notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<h1 align=\"center\">Yelp Rating Prediction</h1>\n",
    "\n",
    "In this Jupyter Notebook project, a suspected correlation is explored between the text content of a business' user reviews and the business' respective star rating. By applying natural language processing (NLP) to the text content of user reviews, a supervised machine learning model in [TensorFlow](https://www.tensorflow.org/) can be constructed to predict star ratings (i.e. $f(reviews)=star\\ rating$). Despite discrete star rating values (e.g. $[1.0, 1.5, ..., 4.5, 5.0]$), a regression model is used here instead of classification since the star ratings are within a numerical range.\n",
    "\n",
    "The dataset used for this project is sourced from the [Yelp Open Dataset](https://www.yelp.com/dataset), a public and non-commercial dataset for educational purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Development Environment\n",
    "In this section, the development environment for this Jupyter notebook project is configured by collecting Python dependencies, importing libraries, and configuring the runtime environment."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Python Dependencies\n",
    "External Python dependencies are collected through the [Pipenv](https://pypi.org/project/pipenv/) package manager since it supports dependency lock-files. In the following script, `pipenv` and its transitive dependencies are installed from `pipenv-requirements.txt` and then the notebook's dependencies are installed from `Pipfile` and `Pipfile.lock`."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "!pip install -r pipenv-requirements.txt\n",
    "!pipenv install"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Python Imports\n",
    "All relevant project libraries and utilities for the notebook are imported here."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from typing import TypedDict, Dict, List\n",
    "from statistics import mean, stdev\n",
    "from io import StringIO\n",
    "from time import time\n",
    "import sys\n",
    "import os\n",
    "import json\n",
    "import spectra\n",
    "import math\n",
    "import pandas\n",
    "import numpy\n",
    "import sklearn\n",
    "import tensorflow\n",
    "import matplotlib.pyplot as pyplot\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from IPython.core.display import display, HTML\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "# store standard out stream\n",
    "original_stdout = sys.stdout\n",
    "\n",
    "print(f\"Python: {sys.version}\")\n",
    "print(f\"Pandas {pandas.__version__}\")\n",
    "print(f\"Scikit-Learn {sklearn.__version__}\")\n",
    "print(f\"Tensor Flow Version: {tensorflow.__version__} (Keras Version: {tensorflow.keras.__version__})\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.8.5 (tags/v3.8.5:580fbb0, Jul 20 2020, 15:57:54) [MSC v.1924 64 bit (AMD64)]\n",
      "Pandas 1.1.2\n",
      "Scikit-Learn 0.23.2\n",
      "Tensor Flow Version: 2.3.0 (Keras Version: 2.4.0)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Configure PRNG Seed\n",
    "Numpy, Tensorflow, and SciKit rely on [pseudorandom number generators](https://en.wikipedia.org/wiki/Pseudorandom_number_generator) for internal operations. To guarantee deterministic behaviour and reproducible results when executing this notebook, an arbitrary seed is configured and used."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# arbitrary changeable seed constant\n",
    "PRNG_SEED = 42\n",
    "\n",
    "numpy.random.seed(PRNG_SEED)\n",
    "tensorflow.random.set_seed(PRNG_SEED)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# I. Data Importation\n",
    "The Yelp data needs to be imported from its JSON file format into usable in-memory data structures. Yelp provides documentation on their dataset file formats and structures [here](https://www.yelp.com/dataset/documentation/main)."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Dataset Location\n",
    "For this project, all data is stored in the `data` folder in the repository root directory. Historically, Yelp has changed the file names a few times; thus, they are enumerated below at the time of implementing this project. A few path constants are set here and used later when importing the dataset."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset_User_Agreement.pdf\n",
      "yelp_academic_dataset_business.json\n",
      "yelp_academic_dataset_checkin.json\n",
      "yelp_academic_dataset_review.json\n",
      "yelp_academic_dataset_tip.json\n",
      "yelp_academic_dataset_user.json\n"
     ]
    }
   ],
   "source": [
    "!dir /b \"./data/\"\n",
    "\n",
    "FILE_BUSINESSES = r\"data/yelp_academic_dataset_business.json\"\n",
    "FILE_USER_REVIEWS = r\"data/yelp_academic_dataset_review.json\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Import Yelp Businesses\n",
    "All public registered businesses are loaded from the new-line (e.g. `\\n`) delimited JSON file and are indexed into a dictionary by their corresponding `business_id`. Of only particular interest, are the business' `business_id`, `review_count`, and `stars` attributes."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported 209,393 distinct businesses in 4.126696 seconds\n"
     ]
    }
   ],
   "source": [
    "# metrics\n",
    "start_time = time()\n",
    "\n",
    "# business data structure\n",
    "class Business(TypedDict):\n",
    "    business_id: str\n",
    "    review_count: int\n",
    "    stars: float\n",
    "\n",
    "# businesses indexed by business_id (i.e. {business['business_id']: Business}\n",
    "businesses_by_id: Dict[str, Business] = {}\n",
    "\n",
    "# parse all businesses\n",
    "with open(FILE_BUSINESSES, 'r', encoding='utf-8') as file:\n",
    "    # iterate over newline-delimited JSON records\n",
    "    record: str\n",
    "    for record in file:\n",
    "        # parse JSON record\n",
    "        business: Business = json.loads(record)\n",
    "        # map Business by business_id\n",
    "        businesses_by_id[business['business_id']] = business\n",
    "\n",
    "# metrics\n",
    "print(f\"Imported {len(businesses_by_id):,} distinct businesses in {time() - start_time:.6f} seconds\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Import User Reviews\n",
    "All public user reviews of businesses are loaded from the new-line (e.g. `\\n`) delimited JSON file and are indexed into a dictionary array by matching `business_id`s. Of only particular interest, are the business' `business_id` and `text` attributes - no other data from user reviews is used."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported 8,021,122 user reviews in 96.604981 seconds\n"
     ]
    }
   ],
   "source": [
    "# metrics\n",
    "start_time = time()\n",
    "\n",
    "# user review data structure\n",
    "class UserReview(TypedDict):\n",
    "    business_id: str\n",
    "    text: str\n",
    "\n",
    "# user reviews indexed by business_id (i.e. {business_id: UserReview['text'][]})\n",
    "business_review_texts: Dict[str, List[str]] = {\n",
    "    business_id: []\n",
    "    for business_id in businesses_by_id.keys()\n",
    "}\n",
    "\n",
    "# parse user reviews\n",
    "with open(FILE_USER_REVIEWS, 'r', encoding='utf-8') as file:\n",
    "    # iterate over newline-delimited JSON records\n",
    "    record: str\n",
    "    for record in file:\n",
    "        # parse JSON record\n",
    "        review: UserReview = json.loads(record)\n",
    "        # map user review by business_id\n",
    "        business_review_texts[review['business_id']].append(review[\"text\"])\n",
    "\n",
    "# metrics\n",
    "print(f\"Imported {sum([len(reviews) for reviews in business_review_texts.values()]):,} user reviews in {time() - start_time:.6f} seconds\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## II. Dataset Visualization\n",
    "\n",
    "Before training the machine learning model, some of the business attributes should be explored better investigate the dataset and understand its distributions."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Star Rating Distribution\n",
    "The following graphic displays how discrete star ratings (i.e. $[1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]$) for businesses on Yelp are distributed."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 1 Axes>",
