clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3598\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "cate = ['Mexican', 'Fast Food', 'Pizza', 'American (Traditional)', 'Sandwiches', \n",
    "        'Breakfast & Brunch', 'Chinese', 'Italian']\n",
    "\n",
    "fullcate = []\n",
    "file = open(\"./business.json\",'r')\n",
    "content = []\n",
    "for line in file:\n",
    "    content.append(json.loads(line))\n",
    "\n",
    "for item in content:\n",
    "    if(item[\"city\"] == \"Las Vegas\"):\n",
    "        if((item['categories'] not in fullcate)&('Restaurants' in item['categories'])):\n",
    "            fullcate.append(item['categories'])\n",
    "#         if any(j in cate for j in item['categories']):\n",
    "#             data.append(item)\n",
    "# print(data[0])\n",
    "# print(len(data))\n",
    "print(len(fullcate))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "390\n",
      "['Nightlife', 'Bars', 'Barbeque', 'Sports Bars', 'American (New)', 'Restaurants', 'Fast Food', 'Sandwiches', 'Arcades', 'Arts & Entertainment', 'Gastropubs', 'Italian', 'Burgers', 'Cafes', 'Wine Bars', 'American (Traditional)', 'Seafood', 'Pizza', 'Karaoke', 'Mexican', 'Dance Clubs', 'Thai', 'Food', 'Coffee & Tea', 'Salad', 'Breakfast & Brunch', 'Chinese', 'Specialty Food', 'Candy Stores', 'Ice Cream & Frozen Yogurt', 'Pubs', 'Irish', 'Japanese', 'Delis', 'Shaved Ice', 'Hot Dogs', 'Creperies', 'Buffets', 'Event Planning & Services', 'Caterers', 'Greek', 'Middle Eastern', 'Mediterranean', 'Breweries', 'Hawaiian', 'Asian Fusion', 'Korean', 'Casinos', 'Chicken Shop', 'Chicken Wings', 'Diners', 'Desserts', 'Sushi Bars', 'Lounges', 'Cuban', 'Food Trucks', 'Music Venues', 'Persian/Iranian', 'Cocktail Bars', 'Steakhouses', 'Filipino', 'Tex-Mex', 'Beer Bar', 'Bakeries', 'Tapas Bars', 'Donuts', 'Soup', 'Street Vendors', 'Poke', 'Irish Pub', 'Vietnamese', 'Do-It-Yourself Food', 'Hot Pot', 'Fondue', 'Mongolian', 'Juice Bars & Smoothies', 'Vegan', 'Vegetarian', 'Tacos', 'Local Flavor', 'Cheesesteaks', 'Beer', 'Wine & Spirits', 'Ramen', 'Shopping', 'Shopping Centers', 'Fashion', 'Outlet Stores', 'Noodles', 'Food Delivery Services', 'Beauty & Spas', 'South African', 'Personal Shopping', 'African', 'Cosmetics & Beauty Supply', 'Gluten-Free', 'Ethnic Food', 'Latin American', 'Food Stands', 'Peruvian', 'Meat Shops', 'Soul Food', 'Personal Chefs', 'Imported Food', 'Indian', 'Bubble Tea', 'Salvadoran', 'Live/Raw Food', 'Waffles', 'Comfort Food', 'Hotels', 'Day Spas', 'Hotels & Travel', 'Szechuan', 'Venues & Event Spaces', 'French', 'Pakistani', 'Kosher', 'Tapas/Small Plates', 'Farmers Market', 'Health Markets', 'Grocery', 'Fruits & Veggies', 'Food Court', 'Southern', 'Caribbean', 'Halal', 'Japanese Curry', 'Izakaya', 'Jazz & Blues', 'Macarons', 'Chocolatiers & Shops', 'Smokehouse', 'Internet Cafes', 'Dive Bars', 'Country Dance Halls', 'Social Clubs', 'Performing Arts', 'Resorts', 'Active Life', 'Tuscan', 'Bistros', 'Golf', 'Bagels', 'Local Services', 'Home Cleaning', 'Home Services', 'Automotive', 'Interior Design', 'Hair Salons', 'Heating & Air Conditioning/HVAC', 'Aircraft Repairs', 'Airsoft', 'Auto Repair', 'Air Duct Cleaning', 'Modern European', 'Department Stores', 'Hookah Bars', 'Taiwanese', 'Tea Rooms', 'Venezuelan', 'Adult Entertainment', 'Landmarks & Historical Buildings', 'Tours', 'Public Services & Government', 'Gun/Rifle Ranges', 'Gelato', 'Cantonese', 'Dim Sum', 'Pasta Shops', 'Spanish', 'Organic Stores', 'Pop-Up Restaurants', 'Falafel', 'Cajun/Creole', 'Ukrainian', 'Gas Stations', 'Pretzels', 'Ethiopian', 'Pan Asian', 'Pet Services', 'Pets', 'Pet Adoption', 'Pet Stores', 'Afghan', 'British', 'Brasseries', 'Food Tours', 'Walking Tours', 'Patisserie/Cake Shop', 'Turkish', 'New Mexican Cuisine', 'Acai Bowls', 'Ethnic Grocery', 'Wedding Chapels', 'Party & Event Planning', 'Wraps', 'Moroccan', 'Himalayan/Nepalese', 'Puerto Rican', 'Butcher', 