: right;\"> 0.0126064 </td><td style=\"text-align: right;\">-0.0556719</td><td style=\"text-align: right;\"> 0.055117  </td><td style=\"text-align: right;\">-0.00705287</td><td style=\"text-align: right;\"> 0.113799  </td><td style=\"text-align: right;\">-0.0981764</td><td style=\"text-align: right;\">-0.118228 </td><td style=\"text-align: right;\">-0.162    </td><td style=\"text-align: right;\"> 0.049193  </td><td style=\"text-align: right;\">-0.00897845</td><td style=\"text-align: right;\"> 0.0112516</td><td style=\"text-align: right;\">-0.1093   </td><td style=\"text-align: right;\"> 0.0846343</td><td style=\"text-align: right;\"> 0.123794   </td><td style=\"text-align: right;\">-0.0117476  </td><td style=\"text-align: right;\">0.187768 </td><td style=\"text-align: right;\">-0.112331 </td><td style=\"text-align: right;\">0.0810788</td><td style=\"text-align: right;\"> 0.157419 </td><td style=\"text-align: right;\"> 0.146585 </td><td style=\"text-align: right;\">-0.0548345 </td><td style=\"text-align: right;\"> 0.106937  </td><td style=\"text-align: right;\">-0.0115633 </td><td style=\"text-align: right;\"> 0.0721555 </td><td style=\"text-align: right;\">0.0592547</td><td style=\"text-align: right;\"> 0.0510364</td><td style=\"text-align: right;\"> 0.0982434 </td><td style=\"text-align: right;\">-0.0223077 </td><td style=\"text-align: right;\">-0.0753102</td><td style=\"text-align: right;\">-0.0928342  </td><td style=\"text-align: right;\"> 0.0154012 </td><td style=\"text-align: right;\"> 0.0562391</td><td style=\"text-align: right;\"> 0.0659295 </td><td style=\"text-align: right;\"> 0.000222585</td><td style=\"text-align: right;\"> 0.0582499 </td><td style=\"text-align: right;\">-0.0134955</td><td style=\"text-align: right;\">-0.0377504</td><td style=\"text-align: right;\">-0.10022  </td><td style=\"text-align: right;\"> 0.171738  </td><td style=\"text-align: right;\">-0.0884614 </td><td style=\"text-align: right;\">-0.2505  </td></tr>\n",
       "<tr><td style=\"text-align: right;\">-0.0732007</td><td style=\"text-align: right;\">-0.16155  </td><td style=\"text-align: right;\"> 0.169509 </td><td style=\"text-align: right;\">-0.0588709</td><td style=\"text-align: right;\">0.137003 </td><td style=\"text-align: right;\">-0.101893  </td><td style=\"text-align: right;\"> 0.0712918</td><td style=\"text-align: right;\"> 0.0538054</td><td style=\"text-align: right;\">-0.179305 </td><td style=\"text-align: right;\">-0.155106  </td><td style=\"text-align: right;\">-0.0516661</td><td style=\"text-align: right;\">-0.0432677</td><td style=\"text-align: right;\">-0.114111 </td><td style=\"text-align: right;\">-0.0995693 </td><td style=\"text-align: right;\">-0.0274235</td><td style=\"text-align: right;\">-0.0690333</td><td style=\"text-align: right;\"> 0.00642208</td><td style=\"text-align: right;\"> 0.0230132</td><td style=\"text-align: right;\">0.226017</td><td style=\"text-align: right;\">-0.0343685</td><td style=\"text-align: right;\"> 0.0156354  </td><td style=\"text-align: right;\"> 0.0641474</td><td style=\"text-align: right;\">0.178739 </td><td style=\"text-align: right;\">-0.0415269 </td><td style=\"text-align: right;\">-0.0756107</td><td style=\"text-align: right;\">-0.109671  </td><td style=\"text-align: right;\"> 0.00291719</td><td style=\"text-align: right;\"> 0.154853  </td><td style=\"text-align: right;\"> 0.0332205 </td><td style=\"text-align: right;\"> 0.0859559  </td><td style=\"text-align: right;\">-0.0376196  </td><td style=\"text-align: right;\">-0.0545254  </td><td style=\"text-align: right;\">-0.0172261 </td><td style=\"text-align: right;\">-0.0518112</td><td style=\"text-align: right;\">-0.0031721</td><td style=\"text-align: right;\"> 0.00489847</td><td style=\"text-align: right;\">-0.031869  </td><td style=\"text-align: right;\">-0.068588 </td><td style=\"text-align: right;\"> 0.0180251</td><td style=\"text-align: right;\">-0.0965746</td><td