{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bhawana Sharma\n",
    "### Epochs = 150 LR = 0.001 Activation = Relu Optimizer = Adam\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  OHPL Mean Loss Function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Metrics to assess performance on ordinal classification task given class prediction\n",
    "   using hyper plane loss techniques \n",
    "\"\"\"\n",
    "\n",
    "# Authors: Bob Vanderheyden <rvanderh@us.ibm.com>\n",
    "#          Ying Xie <yxie2@kennesaw.edu>\n",
    "#         \n",
    "# Contributor: Shayan Shamskolahi\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "def hpall_mean_loss(y_true, y_pred, minlabel, maxlabel, margin=0.1, ordering_loss_weight=1):\n",
    "    \"\"\" Evaluate the ordinal hyperplane ordering loss and point loss of the predictions y_pred\\\n",
    "        (using reduce mean).\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        y_true : array-like\n",
    "        y_pred : array-like\n",
    "        minlabel : integer\n",
    "        maxlabel : integer\n",
    "        margin : float\n",
    "        ordering_loss_weight : float\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        loss: float\n",
    "        A non-negative floating point value (best value is 0.0)\n",
    "        \n",
    "        Usage\n",
    "        -------\n",
    "        loss = hp_all_loss([4,1,2,0,4,2,1], [6.0,3.1,5.2,1.0,4.0,2.2,3.7],0,4,.3,0.1)\n",
    "        print('Loss: ', loss.numpy()) # Loss: 0.7228571\n",
    "        \n",
    "        \n",
    "        Usage with the `compile` API:\n",
    "        \n",
    "        ```python\n",
    "        \n",
    "        Example Keras wrapper for hp_all_loss:\n",
    "        \n",
    "        def get_ohpl_wrapper (min_label, max_label, margin, ordering_loss_weight):\n",
    "            def ohpl(y_true, y_pred):\n",
    "                return hpall_mean_loss(y_true, y_pred, min_label, max_label, margin, ordering_loss_weight)\n",
    "            return ohpl\n",
    "\n",
    "        loss = get_ohpl_wrapper(2,7,.3,1) # ordering_loss_weight must not be less that 1\n",
    "        \n",
    "        model = tf.keras.Model(inputs, outputs)\n",
    "        model.compile(loss=hp_all_loss, optimizer='adam', loss=ohpl_point_loss)\n",
    "        ```\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    min_label = tf.constant(minlabel, dtype=tf.float32)\n",
    "    max_label = tf.constant(maxlabel, dtype=tf.float32)\n",
    "    margin = tf.constant(margin, dtype=tf.float32) # centroid margin\n",
    "    ordering_loss_weight = tf.constant(ordering_loss_weight, dtype=tf.float32) \n",
    "    \n",
    "    y_pred = tf.convert_to_tensor(y_pred)\n",
    "    y_true = tf.dtypes.cast(y_true, y_pred.dtype)\n",
    "    y_pred = tf.reshape(tf.transpose(y_pred),[-1,1])\n",
    "    \n",
    "    # OHPL ordering loss\n",
    "    # one hot vector for y_true\n",
    "    ords, idx = tf.unique(tf.reshape(y_true, [-1])) \n",
    "    num = tf.shape(ords)[0]\n",
    "    y_true_1hot = tf.one_hot(idx, num)\n",
    "\n",
    "    # mean distance for each class\n",
    "    yO = tf.matmul(tf.transpose(y_pred),y_true_1hot)\n",
    "    yc = tf.reduce_sum(y_true_1hot,0)\n",
    "    class_mean = tf.divide(yO,yc)  \n",
    "\n",
    "    # min. distance\n",
    "    ords = tf.dtypes.cast(ords, tf.float32)\n",
    "    ords0 = tf.reshape(ords, [-1,1])\n",
    "    ords1 = tf.reshape(ords, [1,-1])\n",
    "    \n",
    "    min_distance = tf.subtract(ords0, ords1)\n",
    "    # apply ReLU\n",
    "    min_distance = tf.nn.relu (min_distance)\n",
    "    \n",
    "    # keeps min. distance\n",
    "    keep = tf.minimum(min_distance,1)\n",
    "\n",
    "    # distance to centroid     \n",
    "    class_mean0 = tf.reshape(class_mean, [-1,1])\n",
    "    class_mean1 = tf.reshape(class_mean, [1,-1])\n",
    "    class_mean = tf.subtract(class_mean0, class_mean1)  \n",
    "    # apply ReLU    \n",
    "    class_mean = tf.nn.relu(class_mean)\n",
    "    centroid_distance = tf.multiply(keep, class_mean)\n",
    "    \n",
    "    hp_ordering_loss = tf.subtract(min_distance,centroid_distance)\n",
    "    # apply ReLU\n",
    "    hp_ordering_loss = tf.nn.relu(hp_ordering_loss)\n",
    "    hp_ordering_loss = tf.reduce_sum(hp_ordering_loss)\n",
    "    \n",
    "    # OHPL point loss\n",
    "    # Centroid for point\n",
    "    point_cent = tf.matmul(y_true_1hot, class_mean0)\n",
    "    \n",
    "    lower_bound = tf.subtract(min_label,y_true)\n",
    "    lower_bound = tf.add(lower_bound,1)\n",
    "    lower_bound = tf.multiply(lower_bound,1e9)\n",
    "    # apply ReLU    \n",
    "    lower_bound = tf.nn.relu(lower_bound)\n",
    "    lower_bound = tf.add(margin, lower_bound)\n",
    "\n",
    "    upper_bound = tf.subtract(y_true,max_label)\n",
    "    upper_bound = tf.add(upper_bound,1)\n",
    "    upper_bound = tf.multiply(upper_bound,1e9)\n",
    "    # apply ReLU    \n",
    "    upper_bound = tf.nn.relu(upper_bound)\n",
    "    upper_bound = tf.add(margin, upper_bound)    \n",
    "\n",
    "    upper_loss = tf.add(point_cent,upper_bound[:,tf.newaxis])\n",
    "    upper_loss = tf.subtract(y_pred,upper_loss)\n",
    "    # apply ReLU    \n",
    "    upper_loss = tf.nn.relu(upper_loss)\n",
    "    \n",
    "    lower_loss = tf.add(lower_bound[:,tf.newaxis],y_pred)\n",
    "    lower_loss = tf.subtract(point_cent,lower_loss)\n",
    "    # apply ReLU    \n",
    "    lower_loss = tf.nn.relu(lower_loss)\n",
    "   \n",
    "    hp_point_loss = tf.add(upper_loss, lower_loss)\n",
    "    hp_point_loss = tf.reduce_mean(hp_point_loss)\n",
    "\n",
    "    # aggregate ordering loss and point loss     \n",
    "    mean_loss = tf.add(hp_point_loss,tf.multiply(ordering_loss_weight, hp_ordering_loss))\n",
    "    \n",
    "    return mean_loss\n",
    "\n",
    "   \n",
    "    \"\"\"    \n",
    "        References\n",
    "        ----------\n",
    "        .. [1] Vanderheyden, Bob and Ying Xie. Ordinal Hyperplane Loss. (2018). \n",
    "           2018 IEEE International Conference on Big Data (Big Data), \n",
    "           2018 IEEE International Conference On, 2337. https://doi-org.proxy.kennesaw.edu/10.1109/BigData.2018.8622079\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OHPL Sum Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hpall_sum_loss(y_true, y_pred, minlabel, maxlabel, margin=0.1, ordering_loss_weight=1):\n",
    "    \"\"\" Evaluate the ordinal hyperplane ordering loss and point loss of the predictions y_pred\\\n",
    "        (using reduce sum).\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        y_true : array-like\n",
    "        y_pred : array-like\n",
    "        minlabel : integer\n",
    "        maxlabel : integer\n",
    "        margin : float\n",
    "        ordering_loss_weight : float\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        loss: float\n",
    "        A non-negative floating point value (best value is 0.0)\n",
    "        \n",
    "        Usage\n",
    "        -------\n",
    "        loss = hp_all_loss([4,1,2,0,4,2,1], [6.0,3.1,5.2,1.0,4.0,2.2,3.7],0,4,.3,0.1)\n",
    "        print('Loss: ', loss.numpy()) # Loss: 3.48\n",
    "        \n",
    "        \n",
    "        Usage with the `compile` API:\n",
    "        \n",
    "        ```python\n",
    "        \n",
    "        Example Keras wrapper for hp_all_loss:\n",
    "        \n",
    "        def get_ohpl_wrapper (min_label, max_label, margin, ordering_loss_weight):\n",
    "            def ohpl(y_true, y_pred):\n",
    "                return hpall_sum_loss(y_true, y_pred, min_label, max_label, margin, ordering_loss_weight)\n",
    "            return ohpl\n",
    "\n",
    "        loss = get_ohpl_wrapper(0,4,1,1)\n",
    "        \n",
    "        model = tf.keras.Model(inputs, outputs)\n",
    "        model.compile(loss=hp_all_loss, optimizer='adam', loss=ohpl_point_loss)\n",
    "        ```\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    min_label = tf.constant(minlabel, dtype=tf.float32)\n",
    "    max_label = tf.constant(maxlabel, dtype=tf.float32)\n",
    "    margin = tf.constant(margin, dtype=tf.float32) # centroid margin\n",
    "    ordering_loss_weight = tf.constant(ordering_loss_weight, dtype=tf.float32) \n",
    "    \n",
    "    y_pred = tf.convert_to_tensor(y_pred)\n",
    "    y_true = tf.dtypes.cast(y_true, y_pred.dtype)\n",
    "    y_pred = tf.reshape(tf.transpose(y_pred),[-1,1])\n",
    "    \n",
    "    # OHPL ordering loss\n",
    "    # one hot vector for y_true\n",
    "    ords, idx = tf.unique(tf.reshape(y_true, [-1])) \n",
    "    num = tf.shape(ords)[0]\n",
    "    y_true_1hot = tf.one_hot(idx, num)\n",
    "\n",
    "    # mean distance for each class\n",
    "    yO = tf.matmul(tf.transpose(y_pred),y_true_1hot)\n",
    "    yc = tf.reduce_sum(y_true_1hot,0)\n",
    "    class_mean = tf.divide(yO,yc)  \n",
    "\n",
    "    # min. distance\n",
    "    ords = tf.dtypes.cast(ords, tf.float32)\n",
    "    ords0 = tf.reshape(ords, [-1,1])\n",
    "    ords1 = tf.reshape(ords, [1,-1])\n",
    "    \n",
    "    min_distance = tf.subtract(ords0, ords1)\n",
    "    # apply ReLU\n",
    "    min_distance = tf.nn.relu (min_distance)\n",
    "    \n",
    "    # keeps min. distance\n",
    "    keep = tf.minimum(min_distance,1)\n",
    "\n",
    "    # distance to centroid     \n",
    "    class_mean0 = tf.reshape(class_mean, [-1,1])\n",
    "    class_mean1 = tf.reshape(class_mean, [1,-1])\n",
    "    class_mean = tf.subtract(class_mean0, class_mean1)  \n",
    "    # apply ReLU    \n",
    "    class_mean = tf.nn.relu(class_mean)\n",
    "    centroid_distance = tf.multiply(keep, class_mean)\n",
    "    \n",
    "    hp_ordering_loss = tf.subtract(min_distance,centroid_distance)\n",
    "    # apply ReLU\n",
    "    hp_ordering_loss = tf.nn.relu(hp_ordering_loss)\n",
    "    hp_ordering_loss = tf.reduce_sum(hp_ordering_loss)\n",
    "    \n",
    "    # OHPL point loss\n",
    "    # Centroid for point\n",
    "    point_cent = tf.matmul(y_true_1hot, class_mean0)\n",
    "    \n",
    "    lower_bound = tf.subtract(min_label,y_true)\n",
    "    lower_bound = tf.add(lower_bound,1)\n",
    "    lower_bound = tf.multiply(lower_bound,1e9)\n",
    "    # apply ReLU    \n",
    "    lower_bound = tf.nn.relu(lower_bound)\n",
    "    lower_bound = tf.add(margin, lower_bound)\n",
    "\n",
    "    upper_bound = tf.subtract(y_true,max_label)\n",
    "    upper_bound = tf.add(upper_bound,1)\n",
    "    upper_bound = tf.multiply(upper_bound,1e9)\n",
    "    # apply ReLU    \n",
    "    upper_bound = tf.nn.relu(upper_bound)\n",
    "    upper_bound = tf.add(margin, upper_bound)    \n",
    "\n",
    "    upper_loss = tf.add(point_cent,upper_bound[:,tf.newaxis])\n",
    "    upper_loss = tf.subtract(y_pred,upper_loss)\n",
    "    # apply ReLU    \n",
    "    upper_loss = tf.nn.relu(upper_loss)\n",
    "    \n",
    "    lower_loss = tf.add(lower_bound[:,tf.newaxis],y_pred)\n",
    "    lower_loss = tf.subtract(point_cent,lower_loss)\n",
    "    # apply ReLU    \n",
    "    lower_loss = tf.nn.relu(lower_loss)\n",
    "   \n",
    "    hp_point_loss = tf.add(upper_loss, lower_loss)\n",
    "    hp_point_loss = tf.reduce_sum(hp_point_loss)\n",
    "\n",
    "    # aggregate ordering loss and point loss     \n",
    "    sum_loss = tf.add(hp_point_loss,tf.multiply(ordering_loss_weight, hp_ordering_loss))\n",
    "    \n",
    "    return sum_loss\n",
    "\n",
    "\n",
    "    \"\"\"    \n",
    "        References\n",
    "        ----------\n",
    "        .. [1] Vanderheyden, Bob and Ying Xie. Ordinal Hyperplane Loss. (2018). \n",
    "           2018 IEEE International Conference on Big Data (Big Data), \n",
    "           2018 IEEE International Conference On, 2337. https://doi-org.proxy.kennesaw.edu/10.1109/BigData.2018.8622079\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Application in Keras (mean loss):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example Keras wrapper for hpall_mean_loss\n",
    "\n",
    "def get_ohpl_wrapper (min_label, max_label, margin, ordering_loss_weight):\n",
    "    def ohpl(y_true, y_pred):\n",
    "        return hpall_mean_loss(y_true, y_pred, min_label, max_label, margin, ordering_loss_weight)\n",
    "    return ohpl\n",
    "\n",
    "loss = get_ohpl_wrapper(1,5,.1,1) # ordering_loss_weight must not be less that 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from numpy import array\n",
    "from keras.preprocessing.text import one_hot\n",
    "from keras.preprocessing.text import Tokenizer \n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "#from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from keras.initializers import Constant\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "from keras.models import Sequential\n",
    "#from keras.layers import Dense\n",
    "from keras.layers import Flatten, BatchNormalization, LSTM, Dropout, Dense, GRU\n",
    "#from keras.layers import LSTM\n",
    "#from keras.layers import Dropout\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from sklearn.model_selection import train_test_split \n",
    "import string, os\n",
    "import nltk\n",
    "\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 34626 entries, 0 to 34625\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Rating  34626 non-null  int64 \n",
      " 1   Review  34626 non-null  object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 811.5+ KB\n",
      "['Rating' 'Review']\n"
     ]
    }
   ],
   "source": [
    "pwd = !pwd\n",
    "df = pd.read_csv('RatingReview.csv')\n",
    "df = df.dropna()\n",
    "df.info()\n",
    "print(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>This product so far has not disappointed. My children love to use it and I like the ability to monitor control what content they see with ease.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>great for beginner or experienced person. Bought as a gift and she loves it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Inexpensive tablet for him to use and learn on, step up from the NABI. He was thrilled with it, learn how to Skype on it already...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>I've had my Fire HD 8 two weeks now and I love it. This tablet is a great value.We are Prime Members and that is where this tablet SHINES. I love being able to easily access all of the Prime content as well as movies you can download and watch laterThis has a 1280/800 screen which has some really nice look to it its nice and crisp and very bright infact it is brighter then the ipad pro costing $900 base model. The build on this fire is INSANELY AWESOME running at only 7.7mm thick and the smooth glossy feel on the back it is really amazing to hold its like the futuristic tab in ur hands.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>I bought this for my grand daughter when she comes over to visit. I set it up with her as the user, entered her age and name and now Amazon makes sure that she only accesses sites and content that are appropriate to her age. Simple to do and she loves the capabilities. I also bought and installed a 64gig SD card which gives this little tablet plenty of storage. For the price I think this tablet is best one out there. You can spend hundreds of dollars more for additional speed and capacity but when it comes to the basics this tablets does everything that most people will ever need at a fraction of the cost.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>This amazon fire 8 inch tablet is the perfect size. I purchased it for my husband so that he has a bigger screen than just his phone. He had gotten me one a few years ago so I knew it would be a good purchase.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>Great for e-reading on the go, nice and light weight, and for the price point given, definitely worth the purchase.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>I gave this as a Christmas gift to my inlaws, husband and uncle. They loved it and how easy they are to use with fantastic features!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>Great as a device to read books. I like that it links with my borrowed library e-books. Switched from another popular tablet brand and I am happy with the choice I made. It took some time to get books from my previous non-Kindle reader, but finally figured out a way!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>I love ordering books and reading them with the reader.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating  \\\n",
       "0  5        \n",
       "1  5        \n",
       "2  5        \n",
       "3  4        \n",
       "4  5        \n",
       "5  5        \n",
       "6  4        \n",
       "7  5        \n",
       "8  5        \n",
       "9  5        \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Review  \n",
       "0  This product so far has not disappointed. My children love to use it and I like the ability to monitor control what content they see with ease.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n",
       "1  great for beginner or experienced person. Bought as a gift and she loves it                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            \n",
       "2  Inexpensive tablet for him to use and learn on, step up from the NABI. He was thrilled with it, learn how to Skype on it already...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    \n",
       "3  I've had my Fire HD 8 two weeks now and I love it. This tablet is a great value.We are Prime Members and that is where this tablet SHINES. I love being able to easily access all of the Prime content as well as movies you can download and watch laterThis has a 1280/800 screen which has some really nice look to it its nice and crisp and very bright infact it is brighter then the ipad pro costing $900 base model. The build on this fire is INSANELY AWESOME running at only 7.7mm thick and the smooth glossy feel on the back it is really amazing to hold its like the futuristic tab in ur hands.                      \n",
       "4  I bought this for my grand daughter when she comes over to visit. I set it up with her as the user, entered her age and name and now Amazon makes sure that she only accesses sites and content that are appropriate to her age. Simple to do and she loves the capabilities. I also bought and installed a 64gig SD card which gives this little tablet plenty of storage. For the price I think this tablet is best one out there. You can spend hundreds of dollars more for additional speed and capacity but when it comes to the basics this tablets does everything that most people will ever need at a fraction of the cost.  \n",
       "5  This amazon fire 8 inch tablet is the perfect size. I purchased it for my husband so that he has a bigger screen than just his phone. He had gotten me one a few years ago so I knew it would be a good purchase.                                                                                                                                                                                                                                                                                                                                                                                                                      \n",
       "6  Great for e-reading on the go, nice and light weight, and for the price point given, definitely worth the purchase.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    \n",
       "7  I gave this as a Christmas gift to my inlaws, husband and uncle. They loved it and how easy they are to use with fantastic features!                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
       "8  Great as a device to read books. I like that it links with my borrowed library e-books. Switched from another popular tablet brand and I am happy with the choice I made. It took some time to get books from my previous non-Kindle reader, but finally figured out a way!                                                                                                                                                                                                                                                                                                                                                            \n",
       "9  I love ordering books and reading them with the reader.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', -1)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34616</th>\n",
       "      <td>2</td>\n",
       "      <td>Although the description states that this will work on my Kindle Fire HDX 8.9, when I plug the charger to the Kindle, I receive a message that the charger is not of sufficient wattage to properly charge the Kindle. I bought this charger as a back up for when I travel. The original charger which came with the Kindle does not show this warning when I plug it in.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34617</th>\n",
       "      <td>5</td>\n",
       "      <td>Connects snugly!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34618</th>\n",
       "      <td>1</td>\n",
       "      <td>So I was already not too pleased with Amazon's choice to only include an essentially useless charger when purchasing a kindle hd, and caved to ordering the fast charger so it would not take hours of non-use to recharge. For 20 you don't even get a full charger, just an adapter to put on to the cord they already gave you. Amazon please just charge the real price for the device instead of settling to slowly rip people off with essential accessories, this sort of thing cheapens your image and erodes loyalty.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34619</th>\n",
       "      <td>1</td>\n",
       "      <td>As a charger, it works but is nothing special. With a Mk 1 Kindle Fire, it doesn't seem to charge any faster than the standard charger. And NO CABLE is included!! A Kindle-specific cable must be purchased separately! This is not mentioned in the descriptions.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34620</th>\n",
       "      <td>1</td>\n",
       "      <td>This is exactly like any other usb power charger, making it unnecessary. A basic charger used for an ipod works to charge the kindle fire within 3-4 hours.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34621</th>\n",
       "      <td>3</td>\n",
       "      <td>This is not appreciably faster than any other 1.8 or higher amp charger when used with my Kindle, Kindle Fire, Kindle Fire HD, various Android tablets, and other devices that have USB charging cables.It also blocks an adjacent outlet. Why didn't they put the plug on the short end like the original black-and-white Kindle chargerIt is an exceptionally good deal when purchased together with a Kindle Fire HD on promotion, but the regular price is not competitive for a supposedly premium charger.Additionally, it does not come with a cable, an omission that many people won't realize.**EDIT** I appreciate the many comments, but Amazon promotes this charger as something that charges faster than other chargers, but I haven't seen any such advantage. At promotional prices, it's a good deal, but not at almost twenty dollars. Otherwise, look elsewhere.**EDIT** When the review was written, the description of the item did not have this statement Optimized for Kindle tablets: PowerFast is a 9W charger and will charge your Kindle tablets faster than the white 5W Kindle charger will. That line was added a considerable amount of time after I wrote my review.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34622</th>\n",
