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In [1]:

    
from datetime import datetime
import random
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from itertools import combinations
from collections import defaultdict



In [2]:

    
import pickle, os
def store(variable, name):
    try:
        out_file = open('data/'+name+'.pkl', 'wb')
        pickle.dump(variable, out_file)
        out_file.close()
        return True
    except:
        return False

def load(name):
    file_name = 'data/'+name+'.pkl'
    if os.path.exists(file_name):
        in_file = open(file_name, 'rb')
        variable = pickle.load(in_file)
        in_file.close()
        return variable
    else:
        return None



In [3]:

    
def load_data(in_file, brands_to_load = None, max_limit = 1404, verbose = False, randomize = True):
    """Loads data from the given data file.
    If the brands to be loaded is less than the maximum rows in the datafile, randomize the loading.
    Args:
      brands_to_load .... Number of brands to be loaded.
      in_file       .... A string representing the location and datafile name to be loaded.
    Returns:
      None. The data is loaded into the global variables."""
    
    if brands_to_load is None:
        brands_to_load = max_limit 
    
    #Randomize Loading
    if brands_to_load != max_limit:
        if randomize:
            choices = sorted(random.sample(range(max_limit), brands_to_load))
            max_row_id = max(choices)
        else:
            choices = range(brands_to_load)
            max_row_id = max(choices)
    else:
        choices = range(max_limit)
        max_row_id = max_limit-1
    
    brand_no = 0
    cur_idx = 0
            
    with open(in_file) as data_file:
        for row in data_file:
            if brand_no > max_row_id:
                break
            if brand_no != choices[cur_idx]:
                brand_no += 1
                continue
            else:
                followers = row.split()
                brand = followers.pop(0)
                followers_count = len(followers)
                
                #Ignoring brands with less than 1000 followers
                if followers_count > 1000:  
                    followers = [int(x) for x in followers]
                    BRAND_FOLLOWERS[brand] = set(followers)
                    BRANDS_LIST.append(brand)
                    
                    for f in followers:
                        if f not in UNIQUE_FOLLOWERS:
                            UNIQUE_FOLLOWERS[f] = set()
                        UNIQUE_FOLLOWERS[f].add(brand)
                    print('Loaded',brand,'-',followers_count,'followers')
                else:
                    print('Skipped',brand,'-',followers_count,'followers')
                    
                brand_no += 1
                cur_idx += 1
                    
    return



In [ ]:

    
BRANDS_LIST = []
UNIQUE_FOLLOWERS = {}
BRAND_FOLLOWERS = {}
verbose = True
#randomize = False
randomize = True
start = datetime.now()
#brands_to_load = 50
#max_rows = 1404
#load_data('data/brand_followers_final.tsv', brands_to_load, max_rows, verbose, randomize)
brands_to_load = 200
max_rows = 571
load_data('data/brand_followers_corrected_full.tsv', brands_to_load, max_rows, verbose, randomize)
print('Elapsed:',datetime.now()-start)
N_BRANDS = len(BRAND_FOLLOWERS)
N_FOLLOWERS = len(UNIQUE_FOLLOWERS)
print('No. of Unique Followers', N_FOLLOWERS)
print('No. of Brands', N_BRANDS)



In [4]:

    
BRAND_SECTOR = {}
row_no = 0
with open('data/sectors.txt') as data_file:
        for row in data_file:
            if row_no == 0:
                row_no += 1
                continue
            data = row.strip().split(',')
            sector = data[0].lower().replace(' ','_')
            brand = data[1].lower()
            if sector in BRAND_SECTOR:
                BRAND_SECTOR[sector].add(brand)
            else:
                BRAND_SECTOR[sector] =set([brand])
SECTORS = sorted(list(BRAND_SECTOR.keys()))
brands = set()
for s in SECTORS:
    print(s, len(BRAND_SECTOR[s]),BRAND_SECTOR[s])
    brands = brands | set(BRAND_SECTOR[s])
len(brands)









    



apparel 24 {'benetton', 'adidas', 'burberry', 'ellentracynyc', 'blueqness', 'hugoboss', 'dolcegabbana', 'freepeople', 'azzaro', 'anntaylor', 'eckounltd', 'juicycouture', 'fendi', 'anthropologie', 'guess', 'givenchy', 'prana', 'dior', 'armani', 'hollisterco', 'americanapparel', 'chanel', 'ralphlauren', 'calvinklein'}
appliances 12 {'amanabrand', 'fab', 'frigidaire', 'dacorkitchen', 'ge_reports', 'fagorind_eng', 'monogramappl', 'jennairusa', 'cuisinart', 'electrolux', 'fisherpaykelus', 'earthlite'}
baby 10 {'babyblanketgift', 'bumboosa', 'babiesrus', 'junebeebaby', 'babyganics', 'bbuttonsbabies', 'babobotanicals', 'boogiemom', 'carabnaturally', 'bambinomio'}
banking 1 {'discover'}
car 18 {'ford', 'bentleymotors', 'dodge', 'jaguarusa', 'bmwusa', 'acura_insider', 'honda', 'ferrariusa', 'cadillac', 'hyundai', 'astonmartin', 'jeep', 'buick', 'chevrolet', 'audi', 'kia', 'bugatti', 'chrysler'}
electronics 26 {'humminbirdfish', 'jblaudio', 'clarisonic', 'harmankardon', 'dlink', 'garmin', 'dell', 'att', 'getolympus', 'htcusa', 'cisco', 'jvc_usa', 'bose', 'belkin', 'jabra_us', 'hp', 'asus', 'disney', 'casio_usa', 'hitachi_us', 'headblade', 'acer', 'canonusa', 'ibm', 'blackberry', 'duracell'}
entertainment 1 {'dragonage'}
food 226 {'dietcoke', 'drpepper', 'dum_dums', 'guayaki', 'crackerbarrel', 'fritolay', 'jif', 'eggplantpeople', 'greengiant', 'flatoutbread', 'cheerios', 'jello', 'basic4veg', 'hickoryfarms', 'finlandiavodka', 'bigelowtea', 'cartenoireuk', 'illyissimo', 'ancientharvest', 'dunkindonuts', 'hannaford', 'jonessodaco', 'blue_bunny', 'eatwholly', 'pr_organicsusa', 'davesgourmet', 'ampenergy', 'reallunchables', 'redvines', 'athenos', 'fttv', 'fanta', 'flatearthbrew', '8oclockcoffee', 'cabotcheese', '8thcontinentsoy', 'caffesanora', 'elementbars', 'earthsbest', 'gopicnic', 'jewelosco', 'droetkerbaking', 'bestfoods', 'birdseye', 'feelgoodfoods', 'formaggio', 'eas', 'honesttea', 'combos', 'propel_water', 'aquafina', 'dovechocolate', 'grandmamauds', 'chipsahoy', 'bolthousefarms', 'powerbar', 'dannon', 'bertolli', 'hormelfoods', 'herbalwater', 'danieleinc', 'pukkaherbs', 'gmtgiftbaskets', 'cream_of_wheat', 'foodsalive', 'bobsredmill', 'republicoftea', 'activia', 'deans_beans', 'homefreetreats', 'ellaskitchenusa', '18rabbits', 'frenchmeadow', 'chefboyardee', 'evolvekefir', 'chobani', 'gonecrackers', 'barilla', 'cedarlanefoods', 'quornus', 'indelight', 'cosmermaid', 'alvaradostreet', 'drinktampico', 'reedsgingerbrew', 'empirekosher', 'jimmydean', 'annieshomegrown', 'jacklinks', 'cascadianfarm', 'better_oats', 'hansensnatural', 'ctcsquares', 'fageusa', '7up', 'fosterfarms', 'gatorade', 'goodearthtea', 'kashifoods', 'kettlebrand', 'frs', 'drpraegers', 'ito_en', 'fischernwieser', 'eatliquidgold', 'bettycrocker', 'fmchocolate', 'haagendazs_us', 'freschetta', '5hourenergy', 'homeruninn', 'hotpockets', 'crackerjackd', 'joseolecentral', 'barsfoods', 'benandjerrys', 'godiva', 'hboats', 'hersheys', 'fstg_chips', 'bluediamond', 'govoskos', 'jarritosnation', 'hillshirefarm', 'faygo', 'fyhmarket', 'gf_insider', 'airheads', 'allegrocoffee', 'barbarasbakery', 'cadburyuk', 'chuaochocoholic', 'bostonmarket', 'crushfoods', 'essentiaphwater', 'iansnaturalfood', 'dpolaner', 'jackdaniels_us', 'fiberone', 'glutenfreeda', 'pringles', 'hichew', 'kikkomankitchen', 'bahlsenbiscuits', 'horizon_organic', 'drinkcheerwine', 'evianwater', 'butterfinger', 'doritos', 'doletweets', 'redbull', 'gortonsseafood', 'hostess_snacks', 'bakeryonmain', 'flavorice_pops', 'pretzelcrisps', 'quaker', 'digiornopizza', 'killerbread', 'naturespath', 'funkymonkeysnck', 'purityvodka', 'breyers', 'dietpepsi', 'huntschef', 'balancebar', 'campbellschunky', '34degrees', 'dasaniwater', 'delmonte', 'islandseasoning', 'food_for_life', 'kindsnacks', 'claim_jumper', 'edenfoods', 'harneytea', 'crystallight', 'kerrygoldusa', 'hodgsonmill', 'claussenpickles', 'redbaronpizza', 'farmrichsnacks', 'earthboundfarm', 'frenchsuk', 'goldpeaktea', 'fishernutsbrand', 'izze', 'delallofoods', 'glutinofoods', 'caribou_coffee', 'coffee_mate', 'casbahhamilton', 'bumblebar', 'eatsmartscales', 'johnsonville', 'croftersorganic', 'buitoniusa', 'glennys', 'jollytimepops', 'cocacola', 'healthy_choice', 'kavajavacoffee', 'cheezit', 'powerade', 'artisanafoods', 'ciaobellagelato', 'honeystinger', 'clifbar', 'chexcereal', 'eckrichmeats', 'hubertslemonade', 'celestialtea', 'hinoderice', 'thecoffeebean', 'corazonas', 'genesistoday', 'kelloggcompany', 'bettylousinc', 'kraftheinzco', 'generalmills', 'chavrie', 'honeymaidsnacks', 'daisyhealthrd', 'hellmanns', 'hiballenergy', 'gardein'}
garden 1 {'gardentime1'}
household_che 33 {'celadrin1', 'droppslaundry', 'ecotouch', 'allegraotc', 'bissellclean', 'hairformula37', 'dawndish', 'kingbiodotcom', 'glade', 'coldeeze', 'auracacia', 'eco_me', 'bachflowerdfn', 'bmsnews', 'gnclivewell', 'biokleenhome', 'ecodiscoveries', 'avoninsider', 'downy', 'bayescleaners', 'barkeeperfriend', 'hylandshealth', 'greenworks', 'clorox', 'boironusa', 'febreze_fresh', 'airwickus', 'doctorsmiths', 'purex', 'dreft', 'gain', 'grabgreen', 'estroven'}
lighting_prod 1 {'bulbrite'}
paper_product 4 {'7daysinutopia', 'puffs', 'quiltednorthern', 'domtarcorp'}
personal_care 205 {'caneandaustin', 'kevyn_aucoin', 'cardinalhealth', 'cleanandclear', 'fekkai', 'iqnatural', 'jrwatkins', 'footsmart', 'benefitbeauty', 'bananaboat', 'drmiracles', 'ardell_lashes', 'barbasolshave', 'cremeofnature', 'cwhairlondon', 'eccobella', 'bumgenius', 'foxandjane', 'janmarini', 'redflowernyc', 'faithinnature', 'h2oplususa', 'acne_free', 'always', 'dermalogica', 'herbacinusa', 'cobigelowny', 'allure_magazine', 'coastalscents', 'bblondehair', 'duwopcosmetics', 'firstaidbeauty', 'indigowild', 'instyle', 'chinaglaze', 'bodyimage', 'ancientminerals', 'customnails', 'pritinyc', 'dermadoctor', 'realrevivalabs', 'obagi', 'antimonkeybutt', 'beauticontrol', 'dermorganic', 'puresilkshave', 'kaplanmd', 'ddfskincare', 'curve', 'elemis', 'elfcosmetics', 'jurliqueusa', 'caudalieus', 'dial', 'got2busa', 'jouercosmetics', 'gdiapers', 'hydroxatone', 'artistryus', 'josie_maran', 'bauschlomb', 'aveda', 'spraywithprotan', 'kyanicorp', 'abreva', 'covergirl', 'diapersdotcom', 'hdnaturals', 'aprioribeauty', 'ghostfragrances', 'kerastaseusa', 'bathbodyworks', 'deborahlippmann', 'cottonelle', 'johnfriedaus', 'biooiluk', 'bhcosmetics', 'jergensus', 'devotedcreation', 'redken5thave', 'clearasil', 'farouksystems', 'baby_magic', 'crabtreeevelyn', 'embryolisseusa', 'thedivacup', 'fusion_beauty', 'bronnerbros', 'prttypeaushun', 'purell', 'arbonne', 'cleanwell', 'bulldogskincare', 'colorescience', 'bluebeardsusa', 'decleorskincare', 'dentek', 'glominerals', 'bareminerals', 'derma_e', 'alchimieforever', 'bodyglide', 'aubreyorganicsi', 'coola_suncare', 'afterglow', 'axe', 'hypehair', 'renefurtererus', 'kiehls', 'eltamd', 'barielle', 'premierdeadsea_', 'ballaformen', 'freemanbeauty', 'regenepureqanda', 'julepmaven', 'acneorg', 'alternahaircare', 'american_girl', 'evelom', 'itcosmetics', 'celazome', 'hydropeptide', 'guerlain', 'chapstick', 'clairolcolor', 'bosciaskincare', 'colgate', 'cosmedix', 'carmex', 'cvsbeautyclub', 'gillette', 'dove', 'billyjealousy', 'brazilianblwout', 'carolsdaughter', 'deliskincare', 'realcaboodles', 'butterlondon', 'aquaphorus', '100percentpure', 'illamasqua', 'ecolips', 'dgskincare', 'herbalessences', 'groominglounge', 'braunus', 'elizabetharden', 'davinesofficial', 'durex_usa', 'ghdnorthamerica', 'blinc', 'hourglassmakeup', 'ferrocosmetics', 'biodermalabuk', 'itsa10haircare', 'advilrelief', 'hennahut', 'bioelements', 'graftobian', 'ivory', 'qtips', 'crest', 'alimapure', 'caress', 'ecotools', 'goodcleanlove', 'bummis', 'everymanjack', 'acureorganics', 'alaffiaskincare', 'bdbrows', 'blissspa', 'goddessgarden', 'conairbeauty', 'herban_cowboy', 'gabrielcosmetic', 'burtsbees', 'prescriptives', 'huggies', 'repechage', 'dreamdrystyle', 'candiesbrand', 'drhauschka_usa', 'bravesldier', 'goldenpath', 'fuzzibunz', 'aveneusa', 'no7usa', 'bumble', 'drbronner', 'realpurity', 'bayerus', 'proactiv', 'joico', 'edgeshavegel', 'isseymiyakenyc', 'blackopalbeauty', 'arcona_la', 'biothermusa', 'eucerinus', 'kenra_haircare', 'joyfulbathco', 'fisherprice', 'caswellmassey'}
pet_food 11 {'beneful', 'holisticselect', 'fancyfeast', 'eukanuba', 'friskies', 'iams', 'purinaonedog', 'kibblesnbits', 'eaglepack', 'canidaepetfood', 'cuddleclones'}