style=\"text-align: right;\">-0.0793085</td><td style=\"text-align: right;\">-0.056131 </td><td style=\"text-align: right;\">-0.00547694</td><td style=\"text-align: right;\"> 0.00476019</td><td style=\"text-align: right;\">-0.0106659 </td><td style=\"text-align: right;\">-0.140296  </td><td style=\"text-align: right;\">-0.199387  </td><td style=\"text-align: right;\">-0.116954 </td><td style=\"text-align: right;\"> 0.0926532</td><td style=\"text-align: right;\">-0.0951169</td><td style=\"text-align: right;\">-0.0162338</td><td style=\"text-align: right;\"> 0.0793405  </td><td style=\"text-align: right;\"> 0.0164822</td><td style=\"text-align: right;\"> 0.0691578 </td><td style=\"text-align: right;\"> 0.011476 </td><td style=\"text-align: right;\">0.114886 </td><td style=\"text-align: right;\">-0.148701  </td><td style=\"text-align: right;\">-0.0510924</td><td style=\"text-align: right;\">0.0433333</td><td style=\"text-align: right;\">-0.00145437</td><td style=\"text-align: right;\"> 0.0693071</td><td style=\"text-align: right;\"> 0.0793733 </td><td style=\"text-align: right;\"> 0.0181438 </td><td style=\"text-align: right;\">-0.0414775 </td><td style=\"text-align: right;\">-0.0484972</td><td style=\"text-align: right;\">-0.140597 </td><td style=\"text-align: right;\">-0.0127534</td><td style=\"text-align: right;\">-0.044307  </td><td style=\"text-align: right;\"> 0.033697  </td><td style=\"text-align: right;\"> 0.0898299</td><td style=\"text-align: right;\">-0.0787719</td><td style=\"text-align: right;\"> 0.138622 </td><td style=\"text-align: right;\"> 0.0639041  </td><td style=\"text-align: right;\"> 0.000566445</td><td style=\"text-align: right;\">0.179113 </td><td style=\"text-align: right;\">-0.117074 </td><td style=\"text-align: right;\">0.17331  </td><td style=\"text-align: right;\"> 0.135503 </td><td style=\"text-align: right;\"> 0.0465885</td><td style=\"text-align: right;\">-0.00409158</td><td style=\"text-align: right;\"> 0.0979131 </td><td style=\"text-align: right;\">-0.0765709 </td><td style=\"text-align: right;\"> 0.192878  </td><td style=\"text-align: right;\">0.117089 </td><td style=\"text-align: right;\"> 0.0656053</td><td style=\"text-align: right;\"> 0.109539  </td><td style=\"text-align: right;\">-0.0299847 </td><td style=\"text-align: right;\">-0.0677924</td><td style=\"text-align: right;\">-0.0797814  </td><td style=\"text-align: right;\"> 0.0141459 </td><td style=\"text-align: right;\"> 0.110807 </td><td style=\"text-align: right;\"> 0.036249  </td><td style=\"text-align: right;\">-0.0577235  </td><td style=\"text-align: right;\"> 0.118674  </td><td style=\"text-align: right;\">-0.147738 </td><td style=\"text-align: right;\">-0.0263496</td><td style=\"text-align: right;\">-0.239927 </td><td style=\"text-align: right;\"> 0.0611369 </td><td style=\"text-align: right;\">-0.00924327</td><td style=\"text-align: right;\">-0.27347 </td></tr>\n",
       "<tr><td style=\"text-align: right;\">-0.355958 </td><td style=\"text-align: right;\">-0.196831 </td><td style=\"text-align: right;\">-0.015794 </td><td style=\"text-align: right;\"> 0.0348775</td><td style=\"text-align: right;\">0.103654 </td><td style=\"text-align: right;\"> 0.018093  </td><td style=\"text-align: right;\"> 0.156415 </td><td style=\"text-align: right;\">-0.015333 </td><td style=\"text-align: right;\">-0.0551591</td><td style=\"text-align: right;\">-0.483309  </td><td style=\"text-align: right;\">-0.181961 </td><td style=\"text-align: right;\">-0.240734 </td><td style=\"text-align: right;\">-0.0356244</td><td style=\"text-align: right;\">-0.00285861</td><td style=\"text-align: right;\"> 0.166403 </td><td style=\"text-align: right;\">-0.0125618</td><td style=\"text-align: right;\"> 0.0389355 </td><td style=\"text-align: right;\"> 0.16153  </td><td style=\"text-align: right;\">0.211653</td><td style=\"text-align: right;\"> 0.145215 </td><td style=\"text-align: right;\">-0.197268   </td><td style=\"text-align: right;\"> 0.214232 </td><td style=\"text-align: right;\">0.296793 </td><td style=\"text-align: right;\"> 0.201123  </td><td style=\"text-align: right;\">-0.0418813</td><td style=\"text-align: right;\">-0.00285069</td><td style=\"text-align: right;\"> 0.121268  </td><td style=\"text-align: right;\"> 0.00162978</td><td style=\"text-align: right;\"> 0.267035  </td><td style=\"text-align: right;\"> 0.0195831  </td><td style=\"text-align: right;\">-0.0611562  </td><td style=\"text-align: right;\"> 0.182813   </td><td style=\"text-align: right;\">-0.11171   </td><td style=\"text-align: right;\">-0.161658 </td><td style=\"text-align: right;\">-0.0988285</td><td style=\"text-align: right;\">-0.187027  </td><td style=\"text-align: right;\"> 0.0139657 </td><td style=\"text-align: right;\"> 0.0544618</td><td style=\"text-align: right;\"> 0.0748887</td><td style=\"text-align: right;\">-0.130175 </td><td style=\"text-align: right;\">-0.25456  </td><td style=\"text-align: right;\">-0.0119142</td><td style=\"text-align: right;\"> 0.0753773 </td><td style=\"text-align: right;\"> 0.0693355 </td><td style=\"text-align: right;\">-0.087481  </td><td style=\"text-align: right;\">-0.218356  </td><td style=\"text-align: right;\">-0.163171  </td><td style=\"text-align: right;\">-0.19476  </td><td style=\"text-align: right;\">-0.0369585</td><td style=\"text-align: right;\">-0.135052 </td><td style=\"text-align: right;\"> 0.114671 </td><td style=\"text-align: right;\">-0.142072   </td><td style=\"text-align: right;\"> 0.0492515</td><td style=\"text-align: right;\"> 0.212625  </td><td style=\"text-align: right;\">-0.0653282</td><td style=\"text-align: right;\">0.0682745</td><td style=\"text-align: right;\">-0.190866  </td><td style=\"text-align: right;\"> 0.0707219</td><td style=\"text-align: right;\">0.0442442</td><td style=\"text-align: right;\"> 0.0213841 </td><td style=\"text-align: right;\"> 0.169321 </td><td style=\"text-align: right;\"> 0.0725536 </td><td style=\"text-align: right;\"> 0.0927394 </td><td style=\"text-align: right;\">-0.18728   </td><td style=\"text-align: right;\">-0.0528623</td><td style=\"text-align: right;\">-0.493245 </td><td style=\"text-align: right;\">-0.344943 </td><td style=\"text-align: right;\">-0.170402  </td><td style=\"text-align: right;\"> 0.0942355 </td><td style=\"text-align: right;\">-0.24907  </td><td style=\"text-align: right;\">-0.0165013</td><td style=\"text-align: right;\">-0.0617929</td><td style=\"text-align: right;\"> 0.337667   </td><td style=\"text-align: right;\"> 0.140358   </td><td style=\"text-align: right;\">0.179328 </td><td style=\"text-align: right;\"> 0.0374235</td><td style=\"text-align: right;\">0.253994 </td><td style=\"text-align: right;\">-0.0305166</td><td style=\"text-align: right;\"> 0.157847 </td><td style=\"text-align: right;\"> 0.307999  </td><td style=\"text-align: right;\"> 0.205927  </td><td style=\"text-align: right;\"> 0.16942   </td><td style=\"text-align: right;\"> 0.195855  </td><td style=\"text-align: right;\">0.0503341</td><td style=\"text-align: right;\"> 0.317202 </td><td style=\"text-align: right;\"> 0.170732  </td><td style=\"text-align: right;\"> 0.128417  </td><td style=\"text-align: right;\">-0.17811  </td><td style=\"text-align: right;\"> 0.0888614  </td><td style=\"text-align: right;\">-0.106913  </td><td style=\"text-align: right;\">-0.0785357</td><td style=\"text-align: right;\">-0.0674368 </td><td style=\"text-align: right;\">-0.0956883  </td><td style=\"text-align: right;\">-0.0672496 </td><td style=\"text-align: right;\">-0.0158395</td><td style=\"text-align: right;\"> 0.0901865</td><td style=\"text-align: right;\">-0.185872 </td><td style=\"text-align: right;\"> 0.187507  </td><td style=\"text-align: right;\">-0.0399294 </td><td style=\"text-align: right;\">-0.418681</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_vecs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add aggregated word embeddings \n",