       "      <td>1</td>\n",
       "      <td>Amazon should include this charger with the Kindle. The fact that they're charging us extra for something that should be included is a sign of cheapness. Plus, you can use any micro-USB phone charger instead of this to charge your Kindle. Save your money.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34623</th>\n",
       "      <td>1</td>\n",
       "      <td>Love my Kindle Fire but I am really disappointed in the Kindle Power Fast Charging Unit. I've had it two months and I've used it many times - The first two times it worked okay but failerd on the third and many subsequent tries. I've disposed of it and use my wife's iPad Nano charger which always works just fine.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34624</th>\n",
       "      <td>1</td>\n",
       "      <td>I was surprised to find it did not come with any type of charging cords so I had to purchase one and then found my Sprint HTC 3D charger is faster. I would not purchase again- 1st item I've ever not liked I've purchased from Amazon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34625</th>\n",
       "      <td>1</td>\n",
       "      <td>to spite the fact that i have nothing but good things to say about amazon and anthing i've ever gotten from them. and that i love my fire. i find it greedy that the wall charger doesn't come with the kindle. not everyone, ok most people, but still not everyone has a usb port to plug into. i'm taking my charger back. i think amazon should make things right and let anyone who purchased a kindle without a charger have one for free, or credit those who had to buy one.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rating  \\\n",
       "34616  2        \n",
       "34617  5        \n",
       "34618  1        \n",
       "34619  1        \n",
       "34620  1        \n",
       "34621  3        \n",
       "34622  1        \n",
       "34623  1        \n",
       "34624  1        \n",
       "34625  1        \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Review  \n",
       "34616  Although the description states that this will work on my Kindle Fire HDX 8.9, when I plug the charger to the Kindle, I receive a message that the charger is not of sufficient wattage to properly charge the Kindle. I bought this charger as a back up for when I travel. The original charger which came with the Kindle does not show this warning when I plug it in.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              \n",
       "34617  Connects snugly!                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n",
       "34618  So I was already not too pleased with Amazon's choice to only include an essentially useless charger when purchasing a kindle hd, and caved to ordering the fast charger so it would not take hours of non-use to recharge. For 20 you don't even get a full charger, just an adapter to put on to the cord they already gave you. Amazon please just charge the real price for the device instead of settling to slowly rip people off with essential accessories, this sort of thing cheapens your image and erodes loyalty.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "34619  As a charger, it works but is nothing special. With a Mk 1 Kindle Fire, it doesn't seem to charge any faster than the standard charger. And NO CABLE is included!! A Kindle-specific cable must be purchased separately! This is not mentioned in the descriptions.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     \n",
       "34620  This is exactly like any other usb power charger, making it unnecessary. A basic charger used for an ipod works to charge the kindle fire within 3-4 hours.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n",
       "34621  This is not appreciably faster than any other 1.8 or higher amp charger when used with my Kindle, Kindle Fire, Kindle Fire HD, various Android tablets, and other devices that have USB charging cables.It also blocks an adjacent outlet. Why didn't they put the plug on the short end like the original black-and-white Kindle chargerIt is an exceptionally good deal when purchased together with a Kindle Fire HD on promotion, but the regular price is not competitive for a supposedly premium charger.Additionally, it does not come with a cable, an omission that many people won't realize.**EDIT** I appreciate the many comments, but Amazon promotes this charger as something that charges faster than other chargers, but I haven't seen any such advantage. At promotional prices, it's a good deal, but not at almost twenty dollars. Otherwise, look elsewhere.**EDIT** When the review was written, the description of the item did not have this statement Optimized for Kindle tablets: PowerFast is a 9W charger and will charge your Kindle tablets faster than the white 5W Kindle charger will. That line was added a considerable amount of time after I wrote my review.  \n",
       "34622  Amazon should include this charger with the Kindle. The fact that they're charging us extra for something that should be included is a sign of cheapness. Plus, you can use any micro-USB phone charger instead of this to charge your Kindle. Save your money.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         \n",
       "34623  Love my Kindle Fire but I am really disappointed in the Kindle Power Fast Charging Unit. I've had it two months and I've used it many times - The first two times it worked okay but failerd on the third and many subsequent tries. I've disposed of it and use my wife's iPad Nano charger which always works just fine.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              \n",
       "34624  I was surprised to find it did not come with any type of charging cords so I had to purchase one and then found my Sprint HTC 3D charger is faster. I would not purchase again- 1st item I've ever not liked I've purchased from Amazon                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 \n",
       "34625  to spite the fact that i have nothing but good things to say about amazon and anthing i've ever gotten from them. and that i love my fire. i find it greedy that the wall charger doesn't come with the kindle. not everyone, ok most people, but still not everyone has a usb port to plug into. i'm taking my charger back. i think amazon should make things right and let anyone who purchased a kindle without a charger have one for free, or credit those who had to buy one.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Clean-Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from nltk.stem import SnowballStemmer\n",
    "\n",
    "def text_to_wordlist(text, remove_stopwords=False, stem_words=False):\n",
    "    # Clean the text, with the option to remove stopwords and to stem words.\n",
    "    \n",
    "    # Convert words to lower case\n",
    "    text = text.lower()\n",
    "\n",
    "    # Clean the text\n",
    "    text = re.sub(r\"[^A-Za-z0-9^,!.\\/'+-=]\", \" \", str(text))\n",
    "    text = re.sub(r\"what's\", \"what is\", str(text))\n",
    "    text = re.sub(r\"\\'s\", \" \", str(text))\n",
    "    text = re.sub(r\"\\'ve\", \" have \", str(text))\n",
    "    text = re.sub(r\"can't\", \"cannot \", str(text))\n",
    "    text = re.sub(r\"n't\", \" not \", str(text))\n",
    "    text = re.sub(r\"dont\", \"do not \", str(text))\n",
    "    text = re.sub(r\"didn t\", \"did not \", str(text))\n",
    "    text = re.sub(r\"wasn\", \"was not \", str(text))\n",
    "    text = re.sub(r\"shouldn't\", \"should not \", str(text))\n",
    "    text = re.sub(r\"shouldnt\", \"should not \", str(text))\n",
    "    text = re.sub(r\"aren\", \"are not \", str(text))\n",
    "    text = re.sub(r\"awsome\", \"awesome \", str(text))\n",
    "    text = re.sub(r\"tho\", \"though \", str(text))\n",
    "    text = re.sub(r\"wi fi\", \"wi-fi \", str(text))\n",
    "    text = re.sub(r\"i'm\", \"i am \", str(text))\n",
    "    text = re.sub(r\"\\'re\", \" are \", str(text))\n",
    "    text = re.sub(r\"\\'d\", \" would \", str(text))\n",
    "    text = re.sub(r\"\\'ll\", \" will \", str(text))\n",
    "    text = re.sub(r\",\", \" \", str(text))\n",
    "    text = re.sub(r\"\\.\", \" \", str(text))\n",
    "    text = re.sub(r\"!\", \" ! \", str(text))\n",
    "    text = re.sub(r\"\\/\", \" \", str(text))\n",
    "    text = re.sub(r\"\\^\", \" ^ \", str(text))\n",
    "    text = re.sub(r\"\\+\", \" + \", str(text))\n",
    "    text = re.sub(r\"\\-\", \" - \", str(text))\n",
    "    text = re.sub(r\"\\=\", \" = \", str(text))\n",
    "    text = re.sub(r\"'\", \" \", str(text))\n",
    "    text = re.sub(r\":\", \" : \", str(text))\n",
    "    text = re.sub(r\" e g \", \" eg \", str(text))\n",
    "    text = re.sub(r\" b g \", \" bg \", str(text))\n",
    "    text = re.sub(r\" u s \", \" american \", str(text))\n",
    "    text = re.sub(r\" 9 11 \", \"911\", str(text))\n",
    "    text = re.sub(r\"e - mail\", \"email\", str(text))\n",
    "    text = re.sub(r\"e-mail\", \"email\", str(text))\n",
    "    text = re.sub(r\" j k \", \" jk \", str(text))\n",
    "    \n",
    "    text = re.sub(r\"\\0s\", \"0\", str(text))\n",
    "    text = re.sub(r\"(\\d+)(k)\", r\"\\g<1>000\", str(text))\n",
    "    text = re.sub(r\"\\s{2,}\", \" \", str(text))\n",
    "    \n",
    "\n",
    "    \n",
    "    # Optionally, remove stop words\n",
    "    if remove_stopwords:\n",
    "        text = text.split(\" \")\n",
    "        stops = set(stopwords.words(\"english\"))\n",
    "        text = [w for w in text if not w in stops]\n",
    "        text = \" \".join(text)\n",
    "    \n",
    "    # Optionally, shorten words to their stems\n",
    "    if stem_words:\n",
    "        text = text.split(\" \")\n",
    "        stemmer = SnowballStemmer('english')\n",
    "        stemmed_words = [stemmer.stem(word) for word in text]\n",
    "        text = \" \".join(stemmed_words)\n",
    "    \n",
    "    # Return a list of words\n",
    "    return(text)\n",
    "#lower case of the word\n",
    "df['Review'] = df['Review'].apply(lambda x: str(x).lower()) \n",
    "#remove anything that is not a number or alphabet\n",
    "df['Review'] = df['Review'].apply((lambda x: re.sub('[^a-zA-Z0-9\\s]', ' ', x)))  \n",
    "\n",
    "X = df['Review'].map(lambda x: text_to_wordlist(x, remove_stopwords=False, stem_words=False))\n",
    "y = df['Rating'].values \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Review Word Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.0 1902 0 35.18256235072377\n",
      "33932 34626 0.02004274244787154 17\n",
      "21.0 120 0 20.12575267012719\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sentence_length = np.array([len(sentence.split()) for sentence in X])\n",
    "print(np.median(sentence_length), np.max(sentence_length), np.min(sentence_length), np.std(sentence_length))\n",
    "\n",
    "comment_len = 120\n",
    "short = sentence_length[(sentence_length < comment_len+1)]\n",
    "print(len(short), len(sentence_length), (len(sentence_length)-len(short))/len(sentence_length), len(sentence_length[(sentence_length==comment_len+1)]))\n",
    "\n",
    "num_bins = min(100,comment_len)\n",
    "print(np.median(short), np.max(short), np.min(short), np.std(short))\n",
    "n, bins, patches = plt.hist(short, num_bins, alpha=0.5)\n",
    "plt.title('Histogram of Review Word Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Words in Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "\n",
    "rt = df['Review']\n",
    "wordcloud = WordCloud(background_color='white',\n",
    "                      width=700,\n",
    "                      height=400\n",
    "                     ).generate(\" \".join(rt))\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.imshow(wordcloud)\n",
    "plt.title('All Words in the Reviews\\n',size=20)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df['Rating'])\n",
    "plt.title('Count ratings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([1, 2, 3, 4, 5], dtype=int64), array([  410,   402,  1499,  8541, 23774], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(df['Rating'], return_counts=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check X and  y Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34626,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34626,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Training, Validation and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, Xtest, y_train, y_test = train_test_split(X, y, test_size=1/10, stratify=y, random_state=42)\n",
    "#Xtrain, Xval, y_train, y_val = train_test_split(X_train, y_train, test_size=1/9, stratify=y_train, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31163,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31163,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([1, 2, 3, 4, 5], dtype=int64), array([  369,   362,  1349,  7687, 21396], dtype=int64))\n",
      "(array([1, 2, 3, 4, 5], dtype=int64), array([  41,   40,  150,  854, 2378], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(y_train, return_counts=True))\n",
    "\n",
    "print(np.unique(y_test, return_counts=True))\n",
    "\n",
    "#print(np.unique(y_val, return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2, 3, 4, 5], dtype=int64),\n",
       " array([10701, 10860, 10792, 15374, 21396], dtype=int64))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "import numpy as np\n",
    "\n",
    "labels, num = np.unique(y_train, return_counts=True)\n",
    "#print(labels)\n",
    "u = min(labels)\n",
    "initial = 1\n",
    "\n",
    "# set the desired size of the oversampled cells\n",
    "maxcnt = np.int(max(num)/2)\n",
    "\n",
    "for labl, n in zip(labels, num):\n",
    "    x0 = X_train[y_train==labl]\n",
    "    y0 = y_train[y_train==labl]\n",
    "    #print(x0)\n",
    "\n",
    "    remain = maxcnt\n",
    "   # print (remain)\n",
    "    while remain >= n:\n",
    "        if labl == u and initial == 1: \n",
    "            X_Train = x0\n",
    "            y_Train = y0\n",
    "            remain -= n\n",
    "            initial = 0\n",
    "        else:\n",
    "            X_Train = np.concatenate((X_Train, x0), axis=0)\n",
    "            y_Train = np.concatenate((y_Train, y0), axis=0)\n",
    "            remain -= n\n",
    "    if remain > 0 and remain < n: \n",
    "        \n",
    "        idx = np.random.choice(np.arange(len(y0)), remain, replace=False)\n",
    "#        print(idx)\n",
    "        X_Train = np.concatenate((X_Train, x0), axis=0)\n",
    "        y_Train = np.concatenate((y_Train, y0), axis=0)\n",
    "        remain -= n\n",
    "\n",
    "X_Train, y_Train = shuffle(X_Train, y_Train)\n",
    "np.unique(y_Train, return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Glovec Word Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "embeddings_index = {}\n",
    "f = open('glove.6B.50d.txt', encoding=\"utf8\")\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenize Words in Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13948\n",
      "{'the': 1, 'it': 2, 'i': 3, 'and': 4, 'to': 5, 'for': 6, 'a': 7, 'is': 8, 'this': 9, 'my': 10, 'great': 11, 'of': 12, 'with': 13, 'tablet': 14, 'you': 15, 'on': 16, 'have': 17, 'that': 18, 'use': 19, 'not': 20, 'in': 21, 'but': 22, 'was': 23, 'love': 24, 'can': 25, 'as': 26, 'easy': 27, 'so': 28, 's': 29, 'amazon': 30, 'very': 31, 'kindle': 32, 't': 33, 'good': 34, 'bought': 35, 'one': 36, 'fire': 37, 'she': 38, 'price': 39, 'all': 40, 'has': 41, 'are': 42, 'like': 43, 'we': 44, 'an': 45, 'product': 46, 'up': 47, 'be': 48, 'more': 49, 'tv': 50, 'would': 51, 'works': 52, 'if': 53, 'or': 54, 'at': 55, 'just': 56, 'get': 57, 'they': 58, 'echo': 59, 'had': 60, 'much': 61, 'music': 62, 'read': 63, 'alexa': 64, 'kids': 65, 'loves': 66, 'from': 67, 'apps': 68, 'do': 69, 'than': 70, 'well': 71, 'books': 72, 'reading': 73, 'device': 74, 'when': 75, 'best': 76, 'really': 77, 'me': 78, 'buy': 79, 'what': 80, 'games': 81, 'time': 82, 'her': 83, 'old': 84, 'screen': 85, 'purchased': 86, 'also': 87, 'only': 88, 'no': 89, 'he': 90, 'play': 91, 'your': 92, 'got': 93, 'gift': 94, 'better': 95, 'does': 96, 'will': 97, 'recommend': 98, 'them': 99, 'out': 100, 'other': 101, 'set': 102, 'am': 103, 'year': 104, 'perfect': 105, 'our': 106, 'nice': 107, 'new': 108, 'home': 109, 'about': 110, 'little': 111, 'there': 112, 'now': 113, 'light': 114, 'its': 115, 'because': 116, 'even': 117, 'don': 118, 'quality': 119, 'purchase': 120, 'using': 121, 'prime': 122, 'though': 123, 'need': 124, 'did': 125, 'lot': 126, 'm': 127, 'christmas': 128, 'some': 129, 'battery': 130, 'how': 131, 'been': 132, 'too': 133, 'size': 134, 'any': 135, 'able': 136, 'movies': 137, 'want': 138, 'which': 139, 'first': 140, 'everything': 141, 'happy': 142, 'many': 143, 'son': 144, 'work': 145, 'sound': 146, 'still': 147, 'daughter': 148, 'go': 149, 'things': 150, 'used': 151, 'app': 152, 'fun': 153, 'reader': 154, 'these': 155, 'watch': 156, 'fast': 157, 'could': 158, 'far': 159, 'awesome': 160, 'ipad': 161, 'tablets': 162, '2': 163, 'after': 164, 'who': 165, 'features': 166, 'by': 167, 've': 168, 'excellent': 169, 'family': 170, 'money': 171, 'speaker': 172, 'life': 173, 'ut': 174, 'paperwhite': 175, 'cable': 176, 'withough': 177, 'free': 178, 'stick': 179, 'wife': 180, 'voice': 181, 'worth': 182, 'day': 183, 'streaming': 184, 'box': 185, 'while': 186, 'having': 187, 'store': 188, 'small': 189, 'thing': 190, 'way': 191, 'enjoy': 192, 'amazing': 193, 'wanted': 194, 'then': 195, 'internet': 196, 'getting': 197, 'google': 198, 'looking': 199, 'item': 200, 'every': 201, 'smart': 202, 'over': 203, 'back': 204, 'weather': 205, 'since': 206, 'long': 207, 'makes': 208, 'book': 209, 'ask': 210, 'doesn': 211, 'download': 212, 'his': 213, 'two': 214, 'off': 215, 'playing': 216, '3': 217, 'phone': 218, 'take': 219, 'control': 220, 'e': 221, 'another': 222, 'highly': 223, 'most': 224, 'their': 225, 'questions': 226, 'access': 227, 'something': 228, 'house': 229, 'pretty': 230, 'around': 231, 'netflix': 232, 'enough': 233, 'find': 234, 'being': 235, 'right': 236, 'simple': 237, 'make': 238, 'definitely': 239, 'user': 240, 'ugh': 241, 'devices': 242, 'say': 243, 'overall': 244, '5': 245, 'etc': 246, 'always': 247, 'lights': 248, 'loved': 249, 'before': 250, 'were': 251, 'pare': 252, 'few': 253, 'memory': 254, '7': 255, 'think': 256, '4': 257, 'wifi': 258, 'apple': 259, 'years': 260, 'wish': 261, 'available': 262, 'uses': 263, 'needs': 264, 'know': 265, 'learning': 266, 'friendly': 267, 'into': 268, 'both': 269, 'needed': 270, 'see': 271, 'faster': 272, 'case': 273, 'easier': 274, 'down': 275, 'videos': 276, 'turn': 277, 'setup': 278, 'black': 279, 'android': 280, 'anything': 281, 'value': 282, 'especially': 283, 'own': 284, 'shows': 285, 'add': 286, 'room': 287, 'sure': 288, '8': 289, 'news': 290, 'keep': 291, 'absolutely': 292, 'ever': 293, 'charge': 294, 'same': 295, 'anyone': 296, 'through': 297, 'however': 298, 'sale': 299, 'remote': 300, 'watching': 301, 'web': 302, 'going': 303, 'deal': 304, 'never': 305, '4000': 306, 'put': 307, 'lots': 308, 'bit': 309, 'made': 310, 'card': 311, 're': 312, 'hd': 313, 'already': 314, 'problem': 315, 'children': 316, 'ught': 317, 'second': 318, 'issues': 319, 'version': 320, 'picture': 321, 'seems': 322, 'plus': 323, 'child': 324, 'him': 325, 'content': 326, 'kid': 327, 'myself': 328, 'easily': 329, 'should': 330, 'slow': 331, 'different': 332, 'big': 333, 'several': 334, 'again': 335, 'yet': 336, 'give': 337, 'listen': 338, 'night': 339, 'found': 340, 'dot': 341, 'video': 342, 'feature': 343, 'favorite': 344, '50': 345, 'camera': 346, 'system': 347, 'last': 348, 'eyes': 349, 'storage': 350, 'list': 351, 'buying': 352, 'cheap': 353, 'mom': 354, 'times': 355, 'friday': 356, 'carry': 357, 'beat': 358, 'basic': 359, 'products': 360, 'unit': 361, 'such': 362, 'must': 363, 'controls': 364, 'pleased': 365, 'account': 366, 'shopping': 367, 'grandson': 368, 'gave': 369, 'fine': 370, 'cost': 371, 'low': 372, 'older': 373, 'decided': 374, 'mother': 375, 'super': 376, 'hard': 377, 'tal': 378, 'husband': 379, 'clear': 380, 'white': 381, '1': 382, 'plays': 383, 'replace': 384, 'less': 385, 'library': 386, 'almost': 387, 'once': 388, 'tap': 389, 'online': 390, 'pay': 391, 'haven': 392, 'expensive': 393, 'look': 394, 'navigate': 395, 'useful': 396, 'problems': 397, 'bluetooth': 398, 'tell': 399, 'stream': 400, 'upgrade': 401, 'hold': 402, 'weight': 403, 'se': 404, '10': 405, 'expected': 406, 'each': 407, 'glad': 408, 'ok': 409, 'resolution': 410, 'working': 411, 'paper': 412, 'quick': 413, 'friends': 414, 'page': 415, 'member': 416, 'roku': 417, 'extra': 418, 'connect': 419, 'enjoying': 420, 'service': 421, 'options': 422, 'ability': 423, 'speed': 424, 'friend': 425, 'email': 426, 'cool': 427, 'convenient': 428, 'fact': 429, 'daily': 430, 'us': 431, 'model': 432, 'everyone': 433, 'ease': 434, 'touch': 435, 'where': 436, 'sd': 437, 'help': 438, 'charger': 439, '6': 440, 'cover': 441, 'display': 442, 'decent': 443, 'birthday': 444, 'sometimes': 445, 'likes': 446, 'answer': 447, 'everyday': 448, 'experience': 449, 'hands': 450, 'learn': 451, 'granddaughter': 452, 'travel': 453, 'isn': 454, 'portable': 455, 'went': 456, 'issue': 457, 'original': 458, 'comes': 459, 'allows': 460, 'feel': 461, 'wonderful': 462, 'ads': 463, 'option': 464, 'job': 465, 'may': 466, 'hours': 467, 'instead': 468, 'quite': 469, 'asking': 470, 'lighting': 471, 'come': 472, 'voyage': 473, 'couldn': 474, 'youtube': 475, 'computer': 476, 'keeps': 477, 'recommended': 478, 'd': 479, 'addition': 480, 'gets': 481, 'liked': 482, 'lightweight': 483, 'install': 484, 'quickly': 485, 'full': 486, 'll': 487, 'high': 488, 'present': 489, 'inexpensive': 490, 'actually': 491, 'information': 492, 'check': 493, 'worked': 494, 'listening': 495, 'commands': 496, 'gifts': 497, 'month': 498, 'technology': 499, 'someone': 500, 'stuff': 501, 'helpful': 502, 'takes': 503, 'search': 504, 'came': 505, 'enjoyed': 506, 'took': 507, 'entertainment': 508, 'interface': 509, 'outside': 510, 'said': 511, 'anywhere': 512, 'people': 513, 'start': 514, 'durable': 515, 'order': 516, 'cannot': 517, 'media': 518, 'bad': 519, 'tech': 520, 'color': 521, 'skills': 522, 'won': 523, 'connected': 524, 'mostly': 525, 'added': 526, 'reviews': 527, 'cord': 528, 'performance': 529, 'longer': 530, 'satisfied': 531, 'compared': 532, 'charging': 533, 'connection': 534, 'days': 535, 'exactly': 536, 'load': 537, 'away': 538, 'try': 539, 'bright': 540, 'affordable': 541, 'done': 542, 'understand': 543, 'kindles': 544, 'fantastic': 545, 'next': 546, 'nothing': 547, 'limited': 548, 'why': 549, 'built': 550, 'replacement': 551, 'kodi': 552, 'tried': 553, 'speakers': 554, 'wrong': 555, 'cheaper': 556, 'point': 557, 'fits': 558, 'space': 559, 'previous': 560, 'others': 561, 'couple': 562, 'during': 563, 'purchasing': 564, 'stars': 565, 'whole': 566, 'disappointed': 567, 'samsung': 568, 'browsing': 569, 'nephew': 570, 'bed': 571, 'power': 572, 'backlight': 573, 'enjoys': 574, 'extremely': 575, 'items': 576, 'else': 577, 'purse': 578, 'real': 579, 'game': 580, 