      "image/png": 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OOceSJUt44IEHMDN69OjB5MmT43qNIlJzWaItGpKVleU5OTnV3QwREZG40BxwkepjZivcPat8XFNQRERERETiSFNQRESkTliSV3dHegF6pmq0V6Su0Ai4iIiIiEgcKQEXEREREYkjJeAiIiIiInGkBFxEREREJI6UgIuIiIiIxJEScBERERGROFICLiIiIiISR0rARURERETiSAm4iIiIiEgcKQEXEREREYkjJeAiIiIiInGkBFxEREREJI5iloCbWX0zW25mH5nZGjMbE8QfNrPNZpYbfK4IK/OAmW0ws3Vm1jcs3sXMVgf7JpqZBfGTzeyFIP6BmaXG6npERERERI6HWI6AFwGXunsnIBPoZ2bdg33j3T0z+CwAMLN2wGAgA+gHPGlm9YLjpwDDgLTg0y+IDwW+cvc2wHjg0Rhej4iIiIjIMYtZAu4he4KvJwYfr6LINcAsdy9y943ABqCrmbUEGrr7Mnd34Fng2rAy04PtOUDv0tFxEREREZGaKKZzwM2snpnlAluBhe7+QbBrhJmtMrNnzKxJEGsF5IcV3xTEWgXb5eOHlHH3YmAXcHqEdgwzsxwzy9m2bdvxuTgRERERkaMQ0wTc3UvcPRNIITSa3Z7QdJJzCU1LKQAeDw6PNHLtVcSrKlO+HVPdPcvds5o3b35E1yAiIiIicjzFZRUUd98JLAH6ufsXQWJ+EHgK6BoctgloHVYsBdgSxFMixA8pY2ZJQCPgy9hchYiIiIjIsYvlKijNzaxxsP0t4DLg02BOd6kBwMfB9nxgcLCyydmEHrZc7u4FQKGZdQ/md98MzAsrMyTYzgYWB/PERURERERqpKQY1t0SmB6sZHICMNvd/2JmM8wsk9BUkTzgJwDuvsbMZgNrgWLgDncvCeq6HZgGfAt4LfgAPA3MMLMNhEa+B8fwekREREREjlnMEnB3XwVcECH+oyrKjAXGRojnAO0jxPcBA4+tpSIiIiIi8aM3YYqIiIiIxJEScBERERGROFICLiIiIiISR0rARURERETiSAm4iIiIiEgcKQEXEREREYkjJeAiIiIiInGkBFxEREREJI6UgIuIiIiIxJEScBERERGROFICLiIiIiISR0rARURERCqRn59Pr169SE9PJyMjg9///vcAfPTRR1x44YV06NCBq6++mt27dwOwf/9+brnlFjp06ECnTp1YsmRJhTr79+9P+/bty74XFRVx/fXX06ZNG7p160ZeXl48Lk2qkRJwERERkUokJSXx+OOP88knn/D+++8zefJk1q5dy2233cYjjzzC6tWrGTBgAL/73e8AeOqppwBYvXo1Cxcu5J577uHgwYNl9c2dO5cGDRocco6nn36aJk2asGHDBu666y7uv//++F2gVAsl4CIiIiKVaNmyJZ07dwbgtNNOIz09nc2bN7Nu3Tp69OgBQJ8+fXjppZcAWLt2Lb179wagRYsWNG7cmJycHAD27NnDE088wYMPPnjIOebNm8eQIUMAyM7OZtGiRbh7XK5PqocScBEREZEo5OXlsXLlSrp160b79u2ZP38+AC+++CL5+fkAdOrUiXnz5lFcXMzGjRtZsWJF2b6HHnqIe+65h1NOOeWQejdv3kzr1q2B0Ih7o0aN2LFjRxyvTOJNCbiIiIjIYezZs4frrruOCRMm0LBhQ5555hkmT55Mly5dKCws5KSTTgLg1ltvJSUlhaysLO68804uuugikpKSyM3NZcOGDQwYMKBC3ZFGu80s5tck1SepuhsgIiIiUpMdOHCA6667jhtvvJEf/OAHALRt25Y333wTgM8++4xXX30VCI1gjx8/vqzsRRddRFpaGkuXLmXFihWkpqZSXFzM1q1b6dmzJ0uWLCElJYX8/HxSUlIoLi5m165dNG3aNP4XKnETsxFwM6tvZsvN7CMzW2NmY4J4UzNbaGbrg59Nwso8YGYbzGydmfUNi3cxs9XBvokW/LPQzE42sxeC+Admlhqr6xEREZHE4+4MHTqU9PR07r777rL41q1bATh48CC//vWvGT58OABff/01e/fuBWDhwoUkJSXRrl07br/9drZs2UJeXh7vvvsu5513XtkKKf3792f69OkAzJkzh0svvVQj4HVcLKegFAGXunsnIBPoZ2bdgZ8Ci9w9DVgUfMfM2gGDgQygH/CkmdUL6poCDAPSgk+/ID4U+Mrd2wDjgUdjeD0iIrVSZcuo5ebm0r17dzIzM8nKymL58uVlZVatWsWFF15IRkYGHTp0YN++fQD069ePTp06kZGRwfDhwykpKTnkXHPmzMHMyh46E6nt3nvvPWbMmMHixYvJzMwkMzOTBQsWMHPmTM477zzatm1LcnIyt9xyCxBKzDt37kx6ejqPPvooM2bMOOw5hg4dyo4dO2jTpg1PPPEEjzzySKwvS6qZxeMpWzM7BXgXuB14Fujp7gVm1hJY4u7nm9kDAO4+LijzBvAwkAe87e5tg/gNQfmflB7j7svMLAn4F9Dcq7iorKws118MIpJICgoKKCgooHPnzhQWFtKlSxdeeeUV7rzzTu666y4uv/xyFixYwG9/+1uWLFlCcXExnTt3ZsaMGXTq1IkdO3bQuHFj6tWrx+7du2nYsCHuTnZ2NgMHDmTw4MEAFBYWcuWVV7J//34mTZpEVlZWXK9zSV7dHjHsmXp0f1+PWVp3+2X0JVopRGo2M1vh7hX+MIzpQ5hmVs/McoGtwEJ3/wA4w90LAIKfLYLDWwH5YcU3BbFWwXb5+CFl3L0Y2AWcHqEdw8wsx8xytm3bdpyuTkSkdqhsGTUzK3t5yK5du0hOTgbgzTffpGPHjnTq1AmA008/nXr1Qr+QbNiwIQDFxcXs37//kF+TP/TQQ4waNYr69evH7dpERGqjmD6E6e4lQKaZNQZeNrP2VRwe6Z/oXkW8qjLl2zEVmAqhEfCq2iwiUpeFL6M2YcIE+vbty7333svBgwf529/+BoQeKDMz+vbty7Zt2xg8eDCjRo0qq6Nv374sX76cyy+/nOzsbABWrlxJfn4+V111FY899li1XJtItHpNr7u/FXh7iNKc2iAuyxC6+05gCaG5218EU08Ifm4NDtsEtA4rlgJsCeIpEeKHlAmmoDQCvozFNYiI1Hbll1GbMmUK48ePJz8/n/HjxzN06FAgNLr97rvv8vzzz/Puu+/y8ssvs2jRorJ63njjDQoKCigqKmLx4sUcPHiQu+66i8cff7y6Lk1EpFaJ5SoozYORb8zsW8BlwKfAfGBIcNgQYF6wPR8YHKxscjahhy2XB9NUCs2se7D6yc3lypTWlQ0srmr+t4hIooq0jNr06dPLtgcOHFj2EGZKSgqXXHIJzZo145RTTuGKK67gww8/PKS++vXr079/f+bNm0dhYSEff/wxPXv2JDU1lffff5/+/fvrQUwRkUrEcgS8JfC2ma0C/k5oDvhfgEeAPma2HugTfMfd1wCzgbXA68AdwRQWCD28+SdgA/B/wGtB/GngdDPbANxNsKKKiIj8W2XLqCUnJ7N06VIAFi9eTFpaGhCaYrJq1Sq+/vpriouLWbp0Ke3atWPPnj0UFBQAoVHyBQsW0LZtWxo1asT27dvJy8sjLy+P7t27M3/+/Lg/hCkiUlvEbA64u68CLogQ3wH0rqTMWGBshHgOUGH+uLvvAwYec2NFROqw0mXUOnToQGZmJgC/+c1veOqppxg5ciTFxcXUr1+fqVOnAtCkSRPuvvtuvvOd72BmXHHFFVx55ZV88cUX9O/fn6KiIkpKSrj00kvL1j4WEZHo6U2YIiJ13MUXXxzxVdcAK1asiBi/6aabuOmmmw6JnXHGGfz9738/7PlKXy4iInVTfn4+N998M//617844YQTGDZsGCNHjuT6669n3bp1AOzcuZPGjRuTm5sLwLhx43j66aepV68eEydOpG/f0PsWZ86cyW9+8xvMjOTkZJ577jmaNWtGUVERN998MytWrOD000/nhRdeIDU1tZqu+PhTAi4iIiIiUUtKSuLxxx8/5N0Cffr04YUXXig75p577qFRo0YArF27llmzZrFmzRq2bNnCZZddxmeffYa7M3LkSNauXUuzZs0YNWoUkyZN4uGHH+bpp5+mSZMmbNiwgVmzZnH//fcfUn9tF5dVUERE5HizOv4RkZqqsncLlHJ3Zs+ezQ033ADAvHnzGDx4MCeffDJnn302bdq0Yfny5bg77s7evXtxd3bv3l32PoJ58+YxZEhonY3s7GwWLVpU6W/yaiMl4CIiIiJyVMLfLVDqr3/9K2eccUbZg92bN2+mdet/rzSdkpLC5s2bOfHEE5kyZQodOnQgOTmZtWvXli2HGl4mKSmJRo0asWPHjjheWWwpARcRERGRI1b+3QKlZs6cWTb6DUQcuTYzDhw4wJQpU1i5ciVbtmyhY8eOjBs3rsoydYUScBERERE5IpHeLQChJUrnzp3L9ddfXxZLSUkhPz+/7PumTZtITk4ue0Dz3HPPxcwYNGhQ2Rt5w8sUFxeza9cumjZtGocriw8l4CIiIiIStcreLQDw1ltv0bZtW1JS/v0S8/79+zNr1iyKiorYuHEj69evp2vXrrRq1Yq1a9eybds2ABYuXEh6enpZmenTpwMwZ84cLr300jo1Aq5VUEREREQkapW9W+CKK65g1qxZh0w/AcjIyGDQoEG0a9eOpKQkJk+eTL169UhOTmb06NH06NGDE088kbPOOotp06YBMHToUH70ox/Rpk0bmjZtyqxZs+J8lbFldemJ0mhkZWW5Xo8sIrVf3RkJiuzI/25akle3+6Rn6tH9fT1mad3tl9GXHF2f9Jped/vk7SGJldfVdGa2wt0rvBZYU1BEREREROJIU1BEREREEpyNqbu/FfDRNe+3AhoBFxERERGJIyXgIiIiIiJxpARcRERERCSOlICLiIiIiMSREnARERERkThSAi4iIiIiEkdKwEVERERE4ihmCbiZtTazt83sEzNbY2Yjg/jDZrbZzHKDzxVhZR4wsw1mts7M+obFu5jZ6mDfRDOzIH6ymb0QxD8ws9RYXY+IiIiIyPEQyxHwYuAed08HugN3mFm7YN94d88MPgsAgn2DgQygH/CkmdULjp8CDAPSgk+/ID4U+Mrd2wDjgUdjeD0iIiIiIscsZgm4uxe4+4fBdiHwCdCqiiLXALPcvcjdNwIbgK5m1hJo6O7L3N2BZ4Frw8pMD7bnAL1LR8dFRERERGqiqBJwM2t/LCcJpoZcAHwQhEaY2Soze8bMmgSxVkB+WLFNQaxVsF0+fkgZdy8GdgGnRzj/MDPLMbOcbdu2HculiIiIiIgck2hHwP9oZsvN7D/NrPGRnMDMGgAvAXe6+25C00nOBTKBAuDx0kMjFPcq4lWVOTTgPtXds9w9q3nz5kfSfBERERGR4yqqBNzdLwZuBFoDOWb2ZzPrc7hyZnYioeT7eXefG9T1hbuXuPtB4Cmga3D4pqD+UinAliCeEiF+SBkzSwIaAV9Gc00iIiIiItUh6jng7r4eeBC4H7gEmGhmn5rZDyIdH8zFfhr4xN2fCIu3DDtsAPBxsD0fGBysbHI2oYctl7t7AVBoZt2DOm8G5oWVGRJsZwOLg3niIiIiIiI1UlI0B5lZR+AW4EpgIXC1u39oZsnAMmBuhGLfBX4ErDaz3CD2M+AGM8skNFUkD/gJgLuvMbPZwFpCK6jc4e4lQbnbgWnAt4DXgg+EEvwZZraB0Mj34GiuR0RERESkukSVgAOTCE0X+Zm7f1MadPctZvZgpALu/i6R52gvqOwk7j4WGBshngNUeBDU3fcBAw/behERERGRGiLaBPwK4JvSEWkzOwGo7+5fu/uMmLVORERERKSOiXYO+FuEpn+UOiWIiYiIiIjIEYg2Aa/v7ntKvwTbp8SmSSIiIiIidVe0CfheM+tc+sXMugDfVHG8iEi1yM/Pp1evXqSnp5ORkcHvf/97AF588UUyMjI44YQTyMnJqVDun//8Jw0aNOCxxx4ri/Xr149OnTqRkZHB8OHDKSkJPRc+bdo0mjdvTmZmJpmZmfzpT3+Kz8WJiEidEO0c8DuBF82sdP3tlsD1MWmRiMgxSEpK4vHHH6dz584UFhbSpUsX+vTpQ/v27Zk7dy4/+clPIpa76667uPzyyw+JzZ49m4YNG+LuZGdn8+KLLzJ4cGixpeuvv55JkybF/HpERKTuiSoBd/e/m1lb4HxCK5t86u4HYtoyEZGj0LJlS1q2DL1u4LTTTiM9PZ3NmzfTp0/l7w575ZVXOOecczj11FMPiTds2BCA4uJi9u/fT+hVBCIiIscm6hfxAN8BOgIXEFrL++bYNElE5PjIy8tj5cqVdOvWrdJj9u7dy6OPPsro0aMj7u/bty8tWrTgtNNOIzs7uyz+0ksv0bFjR7Kzs8nPzz/ubRcRkborqgTczGYAjwEXE0rEvwNkxbBdIiLHZM+ePVx33XVMmDChbCQ7ktGjR3PXXXfRoEGDiPvfeOMNCgoKKCoqYvHixQBcffXV5OXlsWrVKi677DKGDBkSsayIiEgk0c4BzwLa6TXvIlIbHDhwgOuuu44bb7yRH/zgB1Ue+8EHHzBnzhxGjRrFzp07OeGEE6hfvz4jRowoO6Z+/fr079+fefPm0adPH04//fSyff/xH//B/fffH7NrERGRuifaBPxj4NtAQQzbIiJyzNydoUOHkp6ezt13333Y4//617+WbT/88MM0aNCAESNGsGfPHgoLC2nZsiXFxcUsWLCA733vewAUFBSUzTOfP38+6enpsbkYERGpk6JNwJsBa81sOVBUGnT3/jFplYjIUXrvvfeYMWMGHTp0IDMzE4Df/OY3FBUV8f/+3/9j27ZtXHnllWRmZvLGG29UWs/evXvp378/RUVFlJSUcOmllzJ8+HAAJk6cyPz580lKSqJp06ZMmzYtDlcmIiJ1hUUzq8TMLokUd/elx71FMZaVleWR1gAWEald6vqKLEc+43FJXt3uk56pRzcLdMzSutsvoy85uj7pNb3u9snbQ46uT2xM3e0TH119M6jNbIW7V3huMtplCJea2VlAmru/ZWanAPWOdyNFREREROq6qBJwM/sPYBjQFDgXaAX8Eegdu6aJiASW1N2RGXrq2XYRkUQT7TrgdwDfBXYDuPt6oEWsGiUiIiIiUldFm4AXufv+0i9mlsTRTNATEREREUlw0SbgS83sZ8C3zKwP8CLwv7FrloiIiIhI3RRtAv5TYBuwGvgJsAB4sKoCZtbazN42s0/MbI2ZjQziTc1soZmtD342CSvzgJltMLN1ZtY3LN7FzFYH+yaamQXxk83shSD+gZmlHtHVi4iIiIjEWVQJuLsfdPen3H2gu2cH24ebglIM3OPu6UB34A4za0comV/k7mnAouA7wb7BQAbQD3jSzEpXWplC6CHQtODTL4gPBb5y9zbAeODRqK5aRERERKSaRJWAm9lGM/u8/KeqMu5e4O4fBtuFwCeEVk+5BpgeHDYduDbYvgaY5e5F7r4R2AB0NbOWQEN3XxYk/c+WK1Na1xygd+nouIiIiIhITRTtmzDDFxCvDwwktCRhVIKpIRcAHwBnuHsBhJJ0MytdTaUV8H5YsU1B7ECwXT5eWiY/qKvYzHYBpwPby51/GKERdM4888xomy0iIiIictxFOwVlR9hns7tPAC6NpqyZNQBeAu50991VHRrp1FXEqypzaMB9qrtnuXtW8+bND9dkEREREZGYifZFPJ3Dvp5AaET8tCjKnUgo+X7e3ecG4S/MrGUw+t0S2BrENwGtw4qnAFuCeEqEeHiZTcHSiI2AL6O5JhERERGR6hDtFJTHw7aLgTxgUFUFgrnYTwOfuPsTYbvmA0OAR4Kf88LifzazJ4BkQg9bLnf3EjMrNLPuhKaw3Az8oVxdy4BsYHEUD4eKiIiIiFSbqBJwd+91FHV/F/gRsNrMcoPYzwgl3rPNbCjwT0LzyXH3NWY2G1hLKMm/w91LgnK3A9OAbwGvBR8IJfgzzGwDoZHvwUfRThERERGRuIl2CsrdVe0vN8JdGnuXyHO0AXpXUs9YYGyEeA7QPkJ8H0ECLyIiIiJSGxzJKijfITTlA+Bq4B2CFUhERERERCQ60SbgzYDOwXremNnDwIvuflusGiYiIiIiUhdF+yr6M4H9Yd/3A6nHvTUiIiIiInVctCPgM4DlZvYyoXW2BxB6I6WIiIiIiByBaFdBGWtmrwHfC0K3uPvK2DVLRERERKRuinYKCsApwG53/z2hF9+cHaM2iYiIiIjUWVEl4GY2GrgfeCAInQg8F6tGiYiIiIjUVdGOgA8A+gN7Adx9C1G8il5ERERERA4VbQK+P3jFuwOY2amxa5KIiIiISN0VbQI+28z+G2hsZv8BvAU8FbtmiYiIiIjUTYddBcXMDHgBaAvsBs4HfuHuC2PcNhERERGROuewCbi7u5m94u5dACXdIiIiIiLHINopKO+b2Xdi2hIRERERkQQQ7ZswewHDzSyP0EooRmhwvGOsGiYiIiIiUhdVmYCb2Znu/k/g8ji1R0RERESkTjvcCPgrQGd3/4eZveTu18WhTSIiIiIiddbh5oBb2PY5sWyIiIiIiEgiOFwC7pVsH5aZPWNmW83s47DYw2a22cxyg88VYfseMLMNZrbOzPqGxbuY2epg38RgWUTM7GQzeyGIf2BmqUfSPhERERGR6nC4BLyTme02s0KgY7C928wKzWz3YcpOA/pFiI9398zgswDAzNoBg4GMoMyTZlYvOH4KMAxICz6ldQ4FvnL3NsB44NHDtEdEREREpNpVmYC7ez13b+jup7l7UrBd+r3hYcq+A3wZZTuuAWa5e5G7bwQ2AF3NrCXQ0N2XubsDzwLXhpWZHmzPAXqXjo6LiIiIiNRU0a4DfjyNMLNVwRSVJkGsFZAfdsymINYq2C4fP6SMuxcDu4DTY9lwEREREZFjFe8EfApwLpAJFACPB/FII9deRbyqMhWY2TAzyzGznG3