    "ext_reviews = reviews.cbind(review_vecs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train GBM Model to Predict Good Review\n",
    "\n",
    "We will train a GBM model with the same parameters as our baseline gbm.  This time, however, we will add the aggregated word embeddings as predictors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ext_train = ext_reviews[ext_reviews[\"Train\"] == \"Yes\"]\n",
    "ext_test = ext_reviews[ext_reviews[\"Train\"] == \"No\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "predictors = predictors + review_vecs.names\n",
    "response = 'PositiveReview'\n",
    "\n",
    "gbm_embeddings = H2OGradientBoostingEstimator(stopping_metric = \"AUC\", stopping_tolerance = 0.001,\n",
    "                                              stopping_rounds = 5, score_tree_interval = 10,\n",
    "                                              model_id = \"gbm_embeddings.hex\"\n",
    "                                             )\n",
    "gbm_embeddings.train(x = predictors, y = response, \n",
    "                   training_frame = ext_train, validation_frame = ext_test\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline AUC: 0.616\n",
      "With Embeddings AUC: 0.874\n"
     ]
    }
   ],
   "source": [
    "print(\"Baseline AUC: \" + str(round(gbm_baseline.auc(valid = True), 3)))\n",
    "print(\"With Embeddings AUC: \" + str(round(gbm_embeddings.auc(valid = True), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.54593742414: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>2034.0</td>\n",
       "<td>2626.0</td>\n",
       "<td>0.5635</td>\n",
       "<td> (2626.0/4660.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>551.0</td>\n",
       "<td>14797.0</td>\n",
       "<td>0.0359</td>\n",
       "<td> (551.0/15348.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>2585.0</td>\n",
       "<td>17423.0</td>\n",
       "<td>0.1588</td>\n",
       "<td> (3177.0/20008.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0     1      Error    Rate\n",
       "-----  ----  -----  -------  ----------------\n",
       "0      2034  2626   0.5635   (2626.0/4660.0)\n",
       "1      551   14797  0.0359   (551.0/15348.0)\n",
       "Total  2585  17423  0.1588   (3177.0/20008.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_embeddings.confusion_matrix(valid = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Summary\n",
    "\n",
    "We saw that the review column is not the only column with text.  We also have a column called `Summary` which summarizes the review.  We will add the word embeddings of the summary to see if this improves our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Break summaries into sequence of words\n",
    "summary_words = tokenize(reviews[\"Summary\"].ascharacter())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add aggregated summary word embeddings\n",
    "summary_vecs = w2v_model.transform(summary_words, aggregate_method = \"AVERAGE\")\n",
    "summary_vecs.names = [\"summary_\" + s for s in summary_vecs.names]\n",
    "\n",
    "ext_reviews = ext_reviews.cbind(summary_vecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ext_train = ext_reviews[ext_reviews[\"Train\"] == \"Yes\"]\n",
    "ext_test = ext_reviews[ext_reviews[\"Train\"] == \"No\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "predictors = predictors + summary_vecs.names\n",
    "response = 'PositiveReview'\n",
    "\n",
    "gbm_plus_summary = H2OGradientBoostingEstimator(stopping_metric = \"AUC\", stopping_tolerance = 0.001,\n",
    "                                              stopping_rounds = 5, score_tree_interval = 10,\n",
    "                                              model_id = \"gbm_plus_summary.hex\"\n",
    "                                             )\n",