'side': 581, 'months': 582, 'gives': 583, 'choice': 584, 'smaller': 585, 'setting': 586, 'market': 587, 'weeks': 588, 'bigger': 589, 'handy': 590, 'run': 591, 'started': 592, 'paid': 593, 'looks': 594, 'mine': 595, 'dark': 596, 'ago': 597, 'compact': 598, 'three': 599, 'trying': 600, 'firestick': 601, 'due': 602, 'automation': 603, 'functions': 604, 'niece': 605, 'spend': 606, 'services': 607, 'person': 608, 'reason': 609, 'hand': 610, 'yr': 611, 'doing': 612, 'school': 613, 'answers': 614, 'vue': 615, 'downloaded': 616, 'grandkids': 617, 'responsive': 618, 'perfectly': 619, 'plug': 620, 'glare': 621, 'surfing': 622, 'change': 623, 'part': 624, 'wait': 625, 'expect': 626, 'between': 627, 'young': 628, 'although': 629, 'returned': 630, 'operate': 631, 'kitchen': 632, 'firetv': 633, '9': 634, 'runs': 635, 'generation': 636, 'lasts': 637, 'helps': 638, 'regular': 639, 'law': 640, 'capabilities': 641, 'top': 642, 'offers': 643, 'wouldn': 644, 'probably': 645, 'finally': 646, 'owned': 647, 'facebook': 648, 'radio': 649, 'wants': 650, 'larger': 651, 'making': 652, 'impressed': 653, 'week': 654, 'received': 655, 'cut': 656, 'often': 657, 'here': 658, 'button': 659, 'command': 660, 'traveling': 661, 'along': 662, 'ts': 663, 'processor': 664, 'laptop': 665, 'pandora': 666, '100': 667, 'buffering': 668, 'hue': 669, 'plenty': 670, 'place': 671, 'let': 672, 'question': 673, 'updates': 674, 'end': 675, 'movie': 676, 'via': 677, 'table': 678, 'nook': 679, 'seem': 680, 'brought': 681, 'age': 682, 'tab': 683, 'under': 684, 'ones': 685, 'ereader': 686, 'everywhere': 687, 'replaced': 688, 'fit': 689, 'complaints': 690, 'might': 691, 'hulu': 692, 'volume': 693, 'live': 694, 'jokes': 695, 'pictures': 696, 'expectations': 697, 'hear': 698, 'functionality': 699, 'educational': 700, 'morning': 701, 'text': 702, 'difference': 703, 'choose': 704, 'mini': 705, 'taking': 706, 'mainly': 707, 'either': 708, 'upgraded': 709, 'wireless': 710, 'sounds': 711, 'figure': 712, 'purchases': 713, 'lists': 714, 'warranty': 715, 'goes': 716, 'customer': 717, 'maybe': 718, 'playstation': 719, 'sister': 720, 'port': 721, 'downloading': 722, 'huge': 723, 'plan': 724, 'purpose': 725, 'response': 726, 'grand': 727, 'tool': 728, 'surprised': 729, 'future': 730, 'name': 731, 'otherwise': 732, 'simply': 733, 'advertised': 734, 'pages': 735, 'channels': 736, 'additional': 737, 'large': 738, 'third': 739, 'pick': 740, 'sunlight': 741, 'complaint': 742, 'until': 743, 'siri': 744, 'forward': 745, 'thinking': 746, 'provides': 747, 'adding': 748, 'thermostat': 749, 'multiple': 750, 'sun': 751, 'songs': 752, 'timer': 753, 'thanks': 754, 'audio': 755, 'readers': 756, 'alarm': 757, 'bedroom': 758, 'given': 759, 'difficult': 760, 'prefer': 761, 'minutes': 762, 'asked': 763, 'usb': 764, 'show': 765, 'micro': 766, 'ages': 767, 'recognition': 768, 'surf': 769, 'level': 770, 'open': 771, 'iphone': 772, 'amount': 773, 'dad': 774, 'beach': 775, 'line': 776, 'reliable': 777, 'tons': 778, 'support': 779, 'installed': 780, 'members': 781, 'ready': 782, 'says': 783, 'return': 784, '99': 785, 'update': 786, 'os': 787, 'expandable': 788, 'rather': 789, 'loud': 790, 'personal': 791, 'adults': 792, 'software': 793, 'kind': 794, 'holds': 795, 'programs': 796, 'buttons': 797, 'save': 798, 'turning': 799, 'range': 800, 'ordered': 801, 'constantly': 802, 'timers': 803, 'ethernet': 804, 'starter': 805, 'allow': 806, 'soon': 807, 'local': 808, 'blue': 809, 'bucks': 810, 'picked': 811, 'break': 812, 'beginner': 813, 'emails': 814, 'keyboard': 815, 'wi': 816, 'info': 817, 'smooth': 818, 'annoying': 819, 'believe': 820, 'viewing': 821, 'sports': 822, 'selection': 823, 'living': 824, 'fi': 825, 'talk': 826, 'gadget': 827, 'priced': 828, 'loving': 829, 'review': 830, 'font': 831, 'car': 832, 'assistant': 833, 'checking': 834, 'reads': 835, 'totally': 836, 'mind': 837, 'told': 838, 'ended': 839, 'saw': 840, 'comfortable': 841, '2nd': 842, 'compatible': 843, 'graphics': 844, 'grandchildren': 845, 'solid': 846, 'dots': 847, 'based': 848, 'ebooks': 849, 'player': 850, 'feels': 851, 'loaded': 852, 'piece': 853, 'fan': 854, 'broke': 855, 'certain': 856, '35': 857, 'song': 858, 'worry': 859, 'alternative': 860, 'subscription': 861, 'cant': 862, 'design': 863, 'handle': 864, 'brand': 865, 'past': 866, 'membership': 867, 'alot': 868, 'brightness': 869, 'yes': 870, 'lag': 871, 'itself': 872, 'recently': 873, 'settings': 874, 'unlimited': 875, 'dropped': 876, 'nest': 877, 'main': 878, 'slot': 879, 'heavy': 880, '3rd': 881, 'excited': 882, 'electronic': 883, 'turns': 884, 'non': 885, 'trips': 886, 'giving': 887, 'front': 888, 'become': 889, 'turned': 890, 'thank': 891, 'least': 892, 'savvy': 893, 'powerful': 894, 'bill': 895, 'adjust': 896, 'within': 897, 'half': 898, 'mode': 899, 'view': 900, 'trip': 901, 'research': 902, '30': 903, 'spent': 904, 'colors': 905, 'lighter': 906, 'including': 907, 'crisp': 908, 'running': 909, 'gaming': 910, 'clarity': 911, 'unless': 912, 'busy': 913, 'inch': 914, 'father': 915, 'browser': 916, 'budget': 917, 'previously': 918, 'finding': 919, 'entertaining': 920, 'reasonable': 921, 'offer': 922, 'happier': 923, 'hit': 924, 'higher': 925, 'similar': 926, 'idea': 927, 'paying': 928, 'improvement': 929, '40': 930, 'move': 931, 'cooking': 932, 'traffic': 933, 'charged': 934, 'included': 935, 'stop': 936, 'function': 937, 'course': 938, 'integration': 939, '20': 940, 'okay': 941, 'four': 942, 'type': 943, 'later': 944, 'star': 945, 'knew': 946, 'primarily': 947, 'avid': 948, 'lock': 949, 'various': 950, 'trouble': 951, 'tells': 952, 'brother': 953, 'completely': 954, 'call': 955, 'spotify': 956, 'gotten': 957, 'sharp': 958, '15': 959, 'program': 960, 'talking': 961, 'latest': 962, 'seen': 963, 'electronics': 964, 'intuitive': 965, 'short': 966, 'entertained': 967, 'u': 968, 'waiting': 969, 'except': 970, 'variety': 971, 'homework': 972, 'world': 973, 'specific': 974, 'hooked': 975, 'capable': 976, 'calendar': 977, 'photos': 978, 'applications': 979, 'general': 980, 'sleep': 981, 'truly': 982, 'browse': 983, 'lost': 984, 'close': 985, 'kept': 986, 'alone': 987, 'fairly': 988, 'left': 989, 'hub': 990, 'pad': 991, 'instructions': 992, 'hope': 993, 'toy': 994, 'rid': 995, 'considering': 996, 'functional': 997, 'background': 998, 'beginners': 999, 'special': 1000, 'gb': 1001, 'party': 1002, 'downside': 1003, 'models': 1004, 'looked': 1005, 'step': 1006, 'users': 1007, 'operating': 1008, 'speak': 1009, 'direct': 1010, 'ways': 1011, 'opinion': 1012, 'capability': 1013, 'gen': 1014, 'road': 1015, 'ton': 1016, 'words': 1017, 'alarms': 1018, 'figured': 1019, 'lol': 1020, 'usually': 1021, 'current': 1022, 'toddler': 1023, 'network': 1024, 'dollars': 1025, 'portability': 1026, 'responds': 1027, 'originally': 1028, 'heard': 1029, 'sturdy': 1030, 'directly': 1031, 'plugged': 1032, 'whatever': 1033, 'stopped': 1034, 'television': 1035, 'guess': 1036, 'number': 1037, 'leave': 1038, 'wanting': 1039, 'word': 1040, 'searching': 1041, 'xmas': 1042, 'profile': 1043, 'performs': 1044, 'connects': 1045, 'galaxy': 1046, 'tasks': 1047, 'drop': 1048, 'lack': 1049, 'office': 1050, 'currently': 1051, 'saying': 1052, 'chromecast': 1053, 'died': 1054, 'investment': 1055, 'basically': 1056, '1st': 1057, 'onto': 1058, 'charges': 1059, 'switch': 1060, 'limits': 1061, 'lower': 1062, 'today': 1063, 'supposed': 1064, 'stock': 1065, 'wake': 1066, 'loading': 1067, 'boxes': 1068, 'entire': 1069, 'improved': 1070, '00': 1071, 'company': 1072, 'hook': 1073, 'whether': 1074, 'stand': 1075, 'adult': 1076, 'harmony': 1077, 'bring': 1078, 'build': 1079, 'newer': 1080, 'hd8': 1081, 'social': 1082, 'phones': 1083, 'stay': 1084, 'entry': 1085, 'adds': 1086, 'ipads': 1087, 'fully': 1088, 'beautiful': 1089, '49': 1090, 'returning': 1091, 'miss': 1092, 'convenience': 1093, 'matter': 1094, 'limit': 1095, 'updated': 1096, 'daughters': 1097, 'source': 1098, 'print': 1099, 'digital': 1100, 'downloads': 1101, 'cell': 1102, 'household': 1103, 'anymore': 1104, 'younger': 1105, 'opened': 1106, 'played': 1107, 'streams': 1108, 'versions': 1109, 'expand': 1110, 'accounts': 1111, 'broken': 1112, 'care': 1113, 'remember': 1114, 'five': 1115, 'link': 1116, 'router': 1117, 'hoping': 1118, 'expecting': 1119, 'pass': 1120, 'bonus': 1121, 'sales': 1122, 'flawlessly': 1123, 'regret': 1124, 'edition': 1125, 'incredible': 1126, 'lit': 1127, 'potential': 1128, 'strain': 1129, 'adjustable': 1130, 'bargain': 1131, 'outstanding': 1132, 'chose': 1133, 'usage': 1134, 'immediately': 1135, 'controlling': 1136, 'rooms': 1137, 'menu': 1138, 'vacation': 1139, 'serves': 1140, 'bag': 1141, 'holding': 1142, 'respond': 1143, 'grocery': 1144, 'stations': 1145, 'tired': 1146, 'college': 1147, 'decision': 1148, 'seemed': 1149, 'press': 1150, 'sticks': 1151, 'thrilled': 1152, 'bottom': 1153, 'im': 1154, '11': 1155, 'geek': 1156, 'pros': 1157, 'helped': 1158, 'sling': 1159, 'satellite': 1160, 'mail': 1161, 'normal': 1162, 'shop': 1163, 'hasn': 1164, 'complain': 1165, 'major': 1166, 'external': 1167, 'protective': 1168, 'cause': 1169, 'prices': 1170, 'package': 1171, 'area': 1172, 'fires': 1173, 'earlier': 1174, 'hdmi': 1175, 'negative': 1176, 'request': 1177, 'across': 1178, 'bestbuy': 1179, 'knows': 1180, 'eye': 1181, 'advantage': 1182, 'conditions': 1183, 'outdoors': 1184, 'purposes': 1185, 'together': 1186, 'yourself': 1187, 'means': 1188, 'tvs': 1189, 'oasis': 1190, 'cutting': 1191, 'ordering': 1192, 'actual': 1193, 'amazed': 1194, 'offered': 1195, 'example': 1196, 'internal': 1197, 'boy': 1198, 'flash': 1199, 'buck': 1200, 'alexia': 1201, 'endless': 1202, 'learned': 1203, '12': 1204, 'receive': 1205, 'date': 1206, 'nephews': 1207, 'process': 1208, 'joke': 1209, 'pre': 1210, 'unlike': 1211, 'choices': 1212, 'freeze': 1213, 'cases': 1214, 'cameras': 1215, 'continue': 1216, 'exceeded': 1217, 'standard': 1218, 'send': 1219, 'boys': 1220, 'penny': 1221, 'backlit': 1222, 'wemo': 1223, 'keeping': 1224, 'compare': 1225, 'mobile': 1226, 'bunch': 1227, 'near': 1228, 'carrying': 1229, 'coming': 1230, 'interested': 1231, 'switches': 1232, 'lives': 1233, 'breeze': 1234, 'remove': 1235, 'unfortunately': 1236, 'hour': 1237, 'note': 1238, 'saved': 1239, 'cons': 1240, '8gb': 1241, 'drawback': 1242, 'frustrating': 1243, 'understands': 1244, 'meets': 1245, 'sold': 1246, 'plane': 1247, 'fix': 1248, 'versatile': 1249, 'companion': 1250, 'protection': 1251, 'required': 1252, 'poor': 1253, 'sync': 1254, 'telling': 1255, 'automatically': 1256, 'strong': 1257, 'security': 1258, 'pocket': 1259, 'drive': 1260, 'vs': 1261, 'audible': 1262, 'bang': 1263, 'realize': 1264, 'imagine': 1265, 'possible': 1266, 'create': 1267, 'processing': 1268, 'net': 1269, 'biggest': 1270, 'magazines': 1271, 'slightly': 1272, 'loads': 1273, 'navigation': 1274, 'initially': 1275, 'shipping': 1276, 'neat': 1277, 'linked': 1278, 'lets': 1279, 'share': 1280, 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1341, 'echos': 1342, 'quicker': 1343, 'slower': 1344, 'bills': 1345, 'numerous': 1346, 'somewhat': 1347, 'pleasure': 1348, 'areas': 1349, 'true': 1350, 'doesnt': 1351, 'smarter': 1352, 'ecosystem': 1353, 'paired': 1354, 'single': 1355, 'hdx': 1356, 'wow': 1357, '16': 1358, 'monthly': 1359, 'stereo': 1360, 'seeing': 1361, 'improve': 1362, 'specifically': 1363, 'reasonably': 1364, 'literally': 1365, 'random': 1366, 'connectivity': 1367, 'minor': 1368, 'controller': 1369, 'base': 1370, 'data': 1371, 'ideal': 1372, 'missing': 1373, 'nieces': 1374, 'held': 1375, 'sooner': 1376, 'enjoyable': 1377, 'oh': 1378, 'physical': 1379, 'chance': 1380, 'taken': 1381, 'surprise': 1382, '39': 1383, 'wherever': 1384, 'nearly': 1385, 'separate': 1386, '33': 1387, 'ink': 1388, 'elderly': 1389, 'important': 1390, 'nicely': 1391, 'freetime': 1392, 'kinds': 1393, 'ram': 1394, 'pool': 1395, 'exchange': 1396, 'activities': 1397, 'awhile': 1398, 'waste': 1399, 'frequently': 1400, 'hardware': 1401, 'premium': 1402, 'provide': 1403, 'necessary': 1404, 'cloud': 1405, 'interesting': 1406, 'whenever': 1407, 'reports': 1408, 'besides': 1409, 'allowed': 1410, 'smoothly': 1411, 'basis': 1412, 'early': 1413, 'presents': 1414, 'form': 1415, 'solution': 1416, 'thru': 1417, 'forever': 1418, 'appear': 1419, 'appreciate': 1420, '200': 1421, 'leather': 1422, 'scores': 1423, 'consider': 1424, 'specs': 1425, 'throughout': 1426, 'class': 1427, 'smartphone': 1428, 'bother': 1429, 'downfall': 1430, 'rest': 1431, 'calls': 1432, 'begin': 1433, '80': 1434, 'air': 1435, 'units': 1436, 'occasionally': 1437, 'reasons': 1438, 'whistles': 1439, 'clean': 1440, 'anyway': 1441, 'instantly': 1442, 'hundreds': 1443, 'pop': 1444, 'forget': 1445, 'putting': 1446, 'ebook': 1447, 'image': 1448, 'surprisingly': 1449, 'complicated': 1450, 'saves': 1451, 'crazy': 1452, 'series': 1453, 'normally': 1454, 'intended': 1455, 'bells': 1456, 'answering': 1457, 'adapter': 1458, 'wall': 1459, 'recipes': 1460, 'philips': 1461, 'reset': 1462, 'places': 1463, 'microsd': 1464, 'credit': 1465, 'usands': 1466, 'dim': 1467, 'material': 1468, 'dead': 1469, 'particular': 1470, '300': 1471, 'moving': 1472, 'opening': 1473, 'rating': 1474, '2015': 1475, 'honestly': 1476, 'girlfriend': 1477, 'catch': 1478, 'dedicated': 1479, 'weak': 1480, 'protect': 1481, 'trivia': 1482, 'pro': 1483, '60': 1484, 'sit': 1485, 'grandsons': 1486, 'rate': 1487, 'pair': 1488, 'adequate': 1489, 'holidays': 1490, 'lasted': 1491, 'possibilities': 1492, 'password': 1493, 'finger': 1494, 'happen': 1495, 'informative': 1496, 'accurate': 1497, 'sitting': 1498, 'fall': 1499, 'spot': 1500, 'require': 1501, 'sense': 1502, 'pull': 1503, 'gadgets': 1504, 'wired': 1505, 'requests': 1506, 'recipient': 1507, 'replacing': 1508, 'locked': 1509, 'youngest': 1510, 'surface': 1511, 'ahead': 1512, 'thin': 1513, 'face': 1514, 'hate': 1515, 'constant': 1516, 'nexus': 1517, 'sign': 1518, 'terrific': 1519, 'explore': 1520, 'advertising': 1521, 'application': 1522, 'further': 1523, 'impressive': 1524, 'upon': 1525, 'tooth': 1526, 'described': 1527, 'complete': 1528, 'stuck': 1529, 'sonos': 1530, 'thermostats': 1531, 'monitor': 1532, 'freezes': 1533, 'cards': 1534, 'minimal': 1535, 'listens': 1536, 'roughly': 1537, 'efficient': 1538, 'proof': 1539, 'sell': 1540, 'middle': 1541, 'sons': 1542, 'damage': 1543, 'eventually': 1544, 'correct': 1545, 'girl': 1546, 'con': 1547, 'watches': 1548, 'location': 1549, 'confusing': 1550, 'hesitant': 1551, 'kinda': 1552, 'w': 1553, 'garage': 1554, 'sites': 1555, 'push': 1556, 'bulky': 1557, 'likely': 1558, 'enabled': 1559, 'notes': 1560, '16gb': 1561, 'wide': 1562, 'terrible': 1563, 'mention': 1564, 'core': 1565, 'provided': 1566, 'platform': 1567, 'practical': 1568, 'points': 1569, 'signal': 1570, 'win': 1571, 'protector': 1572, 'advanced': 1573, 'upstairs': 1574, 'useless': 1575, 'casual': 1576, 'files': 1577, 'worried': 1578, 'stores': 1579, 'increase': 1580, 'beats': 1581, 'thus': 1582, 'fastest': 1583, 'integrated': 1584, 'curve': 1585, 'occasional': 1586, 'hurt': 1587, 'equipment': 1588, 'bose': 1589, 'sensitive': 1590, 'delivery': 1591, 'girls': 1592, 'popular': 1593, 'utilize': 1594, 'moment': 1595, 'unable': 1596, 'careful': 1597, 'rear': 1598, 'baby': 1599, 'wont': 1600, 'twice': 1601, 'ran': 1602, 'tough': 1603, 'staff': 1604, 'six': 1605, 'clearly': 1606, 'cracked': 1607, 'man': 1608, 'pain': 1609, 'throw': 1610, 'demand': 1611, 'jeopardy': 1612, 'silk': 1613, 'appointments': 1614, 'pics': 1615, 'subscribe': 1616, 'holiday': 1617, 'limitations': 1618, 'aware': 1619, 'pleasantly': 1620, 'beyond': 1621, 'x': 1622, 'profiles': 1623, 'realized': 1624, 'granddaughters': 1625, 'menus': 1626, 'receiver': 1627, 'o': 1628, 'recent': 1629, 'perform': 1630, 'asleep': 1631, 'thanksgiving': 1632, 'track': 1633, 'include': 1634, 'enable': 1635, 'fixed': 1636, 'smoother': 1637, 'batteries': 1638, 'trial': 1639, 'ai': 1640, 'forecast': 1641, 'temperature': 1642, 'flexibility': 1643, 'afford': 1644, 'occupied': 1645, 'incredibly': 1646, 'fee': 1647, 'total': 1648, 'notice': 1649, 'abilities': 1650, 'pc': 1651, 'behind': 1652, 'quad': 1653, 'dollar': 1654, 'plastic': 1655, 'floor': 1656, 'hot': 1657, 'primary': 1658, 'water': 1659, 'artist': 1660, 'basics': 1661, 'switched': 1662, 'didnt': 1663, 'fancy': 1664, 'pricey': 1665, 'yrs': 1666, 'mean': 1667, 'recharge': 1668, 'sort': 1669, 'concerned': 1670, 'lacks': 1671, 'tag': 1672, 'task': 1673, 'thats': 1674, 'pdf': 1675, 'improvements': 1676, 'supports': 1677, 'seconds': 1678, 'plugs': 1679, 'controlled': 1680, 'sony': 1681, 'aunt': 1682, 'difficulty': 1683, 'charm': 1684, 'costs': 1685, 'ive': 1686, 'unbelievable': 1687, 'events': 1688, 'outlet': 1689, 'rough': 1690, 'readable': 1691, 'bb': 1692, '90': 1693, 'glass': 1694, 'includes': 1695, 'needing': 1696, 'understanding': 1697, 'fingertips': 1698, 'guy': 1699, 'depending': 1700, 'grandma': 1701, 'deals': 1702, 'extended': 1703, 'straight': 1704, 'newest': 1705, 'guide': 1706, 'ui': 1707, 'continues': 1708, 'borrow': 1709, 'accidentally': 1710, 'backlighting': 1711, 'pleasant': 1712, 'channel': 1713, 'microphone': 1714, 'skill': 1715, 'integrate': 1716, 'plex': 1717, 'funny': 1718, 'streamer': 1719, 'preloaded': 1720, 'olds': 1721, 'effective': 1722, 'hoped': 1723, 'despite': 1724, 'lighted': 1725, 'gone': 1726, 'hearing': 1727, 'wise': 1728, 'chrome': 1729, 'above': 1730, '128gb': 1731, 'site': 1732, 'operation': 1733, 'became': 1734, 'discovered': 1735, 'finds': 1736, 'appears': 1737, 'feeling': 1738, 'quiet': 1739, 'breaks': 1740, 'factor': 1741, 'com': 1742, 'edge': 1743, 'types': 1744, 'seriously': 1745, 'fraction': 1746, 'relatively': 1747, 'clearer': 1748, 'fills': 1749, 'lacking': 1750, 'noticeable': 1751, 'nicer': 1752, 'barely': 1753, 'happened': 1754, 'ios': 1755, 'follow': 1756, 'covers': 1757, 'showed': 1758, 'b': 1759, 'superior': 1760, 'ultra': 1761, 'touchscreen': 1762, 'hassle': 1763, 'minute': 1764, 'led': 1765, 'answered': 1766, 'outdoor': 1767, 'bumper': 1768, 'rarely': 1769, 'ps': 1770, 'directv': 1771, 'bass': 1772, 'ifttt': 1773, 'buffer': 1774, 'skype': 1775, 'knowledgeable': 1776, 'joy': 1777, 'manage': 1778, 'released': 1779, 'hers': 1780, 'multi': 1781, 'log': 1782, 'pack': 1783, 'accessible': 1784, 'damaged': 1785, 'shield': 1786, 'directions': 1787, 'hardly': 1788, 'knowing': 1789, 'chargers': 1790, 'moved': 1791, 'defective': 1792, 'flawless': 1793, 'selections': 1794, 'sufficient': 1795, 'key': 1796, 'test': 1797, 'swipe': 1798, 'suggested': 1799, 'public': 1800, 'country': 1801, 'planning': 1802, 'growing': 1803, 'correctly': 1804, 'accessories': 1805, 'technical': 1806, 'speaking': 1807, 'instant': 1808, '3g': 1809, 'facts': 1810, 'integrates': 1811, 'links': 1812, 'grandmother': 1813, 'disappoint': 1814, 'initial': 1815, 'tabs': 1816, 'late': 1817, 'seamlessly': 1818, 'owner': 1819, 'travels': 1820, 'speech': 1821, 'exchanged': 1822, 'transfer': 1823, '70': 1824, 'starts': 1825, 'searches': 1826, 'train': 1827, 'tiny': 1828, 'quit': 1829, 'bank': 1830, 'suppose': 1831, 'comparable': 1832, 'recomend': 1833, 'frustrated': 1834, 'math': 1835, 'wonderfully': 1836, 'headphones': 1837, 'effort': 1838, 'serious': 1839, 'students': 1840, '34': 1841, 'teach': 1842, 'report': 1843, 'responses': 1844, 'teenage': 1845, '13': 1846, 'snappy': 1847, 'junk': 1848, 'windows': 1849, 'titles': 1850, 'def': 1851, 'restart': 1852, 'greatly': 1853, 'shut': 1854, 'discount': 1855, 'loose': 1856, 'arrived': 1857, 'boyfriend': 1858, 'navigating': 1859, 'dictionary': 1860, 'systems': 1861, 'acceptable': 1862, 'commute': 1863, 'wink': 1864, 'logitech': 1865, 'antenna': 1866, 'teens': 1867, 'versus': 1868, 'playback': 1869, 'meant': 1870, 'comparison': 1871, 'god': 1872, 'upgrading': 1873, 'lasting': 1874, 'fabulous': 1875, 'durability': 1876, 'nvidia': 1877, 'installing': 1878, 'particularly': 1879, 'hey': 1880, 'watched': 1881, 'rides': 1882, 'changes': 1883, 'caught': 1884, 'becomes': 1885, 'opportunity': 1886, 'commercials': 1887, 'letters': 1888, 'decide': 1889, 'offering': 1890, 'style': 1891, 'sets': 1892, '1080p': 1893, 'hang': 1894, 'vudu': 1895, 'phillips': 1896, 'brighter': 1897, 'visit': 1898, 'speedy': 1899, 'expansion': 1900, 'advertisements': 1901, 'sorts': 1902, 'breaking': 1903, 'delivered': 1904, 'pink': 1905, 'researched': 1906, 'knock': 1907, 'aspect': 1908, 'appstore': 1909, 'exact': 1910, 'airplane': 1911, 'terms': 1912, 'fell': 1913, 'cousin': 1914, 'itunes': 1915, 'manually': 1916, 'click': 1917, 'student': 1918, 'forth': 1919, 'learns': 1920, 'contrast': 1921, 'secure': 1922, 'relative': 1923, 'anybody': 1924, 'attached': 1925, 'louder': 1926, 'message': 1927, 'dinner': 1928, 'daylight': 1929, 'placed': 1930, 'soft': 1931, 'provider': 1932, 'novelty': 1933, 'automated': 1934, 'firebox': 1935, 'tables': 1936, 'contact': 1937, 'offline': 1938, 'sorry': 1939, 'existing': 1940, 'crystal': 1941, 'concern': 1942, 'cumbersome': 1943, 'supported': 1944, 'sources': 1945, 'powered': 1946, 'falls': 1947, 'sent': 1948, 'picking': 1949, 'summer': 1950, 'saver': 1951, 'tips': 1952, 'fair': 1953, 'generally': 1954, 'voyager': 1955, 'protected': 1956, 'fault': 1957, 'removed': 1958, 'admit': 1959, 'paperback': 1960, 'freezing': 1961, 'history': 1962, 'engine': 1963, 'walk': 1964, 'downstairs': 1965, 'expanding': 1966, 'schedule': 1967, 'firesticks': 1968, 'playlist': 1969, 'activation': 1970, 'experienced': 1971, 'easiest': 1972, '2016': 1973, 'maneuver': 1974, 'santa': 1975, 'fourth': 1976, 'aside': 1977, 'increased': 1978, 'images': 1979, 'disappointing': 1980, 'known': 1981, 'tend': 1982, 'brings': 1983, 'benefit': 1984, 'register': 1985, 'mentioned': 1986, 'missed': 1987, 'outlets': 1988, 'assistance': 1989, 'grow': 1990, 'creating': 1991, 'mouse': 1992, 'thumbs': 1993, 'theirs': 1994, 'locks': 1995, 'modem': 1996, 'lamp': 1997, 'installation': 1998, 'collection': 1999, 'noise': 2000, 'hears': 2001, 'smartthings': 2002, 'unlock': 2003, 'avoid': 2004, 'prize': 2005, 'ipod': 2006, 'upload': 2007, 'dish': 2008, 'layout': 2009, 'adjustment': 2010, 'backup': 2011, 'aps': 2012, 'adjusting': 2013, 'comment': 2014, 'ps4': 2015, 'related': 2016, 'pixels': 2017, 'upgrades': 2018, 'slim': 2019, 'losing': 2020, 'senior': 2021, 'versatility': 2022, 'recognize': 2023, 'attractive': 2024, 'updating': 2025, 'refund': 2026, 'write': 2027, 'activate': 2028, 'weekend': 2029, 'lil': 2030, 'customize': 2031, 'couch': 2032, 'dual': 2033, 'band': 2034, 'winner': 2035, 'mess': 2036, 'sounding': 2037, 'situations': 2038, 'cancel': 2039, 'results': 2040, 'interact': 2041, 'briefing': 2042, 'interactive': 2043, 'worse': 2044, 'restrictions': 2045, 'devise': 2046, 'automatic': 2047, 'failed': 2048, 'common': 2049, 'computers': 2050, 'season': 2051, 'relaxing': 2052, 'extensive': 2053, 'tied': 2054, 'glitches': 2055, 'oldest': 2056, 'puts': 2057, 'thinks': 2058, 'heavier': 2059, 'weren': 2060, 'period': 2061, 'typing': 2062, 'techie': 2063, 'manual': 2064, 'phenomenal': 2065, 'playstore': 2066, 'rubber': 2067, 'wore': 2068, 'situation': 2069, 'delete': 2070, 'calling': 2071, 'helping': 2072, '128': 2073, 'silly': 2074, 'gonna': 2075, 'environment': 2076, 'bar': 2077, 'head': 2078, 'exploring': 2079, 'delivers': 2080, 'heart': 2081, 'liking': 2082, 'towards': 2083, 'wonder': 2084, 'slowly': 2085, 'stolen': 2086, 'connections': 2087, 'definition': 2088, 'appliances': 2089, 'receiving': 2090, 'allowing': 2091, 'perhaps': 2092, 'superb': 2093, 'lags': 2094, 'sized': 2095, 'bugs': 2096, 'zero': 2097, 'advertisement': 2098, 'owning': 2099, 'sucks': 2100, 'blows': 2101, 'disappointment': 2102, 'repeat': 2103, 'action': 