bth1Rg0VqsltvvZUWLVrQvn37sthDDz1Ex44dyczM5Pvf/z5btmwBYMeOHfTq1YsGDRowYsSIsuO//vprrrzyStq2bUtGRgY//elPK5xnzpw5mBk5OTmxvygREZE6Lq4JuLt/4e4l7n4QeAroGuzaBLQOOzQF2BLEUyLEDyljZklAIyqZ8uLuU909y92zmjdvfrwuR6Ta/fjHP+b1118/JHbfffexatUqcnNzueqqq/jlL38JQP369fnVr37FY489VqGee++9l08//ZSVK1fy3nvv8dprr5XtKywsZOLEiXTr1i22FyMiIpIg4pqAB3O6Sw0ASldImQ8MDlY2OZvQw5bL3b0AKDSz7sH87puBeWFlhgTb2cDiYJ64SMLo0aMHTZs2PSTWsOG/H8/Yu3cvpY9GnHrqqVx88cXUr1//kONPOeUUevXqBcBJJ51E586d2bTp3zO/HnroIUaNGlWhnIiIiBydmCXgZjYTWAacb2abzGwo8NtgScFVhF5vfxeAu68BZgNrgdeBO9y9JKjqduBPhB7M/D+gdGjuaeB0M9sA3A1U/L25SIL6+c9/TuvWrXn++efLRsCjsXPnTv73f/+X3r17A7By5Ury8/O56qqrYtVUERGRhHO4N2EeNXe/IUL46SqOHwuMjRDPAdpHiO8DBh5LG0XqqrFjxzJ27FjGjRvHpEmTGDNmzGHLFBcXc8MNN/Bf//VfnHPOORw8eJC77rqLadOmxb7BIiIiCaQ6VkERkTj54Q9/yEsvvRTVscOGDSMtLY0777wTCM39/vjjj+nZsyepqam8//779O/fXw9iioiIHCMl4CJ1zPr168u258+fT9u2bQ9b5sEHH2TXrl1MmDChLNaoUSO2b99OXl4eeXl5dO/enfnz55OVlRWLZouIiCSMmE1BEZHYu+GGG1iyZAnbt28nJSWFMWPGsGDBAtatW8cJJ5zAWWedxR//+Mey41NTU9m9ezf79+/nlVde4c0336Rhw4aMHTuWtm3b0rlzZwBGjBjBbbfdVl2XJSIiUqcpARepxWbOnFkhNnTo0EqPz8vLixiPZgGhJUuWRNssERERqYIScJGaZkykd0zVEaO1UqiIiIjmgIuIiIiIxJEScBERERGROFICLiIiIiISR0rARURERETiSAm4iIiIiEgcKQEXEREREYkjJeAiIiIiInGkBFxEREREJI6UgIuIiIiIxJEScBERERGROFICLiIiIiISR0rARURERETiSAm4iIiIiEgcxSwBN7NnzGyrmX0cFmtqZgvNbH3ws0nYvgfMbIOZrTOzvmHxLma2Otg30cwsiJ9sZi8E8Q/MLDVW1yIiIiIicrzEcgR8GtCvXOynwCJ3TwMWBd8xs3bAYCAjKPOkmdULykwBhgFpwae0zqHAV+7eBhgPPBqzKxEREREROU5iloC7+zvAl+XC1wDTg+3pwLVh8VnuXuTuG4ENQFczawk0dPdl7u7As+XKlNY1B+hdOjouIiIiIlJTxXsO+BnuXgAQ/GwRxFsB+WHHbQpirYLt8vFDyrh7MbALOD3SSc1smJnlmFnOtm3bjtOliIiIiIgcuZryEGakkWuvIl5VmYpB96nunuXuWc2bNz/KJoqIiIiIHLt4J+BfBNNKCH5uDeKbgNZhx6UAW4J4SoT4IWXMLAloRMUpLyIiIiIiNUq8E/D5wJBgewgwLyw+OFjZ5GxCD1suD6apFJpZ92B+983lypTWlQ0sDuaJi4iIiIjUWEmxqtjMZgI9gWZmtgkYDTwCzDazocA/gYEA7r7GzGYDa4Fi4A53Lwmqup3QiirfAl4LPgBPAzPMbAOhke/BsboWEREREZHjJWYJuLvfUMmu3pUcPxYYGyGeA7SPEN9HkMCLiIiIiNQWNeUhTBERERGRhKAEXEREREQkjpSAi4iIiIjEkRJwEREREZE4UgIuIiIiIhJHSsBFREREROJICXgNlpqaSocOHcjMzCQrKwuA3NxcunfvXhZbvnw5APv37+eWW26hQ4cOdOrUiSVLlpTVM3PmTDp06EDHjh3p168f27dvr47LOS527txJdnY2bdu2JT09nWXLlvHll1/Sp08f0tLS6NOnD1999VXZ8ePGjaNNmzacf/75vPHGGxXq69+/P+3bV1jlUkRERCRmlIDXcG+//Ta5ubnk5OQAMGrUKEaPHk1ubi6//OUvGTVqFABPPfUUAKtXr2bhwoXcc889HDx4kOLiYkaOHMnbb7/NqlWr6NixI5MmTaq26zlWI0eOpF+/fnz66ad89NFHpKen88gjj9C7d2/Wr19P7969eeSRRwBYu3Yts2bNYs2aNbz++uv853/+JyUlJWV1zZ07lwYNGlTXpYiIiEiCUgJey5gZu3fvBmDXrl0kJycDoWSzd+/QO45atGhB48aNycnJwd1xd/bu3Yu7s3v37rIytc3u3bt55513GDp0KAAnnXQSjRs3Zt68eQwZMgSAIUOG8MorrwAwb948Bg8ezMknn8zZZ59NmzZtyn5jsGfPHp544gkefPDBarkWERERSVxKwGswM+P73/8+Xbp0YerUqQBMmDCB++67j9atW3Pvvfcybtw4ADp16sS8efMoLi5m48aNrFixgvz8fE488USmTJlChw4dSE5OZu3atWUJbG3z+eef07x5c2655RYuuOACbrvtNvbu3csXX3xBy5YtAWjZsiVbt24FYPPmzbRu3bqsfEpKCps3bwbgoYce4p577uGUU06J/4WIiIhIQlMCXoO99957fPjhh7z22mtMnjyZd955hylTpjB+/Hjy8/MZP358WTJ96623kpKSQlZWFnfeeScXXXQRSUlJHDhwgClTprBy5Uq2bNlCx44dy5L22qa4uJgPP/yQ22+/nZUrV3LqqaeWTTeJxN0rxMyM3NxcNmzYwIABA2LZXBEREZGIlIDXYKVTRVq0aMGAAQNYvnw506dP5wc/+AEAAwcOLJtSkZSUxPjx48nNzWXevHns3LmTtLQ0cnNzATj33HMxMwYNGsTf/va3armeY5WSkkJKSgrdunUDIDs7mw8//JAzzjiDgoICAAoKCmjRokXZ8fn5+WXlN23aRHJyMsuWLWPFihWkpqZy8cUX89lnn9GzZ8+4X4+IiIgkJiXgNdTevXspLCws237zzTdp3749ycnJLF26FIDFixeTlpYGwNdff83evXsBWLhwIUlJSbRr145WrVqxdu1atm3bVrYvPT29Gq7o2H3729+mdevWrFu3DoBFixbRrl07+vfvz/Tp0wGYPn0611xzDRBa4WTWrFkUFRWxceNG1q9fT9euXbn99tvZsmULeXl5vPvuu5x33nmHrBojIiIiEktJ1d0AieyLL74omyJRXFzMD3/4Q/r160eDBg0YOXIkxcXF1K9fv2xu+NatW+nbty8nnHACrVq1YsaMGUBoFH306NH06NGDE088kbPOOotp06ZV12Udsz/84Q/ceOON7N+/n3POOYf/+Z//4eDBgwwaNIinn36aM888kxdffBGAjIwMBg0aRLt27UhKSmLy5MnUq1evmq9AREREEp1Fmidbl2VlZXnpkn4iNdIYq+4WxM7oo/zzZkkd7pOeR/tncB3uEwCOvF+W5NXtPumZenT3ypildbdfRl9ydH3Sa3rd7ZO3hxxdn1gd/rvHj/bvnuPAzFa4e1b5uEbApXr1qrv/w/N2Yv3jVkRERKKjBDyerA4nmwn2mxQRERGRo1UtD2GaWZ6ZrTazXDPLCWJNzWyhma0PfjYJO/4BM9tgZuvMrG9YvEtQzwYzm2hWlzNcEREREakLqnMVlF7unhk2L+anwCJ3TwMWBd8xs3bAYCAD6Ac8aWalT9JNAYYBacGnXxzbLyIiIiJyxGrSMoTXANOD7enAtWHxWe5e5O4bgQ1AVzNrCTR092UeepL02bAyIiIiIiI1UnUl4A68aWYrzGxYEDvD3QsAgp8tgngrID+s7KYg1irYLh+vwMyGmVmOmeWUroctIiIiIlIdqushzO+6+xYzawEsNLNPqzg20rxuryJeMeg+FZgKoWUIj7SxIiIiIiLHS7WMgLv7luDnVuBloCvwRTCthODn1uDwTUDrsOIpwJYgnhIhLiIiIiJSY8U9ATezU83stNJt4PvAx8B8YEhw2BBgXrA9HxhsZieb2dmEHrZcHkxTKTSz7sHqJzeHlRERERERqZGqYwrKGcDLwYqBScCf3f11M/s7MNvMhgL/BAYCuPsaM5sNrAWKgTvcvSSo63ZgGvAt4LXgIyIiIiJSY8U9AXf3z4FOEeI7gN6VlBkLjI0QzwHaH+82ioiIiIjESk1ahlBEREREpM5TAi4iIiIiEkdKwEVERERE4kgJuIiIiIhIHCkBFxERERGJIyXgIiIiIiJxpARcRERERCSOlICLiIiIiMSREnARERERkThSAi4iIiIiEkdKwEVERERE4kgJuIiIiIhIHCkBFxERERGJIyXgIiIiIiJxpARcRERERCSOlICLiIiIiMSREnARERERkThSAi4iIiIiEke1PgE3s35mts7MNpjZT6u7PSIiIiIiVanVCbiZ1QMmA5cD7YAbzKxd9bZKRERERKRytToBB7oCG9z9c3ffD8wCrqnmNomIiIiIVMrcvbrbcNTMLBvo5+63Bd9/BHRz9xHljhsGDAu+ng+si2tDq0czYHt1N6KGUZ9Epn6pSH1SkfqkIvVJReqTitQnkSVKv5zl7s3LB5OqoyXHkUWIVfgXhbtPBabGvjk1h5nluHtWdbejJlGfRKZ+qUh9UpH6pCL1SUXqk4rUJ5Eler/U9ikom4DWYd9TgC3V1BYRERERkcOq7Qn434E0MzvbzE4CBgPzq7lNIiIiIiKVqtVTUNy92MxGAG8A9YBn3H1NNTerpkioKTdRUp9Epn6pSH1SkfqkIvVJReqTitQnkSV0v9TqhzBFRERERGqb2j4FRURERESkVlECLiIiIiISR0rAazEze8bMtprZx5XsNzObaGYbzGyVmXWOdxvjLYo+6Wlmu8wsN/j8It5tjDcza21mb5vZJ2a2xsxGRjgmoe6VKPskEe+V+ma23Mw+CvplTIRjEu1eiaZPEu5egdDbqM1spZn9JcK+hLpPSh2mTxLuPjGzPDNbHVxvToT9CXmfQC1/CFOYBkwCnq1k/+VAWvDpBkwJftZl06i6TwD+6u5Xxac5NUIxcI+7f2hmpwErzGyhu68NOybR7pVo+gQS714pAi519z1mdiLwrpm95u7vhx2TaPdKNH0CiXevAIwEPgEaRtiXaPdJqar6BBLzPunl7pW9cCdR7xONgNdm7v4O8GUVh1wDPOsh7wONzaxlfFpXPaLok4Tj7gXu/mGwXUjoL4dW5Q5LqHslyj5JOMF//z3B1xODT/kn9RPtXommTxKOmaUAVwJ/quSQhLpPIKo+kYoS7j4ppQS8bmsF5Id934SSDIALg18nv2ZmGdXdmHgys1TgAuCDcrsS9l6pok8gAe+V4FfoucBWYKG7J/y9EkWfQOLdKxOAUcDBSvYn3H3C4fsEEu8+ceBNM1thZsMi7E/E+wRQAl7XWYRYoo/cfAic5e6dgD8Ar1Rvc+LHzBoALwF3uvvu8rsjFKnz98ph+iQh7xV3L3H3TEJvFu5qZu3LHZJw90oUfZJQ94qZXQVsdfcVVR0WIVZn75Mo+ySh7pPAd929M6GpJneYWY9y+xPqPgmnBLxu2wS0DvueAmypprbUCO6+u/TXye6+ADjRzJpVc7NiLpi7+hLwvLvPjXBIwt0rh+uTRL1XSrn7TmAJ0K/croS7V0pV1icJeK98F+hvZnnALOBSM3uu3DGJdp8ctk8S8D7B3bcEP7cCLwNdyx2SaPdJGSXgddt84ObgKePuwC53L6juRlUnM/u2mVmw3ZXQ/wM7qrdVsRVc79PAJ+7+RCWHJdS9Ek2fJOi90tzMGgfb3wIuAz4td1ii3SuH7ZNEu1fc/QF3T3H3VGAwsNjdbyp3WELdJ9H0SaLdJ2Z2avCQO2Z2KvB9oPwKZQl1n4TTKii1mJnNBHoCzcxsEzCa0ANCuPsfgQXAFcAG4GvgluppafxE0SfZwO1mVgx8Awz2uv862O8CPwJWB/NYAX4GnAkJe69E0yeJeK+0BKabWT1CycFsd/+LmQ2HhL1XoumTRLxXKkjw+ySiBL9PzgBeDv7NkQT82d1f130SolfRi4iIiIjEkaagiIiIiIjEkRJwEREREZE4UgIuIiIiIhJHSsBFREREROJICbiIiIiISBwpARcRqaXM7OdmtsbMVplZrpl1C+J3mtkpx1j3w2a2Oah3rZndEEWZQ85rZgtK19AWEZF/0zKEIiK1kJldCDwB9HT3ouCNeie5+5bgbXxZ7r79COqr5+4lYd8fBva4+2NmlgasAE539wNV1HHE5xURSUQaARcRqZ1aAtvdvQjA3bcHyfd/AcnA22b2NoCZTTGznGC0fExpBWaWZ2a/MLN3gYGVncjd1xN6SUaTyuqr5Lx5ZtbMzFLN7BMzeyoo82bwVknM7DvBCP4yM/udmZV/U56ISJ2jBFxEpHZ6E2htZp+Z2ZNmdgmAu08EtgC93L1XcOzP3T0L6AhcYmYdw+rZ5+4Xu/usyk5kZp2B9e6+tbL6KjlvuDRgsrtnADuB64L4/wDD3f1CoCRCORGROkcJuIhILeTue4AuwDBgG/CCmf24ksMHmdmHwEogA2gXtu+FKk5zl5mtAz4AHo6yvspsdPfcYHsFkBrMDz/N3f8WxP8cRT0iIrWeEnARkVrK3UvcfYm7jwZG8O9R5TJmdjZwL9Db3TsCrwL1ww7ZW8Upxrv7+cD1wLNmVj+K+ipTFLZdAiQBFkU5EZE6Rwm4iEgtZGbnBw9HlsoE/hFsFwKnBdsNCSXZu8zsDODyIz2Xu88FcoAhh6kv/LzR1PsVUGhm3YPQ4CNtm4hIbZRU3Q0QEZGj0gD4QzCNoxjYQGg6CsBU4DUzK3D3Xma2ElgDfA68d5Tn+yWhKSLphKaeRKrvkPNGWe9Q4Ckz2wssAXYdZftERGoNLUMoIiLVxswaBPPZMbOfAi3dfWQ1N0tEJKY0Ai4iItXpSjN7gNDfR/8Afly9zRERiT2NgIuIiIiIxJEewhQRERERiSMl4CIiIiIicaQEXEREREQkjpSAi4iIiIjEkRJwEREREZE4+v/oKxk0mtL8qAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rendered graphic in 0.559138 seconds\n"
     ]
    }
   ],
   "source": [
    "# metrics\n",
    "start_time = time()\n",
    "\n",
    "pyplot.figure(figsize=(12, 4))\n",
    "pyplot.title(\"Star Rating Histogram\")\n",
    "pyplot.xlabel(\"Star Rating\")\n",
    "pyplot.ylabel(\"Frequency\")\n",
    "star_color = spectra.scale(['red', 'yellow', 'green'])\n",
    "for star_rating, frequency in pandas.DataFrame(businesses_by_id.values(), columns=['stars'])['stars'].value_counts().sort_index().items():\n",
    "    pyplot.bar(str(star_rating), frequency, color=star_color((star_rating - 1)/ 4).hexcode)\n",
    "    pyplot.text(star_rating * 2 - 2.25, frequency + 400, frequency)\n",
    "pyplot.show()\n",
    "\n",
    "# metrics\n",
    "print(f\"Rendered graphic in {time() - start_time:.6f} seconds\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Review Count Distribution\n",
    "The following graphic displays a frequency count and cumulative distribution of review counts for businesses on Yelp.\n",
    "\n",
    "It becomes quite obvious our dataset is skewed and that there might be dataset noise and significant biases at play when trying to find a correlation between user reviews and a business' star rating."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rendered graphic in 39.499829 seconds\n"
     ]
    }
   ],
   "source": [
    "# metrics\n",
    "start_time = time()\n",
    "\n",
    "review_counts = [business['review_count'] for business in businesses_by_id.values()]\n",