    Out[4]:





573



In [ ]:

    
UNIQUE_FOLLOWERS = load('UNIQUE_FOLLOWERS')
BRAND_FOLLOWERS = load('BRAND_FOLLOWERS')
BRANDS_LIST = load('BRANDS_LIST')



In [ ]:

    
def get_all_sectors():
    return set(BRAND_SECTOR.keys())

def get_brands_in_sector(sector):
    return BRAND_SECTOR[sector]

def get_sector(brand):
    for s in BRAND_SECTOR:
        if brand in BRAND_SECTOR[s]:
            return s
    print('No sector found for',brand)
    return None

def get_followers(brand):
    return BRAND_FOLLOWERS[brand]

def set_followers(brand, followers):
    BRAND_FOLLOWERS[brand] = followers
    
def set_brands(user, brands):
    UNIQUE_FOLLOWERS[user] = brands

def get_brands(user):
    return UNIQUE_FOLLOWERS[user]

def get_brand_id(brand):
    return BRANDS_LIST.index(brand)



In [10]:

    
def plot_histogram(data,title):
    hist, bins = np.histogram(np.asarray(data),bins=50)    
    width = 0.9 * (bins[1] - bins[0])
    center = (bins[:-1] + bins[1:]) / 2
    plt.yscale("log")
    plt.bar(center, hist, align='center', width=width)
    plt.title(title)

plot_histogram([len(v) for v in list(UNIQUE_FOLLOWERS.values())], 'Followers Count Vs Brands Followed')



In [11]:

    
def sample_followers(brands_min, sample_pct, split_ratio):
    ffollowers = list(map(lambda x: x[0], filter(lambda x: len(x[1]) >= brands_min, UNIQUE_FOLLOWERS.items())))
    sample = random.sample(ffollowers, int(len(ffollowers) * sample_pct))
    print('Sample taken: %d/%d followers.' % (len(sample),len(list(ffollowers))))
    train_sample_size = int(split_ratio * len(sample))
    train_sample = sample[:train_sample_size]
    test_sample = sample[train_sample_size:]
    print('Training samples:',train_sample_size)
    print('Testing samples:',len(sample)-train_sample_size)
    return train_sample, test_sample #return sample

train_samples, test_samples = sample_followers(brands_min=5, sample_pct= 0.01, split_ratio=0.7)
#follower_ids = sample_followers(brands_min=5, sample_pct= 0.01, split_ratio=0.75)









    



Sample taken: 3246/324683 followers.
Training samples: 2434
Testing samples: 812



In [9]:

    
store(BRANDS_LIST, 'BRANDS_LIST')









    Out[9]:





True



In [10]:

    
store(BRAND_FOLLOWERS, 'BRAND_FOLLOWERS')









    Out[10]:





True



In [17]:

    
store(UNIQUE_FOLLOWERS, 'UNIQUE_FOLLOWERS')









    Out[17]:





True



In [12]:

    
N_BRANDS = len(BRAND_FOLLOWERS)
N_FOLLOWERS = len(UNIQUE_FOLLOWERS)



In [13]:

    
def get_negative_samples(follower_ids):
    brands = {}
    for follower_id in follower_ids:
        brands_not_followed = set(BRANDS_LIST) - get_brands(follower_id)
        brands[follower_id] = random.choice(list(brands_not_followed))
    return brands

brands_for_training = get_negative_samples(train_samples)



In [14]:

    
def remove_links(followers):
    brands_to_remove = {}
    for follower_id in followers:
        brand = random.choice(list(get_brands(follower_id)))
        brands_to_remove[follower_id] = brand
        set_brands(follower_id,get_brands(follower_id) - set([brand]))
        set_followers(brand, get_followers(brand) - set([follower_id]))
        print('Removed link', follower_id,'-',brand)
    return brands_to_remove
removed_brands = remove_links(train_samples)









    



Removed link 717842023265337344 - hyundai
Removed link 3020805899 - bmwusa
Removed link 3044535087 - asus
Removed link 98413674 - bareminerals
Removed link 2347666884 - doritos
Removed link 465092582 - kevyn_aucoin
Removed link 2497000586 - godiva
Removed link 3017735881 - chevrolet
Removed link 19053178 - hyundai
Removed link 16180619 - jaguarusa
Removed link 2789615972 - jeep
Removed link 277746214 - covergirl
Removed link 3241932850 - pringles
Removed link 1909104458 - jaguarusa
Removed link 3929192133 - bareminerals
Removed link 2899074779 - hugoboss
Removed link 356456325 - doritos
Removed link 4910833989 - breyers
Removed link 1744276482 - pringles
Removed link 4706221112 - doritos
Removed link 15244831 - blue_bunny
Removed link 2343837620 - chevrolet
Removed link 2907705488 - pringles
Removed link 3086687958 - pringles
Removed link 3275187505 - bmwusa
Removed link 2435457463 - auracacia
Removed link 2481299223 - att
Removed link 3138028317 - chrysler
Removed link 2391703808 - breyers
Removed link 703280009 - breyers
Removed link 109881591 - bmwusa
Removed link 731904271990448128 - dior
Removed link 2935372363 - covergirl
Removed link 255737687 - hyundai
Removed link 1472973786 - puffs
Removed link 2748439858 - chevrolet
Removed link 1076514884 - cardinalhealth
Removed link 2347768724 - butterfinger
Removed link 3345541611 - jeep
Removed link 40892433 - essentiaphwater
Removed link 2255408168 - clorox
Removed link 732995200373645312 - jaguarusa
Removed link 1546562874 - att
Removed link 3553929736 - dior
Removed link 3369869637 - doritos
Removed link 611622155 - doritos
Removed link 21149226 - bareminerals
Removed link 480352808 - jeep
Removed link 2529428200 - ralphlauren
Removed link 1527909055 - butterfinger
Removed link 1107997080 - att
Removed link 2165446842 - dragonage
Removed link 17240550 - pretzelcrisps
Removed link 2576024966 - dasaniwater
Removed link 2485674343 - hyundai
Removed link 392448665 - kelloggcompany
Removed link 1331599566 - purinaonedog
Removed link 4229364371 - hugoboss
Removed link 2236662282 - jaguarusa
Removed link 2768453820 - hyundai
Removed link 713117748 - hersheys
Removed link 787526407958040576 - ralphlauren
Removed link 3560121857 - jaguarusa
Removed link 14400440 - breyers
Removed link 2892867995 - jeep
Removed link 322810405 - downy
Removed link 3060188100 - bmwusa
Removed link 1453967282 - dietcoke
Removed link 2816789940 - bmwusa
Removed link 1926910892 - doritos
Removed link 19424873 - cottonelle
Removed link 3630597556 - chevrolet
Removed link 701118192 - hersheys
Removed link 4531053732 - pringles
Removed link 626085494 - blue_bunny
Removed link 705765403046313984 - bmwusa
Removed link 743944207371493380 - doritos
Removed link 1786773788 - 34degrees
Removed link 2289877973 - hourglassmakeup
Removed link 65684786 - clifbar
Removed link 738353139175264256 - instyle
Removed link 199284481 - chrysler
Removed link 517032683 - kia
Removed link 65728108 - jeep
Removed link 760753621 - bareminerals
Removed link 2402518214 - hyundai
Removed link 221990963 - jeep
Removed link 4242283877 - ralphlauren
Removed link 1429052293 - breyers
Removed link 2312589577 - chevrolet
Removed link 2595186088 - blue_bunny
Removed link 2337348044 - dannon
Removed link 2813660455 - hyundai
Removed link 95083857 - jaguarusa
Removed link 515899712 - fanta
Removed link 774366980 - bhcosmetics
Removed link 877892958 - chrysler
Removed link 427161414 - purell
Removed link 4921532504 - hyundai
Removed link 71053861 - hugoboss
Removed link 27123252 - annieshomegrown
Removed link 3304430832 - hourglassmakeup
Removed link 115996588 - jaguarusa
Removed link 50497151 - att
Removed link 2161243969 - chrysler
Removed link 2298607981 - chevrolet
Removed link 766689210434588672 - ralphlauren
Removed link 2839587982 - elfcosmetics
Removed link 1647078121 - pringles
Removed link 230637551 - kia
Removed link 255018247 - att
Removed link 4909826532 - hyundai
Removed link 760532144544358401 - kia
Removed link 4075218920 - dior
Removed link 1456909177 - dior
Removed link 1479464454 - bahlsenbiscuits
Removed link 3291706996 - chinaglaze
Removed link 2161322077 - canonusa
Removed link 2871696663 - chevrolet
Removed link 776615453850427393 - hugoboss
Removed link 2998523758 - doritos
Removed link 3130821430 - chevrolet
Removed link 2512702789 - butterfinger
Removed link 184783496 - elfcosmetics
Removed link 2182653312 - flatoutbread
Removed link 2352299106 - bareminerals
Removed link 2793682580 - kelloggcompany
Removed link 732403083804696576 - hourglassmakeup
Removed link 397295317 - blue_bunny
Removed link 16716983 - downy
Removed link 95930419 - hugoboss
Removed link 1409722686 - armani
Removed link 1421578064 - jeep
Removed link 2189645460 - jaguarusa
Removed link 319219127 - bmsnews
Removed link 2608421634 - doritos
Removed link 2537147738 - bmwusa
Removed link 28108980 - hickoryfarms
Removed link 407921702 - hugoboss
Removed link 2786293469 - hyundai
Removed link 793018026304106496 - kia
Removed link 1350515401 - kia
Removed link 566715803 - pringles
Removed link 31200818 - hourglassmakeup
Removed link 4047440429 - chevrolet
Removed link 3091715255 - pringles
Removed link 725939856338092032 - chevrolet
Removed link 2916772200 - hersheys
Removed link 790371570 - jaguarusa
Removed link 3011552201 - jeep
Removed link 3001148823 - jeep
Removed link 796849734 - fanta
Removed link 48160244 - jeep
Removed link 446369971 - downy
Removed link 154270534 - bumble
Removed link 313867966 - dasaniwater
Removed link 2217128876 - jaguarusa
Removed link 1315284074 - deliskincare
Removed link 3168065978 - bmwusa
Removed link 3309139548 - hugoboss
Removed link 231153839 - jeep
Removed link 727705360946102272 - hourglassmakeup
Removed link 755902753696808960 - dior
Removed link 2561736284 - bareminerals
Removed link 586953485 - ralphlauren
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In [15]:

    
def get_similarity_matrix():
    DIST_MAT = np.zeros((N_BRANDS,N_BRANDS))
    
    for tuple_pair in combinations(enumerate(BRANDS_LIST),2):
        b1 = get_followers(tuple_pair[0][1])
        b2 = get_followers(tuple_pair[1][1])
        
        #Jaccard Similarity Measure
        common_connections = len(b1.intersection(b2))
        total_connections = len(b1) + len(b2) - common_connections #More efficient than set(b1).union(b2)
        sim = common_connections/total_connections
        
        #Symmetrix Distance Matrix
        DIST_MAT[tuple_pair[0][0] , tuple_pair[1][0]] = DIST_MAT[tuple_pair[1][0] , tuple_pair[0][0]] = sim
    return DIST_MAT

DIST_MAT = get_similarity_matrix()
print('%d x %d Similarity Matrix created.'%(N_BRANDS,N_BRANDS))









    



200 x 200 Similarity Matrix created.