'Tobacco Shops', 'Fish & Chips', 'Education', 'Special Education', 'Seafood Markets', 'Belgian', 'Brazilian', 'Cupcakes', 'Custom Cakes', 'Malaysian', 'Singaporean', 'Swimming Pools', 'Professional Services', 'Appliances & Repair', 'Pool & Hot Tub Service', 'Home & Garden', 'Appliances', 'Health & Medical', 'Nutritionists', 'Airports', 'Convenience Stores', 'Bowling', 'Furniture Rental', 'Fitness & Instruction', 'Nail Salons', 'Party Equipment Rentals', 'Electronics', 'Pilates', 'Furniture Repair', 'DJs', 'Lighting Fixtures & Equipment', 'Ice Delivery', 'Fur Clothing', 'Photo Booth Rentals', 'Furniture Stores', 'Teppanyaki', 'Party Supplies', 'Basque', 'Scandinavian', 'Gay Bars', 'Coffee Roasteries', 'Musicians', 'Laotian', 'Conveyor Belt Sushi', 'German', 'Car Wash', 'Pool Halls', 'Wholesalers', 'Massage Therapy', 'Acupuncture', 'Physical Therapy', 'Rehabilitation Center', 'Plumbing', 'Occupational Therapy', 'Massage', 'Reflexology', 'Limos', 'Transportation', 'Party Bus Rentals', 'Books', 'Mags', 'Music & Video', 'Newspapers & Magazines', 'Honduran', 'Pub Food', 'Wedding Planning', 'Russian', 'Septic Services', 'Water Heater Installation/Repair', 'Colombian', 'Festivals', 'Piano Bars', 'Kebab', 'Portuguese', 'Lebanese', 'Cinema', 'Dinner Theater', 'Wineries', 'Shanghainese', 'Medical Centers', 'International Grocery', 'Travel Services', 'Car Rental', 'Dry Cleaning', 'Laundry Services', 'Security Services', 'Personal Assistants', 'Airport Shuttles', 'Videos & Video Game Rental', 'Tabletop Games', \"Men's Clothing\", 'Gift Shops', 'Flowers & Gifts', \"Women's Clothing\", 'Hong Kong Style Cafe', 'Cafeteria', 'Weight Loss Centers', 'Honey', 'Poutineries', 'Hobby Shops', 'Aquarium Services', 'Local Fish Stores', 'Aquariums', 'Beer Garden', 'Arabian', 'Drugstores', 'Delicatessen', 'Oil Change Stations', 'Auto Insurance', 'Auto Parts & Supplies', 'Financial Services', 'Windshield Installation & Repair', 'Auto Glass Services', 'Insurance', 'Towing', 'Transmission Repair', 'Argentine', 'Historical Tours', 'Strip Clubs', 'Family Practice', 'Doctors', 'Antiques', 'Amusement Parks', 'Kitchen & Bath', 'Baby Gear & Furniture', 'Home Decor', 'Screen Printing', 'Printing Services', 'Bangladeshi', 'Drive-Thru Bars', 'Feng Shui', 'Specialty Schools', 'Sewing & Alterations', 'Formal Wear', 'Bridal', 'Bar Crawl', 'Hostels', 'Gyms', 'Trainers', 'Martial Arts', 'Ski Resorts', 'Kids Activities', 'Water Stores', 'Paint & Sip', 'Art Classes', 'Bulgarian', 'Real Estate', 'Mortgage Brokers', 'Armenian', 'Bed & Breakfast', 'Hungarian', 'Beverage Store', 'Meditation Centers', 'Yoga', 'Car Dealers', 'Truck Rental', 'RV Parks', 'Polish', 'Ethical Grocery', 'Coffeeshops', 'Eyebrow Services', 'Makeup Artists', 'Airport Terminals', 'Cultural Center', 'Guamanian', 'Bingo Halls', 'Beer Gardens', 'Colleges & Universities', 'Art Galleries', 'Nicaraguan', 'Signmaking', 'Vehicle Wraps', 'Car Window Tinting', 'Auto Customization', 'Cheese Shops', 'Bartenders', 'Restaurant Supplies', 'Discount Store', 'Grilling Equipment', 'Outdoor Furniture Stores', 'Contractors', 'Fireplace Services', 'Mailbox Centers', 'Vitamins & Supplements', 'Eatertainment', 'Playgrounds', 'Cabaret', 'Soba', 'Themed Cafes']\n"
     ]
    }
   ],
   "source": [
    "uniquecate = []\n",
    "for i in range(len(fullcate)):\n",
    "    for item in fullcate[i]:\n",
    "        if item not in uniquecate:\n",
    "            uniquecate.append(item)\n",
    "print(len(uniquecate))\n",
    "print(uniquecate)\n",
    "#['Mexican', 'Fast Food', 'Pizza','American (Traditional)', \n",
    "#'Sandwiches', 'Breakfast & Brunch', 'Chinese', 'Italian'\n",
    "#'Cafes', 'Sandwiches', 'Sandwiches', 'Bars',\n",
    "#'Chicken Wings', 'Hot Dogs', 'Burgers', 'Seafood'\n",
    "#'Jazz & Blues','Korean', 'Donuts', 'Hot Pot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 5899 restaurants in Las Vegas.\n",