2104, '14': 2105, 'fonts': 2106, 'comparing': 2107, 'dog': 2108, 'sweet': 2109, 'ours': 2110, 'regularly': 2111, 'par': 2112, 'pricing': 2113, 'performed': 2114, 'coffee': 2115, 'overpriced': 2116, 'justify': 2117, 'deck': 2118, 'periods': 2119, 'finish': 2120, 'match': 2121, 'glasses': 2122, 'adjusts': 2123, 'lived': 2124, 'sunshine': 2125, 'proper': 2126, 'block': 2127, '0': 2128, 'stories': 2129, 'happens': 2130, 'input': 2131, 'default': 2132, 'luck': 2133, 'stable': 2134, 'levels': 2135, 'amazingly': 2136, 'interaction': 2137, 'theater': 2138, 'cancelled': 2139, 'bing': 2140, 'nabi': 2141, 'lite': 2142, 'afraid': 2143, 'position': 2144, 'pressure': 2145, 'mails': 2146, 'websites': 2147, 'documents': 2148, 'bible': 2149, 'shade': 2150, 'articles': 2151, 'preferred': 2152, 'sideload': 2153, 'invested': 2154, 'seamless': 2155, 'messages': 2156, 'selling': 2157, 'grandchild': 2158, 'hesitate': 2159, 'convert': 2160, 'importantly': 2161, 'amazons': 2162, 'trigger': 2163, 'aspects': 2164, 'checked': 2165, 'boot': 2166, 'cast': 2167, 'interest': 2168, 'honest': 2169, 'closed': 2170, 'jump': 2171, 'positive': 2172, 'output': 2173, 'organized': 2174, 'below': 2175, 'stated': 2176, 'gotta': 2177, 'dvd': 2178, 'discover': 2179, 'virtually': 2180, 'disturbing': 2181, 'coolest': 2182, 'blast': 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'tcm': 6455, '73': 6456, 'elder': 6457, 'mkv': 6458, 'outdid': 6459, 'jigsaw': 6460, 'vacationing': 6461, 'dare': 6462, 'sonce': 6463, 'varying': 6464, 'dojo': 6465, 'tabet': 6466, 'accesible': 6467, 'ceased': 6468, 'essentials': 6469, 'candle': 6470, 'typos': 6471, 'assess': 6472, 'prine': 6473, 'annotate': 6474, 'intensive': 6475, 'annotation': 6476, 'fantasy': 6477, 'commentaries': 6478, 'scripture': 6479, 'cycle': 6480, 'wwe': 6481, 'trashed': 6482, 'netfix': 6483, 'sealed': 6484, 'excellently': 6485, 'navigateperfect': 6486, 'fiire': 6487, 'hacking': 6488, 'studies': 6489, 'unrestricted': 6490, 'inbox': 6491, 'kiddie': 6492, 'enrolled': 6493, 'noob': 6494, 'noticible': 6495, 'bear': 6496, 'unsuccessful': 6497, 'annoyances': 6498, 'hop': 6499, 'somthing': 6500, 'boring': 6501, 'fooled': 6502, 'sf': 6503, 'apks': 6504, 'winning': 6505, 'compliments': 6506, 'skips': 6507, 'disagree': 6508, 'apples': 6509, 'cared': 6510, 'notches': 6511, 'gameplay': 6512, 'sponsored': 6513, 'approaching': 6514, 'bookshelf': 6515, 'dent': 6516, '16th': 6517, 'remorse': 6518, 'valentines': 6519, 'kindell': 6520, 'silky': 6521, 'tge': 6522, 'respectively': 6523, 'overlooked': 6524, 'distant': 6525, 'ships': 6526, 'instrument': 6527, 'painless': 6528, 'sidetracked': 6529, 'initally': 6530, 'shooting': 6531, 'moral': 6532, 'chemo': 6533, 'triplets': 6534, 'ministry': 6535, 'vote': 6536, 'intending': 6537, 'crippled': 6538, 'investigate': 6539, 'hd6': 6540, 'sufficiently': 6541, 'remodeling': 6542, 'resembles': 6543, 'brake': 6544, 'demonstrated': 6545, 'misinformed': 6546, 'revenue': 6547, 'compromising': 6548, 'preteens': 6549, 'preformed': 6550, 'stephanie': 6551, 'ixl': 6552, 'freed': 6553, 'godmother': 6554, 'advertises': 6555, 'bloat': 6556, 'excellence': 6557, 'bottomline': 6558, 'kim': 6559, '5yo': 6560, 'ea': 6561, 'dis': 6562, 'concise': 6563, 'schoolwork': 6564, 'giveaway': 6565, 'vids': 6566, 'valid': 6567, 'sideloading': 6568, 'reworking': 6569, 'useit': 6570, 'nailed': 6571, 'thrill': 6572, 'prises': 6573, 'expendable': 6574, 'advisor': 6575, 'dated': 6576, 'rolling': 6577, 'personalization': 6578, 'seller': 6579, 'graphs': 6580, 'missus': 6581, 'smoothing': 6582, 'begginers': 6583, '3s': 6584, 'nap': 6585, 'applied': 6586, 'crispy': 6587, 'passing': 6588, 'durning': 6589, 'i3': 6590, 'bam': 6591, 'prone': 6592, 'balances': 6593, 'existence': 6594, 'mundane': 6595, 'cnbc': 6596, 'wold': 6597, 'inevitably': 6598, 'trading': 6599, 'pok': 6600, 'mon': 6601, 'triangulation': 6602, 'dinosaur': 6603, 'relation': 6604, 'unions': 6605, 'inlaw': 6606, 'neiece': 6607, 'naught': 6608, 'indefinite': 6609, 'continent': 6610, 'leappad': 6611, 'needy': 6612, 'tanks': 6613, 'deff': 6614, 'ny': 6615, 'rite': 6616, 'owe': 6617, 'permanent': 6618, 'winding': 6619, 'strips': 6620, 'interfacing': 6621, 'twelve': 6622, 'grandaughters': 6623, 'wild': 6624, 'grafics': 6625, 'caribbean': 6626, 'trivial': 6627, 'friendlier': 6628, 'careless': 6629, 'tiles': 6630, 'painfully': 6631, 'sketchy': 6632, 'elders': 6633, 'diy': 6634, 'clunkier': 6635, '61': 6636, 'guns': 6637, 'chargeable': 6638, 'dilemma': 6639, 'nuisance': 6640, '71': 6641, 'systen': 6642, 'agood': 6643, 'quickness': 6644, 'disaster': 6645, 'lime': 6646, 'alive': 6647, 'accidents': 6648, 'ther': 6649, 'ruins': 6650, 'ppl': 6651, 'highschool': 6652, 'van': 6653, 'credited': 6654, 'determined': 6655, 'functionally': 6656, 'everthing': 6657, 'od': 6658, 'illiterate': 6659, 'googlecast': 6660, 'crossword': 6661, 'guided': 6662, 'underestimate': 6663, 'ownership': 6664, 'prong': 6665, 'sea': 6666, 'exiting': 6667, 'mailing': 6668, 'weekdays': 6669, 'servive': 6670, 'combining': 6671, 'kool': 6672, 'confusion': 6673, 'moth': 6674, 'dec': 6675, '17th': 6676, 'auxiliary': 6677, 'persistence': 6678, 'fixing': 6679, 'cried': 6680, 'powers': 6681, 'screensavers': 6682, 'incomplete': 6683, 'catalogs': 6684, 'commander': 6685, 'override': 6686, 'educationally': 6687, 'funds': 6688, 'compromised': 6689, 'galore': 6690, 'scattered': 6691, 'messaging': 6692, 'royale': 6693, 'housing': 6694, 'definitley': 6695, 'fur': 6696, '55': 6697, 'unreadable': 6698, 'affordability': 6699, 'adopt': 6700, 'ahem': 6701, '3000': 6702, 'animals': 6703, 'vey': 6704, 'hiccup': 6705, 'hacked': 6706, 'scanned': 6707, 'notifies': 6708, 'thy': 6709, 'freinds': 6710, 'nt': 6711, 'dealership': 6712, 'kiss': 6713, 'inform': 6714, '64g': 6715, 'weekday': 6716, 'supper': 6717, 'treasure': 6718, 'allowance': 6719, 'grasp': 6720, 'customs': 6721, 'filters': 6722, 'bd': 6723, 'slipped': 6724, 'interfaced': 6725, 'coating': 6726, 'showcase': 6727, 'awfully': 6728, 'stickers': 6729, 'ia': 6730, 'confidence': 6731, 'upto': 6732, '7yrs': 6733, 'dangerous': 6734, 'ser': 6735, 'agreat': 6736, 'cont': 6737, 'duh': 6738, 'favortie': 6739, 'figures': 6740, 'virgin': 6741, 'billing': 6742, 'chagrin': 6743, 'vpn': 6744, 'obnoxious': 6745, 'defintely': 6746, 'friendliness': 6747, 'costco': 6748, 'exists': 6749, 'capacitive': 6750, 'swears': 6751, 'seats': 6752, 'useable': 6753, 'boss': 6754, 'crossing': 6755, '149': 6756, 'imported': 6757, 'kindke': 6758, 'blessing': 6759, 'occurred': 6760, 'amason': 6761, 'david': 6762, 'zeepad': 6763, 'dan': 6764, 'resent': 6765, 'investing': 6766, 'electric': 6767, 'sharpener': 6768, 'docking': 6769, 'mama': 6770, 'sgreat': 6771, 'positional': 6772, 'cringe': 6773, 'respects': 6774, 'blinded': 6775, 'cruises': 6776, 'parted': 6777, 'becasue': 6778, 'hastle': 6779, 'polaroid': 6780, 'motivated': 6781, 'kiddy': 6782, 'afordable': 6783, 'quieter': 6784, 'toe': 6785, 'circling': 6786, 'availabe': 6787, 'lo': 6788, 'behold': 6789, 'transportable': 6790, 'lessons': 6791, 'peppa': 6792, 'pig': 6793, 'anticipating': 6794, 'intentions': 6795, 'yup': 6796, 'janky': 6797, 'junkie': 6798, 'paranoid': 6799, 'drained': 6800, 'daddy': 6801, 'paste': 6802, 'glade': 6803, 'couples': 6804, 'attracted': 6805, 'measure': 6806, 'fulfills': 6807, 'urge': 6808, 'camp': 6809, 'edited': 6810, 'boasts': 6811, 'discarded': 6812, 'snagged': 6813, 'mircosd': 6814, '42': 6815, 'oil': 6816, 'notion': 6817, 'idiot': 6818, 'stuffs': 6819, 'accomplishes': 6820, 'verry': 6821, 'frog': 6822, 'forgiven': 6823, 'fanciest': 6824, 'trailer': 6825, 'cure': 6826, 'miracle': 6827, 'army': 6828, 'drinking': 6829, 'exercising': 6830, 'hooray': 6831, 'restock': 6832, 'rake': 6833, 'fitted': 6834, 'transformer': 6835, 's3': 6836, 'apparatus': 6837, 'scene': 6838, 'nov': 6839, 'stink': 6840, 'warehouse': 6841, 'nurse': 6842, 'subway': 6843, '81': 6844, 'courtesy': 6845, 'handing': 6846, 'routinely': 6847, 'cent': 6848, 'producing': 6849, 'popup': 6850, 'banner': 6851, 'remedied': 6852, 'oriented': 6853, 'conscious': 6854, 'tile': 6855, 'roadtrip': 6856, 'summed': 6857, 'heft': 6858, 'rendered': 6859, 'calander': 6860, 'established': 6861, 'lousy': 6862, 'someones': 6863, 'stalls': 6864, 'obtrusive': 6865, 'storm': 6866, 'stocked': 6867, 'modifications': 6868, 'expecially': 6869, 'cams': 6870, 'wa': 6871, 'skullcandy': 6872, 'spirit': 6873, 'nick': 6874, 'impeccable': 6875, 'rebooted': 6876, 'idem': 6877, 'downs': 6878, 'skypes': 6879, 'squint': 6880, 'tabelt': 6881, 'supplemental': 6882, 'raves': 6883, 'toting': 6884, 'nightime': 6885, 'teaches': 6886, 'disneyworld': 6887, 'italy': 6888, 'recovering': 6889, 'ore': 6890, '36': 6891, 'dell': 6892, 'muted': 6893, 'shelled': 6894, 'exacting': 6895, 'seals': 6896, 'ticket': 6897, 'branch': 6898, 'fearing': 6899, 'decreased': 6900, 'logs': 6901, 'naby': 6902, 'nobles': 6903, 'fire7': 6904, 'marketed': 6905, 'firehd': 6906, 'resolves': 6907, 'land': 6908, 'riddled': 6909, 'noting': 6910, 'miracast': 6911, 'funky': 6912, 'theyll': 6913, 'clearness': 6914, 'sw': 6915, 'workmanship': 6916, 'busted': 6917, 'married': 6918, 'nieceshe': 6919, 'papa': 6920, 'duarable': 6921, 'redemption': 6922, 'margins': 6923, 'lightings': 6924, 'revolutionary': 6925, 'evolution': 6926, 'unnoticeable': 6927, 'transportation': 6928, 'annoys': 6929, 'published': 6930, 'india': 6931, 'rightly': 6932, 'giveaways': 6933, 'pounds': 6934, 'downtime': 6935, 'rehab': 6936, 'iceland': 6937, 'wipe': 6938, 'matthew': 6939, 'compatable': 6940, 'wizard': 6941, 'preschool': 6942, 'snob': 6943, '23': 6944, 'neil': 6945, 'tony': 6946, 'tweens': 6947, 'existed': 6948, 'slipping': 6949, 'musically': 6950, 'dat': 6951, 'efficiency': 6952, 'piano': 6953, 'endured': 6954, 'ignorant': 6955, 'hugh': 6956, 'tryed': 6957, 'shy': 6958, 'steelbook': 6959, 'equivalent': 6960, 'graduates': 6961, 'macs': 6962, 'k3': 6963, 'dodo': 6964, 'amazonbasics': 6965, 'rubberized': 6966, 'strap': 6967, 'inner': 6968, 'fastener': 6969, 'reactions': 6970, 'tastes': 6971, 'splash': 6972, 'teeth': 6973, 'granddaugther': 6974, 'recomended': 6975, 'loooong': 6976, 'preschooler': 6977, 'bounced': 6978, 'proudly': 6979, 'withstands': 6980, 'unconditional': 6981, 'minnie': 6982, 'rule': 6983, 'awseome': 6984, 'breakage': 6985, 'frustrates': 6986, 'healthy': 6987, 'trending': 6988, 'vocal': 6989, 'identifying': 6990, 'whit': 6991, 'silicon': 6992, 'enclosure': 6993, 'interpreted': 6994, 'grouping': 6995, 'inaccurate': 6996, '256': 6997, 'shoots': 6998, 'habe': 6999, 'encounter': 7000, 'branding': 7001, 'prepaid': 7002, 'conection': 7003, 'sprout': 7004, 'varieties': 7005, 'conducive': 7006, 'breakable': 7007, 'suggesting': 7008, 'aplication': 7009, 'insure': 7010, 'nails': 7011, 'onother': 7012, 'jammed': 7013, '3yo': 7014, '5year': 7015, 'eady': 7016, 'warrantee': 7017, 'broad': 7018, 'savior': 7019, 'aa': 7020, 'kickstand': 7021, 'fascinated': 7022, 'everynight': 7023, 'laughing': 7024, 'optimum': 7025, 'lovethis': 7026, '10th': 7027, 'gold': 7028, 'patents': 7029, 'exaggerating': 7030, 'reread': 7031, 'baggage': 7032, 'leary': 7033, 'infinitely': 7034, 'balloon': 7035, 'trumps': 7036, 'addiction': 7037, 'harry': 7038, 'potter': 7039, 'fatigued': 7040, 'illustrations': 7041, 'feather': 7042, 'mildly': 7043, 'loans': 7044, '190': 7045, 'slept': 7046, 'lookup': 7047, 'wholeheartedly': 7048, 'facilities': 7049, 'predecessor': 7050, 'libro': 7051, 'puedes': 7052, 'tener': 7053, 'todos': 7054, 'libros': 7055, 'sleepy': 7056, 'exclaimed': 7057, 'longing': 7058, 's5': 7059, 'strenuous': 7060, 'flickers': 7061, 'abandoned': 7062, 'toolbar': 7063, 'bars': 7064, 'expensively': 7065, 'improvment': 7066, 'domain': 7067, 'corporate': 7068, 'bro': 7069, 'analysis': 7070, 'doze': 7071, 'rockin': 7072, 'references': 7073, 'lounging': 7074, 'accumulate': 7075, 'permits': 7076, '94': 7077, 'reverse': 7078, 'hardbacks': 7079, 'reinvigorated': 7080, 'underlined': 7081, 'nightlight': 7082, 'mimic': 7083, 'drawer': 7084, '119': 7085, 'portrate': 7086, 'behemoth': 7087, 'amazom': 7088, 'curling': 7089, 'favourite': 7090, 'ranting': 7091, 'adverse': 7092, 'hobby': 7093, 'greet': 7094, 'bitten': 7095, 'delegated': 7096, 'advocate': 7097, 'shone': 7098, 'negligible': 7099, 'flicker': 7100, 'spacing': 7101, 'decades': 7102, 'squirrelly': 7103, 'tactile': 7104, 'irritate': 7105, 'paperwhite1': 7106, 'excellent2': 7107, 'highlighters': 7108, 'stephen': 7109, 'enhancing': 7110, 'halfway': 7111, 'earn': 7112, 'emanating': 7113, 'substantially': 7114, 'induce': 7115, 'savor': 7116, 'guatemala': 7117, 'jumpy': 7118, 'engaging': 7119, 'temptation': 7120, 'delayed': 7121, 'booklights': 7122, 'rushing': 7123, 'lightnings': 7124, 'french': 7125, 'sheer': 7126, 'infrastructure': 7127, 'classy': 7128, 'editions': 7129, 'ears': 7130, 'whites': 7131, 'enormous': 7132, 'migrate': 7133, 'doesnot': 7134, 'accompany': 7135, 'interrupt': 7136, 'alter': 7137, 'paperwite': 7138, 'russian': 7139, 'rack': 7140, 'draws': 7141, 'participates': 7142, 'juts': 7143, 'bombarded': 7144, 'annoyingly': 7145, 'shipment': 7146, 'smiling': 7147, 'england': 7148, 'conked': 7149, 'exceedingly': 7150, 'nyt': 7151, 'pane': 7152, 'obsessive': 7153, 'causes': 7154, 'advancing': 7155, 'rocking': 7156, 'aroumd': 7157, 'loveit': 7158, 'foward': 7159, 'shelve': 7160, 'swear': 7161, 'transformed': 7162, 'instantaneous': 7163, 'transfered': 7164, 'silver': 7165, 'squeezy': 7166, 'skins': 7167, 'prefered': 7168, 'relaxed': 7169, 'devicegood': 7170, '300dpi': 7171, 'deficated': 7172, 'paoerwhite': 7173, 'bevel': 7174, 'constantantly': 7175, 'returing': 7176, 'relieved': 7177, 'recycling': 7178, 'spectacularly': 7179, 'leadership': 7180, 'caf': 7181, 'upwards': 7182, 'fade': 7183, 'doubts': 7184, 'sticking': 7185, 'mentions': 7186, 'cats': 7187, 'tucked': 7188, 'equals': 7189, 'clippings': 7190, 'medical': 7191, 'assigned': 7192, 'retailers': 7193, 'rice': 7194, 'disrupt': 7195, 'tiring': 7196, 'forego': 7197, 'wondered': 7198, 'absence': 7199, 'prove': 7200, 'tilt': 7201, 'supporter': 7202, 'crank': 7203, 'bluish': 7204, 'implementation': 7205, 'downgrade': 7206, 'marked': 7207, 'surrounded': 7208, 'nonexistent': 7209, 'movers': 7210, 'receives': 7211, 'readeri': 7212, 'papperwhite': 7213, 'basket': 7214, 'whiter': 7215, 'lowering': 7216, 'styles': 7217, 'mentioning': 7218, 'compensates': 7219, 'sorting': 7220, 'experts': 7221, 'procrastinated': 7222, 'split': 7223, 'fm': 7224, 'tract': 7225, 'symbol': 7226, 'vibration': 7227, 'aluminum': 7228, 'ligths': 7229, 'discoloration': 7230, 'forwards': 7231, 'opinions': 7232, 'defense': 7233, 'unusual': 7234, 'appreciates': 7235, 'concentration': 7236, 'gathers': 7237, 'otter': 7238, 'dorms': 7239, 'humble': 7240, 'roku2': 7241, '1984': 7242, 'relate': 7243, 'critique': 7244, 'canadian': 7245, 'adverts': 7246, 'legally': 7247, 'en': 7248, 'sur': 7249, 'que': 7250, 'dramatic': 7251, 'outages': 7252, 'surge': 7253, 'chewed': 7254, 'screwed': 7255, 'kitten': 7256, 'famous': 7257, 'ca': 7258, '93': 7259, 'cathough': 7260, 'lic': 7261, 'richer': 7262, 'iy': 7263, 'breadth': 7264, 'infinite': 7265, '28': 7266, 'fiber': 7267, 'optic': 7268, 'desperately': 7269, 'messy': 7270, 'tease': 7271, 'worship': 7272, 'ingredients': 7273, 'cookies': 7274, 'omission': 7275, 'nobody': 7276, 'pokmon': 7277, '1960': 7278, 'temperatures': 7279, 'tweeks': 7280, 'fixture': 7281, 'attachments': 7282, 'redo': 7283, 'furniture': 7284, 'hues': 7285, 'chicken': 7286, 'bottle': 7287, 'handsfree': 7288, 'convience': 7289, 'noice': 7290, 'thunderstorm': 7291, 'paces': 7292, 'logic': 7293, 'united': 7294, 'blinds': 7295, 'imperative': 7296, 'seemlessly': 7297, 'aws': 7298, 'mucic': 7299, 'potato': 7300, 'execution': 7301, 'addicting': 7302, 'sir': 7303, 'italian': 7304, 'underutilized': 7305, 'wantedto': 7306, 'gather': 7307, 'regulating': 7308, 'deeper': 7309, 'broadband': 7310, 'spotifly': 7311, 'reacts': 7312, 'overkill': 7313, 'instal': 7314, '911': 7315, 'concierge': 7316, 'comedian': 7317, 'musician': 7318, 'casts': 7319, '50s': 7320, 'compatability': 7321, 'dcor': 7322, 'ranked': 7323, 'applicable': 7324, 'crowded': 7325, 'equipments': 7326, 'dialog': 7327, 'fr': 7328, 'composer': 7329, '15min': 7330, 'goofy': 7331, 'superfun': 7332, 'herd': 7333, 'halo': 7334, 'intimidated': 7335, 'sponsors': 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7394, 'vj': 7395, 'beck': 7396, 'lullabies': 7397, 'instruments': 7398, 'stroke': 7399, 'informing': 7400, 'partnership': 7401, 'jeff': 7402, 'bezos': 7403, 'marry': 7404, 'posting': 7405, 'president': 7406, 'radios': 7407, 'hallway': 7408, 'ws': 7409, 'evolve': 7410, 'knife': 7411, 'caution': 7412, 'buckie': 7413, 'oth': 7414, 'fuller': 7415, 'pot': 7416, 'zwave': 7417, 'echobee': 7418, 'alexsa': 7419, 'scratching': 7420, 'greets': 7421, 'neatest': 7422, 'unleash': 7423, 'cooks': 7424, 'singer': 7425, 'surroundings': 7426, 'dud': 7427, 'amz': 7428, 'og': 7429, 'refers': 7430, 'detergent': 7431, 'chest': 7432, 'scissors': 7433, 'memeber': 7434, 'glossary': 7435, 'equivalents': 7436, 'furnace': 7437, 'developing': 7438, 'upped': 7439, 'strip': 7440, 'monster': 7441, 'implement': 7442, 'pronounce': 7443, 'bby': 7444, 'yopu': 7445, 'gr8': 7446, 'rented': 7447, 'naming': 7448, 'protocols': 7449, '100x': 7450, 'washing': 7451, 'humour': 7452, 'planets': 7453, 'adt': 7454, 'wedding': 7455, 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10380, 'actively': 10381, 'inperfections': 10382, 'pariah': 10383, 'previoulsy': 10384, 'kindl': 10385, 'lightingweight': 10386, 'exquisite': 10387, 'shockingly': 10388, 'atlas': 10389, 'shrugged': 10390, 'furthest': 10391, 'reopen': 10392, '191': 10393, 'license': 10394, 'rebuy': 10395, 'suppos': 10396, 'wrk': 10397, 'shifts': 10398, 'lifeless': 10399, 'execellent': 10400, 'metro': 10401, 'commuters': 10402, 'riders': 10403, 'hardcovers': 10404, 'consequence': 10405, 'gpad': 10406, 'untimely': 10407, 'beam': 10408, 'optimistic': 10409, 'excite': 10410, 'dudes': 10411, 'linger': 10412, 'poetry': 10413, 'rocketfish': 10414, 'appetite': 10415, 'lightened': 10416, 'struck': 10417, 'disraction': 10418, 'motorcylce': 10419, 'fellas': 10420, 'ole': 10421, 'everywhereperfect': 10422, 'dusk': 10423, 'detects': 10424, 'shady': 10425, 'shelp': 10426, 'darkest': 10427, 'electr': 10428, 'nico': 10429, 'pr': 10430, 'ctico': 10431, 'somwthing': 10432, 'ughtnwould': 10433, 'sceeen': 10434, 'resuming': 10435, 'campus': 10436, 'readr': 10437, 'complemented': 10438, 'tt': 10439, 'preliminary': 10440, 'whiletraveling': 10441, 'pregnancy': 10442, 'breast': 10443, 'feeding': 10444, 'thisbinsteadbof': 10445, 'stabilization': 10446, 'migrating': 10447, 'bestkindle': 10448, 'nookbattery': 10449, 'ligth': 10450, 'frature': 10451, 'staring': 10452, 'tome': 10453, 'debate': 10454, 'arsenal': 10455, 'appealed': 10456, 'forsook': 10457, 'dispatched': 10458, 'parter': 10459, 'minimized': 10460, 'and1st': 10461, 'shareware': 10462, 'converters': 10463, 'offshore': 10464, 'iteration': 10465, 'damp': 10466, 'wrinkled': 10467, 'sunscreen': 10468, 'icky': 10469, 'greta': 10470, 'tk': 10471, 'bix': 10472, 'contemplation': 10473, 'excepted': 10474, 'express': 10475, 'abnormal': 10476, 'import': 10477, 'instapaper': 10478, 'dismiss': 10479, 'bashing': 10480, '9000': 10481, 'finnally': 10482, 'helful': 10483, 'readno': 10484, 'strainlong': 10485, 'lifesimple': 10486, 'useinterfaces': 10487, 'computernot': 10488, 'browsingvery': 10489, 'ergonomiclightweightsturdy': 10490, 'diid': 10491, 'ebookwhich': 10492, 'stimulating': 10493, 'hardbound': 10494, 'xray': 10495, 'brightnesses': 10496, '9hours': 10497, 'wiped': 10498, 'oils': 10499, 'prevoius': 10500, 'differencemuch': 10501, 'paperwhiye': 10502, 'challange': 10503, 'paperbook': 10504, 'believing': 10505, 'paperwight': 10506, 'nysecond': 10507, 'wihite': 10508, 'abook': 10509, 'cheapo': 10510, 'underlines': 10511, 'whoever': 10512, 'suiting': 10513, 'barnesand': 10514, 'warms': 10515, 'sprang': 10516, 'techshe': 10517, 'heaver': 10518, 'lefty': 10519, 'righty': 10520, 'l8ght': 10521, 'eldery': 10522, 'ignores': 10523, 'goto': 10524, 'librarian': 10525, 'shd': 10526, 'superbowl': 10527, 'snacks': 10528, 'beverages': 10529, 'swiped': 10530, 'acquire': 10531, 'vulnerable': 10532, 'hadnt': 10533, 'appproslight': 10534, 'weightsmall': 10535, 'purseback': 10536, 'lightcons1': 10537, 'ads2': 10538, 'store3': 10539, 'simple4': 10540, 'other5': 10541, 'anything6': 10542, 'juggle': 10543, 'migrated': 10544, '1200': 10545, 'carting': 10546, 'lamplight': 10547, 'astronomical': 10548, 'technnology': 10549, 'definalty': 10550, 'smudgy': 10551, 'parcel': 10552, 'prolonged': 10553, 'suppression': 10554, 'enamored': 10555, 'briught': 10556, 'connivence': 10557, 'imitates': 10558, 'frickin': 10559, 'devote': 10560, 'waffling': 10561, 'commitment': 10562, 'strained': 10563, 'smokey': 10564, 'reversing': 10565, 'purchse': 10566, 'ong': 10567, 'envy': 10568, 'adores': 10569, 'leaked': 10570, 'pawnshop': 10571, 'paprrwjite': 10572, 'synching': 10573, 'cheeky': 10574, 'enjoyments': 10575, 'alphabetizing': 10576, 'infrequently': 10577, 'dedication': 10578, 'tempted': 10579, 'originalkindle': 10580, 'quailty': 10581, 'creaks': 10582, 'persists': 10583, 'unexpectedly': 10584, 'purely': 10585, 'bibliophile': 10586, 'obsolutely': 10587, 'glarefree': 10588, 'aholic': 10589, 'backordered': 10590, 'extending': 10591, 'originial': 10592, 'backlid': 10593, 'dyslexics': 10594, 'helvetica': 10595, 'annotating': 10596, 'murder': 10597, 'itty': 10598, 'bitty': 10599, 'maneurving': 10600, 'renditions': 10601, 'abound': 10602, 'displeased': 10603, 'quadrupled': 10604, 'haul': 10605, 'oniy': 10606, 'dale': 10607, '6hr': 10608, 'stretches': 10609, 'municipal': 10610, 'ryes': 10611, 'wihich': 10612, 'typefaces': 10613, 'illuminate': 10614, 'bookie': 10615, 'suffering': 10616, 'paparwhite': 10617, 'akers': 10618, 'sandpfoof': 10619, 'maneuvering': 10620, 'departments': 10621, 'itc': 10622, 'synk': 10623, 'llight': 10624, 'subdued': 10625, 'shadings': 10626, 'lightwieght': 10627, 'backllit': 10628, 'paging': 10629, 'myth': 10630, 'masters': 10631, 'spindle': 10632, 'dwindle': 10633, 'opendyslexic': 10634, 'applauded': 10635, 'b00oqvzdjm': 10636, 'carta': 10637, 'epaper': 10638, 'notbing': 10639, '1400': 10640, 'giftee': 10641, 'advent': 10642, 'replicates': 10643, 'wikis': 10644, 'zones': 10645, 'contrary': 10646, 'defines': 10647, 'ooks': 10648, 'kindlepaper': 10649, 'dissappointing': 10650, 'divorce': 10651, 'shaft': 10652, 'inverse': 10653, 'retains': 10654, 'stressing': 10655, 'amat': 10656, 'cringed': 10657, 'accomplishment': 10658, 'danger': 10659, 'gripes': 10660, 'brilliantly': 10661, 'penalize': 10662, 'booksvia': 10663, 'andthe': 10664, 'readinstructions': 10665, 'litghting': 10666, 'emits': 10667, 'kinder': 10668, 'defining': 10669, 'inference': 10670, 'propose': 10671, 'purposeful': 10672, 'foregone': 10673, 'magnify': 10674, 'kinlde': 10675, 'compacy': 10676, 'vintage': 10677, 'cheeper': 10678, 'implies': 10679, 'disappointingly': 10680, 'lightweright': 10681, 'readale': 10682, 'shinning': 10683, 'envelop': 10684, 'substitution': 10685, 'pocketbooks': 10686, 'orpaperback': 10687, 'prayers': 10688, 'eyeglasses': 10689, 'focusing': 10690, 'worthiness': 10691, 'okjust': 10692, 'okhate': 10693, 'screennot': 10694, 'averaged': 10695, 'instore': 10696, 'correlate': 10697, 'piled': 10698, 'tucking': 10699, 'dependant': 10700, 'assault': 10701, 'phobe': 10702, 'concentrated': 10703, 'sunroom': 10704, 'vox': 10705, 'naples': 10706, 'bartering': 10707, 