    "gbm_plus_summary.train(x = predictors, y = response, \n",
    "                       training_frame = ext_train, validation_frame = ext_test\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline AUC: 0.616\n",
      "With Embeddings AUC: 0.874\n",
      "With Summary and Review Embeddings AUC: 0.911\n"
     ]
    }
   ],
   "source": [
    "print(\"Baseline AUC: \" + str(round(gbm_baseline.auc(valid = True), 3)))\n",
    "print(\"With Embeddings AUC: \" + str(round(gbm_embeddings.auc(valid = True), 3)))\n",
    "print(\"With Summary and Review Embeddings AUC: \" + str(round(gbm_plus_summary.auc(valid = True), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cXlX3AYcCOwC7VdXKJE9d92cjSZIkSY835WZCkzw5yaVJlgzPECZZmmTrdvu8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\n9wAAAHBYjruV0Cx9IuxlVbU3ye4kF2T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      "text/plain": [
       "<matplotlib.figure.Figure at 0x121890890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_plus_summary.varimp_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |█████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ys3bA3t4Um/U7egbYDiAihgPvB56oN7ykBnLWWfDII/CTn8CSS1adRtJ81HMe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\nu8Lil5SFWbPg5pvfXexbbw1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f/CJMnQprrAFTpsBWWxXX1d99d1hyyapTS9IcFr+yk1JR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\nauR/m7PXwC2Wb78AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1218f2490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_word_vecs = gbm_plus_summary.partial_plot(data = ext_train, cols = [\"C43\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that a low `C43` is associated with a smaller probability of positive review.  Let's see what words have a low `C43` value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get Word Embeddings per Word\n",
    "unique_words = words.asfactor().unique().ascharacter()\n",
    "unique_words.col_names = [\"Word\"]\n",
    "word_embeddings = w2v_model.transform(unique_words, aggregate_method=\"None\")\n",
    "word_embeddings = unique_words.cbind(word_embeddings)\n",
    "word_embeddings = word_embeddings[~(word_embeddings[\"C1\"].isna())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12300ded0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_embeddings[\"C43\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>Word      </th><th style=\"text-align: right;\">     C43</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>answered  </td><td style=\"text-align: right;\">-1.22806</td></tr>\n",
       "<tr><td>contacted </td><td style=\"text-align: right;\">-1.27295</td></tr>\n",
       "<tr><td>credit    </td><td style=\"text-align: right;\">-1.23765</td></tr>\n",
       "<tr><td>email     </td><td style=\"text-align: right;\">-1.3267 </td></tr>\n",
       "<tr><td>emails    </td><td style=\"text-align: right;\">-1.30889</td></tr>\n",
       "<tr><td>expiration</td><td style=\"text-align: right;\">-1.10804</td></tr>\n",
       "<tr><td>expired   </td><td style=\"text-align: right;\">-1.16901</td></tr>\n",
       "<tr><td>phone     </td><td style=\"text-align: right;\">-1.18508</td></tr>\n",
       "<tr><td>refund    </td><td style=\"text-align: right;\">-1.32436</td></tr>\n",
       "<tr><td>refunded  </td><td style=\"text-align: right;\">-1.26201</td></tr>\n",
       "<tr><td>rep       </td><td style=\"text-align: right;\">-1.4429 </td></tr>\n",
       "<tr><td>responded </td><td style=\"text-align: right;\">-1.10283</td></tr>\n",
       "<tr><td>salmonella</td><td style=\"text-align: right;\">-1.22183</td></tr>\n",
       "<tr><td>similac   </td><td style=\"text-align: right;\">-1.16961</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_c43_words = word_embeddings[word_embeddings[\"C43\"] < -1.1]\n",