      "{'business_id': '4srfPk1s8nlm1YusyDUbjg', 'name': 'Subway', 'neighborhood': 'Southeast', 'address': '6889 S Eastern Ave, Ste 101', 'city': 'Las Vegas', 'state': 'NV', 'postal_code': '89119', 'latitude': 36.0646523, 'longitude': -115.1189541, 'stars': 2.5, 'review_count': 6, 'is_open': 1, 'attributes': {'GoodForMeal': {'dessert': False, 'latenight': False, 'lunch': False, 'dinner': False, 'breakfast': False, 'brunch': False}, 'Alcohol': 'none', 'HasTV': False, 'RestaurantsGoodForGroups': True, 'RestaurantsAttire': 'casual', 'RestaurantsReservations': False, 'OutdoorSeating': False, 'BusinessAcceptsCreditCards': True, 'RestaurantsPriceRange2': 1, 'BikeParking': False, 'RestaurantsDelivery': False, 'Ambience': {'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'divey': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}, 'RestaurantsTakeOut': True, 'GoodForKids': True, 'DriveThru': False, 'BusinessParking': {'garage': False, 'street': False, 'validated': False, 'lot': False, 'valet': False}}, 'categories': ['Fast Food', 'Restaurants', 'Sandwiches'], 'hours': {}}\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "for item in content:\n",
    "    if(item[\"city\"] == \"Las Vegas\"):\n",
    "        if any(label in uniquecate for label in item['categories'])&('Restaurants' in item['categories']):\n",
    "            data.append(item)\n",
    "print(\"there are\", len(data), \"restaurants in Las Vegas.\")\n",
    "print(data[1])\n",
    "\n",
    "import json\n",
    "\n",
    "with open('data.json', 'w') as fp:\n",
    "    for item in data:\n",
    "        json.dump(item, fp)\n",
    "        fp.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[36.0669136, -115.1708484]\n"
     ]
    }
   ],
   "source": [
    "ll = []\n",
    "for row in data:\n",
    "    temp = []\n",
    "    temp.append(row['latitude'])\n",
    "    temp.append(row['longitude'])\n",
    "    ll.append(temp)\n",
    "print(ll[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.metrics as metrics\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a44d85b38>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a44d85470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.asarray(ll)\n",
    "plt.figure(figsize = (5,8))\n",
    "_ = plt.scatter(X[:,0],X[:,1],s = 2)\n",
    "plt.plot([36.114647], [-115.172813], marker=\"x\", markersize=20, color=\"black\", label=\"downtown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5899\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "k=8\n",
    "kmeans = KMeans(n_clusters=k, init='k-means++', max_iter=100, n_init=1)\n",
    "labels = kmeans.fit_predict(X)\n",
    "print(len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'longitude')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a4567ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'pink'][p] for p in labels]\n",
    "plt.figure(figsize = (5, 8))\n",
    "plt.scatter(X[:, 0], X[:, 1], color = colors, s = 5)\n",
    "plt.title('kmeans++ clustering')\n",
    "plt.xlabel('latitude')\n",
    "plt.ylabel('longitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'blue': 3.341304347826087, 'green': 3.3526654950205037, 'red': 3.5573979591836733, 'cyan': 3.4453032104637336, 'magenta': 3.584428715874621, 'yellow': 3.3634408602150536, 'black': 3.515833333333333, 'pink': 3.551111111111111}\n"
     ]
    }
   ],
   "source": [
    "average_rate = {}\n",
    "colorname = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'pink']\n",
    "for c in range(8):\n",
    "    rate_sum = 0\n",
    "    rest_count = 0\n",
    "    for i in range(len(labels)):\n",
    "        if(labels[i]==c):\n",
    "            rate_sum = rate_sum + data[i]['stars']\n",
    "            rest_count +=1\n",
    "            average_rate[colorname[c]] = rate_sum/rest_count\n",
    "            \n",