In [16]:

    
def get_statistics(values):
    import statistics
    mean = statistics.mean(values)
    median = statistics.median(values)
    maximum = max(values)
    minimum = min(values)
    return [mean, median, maximum, minimum]



In [17]:

    
def get_rank(sorted_scores, p_brand, n_brand):

    done = 0
    p_rank = None
    n_rank = None
    for idx, brand_score in enumerate(sorted_scores):
        if brand_score[0] == p_brand:
            p_rank = idx + 1
            done += 1
        elif brand_score[0] == n_brand:
            n_rank = idx + 1
            done += 1
        if done == 2:
            break;
    if done < 2:
        print(p_brand,p_rank, n_brand, n_rank)
    return p_rank, n_rank



In [18]:

    
def get_prediction(follower_ids, p_brands, n_brands):
    p_rank = {}
    n_rank = {}
    
    for follower_id in follower_ids:
        brands_followed = get_brands(follower_id)
        brands_not_followed = set(BRANDS_LIST) - brands_followed
            
        scores = {}
        for brand_not_followed in brands_not_followed:
            bnf_idx = get_brand_id(brand_not_followed)
            scores[brand_not_followed] = 0
            
            for brand_followed in brands_followed:
                bf_idx = get_brand_id(brand_followed)
                scores[brand_not_followed] += DIST_MAT[bf_idx, bnf_idx]
    
        prediction = sorted(scores.items(), key=lambda x: x[1]/len(brands_followed), reverse=True)
        p_rank[follower_id], n_rank[follower_id] = get_rank(prediction, p_brands[follower_id], n_brands[follower_id])
        
    return p_rank, n_rank

positive_rank_prediction, negative_rank_prediction = get_prediction(train_samples,removed_brands,brands_for_training)



In [19]:

    
plot_histogram(list(positive_rank_prediction.values()),'Jaccard Rank Distribution(+)')



In [20]:

    
plot_histogram(list(negative_rank_prediction.values()),'Jaccard Rank Distribution(-)')



In [21]:

    
print('\t\t\t  ',['mean', 'median', 'max', 'min'])
positive_statistics = get_statistics(positive_rank_prediction.values())
print('Jaccard Rank Statistics(+)', positive_statistics)
negative_statistics = get_statistics(negative_rank_prediction.values())
print('Jaccard Rank Statistics(-)', negative_statistics)









    



			   ['mean', 'median', 'max', 'min']
Jaccard Rank Statistics(+) [10.715283483976993, 2.0, 175, 1]
Jaccard Rank Statistics(-) [97.7834839769926, 97.0, 196, 1]



In [22]:

    
def get_worst_prediction(prediction, brands, source='+'):
    if source == '+':
        id_worst,rank_worst = max(prediction.items(), key=lambda x: x[1])
    elif source == '-':
        id_worst,rank_worst = min(prediction.items(), key=lambda x: x[1])
    print('(%s) Worst Jaccard Prediction %d for %s followed by %s'%(source, rank_worst, brands[id_worst], id_worst))
    print('Brands Followed', get_brands(id_worst))
get_worst_prediction(positive_rank_prediction,removed_brands, '+')
get_worst_prediction(negative_rank_prediction,brands_for_training,'-')









    



(+) Worst Jaccard Prediction 175 for cardinalhealth followed by 1076514884
Brands Followed {'butterfinger', 'hyundai', 'fstg_chips', 'kia'}
(-) Worst Jaccard Prediction 1 for kia followed by 339298360
Brands Followed {'jaguarusa', 'hyundai', 'chevrolet', 'jeep', 'hugoboss', 'bmwusa'}



In [23]:

    
def bar_plot(data_points, title):
    x = [p[0] for p in data_points]
    y = [p[1] for p in data_points]
    plt.bar(x,y)
    plt.title(title)



In [24]:

    
def get_rank_by_brands_count(follower_ids, prediction_rank):
    mean_rank = {}
    brands_followed_count = {}
    for follower_id in follower_ids:
        count = len(get_brands(follower_id))
        if count in mean_rank.keys():
            mean_rank[count] += prediction_rank[follower_id]
            brands_followed_count[count] += 1
        else:
            mean_rank[count] = prediction_rank[follower_id]
            brands_followed_count[count] = 1
    for k in mean_rank:
        mean_rank[k] //= brands_followed_count[k]
    return mean_rank



In [25]:

    
positive_mean_ranks = get_rank_by_brands_count(train_samples, positive_rank_prediction)
bar_plot(positive_mean_ranks.items(),'Average Rank by number of brands followed(+)')



In [26]:

    
negative_mean_ranks = get_rank_by_brands_count(train_samples, negative_rank_prediction)
bar_plot(negative_mean_ranks.items(),'Average Rank by number of brands followed(-)')



In [27]:

    
def get_ranks_by_followed_brands_count(follower_ids, rank_prediction):

    statistics = {}
    brands_followed = {}
    for follower_id in follower_ids:
        count = len(get_brands(follower_id))
        if count in brands_followed.keys():
            brands_followed[count].append(rank_prediction[follower_id])
        else:
            brands_followed[count] = [rank_prediction[follower_id]]
    for key in brands_followed:
        statistics[key] = get_statistics(brands_followed[key])
    
    return statistics

positive_binned_statistics = get_ranks_by_followed_brands_count(train_samples, positive_rank_prediction)
negative_binned_statistics = get_ranks_by_followed_brands_count(train_samples, negative_rank_prediction)



In [28]:

    
def get_Jaccard_scores(follower_id):
    
    brands_followed = get_brands(follower_id)
    brands_not_followed = set(BRANDS_LIST) - brands_followed

    count = len(brands_followed)
    scores = {}
    for brand_not_followed in brands_not_followed:
        bnf_idx = BRANDS_LIST.index(brand_not_followed)
        scores[brand_not_followed] = 0
        
        for brand_followed in brands_followed:
            bf_idx = BRANDS_LIST.index(brand_followed)
            scores[brand_not_followed] += DIST_MAT[bf_idx, bnf_idx]
    for b in scores:
        scores[b] /= count
    return scores



In [29]:

    
def get_adamic_adar_score(follower, brand):
    from math import log
    scores = {}
    
    brands_followed = get_brands(follower)
    count = len(brands_followed)
    for b in brands_followed:
        common_neighbours = get_followers(b) & get_followers(brand)
        scores[b] = 0
        for f in common_neighbours:
            scores[b] += 1/log(len(get_brands(f)))
        
    return get_statistics(scores.values())



In [30]:

    
def get_common_neighbor_stats(follower, brand):
    count = {}
    
    brands_followed = get_brands(follower)
    for b in brands_followed:
        common_neighbors = get_followers(b) & get_followers(brand)
        count[b] = len(common_neighbors)        
    return get_statistics(count.values())



In [31]:

    
def get_sector_stats(follower, brand, similarity_scores):
    brands_followed = get_brands(follower)
    sector = get_sector(brand)
    
    count_by_sector = defaultdict(int)
    score_by_sector = defaultdict(float)
    brand_sector = []
    for b in brands_followed:
        s = get_sector(b)
        brand_sector.append((b,s))
        count_by_sector[s] += 1
        similar_brands = get_brands_in_sector(s)
        for s_b in similar_brands:
            if s_b in similarity_scores:
                score_by_sector[s] += similarity_scores[s_b]

    count_list = []
    score_list = []
    wtd_sum = 0
    for s in SECTORS:
        c = count_by_sector[s]
        count_list.append(c)
        if c > 0:
            score_list.append(score_by_sector[s]/c)
        else:
            score_list.append(0.0)
        wtd_sum += c * score_by_sector[s]
    
    avgs = [sum(score_list)/len(score_list), wtd_sum/sum(count_list)]
    
    return count_list + score_list + avgs



In [32]:

    
def get_features(follower, brand, scores):
    
    record = []
    record.append(scores[brand])
    record.extend(get_statistics(scores.values()))
    
    
    n_brand = len(get_brands(follower))
    record.append(n_brand/N_BRANDS)
    
    n_follower = len(get_followers(brand))    
    record.append(n_follower/N_FOLLOWERS)
    
    sector_stats = get_sector_stats(follower, brand, scores)
    record.extend(sector_stats)
    
    #prefrtl_atchmnt = n_brand * n_follower
    #record.append(prefrtl_atchmnt)
    
    #aa_score = get_adamic_adar_score(follower,brand)
    #record.extend(aa_score)
    
    cn_score = get_common_neighbor_stats(follower,brand)
    record.extend(cn_score)
    
    #cosine_sim = get_cosine_stats(follower, brand)
    #sim_rank = get_sim_rank_stats(follower, brand)
    
    return record



In [33]:

    
def get_records(follower_ids, removed_brands, brands):
    positive_records = {}
    negative_records = {}
    for follower_id in follower_ids:
        p_brand = removed_brands[follower_id]
        n_brand = brands[follower_id]
        scores = get_Jaccard_scores(follower_id)
        p_record = get_features(follower_id, p_brand,scores)
        n_record = get_features(follower_id, n_brand,scores)
        positive_records[follower_id] = p_record
        negative_records[follower_id] = n_record
    return positive_records, negative_records

positive_records, negative_records = get_records(train_samples, removed_brands, brands_for_training)



In [34]:

    
def get_sample_stats(records):
    records = np.asarray(records)
    n, m = records.shape
    for i in range(m):
        print('Feature %d:'%(i+1), get_statistics(list(records[:,i])))
print('(+) Samples Stats:')
get_sample_stats(list(positive_records.values()))
print('(-) Samples Stats:')
get_sample_stats(list(negative_records.values()))









    



