plot_spectral_coclustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# A demo of the Spectral Co-Clustering algorithm\n",
    "\n",
    "This example demonstrates how to generate a dataset and bicluster it\n",
    "using the Spectral Co-Clustering algorithm.\n",
    "\n",
    "The dataset is generated using the ``make_biclusters`` function, which\n",
    "creates a matrix of small values and implants bicluster with large\n",
    "values. The rows and columns are then shuffled and passed to the\n",
    "Spectral Co-Clustering algorithm. Rearranging the shuffled matrix to\n",
    "make biclusters contiguous shows how accurately the algorithm found\n",
    "the biclusters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = {'a':3,'b':5}\n",
    "'c' not in a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_matrix_data(df):\n",
    "    sequence = []\n",
    "    past_tid = None\n",
    "    curr_tid = None\n",
    "    num_of_seqs = 0\n",
    "    map_element_id = 0\n",
    "    unique_elements = {}\n",
    "    map_id_to_element = {}\n",
    "#     f = open(\"data/real_application/foursquare_NY/fs_ny_week_sequences.dat\", \"w\")\n",
    "#     f = open(pfile, \"w\")\n",
    "    \n",
    "    for i in tqdm(range(int(len(df))), colour='blue', desc='Processing DataFrame'):\n",
    "        curr_tid = df.loc[i,\"new_tid\"]\n",
    "        index = df.index[df.loc[i,\"new_tid\"]]\n",
    "        element = str(df.loc[i,\"type\"].replace(' ','_'))\n",
    "        if element not in unique_elements:\n",
    "                unique_elements.update({element:map_element_id})\n",
    "                map_id_to_element.update({str(map_element_id):element})\n",
    "                map_element_id +=1\n",
    "        \n",
    "        if index == 0:\n",
    "            sequence.append(element)\n",
    "            past_tid = curr_tid\n",
    "            \n",
    "        elif curr_tid == past_tid:\n",
    "            sequence.append(element)\n",
    "                \n",
    "        elif curr_tid != past_tid:\n",
    "            tmp = ' '.join(sequence)\n",
    "            tmp += '\\n'\n",
    "#             f.write(tmp)\n",
    "            num_of_seqs += 1\n",
    "            sequence = []\n",
    "            sequence.append(element)\n",
    "            past_tid = curr_tid\n",
    "        \n",
    "        if i == (len(df)-1):\n",
    "            tmp = ' '.join(sequence)\n",
    "#             f.write(tmp)\n",
    "            num_of_seqs += 1\n",
    "            \n",
    "#     f.close()\n",
    "\n",
    "    print('Number of sequences: ',num_of_seqs)\n",
    "    print('Number of columns: ',len(unique_elements))\n",
    "    \n",
    "    matrix = np.zeros((num_of_seqs,len(unique_elements)))\n",
    "    print('Matrix: ',matrix.shape)\n",
    "    num_of_seqs = 0\n",
    "    \n",
    "    for i in tqdm(range(int(len(df))), colour='blue', desc='Populating Matrix'):\n",
    "        curr_tid = df.loc[i,\"new_tid\"]\n",
    "        index = df.index[df.loc[i,\"new_tid\"]]\n",
    "        element = str(df.loc[i,\"type\"].replace(' ','_'))\n",
    "        \n",
    "        if index == 0:\n",
    "            sequence.append(element)\n",
    "            past_tid = curr_tid\n",
    "            matrix[num_of_seqs,unique_elements[element]] += 1\n",
    "            \n",
    "        elif curr_tid == past_tid:\n",
    "            sequence.append(element)\n",
    "            matrix[num_of_seqs,unique_elements[element]] += 1\n",