'retrospect': 10708, 'scree': 10709, 'bookworms': 10710, 'glance': 10711, 'whispernet': 10712, 'confortable': 10713, 'mindset': 10714, 'citation': 10715, 'decade': 10716, 'life2': 10717, 'clarity3': 10718, 'circumstances4': 10719, '1005': 10720, 'document6': 10721, 'weightbelieve': 10722, 'oneself': 10723, 'coach': 10724, 'simulating': 10725, 'newfound': 10726, 'bookaholic': 10727, 'relieve': 10728, 'ma': 10729, 'decree': 10730, 'increditable': 10731, 'alleviates': 10732, 'microusb': 10733, 'orginall': 10734, '85yr': 10735, 'unaffected': 10736, 'brushing': 10737, 'backpocket': 10738, 'seating': 10739, 'stressful': 10740, 'observations': 10741, 'varies': 10742, 'fumble': 10743, 'condense': 10744, 'gestures': 10745, 'attachable': 10746, 'gripper': 10747, 'comparative': 10748, 'eases': 10749, 'teamkindlepaperwhite': 10750, 'oerfect': 10751, 'nonsense': 10752, 'sned': 10753, 'aggravated': 10754, 'amazinged': 10755, 'tomes': 10756, 'squeezed': 10757, 'shutterfly': 10758, 'complimented': 10759, 'vvery': 10760, 'aprice': 10761, 'afforadable': 10762, 'lightweightness': 10763, 'paperwhiite': 10764, 'brooks': 10765, 'emergence': 10766, 'leaks': 10767, 'emerging': 10768, 'pinhole': 10769, 'speck': 10770, 'surrendering': 10771, 'towers': 10772, 'tunnels': 10773, 'tolerate': 10774, 'veritable': 10775, 'overwhelmingly': 10776, 'continuation': 10777, 'erratic': 10778, 'pitched': 10779, 'tester': 10780, 'cargo': 10781, 'shorts': 10782, 'outweighed': 10783, 'fliers': 10784, 'beachbag': 10785, 'constructive': 10786, 'feelings': 10787, 'owing': 10788, 'bibliography': 10789, 'worrisome': 10790, 'sour': 10791, 'river': 10792, 'indicate': 10793, 'explanation': 10794, 'conclusions': 10795, 'gloss': 10796, 'kndle': 10797, 'magnifying': 10798, 'rumored': 10799, 'fictions': 10800, 'refreshing': 10801, 'discomfort': 10802, 'bookwork': 10803, 'outlast': 10804, 'hoarding': 10805, 'wouls': 10806, 'gunslinger': 10807, 'midnight': 10808, 'prtability': 10809, 'magnified': 10810, 'paperwork': 10811, 'goodbuy': 10812, 'subscribes': 10813, 'publications': 10814, 'disappears': 10815, 'disallows': 10816, 'manipulating': 10817, 'hero': 10818, 'duration': 10819, 'kindlewhites': 10820, 'purposefully': 10821, 'lifespan': 10822, 'aide': 10823, 'squinting': 10824, '06': 10825, 'kinkle': 10826, 'rhis': 10827, 'wrists': 10828, 'hasatate': 10829, 'destruct': 10830, 'washes': 10831, 'fantactic': 10832, 'granularity': 10833, 'inhibits': 10834, 'backloght': 10835, 'inception': 10836, 'agile': 10837, 'mysteriously': 10838, 'bahamas': 10839, 'bald': 10840, 'kindall': 10841, 'kimball': 10842, 'easytocarry': 10843, 'jaggies': 10844, 'moreover': 10845, 'handier': 10846, 'gained': 10847, 'achieves': 10848, 'copied': 10849, 'soooooooooo': 10850, 'critics': 10851, 'fruity': 10852, 'trainer': 10853, 'decission': 10854, 'gobbled': 10855, 'conserve': 10856, 'squeaking': 10857, 'overwhelmed': 10858, 'convoluted': 10859, 'sprung': 10860, 'curled': 10861, 'recipies': 10862, 'kindie': 10863, 'resisting': 10864, 'crisply': 10865, 'peperwhite': 10866, 'maintaining': 10867, 'selective': 10868, 'span': 10869, 'disgusted': 10870, 'evidently': 10871, 'comforatble': 10872, 'withyour': 10873, 'finance': 10874, 'referenced': 10875, 'quote': 10876, 'expirience': 10877, 'bricking': 10878, 'beige': 10879, 'dyslexia': 10880, 'contrasts': 10881, 'fenomenal': 10882, 'copyrights': 10883, 'alread': 10884, 'scenario': 10885, 'stepmother': 10886, 'feasible': 10887, 'circumstances': 10888, 'bookmarking': 10889, 'thekindle': 10890, 'joband': 10891, 'flippable': 10892, 'contemplated': 10893, 'philsophy': 10894, 'government': 10895, 'lowlight': 10896, 'toothbrush': 10897, 'aready': 10898, 'softly': 10899, 'gosh': 10900, 'purist': 10901, 'alurek': 10902, 'devoured': 10903, 'cured': 10904, 'lifewould': 10905, 'outweigh': 10906, 'goodwas': 10907, 'touchable': 10908, 'diestraftions': 10909, 'custumer': 10910, 'seein': 10911, 'goad': 10912, 'ican': 10913, 'diffuses': 10914, 'ju': 10915, 'healing': 10916, 'readand': 10917, 'recommeded': 10918, 'backlite': 10919, 'achy': 10920, 'glitter': 10921, 'technologies': 10922, 'rader': 10923, 'shoved': 10924, 'fantasticly': 10925, 'uc': 10926, 'irvine': 10927, 'dooper': 10928, 'yourbedmate': 10929, 'cartridges': 10930, 'labeling': 10931, 'syllabus': 10932, 'registers': 10933, 'vovage': 10934, 'blackvery': 10935, 'reliabe': 10936, 'gliches': 10937, 'retire': 10938, 'impressions': 10939, 'usefult': 10940, 'incompatibility': 10941, 'wonderfui': 10942, 'discribed': 10943, 'dominican': 10944, 'rebublic': 10945, 'missionaries': 10946, 'mondays': 10947, 'chill': 10948, 'thins': 10949, 'paperwhits': 10950, 'graders': 10951, 'dummy': 10952, 'bdu': 10953, 'transitioned': 10954, 'unneeded': 10955, 'nonglare': 10956, 'duplicate': 10957, 'resd': 10958, 'schhool': 10959, 'backpighting': 10960, 'nitch': 10961, 'talbets': 10962, 'grudgingly': 10963, 'ebooker': 10964, 'werr': 10965, 'ths': 10966, 'arounnd': 10967, 'carringing': 10968, 'coloured': 10969, 'leggy': 10970, 'moe': 10971, 'bednot': 10972, 'corresponding': 10973, 'tidy': 10974, 'toughing': 10975, '8y': 10976, 'mitigate': 10977, 'catastrophic': 10978, 'tjis': 10979, 'efficacy': 10980, 'humbly': 10981, 'recap': 10982, 'enlarges': 10983, 'resize': 10984, 'seattle': 10985, 'pinging': 10986, 'turners': 10987, 'shud': 10988, 'upcharge': 10989, 'specialties': 10990, 'royce': 10991, 'sticked': 10992, 'gloves': 10993, 'pannier': 10994, 'exactlo': 10995, 'cadillac': 10996, 'distinctive': 10997, 'avaiailable': 10998, 'distracts': 10999, 'consult': 11000, 'situ': 11001, 'disrupts': 11002, 'covering': 11003, 'backlot': 11004, 'tinged': 11005, 'subjective': 11006, 'sued': 11007, 'gradient': 11008, 'visualize': 11009, 'pw1': 11010, 'quietly': 11011, 'hards': 11012, 'toldpeople': 11013, 'helium': 11014, 'adjuster': 11015, 'reimagined': 11016, 'bias': 11017, 'adoption': 11018, 'proposition': 11019, 'thhought': 11020, 'easliy': 11021, 'presume': 11022, 'reallllllllllllllllllllllllllllllllllllllllllllllly': 11023, 'flatness': 11024, 'qualify': 11025, 'dominance': 11026, 'hepatic': 11027, 'madman': 11028, 'honesty': 11029, 'whip': 11030, 'qa': 11031, 'agents': 11032, 'facedown': 11033, 'tilted': 11034, 'lid': 11035, 'luddite': 11036, 'ttry': 11037, 'batterry': 11038, 'kindle4nt': 11039, '4t': 11040, 'hep': 11041, 'pending': 11042, 'screencons': 11043, 'frustratingwith': 11044, 'studio': 11045, 'tackle': 11046, 'kitty': 11047, 'borders': 11048, 'sulight': 11049, 'stupendous': 11050, 'journals': 11051, 'navigated': 11052, 'vertically': 11053, 'horizontally': 11054, 'flew': 11055, 'bearable': 11056, 'taxes': 11057, 'georgia': 11058, 'qualifying': 11059, 'perfected': 11060, 'minors': 11061, 'paris': 11062, 'pol': 11063, 'thelarge': 11064, 'ofmaking': 11065, 'havefound': 11066, 'buyhas': 11067, 'thestore': 11068, 'passion': 11069, 'aura': 11070, 'detachment': 11071, 'hy': 11072, 'indigenous': 11073, 'utilizes': 11074, 'girth': 11075, 'frankfort': 11076, 'boundbook': 11077, 'receipent': 11078, 'duck': 11079, 'dispose': 11080, 'sellers': 11081, 'defunct': 11082, 'disassembling': 11083, 'crimp': 11084, 'rhyme': 11085, 'anywhereview': 11086, 'brightens': 11087, 'voraciously': 11088, 'aquired': 11089, 'browny': 11090, 'woods': 11091, 'costliest': 11092, 'liquid': 11093, 'lightlove': 11094, 'simp': 11095, 'archive': 11096, 'breakcons': 11097, 'thingredients': 11098, 'mandates': 11099, 'interfaceneeds': 11100, 'remarked': 11101, 'dirtying': 11102, 'addresses': 11103, 'binged': 11104, 'transportability': 11105, 'freight': 11106, 'pw3': 11107, 'crumb': 11108, 'catcher': 11109, 'distressed': 11110, 'dallas': 11111, 'variations': 11112, 'jesus': 11113, 'christ': 11114, 'nazareth': 11115, 'voyages': 11116, 'dip': 11117, 'spills': 11118, 'fluidity': 11119, 'proponent': 11120, 'sadness': 11121, 'conform': 11122, 'blackscreen': 11123, 'september': 11124, 'tee': 11125, 'orangeish': 11126, 'blueish': 11127, 'mighty': 11128, 'copperish': 11129, 'toned': 11130, 'prettymuch': 11131, 'noe': 11132, 'littlebit': 11133, 'ipadmini': 11134, 'air2': 11135, 'buttton': 11136, 'aweseome': 11137, 'diagram': 11138, 'pagesif': 11139, 'stockpiling': 11140, 'conistent': 11141, 'baggie': 11142, 'andy': 11143, 'satistfy': 11144, '6weeks': 11145, '3weeks': 11146, 'wandering': 11147, 'jackpot': 11148, 'depleted': 11149, 'disliking': 11150, 'produ': 11151, 'ct': 11152, 'freind': 11153, 'rewinds': 11154, 'irresistible': 11155, 'ultraportable': 11156, 'inc': 11157, 'b004y1wcde': 11158, 'bikerider': 11159, 'pricerange': 11160, 'usefulnot': 11161, 'preface': 11162, 'dragon': 11163, 'tattoo': 11164, 'brave': 11165, 'hunger': 11166, 'trilogy': 11167, 'divergent': 11168, 'hindered': 11169, 'dive': 11170, 'inspire': 11171, 'youd': 11172, 'sundry': 11173, 'logistical': 11174, 'attractions': 11175, 'accumulative': 11176, 'creeping': 11177, 'remained': 11178, 'glacial': 11179, 'consensus': 11180, 'flakey': 11181, 'coroporate': 11182, 'hermetically': 11183, '2005': 11184, '2007': 11185, 'itso': 11186, 'disperses': 11187, 'funcion': 11188, 'aims': 11189, '000s': 11190, 'particulary': 11191, '212': 11192, '1292': 11193, '2093': 11194, 'voyger': 11195, '219': 11196, '400so': 11197, '1ghz': 11198, 'slicker': 11199, 'presentation': 11200, '26g': 11201, 'softens': 11202, 'ave': 11203, 'connectionpersonally': 11204, 'papwerwhite': 11205, 'reada': 11206, 'azw3': 11207, 'azw': 11208, 'txt': 11209, 'unprotected': 11210, 'prc': 11211, 'jpeg': 11212, 'gif': 11213, 'png': 11214, 'bmp': 11215, 'conversionq': 11216, 'formata': 11217, 'um0teoar6zw': 11218, 'simples': 11219, 'wella': 11220, 'booka': 11221, 'sizea': 11222, 'tastesq': 11223, 'childrena': 11224, 'familyq': 11225, 'beda': 11226, 'onq': 11227, 'availablea': 11228, 'copyright': 11229, 'lasta': 11230, 'deviceq': 11231, 'chargea': 11232, 'hoursq': 11233, 'holda': 11234, 'youve': 11235, 'elsea': 11236, 'sew': 11237, 'trough': 11238, 'bookstores': 11239, 'vraiment': 11240, 'bon': 11241, 'petit': 11242, 'appareil': 11243, 'lger': 11244, 'facile': 11245, 'emploij': 11246, 'hte': 11247, 'servir': 11248, 'plages': 11249, 'cet': 11250, 'hiverbelle': 11251, 'bibliothque': 11252, 'disponiblesbon': 11253, 'achat': 11254, 'bref': 11255, 'trs': 11256, 'heureux': 11257, 'soient': 11258, 'aprs': 11259, 'tre': 11260, 'fait': 11261, 'voler': 11262, 'est': 11263, 'bien': 11264, 'pouvoir': 11265, 'retrouver': 11266, 'tous': 11267, 'mes': 11268, 'avec': 11269, 'toutes': 11270, 'j': 11271, 'avais': 11272, 'persisted': 11273, 'creature': 11274, 'immersed': 11275, 'wildly': 11276, 'indulgence': 11277, 'uk': 11278, 'jurisdiction': 11279, 'wrongthank': 11280, 'weeksi': 11281, 'buybuyby': 11282, 'electrnico': 11283, 'prctico': 11284, 'jul': 11285, 'loging': 11286, 'meanwhile': 11287, 'moko': 11288, 'kf8': 11289, 'elk': 11290, 'grove': 11291, 'surcharges': 11292, 'bby01': 11293, '800223005180': 11294, 'ocasional': 11295, 'litterate': 11296, 'rugrat': 11297, 'fashionable': 11298, 'woul': 11299, 'peoplenet': 11300, 'assessible': 11301, 'deceased': 11302, 'otoh': 11303, 'offended': 11304, 'mature': 11305, 'homescreen': 11306, 'competitions': 11307, 'boomer': 11308, 'catechism': 11309, 'faded': 11310, 'unwillingness': 11311, 'fingertip': 11312, 'beacuse': 11313, 'deems': 11314, 'pretense': 11315, 'nordic': 11316, 'hipsters': 11317, 'tryout': 11318, '12gb': 11319, 'unibody': 11320, 'urging': 11321, 'tea': 11322, 'installedcons': 11323, 'gamesnot': 11324, 'upgradeslove': 11325, 'corrupted': 11326, 'refurbishment': 11327, 'xs': 11328, 'stupidly': 11329, 'dts': 11330, 'stare': 11331, 'cycled': 11332, 'showbox': 11333, 'sideclick': 11334, 'wellallows': 11335, 'installationmuch': 11336, 'tvallows': 11337, 'notcons': 11338, 'tvremote': 11339, 'controlsremote': 11340, 'resale': 11341, 'dl': 11342, 'referral': 11343, 'quickest': 11344, 'jub': 11345, 'molasses': 11346, 'proportion': 11347, 'trevmoned': 11348, 'onside': 11349, 'insts': 11350, 'wirelees': 11351, 'investigative': 11352, 'disclaimer': 11353, 'roars': 11354, 'replenish': 11355, 'scoff': 11356, 'failures': 11357, 'warrenty': 11358, 'ithas': 11359, 'leverage': 11360, 'weathered': 11361, 'conveint': 11362, 'pm': 11363, 'tucks': 11364, 'nreices': 11365, 'wondeful': 11366, 'prurple': 11367, 'anoid': 11368, 'usablity': 11369, 'trusting': 11370, 'durabale': 11371, 'proficiency': 11372, 'tab2': 11373, 'billed': 11374, 'craftsmanship': 11375, 'sero': 11376, 'youngins': 11377, 'economically': 11378, 'locating': 11379, 'rehearsal': 11380, 'convent': 11381, 'brochure': 11382, 'bloxels': 11383, 'grandnephew': 11384, 'barney': 11385, 'confuse': 11386, '10t': 11387, 'putchase': 11388, 'litte': 11389, 'wo': 11390, 'freedoms': 11391, 'squirly': 11392, 'fuego': 11393, 'delicious': 11394, 'oatmeal': 11395, 'usecons': 11396, 'busybody': 11397, 'uncomplicated': 11398, '2107': 11399, 'wil': 11400, 'stealing': 11401, 'theyit': 11402, 'featuresdon': 11403, 'adage': 11404, 'america': 11405, 'sa': 11406, 'tabletthat': 11407, 'unannounced': 11408, 'deteriorates': 11409, 'li': 11410, 'tabler': 11411, 'reactive': 11412, 'teachers': 11413, 'photograher': 11414, '680': 11415, 'wrko': 11416, 'vb': 11417, 'john': 11418, 'smith': 11419, 'applience': 11420, 'whereever': 11421, 'lenghty': 11422, 'funniest': 11423, 'shortest': 11424, 'discipline': 11425, 'liights': 11426, 'gargae': 11427, 'invite': 11428, 'discreetly': 11429, 'secondvalexa': 11430, 'sirii': 11431, 'nsc': 11432, 'plant': 11433, 'rities': 11434, 'happenings': 11435, 'abode': 11436, 'retrieving': 11437, 'previews': 11438, 'imitation': 11439, 'supera': 11440, 'allowable': 11441, 'itnow': 11442, 'spoil': 11443, 'synonyms': 11444, 'antonyms': 11445, 'fruition': 11446, 'technoilogy': 11447, 'haiku': 11448, 'cools': 11449, 'denver': 11450, 'broncos': 11451, 'marsala': 11452, 'gag': 11453, 'perfume': 11454, 'tablespoons': 11455, 'liter': 11456, 'sills': 11457, 'amplify': 11458, 'naps': 11459, 'toying': 11460, 'simplifying': 11461, 'smartmode': 11462, 'vivant': 11463, 'bakes': 11464, 'divices': 11465, 'rv': 11466, 'bake': 11467, 'cumulus': 11468, 'clouds': 11469, 'pricinr': 11470, 'recongination': 11471, 'townhouse': 11472, 'lovethe': 11473, '0ff': 11474, 'residence': 11475, 'accurrate': 11476, 'protest': 11477, 'populates': 11478, 'spins': 11479, 'summoning': 11480, 'calibrate': 11481, 'tred': 11482, 'investigating': 11483, 'getying': 11484, 'mudic': 11485, 'dotsgreat': 11486, 'shake': 11487, 'uo': 11488, 'tor': 11489, 'definitly': 11490, 'muffled': 11491, 'askin': 11492, 'barcelona': 11493, 'lole': 11494, 'sp': 11495, 'gooooood': 11496, 'medication': 11497, 'algorithm': 11498, 'sweetheart': 11499, 'peripherals': 11500, 'syste': 11501, 'confess': 11502, 'raspberry': 11503, 'pi': 11504, 'forgave': 11505, 'capabiliies': 11506, 'controled': 11507, 'hsnds': 11508, 'sloppy': 11509, 'googlehome': 11510, 'amaxing': 11511, 'compalibility': 11512, 'shed': 11513, 'irs': 11514, 'multiply': 11515, 'subtract': 11516, 'myriad': 11517, 'inaudible': 11518, 'overrated': 11519, 'sever': 11520, 'thermometer': 11521, 'toes': 11522, 'uttered': 11523, 'taxing': 11524, 'amuses': 11525, 'revolving': 11526, 'stab': 11527, 'swayed': 11528, 'descriptive': 11529, 'feathures': 11530, 'answeri': 11531, 'enoys': 11532, 'veing': 11533, 'driveway': 11534, 'distinct': 11535, 'advertisedsound': 11536, 'jams': 11537, 'sweater': 11538, 'taker': 11539, 'shehelps': 11540, 'ofmy': 11541, 'weatger': 11542, 'expedited': 11543, 'logitec': 11544, 'indexing': 11545, 'br': 11546, 'lr': 11547, 'ow': 11548, 'iffft': 11549, 'radioandpod': 11550, 'asss': 11551, 'aides': 11552, 'ubers': 11553, 'undiscovered': 11554, 'havoc': 11555, '25mgb': 11556, 'spottily': 11557, 'perineum': 11558, 'orbit': 11559, 'bhyve': 11560, 'advancements': 11561, 'thatyou': 11562, 'showtimes': 11563, 'gate': 11564, 'personable': 11565, 'echodot': 11566, 'cabt': 11567, 'reporters': 11568, 'stellar': 11569, 'unto': 11570, 'inseon': 11571, 'orally': 11572, 'catalogue': 11573, '4stars': 11574, 'evenin': 11575, 'sixties': 11576, 'harman': 11577, 'kardon': 11578, 'onyx': 11579, 'succeeded': 11580, 'tidbits': 11581, 'jepordy': 11582, 'intranet': 11583, 'auditory': 11584, 'howling': 11585, 'arch': 11586, 'obeyed': 11587, 'hoo': 11588, 'eager': 11589, 'ismartalarm': 11590, 'kevin': 11591, 'spacey': 11592, 'macaw': 11593, 'blues': 11594, 'peice': 11595, 'eqiptment': 11596, 'sunrise': 11597, 'sunset': 11598, 'initiating': 11599, 'proving': 11600, 'revelry': 11601, 'ecco': 11602, 'rhougjt': 11603, 'innovating': 11604, 'sre': 11605, 'mire': 11606, 'enthusiasm': 11607, 'trees': 11608, 'verse': 11609, 'specialty': 11610, 'obstinate': 11611, 'thatis': 11612, 'whispered': 11613, 'mation': 11614, 'synchs': 11615, 'hsve': 11616, 'rundown': 11617, 'componets': 11618, 'partners': 11619, 'downey': 11620, 'california': 11621, 'irritation': 11622, 'intercoms': 11623, 'accomplishing': 11624, 'energysmart': 11625, 'lowes': 11626, '612026': 11627, '691121': 11628, 'smash': 11629, 'eill': 11630, 'cofee': 11631, 'qith': 11632, 'gens': 11633, 'suri': 11634, 'designer': 11635, 'identically': 11636, 'afterbeing': 11637, '05': 11638, 'asmaking': 11639, 'thermastat': 11640, 'intergration': 11641, 'forme': 11642, 'temosta': 11643, 'expecxted': 11644, 'wrecks': 11645, 'gun': 11646, 'merchant': 11647, 'elexa': 11648, 'demands': 11649, 'favs': 11650, 'covenant': 11651, 'tempature': 11652, 'undoubtedly': 11653, 'housecleaning': 11654, 'answe': 11655, 'calories': 11656, 'tart': 11657, 'calculating': 11658, 'shoping': 11659, 'multifunctional': 11660, 'diownload': 11661, 'inaccessible': 11662, 'aver': 11663, 'improtant': 11664, 'deivce': 11665, 'inter': 11666, 'blended': 11667, 'denon': 11668, '123': 11669, 'lisp': 11670, 'thisome': 11671, 'speechless': 11672, 'posibilities': 11673, 'ford': 11674, 'credenza': 11675, 'entries': 11676, 'ourchase': 11677, 'announcements': 11678, 'wy': 11679, 'sonfor': 11680, 'zigbee': 11681, 'summarize': 11682, 'bodies': 11683, 'wright': 11684, 'alaxa': 11685, 'architect': 11686, 'backgound': 11687, 'denied': 11688, 'tracker': 11689, 'hymn': 11690, 'religious': 11691, 'memorized': 11692, 'misunderstood': 11693, 'witnessing': 11694, 'transmitted': 11695, 'comms': 11696, 'sthe': 11697, '3500': 11698, 'detection': 11699, 'decreasing': 11700, 'automations': 11701, 'experimentation': 11702, 'hahahahahahahhahahahahahahahhaha': 11703, 'wholee': 11704, 'nasa': 11705, 'appellate': 11706, 'fairest': 11707, 'talents': 11708, 'dubai': 11709, 'thailand': 11710, 'cara': 11711, 'boston': 11712, 'shuttle': 11713, 'blanket': 11714, '80th': 11715, 'presences': 11716, 'smoke': 11717, 'ocean': 11718, 'foundation': 11719, 'dictates': 11720, 'bette': 11721, 'superbly': 11722, 'amy': 11723, 'loudest': 11724, 'rap': 11725, 'informationtimers': 11726, 'dismissed': 11727, 'industrial': 11728, 'blend': 11729, 'formula': 11730, 'sparse': 11731, 'inconsistent': 11732, 'thi': 11733, 'military': 11734, 'pac': 11735, 'eastern': 11736, 'iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii': 11737, 'exhausting': 11738, 'nominal': 11739, 'caregivers': 11740, 'auction': 11741, 'sought': 11742, 'bided': 11743, 'reachable': 11744, 'assistive': 11745, 'usefulfeatures': 11746, 'extemely': 11747, 'wag': 11748, 'heartily': 11749, 'vested': 11750, 'objects': 11751, 'collectible': 11752, 'moto': 11753, 'dysfunctional': 11754, 'stumble': 11755, 'geological': 11756, 'potshot': 11757, 'calculate': 11758, 'baffled': 11759, 'pills': 11760, 'seeming': 11761, 'overdramatized': 11762, 'underpriced': 11763, 'amaxon': 11764, 'hmmmmm': 11765, 'annticapated': 11766, 'phon': 11767, 'amnazon': 11768, 'breathtaking': 11769, 'bbc': 11770, 'ynab': 11771, 'countriesi': 11772, 'wasy': 11773, 'rum': 11774, 'tilts': 11775, 'unending': 11776, 'geographical': 11777, 'lightswish': 11778, 'shuffling': 11779, 'proceeds': 11780, 'arrogant': 11781, 'preselected': 11782, 'behaves': 11783, 'wands': 11784, 'vday': 11785, 'sono': 11786, 'dubaiwell': 11787, 'defintly': 11788, 'assitant': 11789, 'circular': 11790, 'helo': 11791, 'fascinates': 11792, 'lemon': 11793, 'ignored': 11794, 'canhelp': 11795, 'concisely': 11796, 'intelligently': 11797, 'spews': 11798, 'wand': 11799, 'enriched': 11800, 'enrich': 11801, 'furthermore': 11802, 'widow': 11803, 'western': 11804, 'trafic': 11805, 'ecchor': 11806, 'impomation': 11807, 'widest': 11808, 'brains': 11809, 'soup': 11810, 'smallfootprint': 11811, 'contril': 11812, 'jars': 11813, 'informayion': 11814, 'straightened': 11815, 'exercises': 11816, 'bollywood': 11817, 'michigan': 11818, 'usa': 11819, 'mumbai': 11820, 'cert': 11821, 'millionaire': 11822, 'issuing': 11823, 'wint': 11824, 'hummingbirds': 11825, 'anticipation': 11826, 'impending': 11827, 'precede': 11828, 'wrongly': 11829, 'angeles': 11830, 'mancave': 11831, 'dominoes': 11832, 'vader': 11833, 'helmet': 11834, 'smartify': 11835, 'caster': 11836, 'arlo': 11837, 'module': 11838, 'gizmos': 11839, 'forthcoming': 11840, 'productgoing': 11841, 'speakerworks': 11842, 'hubworks': 11843, 'plum': 11844, 'snatched': 11845, 'specified': 11846, 'lazier': 11847, 'alzheimers': 11848, 'dementia': 11849, 'medicine': 11850, 'spofity': 11851, 'trendy': 11852, 'exponentially': 11853, 'beatles': 11854, 'complements': 11855, 'weakness': 11856, 'thwe': 11857, 'reportsalso': 11858, 'recites': 11859, 'knob': 11860, 'syllable': 11861, 'soubd': 11862, 'setups': 11863, 'arcade': 11864, 'reminding': 11865, 'vemo': 11866, 'secondarily': 11867, 'upworks': 11868, 'awesomeno': 11869, 'boght': 11870, 'servings': 11871, 'arithmatic': 11872, 'bop': 11873, 'lebron': 11874, 'james': 11875, 'instantaneously': 11876, 'allegedly': 11877, 'unwind': 11878, 'grossly': 11879, 'serius': 11880, 'lever': 11881, 'joan': 11882, 'collins': 11883, 'acheivement': 11884, 'misinterprets': 11885, 'transpare': 11886, 'msuper': 11887, 'bub': 11888, 'teaspoons': 11889, 'nickel': 11890, 'deepy': 11891, 'barry': 11892, 'diller': 11893, 'richest': 11894, 'admits': 11895, 'executed': 11896, 'joneses': 11897, 'barrow': 11898, 'lasik': 11899, 'annunciate': 11900, 'mathematics': 11901, 'interprets': 11902, 'ge': 11903, 'conditioners': 11904, 'relates': 11905, 'herplay': 11906, 'leveraging': 11907, 'calculation': 11908, 'announce': 11909, 'insult': 11910, 'echonfrom': 11911, 'promoted': 11912, 'climate': 11913, 'lowe': 11914, 'ech': 11915, 'soumds': 11916, 'auidobooks': 11917, 'overviews': 11918, 'recaps': 11919, 'rationalize': 11920, 'napster': 11921, 'penetrate': 11922, 'ehco': 11923, 'metaphorical': 11924, 'spinal': 11925, 'injury': 11926, 'broadcasts': 11927, 'successfully': 11928, 'kentucky': 11929, 'wildcat': 11930, 'professionally': 11931, 'stimulation': 11932, 'unassisted': 11933, 'wwere': 11934, 'roommates': 11935, 'congitions': 11936, 'coking': 11937, 'housebound': 11938, 'growth': 11939, 'encouraging': 11940, 'doys': 11941, 'tlink': 11942, 'swiss': 11943, 'lifex': 11944, 'ceases': 11945, 'newsflash': 11946, 'hbr': 11947, 'currency': 11948, 'cincise': 11949, 'crestron': 11950, 'corney': 11951, 'coo': 11952, '1800': 11953, 'noticable': 11954, 'thermastats': 11955, 'loader': 11956, 'kludgy': 11957, 'enunciation': 11958, 'guitar': 11959, 'consolidates': 11960, 'prolly': 11961, 'ical': 11962, 'gauge': 11963, 'anecdotes': 11964, 'livinroom': 11965, 'lakers': 11966, 'decorated': 11967, 'exhaust': 11968, 'rhere': 11969, 'downplays': 11970, 'hats': 11971, '22nd': 11972, 'cleans': 11973, 'ciunter': 11974, 'innthe': 11975, 'aesthetic': 11976, 'ourvsed': 11977, 'simpliar': 11978, 'itll': 11979, 'verifications': 11980, 'salvador': 11981, 'ste': 11982, 'developes': 11983, 'jettsons': 11984, 'looove': 11985, 'prospect': 11986, 