(+) Samples Stats:
Feature 1: [0.087365988012841483, 0.081129530134895822, 0.21578411025917499, 0.00038894538529024559]
Feature 2: [0.0054210544770711619, 0.0051437302087332555, 0.010822847592258353, 0.00088393855801835041]
Feature 3: [0.0019098559763533055, 0.0018487534228088598, 0.0061318482031574445, 0.00017156269118332297]
Feature 4: [0.1124746357371218, 0.10831187296108327, 0.21578411025917499, 0.0089468557379962319]
Feature 5: [1.5656374815730005e-05, 8.6899522592709713e-06, 0.0002067413814810804, 0.0]
Feature 6: [0.031275677896466722, 0.025000000000000001, 0.40999999999999998, 0.02]
Feature 7: [0.032659901258510739, 0.034208585398003248, 0.057942096685436602, 0.00015019466789423173]
Feature 8: [0.32169268693508629, 0.0, 5.0, 0.0]
Feature 9: [0.023829087921117501, 0.0, 3.0, 0.0]
Feature 10: [0.034511092851273621, 0.0, 3.0, 0.0]
Feature 11: [0.0, 0.0, 0.0, 0.0]
Feature 12: [1.8976992604765817, 0.0, 7.0, 0.0]
Feature 13: [0.29663105998356615, 0.0, 7.0, 0.0]
Feature 14: [0.0082169268693508633, 0.0, 1.0, 0.0]
Feature 15: [1.9055053410024652, 0.0, 42.0, 0.0]
Feature 16: [0.0, 0.0, 0.0, 0.0]
Feature 17: [0.28759244042728022, 0.0, 6.0, 0.0]
Feature 18: [0.0024650780608052587, 0.0, 1.0, 0.0]
Feature 19: [0.05135579293344289, 0.0, 2.0, 0.0]
Feature 20: [1.3763352506162696, 0.0, 22.0, 0.0]
Feature 21: [0.049301561216105176, 0.0, 4.0, 0.0]
Feature 22: [0.010625511863200661, 0.0, 0.15201920553440396, 0.0]
Feature 23: [8.8273722500764322e-05, 0.0, 0.0083917114403168, 0.0]
Feature 24: [0.00073088872427023511, 0.0, 0.055909496358025952, 0.0]
Feature 25: [0.0, 0.0, 0.0, 0.0]
Feature 26: [0.16166142341850187, 0.0, 0.57888556069597852, 0.0]
Feature 27: [0.011022928416990237, 0.0, 0.095447566101749079, 0.0]
Feature 28: [0.0, 0.0, 0.0, 0.0]
Feature 29: [0.30056530277265631, 0.0, 1.1741130605497294, 0.0]
Feature 30: [0.0, 0.0, 0.0, 0.0]
Feature 31: [0.016121124066966107, 0.0, 0.16943325962946823, 0.0]
Feature 32: [0.0, 0.0, 0.0, 0.0]
Feature 33: [0.001132035178563857, 0.0, 0.063701265534230112, 0.0]
Feature 34: [0.17000817862534282, 0.0, 0.77315928026422232, 0.0]
Feature 35: [0.0017877721799207096, 0.0, 0.086115418020606488, 0.0]
Feature 36: [0.048124531354922402, 0.043814644769122363, 0.11930031016676623, 0.0]
Feature 37: [1.7564434402497513, 1.5892637413554427, 6.9799877747488459, 0.0]
Feature 38: [50454.791501116502, 35462.300000000003, 147860.5, 21.777777777777779]
Feature 39: [44962.746918652425, 24137.25, 131979.0, 13.0]
Feature 40: [95728.324979457684, 78171.0, 216858.0, 82.0]
Feature 41: [21823.190221857025, 4224.0, 126932.0, 1.0]
(-) Samples Stats:
Feature 1: [0.0048092524576024897, 0.0015305334359486812, 0.15269951042693508, 1.4905249992850578e-06]
Feature 2: [0.0054210544770711619, 0.0051437302087332555, 0.010822847592258353, 0.00088393855801835041]
Feature 3: [0.0019098559763533055, 0.0018487534228088598, 0.0061318482031574445, 0.00017156269118332297]
Feature 4: [0.1124746357371218, 0.10831187296108327, 0.21578411025917499, 0.0089468557379962319]
Feature 5: [1.5656374815730005e-05, 8.6899522592709713e-06, 0.0002067413814810804, 0.0]
Feature 6: [0.031275677896466722, 0.025000000000000001, 0.40999999999999998, 0.02]
Feature 7: [0.0062180710642334835, 0.0012736140669411555, 0.057942096685436602, 0.00011829694621768749]
Feature 8: [0.32169268693508629, 0.0, 5.0, 0.0]
Feature 9: [0.023829087921117501, 0.0, 3.0, 0.0]
Feature 10: [0.034511092851273621, 0.0, 3.0, 0.0]
Feature 11: [0.0, 0.0, 0.0, 0.0]
Feature 12: [1.8976992604765817, 0.0, 7.0, 0.0]
Feature 13: [0.29663105998356615, 0.0, 7.0, 0.0]
Feature 14: [0.0082169268693508633, 0.0, 1.0, 0.0]
Feature 15: [1.9055053410024652, 0.0, 42.0, 0.0]
Feature 16: [0.0, 0.0, 0.0, 0.0]
Feature 17: [0.28759244042728022, 0.0, 6.0, 0.0]
Feature 18: [0.0024650780608052587, 0.0, 1.0, 0.0]
Feature 19: [0.05135579293344289, 0.0, 2.0, 0.0]
Feature 20: [1.3763352506162696, 0.0, 22.0, 0.0]
Feature 21: [0.049301561216105176, 0.0, 4.0, 0.0]
Feature 22: [0.010625511863200661, 0.0, 0.15201920553440396, 0.0]
Feature 23: [8.8273722500764322e-05, 0.0, 0.0083917114403168, 0.0]
Feature 24: [0.00073088872427023511, 0.0, 0.055909496358025952, 0.0]
Feature 25: [0.0, 0.0, 0.0, 0.0]
Feature 26: [0.16166142341850187, 0.0, 0.57888556069597852, 0.0]
Feature 27: [0.011022928416990237, 0.0, 0.095447566101749079, 0.0]
Feature 28: [0.0, 0.0, 0.0, 0.0]
Feature 29: [0.30056530277265631, 0.0, 1.1741130605497294, 0.0]
Feature 30: [0.0, 0.0, 0.0, 0.0]
Feature 31: [0.016121124066966107, 0.0, 0.16943325962946823, 0.0]
Feature 32: [0.0, 0.0, 0.0, 0.0]
Feature 33: [0.001132035178563857, 0.0, 0.063701265534230112, 0.0]
Feature 34: [0.17000817862534282, 0.0, 0.77315928026422232, 0.0]
Feature 35: [0.0017877721799207096, 0.0, 0.086115418020606488, 0.0]
Feature 36: [0.048124531354922402, 0.043814644769122363, 0.11930031016676623, 0.0]
Feature 37: [1.7564434402497513, 1.5892637413554427, 6.9799877747488459, 0.0]
Feature 38: [1581.4333749962068, 311.9666666666667, 88618.5, 0.75]
Feature 39: [1341.64811010682, 261.25, 95707.0, 0.5]
Feature 40: [3486.8225143796221, 563.0, 202553.0, 2.0]
Feature 41: [544.63640098603128, 91.0, 63828.0, 0.0]



In [35]:

    
x_train = []
y_train = []
for follower_id in train_samples:
    x_train.append(positive_records[follower_id])
    y_train.append(1)
    x_train.append(negative_records[follower_id])
    y_train.append(0)

x_train = np.asarray(x_train)
y_train = np.asarray(y_train)



In [36]:

    
def get_metrics(actual, prediction, verbose=False):
    from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, precision_score, recall_score
    metrics = {}
    metrics['cm'] = confusion_matrix(actual,prediction)
    metrics['accuracy'] = accuracy_score(actual,prediction)
    metrics['precision'] = precision_score(actual,prediction)
    metrics['recall'] = recall_score(actual,prediction)
    #metrics['f1'] = f1_score(actual,prediction)
    if verbose:
        print('Confusion Matrix:\n',metrics['cm'])
        #print('F1-Score:',metrics['f1'])
        print('Accuracy:', metrics['accuracy'])
        print('Precision:', metrics['precision'])
        print('Recall:', metrics['recall'])
    return metrics



In [37]:

    
def plot_pr_curve(y_true, y_prob):
    from sklearn.metrics import precision_recall_curve
    precision, recall, thresholds = precision_recall_curve(y_true, y_prob)
    plt.plot(recall, precision, label='Precision-Recall curve')

def plot_auc_curve(y_true, y_prob):
    from sklearn.metrics import roc_auc_score, roc_curve
    print('AUC:',roc_auc_score(y_true, y_prob))
    fpr, tpr, thresholds = roc_curve(y_true, y_prob)
    plt.plot(fpr, tpr, label='ROC Curve')



In [38]:

    
def getFMeasure(precision, recall):
    k = len(precision)
    fm = np.zeros(k)
    for i in range(k):
        fm[i] = 2* precision[i]*recall[i]/(precision[i]+recall[i])
    return fm



In [39]:

    
def get_overall_metrics(metrics, n):
    
    conf_mat = np.zeros((2, 2))
    precision = 0 #np.empty((1,2))
    recall = 0 #np.empty((1,2))
    accuracy=0
    
    for i in range(n):
        conf_mat = np.add(conf_mat, metrics[i]['cm'])
        #precision = np.vstack((precision, metrics[i]['precision']))
        #recall = np.vstack((recall, metrics[i]['recall']))
        precision += metrics[i]['precision']
        recall += metrics[i]['recall']
        accuracy += metrics[i]['accuracy']
    
    precision /= n 
    recall /= n 
    #fMeasure = getFMeasure(precision, recall)
    accuracy/=n
    
    print("Confusion Matrix:")
    print (conf_mat)
    print ("Accuracy\t:\t", accuracy)
    print ("Precision\t:\t", precision)
    print ("Recall\t\t:\t", recall)
    #print ("F-Measure\t:\t", fMeasure[1])



In [40]:

    
def standardize(X):
    from sklearn.preprocessing import StandardScaler
    return StandardScaler().fit_transform(X)
x_train = standardize(x_train)



In [41]:

    
from sklearn.linear_model import LogisticRegression,SGDClassifier
class BaselineClf():
    def __init__(self):
        #param = {'C': 166.81005372000593, 'dual': False, 'penalty': 'l1', 'solver': 'liblinear', 'tol': 0.001}
        #self.clf = LogisticRegression(**param)
        param = {'alpha': 0.0001, 'l1_ratio': 0.75, 'learning_rate': 'optimal', 'loss': 'modified_huber', 'n_iter': 50, 'penalty': 'l1'}
        #param={'alpha': 0.0001, 'learning_rate': 'optimal', 'loss': 'modified_huber', 'n_iter': 50, 'penalty': 'elasticnet'}
        self.clf = SGDClassifier(**param)
    
    def fit(self, X, Y):
        k = 10   #K-Fold 
        from sklearn.model_selection import KFold
        kfold_cv = KFold(n_splits=k)
        results = []
        for train, test in kfold_cv.split(X):
            self.clf.fit(X[train],Y[train])
            y_predict = self.clf.predict(X[test])
            results.append(get_metrics(Y[test], y_predict))
        get_overall_metrics(results, k)
    
    def predict(self, X):
        return self.clf.predict(X)
    
    def predict_proba(self, X):
        return self.clf.predict_proba(X)       
    
    def get_coef(self):
        print('\nCoefficients:')
        for i in range(self.clf.coef_.shape[1]):
            print('Feature %d : %f'%(i+1, self.clf.coef_[0,i]))
    def precision_recall_curve(self):
        plot_pr_curve(y_train, y_train_prob[:,1])
    
    def auc_curve(self):
        plot_auc_curve(y_train, y_train_prob[:,1])



In [42]:

    
clf = BaselineClf()
clf.fit(x_train, y_train)
clf.get_coef()









    



Confusion Matrix:
[[ 2127.   307.]
 [  368.  2066.]]
Accuracy	:	 0.861332927726
Precision	:	 0.875572445978
Recall		:	 0.848821088848

Coefficients:
Feature 1 : 5.987832
Feature 2 : -0.203802
Feature 3 : 0.493264
Feature 4 : -0.299754
Feature 5 : 0.000000
Feature 6 : 0.000000
Feature 7 : 0.686356
Feature 8 : -0.461790
Feature 9 : 0.067350
Feature 10 : 0.000000
Feature 11 : 0.000000
Feature 12 : -0.819609
Feature 13 : 0.000000
Feature 14 : 0.000000
Feature 15 : 0.000000
Feature 16 : 0.000000
Feature 17 : 0.000000
Feature 18 : 0.000000
Feature 19 : 0.000000
Feature 20 : 0.000000
Feature 21 : -0.446412
Feature 22 : 0.000000
Feature 23 : 0.000000
Feature 24 : 0.000000
Feature 25 : 0.000000
Feature 26 : 0.497729
Feature 27 : 0.000000
Feature 28 : 0.000000
Feature 29 : 0.000000
Feature 30 : 0.000000
Feature 31 : 0.000000
Feature 32 : 0.000000
Feature 33 : 0.000000
Feature 34 : 0.000000
Feature 35 : 0.000000
Feature 36 : 0.000000
Feature 37 : 0.000000
Feature 38 : -10.827388
Feature 39 : 2.974216
Feature 40 : 3.466064
Feature 41 : 2.299369



In [44]:

    
#0.0001,modified_huber,l2,optimal 0.85
#'alpha': 0.0001, 'learning_rate': 'optimal', 'loss': 'modified_huber', 'n_iter': 50, 'penalty': 'elasticnet', 0.88
#'alpha': 0.0001, 'l1_ratio': 0.9, 'learning_rate': 'optimal', 'loss': 'hinge', 'n_iter': 50, 'penalty': 'elasticnet', 0.888
#'alpha': 0.0001, 'eta0': 0.1, 'l1_ratio': 0.75, 'learning_rate': 'constant', 'loss': 'hinge', 'n_iter': 100, 'penalty': 'l1'}, 0.8899
#'alpha': 0.0001, 'l1_ratio': 0.75, 'learning_rate': 'optimal', 'loss': 'modified_huber', 'n_iter': 50, 'penalty': 'l1'}, 0.893

from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import SGDClassifier
params = {
    'loss': ['hinge', 'log', 'modified_huber'],
    'penalty':['elasticnet'],
    'alpha': [0.0001, 0.001, 0.01, 0.1, 1], 
    'learning_rate': ['constant','invscaling'],
    'n_iter': [5, 50, 100],
    'l1_ratio': [0.15, 0.3, 0.45, 0.6, 0.75, 0.9],
    'eta0': [0.1, 0.5]
}
search_clf = GridSearchCV(estimator=SGDClassifier(), param_grid=params)



In [45]:

    
search_clf.fit(x_train,y_train)









    Out[45]:





GridSearchCV(cv=None, error_score='raise',
       estimator=SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,
       eta0=0.0, fit_intercept=True, l1_ratio=0.15,
       learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1,
       penalty='l2', power_t=0.5, random_state=None, shuffle=True,
       verbose=0, warm_start=False),
       fit_params={}, iid=True, n_jobs=1,
       param_grid={'alpha': [0.0001, 0.001, 0.01, 0.1, 1], 'penalty': ['elasticnet'], 'learning_rate': ['constant', 'invscaling'], 'l1_ratio': [0.15, 0.3, 0.45, 0.6, 0.75, 0.9], 'loss': ['hinge', 'log', 'modified_huber'], 'eta0': [0.1, 0.5], 'n_iter': [5, 50, 100]},
       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,
       scoring=None, verbose=0)



In [46]:

    
search_clf.best_estimator_









    Out[46]:





SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
       eta0=0.5, fit_intercept=True, l1_ratio=0.9,
       learning_rate='invscaling', loss='hinge', n_iter=100, n_jobs=1,
       penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True,
       verbose=0, warm_start=False)



In [47]:

    
search_clf.best_params_









    Out[47]:





{'alpha': 0.001,
 'eta0': 0.5,
 'l1_ratio': 0.9,
 'learning_rate': 'invscaling',
 'loss': 'hinge',
 'n_iter': 100,
 'penalty': 'elasticnet'}



In [48]:

    
search_clf.best_score_









    Out[48]:





0.87262817924909164



In [51]:

    
#'C': 3593.8136638046258, 'dual': False, 'penalty': 'l2', 'solver': 'lbfgs'
#'C': 27825.594022071258,  'dual': False,  'penalty': 'l2',  'solver': 'liblinear' 0.886
#'C': 27825.594022071258, 'dual': False, 'penalty': 'l2', 'solver': 'lbfgs', 0.886
#'C': 166.81005372000593, 'dual': False, 'penalty': 'l1', 'solver': 'liblinear', 'tol': 0.001}
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression, RidgeClassifier
c_range = np.logspace(0, 5, 10)
params = {
    'penalty': ['l1','l2'],
    'C': c_range,
    'tol': [1e-4, 1e-3, 1e-2],
    'solver': ['liblinear'] #['newton-cg', 'lbfgs'] #['liblinear', 'sag'] 
}
search_clf = GridSearchCV(estimator=LogisticRegression(), param_grid=params)