    "                \n",
    "        elif curr_tid != past_tid:\n",
    "            tmp = ' '.join(sequence)\n",
    "            tmp += '\\n'\n",
    "#             f.write(tmp)\n",
    "            matrix[num_of_seqs,unique_elements[element]] += 1\n",
    "            num_of_seqs += 1\n",
    "            sequence = []\n",
    "            sequence.append(element)\n",
    "            past_tid = curr_tid\n",
    "        \n",
    "        if i == (len(df)-1):\n",
    "            tmp = ' '.join(sequence)\n",
    "#             f.write(tmp)\n",
    "            num_of_seqs += 1\n",
    "    \n",
    "    print(matrix.sum())\n",
    "    print(\"Process completed!\")\n",
    "    return matrix, map_id_to_element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing DataFrame:  37%|\u001b[34m███████████████████▍                                \u001b[0m| 4393/11715 [00:00<00:00, 43792.43it/s]\u001b[0m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing DataFrame: 100%|\u001b[34m███████████████████████████████████████████████████\u001b[0m| 11715/11715 [00:00<00:00, 50540.75it/s]\u001b[0m\n",
      "Populating Matrix:  33%|\u001b[34m██████████████████▏                                    \u001b[0m| 3866/11715 [00:00<00:00, 38566.98it/s]\u001b[0m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of sequences:  352\n",
      "Number of columns:  310\n",
      "Matrix:  (352, 310)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Populating Matrix: 100%|\u001b[34m██████████████████████████████████████████████████████\u001b[0m| 11715/11715 [00:00<00:00, 43461.35it/s]\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11715.0\n",
      "Process completed!\n",
      "<class 'numpy.ndarray'> (352, 310)\n",
      "Done in 0.05s.\n",
      "Row clusters: 10\n",
      "Column clusters: 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x327.019 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x327.019 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x327.019 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster  0\n",
      "Number of rows:  43\n",
      "Elements: Pool, Bank, Doctor's_Office, Automotive_Shop, Salon_/_Barbershop, Bridge, Medical_Center, Electronics_Store, Discount_Store, Hockey_Arena, Wine_Bar, Basketball_Court, Financial_or_Legal_Service, New_American_Restaurant, Tunnel, College_Academic_Building, Salad_Place, Dance_Studio, Pet_Store, Czech_Restaurant, Rest_Area, Bowling_Alley, Snack_Place, Empanada_Restaurant, Scandinavian_Restaurant, Trade_School, Fire_Station, Music_School, \n",
      "\n",
      "Cluster  1\n",
      "Number of rows:  33\n",
      "Elements: Speakeasy, Music_Venue, Restaurant, Pizza_Place, High_School, Home_(private), Building, Office, Gas_Station, Seafood_Restaurant, Italian_Restaurant, Government_Building, Sports_Bar, Church, Pharmacy, Cafe, Beach, Arts_&_Crafts_Store, Gymnastics_Gym, Animal_Shelter, Music_Store, German_Restaurant, Clothing_Store, Golf_Course, Nightclub, Gourmet_Shop, Dentist's_Office, Supermarket, Gay_Bar, Paper_/_Office_Supplies_Store, Deli_/_Bodega, Miscellaneous_Shop, Southern_/_Soul_Food_Restaurant, Sandwich_Place, Cafeteria, Optical_Shop, Bakery, Bagel_Shop, Food_Truck, Laundry_Service, Food_&_Drink_Shop, Fair, Art_Gallery, Nail_Salon, Movie_Theater, Racetrack, Baseball_Stadium, Tech_Startup, Women's_Store, Bar, Board_Shop, Basketball_Stadium, French_Restaurant, Concert_Hall, College_History_Building, Monument_/_Landmark, Art_Museum, Falafel_Restaurant, Spa, Tea_Room, Soup_Place, Liquor_Store, Big_Box_Store, Light_Rail_Station, Theater, Chocolate_Shop, Boutique, Bookstore, Outdoor_Sculpture, Thai_Restaurant, Flea_Market, Hot_Dog_Joint, Comedy_Club, Playground, Pie_Shop, Russian_Restaurant, Roof_Deck, College_Classroom, College_Bookstore, Colombian_Restaurant, Irish_Pub, Jazz_Club, Indie_Movie_Theater, Border_Crossing, Belgian_Restaurant, Business_Service, IT_Services, Corporate_Cafeteria, English_Restaurant, Taco_Place, Memorial_Site, Public_Art, Newsstand, Casino, Check_Cashing_Service, Shop_&_Service, Shanghai_Restaurant, Outdoors_&_Recreation, Beer_Store, Cable_Car, Swiss_Restaurant, Ramen_Restaurant, Convention_Center, College_Technology_Building, Arcade, Harbor_/_Marina, Waterfront, Warehouse_Store, \n",
      "\n",
      "Cluster  2\n",
      "Number of rows:  40\n",
      "Elements: Other_Nightlife, Bus_Line, Radio_Station, Voting_Booth, Camera_Store, Market, Chiropractor, Noodle_House, Gastropub, Brewery, South_American_Restaurant, Lingerie_Store, Israeli_Restaurant, Historic_Site, Candy_Store, Spiritual_Center, Indian_Restaurant, University, Platform, Campground, Men's_Store, Skating_Rink, Airport_Lounge, Turkish_Restaurant, Australian_Restaurant, Sake_Bar, Baseball_Field, Bike_Shop, \n",
      "\n",
      "Cluster  3\n",
      "Number of rows:  35\n",
      "Elements: Burger_Joint, Airport_Gate, Latin_American_Restaurant, BBQ_Joint, Advertising_Agency, \n",
      "\n",
      "Cluster  4\n",
      "Number of rows:  71\n",
      "Elements: Strip_Club, Park, Airport_Terminal, Farm, Furniture_/_Home_Store, Gym_/_Fitness_Center, Convenience_Store, Metro_Station, Cemetery, Housing_Development, Scenic_Lookout, Garden, Pub, Beer_Garden, Shopping_Mall, Multiplex, Luggage_Store, Toll_Plaza, Rock_Club, Tapas_Restaurant, Hotel_Bar, Austrian_Restaurant, Event_Space, Supplement_Shop, Festival, Vegetarian_/_Vegan_Restaurant, Toy_/_Game_Store, Theme_Park, Eastern_European_Restaurant, Portuguese_Restaurant, Trail, \n",
      "\n",
      "Cluster  5\n",
      "Number of rows:  15\n",
      "Elements: Coffee_Shop, Cuban_Restaurant, Meeting_Room, Library, Hardware_Store, Airport_Tram, Chinese_Restaurant, Mobile_Phone_Shop, Fried_Chicken_Joint, Fish_Market, Parking, Bubble_Tea_Shop, Science_Museum, Cupcake_Shop, Greek_Restaurant, Food_Court, City_Hall, Track, Hotpot_Restaurant, \n",
      "\n",
      "Cluster  6\n",
      "Number of rows:  8\n",
      "Elements: Hotel, Sporting_Goods_Shop, Vietnamese_Restaurant, \n",
      "\n",
      "Cluster  7\n",
      "Number of rows:  61\n",
      "Elements: American_Restaurant, Post_Office, Elementary_School, Donut_Shop, Museum, Gift_Shop, Lounge, Breakfast_Spot, General_Entertainment, Gym, Hookah_Bar, Steakhouse, Bus_Stop, Cosmetics_Shop, Caribbean_Restaurant, Bus_Station, Plaza, Train, Funeral_Home, Storage_Facility, Performing_Arts_Venue, Field, Soccer_Stadium, Dive_Bar, Dessert_Shop, Distillery, Wine_Shop, Car_Wash, Pier, Asian_Restaurant, Farmers_Market, Brazilian_Restaurant, Resort, Karaoke_Bar, Street_Food_Gathering, Peruvian_Restaurant, Yoga_Studio, Burrito_Place, Airport, Korean_Restaurant, Thrift_/_Vintage_Store, Video_Game_Store, Cajun_/_Creole_Restaurant, Kitchen_Supply_Store, Temple, Synagogue, Aquarium, Theme_Park_Ride_/_Attraction, Butcher, Dumpling_Restaurant, Filipino_Restaurant, Street_Fair, Middle_Eastern_Restaurant, Pakistani_Restaurant, Christmas_Market, Assisted_Living, Whisky_Bar, Football_Stadium, Island, Credit_Union, Courthouse, \n",
      "\n",
      "Cluster  8\n",