    "figure = pyplot.figure(figsize=(12, 4))\n",
    "\n",
    "axes = figure.add_subplot(1, 2, 1)\n",
    "axes.set_title(\"Frequency of Review Counts\")\n",
    "axes.set_xlabel(\"Business Review Count\")\n",
    "axes.axis(xmin=0, xmax=mean(review_counts) + stdev(review_counts))\n",
    "axes.set_ylabel(\"Frequency\")\n",
    "axes.grid(True)\n",
    "axes.hist(review_counts, bins = list(range(0, max(review_counts))))\n",
    "\n",
    "axes = figure.add_subplot(1, 2, 2)\n",
    "axes.set_title(\"Cumulative Distribution of Review Counts\")\n",
    "axes.set_xlabel(\"Business Review Count\")\n",
    "axes.axis(xmin=0, xmax=mean(review_counts) + stdev(review_counts))\n",
    "axes.set_ylabel(\"Cumulative Proportion\")\n",
    "axes.yaxis.set_major_formatter(FuncFormatter('{0:.0%}'.format))\n",
    "axes.grid(True)\n",
    "pyplot.hist(review_counts, bins=list(range(0, max(review_counts))), cumulative=True, density=True)\n",
    "\n",
    "pyplot.show()\n",
    "\n",
    "# metrics\n",
    "print(f\"Rendered graphic in {time() - start_time:.6f} seconds\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## III. Data Preprocessing\n",
    "Before training a machine learning model, the raw text content has to be preprocessed into numerical matrices for TensorFlow. In the following subsections, the dataset is prepared for the machine learning model."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Filter Businesses with Less than 15 Reviews\n",
    "As seen in the visualization of star ratings, a significant amount of businesses do not have many user reviews. This is problematic, creating strong biases and word associations in user reviews on less-reviewed businesses. To mitigate bias and variance, a business must have a minimum threshold of 15 or more user reviews to be considered for the remainder of the project. The results should still be statistically significant, as close to half of all businesses have 15 or more reviews."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 80,355 businesses (filtered 129,038)\n"
     ]
    }
   ],
   "source": [
    "# minimum business['review_count'] required to be selected\n",
    "MINIMUM_REVIEW_COUNT = 15\n",
    "\n",
    "selected_businesses = [business for business in businesses_by_id.values() if MINIMUM_REVIEW_COUNT <= business['review_count']]\n",
    "\n",
    "print(f\"Selected {len(selected_businesses):,} businesses (filtered {len(businesses_by_id) - len(selected_businesses):,})\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Data Partitioning: Training, Validation, and Testing\n",
    "It is rather computationally expensive to use the entire dataset; instead a subset of the selected businesses are split into 3 disjoint partitions:\n",
    "- **training**: used for training the model\n",
    "- **validation**: provides an evaluation of the model fit during training\n",
    "- **testing**: provides an evaluation of the final fitted model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partitioned 209,393 businesses into {'train': 25000, 'validate': 1000, 'test': 10000} in 0.336058 seconds\n",
      "Remaining unused businesses: 173,393\n"
     ]
    }
   ],
   "source": [
    "# partition size constants\n",
    "TRAINING_SIZE = 25_000\n",
    "VALIDATE_SIZE = 1_000\n",
    "TESTING_SIZE = 10_000\n",
    "\n",
    "# metrics\n",
    "start_time = time()\n",
    "\n",
    "# perform partitions; note: train_test_split does not support 3-way splitting\n",
    "partitions = {}\n",
    "partitions['train'], _ = train_test_split(\n",
    "    # select all businesses that are greater than or equal to MINIMUM_REVIEW_COUNT\n",
    "    selected_businesses,\n",
    "    # partition proportions\n",
    "    train_size=TRAINING_SIZE,\n",
    "    shuffle=True,\n",
    "    # PRNG seed for deterministic behaviour\n",
    "    random_state=PRNG_SEED,\n",
    ")\n",
    "partitions['validate'], partitions['test'] = train_test_split(\n",
    "    # select all businesses not in partitions['train']\n",
    "    [\n",
    "        businesses_by_id[business_id]\n",
    "        for business_id in set([business['business_id'] for business in selected_businesses]).difference(set([business['business_id'] for business in partitions['train']]))\n",
    "    ],\n",
    "    # partition proportions\n",
    "    train_size=VALIDATE_SIZE,\n",
    "    test_size=TESTING_SIZE,\n",
    "    shuffle=True,\n",
    "    # PRNG seed for deterministic behaviour\n",
    "    random_state=PRNG_SEED,\n",
    ")\n",
    "\n",
    "# metrics\n",
    "print(f\"Partitioned {len(businesses_by_id):,} businesses into { {category: len(partition) for category, partition in partitions.items()}} in {time() - start_time:.6f} seconds\")\n",
    "print(f\"Remaining unused businesses: {len(businesses_by_id) - sum([len(partition) for partition in partitions.values()]):,}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Input Features: Preprocess Review Texts using TF-IDF Vectorization\n",
    "Natural language processing must be applied onto the user reviews to prepare the natural language input data into numerical matrices. Concatenated user reviews are vectorized into a [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) weighted word matrix.\n",
    "\n",
    "An important note is that this operation mitigates bias by splitting the training, validation, and testing subsets prior to fitting the TF-IDF vectorization model. Training data is used to fit the model for vectorization features and is later transformed, while validation testing data is _only_ transformed by the model.\n",
    "\n",
    "> **Note**: Increasing the `max_features` vectorizer parameter decreases the resulting RMSE score, but with diminishing returns."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word features cardinality: 1,000\n",
      "Word features: ['00', '10', '100', '11', '12', '15', '20', '24', '25', '30', '40', '45', '50', '99', 'able', 'absolutely', 'accommodating', 'actual', 'actually', 'add', 'added', 'addition', 'affordable', 'afternoon', 'ago', 'ahead', 'air', 'airport', 'amazing', 'ambiance', 'american', 'answer', 'apparently', 'appetizer', 'appetizers', 'appointment', 'appreciate', 'area', 'aren', 'arrived', 'asian', 'ask', 'asked', 'asking', 'ate', 'atmosphere', 'attention', 'attentive', 'attitude', 'authentic', 'available', 'average', 'avocado', 'avoid', 'away', 'awesome', 'awful', 'baby', 'bacon', 'bad', 'bag', 'baked', 'bar', 'barely', 'bartender', 'based', 'basic', 'basically', 'bathroom', 'bbq', 'beans', 'beautiful', 'bed', 'beef', 'beer', 'beers', 'believe', 'best', 'better', 'big', 'birthday', 'bit', 'bite', 'black', 'bland', 'blue', 'book', 'bottle', 'bought', 'bowl', 'box', 'boyfriend', 'bread', 'break', 'breakfast', 'bring', 'brisket', 'broth', 'brought', 'brunch', 'buffet', 'building', 'bun', 'bunch', 'burger', 'burgers', 'burrito', 'business', 'busy', 'butter', 'buy', 'cafe', 'cake', 'called', 'came', 'car', 'card', 'care', 'case', 'cash', 'cashier', 'casino', 'casual', 'cause', 'center', 'certainly', 'chain', 'chairs', 'chance', 'change', 'changed', 'charge', 'charged', 'charlotte', 'cheap', 'check', 'checked', 'checking', 'cheese', 'chef', 'chicken', 'chili', 'chinese', 'chips', 'chocolate', 'choice', 'choices', 'choose', 'chose', 'city', 'class', 'clean', 'cleaning', 'clear', 'clearly', 'close', 'closed', 'club', 'cocktail', 'cocktails', 'coffee', 'cold', 'color', 'combo', 'come', 'comes', 'comfortable', 'coming', 'company', 'compared', 'complaint', 'complete', 'completely', 'considering', 'convenient', 'conversation', 'cook', 'cooked', 'cool', 'corn', 'corner', 'cost', 'couldn', 'counter', 'couple', 'course', 'covered', 'crab', 'craving', 'crazy', 'cream', 'creamy', 'credit', 'crispy', 'crowd', 'crowded', 'crust', 'cup', 'curry', 'customer', 'customers', 'cut', 'cute', 'dark', 'date', 'daughter', 'day', 'days', 'deal', 'decent', 'decided', 'decor', 'deep', 'definitely', 'delicious', 'delivered', 'delivery', 'desk', 'despite', 'dessert', 'desserts', 'did', 'didn', 'die', 'different', 'difficult', 'dining', 'dinner', 'dip', 'dirty', 'disappointed', 'disappointing', 'discount', 'dish', 'dishes', 'doctor', 'does', 'doesn', 'dog', 'dogs', 'doing', 'dollars', 'don', 'dont', 'door', 'double', 'downtown', 'dr', 'dress', 'dressing', 'drink', 'drinks', 'drive', 'dry', 'duck', 'early', 'easily', 'easy', 'eat', 'eaten', 'eating', 'efficient', 'egg', 'eggs', 'employee', 'employees', 'end', 'ended', 'enjoy', 'enjoyed', 'entire', 'entree', 'entrees', 'environment', 'especially', 'establishment', 'evening', 'event', 'exactly', 'excellent', 'excited', 'expect', 'expectations', 'expected', 'expecting', 'expensive', 'experience', 'explained', 'extra', 'extremely', 'eye', 'fabulous', 