    "print(average_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "178\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(36.1146,-115.183,'downtown')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a45724860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best = []\n",
    "bestll = []\n",
    "worst = []\n",
    "worstll = []\n",
    "\n",
    "for item in data:\n",
    "    temp = []\n",
    "    if(item['stars']>=5):\n",
    "        best.append(item)\n",
    "        temp.append(item['latitude'])\n",
    "        temp.append(item['longitude'])\n",
    "        bestll.append(temp)\n",
    "        \n",
    "\n",
    "for item in data:\n",
    "    temp = []\n",
    "    if(item['stars']<=2.0):\n",
    "        worst.append(item)\n",
    "        temp.append(item['latitude'])\n",
    "        temp.append(item['longitude'])\n",
    "        worstll.append(temp)\n",
    "\n",
    "\n",
    "print(len(bestll))\n",
    "X = np.asarray(bestll)\n",
    "Y = np.asarray(worstll)\n",
    "plt.figure(figsize = (5,8))\n",
    "_ = plt.scatter(X[:,0], X[:,1], color = 'r', s = 2)\n",
    "_ = plt.scatter(Y[:,0], Y[:,1], color = 'b', s = 2)\n",
    "\n",
    "plt.plot([36.114647], [-115.172813], marker=\"x\", markersize=20, color=\"black\", label = \"downtown\")\n",
    "plt.text(36.114647, -115.182813,r'downtown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.04: 3.518331226295828, 0.0405: 3.521144278606965, 0.041: 3.517747858017136, 0.0415: 3.510856453558504, 0.042: 3.506563245823389, 0.0425: 3.50401376146789, 0.043000000000000003: 3.5062217194570136, 0.043500000000000004: 3.519913885898816, 0.044000000000000004: 3.5105042016806722, 0.044500000000000005: 3.509733606557377, 0.045000000000000005: 3.502, 0.045500000000000006: 3.4995093228655545, 0.046000000000000006: 3.500965250965251, 0.04650000000000001: 3.4844606946983547, 0.04700000000000001: 3.487676056338028, 0.04750000000000001: 3.483599663582843, 0.04800000000000001: 3.483779399837794, 0.04850000000000001: 3.4864, 0.04900000000000001: 3.4907048799380327, 0.04950000000000001: 3.4898036253776437, 0.05000000000000001: 3.4861733931240657, 0.05050000000000001: 3.4829881656804735, 0.05100000000000001: 3.482443306510607, 0.05150000000000001: 3.4805755395683455, 0.05200000000000001: 3.4843304843304845, 0.05250000000000001: 3.4877365101611773, 0.05300000000000001: 3.4874389392882064, 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     ]
    }
   ],
   "source": [
    "#now devide according to the distance to downtown point.\n",
    "nearest = []\n",
    "near_stars = []\n",
    "def distance(x,y):\n",
    "    dist = ((x-36.114647)**2 + (y+115.182813)**2)**0.5\n",
    "    return dist\n",
    "\n",
    "\n",
    "def relat_l_s(r1,r2):\n",
    "    for item in data:\n",
    "        temp = []\n",
    "        x = item['latitude']\n",
    "        y = item['longitude']\n",
    "        if(r1<distance(x,y)<r2):\n",
    "            temp.append(item['latitude'])\n",
    "            temp.append(item['longitude'])\n",
    "            nearest.append(temp)\n",
    "            near_stars.append(item['stars'])\n",
    "    ave_star = sum(near_stars)/(len(near_stars))\n",
    "    return ave_star\n",
    "\n",
    "\n",
    "relate = {}\n",
    "r1 = 0.02\n",
    "for r2 in np.arange(0.04, 0.15, 0.0005):\n",
    "    relate[r2] = relat_l_s(r1,r2)\n",
    "    r1 = r2\n",
    "\n",
    "# X = np.asarray(nearest)\n",
    "# plt.figure(figsize = (5,5))\n",
    "# _ = plt.scatter(X[:,0], X[:,1], color = 'r', s = 2)\n",
    "# print(ave_star)\n",
    "print(relate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a46e14c50>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a46dc11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = relate.keys()\n",
    "y = relate.values()\n",
    "fig, ax = plt.subplots( nrows=1, ncols=1 )\n",
    "ax.plot(x,y,'r-')\n",
    "ax.plot(x,y,'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"location-star(0.15).jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}