      "Number of rows:  24\n",
      "Elements: Fast_Food_Restaurant, Ice_Cream_Shop, Cocktail_Bar, Sushi_Restaurant, Other_Repair_Shop, Smoke_Shop, Grocery_Store, Dog_Run, Frozen_Yogurt_Shop, Department_Store, Mediterranean_Restaurant, School, Spanish_Restaurant, Japanese_Restaurant, Fraternity_House, Coworking_Space, College_Arts_Building, Auto_Dealership, \n",
      "\n",
      "Cluster  9\n",
      "Number of rows:  22\n",
      "Elements: Mexican_Restaurant, Residential_Building_(Apartment_/_Condo), Diner, Shoe_Store, Juice_Bar, Wings_Joint, College_Administrative_Building, Indie_Theater, Cheese_Shop, \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "# Author: Kemal Eren <kemal@kemaleren.com>\n",
    "# License: BSD 3 clause\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from time import time\n",
    "\n",
    "# from sklearn.datasets import make_biclusters\n",
    "from sklearn.cluster import SpectralCoclustering\n",
    "# from sklearn.metrics import consensus_score\n",
    "\n",
    "# data, rows, columns = make_biclusters(\n",
    "#     shape=(300, 300), n_clusters=5, noise=5,\n",
    "#     shuffle=False, random_state=0)\n",
    "\n",
    "k = 10\n",
    "df_fs_top = pd.read_csv('data/real_application/foursquare_NY/fs_ny_top_users_10.csv', sep=\";\")\n",
    "data, map_id_to_element = create_matrix_data(df_fs_top)\n",
    "\n",
    "print(type(data),data.shape)\n",
    "\n",
    "# plt.matshow(data, cmap=plt.cm.Blues)\n",
    "plt.matshow(data, cmap=plt.cm.hot)\n",
    "plt.title(\"Original dataset\")\n",
    "\n",
    "# shuffle clusters\n",
    "rng = np.random.RandomState(0)\n",
    "row_idx = rng.permutation(data.shape[0])\n",
    "col_idx = rng.permutation(data.shape[1])\n",
    "data = data[row_idx][:, col_idx]\n",
    "\n",
    "plt.matshow(data, cmap=plt.cm.Blues)\n",
    "plt.title(\"Shuffled dataset\")\n",
    "\n",
    "start_time = time()\n",
    "# model = SpectralCoclustering(n_clusters=193, random_state=0)\n",
    "model = SpectralCoclustering(n_clusters=10, random_state=0)\n",
    "model.fit(data)\n",
    "end_time = time()\n",
    "print(\"Done in {:.2f}s.\".format(end_time-start_time))\n",
    "\n",
    "# score = consensus_score(model.biclusters_,\n",
    "#                         (rows[:, row_idx], columns[:, col_idx]))\n",
    "\n",
    "# print(\"consensus score: {:.3f}\".format(score))\n",
    "\n",
    "fit_data = data[np.argsort(model.row_labels_)]\n",
    "fit_data = fit_data[:, np.argsort(model.column_labels_)]\n",
    "# print('Row labels: ',len(model.row_labels_))\n",
    "# print(type(model.row_labels_))\n",
    "df_r = pd.DataFrame({'row_labels':list(model.row_labels_)})\n",
    "df_c = pd.DataFrame({'column_labels':list(model.column_labels_)})\n",
    "print('Row clusters:',df_r['row_labels'].nunique())\n",
    "print('Column clusters:',df_c['column_labels'].nunique())\n",
    "\n",
    "plt.matshow(fit_data, cmap=plt.cm.hot)\n",
    "plt.title(\"After biclustering; rearranged to show biclusters\")\n",
    "plt.show()\n",
    "\n",
    "for i in range(k):\n",
    "    print('Cluster ',i)\n",
    "#     print(model.get_indices(i))\n",
    "    print('Number of rows: ',len(model.get_indices(i)[0]))\n",
    "    print('Elements: ',end='')\n",
    "    for col in model.get_indices(i)[1]:\n",
    "        print(map_id_to_element[str(col)],end=\", \")\n",
    "#         print(col, end=\", \")\n",
    "        \n",
    "    print('\\n')"
   ]
  }
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