'unresponsiveness': 11987, 'snapshot': 11988, 'egg': 11989, 'carton': 11990, 'ffiend': 11991, 'controllong': 11992, 'petty': 11993, 'dungeons': 11994, 'dragons': 11995, 'algebra': 11996, 'aired': 11997, 'incidentaly': 11998, 'starwars': 11999, 'pleases': 12000, 'fuctions': 12001, 'betwee': 12002, 'honey': 12003, 'hus': 12004, 'automobile': 12005, 'revolutionize': 12006, 'jamming': 12007, 'centerpieces': 12008, 'worded': 12009, 'nyc': 12010, 'metric': 12011, 'rephrase': 12012, 'plate': 12013, 'imitations': 12014, 'questioned': 12015, 'quizzes': 12016, 'whisper': 12017, 'vloume': 12018, 'triva': 12019, 'unwraps': 12020, 'quantify': 12021, 'wemu': 12022, 'rtr': 12023, 'germ': 12024, 'aphobic': 12025, 'putz': 12026, '5stars': 12027, 'installable': 12028, 'romantic': 12029, 'mass': 12030, 'arctic': 12031, 'interpretation': 12032, 'pronunciations': 12033, 'hiwever': 12034, 'milk': 12035, 'tokd': 12036, 'wfi': 12037, 'packers': 12038, 'alea': 12039, 'toyota': 12040, 'measurement': 12041, 'automatons': 12042, 'wirhout': 12043, 'cist': 12044, 'ballgame': 12045, 'alltimes': 12046, 'grocerys': 12047, 'alessa': 12048, 'qualit': 12049, 'nonneed': 12050, 'worls': 12051, 'velop': 12052, 'diamond': 12053, 'terminology': 12054, 'sensi': 12055, 'littetly': 12056, 'debice': 12057, 'inquisitive': 12058, 'bantering': 12059, 'facinating': 12060, 'enlightening': 12061, 'yuh': 12062, 'lifelike': 12063, 'shipboard': 12064, 'nicked': 12065, 'echolettes': 12066, 'simpilar': 12067, 'infection': 12068, 'roof': 12069, 'smart5': 12070, 'addon': 12071, 'suckered': 12072, 'broadens': 12073, 'simplification': 12074, 'multiroom': 12075, 'somethings': 12076, 'ike': 12077, 'happerier': 12078, 'dean': 12079, 'martin': 12080, '2m': 12081, 'expections': 12082, 'grilling': 12083, 'simon': 12084, 'outlr': 12085, 'undeniably': 12086, 'slate': 12087, 'agoura': 12088, 'hills': 12089, 'pronounciation': 12090, 'renamed': 12091, 'neato': 12092, 'botvac': 12093, 'irrigation': 12094, 'meditations': 12095, 'lung': 12096, '1969': 12097, 'suppprt': 12098, 'disingenuous': 12099, 'detecting': 12100, 'x2': 12101, 'chatty': 12102, 'logical': 12103, 'satisfiedgood': 12104, 'prepping': 12105, 'attire': 12106, 'idecided': 12107, 'police': 12108, 'foo': 12109, 'fighters': 12110, 'zeppelin': 12111, 'jewelry': 12112, 'tng': 12113, 'funnest': 12114, 'ayer': 12115, 'fledgling': 12116, '18000': 12117, 'handsfreedom': 12118, 'dwelling': 12119, 'wellgreat': 12120, 'play1': 12121, '122': 12122, 'stoked': 12123, 'property': 12124, 'modtly': 12125, 'recomed': 12126, 'judi': 12127, 'rejected': 12128, 'mis': 12129, 'interpret': 12130, 'hesitantly': 12131, 'rewarding': 12132, 'adjoining': 12133, 'anwsers': 12134, 'updatesshe': 12135, 'yetlove': 12136, 'fahrenheit': 12137, 'celsius': 12138, 'perforrm': 12139, 'mathematical': 12140, 'unforeseen': 12141, 'leasing': 12142, 'acceries': 12143, 'disarm': 12144, 'diego': 12145, 'valley': 12146, 'offset': 12147, 'takeout': 12148, 'jeapordy': 12149, 'elarning': 12150, 'curates': 12151, 'toanyone': 12152, 'sirrius': 12153, 'satelite': 12154, 'lidten': 12155, 'lyric': 12156, 'friendley': 12157, 'paralyzed': 12158, 'roomba': 12159, 'tacos': 12160, 'facinated': 12161, 'helicopters': 12162, 'amizon': 12163, '2thumbs': 12164, 'miore': 12165, 'alt': 12166, 'nation': 12167, 'destination': 12168, 'movable': 12169, 'conditioning': 12170, 'bloom': 12171, 'formate': 12172, 'funtions': 12173, 'sinc': 12174, 'cups': 12175, 'quarts': 12176, 'unproductive': 12177, 'wheels': 12178, 'puchased': 12179, 'capabilties': 12180, 'bloomberg': 12181, 'reuters': 12182, 'concerning': 12183, 'quizzies': 12184, 'verb': 12185, 'setu': 12186, 'cynical': 12187, 'pump': 12188, 'profits': 12189, 'contributes': 12190, 'deny': 12191, 'adopting': 12192, 'uss': 12193, 'disconects': 12194, 'querys': 12195, 'shortfalls': 12196, 'floating': 12197, 'multiform': 12198, 'simultaneousso': 12199, 'ihart': 12200, 'ayou': 12201, 'insulted': 12202, 'trully': 12203, 'positioning': 12204, 'recomment': 12205, 'consistency': 12206, 'bases': 12207, 'aamazon': 12208, 'fitting': 12209, 'exceptions': 12210, 'respresented': 12211, 'bizarrely': 12212, 'artistgreat': 12213, 'rainstorm': 12214, 'soundtracks': 12215, 'polka': 12216, 'generas': 12217, 'polkas': 12218, 'unintuitive': 12219, 'megaboom': 12220, 'goods': 12221, 'friendhavi': 12222, 'myst': 12223, 'stil': 12224, 'bach': 12225, 'smarts': 12226, 'greeting': 12227, 'esoteric': 12228, 'decibel': 12229, 'gsuite': 12230, 'biz': 12231, 'freebie': 12232, 'tailor': 12233, 'withstanding': 12234, 'propensity': 12235, 'restocking': 12236, 'quantities': 12237, 'exhausted': 12238, 'progresses': 12239, 'omnidirectional': 12240, 'valuing': 12241, 'aficionado': 12242, 'miscellaneous': 12243, 'conspicuously': 12244, 'sidney': 12245, 'michelle': 12246, 'ialexa': 12247, 'gathered': 12248, 'row': 12249, 'consecutive': 12250, 'slecting': 12251, 'addedand': 12252, 'deivices': 12253, 'peoduct': 12254, 'documented': 12255, 'serive': 12256, 'qickly': 12257, 'interupting': 12258, 'istall': 12259, 'nerds': 12260, 'reliance': 12261, 'sinking': 12262, 'funnier': 12263, 'relaunch': 12264, 'twisting': 12265, 'querying': 12266, 'bruno': 12267, 'mars': 12268, 'phenomenon': 12269, 'townhome': 12270, 'puttering': 12271, 'naysayers': 12272, 'judge': 12273, 'firing': 12274, 'rightit': 12275, 'qiestions': 12276, 'acquiring': 12277, 'prieo': 12278, 'heavenly': 12279, 'misinterpretation': 12280, 'cardinal': 12281, 'engagements': 12282, 'inconsistency': 12283, 'carfull': 12284, 'genera': 12285, 'motorola': 12286, 'elected': 12287, 'phil': 12288, 'misorder': 12289, 'lowered': 12290, 'smug': 12291, 'selects': 12292, 'bella': 12293, 'anunciate': 12294, 'misinterpret': 12295, 'tempter': 12296, 'joked': 12297, 'comedy': 12298, 'cooling': 12299, 'devotion': 12300, 'medley': 12301, 'meals': 12302, 'pranks': 12303, 'vital': 12304, 'goof': 12305, 'delved': 12306, 'anwser': 12307, 'caseta': 12308, 'lolshe': 12309, 'rosie': 12310, 'inspiration': 12311, 'broader': 12312, 'excetera': 12313, 'fickle': 12314, 'uncertainty': 12315, 'controling': 12316, 'vocabs': 12317, 'query': 12318, 'historical': 12319, 'realised': 12320, 'ummmmmmm': 12321, 'wth': 12322, 'notify': 12323, 'paraplegic': 12324, 'unpacked': 12325, 'wuestions': 12326, 'standoff': 12327, 'statements': 12328, 'timerssets': 12329, 'warmth': 12330, 'coolness': 12331, 'keen': 12332, 'conservation': 12333, 'detected': 12334, 'pronounced': 12335, 'woke': 12336, 'kiddoes': 12337, 'everyboby': 12338, 'favoite': 12339, 'extreamly': 12340, 'ejoy': 12341, 'showroomevery': 12342, 'remarks': 12343, 'thermos': 12344, 'reconitiona': 12345, 'guestions': 12346, 'outlook': 12347, 'lites': 12348, 'mucheasy': 12349, 'blasts': 12350, 'discrete': 12351, 'casetta': 12352, 'und': 12353, 'reword': 12354, 'crosby': 12355, 'giftlistening': 12356, 'sil': 12357, 'tides': 12358, 'spontaneous': 12359, 'styling': 12360, 'cinnamon': 12361, 'underestimated': 12362, 't5o': 12363, 'lear4n': 12364, 'balky': 12365, 'bladerunner': 12366, 'luvit': 12367, 'myers': 12368, 'vinyl': 12369, 'likeabroken': 12370, 'tickled': 12371, 'wikimedia': 12372, 'aroud': 12373, 'shouting': 12374, '50lbs': 12375, 'cookie': 12376, 'unfold': 12377, 'brunsch': 12378, 'drawl': 12379, 'litle': 12380, 'towels': 12381, 'reap': 12382, 'appoint': 12383, 'thermo': 12384, 'sightseeing': 12385, 'machines': 12386, 'hectic': 12387, 'commanding': 12388, 'perimeter': 12389, 'vasr': 12390, 'purshased': 12391, 'undertake': 12392, 'tuesday': 12393, 'receivers': 12394, 'marantz': 12395, 'equalize': 12396, 'kasa': 12397, 'mesmerized': 12398, 'greetings': 12399, 'nixes': 12400, 'compatabile': 12401, 'pierce': 12402, 'catches': 12403, 'arbitrary': 12404, 'admission': 12405, 'director': 12406, 'deadbolts': 12407, 'temps': 12408, 'hookups': 12409, 'settles': 12410, 'featuring': 12411, 'fantastics': 12412, 'bow': 12413, 'woof': 12414, 'sigh': 12415, 'belief': 12416, 'widowed': 12417, 'literate': 12418, 'interractions': 12419, 'realtyusa': 12420, 'jacked': 12421, 'bleek': 12422, 'farfield': 12423, 'asr': 12424, 'faqs': 12425, 'argued': 12426, 'comletely': 12427, 'configuring': 12428, 'hopeful': 12429, 'upgreat': 12430, 'musicamazinggreat': 12431, 'casta': 12432, 'avaid': 12433, 'perview': 12434, 'exho': 12435, 'lunches': 12436, 'obeys': 12437, 'freaky': 12438, 'correcting': 12439, 'relayed': 12440, 'fareasy': 12441, 'understandentertaining': 12442, 'sin': 12443, 'wrestling': 12444, 'themone': 12445, 'uswe': 12446, 'startrek': 12447, 'bundles': 12448, 'cutit': 12449, 'yells': 12450, 'ithe': 12451, 'freestanding': 12452, 'joint': 12453, 'kirk': 12454, 'cues': 12455, 'cue': 12456, 'destinations': 12457, 'cocktail': 12458, 'gh': 12459, 'guft': 12460, 'everside': 12461, 'minimizing': 12462, 'competitively': 12463, 'computerized': 12464, 'unintelligible': 12465, 'composers': 12466, 'abruptly': 12467, 'singers': 12468, 'nother': 12469, 'wildest': 12470, 'dianostic': 12471, 'decline': 12472, 'scoring': 12473, 'huawei': 12474, 'nites': 12475, 'nrws': 12476, 'speakfor': 12477, 'fimiliar': 12478, 'malicious': 12479, 'funis': 12480, 'smy': 12481, '74': 12482, 'rests': 12483, 'fixtures': 12484, 'severe': 12485, 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'saturn': 12700, 'holley': 12701, 'aboutjust': 12702, 'alexacan': 12703, 'streamlines': 12704, 'jury': 12705, 'analog': 12706, 'questioning': 12707, 'motives': 12708, 'philps': 12709, 'frontier': 12710, 'buyin': 12711, 'twas': 12712, 'newcomer': 12713, 'imply': 12714, 'parlor': 12715, 'sillly': 12716, 'thee': 12717, 'othe': 12718, 'dawn': 12719, 'steadfast': 12720, 'echoi': 12721, 'mindfulness': 12722, 'bmw': 12723, 'monthy': 12724, 'efficent': 12725, 'aand': 12726, 'saveing': 12727, 'toown': 12728, 'tinker': 12729, 'synopsis': 12730, 'dropcam': 12731, 'hackers': 12732, 'spiffy': 12733, 'checklists': 12734, 'restate': 12735, 'investigations': 12736, 'teaming': 12737, 'tchnology': 12738, 'remedial': 12739, 'vastly': 12740, 'togethers': 12741, 'bookseller': 12742, 'addtoo': 12743, 'trackers': 12744, 'snooty': 12745, 'inspiring': 12746, 'gains': 12747, 'fishtank': 12748, 'tonmake': 12749, 'echoing': 12750, 'clap': 12751, 'holler': 12752, 'communicates': 12753, 'marekting': 12754, 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'listin': 12809, 'sink': 12810, 'pso': 12811, 'thinghere': 12812, 'sopranos': 12813, 'blahoverall': 12814, 'revision': 12815, 'shelving': 12816, 'smatphone': 12817, 'kno': 12818, 'theme': 12819, 'mixes': 12820, 'redirect': 12821, 'melt': 12822, 'figuratively': 12823, 'cas': 12824, 'ta': 12825, 'warrants': 12826, 'refueling': 12827, 'cells': 12828, 'gratifying': 12829, 'programmers': 12830, 'rewording': 12831, 'sked': 12832, 'shocker': 12833, 'floats': 12834, '5ft': 12835, '10w': 12836, 'birth': 12837, 'corrects': 12838, 'disgrace': 12839, 'blocky': 12840, 'supplier': 12841, 'narrower': 12842, 'representatives': 12843, 'discharge': 12844, 'crooked': 12845, 'padding': 12846, 'shutter': 12847, 'citron': 12848, 'neon': 12849, '1of': 12850, 'acceptably': 12851, 'advantaged': 12852, '500ma': 12853, 'underhanded': 12854, 'estimated': 12855, 'oldie': 12856, 'goodie': 12857, 'pointy': 12858, 'griendly': 12859, 'distorts': 12860, 'drm': 12861, 'onternet': 12862, 'kerplot': 12863, 'gona': 12864, 'pound': 12865, 'eatching': 12866, 'arthritic': 12867, 'joints': 12868, 'usefulto': 12869, 'fount': 12870, 'qwerks': 12871, 'saturday': 12872, 'letterbox': 12873, 'prints': 12874, 'scrolls': 12875, 'dwarfs': 12876, 'cream': 12877, 'htey': 12878, 'relacement': 12879, 'disproportionate': 12880, 'width': 12881, 'abysmal': 12882, 'yetfastestand': 12883, 'screenis': 12884, 'unimpressed': 12885, 'activites': 12886, 'aol': 12887, 'bob': 12888, 'jones': 12889, 'perfectsound': 12890, 'ittle': 12891, 'timeit': 12892, 'plants': 12893, 'zombies': 12894, 'enuf': 12895, 'sinks': 12896, 'spousal': 12897, 'phne': 12898, 'ph': 12899, 'ne': 12900, 'nikon': 12901, 'sigma': 12902, '600mm': 12903, 'dissecting': 12904, 'd810': 12905, 'territory': 12906, 'upholstery': 12907, 'stain': 12908, 'woken': 12909, 'announcement': 12910, 'marino': 12911, 'fishing': 12912, 'usd150': 12913, 'excess': 12914, 'boughy': 12915, 'alezxa': 12916, 'rechargable': 12917, 'joule': 12918, 'outdside': 12919, 'acoustics': 12920, 'horoscope': 12921, 'hut': 12922, 'weary': 12923, 'adoptor': 12924, 'usefule': 12925, 'guam': 12926, 'instructio': 12927, 'interconnected': 12928, 'circlers': 12929, 'dystopia': 12930, 'eggers': 12931, 'hail': 12932, 'mids': 12933, 'luke': 12934, 'grille': 12935, 'mesh': 12936, 'grilles': 12937, 'micrphones': 12938, 'flex': 12939, 'pits': 12940, 'satchel': 12941, 'andespn': 12942, 'avarage': 12943, 'uncanny': 12944, 'yearbook': 12945, 'printable': 12946, 'thunder': 12947, 'batterey': 12948, 'worldwide': 12949, 'eavsdropping': 12950, 'peak': 12951, 'cloth': 12952, 'likenit': 12953, 'forte': 12954, 'surreptitiously': 12955, 'portablebility': 12956, 'lithium': 12957, 'smartness': 12958, 'paradigm': 12959, 'greatdifficult': 12960, 'sseems': 12961, 'defeat': 12962, 'inventions': 12963, 'pf': 12964, 'uh': 12965, 'ohs': 12966, 'mariachi': 12967, 'colorado': 12968, 'kdis': 12969, 'ittt': 12970, 'renders': 12971, 'retrieves': 12972, 'handsyou': 12973, 'sweden': 12974, 'portugal': 12975, 'namesake': 12976, 'decks': 12977, 'claiming': 12978, 'ruining': 12979, 'ijnfo': 12980, '5star': 12981, 'pill': 12982, 'duly': 12983, 'huuuge': 12984, 'inna': 12985, 'summon': 12986, 'prouducts': 12987, 'conpack': 12988, 'tappy': 12989, 'wast': 12990, 'someways': 12991, 'michael': 12992, 'bolton': 12993, 'bruce': 12994, 'springsteen': 12995, 'predicted': 12996, 'unknowingly': 12997, 'independant': 12998, 'scratchy': 12999, 'commandable': 13000, 'coll': 13001, 'graduated': 13002, 'bemoaning': 13003, 'tin': 13004, 'balcony': 13005, 'audiophiles': 13006, 'fluke': 13007, 'covenient': 13008, 'cult': 13009, 'trashing': 13010, 'triby': 13011, 'partnerships': 13012, 'draugther': 13013, 'ganes': 13014, 'iceberg': 13015, 'ruled': 13016, 'cnet': 13017, 'frustrations': 13018, 'fot': 13019, 'heaper': 13020, 'repository': 13021, 'booming': 13022, 'bluetoothdecent': 13023, 'instructive': 13024, 'gatherings': 13025, 'siriamazon': 13026, 'alley': 13027, 'heres': 13028, 'muti': 13029, 'amazement': 13030, 'vis': 13031, 'commandscons': 13032, 'speakersoverall': 13033, 'steup': 13034, 'polish': 13035, 'username': 13036, 'riddance': 13037, 'intall': 13038, 'mny': 13039, '4000000': 13040, 'vueso': 13041, 'appscons': 13042, 'cinemax': 13043, 'marshmallow': 13044, 'centricplease': 13045, 'hd1': 13046, 'dynamite': 13047, 'expierence': 13048, 'chromescast': 13049, 'frist': 13050, 'scripts': 13051, 'temperamental': 13052, 'appli': 13053, 'progrant': 13054, 'reconment': 13055, 'grace': 13056, 'valueble': 13057, 'frend': 13058, 'broughts': 13059, 'smoothest': 13060, 'infomercials': 13061, 'hdhomerun': 13062, 'outlandish': 13063, 'supped': 13064, 'saids': 13065, 'producg': 13066, 'bitstream': 13067, 'atmos': 13068, 'prison': 13069, 'staggering': 13070, 'offsprings': 13071, 'syfy': 13072, 'bogging': 13073, 'races': 13074, 'unsubscribe': 13075, 'af': 13076, 'wouldve': 13077, 'attack': 13078, 'sorround': 13079, 'anotherone': 13080, 'becuse': 13081, 'brace': 13082, 'waist': 13083, 'tok': 13084, 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13139, 'suuuuuper': 13140, 'delve': 13141, 'quagmire': 13142, 'mousepad': 13143, 'exclusion': 13144, 'voiced': 13145, 'slgihtly': 13146, 'pdif': 13147, 'scard': 13148, 'constraints': 13149, 'upscaling': 13150, 'unsmart': 13151, 'planty': 13152, 'streamin': 13153, 'oomph': 13154, 'primer': 13155, 'diehard': 13156, 'cw': 13157, 'evrything': 13158, 'zap': 13159, 'programmings': 13160, 'netflixi': 13161, 'systym': 13162, 'alexander': 13163, 'workshould': 13164, 'firend': 13165, 'thrust': 13166, 'passive': 13167, '60in': 13168, 'hardline': 13169, 'prominently': 13170, 'nut': 13171, 'heh': 13172, 'outputs': 13173, 'duper': 13174, 'perfecto': 13175, 'versility': 13176, '540': 13177, 'subcriptions': 13178, 'jist': 13179, 'unbelievible': 13180, 'freatures': 13181, 'regulat': 13182, 'footbal': 13183, 'lockups': 13184, 'mainstay': 13185, 'taped': 13186, 'reinvented': 13187, 'willy': 13188, 'gen2': 13189, 'chocked': 13190, 'gigantic': 13191, 'osmo': 13192, 'releived': 13193, 'cleaned': 13194, 'poweruser': 13195, '4000tv': 13196, 'bugged': 13197, 'regreted': 13198, 'appel': 13199, 'eh': 13200, 'walahhh': 13201, 'nerves': 13202, 'tom': 13203, 'centers': 13204, 'film': 13205, 'emphasizes': 13206, 'tihs': 13207, 'cordcutting': 13208, 'clone': 13209, 'esasy': 13210, 'amonth': 13211, '1500': 13212, 'redirected': 13213, '12mpb': 13214, 'okk': 13215, '1958': 13216, 'extraordinarily': 13217, 'cabls': 13218, 'greatproduct': 13219, 'halpy': 13220, 'experice': 13221, 'sandwich': 13222, 'daughterthere': 13223, 'peck': 13224, 'iton': 13225, 'phasing': 13226, 'lease': 13227, 'goft': 13228, 'moerits': 13229, 'choked': 13230, 'hick': 13231, 'netfliz': 13232, 'serach': 13233, 'mpbs': 13234, 'tamed': 13235, 'andmy': 13236, 'stall': 13237, 'mitsubishi': 13238, 'minimalist': 13239, 'ungraded': 13240, 'wastes': 13241, 'proportioned': 13242, 'cellophane': 13243, 'hogjly': 13244, 'plaform': 13245, 'monstrosties': 13246, 'storms': 13247, 'outletsavailable': 13248, 'cosmetically': 13249, 'fullfill': 13250, 'comparability': 13251, 'versital': 13252, 'resellers': 13253, 'tapered': 13254, 'somuch': 13255, 'intenet': 13256, 'x6': 13257, 'grail': 13258, 'mounting': 13259, 'fairness': 13260, 'insall': 13261, 'tickets': 13262, 'u450': 13263, 'di': 13264, 'criends': 13265, 'irrelevant': 13266, 'ni': 13267, 'sizzled': 13268, 'compability': 13269, 'whatsover': 13270, 'modifiable': 13271, 'trump': 13272, 'amateur': 13273, 'feud': 13274, 'firestck': 13275, 'foxfire': 13276, 'percentages': 13277, 'recommened': 13278, 'recient': 13279, 'f8nd': 13280, 'tradional': 13281, 'firestorm': 13282, 'absoutley': 13283, 'lana': 13284, 'cliche': 13285, 'cery': 13286, 'corresponds': 13287, 'increment': 13288, '64x': 13289, 'friendlyi': 13290, 'solidified': 13291, 'usenet': 13292, 'kody': 13293, 'laods': 13294, 'quic': 13295, 'screw': 13296, 'havint': 13297, 'sean': 13298, 'tveasy': 13299, 'strraming': 13300, 'implementations': 13301, 'refreshed': 13302, 'replugging': 13303, 'decreases': 13304, 'becaus': 13305, 'mobdro': 13306, 'everythings': 13307, 'dtn': 13308, 'hull': 13309, 'penthough': 13310, 'hustlers': 13311, '15mbps': 13312, 'labels': 13313, 'jusy': 13314, 'pucture': 13315, 'reproduce': 13316, 'strings': 13317, 'hahaha': 13318, 'boxwish': 13319, 'betterspeed': 13320, 'pirates': 13321, 'dvi': 13322, 'amazonproducts': 13323, 'facilitate': 13324, 'alexathe': 13325, 'rural': 13326, 'fob': 13327, '0ghz': 13328, 'idk': 13329, 'alon': 13330, 'sasy': 13331, 'p65': 13332, 'c1': 13333, 'interms': 13334, 'praises': 13335, 'nividi': 13336, 'hdd': 13337, 'enforcing': 13338, 'fwd': 13339, 'vincent': 13340, 'cody': 13341, 'hallmark': 13342, 'keboard': 13343, 'xtra': 13344, 'subscripton': 13345, 'storageworth': 13346, 'disadvantages': 13347, 'steamer': 13348, 'joystick': 13349, 'gentleman': 13350, 'wan': 13351, 'weaker': 13352, 'memberkodi': 13353, 'retiree': 13354, 'incorporates': 13355, 'mist': 13356, 'pluged': 13357, 'patch': 13358, 'flatscreen': 13359, 'hq': 13360, 'nexflix': 13361, 'evil': 13362, 'nefarious': 13363, 'empire': 13364, 'subjected': 13365, 'fret': 13366, 'exorbitant': 13367, 'contracts': 13368, 'giants': 13369, 'unecessary': 13370, 'navagating': 13371, 'ells': 13372, 'indefinitely': 13373, 'insanly': 13374, 'eith': 13375, 'sixty': 13376, 'diligence': 13377, 'entirety': 13378, 'loophole': 13379, 'chunks': 13380, 'mindlessly': 13381, 'liberating': 13382, 'mackbook': 13383, 'airs': 13384, 'tvsi': 13385, 'modding': 13386, 'sexy': 13387, 'nitpick': 13388, 'preferable': 13389, 'wookie': 13390, 'beausbuild': 13391, 'okc': 13392, 'saud': 13393, 'firesticktv': 13394, '50mbps': 13395, 'infrequent': 13396, 'transform': 13397, 'sofar': 13398, 'turkey': 13399, 'accommodates': 13400, 'use6': 13401, 'graphics7': 13402, 'dislikes': 13403, 'provision': 13404, '2x': 13405, 'boxee': 13406, 'onable': 13407, 'collects': 13408, 'controler': 13409, 'domestic': 13410, 'ch': 13411, 'b4': 13412, 'firstly': 13413, 'dived': 13414, 'heartedly': 13415, 'aesthetically': 13416, 'appreciating': 13417, 'inabled': 13418, 'preconfigured': 13419, 'xiaomi': 13420, 'cheaping': 13421, 'crazed': 13422, 'videophile': 13423, 'overpaying': 13424, 'abort': 13425, 'kodiak': 13426, 'dreaming': 13427, 'korea': 13428, '100mbps': 13429, 'hungry': 13430, 'saturated': 13431, 'ween': 13432, 'acquaintances': 13433, 'ooma': 13434, 'legit': 13435, 'qwerty': 13436, 'mlbtv': 13437, 'cuddling': 13438, 'fred': 13439, 'bracket': 13440, 'advirtized': 13441, 'ithighly': 13442, 'alexus': 13443, 'weve': 13444, 'locals': 13445, 'slanted': 13446, 'entitled': 13447, 'organizes': 13448, 'tubes': 13449, 'privilege': 13450, 'sheild': 13451, 'turbomode': 13452, 'hologram': 13453, 'amaxom': 13454, 'snow': 13455, 'acceleration': 13456, 'h264': 13457, 'sideloads': 13458, 'microsdhc': 13459, 'dismal': 13460, 'customizeable': 13461, 'portland': 13462, 'phoenix': 13463, 'summarizes': 13464, '10it': 13465, 'ridding': 13466, 'rj': 13467, 'myrtle': 13468, 'downsizing': 13469, 'flexibilitymore': 13470, 'greenlight': 13471, 'buzz': 13472, 'onced': 13473, 'rattles': 13474, 'neflx': 13475, 'recover': 13476, 'onward': 13477, 'awesomeur': 13478, 'whever': 13479, 'boxing': 13480, 'snot': 13481, 'lookout': 13482, '30hz': 13483, 'urself': 13484, 'coast': 13485, 'puerto': 13486, 'rico': 13487, 'bil': 13488, 'avenues': 13489, 'mange': 13490, 'mot': 13491, 'ventures': 13492, 'reagy': 13493, 'oversees': 13494, 'fxnow': 13495, '209': 13496, 'androidtv': 13497, 'framework': 13498, 'crossy': 13499, 'divine': 13500, 'flowing': 13501, 'superfast': 13502, 'sameproblems': 13503, 'goldengate': 13504, 'sybase': 13505, 'ik': 13506, 'cabable': 13507, 'nefflix': 13508, 'chucks': 13509, 'msnufactures': 13510, 'acquisition': 13511, 'ina': 13512, 'fmily': 13513, 'discontinuing': 13514, 'prevented': 13515, 'stud': 13516, 'thatn': 13517, 'indirect': 13518, 'tivi': 13519, 'poeerfyul': 13520, 'fastet': 13521, 'servise': 13522, 'waranty': 13523, 'racked': 13524, 'passthough': 13525, 'iq': 13526, 'frustrate': 13527, 'opitions': 13528, 'avon': 13529, 'indiana': 13530, 'ted': 13531, 'deprogrammed': 13532, 'preachers': 13533, 'broadcasters': 13534, 'sometimesnot': 13535, 'rization': 13536, 'entratainment': 13537, 'micsosd': 13538, 'prodigious': 13539, 'beatprice': 13540, 'valuehighly': 13541, 'neigbor': 13542, 'processed': 13543, 'bridged': 13544, 'weakens': 13545, 'subtitles': 13546, '100m': 13547, 'finnicky': 13548, 'tended': 13549, 'transforming': 13550, 'administrator': 13551, 'yankees': 13552, '650': 13553, 'andcis': 13554, 'acorn': 13555, 'british': 13556, 'productions': 13557, 'tunable': 13558, 'immediatel': 13559, 'llocation': 13560, 'tetevision': 13561, 'ownpro': 13562, 'setupapp': 13563, 'choicesability': 13564, 'appsalexa': 13565, 'integrationcon': 13566, 'roku3': 13567, 'distinguishing': 13568, 'le': 13569, 'yoi': 13570, 'lmovies': 13571, 'cutomize': 13572, 'clams': 13573, 'labor': 13574, 'itune': 13575, '25mbps': 13576, 'advises': 13577, 'eligible': 13578, 'cleanest': 13579, 'svc': 13580, 'layers': 13581, 'authentication': 13582, 'grea': 13583, 'defusing': 13584, 'jn': 13585, 'audios': 13586, 'apocalypse': 13587, 'commenting': 13588, 'goood': 13589, 'dlna': 13590, 'crashing': 13591, 'spreed': 13592, 'dualcore': 13593, '1299': 13594, '1275': 13595, 'havei': 13596, 'hulk': 