'face', 'fact', 'fair', 'fairly', 'family', 'fan', 'fancy', 'fantastic', 'far', 'fast', 'favorite', 'fee', 'feel', 'feeling', 'felt', 'filled', 'filling', 'finally', 'fine', 'finish', 'finished', 'fish', 'fix', 'fixed', 'flavor', 'flavorful', 'flavors', 'flavour', 'floor', 'food', 'foods', 'forever', 'forget', 'forgot', 'forward', 'free', 'french', 'fresh', 'friday', 'fried', 'friend', 'friendly', 'friends', 'fries', 'frozen', 'fruit', 'fun', 'future', 'game', 'games', 'garlic', 'gave', 'gem', 'general', 'generous', 'gets', 'getting', 'girl', 'girls', 'given', 'giving', 'glad', 'glass', 'glasses', 'goes', 'going', 'gone', 'good', 'got', 'gotten', 'grab', 'greasy', 'great', 'green', 'greeted', 'grill', 'grilled', 'group', 'guess', 'guests', 'guy', 'guys', 'hair', 'half', 'hand', 'hands', 'happen', 'happened', 'happy', 'hard', 'hate', 'haven', 'having', 'head', 'healthy', 'hear', 'heard', 'heat', 'help', 'helped', 'helpful', 'high', 'higher', 'highly', 'hit', 'hold', 'home', 'homemade', 'honest', 'honestly', 'hope', 'horrible', 'hostess', 'hot', 'hotel', 'hour', 'hours', 'house', 'huge', 'hungry', 'husband', 'ice', 'idea', 'immediately', 'impressed', 'included', 'including', 'incredible', 'incredibly', 'indian', 'ingredients', 'inside', 'instead', 'interesting', 'interior', 'isn', 'issue', 'issues', 'italian', 'item', 'items', 'japanese', 'job', 'joint', 'juice', 'juicy', 'just', 'kept', 'kids', 'kind', 'kinda', 'kitchen', 'knew', 'know', 'knowledgeable', 'knows', 'korean', 'la', 'lack', 'lady', 'lamb', 'large', 'las', 'late', 'later', 'leave', 'leaving', 'left', 'lemon', 'let', 'lettuce', 'level', 'life', 'light', 'like', 'liked', 'limited', 'line', 'lines', 'list', 'literally', 'little', 'live', 'lived', 'll', 'lobster', 'local', 'located', 'location', 'locations', 'lol', 'long', 'longer', 'look', 'looked', 'looking', 'looks', 'lost', 'lot', 'lots', 'loud', 'love', 'loved', 'lovely', 'low', 'lunch', 'mac', 'main', 'make', 'makes', 'making', 'mall', 'man', 'management', 'manager', 'mango', 'market', 'massage', 'matter', 'maybe', 'meal', 'meals', 'mean', 'means', 'meat', 'meats', 'mediocre', 'medium', 'mention', 'mentioned', 'menu', 'met', 'mexican', 'middle', 'milk', 'min', 'mind', 'mins', 'minute', 'minutes', 'miss', 'missing', 'mistake', 'mix', 'mixed', 'modern', 'mom', 'monday', 'money', 'month', 'months', 'morning', 'mouth', 'moved', 'moving', 'multiple', 'music', 'nail', 'nails', 'near', 'need', 'needed', 'needs', 'negative', 'neighborhood', 'new', 'nice', 'nicely', 'night', 'nights', 'non', 'noodle', 'noodles', 'normal', 'normally', 'notch', 'note', 'noticed', 'number', 'obviously', 'offer', 'offered', 'office', 'oh', 'oil', 'ok', 'okay', 'old', 'ones', 'onion', 'onions', 'online', 'open', 'opened', 'opinion', 'option', 'options', 'orange', 'order', 'ordered', 'ordering', 'orders', 'original', 'outdoor', 'outside', 'outstanding', 'overall', 'overpriced', 'owner', 'owners', 'packed', 'pad', 'paid', 'pancakes', 'park', 'parking', 'party', 'pass', 'past', 'pasta', 'patient', 'patio', 'pay', 'paying', 'people', 'pepper', 'perfect', 'perfection', 'perfectly', 'person', 'personal', 'personally', 'pho', 'phoenix', 'phone', 'pick', 'picked', 'pictures', 'pie', 'piece', 'pieces', 'pizza', 'place', 'placed', 'places', 'plan', 'plate', 'plates', 'play', 'playing', 'pleasant', 'pleased', 'plenty', 'plus', 'pm', 'point', 'polite', 'pool', 'poor', 'pork', 'portion', 'portions', 'positive', 'possible', 'potato', 'potatoes', 'prefer', 'prepared', 'presentation', 'pretty', 'previous', 'price', 'priced', 'prices', 'pricey', 'pricing', 'prime', 'probably', 'problem', 'process', 'products', 'professional', 'provided', 'pulled', 'purchase', 'purchased', 'quality', 'questions', 'quick', 'quickly', 'quiet', 'quite', 'ramen', 'rare', 'rating', 'read', 'reading', 'ready', 'real', 'really', 'reason', 'reasonable', 'received', 'recently', 'recommend', 'recommended', 'red', 'regular', 'remember', 'request', 'reservation', 'reservations', 'resort', 'rest', 'restaurant', 'restaurants', 'return', 'returning', 'review', 'reviews', 'rib', 'ribs', 'rice', 'rich', 'ridiculous', 'right', 'rock', 'roll', 'rolls', 'room', 'rooms', 'rude', 'run', 'running', 'rush', 'sad', 'said', 'salad', 'salads', 'sales', 'salmon', 'salon', 'salsa', 'salt', 'salty', 'sandwich', 'sandwiches', 'sat', 'satisfied', 'saturday', 'sauce', 'sauces', 'sausage', 'save', 'saw', 'say', 'saying', 'says', 'school', 'scottsdale', 'seafood', 'seasoned', 'seat', 'seated', 'seating', 'seats', 'second', 'section', 'security', 'seeing', 'seen', 'selection', 'sell', 'sent', 'seriously', 'serve', 'served', 'server', 'servers', 'service', 'services', 'serving', 'set', 'share', 'shared', 'shop', 'shopping', 'shops', 'short', 'shot', 'showed', 'shows', 'shrimp', 'sides', 'sign', 'similar', 'simple', 'simply', 'single', 'sit', 'sitting', 'situation', 'size', 'slightly', 'slow', 'small', 'smaller', 'smell', 'smile', 'soft', 'solid', 'son', 'soon', 'sorry', 'sort', 'soup', 'sour', 'spa', 'space', 'speak', 'special', 'specials', 'spend', 'spent', 'spice', 'spicy', 'split', 'spot', 'spring', 'staff', 'stand', 'standard', 'star', 'stars', 'start', 'started', 'station', 'stay', 'stayed', 'staying', 'steak', 'stop', 'stopped', 'store', 'stores', 'street', 'strip', 'strong', 'stuff', 'stuffed', 'style', 'sugar', 'suggest', 'summer', 'sunday', 'super', 'supposed', 'sure', 'surprise', 'surprised', 'sushi', 'sweet', 'table', 'tables', 'taco', 'tacos', 'taken', 'takes', 'taking', 'talk', 'talking', 'taste', 'tasted', 'tastes', 'tasting', 'tasty', 'tea', 'team', 'tell', 'tender', 'terrible', 'texture', 'thai', 'thank', 'thanks', 'thing', 'things', 'think', 'thinking', 'thought', 'time', 'times', 'tiny', 'tip', 'toast', 'today', 'tofu', 'told', 'tomato', 'tonight', 'took', 'toppings', 'toronto', 'total', 'totally', 'touch', 'town', 'traditional', 'treat', 'treated', 'tried', 'trip', 'true', 'truly', 'try', 'trying', 'tuna', 'turn', 'turned', 'tv', 'twice', 'type', 'typical', 'understand', 'unfortunately', 'unique', 'unless', 'update', 'use', 'used', 'using', 'usual', 'usually', 'valley', 'value', 'variety', 've', 'vegan', 'vegas', 'vegetables', 'vegetarian', 'veggie', 'veggies', 'vehicle', 'vibe', 'view', 'visit', 'visited', 'visiting', 'waffles', 'wait', 'waited', 'waiter', 'waiting', 'waitress', 'walk', 'walked', 'walking', 'wall', 'want', 'wanted', 'warm', 'wash', 'wasn', 'waste', 'watch', 'watching', 'water', 'way', 'wedding', 'week', 'weekend', 'weeks', 'weird', 'welcoming', 'went', 'weren', 'white', 'wife', 'window', 'wine', 'wings', 'wish', 'woman', 'won', 'wonderful', 'word', 'work', 'worked', 'workers', 'working', 'works', 'world', 'worse', 'worst', 'worth', 'wouldn', 'wow', 'write', 'wrong', 'year', 'years', 'yelp', 'yes', 'yesterday', 'young', 'yum', 'yummy']\n",
      "IDF Vectorized 36,000 businesses review texts in 337.020741 seconds\n",
      "\n",
      "Training Shape: (25000, 1000)\n",
      "Validate Shape: (1000, 1000)\n",
      "Testing Shape: (10000, 1000)\n"
     ]
    }
   ],
   "source": [
    "# metrics\n",
    "start_time = time()\n",
    "\n",
    "# construct vectorizer\n",
    "vectorizer: TfidfVectorizer = TfidfVectorizer(\n",
    "    # maximum word features\n",
    "    max_features=1000,\n",
    "    # prune english stop words\n",
    "    stop_words='english',\n",
    ")\n",
    "\n",
    "# construct corpus partitions\n",
    "tf_input = {\n",
    "    category: getattr(vectorizer, 'fit_transform' if category == 'train' else 'transform')\n",
    "              (['\\n'.join(business_review_texts[business['business_id']]) for business in partition])\n",
    "              .toarray()\n",
    "    for category, partition in partitions.items()\n",
    "}\n",
    "\n",
    "# metrics\n",
    "print(f\"Word features cardinality: {len(vectorizer.get_feature_names()):,}\")\n",
    "print(f\"Word features: {vectorizer.get_feature_names()}\")\n",
    "print(f\"IDF Vectorized {sum([len(matrix) for matrix in tf_input.values()]):,} businesses review texts in {time() - start_time:.6f} seconds\")\n",
    "print()\n",
    "print(f\"Training Shape: {tf_input['train'].shape}\")\n",
    "print(f\"Validate Shape: {tf_input['validate'].shape}\")\n",
    "print(f\"Testing Shape: {tf_input['test'].shape}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Output Features: Select Star Rating\n",
    "The only output feature for our training model is a business' unnormalized star rating. Despite the discrete values for star ratings, regression is used here and the predicted result can be rounded to the nearest half star."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Shape: (25000, 1)\n",
      "Validate Shape: (1000, 1)\n",