In [52]:

    
search_clf.fit(x_train,y_train)
search_clf.best_estimator_









    Out[52]:





LogisticRegression(C=2154.4346900318847, class_weight=None, dual=False,
          fit_intercept=True, intercept_scaling=1, max_iter=100,
          multi_class='ovr', n_jobs=1, penalty='l1', random_state=None,
          solver='liblinear', tol=0.001, verbose=0, warm_start=False)



In [53]:

    
search_clf.best_params_









    Out[53]:





{'C': 2154.4346900318847, 'penalty': 'l1', 'solver': 'liblinear', 'tol': 0.001}



In [54]:

    
search_clf.best_score_









    Out[54]:





0.86737989503431567



In [49]:

    
#'alpha': 0.0001, 'solver': 'lsqr', 'tol': 0.001, 0.86
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import RidgeClassifier
params = {
    'alpha': [1,0.1, 0.01, 0.001, 0.0001, 0.00001],
    'tol': [1e-4, 1e-3, 1e-2],
    'solver': ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag'] 
}
search_clf = GridSearchCV(estimator=RidgeClassifier(), param_grid=params)



In [50]:

    
search_clf.fit(x_train,y_train)
search_clf.best_estimator_









    Out[50]:





RidgeClassifier(alpha=0.1, class_weight=None, copy_X=True, fit_intercept=True,
        max_iter=None, normalize=False, random_state=None, solver='lsqr',
        tol=0.001)



In [43]:

    
def add_removed_links(follower_ids, removed_brands):
    for follower in follower_ids:
        brand = removed_brands[follower]
        set_brands(follower, get_brands(follower) | set([brand]))
        set_followers(brand, get_followers(brand) | set([follower]))

add_removed_links(train_samples, removed_brands)



In [44]:

    
removed_brands_test = remove_links(test_samples)









    