13597, 'thear': 13598, 'optio': 13599, 'onetime': 13600, 'usking': 13601, 'visio': 13602, 'profound': 13603, '80mps': 13604, 'royalist': 13605, 'centerpoint': 13606, 'pusrchases': 13607, 'kiddish': 13608, 'tomany': 13609, 'heated': 13610, 'han': 13611, 'otis': 13612, 'mend': 13613, 'vising': 13614, 'interestingly': 13615, 'libray': 13616, 'regrettable': 13617, 'attended': 13618, 'cursor': 13619, 'eathernet': 13620, 'abandoning': 13621, 'againhave': 13622, 'concil': 13623, 'fellow': 13624, 'bananas': 13625, 'pointer': 13626, 'systemes': 13627, 'replacedvoice': 13628, 'madeno': 13629, 'featurestill': 13630, 'telly': 13631, 'capped': 13632, 'peripheral': 13633, 'aethetic': 13634, 'bouth': 13635, 'thn': 13636, 'stampede': 13637, 'ott': 13638, 'adfter': 13639, 'duplicates': 13640, 'aweomse': 13641, 'throttling': 13642, 'amazn': 13643, 'allowes': 13644, 'everyhting': 13645, 'allied': 13646, 'magnolia': 13647, 'standardize': 13648, 'chews': 13649, 'soccer': 13650, 'lacrosse': 13651, '3mb': 13652, '720': 13653, 'tranfer': 13654, 'ruko': 13655, 'multimedia': 13656, 'woujld': 13657, 'perferomance': 13658, 'wor': 13659, 'anniversaries': 13660, 'blaze': 13661, 'occurrence': 13662, 'ihave': 13663, 'goodi': 13664, 'affecting': 13665, 'downcovert': 13666, 'legacy': 13667, 'perpose': 13668, 'strean': 13669, 'ming': 13670, 'brighthough': 13671, 'frieds': 13672, 'device4000': 13673, 'nicegreat': 13674, 'supportnegative': 13675, 'cach': 13676, 'tgey': 13677, 'mantle': 13678, 'remort': 13679, 'controll': 13680, 'retuned': 13681, 'forgone': 13682, 'vc': 13683, 'thirt': 13684, 'stady': 13685, 'tales': 13686, 'borderlands': 13687, 'combat': 13688, 'knights': 13689, 'republic': 13690, 'qlty': 13691, 'docsis': 13692, 'gbox': 13693, 'wd': 13694, 'immovable': 13695, 'scaring': 13696, 'spotted': 13697, 'glimpse': 13698, 'spinning': 13699, 'blood': 13700, 'rises': 13701, 'minority': 13702, 'yanked': 13703, 'acquired': 13704, 'uhdtv': 13705, 'fulfiled': 13706, 'forefront': 13707, 'thief': 13708, 'mercy': 13709, 'infra': 13710, 'remot': 13711, 'ud': 13712, 'sourced': 13713, 'preditable': 13714, 'vig': 13715, 'seeems': 13716, 'overview': 13717, 'livetv': 13718, 'discplayer': 13719, 'aliza': 13720, 'ignorance': 13721, 'dimension': 13722, 'forgiving': 13723, 'biggy': 13724, '3mbps': 13725, 'treating': 13726, 'fite': 13727, 'assignment': 13728, 'yamaha': 13729, 'routers': 13730, 'setuphttps': 13731, 'github': 13732, 'sphinx02': 13733, '28only': 13734, 'bes': 13735, 'grunt': 13736, 'afforable': 13737, 'unplayable': 13738, 'reciprocate': 13739, 'karaoke': 13740, 'sock': 13741, 'hest': 13742, 'generator': 13743, 'circles': 13744, 'modded': 13745, 'recordings': 13746, 'bolt': 13747, 'tweets': 13748, 'productthe': 13749, 'buyback': 13750, 'erthernet': 13751, 'arte': 13752, 'goodwork': 13753, 'transcoded': 13754, 'cinch': 13755, 'rediculously': 13756, 'zipper': 13757, 'charities': 13758, '108': 13759, 'jailbreaker': 13760, 'jb': 13761, 'amazonfire': 13762, 'lockup': 13763, '40x': 13764, 'nationwide': 13765, 'hdhomerrun': 13766, 'demos': 13767, 'mxiii': 13768, 'ouya': 13769, 'automaticly': 13770, 'tittle': 13771, 'tvos': 13772, 'strem': 13773, 'agreed': 13774, 'sticksbut': 13775, 'suppirior': 13776, 'compitetive': 13777, 'thinj': 13778, 'primeits': 13779, 'tick': 13780, 'crispness': 13781, 'farm': 13782, 'clearstream': 13783, 'sjdhfhdhhsjx': 13784, 'flies': 13785, 'beep': 13786, 'drama': 13787, 'fever': 13788, 'proofed': 13789, 'documentaries': 13790, 'cycling': 13791, 'endorsement': 13792, 'unexceptionable': 13793, 'ypu': 13794, 'newbies': 13795, 'mediacom': 13796, 'accessable': 13797, 'cinema': 13798, 'nutt': 13799, 'minuses': 13800, 'exhibits': 13801, 'fluent': 13802, 'iream': 13803, 'extenders': 13804, 'convienece': 13805, 'wel': 13806, 'prepay': 13807, 'sweetest': 13808, 'zelda': 13809, 'chanels': 13810, 'perpetual': 13811, 'recoverable': 13812, 'soured': 13813, 'additon': 13814, 'canle': 13815, 'vueing': 13816, 'exelente': 13817, 'smarttvs': 13818, 'stucked': 13819, 'commend': 13820, 'jitter': 13821, 'caption': 13822, 'moded': 13823, 'tvits': 13824, 'impose': 13825, '65mbps': 13826, '264': 13827, 'hi10p': 13828, 'decoded': 13829, 'coded': 13830, 'bitrate': 13831, 'cpus': 13832, 'spike': 13833, '44': 13834, 'agnostic': 13835, 'presented': 13836, 'disappointedpros': 13837, 'slotcons': 13838, 'maximal': 13839, 'posible': 13840, 'firetv1': 13841, 'firetv2': 13842, 'serials': 13843, 'additive': 13844, 'sucker': 13845, '2also': 13846, 'aint': 13847, 'ht': 13848, 'boingo': 13849, 'girts': 13850, 'bufferinggreat': 13851, 'qualityendless': 13852, 'programmingif': 13853, 'appl': 13854, 'kart': 13855, 'donkey': 13856, 'kong': 13857, 'centierpede': 13858, 'onscreen': 13859, 'inputing': 13860, 'dfficult': 13861, 'idevice': 13862, 'pbskids': 13863, '182': 13864, 'offload': 13865, 'sreaming': 13866, 'gaget': 13867, 'reinsert': 13868, 'emitting': 13869, 'diode': 13870, 'shotty': 13871, 'noone': 13872, 'programmes': 13873, 'thang': 13874, 'astetically': 13875, 'boa': 13876, 'occasionly': 13877, 'smarttv': 13878, 'mos': 13879, 'timewarner': 13880, 'alllow': 13881, 'appletvs': 13882, 'permeated': 13883, 'rectify': 13884, 'mystical': 13885, 'lamented': 13886, 'unskilled': 13887, 'wayyy': 13888, 'jokingly': 13889, 'proceeded': 13890, 'forgo': 13891, 'cablecutters': 13892, 'mopping': 13893, 'cinderella': 13894, 'mei': 13895, 'demographic': 13896, 'thirties': 13897, 'partying': 13898, 'suburban': 13899, 'hdml': 13900, 'instants': 13901, 'scarce': 13902, 'flyovers': 13903, 'dx2': 13904, 'graphite': 13905, 'fidgety': 13906, 'accidentlly': 13907, 'cased': 13908, 'summing': 13909, 'comfortthis': 13910, 'unfolded': 13911, 'timethe': 13912, 'neglegible': 13913, 'has5': 13914, 'indicates': 13915, 'unintentionally': 13916, 'problemcon': 13917, 'overstock': 13918, 'itself2': 13919, 'untouched': 13920, 'firt': 13921, 'lend': 13922, 'handsome': 13923, 'tug': 13924, 'bubbled': 13925, 'moisture': 13926, 'framewhere': 13927, 'problembut': 13928, 'uncluttered': 13929, '9in': 13930, 'seperately': 13931, 'hdxs': 13932, 'wattage': 13933, 'cheapens': 13934, 'erodes': 13935, 'mk': 13936, 'descriptions': 13937, 'appreciably': 13938, 'adjacent': 13939, 'chargerit': 13940, 'cheapness': 13941, 'failerd': 13942, 'subsequent': 13943, 'disposed': 13944, 'sprint': 13945, 'htc': 13946, 'anthing': 13947, 'greedy': 13948}\n"
     ]
    }
   ],
   "source": [
    "vocab_size = 4648\n",
    "\n",
    "tokenizer = Tokenizer(vocab_size, filters='!\"#$%&()*+,-./:;<=>?@[\\]^_`{\"}~\\t\\n',lower=True, split=' ')\n",
    "tokenizer.fit_on_texts(X)\n",
    "word_index = tokenizer.word_index\n",
    "print(len(word_index))\n",
    "print(tokenizer.word_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('the', 43955), ('it', 34950), ('i', 34277), ('and', 33768), ('to', 33658), ('for', 26324), ('a', 24883), ('is', 20768), ('this', 17663), ('my', 17301), ('great', 11858), ('of', 11026), ('with', 9470), ('tablet', 9084), ('you', 8518), ('on', 8386), ('have', 8129), ('that', 7818), ('use', 7685), ('not', 7243), ('in', 7144), ('but', 6815), ('was', 6737), ('love', 6632), ('can', 6545), ('as', 6392), ('easy', 6209), ('so', 6098), ('s', 5954), ('amazon', 5874), ('very', 5750), ('kindle', 5311), ('t', 5302), ('good', 5082), ('bought', 5059), ('one', 4994), ('fire', 4826), ('she', 4237), ('price', 4197), ('all', 4165), ('has', 3998), ('are', 3839), ('like', 3819), ('we', 3712), ('an', 3648), ('product', 3583), ('up', 3552), ('be', 3424), ('more', 3388), ('tv', 3311), ('would', 3218), ('works', 3108), ('if', 3095), ('or', 3090), ('at', 3075), ('just', 3055), ('get', 2990), ('they', 2984), ('echo', 2945), ('had', 2927), ('much', 2784), ('music', 2782), ('read', 2756), ('alexa', 2724), ('kids', 2697), ('loves', 2633), ('from', 2619), ('apps', 2612), ('do', 2610), ('than', 2599), ('well', 2587), ('books', 2534), ('reading', 2506), ('device', 2444), ('when', 2415), ('best', 2408), ('really', 2389), ('me', 2370), ('buy', 2334), ('what', 2315), ('games', 2278), ('time', 2261), ('her', 2248), ('old', 2242), ('screen', 2228), ('purchased', 2220), ('also', 2209), ('only', 2188), ('no', 2173), ('he', 2140), ('play', 2139), ('your', 2073), ('got', 2064), ('gift', 2030), ('better', 2022), ('does', 1984), ('will', 1982), ('recommend', 1982), ('them', 1840), ('out', 1829), ('other', 1803), ('set', 1759), ('am', 1651), ('year', 1646), ('perfect', 1629), ('our', 1623), ('nice', 1606), ('new', 1588), ('home', 1566), ('about', 1566), ('little', 1552), ('there', 1540), ('now', 1513), ('light', 1496), ('its', 1471), ('because', 1459), ('even', 1453), ('don', 1449), ('quality', 1447), ('purchase', 1400), ('using', 1383), ('prime', 1381), ('though', 1377), ('need', 1368), ('did', 1355), ('lot', 1347), ('m', 1343), ('christmas', 1336), ('some', 1329), ('battery', 1313), ('how', 1305), ('been', 1286), ('too', 1270), ('size', 1266), ('any', 1266), ('able', 1260), ('movies', 1259), ('want', 1253), ('which', 1227), ('first', 1218), ('everything', 1191), ('happy', 1164), ('many', 1164), ('son', 1162), ('work', 1157), ('sound', 1146), ('still', 1133), ('daughter', 1126), ('go', 1124), ('things', 1123), ('used', 1119), ('app', 1115), ('fun', 1114), ('reader', 1104), ('these', 1098), ('watch', 1094), ('fast', 1089), ('could', 1089), ('far', 1021), ('awesome', 1021), ('ipad', 1013), ('tablets', 1002), ('2', 994), ('after', 984), ('who', 965), ('features', 961), ('by', 957), ('ve', 938), ('excellent', 933), ('family', 925), ('money', 920), ('speaker', 920), ('life', 918), ('ut', 914), ('paperwhite', 914), ('cable', 914), ('withough', 912), ('free', 912), ('stick', 900), ('wife', 899), ('voice', 896), ('worth', 892), ('day', 888), ('streaming', 887), ('box', 879), ('while', 865), ('having', 862), ('store', 860), ('small', 860), ('thing', 847), ('way', 845), ('enjoy', 838), ('amazing', 827), ('wanted', 824), ('then', 820), ('internet', 820), ('getting', 813), ('google', 811), ('looking', 810), ('item', 804), ('every', 801), ('smart', 792), ('over', 787), ('back', 779), ('weather', 776), ('since', 773), ('long', 769), ('makes', 767), ('book', 766), ('ask', 765), ('doesn', 760), ('download', 756), ('his', 754), ('two', 751), ('off', 751), ('playing', 743), ('3', 743), ('phone', 738), ('take', 733), ('control', 715), ('e', 713), ('another', 707), ('highly', 705), ('most', 698), ('their', 688), ('questions', 688), ('access', 686), ('something', 681), ('house', 679), ('pretty', 674), ('around', 674), ('netflix', 674), ('enough', 673), ('find', 673), ('being', 671), ('right', 670), ('simple', 666), ('make', 664), ('definitely', 650), ('user', 645), ('ugh', 644), ('devices', 635), ('say', 633), ('overall', 627), ('5', 625), ('etc', 624), ('always', 623), ('lights', 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('integrationcon', 1), ('roku3', 1), ('distinguishing', 1), ('le', 1), ('yoi', 1), ('lmovies', 1), ('cutomize', 1), ('clams', 1), ('labor', 1), ('itune', 1), ('25mbps', 1), ('advises', 1), ('eligible', 1), ('cleanest', 1), ('svc', 1), ('layers', 1), ('authentication', 1), ('grea', 1), ('defusing', 1), ('jn', 1), ('audios', 1), ('apocalypse', 1), ('commenting', 1), ('goood', 1), ('dlna', 1), ('crashing', 1), ('spreed', 1), ('dualcore', 1), ('1299', 1), ('1275', 1), ('havei', 1), ('hulk', 1), ('thear', 1), ('optio', 1), ('onetime', 1), ('usking', 1), ('visio', 1), ('profound', 1), ('80mps', 1), ('royalist', 1), ('centerpoint', 1), ('pusrchases', 1), ('kiddish', 1), ('tomany', 1), ('heated', 1), ('han', 1), ('otis', 1), ('mend', 1), ('vising', 1), ('interestingly', 1), ('libray', 1), ('regrettable', 1), ('attended', 1), ('cursor', 1), ('eathernet', 1), ('abandoning', 1), ('againhave', 1), ('concil', 1), ('fellow', 1), ('bananas', 1), ('pointer', 1), ('systemes', 1), ('replacedvoice', 1), ('madeno', 1), ('featurestill', 1), ('telly', 1), ('capped', 1), ('peripheral', 1), ('aethetic', 1), ('bouth', 1), ('thn', 1), ('stampede', 1), ('ott', 1), ('adfter', 1), ('duplicates', 1), ('aweomse', 1), ('throttling', 1), ('amazn', 1), ('allowes', 1), ('everyhting', 1), ('allied', 1), ('magnolia', 1), ('standardize', 1), ('chews', 1), ('soccer', 1), ('lacrosse', 1), ('3mb', 1), ('720', 1), ('tranfer', 1), ('ruko', 1), ('multimedia', 1), ('woujld', 1), ('perferomance', 1), ('wor', 1), ('anniversaries', 1), ('blaze', 1), ('occurrence', 1), ('ihave', 1), ('goodi', 1), ('affecting', 1), ('downcovert', 1), ('legacy', 1), ('perpose', 1), ('strean', 1), ('ming', 1), ('brighthough', 1), ('frieds', 1), ('device4000', 1), ('nicegreat', 1), ('supportnegative', 1), ('cach', 1), ('tgey', 1), ('mantle', 1), ('remort', 1), ('controll', 1), ('retuned', 1), ('forgone', 1), ('vc', 1), ('thirt', 1), ('stady', 1), ('tales', 1), ('borderlands', 1), ('combat', 1), ('knights', 1), ('republic', 1), ('qlty', 1), ('docsis', 1), ('gbox', 1), ('wd', 1), ('immovable', 1), ('scaring', 1), ('spotted', 1), ('glimpse', 1), ('spinning', 1), ('blood', 1), ('rises', 1), ('minority', 1), ('yanked', 1), ('acquired', 1), ('uhdtv', 1), ('fulfiled', 1), ('forefront', 1), ('thief', 1), ('mercy', 1), ('infra', 1), ('remot', 1), ('ud', 1), ('sourced', 1), ('preditable', 1), ('vig', 1), ('seeems', 1), ('overview', 1), ('livetv', 1), ('discplayer', 1), ('aliza', 1), ('ignorance', 1), ('dimension', 1), ('forgiving', 1), ('biggy', 1), ('3mbps', 1), ('treating', 1), ('fite', 1), ('assignment', 1), ('yamaha', 1), ('routers', 1), ('setuphttps', 1), ('github', 1), ('sphinx02', 1), ('28only', 1), ('bes', 1), ('grunt', 1), ('afforable', 1), ('unplayable', 1), ('reciprocate', 1), ('karaoke', 1), ('sock', 1), ('hest', 1), ('generator', 1), ('circles', 1), ('modded', 1), ('recordings', 1), ('bolt', 1), ('tweets', 1), ('productthe', 1), ('buyback', 1), ('erthernet', 1), ('arte', 1), ('goodwork', 1), ('transcoded', 1), ('cinch', 1), ('rediculously', 1), ('zipper', 1), ('charities', 1), ('108', 1), ('jailbreaker', 1), ('jb', 1), ('amazonfire', 1), ('lockup', 1), ('40x', 1), ('nationwide', 1), ('hdhomerrun', 1), ('demos', 1), ('mxiii', 1), ('ouya', 1), ('automaticly', 1), ('tittle', 1), ('tvos', 1), ('strem', 1), ('agreed', 1), ('sticksbut', 1), ('suppirior', 1), ('compitetive', 1), ('thinj', 1), ('primeits', 1), ('tick', 1), ('crispness', 1), ('farm', 1), ('clearstream', 1), ('sjdhfhdhhsjx', 1), ('flies', 1), ('beep', 1), ('drama', 1), ('fever', 1), ('proofed', 1), ('documentaries', 1), ('cycling', 1), ('endorsement', 1), ('unexceptionable', 1), ('ypu', 1), ('newbies', 1), ('mediacom', 1), ('accessable', 1), ('cinema', 1), ('nutt', 1), ('minuses', 1), ('exhibits', 1), ('fluent', 1), ('iream', 1), ('extenders', 1), ('convienece', 1), ('wel', 1), ('prepay', 1), ('sweetest', 1), ('zelda', 1), ('chanels', 1), ('perpetual', 1), ('recoverable', 1), ('soured', 1), ('additon', 1), ('canle', 1), ('vueing', 1), ('exelente', 1), ('smarttvs', 1), ('stucked', 1), ('commend', 1), ('jitter', 1), ('caption', 1), ('moded', 1), ('tvits', 1), ('impose', 1), ('65mbps', 1), ('264', 1), ('hi10p', 1), ('decoded', 1), ('coded', 1), ('bitrate', 1), ('cpus', 1), ('spike', 1), ('44', 1), ('agnostic', 1), ('presented', 1), ('disappointedpros', 1), ('slotcons', 1), ('maximal', 1), ('posible', 1), ('firetv1', 1), ('firetv2', 1), ('serials', 1), ('additive', 1), ('sucker', 1), ('2also', 1), ('aint', 1), ('ht', 1), ('boingo', 1), ('girts', 1), ('bufferinggreat', 1), ('qualityendless', 1), ('programmingif', 1), ('appl', 1), ('kart', 1), ('donkey', 1), ('kong', 1), ('centierpede', 1), ('onscreen', 1), ('inputing', 1), ('dfficult', 1), ('idevice', 1), ('pbskids', 1), ('182', 1), ('offload', 1), ('sreaming', 1), ('gaget', 1), ('reinsert', 1), ('emitting', 1), ('diode', 1), ('shotty', 1), ('noone', 1), ('programmes', 1), ('thang', 1), ('astetically', 1), ('boa', 1), ('occasionly', 1), ('smarttv', 1), ('mos', 1), ('timewarner', 1), ('alllow', 1), ('appletvs', 1), ('permeated', 1), ('rectify', 1), ('mystical', 1), ('lamented', 1), ('unskilled', 1), ('wayyy', 1), ('jokingly', 1), ('proceeded', 1), ('forgo', 1), ('cablecutters', 1), ('mopping', 1), ('cinderella', 1), ('mei', 1), ('demographic', 1), ('thirties', 1), ('partying', 1), ('suburban', 1), ('hdml', 1), ('instants', 1), ('scarce', 1), ('flyovers', 1), ('dx2', 1), ('graphite', 1), ('fidgety', 1), ('accidentlly', 1), ('cased', 1), ('summing', 1), ('comfortthis', 1), ('unfolded', 1), ('timethe', 1), ('neglegible', 1), ('has5', 1), ('indicates', 1), ('unintentionally', 1), ('problemcon', 1), ('overstock', 1), ('itself2', 1), ('untouched', 1), ('firt', 1), ('lend', 1), ('handsome', 1), ('tug', 1), ('bubbled', 1), ('moisture', 1), ('framewhere', 1), ('problembut', 1), ('uncluttered', 1), ('9in', 1), ('seperately', 1), ('hdxs', 1), ('wattage', 1), ('cheapens', 1), ('erodes', 1), ('mk', 1), ('descriptions', 1), ('appreciably', 1), ('adjacent', 1), ('chargerit', 1), ('cheapness', 1), ('failerd', 1), ('subsequent', 1), ('disposed', 1), ('sprint', 1), ('htc', 1), ('anthing', 1), ('greedy', 1)]\n"
     ]
    }
   ],
   "source": [
    "print(sorted(tokenizer.word_counts.items(), key=lambda x: x[1], reverse=True))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedd Words in Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# How should we choose vector length\n",
    "\n",
    "embeddings = np.zeros((vocab_size+1, 50))\n",
    "for word, i in word_index.items():\n",
    "    if i <= vocab_size:\n",
    "        vector = embeddings_index.get(word)\n",
    "        print(i)\n",
    "        if i > vocab_size-5: print(word, i)\n",
    "        if vector is not None:\n",
    "            # words not found in embedding index will be all-zeros.\n",
    "            embeddings[i] = vector\n",
    "        else: embeddings[i] = np.random.rand(50) - 0.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4649, 50)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting Review words to Numerical Array and Padding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_length =120\n",
    "X_Train = pad_sequences(tokenizer.texts_to_sequences(X_Train), maxlen=max_length, padding='post')\n",
    "X_test = pad_sequences(tokenizer.texts_to_sequences(Xtest), maxlen=max_length, padding='post')\n",
    "#X_val = pad_sequences(tokenizer.texts_to_sequences(Xval), maxlen=max_length, padding='post')\n",
    "X_train = pad_sequences(tokenizer.texts_to_sequences(X_train), maxlen=max_length, padding='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69123, 120)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69123,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_Train.shape"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([1, 2, 3, 4, 5], dtype=int64), array([  41,   40,  150,  854, 2378], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "#print(np.unique(y_val, return_counts=True))\n",
    "print(np.unique(y_test, return_counts=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 120, 50)           232450    \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 64)                22080     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 25)                1625      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 25)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 6)                 156       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 6)                 0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 7         \n",
      "=================================================================\n",
      "Total params: 256,318\n",
      "Trainable params: 256,318\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(vocab_size+1, 50, input_length=max_length, weights=[embeddings], trainable=True))\n",
    "\n",
    "model.add(GRU(64, activation='relu'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(25, activation='relu'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(6, activation='relu'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(1))\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import numpy as np\n",
    "import keras.backend as K\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "from keras.backend.tensorflow_backend import clear_session\n",
    "from keras.backend.tensorflow_backend import get_session\n",
    "from keras.callbacks import Callback \n",
    "\n",
    "class lvStop(Callback): \n",
    "    def __init__(self, monitor='val_loss', value=0.3, verbose=0): \n",
    "        super(Callback, self).__init__() \n",
    "        self.monitor = monitor \n",
    "        self.value = value \n",
    "        self.verbose = verbose \n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs={}): \n",
    "        current = logs.get(self.monitor) \n",
    "        if current is None: \n",
    "            warnings.warn(\"Early stopping requires %s available!\" % self.monitor, RuntimeWarning) \n",
    "            \n",
    "        if current < self.value: \n",
    "            if self.verbose > 0: \n",
    "                print(\"Epoch %05d: early stopping THR\" % epoch) \n",
    "            self.model.stop_training = True\n",
    "            \n",
    "class ltStop(Callback): \n",
    "    def __init__(self, monitor='loss', value=0.3, verbose=0): \n",
    "        super(Callback, self).__init__() \n",
    "        self.monitor = monitor \n",
    "        self.value = value \n",
    "        self.verbose = verbose \n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs={}): \n",
    "        current = logs.get(self.monitor) \n",
    "        if current is None: \n",
    "             warnings.warn(\"Early stopping requires %s available!\" % self.monitor, RuntimeWarning) \n",
    "            \n",
    "        if current < self.value: \n",
    "            if self.verbose > 0: \n",
    "                print(\"Epoch %05d: early stopping THR\" % epoch) \n",
    "            self.model.stop_training = True\n",
    "             \n",
    "                \n",
    "cb = [lvStop(value=0.5)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 120, 50)           232450    \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 64)                22080     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 25)                1625      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 25)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 6)                 156       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 6)                 0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 7         \n",
      "=================================================================\n",
      "Total params: 256,318\n",
      "Trainable params: 256,318\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, decay=1e-8) \n",
    "\n",
    "model.compile(loss=loss, optimizer=opt)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69123,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_Train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69123, 120)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31163, 120)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31163,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 69123 samples, validate on 31163 samples\n",
      "Epoch 1/150\n",
      "69123/69123 [==============================] - 118s 2ms/step - loss: 6.2326 - val_loss: 2.5875\n",
      "Epoch 2/150\n",
      "69123/69123 [==============================] - 113s 2ms/step - loss: 1.7857 - val_loss: 2.0532\n",
      "Epoch 3/150\n",
      "69123/69123 [==============================] - 115s 2ms/step - loss: 1.3760 - val_loss: 1.6430\n",
      "Epoch 4/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 1.1890 - val_loss: 1.4566\n",
      "Epoch 5/150\n",