      "Testing Shape: (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "tf_output = {\n",
    "    category: numpy.array([[business['stars']] for business in partition])\n",
    "    for category, partition in partitions.items()\n",
    "}\n",
    "\n",
    "print(f\"Training Shape: {tf_output['train'].shape}\")\n",
    "print(f\"Validate Shape: {tf_output['validate'].shape}\")\n",
    "print(f\"Testing Shape: {tf_output['test'].shape}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# IV. Model Training\n",
    "Using the defined input and output feature matrices, a neural network can be trained with a supervised learning regression model. Potentially, a correlation may be discovered between text reviews and the star rating."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Build Model\n",
    "A supervised learning Sequential neural network is constructed for building a regression model. A simple model with fully-connected dense layers is employed here."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"yelp_regression\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                64064     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 66,689\n",
      "Trainable params: 66,689\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# build model\n",
    "model = Sequential(\n",
    "    [\n",
    "        Dense(64, input_dim=tf_input['train'].shape[1]),\n",
    "        Dense(32, activation='relu'),\n",
    "        Dense(16, activation='relu'),\n",
    "        Dense(1)\n",
    "    ],\n",
    "    name=\"yelp_regression\"\n",
    ")\n",
    "# set optimizer for gradient descent\n",
    "model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['mse'])\n",
    "\n",
    "# print model summary\n",
    "model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Train Model\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model trained in 210.069468 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "    <details>        <summary><b>Training Details</b></summary>        <sub><sup>            Epoch 1/1000<br>782/782 - 2s - loss: 0.5006 - mse: 0.5006 - val_loss: 0.1839 - val_mse: 0.1839<br>Epoch 2/1000<br>782/782 - 1s - loss: 0.1471 - mse: 0.1471 - val_loss: 0.1705 - val_mse: 0.1705<br>Epoch 3/1000<br>782/782 - 1s - loss: 0.1271 - mse: 0.1271 - val_loss: 0.1341 - val_mse: 0.1341<br>Epoch 4/1000<br>782/782 - 1s - loss: 0.1162 - mse: 0.1162 - val_loss: 0.1535 - val_mse: 0.1535<br>Epoch 5/1000<br>782/782 - 1s - loss: 0.1098 - mse: 0.1098 - val_loss: 0.1288 - val_mse: 0.1288<br>Epoch 6/1000<br>782/782 - 1s - loss: 0.1033 - mse: 0.1033 - val_loss: 0.1098 - val_mse: 0.1098<br>Epoch 7/1000<br>782/782 - 1s - loss: 0.1002 - mse: 0.1002 - val_loss: 0.1065 - val_mse: 0.1065<br>Epoch 8/1000<br>782/782 - 1s - loss: 0.0957 - mse: 0.0957 - val_loss: 0.1057 - val_mse: 0.1057<br>Epoch 9/1000<br>782/782 - 1s - loss: 0.0918 - mse: 0.0918 - val_loss: 0.1039 - val_mse: 0.1039<br>Epoch 10/1000<br>782/782 - 1s - loss: 0.0896 - mse: 0.0896 - val_loss: 0.0993 - val_mse: 0.0993<br>Epoch 11/1000<br>782/782 - 1s - loss: 0.0883 - mse: 0.0883 - val_loss: 0.1340 - val_mse: 0.1340<br>Epoch 12/1000<br>782/782 - 1s - loss: 0.0857 - mse: 0.0857 - val_loss: 0.1129 - val_mse: 0.1129<br>Epoch 13/1000<br>782/782 - 1s - loss: 0.0844 - mse: 0.0844 - val_loss: 0.1236 - val_mse: 0.1236<br>Epoch 14/1000<br>782/782 - 1s - loss: 0.0835 - mse: 0.0835 - val_loss: 0.0932 - val_mse: 0.0932<br>Epoch 15/1000<br>782/782 - 1s - loss: 0.0823 - mse: 0.0823 - val_loss: 0.0950 - val_mse: 0.0950<br>Epoch 16/1000<br>782/782 - 1s - loss: 0.0813 - mse: 0.0813 - val_loss: 0.1009 - val_mse: 0.1009<br>Epoch 17/1000<br>782/782 - 2s - loss: 0.0811 - mse: 0.0811 - val_loss: 0.0985 - val_mse: 0.0985<br>Epoch 18/1000<br>782/782 - 1s - loss: 0.0802 - mse: 0.0802 - val_loss: 0.0983 - val_mse: 0.0983<br>Epoch 19/1000<br>782/782 - 1s - loss: 0.0797 - mse: 0.0797 - val_loss: 0.0901 - val_mse: 0.0901<br>Epoch 20/1000<br>782/782 - 2s - loss: 0.0794 - mse: 0.0794 - val_loss: 0.1285 - val_mse: 0.1285<br>Epoch 21/1000<br>782/782 - 1s - loss: 0.0786 - mse: 0.0786 - val_loss: 0.1076 - val_mse: 0.1076<br>Epoch 22/1000<br>782/782 - 1s - loss: 0.0778 - mse: 0.0778 - val_loss: 0.1248 - val_mse: 0.1248<br>Epoch 23/1000<br>782/782 - 2s - loss: 0.0775 - mse: 0.0775 - val_loss: 0.0904 - val_mse: 0.0904<br>Epoch 24/1000<br>782/782 - 1s - loss: 0.0769 - mse: 0.0769 - val_loss: 0.0910 - val_mse: 0.0910<br>Epoch 25/1000<br>782/782 - 1s - loss: 0.0766 - mse: 0.0766 - val_loss: 0.0901 - val_mse: 0.0901<br>Epoch 26/1000<br>782/782 - 1s - loss: 0.0760 - mse: 0.0760 - val_loss: 0.1295 - val_mse: 0.1295<br>Epoch 27/1000<br>782/782 - 1s - loss: 0.0756 - mse: 0.0756 - val_loss: 0.0894 - val_mse: 0.0894<br>Epoch 28/1000<br>782/782 - 1s - loss: 0.0754 - mse: 0.0754 - val_loss: 0.0915 - val_mse: 0.0915<br>Epoch 29/1000<br>782/782 - 1s - loss: 0.0745 - mse: 0.0745 - val_loss: 0.1030 - val_mse: 0.1030<br>Epoch 30/1000<br>782/782 - 1s - loss: 0.0743 - mse: 0.0743 - val_loss: 0.1083 - val_mse: 0.1083<br>Epoch 31/1000<br>782/782 - 1s - loss: 0.0741 - mse: 0.0741 - val_loss: 0.0870 - val_mse: 0.0870<br>Epoch 32/1000<br>782/782 - 1s - loss: 0.0738 - mse: 0.0738 - val_loss: 0.0919 - val_mse: 0.0919<br>Epoch 33/1000<br>782/782 - 1s - loss: 0.0735 - mse: 0.0735 - val_loss: 0.0869 - val_mse: 0.0869<br>Epoch 34/1000<br>782/782 - 1s - loss: 0.0729 - mse: 0.0729 - val_loss: 0.0872 - val_mse: 0.0872<br>Epoch 35/1000<br>782/782 - 1s - loss: 0.0732 - mse: 0.0732 - val_loss: 0.0879 - val_mse: 0.0879<br>Epoch 36/1000<br>782/782 - 1s - loss: 0.0728 - mse: 0.0728 - val_loss: 0.0863 - val_mse: 0.0863<br>Epoch 37/1000<br>782/782 - 1s - loss: 0.0722 - mse: 0.0722 - val_loss: 0.1625 - val_mse: 0.1625<br>Epoch 38/1000<br>782/782 - 1s - loss: 0.0723 - mse: 0.0723 - val_loss: 0.0877 - val_mse: 0.0877<br>Epoch 39/1000<br>782/782 - 1s - loss: 0.0719 - mse: 0.0719 - val_loss: 0.1170 - val_mse: 0.1170<br>Epoch 40/1000<br>782/782 - 1s - loss: 0.0717 - mse: 0.0717 - val_loss: 0.1047 - val_mse: 0.1047<br>Epoch 41/1000<br>782/782 - 1s - loss: 0.0711 - mse: 0.0711 - val_loss: 0.1365 - val_mse: 0.1365<br>Epoch 42/1000<br>782/782 - 1s - loss: 0.0712 - mse: 0.0712 - val_loss: 0.0858 - val_mse: 0.0858<br>Epoch 43/1000<br>782/782 - 1s - loss: 0.0708 - mse: 0.0708 - val_loss: 0.0876 - val_mse: 0.0876<br>Epoch 44/1000<br>782/782 - 1s - loss: 0.0708 - mse: 0.0708 - val_loss: 0.0904 - val_mse: 0.0904<br>Epoch 45/1000<br>782/782 - 1s - loss: 0.0701 - mse: 0.0701 - val_loss: 0.1162 - val_mse: 0.1162<br>Epoch 46/1000<br>782/782 - 1s - loss: 0.0703 - mse: 0.0703 - val_loss: 0.0926 - val_mse: 0.0926<br>Epoch 47/1000<br>782/782 - 1s - loss: 0.0702 - mse: 0.0702 - val_loss: 0.0861 - val_mse: 0.0861<br>Epoch 48/1000<br>782/782 - 1s - loss: 0.0700 - mse: 0.0700 - val_loss: 0.1158 - val_mse: 0.1158<br>Epoch 49/1000<br>782/782 - 1s - loss: 0.0698 - mse: 0.0698 - val_loss: 0.0847 - val_mse: 0.0847<br>Epoch 50/1000<br>782/782 - 1s - loss: 0.0693 - mse: 0.0693 - val_loss: 0.0943 - val_mse: 0.0943<br>Epoch 51/1000<br>782/782 - 1s - loss: 0.0693 - mse: 0.0693 - val_loss: 0.0870 - val_mse: 0.0870<br>Epoch 52/1000<br>782/782 - 1s - loss: 0.0690 - mse: 0.0690 - val_loss: 0.0911 - val_mse: 0.0911<br>Epoch 53/1000<br>782/782 - 1s - loss: 0.0687 - mse: 0.0687 - val_loss: 0.0852 - val_mse: 0.0852<br>Epoch 54/1000<br>782/782 - 1s - loss: 0.0690 - mse: 0.0690 - val_loss: 0.1068 - val_mse: 0.1068<br>Epoch 55/1000<br>782/782 - 1s - loss: 0.0686 - mse: 0.0686 - val_loss: 0.0840 - val_mse: 0.0840<br>Epoch 56/1000<br>782/782 - 1s - loss: 0.0685 - mse: 0.0685 - val_loss: 0.0956 - val_mse: 0.0956<br>Epoch 57/1000<br>782/782 - 1s - loss: 0.0684 - mse: 0.0684 - val_loss: 0.0840 - val_mse: 0.0840<br>Epoch 58/1000<br>782/782 - 1s - loss: 0.0682 - mse: 0.0682 - val_loss: 0.0926 - val_mse: 0.0926<br>Epoch 59/1000<br>782/782 - 1s - loss: 0.0682 - mse: 0.0682 - val_loss: 0.0873 - val_mse: 0.0873<br>Epoch 60/1000<br>782/782 - 1s - loss: 0.0678 - mse: 0.0678 - val_loss: 0.0846 - val_mse: 0.0846<br>Epoch 61/1000<br>782/782 - 1s - loss: 0.0679 - mse: 0.0679 - val_loss: 0.0980 - val_mse: 0.0980<br>Epoch 62/1000<br>782/782 - 1s - loss: 0.0675 - mse: 0.0675 - val_loss: 0.0859 - val_mse: 0.0859<br>Epoch 63/1000<br>782/782 - 1s - loss: 0.0674 - mse: 0.0674 - val_loss: 0.0846 - val_mse: 0.0846<br>Epoch 64/1000<br>782/782 - 1s - loss: 0.0676 - mse: 0.0676 - val_loss: 0.0880 - val_mse: 0.0880<br>Epoch 65/1000<br>782/782 - 1s - loss: 0.0672 - mse: 0.0672 - val_loss: 0.0879 - val_mse: 0.0879<br>Epoch 66/1000<br>782/782 - 1s - loss: 0.0670 - mse: 0.0670 - val_loss: 0.0839 - val_mse: 0.0839<br>Epoch 67/1000<br>782/782 - 1s - loss: 0.0669 - mse: 0.0669 - val_loss: 0.0851 - val_mse: 0.0851<br>Epoch 68/1000<br>782/782 - 1s - loss: 0.0667 - mse: 0.0667 - val_loss: 0.0862 - val_mse: 