Removed link 752280715849498624 - kia
Removed link 3313918175 - butterfinger
Removed link 3261386458 - kia
Removed link 2238881191 - dannon
Removed link 2505952320 - breyers
Removed link 864023329 - chinaglaze
Removed link 2181359733 - hersheys
Removed link 553907332 - butterfinger
Removed link 517924648 - bareminerals
Removed link 4562071101 - ralphlauren
Removed link 2332050175 - bmwusa
Removed link 2369966698 - blue_bunny
Removed link 2955656943 - jeep
Removed link 322056312 - iams
Removed link 56255564 - bmwusa
Removed link 3277197810 - bmwusa
Removed link 2674270953 - bmwusa
Removed link 45066889 - kia
Removed link 385036539 - herbalessences
Removed link 26542446 - bareminerals
Removed link 1239752538 - bmwusa
Removed link 2714260648 - jeep
Removed link 22397913 - duracell
Removed link 2837224617 - kia
Removed link 2786919954 - chevrolet
Removed link 117399090 - chrysler
Removed link 834725330 - hyundai
Removed link 3064819575 - bmwusa
Removed link 46134480 - bahlsenbiscuits
Removed link 109705705 - bareminerals
Removed link 719098837244227585 - bmwusa
Removed link 392493606 - chrysler
Removed link 1414990286 - jaguarusa
Removed link 1372736671 - dietcoke
Removed link 23228342 - jif
Removed link 499091235 - kia
Removed link 4431903935 - chevrolet
Removed link 332423327 - hyundai
Removed link 3857011338 - acer
Removed link 388943168 - covergirl
Removed link 506616098 - auracacia
Removed link 347974052 - canonusa
Removed link 628929215 - hyundai
Removed link 228742712 - fanta
Removed link 2932600932 - garmin
Removed link 476311424 - haagendazs_us
Removed link 3422257425 - dietcoke
Removed link 476876257 - clifbar
Removed link 1392988231 - att
Removed link 3058657561 - jaguarusa
Removed link 3140368396 - pringles
Removed link 3073416345 - 8oclockcoffee
Removed link 3016055898 - dietcoke
Removed link 2258783227 - jeep
Removed link 766876268927197184 - jeep
Removed link 191348554 - kevyn_aucoin
Removed link 2324632369 - pringles
Removed link 592155260 - breyers
Removed link 1875468036 - doritos
Removed link 411604202 - chinaglaze
Removed link 588288880 - chevrolet
Removed link 1608553134 - jeep
Removed link 2871828314 - elfcosmetics
Removed link 3246411023 - cottonelle
Removed link 4886431872 - hugoboss
Removed link 196185136 - jeep
Removed link 2549401916 - doritos
Removed link 2309267520 - hersheys
Removed link 2256039696 - breyers
Removed link 704837259766296576 - breyers
Removed link 210579077 - breyers
Removed link 172762179 - bmwusa
Removed link 550892320 - chobani
Removed link 2844380847 - jaguarusa
Removed link 753986808615014400 - hyundai
Removed link 3220610112 - doritos
Removed link 803989454 - acureorganics
Removed link 1608459384 - hugoboss
Removed link 1948030585 - bareminerals
Removed link 3344431239 - kia
Removed link 2435981304 - downy
Removed link 16542710 - croftersorganic
Removed link 226573571 - hyundai
Removed link 10364262 - foodsalive
Removed link 800045941 - chevrolet
Removed link 634974424 - hersheys
Removed link 3452206461 - jaguarusa
Removed link 2688350143 - jeep
Removed link 758496073895731200 - armani
Removed link 3203593133 - bmwusa
Removed link 2883438234 - croftersorganic
Removed link 2274969254 - pretzelcrisps
Removed link 168774273 - duracell
Removed link 506278413 - jewelosco
Removed link 219114397 - duracell
Removed link 3541544773 - armani
Removed link 2203741886 - obagi
Removed link 2331077826 - hugoboss
Removed link 3304843668 - dietcoke
Removed link 83748494 - 5hourenergy
Removed link 814870526 - bmwusa
Removed link 410045967 - bahlsenbiscuits
Removed link 894654955 - clorox
Removed link 2241559153 - chinaglaze
Removed link 786488705670324224 - kelloggcompany
Removed link 2934508707 - advilrelief
Removed link 4713755790 - chevrolet
Removed link 733686385970794496 - att
Removed link 1486332732 - kelloggcompany
Removed link 768698112 - chrysler
Removed link 2798431975 - hyundai
Removed link 2752498511 - covergirl
Removed link 562416614 - hyundai
Removed link 2378301129 - hyundai
Removed link 809029712 - chinaglaze
Removed link 38059841 - doritos
Removed link 701612923093790720 - doritos
Removed link 398703814 - kikkomankitchen
Removed link 3185292802 - clorox
Removed link 3078586799 - chrysler
Removed link 2591936972 - pringles
Removed link 709788070321598464 - bhcosmetics
Removed link 4764953191 - covergirl
Removed link 2589593273 - pringles
Removed link 3119776276 - dietcoke
Removed link 2886345953 - chevrolet
Removed link 4144467280 - pringles
Removed link 981270937 - elfcosmetics
Removed link 193490489 - herbacinusa
Removed link 207497292 - hyundai
Removed link 1519545739 - bmwusa
Removed link 1631709446 - clorox
Removed link 2856755503 - pringles
Removed link 415141507 - pretzelcrisps
Removed link 564866915 - blue_bunny
Removed link 2557108128 - att
Removed link 2585571636 - drbronner
Removed link 282735826 - breyers
Removed link 42808303 - puffs
Removed link 1242337728 - jaguarusa
Removed link 3317356149 - jaguarusa
Removed link 2591791652 - kia
Removed link 2560030784 - jaguarusa
Removed link 33025333 - hersheys
Removed link 4048949729 - hourglassmakeup
Removed link 203034165 - armani
Removed link 241253367 - dasaniwater
Removed link 2689539517 - att
Removed link 2664936348 - bummis
Removed link 1328299086 - jaguarusa
Removed link 2437119780 - kia
Removed link 724979357563899904 - chrysler
Removed link 2849994357 - avoninsider
Removed link 3185309144 - chrysler
Removed link 4871241713 - doritos
Removed link 1964686866 - kia
Removed link 1008488936 - jaguarusa
Removed link 4430302773 - ralphlauren
Removed link 1195841509 - kevyn_aucoin
Removed link 866905962 - jeep
Removed link 276927145 - bumble
Removed link 2685610834 - fanta
Removed link 221141503 - chobani
Removed link 734379792896643072 - doritos
Removed link 2846946718 - canonusa
Removed link 1446201150 - blue_bunny
Removed link 1375762254 - bhcosmetics
Removed link 3006243574 - bmwusa
Removed link 331074042 - att
Removed link 4676392452 - covergirl
Removed link 725657764228423680 - att
Removed link 710505706210852864 - dannon
Removed link 483744783 - jeep
Removed link 3479242576 - jeep
Removed link 1372640130 - chevrolet
Removed link 170680902 - hypehair
Removed link 553663442 - 8oclockcoffee
Removed link 2909569384 - jeep
Removed link 246340630 - chevrolet
Removed link 3130437754 - bhcosmetics
Removed link 918444708 - chrysler
Removed link 1697231240 - pretzelcrisps
Removed link 43968680 - bareminerals
Removed link 1974453835 - bmwusa
Removed link 377286688 - hyundai
Removed link 703455303719919617 - butterfinger
Removed link 286064522 - hyundai
Removed link 313573551 - kia
Removed link 14861579 - fab
Removed link 2261051480 - chevrolet
Removed link 3246601977 - chrysler
Removed link 498594492 - pringles
Removed link 381439794 - aveda
Removed link 733254446235455488 - att
Removed link 2897935814 - eatwholly
Removed link 3054228907 - downy
Removed link 1151377868 - hersheys
Removed link 866320754 - herbalessences
Removed link 2765898150 - bmsnews
Removed link 3245674033 - hugoboss
Removed link 22012206 - cascadianfarm
Removed link 211353429 - kia
Removed link 1003023439 - chevrolet
Removed link 730618664651657216 - bhcosmetics
Removed link 38507773 - garmin
Removed link 3035595945 - chinaglaze
Removed link 10927 - kia
Removed link 3195955434 - jeep
Removed link 2504857929 - hyundai
Removed link 187978013 - advilrelief
Removed link 631014955 - jaguarusa
Removed link 2946935870 - hersheys
Removed link 636111054 - fanta
Removed link 184892720 - drbronner
Removed link 1671012883 - bhcosmetics
Removed link 33820600 - covergirl
Removed link 719748278909276160 - jeep
Removed link 118125071 - joico
Removed link 2282437135 - hourglassmakeup
Removed link 579940710 - hugoboss
Removed link 1037567222 - jaguarusa
Removed link 156957615 - armani
Removed link 3049543767 - jaguarusa
Removed link 317696685 - americanapparel
Removed link 1364337182 - herbalessences
Removed link 2959116148 - chinaglaze
Removed link 2730092595 - jeep
Removed link 474683676 - hugoboss
Removed link 3307247807 - aveda
Removed link 733807471 - kia
Removed link 3067450683 - pringles
Removed link 181288593 - jvc_usa
Removed link 2584286802 - hugoboss
Removed link 701929822885384192 - kia
Removed link 2457512508 - covergirl
Removed link 2834665695 - breyers
Removed link 733281169 - chrysler
Removed link 27329379 - jeep
Removed link 2685405860 - chrysler
Removed link 283812956 - kelloggcompany
Removed link 3092417806 - chevrolet
Removed link 1975089919 - chevrolet
Removed link 572828416 - chevrolet
Removed link 15117375 - butterfinger
Removed link 22018840 - hersheys
Removed link 110258077 - derma_e
Removed link 1137365606 - dietcoke
Removed link 2706112183 - jaguarusa
Removed link 372127153 - jaguarusa
Removed link 1638551268 - jeep
Removed link 117060492 - kelloggcompany
Removed link 604136273 - dietpepsi
Removed link 1918962583 - jaguarusa
Removed link 759157695714824192 - armani
Removed link 873133495 - jeep
Removed link 3381014614 - puffs
Removed link 736931192 - att
Removed link 1421658392 - breyers
Removed link 68999207 - downy
Removed link 176167136 - americanapparel
Removed link 3220325598 - chrysler
Removed link 91497661 - friskies
Removed link 2572994128 - att
Removed link 4634438722 - ralphlauren
Removed link 129654693 - kia
Removed link 902449056 - puffs
Removed link 2523175196 - jaguarusa
Removed link 2288579710 - hersheys
Removed link 3121974694 - auracacia
Removed link 3294253738 - chevrolet
Removed link 2437334624 - att
Removed link 1413298554 - jaguarusa
Removed link 1556791933 - puffs
Removed link 86983000 - jeep
Removed link 3666498280 - pringles
Removed link 1954135638 - breyers
Removed link 2865888129 - att
Removed link 742021325028352000 - hugoboss
Removed link 155536568 - chevrolet
Removed link 2660773477 - att
Removed link 319114537 - bmwusa
Removed link 4670332213 - chrysler
Removed link 19981406 - bareminerals
Removed link 293072803 - fstg_chips
Removed link 361697675 - clifbar
Removed link 3002382215 - duracell
Removed link 1067068202 - chevrolet
Removed link 2742185329 - hyundai
Removed link 1928079716 - hyundai
Removed link 2370012785 - covergirl
Removed link 146167290 - bmwusa
Removed link 741413596475891712 - pringles
Removed link 21501503 - covergirl
Removed link 1596999853 - quiltednorthern
Removed link 3292409209 - kia
Removed link 2451775934 - americanapparel
Removed link 566619631 - doritos
Removed link 715206889169100806 - elfcosmetics
Removed link 1378543099 - bmwusa
Removed link 202640680 - kelloggcompany
Removed link 37291767 - kia
Removed link 3025657611 - bareminerals
Removed link 20236943 - bareminerals
Removed link 196389768 - farmrichsnacks
Removed link 774936774 - chobani
Removed link 1096318603 - covergirl
Removed link 2353872037 - pringles
Removed link 841664209 - doritos
Removed link 737657348 - duracell
Removed link 144580165 - bmwusa
Removed link 320822450 - bareminerals
Removed link 3468792734 - covergirl
Removed link 1957940504 - barbarasbakery
Removed link 399144616 - bareminerals
Removed link 359192597 - hyundai
Removed link 483938642 - bahlsenbiscuits
Removed link 65702638 - butterfinger
Removed link 174044452 - elfcosmetics
Removed link 2625852491 - kelloggcompany
Removed link 854776920 - bmwusa
Removed link 2955677702 - doritos
Removed link 1568566826 - clorox
Removed link 1373392015 - covergirl
Removed link 4320959242 - hyundai
Removed link 4908196797 - jeep
Removed link 2358831282 - jaguarusa
Removed link 457668065 - doritos
Removed link 3156612079 - armani
Removed link 3076791287 - elfcosmetics
Removed link 64997395 - decleorskincare
Removed link 150805000 - chrysler
Removed link 739669446 - clifbar
Removed link 2919055525 - bmwusa
Removed link 1192957597 - bareminerals
Removed link 3148206284 - jeep
Removed link 2353667582 - pretzelcrisps
Removed link 109857473 - covergirl
Removed link 374163970 - ralphlauren
Removed link 527633939 - bissellclean
Removed link 2370164725 - hyundai
Removed link 2972868363 - chrysler
Removed link 256288119 - farmrichsnacks
Removed link 45297023 - bareminerals
Removed link 3929751023 - blue_bunny
Removed link 2320765320 - jaguarusa
Removed link 2159120406 - butterfinger
Removed link 343150094 - hersheys
Removed link 2368543891 - jeep
Removed link 2857371687 - doritos
Removed link 3036728248 - bmwusa
Removed link 3239652088 - breyers
Removed link 1969654849 - americanapparel
Removed link 1679909682 - doritos
Removed link 705114760346525697 - bareminerals
Removed link 3877465098 - armani
Removed link 394098665 - jeep
Removed link 1549437679 - dietcoke
Removed link 2369576497 - fstg_chips
Removed link 700488476756566016 - instyle
Removed link 3142453546 - kelloggcompany
Removed link 697079548593049600 - puffs
Removed link 226702680 - pringles
Removed link 482989930 - downy
Removed link 723496849428992000 - jaguarusa
Removed link 4834572717 - kia
Removed link 2575404826 - aveda
Removed link 362876525 - bareminerals
Removed link 306557319 - ralphlauren
Removed link 304087095 - bmwusa
Removed link 3105118566 - jaguarusa
Removed link 2665833776 - duracell
Removed link 755042793853181953 - hugoboss
Removed link 3138477658 - jeep
Removed link 739765044200800256 - jeep
Removed link 1344188509 - hyundai
Removed link 3028673301 - hourglassmakeup
Removed link 737904502578237440 - armani
Removed link 706906112084332544 - hyundai
Removed link 19221448 - godiva
Removed link 4670384478 - elfcosmetics
Removed link 209692031 - hersheys
Removed link 223078096 - att
Removed link 405064540 - pringles
Removed link 3403411007 - jeep
Removed link 2488905474 - annieshomegrown
Removed link 743702463673315329 - covergirl
Removed link 2484591428 - breyers
Removed link 107572393 - decleorskincare
Removed link 2242392380 - hourglassmakeup
Removed link 131014335 - asus
Removed link 1327474662 - chevrolet
Removed link 44490590 - breyers
Removed link 589643363 - chevrolet
Removed link 2877279999 - kevyn_aucoin
Removed link 2685923779 - chevrolet
Removed link 1305622926 - covergirl
Removed link 732126896964653056 - bareminerals
Removed link 2730540115 - chevrolet
Removed link 52628191 - bareminerals
Removed link 388572173 - aveda
Removed link 1626423468 - pringles
Removed link 743022295233531904 - chrysler
Removed link 132507566 - hyundai
Removed link 2737717066 - butterfinger
Removed link 4352294836 - bmwusa
Removed link 432545134 - covergirl
Removed link 2498141775 - chevrolet
Removed link 739030103858937856 - doritos
Removed link 2990277048 - chevrolet
Removed link 743117524305207296 - kia
Removed link 4398384195 - chrysler
Removed link 360183897 - americanapparel
Removed link 309196250 - jaguarusa
Removed link 743460004963966976 - armani
Removed link 706320471197749248 - jaguarusa
Removed link 384503730 - purinaonedog
Removed link 42689691 - doritos
Removed link 2255315706 - chevrolet
Removed link 4883345867 - puffs
Removed link 28699438 - duracell
Removed link 1332967321 - armani
Removed link 3271598203 - chevrolet
Removed link 2765172245 - hersheys
Removed link 2466448855 - kia
Removed link 3424347407 - breyers
Removed link 2530527500 - chevrolet
Removed link 812049410 - covergirl
Removed link 54580000 - elfcosmetics
Removed link 3041848875 - jaguarusa
Removed link 437750764 - blue_bunny
Removed link 1363047104 - bareminerals
Removed link 261439145 - bmwusa
Removed link 353840144 - hyundai
Removed link 3309811158 - chinaglaze
Removed link 327272313 - hugoboss
Removed link 2696377092 - jaguarusa
Removed link 204458396 - gopicnic
Removed link 2518466490 - hillshirefarm
Removed link 412448673 - elfcosmetics
Removed link 426930894 - jaguarusa
Removed link 2577263310 - drmiracles
Removed link 783724719111626753 - elfcosmetics
Removed link 2858018523 - chevrolet
Removed link 105016378 - hugoboss
Removed link 2959577459 - breyers
Removed link 2319894552 - jeep
Removed link 2380753494 - bhcosmetics
Removed link 94465645 - pringles
Removed link 2940925353 - jeep
Removed link 2935617741 - elfcosmetics
Removed link 1388786490 - hourglassmakeup
Removed link 3159912565 - hugoboss
Removed link 74978729 - aveda
Removed link 265088124 - advilrelief
Removed link 294305368 - jaguarusa
Removed link 717602133529460736 - chevrolet
Removed link 515169269 - ge_reports
Removed link 4264425792 - bareminerals
Removed link 238765562 - bareminerals
Removed link 1668221720 - chinaglaze
Removed link 2913005929 - kia
Removed link 2242696397 - hersheys
Removed link 2680746750 - chevrolet
Removed link 1199014562 - kia
Removed link 2864784725 - godiva
Removed link 4445119694 - bhcosmetics
Removed link 1659388729 - cottonelle
Removed link 2205657067 - chrysler
Removed link 962553996 - hyundai
Removed link 1384314314 - kia
Removed link 1255217780 - covergirl
Removed link 718300103572922368 - jaguarusa
Removed link 224167555 - breyers
Removed link 1486669446 - doritos
Removed link 705873054430306304 - armani
Removed link 3433848657 - jeep
Removed link 524443450 - hyundai
Removed link 88608859 - doritos
Removed link 590299693 - chevrolet
Removed link 3239134203 - hourglassmakeup
Removed link 2266282041 - elfcosmetics
Removed link 147360460 - ralphlauren
Removed link 2474065057 - jaguarusa
Removed link 2644927309 - hyundai
Removed link 4701856159 - bareminerals
Removed link 2573524435 - pringles
Removed link 561423227 - clorox
Removed link 745276276244623360 - chevrolet
Removed link 707912383067525121 - josie_maran
Removed link 705057791489220608 - kia
Removed link 3949736063 - breyers
Removed link 27189717 - jaguarusa
Removed link 736961204078448642 - hyundai
Removed link 574755669 - jeep
Removed link 2910267650 - covergirl
Removed link 3164564171 - chevrolet
Removed link 36071291 - kelloggcompany
Removed link 515024522 - josie_maran
Removed link 325086859 - bareminerals
Removed link 299497310 - jaguarusa
Removed link 1485827845 - ctcsquares
Removed link 1463451248 - chrysler
Removed link 116441571 - hyundai
Removed link 44938913 - kia
Removed link 780955868 - jeep
Removed link 1950521478 - joico
Removed link 106135159 - hyundai
Removed link 2248057092 - fanta
Removed link 458502439 - hyundai
Removed link 487181150 - bhcosmetics
Removed link 2476112558 - blue_bunny
Removed link 65648476 - jif
Removed link 15109603 - clifbar
Removed link 751764065312276480 - jaguarusa
Removed link 473318720 - chinaglaze
Removed link 4568075778 - bareminerals
Removed link 283372235 - chevrolet
Removed link 19245220 - covergirl
Removed link 306601122 - doritos
Removed link 191479685 - jeep
Removed link 429795519 - hugoboss
Removed link 105039519 - hourglassmakeup
Removed link 4161394752 - att
Removed link 2854976888 - doritos
Removed link 389232019 - hyundai
Removed link 2688778983 - hyundai
Removed link 3114391790 - jeep
Removed link 740315318245982208 - doritos
Removed link 1023203270 - doritos
Removed link 2519310695 - chevrolet
Removed link 386765451 - fstg_chips
Removed link 782071678029430784 - jaguarusa
Removed link 1146738865 - clifbar
Removed link 3132251128 - canonusa
Removed link 2895914818 - auracacia
Removed link 2166204375 - hyundai
Removed link 214191358 - essentiaphwater
Removed link 3430280825 - bhcosmetics
Removed link 417318551 - chrysler
Removed link 3313403955 - covergirl
Removed link 365160615 - hyundai
Removed link 734329912438427648 - hyundai
Removed link 2751930825 - jaguarusa
Removed link 461729488 - kikkomankitchen
Removed link 2323721477 - chrysler
Removed link 485201802 - doritos
Removed link 50307593 - blue_bunny
Removed link 111923975 - breyers
Removed link 33623951 - covergirl
Removed link 388994751 - dietpepsi
Removed link 788081836228358144 - jeep
Removed link 268583869 - boogiemom
Removed link 2612379762 - herbalessences
Removed link 458857428 - bumble
Removed link 1701055447 - breyers
Removed link 2863768391 - chrysler
Removed link 47896302 - chevrolet
Removed link 180252153 - bmwusa
Removed link 2849435389 - bmwusa
Removed link 956400852 - butterfinger
Removed link 473611813 - att
Removed link 3366597113 - dietcoke
Removed link 208842859 - kia
Removed link 2954364654 - hyundai
Removed link 1640580542 - jaguarusa
Removed link 107172025 - covergirl
Removed link 2698699783 - hyundai
Removed link 322223538 - eas
Removed link 1328733074 - hyundai
Removed link 469742061 - 8oclockcoffee
Removed link 1612563576 - dietpepsi
Removed link 1435989188 - kia
Removed link 106464304 - pringles
Removed link 59574931 - bareminerals
Removed link 347777880 - cwhairlondon
Removed link 2845707471 - chevrolet
Removed link 2911770339 - chinaglaze
Removed link 4497907708 - american_girl
Removed link 2819159827 - jeep
Removed link 46664117 - dasaniwater
Removed link 36091962 - fttv
Removed link 495883827 - doritos
Removed link 701477477672206337 - jeep
Removed link 41489841 - boogiemom
Removed link 739015809461161984 - doritos
Removed link 782431633639141376 - butterfinger
Removed link 3087096725 - kerastaseusa
Removed link 250431917 - auracacia
Removed link 2158089606 - covergirl
Removed link 613577153 - puffs
Removed link 219322095 - hyundai
Removed link 149304719 - bmwusa
Removed link 4259542533 - jaguarusa
Removed link 1070377020 - ralphlauren
Removed link 270622527 - chrysler
Removed link 743960154 - aveda
Removed link 2718940426 - americanapparel
Removed link 2918601436 - jeep
Removed link 736725114088083456 - hyundai
Removed link 2693683344 - jeep
Removed link 2432200148 - chevrolet
Removed link 59898831 - jaguarusa
Removed link 731973408465010690 - pringles
Removed link 43357548 - butterfinger
Removed link 3973118537 - doritos
Removed link 2462060683 - godiva
Removed link 2846737355 - bmwusa
Removed link 60470572 - purell
Removed link 3069377561 - doritos
Removed link 3095827197 - aquaphorus
Removed link 565815844 - herbalessences
Removed link 1285215506 - avoninsider
Removed link 338497790 - biooiluk
Removed link 3240233155 - chobani
Removed link 568422337 - barbarasbakery
Removed link 279591430 - jaguarusa
Removed link 2596042902 - birdseye
Removed link 3160002226 - elfcosmetics
Removed link 591595201 - armani
Removed link 198762695 - elfcosmetics
Removed link 4875170303 - instyle
Removed link 520474877 - doritos
Removed link 3212853112 - chevrolet
Removed link 1109849240 - chipsahoy
Removed link 2789971177 - chrysler
Removed link 538538749 - breyers
Removed link 2608359372 - eatwholly
Removed link 2398797889 - butterfinger
Removed link 104578214 - cottonelle
Removed link 364622259 - hersheys
Removed link 3302237405 - ralphlauren
Removed link 2496597980 - bmwusa
Removed link 2171950286 - ralphlauren
Removed link 2262097100 - elfcosmetics
Removed link 727827719631847424 - hersheys
Removed link 4106525593 - armani
Removed link 105504485 - aveda
Removed link 1540927770 - jeep
Removed link 25382705 - americanapparel
Removed link 751836157281841152 - chrysler
Removed link 26589876 - pretzelcrisps
Removed link 1032489673 - cottonelle
Removed link 26740835 - dannon
Removed link 564887894 - armani
Removed link 1950899917 - kia
Removed link 2765137877 - americanapparel
Removed link 1524400506 - hyundai
Removed link 282737119 - boogiemom
Removed link 217508793 - 8oclockcoffee
Removed link 2718048884 - fanta
Removed link 2901381814 - fanta
Removed link 154290234 - aveda
Removed link 58057783 - att
Removed link 2270054260 - clorox
Removed link 570363963 - bmwusa
Removed link 25102419 - covergirl
Removed link 3219163585 - chinaglaze
Removed link 2491710637 - hersheys
Removed link 143183436 - chevrolet
Removed link 861730693 - annieshomegrown
Removed link 4207782805 - bareminerals
Removed link 199879008 - clorox
Removed link 3434368761 - jeep
Removed link 1321712900 - jeep
Removed link 3293430088 - doritos
Removed link 1399728127 - hyundai
Removed link 168422798 - hyundai
Removed link 242756427 - deliskincare
Removed link 2767941430 - hyundai
Removed link 39047949 - celadrin1
Removed link 510521159 - jeep
Removed link 16684972 - bissellclean
Removed link 961046749 - kelloggcompany
Removed link 2909463146 - dietcoke
Removed link 1666752800 - butterfinger
Removed link 170045021 - dior
Removed link 3315454235 - elfcosmetics
Removed link 2385577836 - covergirl
Removed link 512540215 - americanapparel
Removed link 2525383473 - hersheys
Removed link 3325213053 - instyle
Removed link 2737037057 - airheads
Removed link 2190579607 - dial
Removed link 1558485211 - pringles
Removed link 342372298 - blue_bunny
Removed link 34301503 - colorescience
Removed link 933306536 - jeep
Removed link 458047951 - dannon
Removed link 724007572798353408 - butterfinger
Removed link 380219755 - bmwusa
Removed link 250458374 - kia
Removed link 216969478 - bmwusa
Removed link 4017421275 - bhcosmetics
Removed link 144189562 - jeep
Removed link 3240923898 - hersheys
Removed link 3350622267 - fstg_chips
Removed link 2742209420 - ctcsquares
Removed link 440219350 - aquaphorus
Removed link 2753128619 - chevrolet
Removed link 1720720038 - elfcosmetics
Removed link 2322432432 - bmwusa
Removed link 195862081 - feelgoodfoods
Removed link 2357320976 - breyers
Removed link 4439720248 - chevrolet
Removed link 2716915418 - jeep
Removed link 1584633206 - breyers
Removed link 2599181796 - clorox
Removed link 2764981133 - fanta
Removed link 47448383 - covergirl
Removed link 2744871262 - pringles
Removed link 2261915960 - pringles
Removed link 389195074 - pringles
Removed link 2386038120 - elfcosmetics
Removed link 712664282647781376 - bmwusa
Removed link 2588990792 - att
Removed link 496131851 - cascadianfarm
Removed link 2929334724 - bhcosmetics
Removed link 1423347302 - att
Removed link 2329595348 - chinaglaze
Removed link 765538781181059073 - bhcosmetics
Removed link 2451421920 - jeep
Removed link 268390324 - bareminerals
Removed link 2861487842 - asus
Removed link 2429228557 - jeep
Removed link 4732270403 - breyers
Removed link 1021648260 - duracell
Removed link 32202890 - fab
Removed link 987147877 - eatwholly
Removed link 2610894582 - dietcoke
Removed link 3267450260 - butterfinger
Removed link 630080650 - hyundai
Removed link 2343667423 - covergirl
Removed link 3340811352 - chevrolet
Removed link 1481779448 - kia
Removed link 3016237375 - jaguarusa
Removed link 3228668962 - chevrolet
Removed link 406698593 - govoskos
Removed link 547978304 - chevrolet
Removed link 347350315 - faygo
Removed link 770299393 - bareminerals
Removed link 1379656268 - chevrolet
Removed link 430605717 - allegraotc
Removed link 3342874313 - jeep
Removed link 48314782 - chevrolet
Removed link 475285050 - chrysler
Removed link 58098786 - instyle
Removed link 160346808 - hyundai
Removed link 721401391957159936 - instyle
Removed link 393484783 - americanapparel
Removed link 2856642425 - kia
Removed link 2254451586 - chevrolet
Removed link 255116046 - hourglassmakeup
Removed link 59623411 - boogiemom
Removed link 974263200 - hyundai
Removed link 755075032372555776 - ralphlauren
Removed link 2318410092 - chrysler
Removed link 721084321 - att
Removed link 1722757003 - bhcosmetics
Removed link 753419152165998592 - armani
Removed link 3214371143 - bmwusa
Removed link 3404638719 - chevrolet
Removed link 2260013787 - bmwusa
Removed link 2218954851 - colorescience
Removed link 431820440 - hugoboss
Removed link 2471416225 - hourglassmakeup
Removed link 972322296 - jeep
Removed link 961235252 - instyle
Removed link 2506060824 - bareminerals
Removed link 443448993 - kia
Removed link 1577070247 - jaguarusa
Removed link 300720519 - jeep
Removed link 399634997 - americanapparel
Removed link 3673872612 - jaguarusa
Removed link 24917532 - clorox
Removed link 760209871413186560 - hourglassmakeup
Removed link 2700919584 - hersheys
Removed link 20229241 - drbronner
Removed link 288924330 - hersheys
Removed link 176057896 - jaguarusa
Removed link 42778965 - hourglassmakeup
Removed link 2835698528 - kenra_haircare
Removed link 2421398091 - republicoftea
Removed link 2881279046 - pringles
Removed link 181658663 - ge_reports
Removed link 2933158102 - kia
Removed link 2744058251 - jeep
Removed link 865409342 - bmwusa
Removed link 2904015206 - hersheys
Removed link 1444755649 - chrysler
Removed link 723754317686013957 - instyle
Removed link 3800557040 - jaguarusa
Removed link 3286389061 - butterfinger
Removed link 30721391 - bmwusa
Removed link 742329844030148608 - chevrolet
Removed link 237695518 - doritos
Removed link 571054434 - bumble
Removed link 2174779914 - covergirl
Removed link 2581284618 - jeep
Removed link 403867104 - covergirl
Removed link 1705659211 - asus
Removed link 3245169446 - duracell
Removed link 1071674432 - chevrolet
Removed link 1832640560 - bareminerals
Removed link 3035201648 - kevyn_aucoin
Removed link 446277448 - bmwusa
Removed link 2158951217 - jeep
Removed link 722392625299464192 - pr_organicsusa
Removed link 183441073 - hichew
Removed link 1653787700 - clorox
Removed link 777986354147840000 - bmwusa
Removed link 52778489 - gopicnic
Removed link 1489291718 - duracell