    "low_c43_words[[\"Word\", \"C43\"]].head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The words with low `C43` like `contacted`, `answered`, `emails`, `phone` and `refund` all seem to be related to contacting for a refund.  Words like: `salmonella` are obviously an indicator of a negative review for a food product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict on New Reviews\n",
    "\n",
    "Now that we've built a model we are satisifed with, we will see how the model performs on new reviews.\n",
    "\n",
    "* *\"The taste is great! especially when you cook it with some vegetable and egg. I like it very much, though it's more expensive than the other noodles\"*\n",
    "\n",
    "\n",
    "* *\"Quite tasteless and they make you order so many. I am stuck with 12 bags of this tasteless stuff. I am not ordering large amounts of anything from Amazon again. So often I don't like it and I am stuck with so much on hand.\"*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(reviews, w2v, gbm):\n",
    "    \n",
    "    words = tokenize(reviews[\"Text\"].ascharacter())\n",
    "    reviews_vec = w2v.transform(words, aggregate_method=\"AVERAGE\")\n",
    "    \n",
    "    summary_words = tokenize(reviews[\"Summary\"].ascharacter())\n",
    "    summary_vec = w2v.transform(summary_words, aggregate_method=\"AVERAGE\")\n",
    "    \n",
    "    model_data = reviews.cbind(reviews_vec).cbind(summary_vec)\n",
    "    print(gbm.predict(model_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "good_review = h2o.H2OFrame([[\"B000EQT574\", \"AISQLBDGS2KXR\", 1, 1, 1263340800, \n",
    "                             \"Delicious\",\n",
    "                             \"These chips are delicious. Salty sweet with a hint of spice. I have no idea how Tamari is supposed to taste, but these chips are awesome. I just finished an entire bag in one day!\"]])\n",
    "\n",
    "good_review.col_names = [\"ProductId\",\"UserId\",\"HelpfulnessNumerator\",\"HelpfulnessDenominator\",\"Time\",\"Summary\",\"Text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "bad_review = h2o.H2OFrame([[\"B003BJZMSM\", \"A2JBZHQVQF7MV0\", 1, 2, 1274832000, \n",
    "                            \"Pretty tasteless\",\n",
    "                            \"Quite tasteless and they make you order so many. I am stuck with 12 bags of this tasteless stuff. I am not ordering large amounts of anything from Amazon again. So often I don't like it and I am stuck with so much on hand.\"]])\n",
    "\n",
    "bad_review.col_names = [\"ProductId\",\"UserId\",\"HelpfulnessNumerator\",\"HelpfulnessDenominator\",\"Time\",\"Summary\",\"Text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Good Review: \n",
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">     p0</th><th style=\"text-align: right;\">     p1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">0.13551</td><td style=\"text-align: right;\">0.86449</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
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     "output_type": "stream",
     "text": [
      "\n",
      "None\n",
      "Bad Review: \n",
      "gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  predict</th><th style=\"text-align: right;\">     p0</th><th style=\"text-align: right;\">       p1</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">0.93553</td><td style=\"text-align: right;\">0.0644696</td></tr>\n",
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       "</table>"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Predict!\n",
    "print(\"Good Review: \")\n",
    "print(predict(good_review, w2v_model, gbm_embeddings))\n",
    "\n",
    "print(\"Bad Review: \")\n",
    "print(predict(bad_review, w2v_model, gbm_embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O session _sid_813b closed.\n"
     ]
    }
   ],
   "source": [
    "h2o.cluster().shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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