      "69123/69123 [==============================] - 120s 2ms/step - loss: 1.0314 - val_loss: 1.1679\n",
      "Epoch 6/150\n",
      "69123/69123 [==============================] - 108s 2ms/step - loss: 0.9604 - val_loss: 1.0885\n",
      "Epoch 7/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.8650 - val_loss: 0.9749\n",
      "Epoch 8/150\n",
      "69123/69123 [==============================] - 106s 2ms/step - loss: 0.8161 - val_loss: 0.9261\n",
      "Epoch 9/150\n",
      "69123/69123 [==============================] - 111s 2ms/step - loss: 0.7843 - val_loss: 0.8187\n",
      "Epoch 10/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 0.7495 - val_loss: 0.8914\n",
      "Epoch 11/150\n",
      "69123/69123 [==============================] - 104s 2ms/step - loss: 0.7088 - val_loss: 0.9778\n",
      "Epoch 12/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.6811 - val_loss: 0.8477\n",
      "Epoch 13/150\n",
      "69123/69123 [==============================] - 124s 2ms/step - loss: 0.6481 - val_loss: 0.7456\n",
      "Epoch 14/150\n",
      "69123/69123 [==============================] - 126s 2ms/step - loss: 0.6341 - val_loss: 0.6149\n",
      "Epoch 15/150\n",
      "69123/69123 [==============================] - 122s 2ms/step - loss: 0.6072 - val_loss: 0.6209\n",
      "Epoch 16/150\n",
      "69123/69123 [==============================] - 122s 2ms/step - loss: 0.5790 - val_loss: 0.5538\n",
      "Epoch 17/150\n",
      "69123/69123 [==============================] - 127s 2ms/step - loss: 0.5611 - val_loss: 0.5160\n",
      "Epoch 18/150\n",
      "69123/69123 [==============================] - 130s 2ms/step - loss: 0.5431 - val_loss: 0.4913\n",
      "Epoch 19/150\n",
      "69123/69123 [==============================] - 132s 2ms/step - loss: 0.5518 - val_loss: 0.4383\n",
      "Epoch 20/150\n",
      "69123/69123 [==============================] - 125s 2ms/step - loss: 0.5323 - val_loss: 0.4847\n",
      "Epoch 21/150\n",
      "69123/69123 [==============================] - 130s 2ms/step - loss: 0.5113 - val_loss: 0.5071\n",
      "Epoch 22/150\n",
      "69123/69123 [==============================] - 130s 2ms/step - loss: 0.5017 - val_loss: 0.4902\n",
      "Epoch 23/150\n",
      "69123/69123 [==============================] - 133s 2ms/step - loss: 0.4884 - val_loss: 0.4235\n",
      "Epoch 24/150\n",
      "69123/69123 [==============================] - 121s 2ms/step - loss: 0.4807 - val_loss: 0.4026\n",
      "Epoch 25/150\n",
      "69123/69123 [==============================] - 126s 2ms/step - loss: 0.4584 - val_loss: 0.3871\n",
      "Epoch 26/150\n",
      "69123/69123 [==============================] - 115s 2ms/step - loss: 0.4631 - val_loss: 0.4319\n",
      "Epoch 27/150\n",
      "69123/69123 [==============================] - 119s 2ms/step - loss: 0.4578 - val_loss: 0.3666\n",
      "Epoch 28/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.4378 - val_loss: 0.3778\n",
      "Epoch 29/150\n",
      "69123/69123 [==============================] - 106s 2ms/step - loss: 0.4343 - val_loss: 0.3561\n",
      "Epoch 30/150\n",
      "69123/69123 [==============================] - 115s 2ms/step - loss: 0.4410 - val_loss: 0.3231\n",
      "Epoch 31/150\n",
      "69123/69123 [==============================] - 134s 2ms/step - loss: 0.4253 - val_loss: 0.3322\n",
      "Epoch 32/150\n",
      "69123/69123 [==============================] - 107s 2ms/step - loss: 0.4252 - val_loss: 0.3241\n",
      "Epoch 33/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.4020 - val_loss: 0.3025\n",
      "Epoch 34/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 0.4031 - val_loss: 0.3213\n",
      "Epoch 35/150\n",
      "69123/69123 [==============================] - 118s 2ms/step - loss: 0.4069 - val_loss: 0.2835\n",
      "Epoch 36/150\n",
      "69123/69123 [==============================] - 117s 2ms/step - loss: 0.4037 - val_loss: 0.2561\n",
      "Epoch 37/150\n",
      "69123/69123 [==============================] - 117s 2ms/step - loss: 0.3897 - val_loss: 0.4093\n",
      "Epoch 38/150\n",
      "69123/69123 [==============================] - 107s 2ms/step - loss: 0.3866 - val_loss: 0.3852\n",
      "Epoch 39/150\n",
      "69123/69123 [==============================] - 112s 2ms/step - loss: 0.3767 - val_loss: 0.2603\n",
      "Epoch 40/150\n",
      "69123/69123 [==============================] - 108s 2ms/step - loss: 0.3811 - val_loss: 0.3276\n",
      "Epoch 41/150\n",
      "69123/69123 [==============================] - 112s 2ms/step - loss: 0.3732 - val_loss: 0.3464\n",
      "Epoch 42/150\n",
      "69123/69123 [==============================] - 119s 2ms/step - loss: 0.3648 - val_loss: 0.2696\n",
      "Epoch 43/150\n",
      "69123/69123 [==============================] - 121s 2ms/step - loss: 0.3662 - val_loss: 0.3090\n",
      "Epoch 44/150\n",
      "69123/69123 [==============================] - 110s 2ms/step - loss: 0.3651 - val_loss: 0.2429\n",
      "Epoch 45/150\n",
      "69123/69123 [==============================] - 110s 2ms/step - loss: 0.3604 - val_loss: 0.2142\n",
      "Epoch 46/150\n",
      "69123/69123 [==============================] - 114s 2ms/step - loss: 0.3491 - val_loss: 0.2372\n",
      "Epoch 47/150\n",
      "69123/69123 [==============================] - 113s 2ms/step - loss: 0.3460 - val_loss: 0.2180\n",
      "Epoch 48/150\n",
      "69123/69123 [==============================] - 104s 2ms/step - loss: 0.3427 - val_loss: 0.2463\n",
      "Epoch 49/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 0.3460 - val_loss: 0.2327\n",
      "Epoch 50/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.3388 - val_loss: 0.2481\n",
      "Epoch 51/150\n",
      "69123/69123 [==============================] - 112s 2ms/step - loss: 0.3367 - val_loss: 0.1946\n",
      "Epoch 52/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.3351 - val_loss: 0.2521\n",
      "Epoch 53/150\n",
      "69123/69123 [==============================] - 117s 2ms/step - loss: 0.3412 - val_loss: 0.2092\n",
      "Epoch 54/150\n",
      "69123/69123 [==============================] - 114s 2ms/step - loss: 0.3344 - val_loss: 0.2267\n",
      "Epoch 55/150\n",
      "69123/69123 [==============================] - 117s 2ms/step - loss: 0.3322 - val_loss: 0.1957\n",
      "Epoch 56/150\n",
      "69123/69123 [==============================] - 118s 2ms/step - loss: 0.3157 - val_loss: 0.2444\n",
      "Epoch 57/150\n",
      "69123/69123 [==============================] - 113s 2ms/step - loss: 0.3239 - val_loss: 0.1999\n",
      "Epoch 58/150\n",
      "69123/69123 [==============================] - 112s 2ms/step - loss: 0.3307 - val_loss: 0.4222\n",
      "Epoch 59/150\n",
      "69123/69123 [==============================] - 106s 2ms/step - loss: 0.3195 - val_loss: 0.2345\n",
      "Epoch 60/150\n",
      "69123/69123 [==============================] - 103s 1ms/step - loss: 0.3146 - val_loss: 0.1660\n",
      "Epoch 61/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 0.3055 - val_loss: 0.2253\n",
      "Epoch 62/150\n",
      "69123/69123 [==============================] - 118s 2ms/step - loss: 0.3081 - val_loss: 0.2054\n",
      "Epoch 63/150\n",
      "69123/69123 [==============================] - 102s 1ms/step - loss: 0.3114 - val_loss: 0.2482\n",
      "Epoch 64/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 0.3089 - val_loss: 0.1540\n",
      "Epoch 65/150\n",
      "69123/69123 [==============================] - 103s 1ms/step - loss: 0.3079 - val_loss: 0.2032\n",
      "Epoch 66/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 0.2996 - val_loss: 0.2133\n",
      "Epoch 67/150\n",
      "69123/69123 [==============================] - 104s 2ms/step - loss: 0.3028 - val_loss: 0.2159\n",
      "Epoch 68/150\n",
      "69123/69123 [==============================] - 104s 2ms/step - loss: 0.3034 - val_loss: 0.1820\n",
      "Epoch 69/150\n",
      "69123/69123 [==============================] - 104s 1ms/step - loss: 0.2981 - val_loss: 0.1673\n",
      "Epoch 70/150\n",
      "69123/69123 [==============================] - 106s 2ms/step - loss: 0.3069 - val_loss: 0.1753\n",
      "Epoch 71/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 0.2922 - val_loss: 0.2215\n",
      "Epoch 72/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 0.2908 - val_loss: 0.2162\n",
      "Epoch 73/150\n",
      "69123/69123 [==============================] - 113s 2ms/step - loss: 0.2941 - val_loss: 0.2025\n",
      "Epoch 74/150\n",
      "69123/69123 [==============================] - 102s 1ms/step - loss: 0.2933 - val_loss: 0.1414\n",
      "Epoch 75/150\n",
      "69123/69123 [==============================] - 102s 1ms/step - loss: 0.3026 - val_loss: 0.2029\n",
      "Epoch 76/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69123/69123 [==============================] - 102s 1ms/step - loss: 0.2892 - val_loss: 0.2191\n",
      "Epoch 77/150\n",
      "69123/69123 [==============================] - 103s 1ms/step - loss: 0.2953 - val_loss: 0.1612\n",
      "Epoch 78/150\n",
      "69123/69123 [==============================] - 104s 1ms/step - loss: 0.2838 - val_loss: 0.1826\n",
      "Epoch 79/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.2858 - val_loss: 0.3254\n",
      "Epoch 80/150\n",
      "69123/69123 [==============================] - 115s 2ms/step - loss: 0.2869 - val_loss: 0.1636\n",
      "Epoch 81/150\n",
      "69123/69123 [==============================] - 107s 2ms/step - loss: 0.2874 - val_loss: 0.2302\n",
      "Epoch 82/150\n",
      "69123/69123 [==============================] - 102s 1ms/step - loss: 0.2902 - val_loss: 0.1652\n",
      "Epoch 83/150\n",
      "69123/69123 [==============================] - 116s 2ms/step - loss: 0.2818 - val_loss: 0.1745\n",
      "Epoch 84/150\n",
      "69123/69123 [==============================] - 119s 2ms/step - loss: 0.2789 - val_loss: 0.1812\n",
      "Epoch 85/150\n",
      "69123/69123 [==============================] - 111s 2ms/step - loss: 0.2821 - val_loss: 0.1585\n",
      "Epoch 86/150\n",
      "69123/69123 [==============================] - 115s 2ms/step - loss: 0.2798 - val_loss: 0.2937\n",
      "Epoch 87/150\n",
      "69123/69123 [==============================] - 118s 2ms/step - loss: 0.2846 - val_loss: 0.1834\n",
      "Epoch 88/150\n",
      "69123/69123 [==============================] - 123s 2ms/step - loss: 0.2760 - val_loss: 0.2086\n",
      "Epoch 89/150\n",
      "69123/69123 [==============================] - 103s 1ms/step - loss: 0.2842 - val_loss: 0.1397\n",
      "Epoch 90/150\n",
      "69123/69123 [==============================] - 112s 2ms/step - loss: 0.2722 - val_loss: 0.1311\n",
      "Epoch 91/150\n",
      "69123/69123 [==============================] - 116s 2ms/step - loss: 0.2736 - val_loss: 0.2370\n",
      "Epoch 92/150\n",
      "69123/69123 [==============================] - 120s 2ms/step - loss: 0.2653 - val_loss: 0.1507\n",
      "Epoch 93/150\n",
      "69123/69123 [==============================] - 114s 2ms/step - loss: 0.2641 - val_loss: 0.17421s - los\n",
      "Epoch 94/150\n",
      "69123/69123 [==============================] - 104s 2ms/step - loss: 0.2731 - val_loss: 0.1441\n",
      "Epoch 95/150\n",
      "69123/69123 [==============================] - 107s 2ms/step - loss: 0.2651 - val_loss: 0.1555\n",
      "Epoch 96/150\n",
      "69123/69123 [==============================] - 106s 2ms/step - loss: 0.2675 - val_loss: 0.1792\n",
      "Epoch 97/150\n",
      "69123/69123 [==============================] - 104s 2ms/step - loss: 0.2645 - val_loss: 0.2795\n",
      "Epoch 98/150\n",
      "69123/69123 [==============================] - 106s 2ms/step - loss: 0.2638 - val_loss: 0.2194\n",
      "Epoch 99/150\n",
      "69123/69123 [==============================] - 120s 2ms/step - loss: 0.2708 - val_loss: 0.1731\n",
      "Epoch 100/150\n",
      "69123/69123 [==============================] - 124s 2ms/step - loss: 0.2667 - val_loss: 0.1402\n",
      "Epoch 101/150\n",
      "69123/69123 [==============================] - 125s 2ms/step - loss: 0.2599 - val_loss: 0.1949\n",
      "Epoch 102/150\n",
      "69123/69123 [==============================] - 126s 2ms/step - loss: 0.2676 - val_loss: 0.1519\n",
      "Epoch 103/150\n",
      "69123/69123 [==============================] - 128s 2ms/step - loss: 0.2660 - val_loss: 0.1542\n",
      "Epoch 104/150\n",
      "69123/69123 [==============================] - 124s 2ms/step - loss: 0.2603 - val_loss: 0.1942\n",
      "Epoch 105/150\n",
      "69123/69123 [==============================] - 126s 2ms/step - loss: 0.2575 - val_loss: 0.1441\n",
      "Epoch 106/150\n",
      "69123/69123 [==============================] - 128s 2ms/step - loss: 0.2678 - val_loss: 0.1469\n",
      "Epoch 107/150\n",
      "69123/69123 [==============================] - 131s 2ms/step - loss: 0.2581 - val_loss: 0.1618\n",
      "Epoch 108/150\n",
      "69123/69123 [==============================] - 123s 2ms/step - loss: 0.2557 - val_loss: 0.1659\n",
      "Epoch 109/150\n",
      "69123/69123 [==============================] - 105s 2ms/step - loss: 0.2558 - val_loss: 0.2163\n",
      "Epoch 110/150\n",
      "69123/69123 [==============================] - 115s 2ms/step - loss: 0.2538 - val_loss: 0.1198\n",
      "Epoch 111/150\n",
      "69123/69123 [==============================] - 119s 2ms/step - loss: 0.2548 - val_loss: 0.2454\n",
      "Epoch 112/150\n",
      "69123/69123 [==============================] - 116s 2ms/step - loss: 0.2554 - val_loss: 0.1719\n",
      "Epoch 113/150\n",
      "69123/69123 [==============================] - 107s 2ms/step - loss: 0.2603 - val_loss: 0.1417\n",
      "Epoch 114/150\n",
      "69123/69123 [==============================] - 110s 2ms/step - loss: 0.2552 - val_loss: 0.1375\n",
      "Epoch 115/150\n",
      "69123/69123 [==============================] - 108s 2ms/step - loss: 0.2558 - val_loss: 0.2595\n",
      "Epoch 116/150\n",
      "69123/69123 [==============================] - 110s 2ms/step - loss: 0.2519 - val_loss: 0.2383\n",
      "Epoch 117/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.2555 - val_loss: 0.1501\n",
      "Epoch 118/150\n",
      "69123/69123 [==============================] - 108s 2ms/step - loss: 0.2502 - val_loss: 0.1412\n",
      "Epoch 119/150\n",
      "69123/69123 [==============================] - 118s 2ms/step - loss: 0.2588 - val_loss: 0.1720\n",
      "Epoch 120/150\n",
      "69123/69123 [==============================] - 125s 2ms/step - loss: 0.2477 - val_loss: 0.1868\n",
      "Epoch 121/150\n",
      "69123/69123 [==============================] - 126s 2ms/step - loss: 0.2502 - val_loss: 0.1321\n",
      "Epoch 122/150\n",
      "69123/69123 [==============================] - 124s 2ms/step - loss: 0.2454 - val_loss: 0.1236\n",
      "Epoch 123/150\n",
      "69123/69123 [==============================] - 125s 2ms/step - loss: 0.2519 - val_loss: 0.1089\n",
      "Epoch 124/150\n",
      "69123/69123 [==============================] - 124s 2ms/step - loss: 0.2520 - val_loss: 0.1567\n",
      "Epoch 125/150\n",
      "69123/69123 [==============================] - 112s 2ms/step - loss: 0.2495 - val_loss: 0.1633\n",
      "Epoch 126/150\n",
      "69123/69123 [==============================] - 108s 2ms/step - loss: 0.2427 - val_loss: 0.2385\n",
      "Epoch 127/150\n",
      "69123/69123 [==============================] - 113s 2ms/step - loss: 0.2477 - val_loss: 0.1163\n",
      "Epoch 128/150\n",
      "69123/69123 [==============================] - 113s 2ms/step - loss: 0.2482 - val_loss: 0.1863\n",
      "Epoch 129/150\n",
      "69123/69123 [==============================] - 115s 2ms/step - loss: 0.2488 - val_loss: 0.1820\n",
      "Epoch 130/150\n",
      "69123/69123 [==============================] - 111s 2ms/step - loss: 0.2454 - val_loss: 0.1272\n",
      "Epoch 131/150\n",
      "69123/69123 [==============================] - 114s 2ms/step - loss: 0.2417 - val_loss: 0.1862\n",
      "Epoch 132/150\n",
      "69123/69123 [==============================] - 114s 2ms/step - loss: 0.2404 - val_loss: 0.1192\n",
      "Epoch 133/150\n",
      "69123/69123 [==============================] - 112s 2ms/step - loss: 0.2450 - val_loss: 0.2987\n",
      "Epoch 134/150\n",
      "69123/69123 [==============================] - 108s 2ms/step - loss: 0.2427 - val_loss: 0.1245\n",
      "Epoch 135/150\n",
      "69123/69123 [==============================] - 103s 1ms/step - loss: 0.2360 - val_loss: 0.1911\n",
      "Epoch 136/150\n",
      "69123/69123 [==============================] - 103s 1ms/step - loss: 0.2386 - val_loss: 0.1097\n",
      "Epoch 137/150\n",
      "69123/69123 [==============================] - 103s 1ms/step - loss: 0.2414 - val_loss: 0.1051\n",
      "Epoch 138/150\n",
      "69123/69123 [==============================] - 107s 2ms/step - loss: 0.2405 - val_loss: 0.1357\n",
      "Epoch 139/150\n",
      "69123/69123 [==============================] - 112s 2ms/step - loss: 0.2410 - val_loss: 0.1116\n",
      "Epoch 140/150\n",
      "69123/69123 [==============================] - 111s 2ms/step - loss: 0.2399 - val_loss: 0.1191\n",
      "Epoch 141/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.2409 - val_loss: 0.1441\n",
      "Epoch 142/150\n",
      "69123/69123 [==============================] - 107s 2ms/step - loss: 0.2464 - val_loss: 0.1539\n",
      "Epoch 143/150\n",
      "69123/69123 [==============================] - 109s 2ms/step - loss: 0.2517 - val_loss: 0.1547\n",
      "Epoch 144/150\n",
      "69123/69123 [==============================] - 117s 2ms/step - loss: 0.2392 - val_loss: 0.1360 ETA: 2s\n",
      "Epoch 145/150\n",
      "69123/69123 [==============================] - 112s 2ms/step - loss: 0.2491 - val_loss: 0.1502\n",
      "Epoch 146/150\n",
      "69123/69123 [==============================] - 120s 2ms/step - loss: 0.2346 - val_loss: 0.2342\n",
      "Epoch 147/150\n",
      "69123/69123 [==============================] - 115s 2ms/step - loss: 0.2366 - val_loss: 0.1170\n",
      "Epoch 148/150\n",
      "69123/69123 [==============================] - 115s 2ms/step - loss: 0.2337 - val_loss: 0.1148\n",
      "Epoch 149/150\n",
      "69123/69123 [==============================] - 116s 2ms/step - loss: 0.2337 - val_loss: 0.1940\n",
      "Epoch 150/150\n",
      "69123/69123 [==============================] - 119s 2ms/step - loss: 0.2417 - val_loss: 0.1589\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x14dcb5422b0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#keeping batch size three times the number of classes\n",
    "\n",
    "model.fit(X_Train, y_Train, batch_size=128, epochs=150, verbose=1, shuffle=True, validation_data=(X_train, y_train))\n",
    "#X_train,  y_train,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69123/69123 [==============================] - 33s 474us/step\n"
     ]
    }
   ],
   "source": [
    "eval_loss = model.evaluate(X_Train, y_Train, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2539649100698617"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 3, ..., 5, 4, 4], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the minimum class\n",
    "min_class = np.min(np.unique(y_Train))\n",
    "y_Train = np.array(y_Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create matrix from on hot encoded training labels to use to calculate class centroids\n",
    "onehot_encoder = OneHotEncoder(sparse=False, categories='auto')\n",
    "onehot = onehot_encoder.fit_transform(y_Train.reshape((-1, 1)))\n",
    "onehot_inverse = 1/np.sum((onehot.T), axis=1)\n",
    "new_y_train = onehot.T*onehot_inverse.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.34492104e-05, 9.20810313e-05, 9.26612305e-05, 6.50448810e-05,\n",
       "       4.67377080e-05])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot_inverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [9.20810313e-05, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 9.26612305e-05, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 6.50448810e-05, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 6.50448810e-05, 6.50448810e-05],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        4.67377080e-05, 0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Score the training set\n",
    "pred = model.predict(X_Train, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.578146  ],\n",
       "       [-0.98088354],\n",
       "       [-1.218465  ],\n",
       "       ...,\n",
       "       [ 1.2213848 ],\n",
       "       [-0.2657959 ],\n",
       "       [-0.2623935 ]], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multiply centroid calculation matrix, new_y_train, by training set scores\n",
    "train_cent = np.matmul(new_y_train, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.57556451],\n",
       "       [-2.25224103],\n",
       "       [-1.14970562],\n",
       "       [-0.07973542],\n",
       "       [ 1.09338553]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_cent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate new data model score\n",
    "new_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify the closest centroid\n",
    "rcenter = train_cent.T # create row matrix of centroids\n",
    "y_pred = np.argmin(abs(new_pred - rcenter), axis=1) + min_class   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 5, 5, ..., 5, 5, 5], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.41900086630089517 0.36240254114929255\n"
     ]
    }
   ],
   "source": [
    "# calculate the mean absolute error and mean zero one error\n",
    "mae = np.mean(abs(y_pred - y_test))\n",
    "mze = np.mean(abs(y_pred - y_test) > 0)   \n",
    "print(mae, mze)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion matrix\n",
    "from sklearn.metrics import confusion_matrix \n",
    "cm_array = confusion_matrix(y_test, y_pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>predicted</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>44</td>\n",
       "      <td>60</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>79</td>\n",
       "      <td>331</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>488</td>\n",
       "      <td>1825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "predicted  1   2   3    4     5\n",
       "actual                         \n",
       "1          3  15  10  6    7   \n",
       "2          4  5   14  12   5   \n",
       "3          1  10  44  60   35  \n",
       "4          2  9   79  331  433 \n",
       "5          1  15  49  488  1825"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(y_test, y_pred, rownames=['actual'], colnames=['predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.419001\n",
      "Mean Zero Error 0.362403\n",
      "Precision: 0.648712\n",
      "Recall: 0.637597\n",
      "F1 score: 0.642061\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "print('Mean Absolute Error: %f' % mae)\n",
    "print('Mean Zero Error %f' % mze)\n",
    "\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(y_test, y_pred,average='weighted')\n",
    "print('Precision: %f' % precision)\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(y_test, y_pred, average='weighted')\n",
    "print('Recall: %f' % recall)\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "print('F1 score: %f' % f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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