In [45]:

    
#Only For Repeating the Experiment
def reset_links(update_brands):
    for f_id in update_brands:
        brand = update_brands[f_id]
        set_brands(f_id,get_brands(follower_id) - set([brand]))
        set_followers(brand, get_followers(brand) - set([follower_id]))
        
#reset_links(removed_brands)
#add_removed_links(test_samples, removed_brands_test)



In [46]:

    
DIST_MAT = get_similarity_matrix()
print('%d x %d Similarity Matrix created.'%(N_BRANDS,N_BRANDS))









    



200 x 200 Similarity Matrix created.



In [49]:

    
k = 15
missing = []
brands_to_rank = {}
brands_to_lookup = {}
for follower_id in test_samples:
    scores = get_Jaccard_scores(follower_id)
    candidates = sorted(scores.items(), key=lambda x: x[1]/len(get_brands(follower_id)), reverse=True)
    brands_to_rank[follower_id] = []
    brands_to_lookup[follower_id] = []
    for brand, score in candidates[:k]:
        record = get_features(follower_id, brand,scores)
        brands_to_rank[follower_id].append(record)
        brands_to_lookup[follower_id].append(brand)
    if removed_brands_test[follower_id] not in brands_to_lookup[follower_id]:
        print('Missing brand %s for %d'%(removed_brands_test[follower_id], follower_id))
        pos = [x+1 for x, y in enumerate(candidates) if y[0] == removed_brands_test[follower_id]]
        print('Current Position:',pos[0])
        missing.append(pos[0])
        print('Brands followed are',','.join(list(UNIQUE_FOLLOWERS[follower_id])))
print('Missing Count:%d/%d'%(len(missing),len(test_samples)))
print(get_statistics(missing))









    