0.0862<br>Epoch 69/1000<br>782/782 - 1s - loss: 0.0667 - mse: 0.0667 - val_loss: 0.0828 - val_mse: 0.0828<br>Epoch 70/1000<br>782/782 - 1s - loss: 0.0666 - mse: 0.0666 - val_loss: 0.0836 - val_mse: 0.0836<br>Epoch 71/1000<br>782/782 - 1s - loss: 0.0664 - mse: 0.0664 - val_loss: 0.0848 - val_mse: 0.0848<br>Epoch 72/1000<br>782/782 - 1s - loss: 0.0660 - mse: 0.0660 - val_loss: 0.0833 - val_mse: 0.0833<br>Epoch 73/1000<br>782/782 - 1s - loss: 0.0660 - mse: 0.0660 - val_loss: 0.0910 - val_mse: 0.0910<br>Epoch 74/1000<br>782/782 - 1s - loss: 0.0660 - mse: 0.0660 - val_loss: 0.0845 - val_mse: 0.0845<br>Epoch 75/1000<br>782/782 - 1s - loss: 0.0661 - mse: 0.0661 - val_loss: 0.0836 - val_mse: 0.0836<br>Epoch 76/1000<br>782/782 - 1s - loss: 0.0655 - mse: 0.0655 - val_loss: 0.0838 - val_mse: 0.0838<br>Epoch 77/1000<br>782/782 - 1s - loss: 0.0656 - mse: 0.0656 - val_loss: 0.0843 - val_mse: 0.0843<br>Epoch 78/1000<br>782/782 - 1s - loss: 0.0652 - mse: 0.0652 - val_loss: 0.0832 - val_mse: 0.0832<br>Epoch 79/1000<br>782/782 - 1s - loss: 0.0653 - mse: 0.0653 - val_loss: 0.0849 - val_mse: 0.0849<br>Epoch 80/1000<br>782/782 - 1s - loss: 0.0649 - mse: 0.0649 - val_loss: 0.0867 - val_mse: 0.0867<br>Epoch 81/1000<br>782/782 - 1s - loss: 0.0651 - mse: 0.0651 - val_loss: 0.0857 - val_mse: 0.0857<br>Epoch 82/1000<br>782/782 - 1s - loss: 0.0650 - mse: 0.0650 - val_loss: 0.0845 - val_mse: 0.0845<br>Epoch 83/1000<br>782/782 - 1s - loss: 0.0650 - mse: 0.0650 - val_loss: 0.1058 - val_mse: 0.1058<br>Epoch 84/1000<br>782/782 - 1s - loss: 0.0645 - mse: 0.0645 - val_loss: 0.0866 - val_mse: 0.0866<br>Epoch 85/1000<br>782/782 - 1s - loss: 0.0647 - mse: 0.0647 - val_loss: 0.0854 - val_mse: 0.0854<br>Epoch 86/1000<br>782/782 - 1s - loss: 0.0644 - mse: 0.0644 - val_loss: 0.0842 - val_mse: 0.0842<br>Epoch 87/1000<br>782/782 - 1s - loss: 0.0645 - mse: 0.0645 - val_loss: 0.0932 - val_mse: 0.0932<br>Epoch 88/1000<br>782/782 - 1s - loss: 0.0642 - mse: 0.0642 - val_loss: 0.0920 - val_mse: 0.0920<br>Epoch 89/1000<br>782/782 - 1s - loss: 0.0641 - mse: 0.0641 - val_loss: 0.0935 - val_mse: 0.0935<br>Epoch 90/1000<br>782/782 - 1s - loss: 0.0639 - mse: 0.0639 - val_loss: 0.0857 - val_mse: 0.0857<br>Epoch 91/1000<br>782/782 - 1s - loss: 0.0638 - mse: 0.0638 - val_loss: 0.0839 - val_mse: 0.0839<br>Epoch 92/1000<br>782/782 - 1s - loss: 0.0639 - mse: 0.0639 - val_loss: 0.0841 - val_mse: 0.0841<br>Epoch 93/1000<br>782/782 - 1s - loss: 0.0638 - mse: 0.0638 - val_loss: 0.0830 - val_mse: 0.0830<br>Epoch 94/1000<br>782/782 - 1s - loss: 0.0635 - mse: 0.0635 - val_loss: 0.0833 - val_mse: 0.0833<br>Epoch 95/1000<br>782/782 - 1s - loss: 0.0633 - mse: 0.0633 - val_loss: 0.0839 - val_mse: 0.0839<br>Epoch 96/1000<br>782/782 - 1s - loss: 0.0633 - mse: 0.0633 - val_loss: 0.0827 - val_mse: 0.0827<br>Epoch 97/1000<br>782/782 - 1s - loss: 0.0631 - mse: 0.0631 - val_loss: 0.0893 - val_mse: 0.0893<br>Epoch 98/1000<br>782/782 - 1s - loss: 0.0631 - mse: 0.0631 - val_loss: 0.0839 - val_mse: 0.0839<br>Epoch 99/1000<br>782/782 - 1s - loss: 0.0630 - mse: 0.0630 - val_loss: 0.0829 - val_mse: 0.0829<br>Epoch 100/1000<br>782/782 - 1s - loss: 0.0628 - mse: 0.0628 - val_loss: 0.0825 - val_mse: 0.0825<br>Epoch 101/1000<br>782/782 - 1s - loss: 0.0625 - mse: 0.0625 - val_loss: 0.0831 - val_mse: 0.0831<br>Epoch 102/1000<br>782/782 - 1s - loss: 0.0628 - mse: 0.0628 - val_loss: 0.0822 - val_mse: 0.0822<br>Epoch 103/1000<br>782/782 - 1s - loss: 0.0625 - mse: 0.0625 - val_loss: 0.0838 - val_mse: 0.0838<br>Epoch 104/1000<br>782/782 - 1s - loss: 0.0624 - mse: 0.0624 - val_loss: 0.0862 - val_mse: 0.0862<br>Epoch 105/1000<br>782/782 - 1s - loss: 0.0621 - mse: 0.0621 - val_loss: 0.0902 - val_mse: 0.0902<br>Epoch 106/1000<br>782/782 - 1s - loss: 0.0620 - mse: 0.0620 - val_loss: 0.0877 - val_mse: 0.0877<br>Epoch 107/1000<br>782/782 - 1s - loss: 0.0619 - mse: 0.0619 - val_loss: 0.0893 - val_mse: 0.0893<br>Epoch 108/1000<br>782/782 - 1s - loss: 0.0619 - mse: 0.0619 - val_loss: 0.0860 - val_mse: 0.0860<br>Epoch 109/1000<br>782/782 - 1s - loss: 0.0618 - mse: 0.0618 - val_loss: 0.0904 - val_mse: 0.0904<br>Epoch 110/1000<br>782/782 - 1s - loss: 0.0616 - mse: 0.0616 - val_loss: 0.0856 - val_mse: 0.0856<br>Epoch 111/1000<br>782/782 - 1s - loss: 0.0616 - mse: 0.0616 - val_loss: 0.0903 - val_mse: 0.0903<br>Epoch 112/1000<br>782/782 - 1s - loss: 0.0616 - mse: 0.0616 - val_loss: 0.0851 - val_mse: 0.0851<br>Epoch 113/1000<br>782/782 - 1s - loss: 0.0616 - mse: 0.0616 - val_loss: 0.0935 - val_mse: 0.0935<br>Epoch 114/1000<br>782/782 - 1s - loss: 0.0612 - mse: 0.0612 - val_loss: 0.0851 - val_mse: 0.0851<br>Epoch 115/1000<br>782/782 - 1s - loss: 0.0612 - mse: 0.0612 - val_loss: 0.0844 - val_mse: 0.0844<br>Epoch 116/1000<br>782/782 - 1s - loss: 0.0608 - mse: 0.0608 - val_loss: 0.0858 - val_mse: 0.0858<br>Epoch 117/1000<br>782/782 - 1s - loss: 0.0610 - mse: 0.0610 - val_loss: 0.0876 - val_mse: 0.0876<br>Epoch 118/1000<br>782/782 - 1s - loss: 0.0609 - mse: 0.0609 - val_loss: 0.0830 - val_mse: 0.0830<br>Epoch 119/1000<br>782/782 - 1s - loss: 0.0607 - mse: 0.0607 - val_loss: 0.0826 - val_mse: 0.0826<br>Epoch 120/1000<br>782/782 - 1s - loss: 0.0606 - mse: 0.0606 - val_loss: 0.0830 - val_mse: 0.0830<br>Epoch 121/1000<br>782/782 - 1s - loss: 0.0606 - mse: 0.0606 - val_loss: 0.0860 - val_mse: 0.0860<br>Epoch 122/1000<br>782/782 - 1s - loss: 0.0602 - mse: 0.0602 - val_loss: 0.1017 - val_mse: 0.1017<br>Epoch 123/1000<br>782/782 - 1s - loss: 0.0602 - mse: 0.0602 - val_loss: 0.0906 - val_mse: 0.0906<br>Epoch 124/1000<br>782/782 - 1s - loss: 0.0599 - mse: 0.0599 - val_loss: 0.0863 - val_mse: 0.0863<br>Epoch 125/1000<br>782/782 - 1s - loss: 0.0601 - mse: 0.0601 - val_loss: 0.0833 - val_mse: 0.0833<br>Epoch 126/1000<br>782/782 - 1s - loss: 0.0599 - mse: 0.0599 - val_loss: 0.0820 - val_mse: 0.0820<br>Epoch 127/1000<br>782/782 - 1s - loss: 0.0601 - mse: 0.0601 - val_loss: 0.0821 - val_mse: 0.0821<br>Epoch 128/1000<br>782/782 - 1s - loss: 0.0597 - mse: 0.0597 - val_loss: 0.0827 - val_mse: 0.0827<br>Epoch 129/1000<br>782/782 - 1s - loss: 0.0596 - mse: 0.0596 - val_loss: 0.0831 - val_mse: 0.0831<br>Epoch 130/1000<br>782/782 - 1s - loss: 0.0595 - mse: 0.0595 - val_loss: 0.0859 - val_mse: 0.0859<br>Epoch 131/1000<br>782/782 - 1s - loss: 0.0592 - mse: 0.0592 - val_loss: 0.0902 - val_mse: 0.0902<br>Epoch 132/1000<br>782/782 - 1s - loss: 0.0590 - mse: 0.0590 - val_loss: 0.0840 - val_mse: 0.0840<br>Epoch 133/1000<br>782/782 - 1s - loss: 0.0592 - mse: 0.0592 - val_loss: 0.0868 - val_mse: 0.0868<br>Epoch 134/1000<br>782/782 - 1s - loss: 0.0591 - mse: 0.0591 - val_loss: 0.0865 - val_mse: 0.0865<br>Epoch 135/1000<br>782/782 - 1s - loss: 0.0588 - mse: 0.0588 - val_loss: 0.0848 - val_mse: 0.0848<br>Epoch 136/1000<br>782/782 - 1s - loss: 0.0586 - mse: 0.0586 - val_loss: 0.0834 - val_mse: 0.0834<br>Epoch 137/1000<br>782/782 - 1s - loss: 0.0587 - mse: 0.0587 - val_loss: 0.0852 - val_mse: 0.0852<br>Epoch 138/1000<br>782/782 - 2s - loss: 0.0585 - mse: 0.0585 - val_loss: 0.0915 - val_mse: 0.0915<br>Epoch 139/1000<br>782/782 - 1s - loss: 0.0584 - mse: 0.0584 - val_loss: 0.0852 - val_mse: 0.0852<br>Epoch 140/1000<br>782/782 - 1s - loss: 0.0581 - mse: 0.0581 - val_loss: 0.1141 - val_mse: 0.1141<br>Epoch 141/1000<br>782/782 - 1s - loss: 0.0579 - mse: 0.0579 - val_loss: 0.0877 - val_mse: 0.0877<br>Epoch 142/1000<br>782/782 - 1s - loss: 0.0578 - mse: 0.0578 - val_loss: 0.0857 - val_mse: 0.0857<br>Epoch 143/1000<br>782/782 - 1s - loss: 0.0581 - mse: 0.0581 - val_loss: 0.0850 - val_mse: 0.0850<br>Epoch 144/1000<br>782/782 - 1s - loss: 0.0574 - mse: 0.0574 - val_loss: 0.0855 - val_mse: 0.0855<br>Epoch 145/1000<br>782/782 - 1s - loss: 0.0575 - mse: 0.0575 - val_loss: 0.0843 - val_mse: 0.0843<br>Epoch 146/1000<br>782/782 - 1s - loss: 0.0577 - mse: 0.0577 - val_loss: 0.0915 - val_mse: 0.0915<br>Epoch 147/1000<br>782/782 - 1s - loss: 0.0573 - mse: 0.0573 - val_loss: 0.0848 - val_mse: 0.0848<br>Epoch 148/1000<br>782/782 - 1s - loss: 0.0572 - mse: 0.0572 - val_loss: 0.0854 - val_mse: 0.0854<br>Epoch 149/1000<br>782/782 - 1s - loss: 0.0569 - mse: 0.0569 - val_loss: 0.0863 - val_mse: 0.0863<br>Epoch 150/1000<br>782/782 - 1s - loss: 0.0567 - mse: 0.0567 - val_loss: 0.0912 - val_mse: 0.0912<br>Epoch 151/1000<br>782/782 - 1s - loss: 0.0567 - mse: 0.0567 - val_loss: 0.0959 - val_mse: 0.0959<br>Epoch 152/1000<br>782/782 - 1s - loss: 0.0566 - mse: 0.0566 - val_loss: 0.1128 - val_mse: 0.1128<br>Epoch 153/1000<br>782/782 - 1s - loss: 0.0567 - mse: 0.0567 - val_loss: 0.0931 - val_mse: 0.0931<br>Epoch 154/1000<br>782/782 - 1s - loss: 0.0568 - mse: 0.0568 - val_loss: 0.0888 - val_mse: 0.0888<br>Epoch 155/1000<br>782/782 - 1s - loss: 0.0563 - mse: 0.0563 - val_loss: 0.0948 - val_mse: 0.0948<br>Epoch 156/1000<br>782/782 - 1s - loss: 0.0561 - mse: 0.0561 - val_loss: 0.0908 - val_mse: 0.0908<br>Epoch 157/1000<br>782/782 - 1s - loss: 0.0560 - mse: 0.0560 - val_loss: 0.0841 - val_mse: 0.0841<br>Epoch 158/1000<br>782/782 - 1s - loss: 0.0557 - mse: 0.0557 - val_loss: 0.0911 - val_mse: 0.0911<br>Epoch 159/1000<br>782/782 - 1s - loss: 0.0557 - mse: 0.0557 - val_loss: 0.1110 - val_mse: 0.1110<br>Epoch 160/1000<br>782/782 - 1s - loss: 0.0556 - mse: 0.0556 - val_loss: 0.1032 - val_mse: 0.1032<br>Epoch 161/1000<br>782/782 - 1s - loss: 0.0555 - mse: 0.0555 - val_loss: 0.0854 - val_mse: 0.0854<br>Epoch 162/1000<br>782/782 - 1s - loss: 0.0555 - mse: 0.0555 - val_loss: 0.0941 - val_mse: 0.0941<br>Epoch 163/1000<br>782/782 - 1s - loss: 0.0553 - mse: 0.0553 - val_loss: 0.0945 - val_mse: 0.0945<br>Epoch 164/1000<br>782/782 - 1s - loss: 0.0550 - mse: 0.0550 - val_loss: 0.0856 - val_mse: 0.0856<br>Epoch 165/1000<br>782/782 - 1s - loss: 0.0551 - mse: 0.0551 - val_loss: 0.0859 - val_mse: 0.0859<br>Epoch 166/1000<br>782/782 - 1s - loss: 0.0547 - mse: 0.0547 - val_loss: 0.0848 - val_mse: 0.0848<br>Epoch 167/1000<br>782/782 - 1s - loss: 0.0549 - mse: 0.0549 - val_loss: 0.0896 - val_mse: 0.0896<br>Epoch 168/1000<br>782/782 - 1s - loss: 0.0547 - mse: 0.0547 - val_loss: 0.0897 - val_mse: 0.0897<br>Epoch 169/1000<br>782/782 - 1s - loss: 0.0542 - mse: 0.0542 - val_loss: 0.0849 - val_mse: 0.0849        </sup></sub>    </details>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# metrics\n",
    "start_time = time()\n",
    "\n",
    "# store standard out stream\n",
    "original_stdout = sys.stdout\n",
    "# replace standard out for intercepting model training logs\n",
    "sys.stdout = captured_stdout = StringIO()\n",
    "\n",
    "try:\n",
    "    # make temporary directory for saved model\n",
    "    if not os.path.exists(\"temp\"):\n",
    "        os.mkdir(\"temp\")\n",
    "\n",
    "    # perform model training\n",
    "    model.fit(\n",
    "        # training data\n",
    "        tf_input['train'], tf_output['train'],\n",
    "        # use test data to validate losses, but not for training\n",
    "        validation_data=(tf_input['validate'], tf_output['validate']),\n",
    "        callbacks=[\n",
    "            # patience: number of epochs with no improvement after which training will be stopped\n",
    "            EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=100, mode='auto', verbose=0),\n",
    "            # save best model from all epochs\n",
    "            ModelCheckpoint(filepath=\"temp/model_best_weights.hdf5\", save_best_only=True, verbose=0)\n",
    "        ],\n",
    "        verbose=2,\n",
    "        epochs=1000,\n",
    "    )\n",
    "finally:\n",
    "    # restore standard out\n",
    "    sys.stdout = original_stdout\n",
    "\n",
    "# metrics\n",
    "print(f\"Model trained in {time() - start_time:.6f} seconds\")\n",
    "# noinspection PyTypeChecker\n",
    "display(HTML(f'\\\n",
    "    <details>\\\n",
    "        <summary><b>Training Details</b></summary>\\\n",
    "        <sub><sup>\\\n",
    "            {\"<br>\".join(captured_stdout.getvalue().splitlines())}\\\n",
    "        </sup></sub>\\\n",
    "    </details>\\\n",
    "'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# V. Model Accuracy\n",
    "The accuracy of the model can be quantified and visualized by comparing the predicted outputs with the actual expected outputs with the testing subset."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Predictions\n",
    "Using the testing subset, predicted star ratings can be quickly computed with our trained model and its weights. Model predictions are computed using the regression model as continuous values and then rounded to the nearest half star as discrete values."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted 10,000 business ratings in 0.679747 seconds\n"
     ]
    }
   ],
   "source": [
    "# metrics\n",
    "start_time = time()\n",
    "\n",
    "# load best model for prediction\n",
    "model.load_weights(\"temp/model_best_weights.hdf5\")\n",
    "\n",
    "# compute predicted ratings for test dataset\n",
    "predicted = model.predict(tf_input['test'])\n",
    "# map predictions to the nearest half-star\n",
    "predicted_rounded = numpy.array([min(round(prediction * 2) / 2, 5) for prediction in predicted.flatten()])\n",
    "\n",
    "# metrics\n",
    "print(f\"Predicted {len(tf_input['test']):,} business ratings in {time() - start_time:.6f} seconds\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Error Scoring (MSE/RSME)\n",
    "Our model's prediction accuracy can be quantified numerically with a [MSE or RMSE](https://en.wikipedia.org/wiki/RSME) score. The score is actually quite statistically significant - the deviation is within the realm of roughly a half star. The scores are shown below for continuous predictions and discrete half-star rounded predictions."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "      Continuous  Discrete\nMSE     0.079469  0.097575\nRMSE    0.281903  0.312370",
      "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>Continuous</th>\n      <th>Discrete</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MSE</th>\n      <td>0.079469</td>\n      <td>0.097575</td>\n    </tr>\n    <tr>\n      <th>RMSE</th>\n      <td>0.281903</td>\n      <td>0.312370</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_score = sklearn.metrics.mean_squared_error(predicted, tf_output['test'])\n",
    "mse_rounded_score = sklearn.metrics.mean_squared_error(predicted_rounded, tf_output['test'])\n",
    "\n",
    "pandas.DataFrame(\n",
    "    [\n",
    "        [mse_score, mse_rounded_score],\n",
    "        numpy.sqrt([mse_score, mse_rounded_score])\n",
    "    ],\n",
    "    columns=[\"Continuous\", \"Discrete\"],\n",
    "    index=[\"MSE\", \"RMSE\"]\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Lift Chart Visualization\n",
    "Quantified error scoring does not lend itself to be easily interpreted; instead, the results may be visualized using a lift chart visualization from our predictions and actual values. Arbitrary businesses are ordered by their actual star rating; the deviations from the predicted value are quickly visible from the actual expected values.\n",
    "\n",
    "Interestingly enough, since a regression model was used here, continuous values above a 5 star rating are predicted. For the discrete visualization, values are clamped to only possible values for star ratings (i.e. clamped to `[0, 5]` and rounded to the nearest half-star)."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = pyplot.figure(figsize=(12, 4))\n",
    "\n",
    "axes = figure.add_subplot(1, 2, 1)\n",
    "axes.set_title(\"Continuous Predictions\")\n",
    "axes.set_xlabel(\"businesses, ordered by ascending star rating\")\n",
    "axes.set_ylabel(\"star rating \")\n",
    "table = pandas.DataFrame({'predicted': predicted.flatten(), 'y': tf_output['test'].flatten()})\n",
    "table.sort_values(by=['y'], inplace=True)\n",
    "axes.plot(table['predicted'].tolist(), label=\"prediction\")\n",
    "axes.plot(table['y'].tolist(), label=\"expected\")\n",
    "axes.legend()\n",
    "\n",
    "axes = figure.add_subplot(1, 2, 2)\n",
    "axes.set_title(\"Discrete Predictions\")\n",
    "axes.set_xlabel(\"businesses, ordered by ascending star rating\")\n",
    "axes.set_ylabel(\"star rating\")\n",
    "table = pandas.DataFrame({'predicted': predicted_rounded.flatten(), 'y': tf_output['test'].flatten()})\n",
    "table.sort_values(by=['y'], inplace=True)\n",
    "axes.plot(table['predicted'].tolist(), label=\"prediction\")\n",
    "axes.plot(table['y'].tolist(), label=\"expected\")\n",
    "axes.legend()\n",
    "\n",
    "pyplot.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# VI. Conclusion\n",
    "\n",
    "If a business has enough reviews (in our case, 15), there is a surprisingly accurate correlation between the text content of the user reviews and the star rating the business receives. The discrete lift chart visualization shows most of the deviation of predicted values occurs within a half-star of the actual expected value.\n",
    "\n",
    "The purpose of this project was not to optimize a model's ability to accurate predict stars, but to inspect a suspected correlation of data. It may be possible to construct a significantly better regression model using more input features of businesses (such as location, hours of operation, tips, etc), but optimization for this project is not quite necessary for identifying a correlation with a business's user reviews and its respective star rating."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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 },
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