Missing brand dannon for 2238881191
Current Position: 19
Brands followed are ge_reports,asus,jewelosco,duracell
Missing brand ralphlauren for 4562071101
Current Position: 54
Brands followed are att,dietcoke,pringles,doritos,fanta
Missing brand iams for 322056312
Current Position: 46
Brands followed are gardein,cottonelle,godiva,bhcosmetics,jurliqueusa,eatwholly,bissellclean,hickoryfarms
Missing brand herbalessences for 385036539
Current Position: 27
Brands followed are annieshomegrown,elfcosmetics,canonusa,chobani,cobigelowny
Missing brand jif for 23228342
Current Position: 23
Brands followed are illyissimo,faygo,butterfinger,josie_maran,blue_bunny,breyers,elfcosmetics,bmwusa,bluediamond
Missing brand covergirl for 388943168
Current Position: 77
Brands followed are downy,derma_e,pretzelcrisps,beneful,jif,eatwholly,asus,flatoutbread,fishernutsbrand,aquaphorus,fischernwieser,friskies,bissellclean,hickoryfarms
Missing brand canonusa for 347974052
Current Position: 28
Brands followed are jaguarusa,covergirl,hyundai,chevrolet,jeep,aveda,kia,elfcosmetics,bmwusa
Missing brand garmin for 2932600932
Current Position: 33
Brands followed are jaguarusa,hyundai,chevrolet,jeep,kia,bmwusa,chrysler
Missing brand haagendazs_us for 476311424
Current Position: 33
Brands followed are hersheys,godiva,jaguarusa,blue_bunny,hyundai,breyers,kelloggcompany,bmwusa,chrysler
Missing brand clifbar for 476876257
Current Position: 65
Brands followed are herbacinusa,american_girl,godiva,friskies
Missing brand 8oclockcoffee for 3073416345
Current Position: 48
Brands followed are hersheys,breyers,kelloggcompany,haagendazs_us,pringles,doritos
Missing brand doritos for 1875468036
Current Position: 19
Brands followed are deliskincare,bhcosmetics,bareminerals,josie_maran,covergirl,elfcosmetics
Missing brand cottonelle for 3246411023
Current Position: 23
Brands followed are bhcosmetics,bareminerals,covergirl,chinaglaze,elfcosmetics
Missing brand acureorganics for 803989454
Current Position: 57
Brands followed are pretzelcrisps,bareminerals,fmchocolate,eatwholly,aquaphorus,8oclockcoffee,farmrichsnacks,hickoryfarms
Missing brand croftersorganic for 16542710
Current Position: 21
Brands followed are clorox,farmrichsnacks,boogiemom,covergirl,hillshirefarm
Missing brand foodsalive for 10364262
Current Position: 152
Brands followed are aveda,americanapparel,chobani,allegrocoffee
Missing brand croftersorganic for 2883438234
Current Position: 22
Brands followed are cottonelle,clorox,covergirl,essentiaphwater,elfcosmetics,bissellclean,farmrichsnacks,pringles,att,barbasolshave,doritos,godiva,annieshomegrown,purell,aveda,eas,bluediamond
Missing brand jewelosco for 506278413
Current Position: 97
Brands followed are barbasolshave,hyundai,hillshirefarm,chevrolet,fmchocolate
Missing brand duracell for 219114397
Current Position: 22
Brands followed are hersheys,bareminerals,clorox,no7usa,jeep
Missing brand armani for 3541544773
Current Position: 26
Brands followed are canonusa,hyundai,chevrolet,jeep,asus,pringles,chrysler,doritos
Missing brand 5hourenergy for 83748494
Current Position: 71
Brands followed are faygo,jaguarusa,chrysler,hyundai,haagendazs_us,kia,bluediamond
Missing brand advilrelief for 2934508707
Current Position: 31
Brands followed are dial,dietcoke,firstaidbeauty,barbarasbakery,clorox,hichew,covergirl,farmrichsnacks,bissellclean,barbasolshave,chobani,doritos,att,godiva,bhcosmetics,dentek,josie_maran,hillshirefarm,aveda,kevyn_aucoin,bluediamond
Missing brand att for 733686385970794496
Current Position: 23
Brands followed are hersheys,downy,butterfinger,blue_bunny,breyers,kelloggcompany,pringles
Missing brand hyundai for 2798431975
Current Position: 80
Brands followed are downy,herbalessences,godiva,hypehair
Missing brand kikkomankitchen for 398703814
Current Position: 27
Brands followed are clorox,jif,duracell,farmrichsnacks,att,pringles,barbasolshave,hersheys,downy,american_girl,purell,dietpepsi,acer,garmin,hillshirefarm
Missing brand herbacinusa for 193490489
Current Position: 118
Brands followed are 34degrees,eatwholly,pretzelcrisps,aquaphorus
Missing brand hersheys for 33025333
Current Position: 41
Brands followed are jergensus,bluediamond,puffs,no7usa
Missing brand dasaniwater for 241253367
Current Position: 38
Brands followed are jaguarusa,hyundai,dietpepsi,fanta,hugoboss,kia,bmwusa,pringles,chrysler,doritos
Missing brand bummis for 2664936348
Current Position: 103
Brands followed are illyissimo,att,annieshomegrown,pringles,doritos
Missing brand jaguarusa for 1328299086
Current Position: 45
Brands followed are downy,dietcoke,clorox,blue_bunny,herbalessences,breyers
Missing brand avoninsider for 2849994357
Current Position: 36
Brands followed are izze,americanapparel,clifbar,hourglassmakeup,breyers,elfcosmetics,pringles,chobani
Missing brand chobani for 221141503
Current Position: 24
Brands followed are jeep,kia,pringles,chevrolet,doritos
Missing brand canonusa for 2846946718
Current Position: 27
Brands followed are dior,instyle,ralphlauren,armani,covergirl,asus,att,doritos
Missing brand hypehair for 170680902
Current Position: 85
Brands followed are jaguarusa,bmwusa,covergirl,chinaglaze
Missing brand 8oclockcoffee for 553663442
Current Position: 37
Brands followed are hugoboss,allegrocoffee,chinaglaze,americanapparel,dannon
Missing brand pretzelcrisps for 1697231240
Current Position: 19
Brands followed are clorox,jaguarusa,blue_bunny,hyundai,breyers,dawndish,duracell,kia,elfcosmetics,bmwusa,pringles,chrysler,doritos,hersheys,downy,butterfinger
Missing brand fab for 14861579
Current Position: 129
Brands followed are jeep,chobani,deliskincare,derma_e,hickoryfarms
Missing brand bmsnews for 2765898150
Current Position: 30
Brands followed are jaguarusa,hugoboss,bmwusa,chevrolet
Missing brand garmin for 38507773
Current Position: 88
Brands followed are doritos,blue_bunny,breyers,dietpepsi,pringles
Missing brand advilrelief for 187978013
Current Position: 17
Brands followed are cottonelle,bhcosmetics,bareminerals,covergirl,purell,purinaonedog,jif,chinaglaze,elfcosmetics,8oclockcoffee
Missing brand drbronner for 184892720
Current Position: 20
Brands followed are barbarasbakery,derma_e,annieshomegrown,clifbar,glutenfreeda,alaffiaskincare,eatwholly,auracacia,cascadianfarm,chobani
Missing brand joico for 118125071
Current Position: 70
Brands followed are dial,godiva,barbarasbakery,herbalessences,got2busa,eatwholly,aquaphorus,barbasolshave
Missing brand americanapparel for 317696685
Current Position: 22
Brands followed are bumble,covergirl,jaguarusa,elfcosmetics
Missing brand jvc_usa for 181288593
Current Position: 79
Brands followed are jaguarusa,covergirl,kia,doritos
Missing brand butterfinger for 15117375
Current Position: 69
Brands followed are hyundai,cedarlanefoods,dermadoctor,elfcosmetics,8oclockcoffee,hickoryfarms,getolympus
Missing brand dietcoke for 1137365606
Current Position: 17
Brands followed are annieshomegrown,butterfinger,blue_bunny,purinaonedog,breyers,haagendazs_us,pringles,bluediamond
Missing brand dietpepsi for 604136273
Current Position: 30
Brands followed are dial,dietcoke,clorox,dentek,josie_maran,purell,babyganics,customnails,jif,aveda,8oclockcoffee,barbasolshave,bluediamond,doritos
Missing brand att for 736931192
Current Position: 24
Brands followed are armani,dior,covergirl,dietcoke
Missing brand americanapparel for 176167136
Current Position: 97
Brands followed are izze,cottonelle,dietcoke,republicoftea,annieshomegrown,clifbar,blue_bunny,breyers,jif,att,chobani
Missing brand friskies for 91497661
Current Position: 46
Brands followed are killerbread,jaguarusa,breyers,kelloggcompany,haagendazs_us,pringles
Missing brand att for 2572994128
Current Position: 19
Brands followed are hersheys,downy,clorox,herbalessences,breyers,fanta,puffs,pringles,hillshirefarm,doritos
Missing brand auracacia for 3121974694
Current Position: 38
Brands followed are biodermalabuk,frenchsuk,pukkaherbs,aveda,bahlsenbiscuits,kevyn_aucoin
Missing brand att for 2865888129
Current Position: 45
Brands followed are clorox,covergirl,chinaglaze,herbalessences
Missing brand clifbar for 361697675
Current Position: 55
Brands followed are americanapparel,clorox,bareminerals,covergirl,chinaglaze,elfcosmetics,doritos
Missing brand duracell for 3002382215
Current Position: 30
Brands followed are hersheys,hugoboss,bahlsenbiscuits,godiva,cartenoireuk
Missing brand quiltednorthern for 1596999853
Current Position: 43
Brands followed are hersheys,hyundai,chevrolet,fanta,breyers,pringles,chobani,doritos
Missing brand americanapparel for 2451775934
Current Position: 49
Brands followed are doritos,hyundai,pringles,breyers,fanta
Missing brand bareminerals for 20236943
Current Position: 41
Brands followed are dietcoke,duracell,dannon,pringles
Missing brand chobani for 774936774
Current Position: 37
Brands followed are att,covergirl,chevrolet,herbalessences
Missing brand duracell for 737657348
Current Position: 49
Brands followed are croftersorganic,farmrichsnacks,annieshomegrown,bluediamond,hickoryfarms
Missing brand armani for 3156612079
Current Position: 59
Brands followed are bareminerals,covergirl,americanapparel,herbalessences
Missing brand clifbar for 739669446
Current Position: 23
Brands followed are 8oclockcoffee,annieshomegrown,allegrocoffee,killerbread
Missing brand americanapparel for 1969654849
Current Position: 23
Brands followed are downy,janmarini,bareminerals,covergirl,chinaglaze,elfcosmetics
Missing brand armani for 3877465098
Current Position: 20
Brands followed are jeep,kia,hyundai,chrysler,chevrolet
Missing brand dietcoke for 1549437679
Current Position: 20
Brands followed are covergirl,att,downy,firstaidbeauty
Missing brand instyle for 700488476756566016
Current Position: 97
Brands followed are hersheys,butterfinger,hyundai,chevrolet,breyers,jeep,kia,cascadianfarm,pringles,chrysler,doritos
Missing brand pringles for 226702680
Current Position: 22
Brands followed are advilrelief,downy,herbalessences,duracell,puffs
Missing brand bareminerals for 362876525
Current Position: 61
Brands followed are jif,ctcsquares,dietcoke,kelloggcompany
Missing brand godiva for 19221448
Current Position: 41
Brands followed are gardein,hyundai,glutenfreeda,flatoutbread,aquaphorus,chobani
Missing brand pringles for 405064540
Current Position: 52
Brands followed are clorox,friskies,derma_e,aquaphorus
Missing brand decleorskincare for 107572393
Current Position: 129
Brands followed are annieshomegrown,farmrichsnacks,boogiemom,blue_bunny
Missing brand chevrolet for 589643363
Current Position: 21
Brands followed are dial,dietcoke,blue_bunny,covergirl,babyganics,farmrichsnacks,barbasolshave,chobani,doritos,kerastaseusa,hersheys,pringles,downy,godiva,dannon,bhcosmetics,iams,kevyn_aucoin,garmin,cottonelle,barbarasbakery,derma_e,pretzelcrisps,clorox,clifbar,hichew,herbalessences,hyundai,advilrelief,haagendazs_us,jif,bestfoods,duracell,fishernutsbrand,elfcosmetics,8oclockcoffee,kia,bissellclean,att,chrysler,hickoryfarms,republicoftea,annieshomegrown,josie_maran,purell,hillshirefarm,quiltednorthern,eatwholly,asus,aveda,eas,puffs,bluediamond,no7usa
Missing brand purinaonedog for 384503730
Current Position: 16
Brands followed are dietcoke,derma_e,canonusa,clorox,ge_reports,blue_bunny,herbalessences,breyers,duracell,pringles,hersheys,downy,butterfinger,quiltednorthern,aveda,iams
Missing brand duracell for 28699438
Current Position: 23
Brands followed are covergirl,breyers,chevrolet,doritos
Missing brand drmiracles for 2577263310
Current Position: 68
Brands followed are hypehair,bhcosmetics,jaguarusa,covergirl,herbalessences,chevrolet,fanta,jeep,chinaglaze,elfcosmetics,att,bmwusa
Missing brand advilrelief for 265088124
Current Position: 30
Brands followed are downy,dietcoke,annieshomegrown,clorox,butterfinger,jaguarusa,blue_bunny,hyundai,breyers,eatwholly,puffs,5hourenergy,chrysler
Missing brand ge_reports for 515169269
Current Position: 59
Brands followed are jaguarusa,att,duracell,kia
Missing brand godiva for 2864784725
Current Position: 25
Brands followed are hersheys,annieshomegrown,butterfinger,blue_bunny,breyers,jeep,hugoboss,jif,pringles,chobani,doritos
Missing brand josie_maran for 515024522
Current Position: 43
Brands followed are dietcoke,firstaidbeauty,americanapparel,clorox,blue_bunny,covergirl,hourglassmakeup,aquaphorus,elfcosmetics,farmrichsnacks,bissellclean,barbasolshave,chrysler,kerastaseusa,downy,godiva,got2busa,jeep,eatwholly,puffs,kevyn_aucoin,garmin,bluediamond
Missing brand ctcsquares for 1485827845
Current Position: 47
Brands followed are dietcoke,godiva,clorox,blue_bunny,eatwholly,bestfoods,barbasolshave,doritos
Missing brand hugoboss for 429795519
Current Position: 34
Brands followed are hersheys,butterfinger,pringles,doritos
Missing brand clifbar for 1146738865
Current Position: 40
Brands followed are hersheys,chobani,jif,eatwholly,flatoutbread,bluediamond
Missing brand canonusa for 3132251128
Current Position: 52
Brands followed are hersheys,americanapparel,butterfinger,blue_bunny,breyers,eatwholly,elfcosmetics,pringles,doritos
Missing brand essentiaphwater for 214191358
Current Position: 61
Brands followed are dial,janmarini,clorox,blue_bunny,breyers,eatwholly
Missing brand kikkomankitchen for 461729488
Current Position: 45
Brands followed are canonusa,covergirl,dietpepsi,breyers,advilrelief,aveda,pringles
Missing brand kia for 208842859
Current Position: 53
Brands followed are canonusa,hugoboss,eatwholly,downy
Missing brand eas for 322223538
Current Position: 72
Brands followed are cottonelle,dietcoke,derma_e,clorox,bareminerals,blue_bunny,covergirl,herbalessences,hyundai,breyers,advilrelief,pringles,chobani,downy,butterfinger,purell,dietpepsi,eatwholly,aveda,fstg_chips,bluediamond
Missing brand 8oclockcoffee for 469742061
Current Position: 37
Brands followed are clorox,butterfinger,blue_bunny,breyers,duracell,chrysler
Missing brand bareminerals for 59574931
Current Position: 54
Brands followed are gardein,auracacia,deliskincare,acureorganics
Missing brand cwhairlondon for 347777880
Current Position: 47
Brands followed are kevyn_aucoin,bareminerals,hourglassmakeup,kerastaseusa
Missing brand american_girl for 4497907708
Current Position: 82
Brands followed are clorox,downy,americanapparel,puffs
Missing brand dasaniwater for 46664117
Current Position: 25
Brands followed are eatwholly,downy,blue_bunny,birdseye,aquaphorus
Missing brand fttv for 36091962
Current Position: 176
Brands followed are fab,clifbar,dietcoke,duracell
Missing brand boogiemom for 41489841
Current Position: 19
Brands followed are izze,annieshomegrown,clorox,blue_bunny,eatwholly,aveda,duracell,farmrichsnacks,cascadianfarm
Missing brand ralphlauren for 1070377020
Current Position: 37
Brands followed are bhcosmetics,bareminerals,covergirl,hourglassmakeup,chinaglaze,elfcosmetics
Missing brand aveda for 743960154
Current Position: 48
Brands followed are hersheys,annieshomegrown,butterfinger,breyers,asus,pringles,doritos
Missing brand americanapparel for 2718940426
Current Position: 17
Brands followed are hugoboss,covergirl,bareminerals,herbalessences
Missing brand godiva for 2462060683
Current Position: 28
Brands followed are hersheys,pretzelcrisps,airheads,bareminerals,butterfinger,kevyn_aucoin
Missing brand purell for 60470572
Current Position: 69
Brands followed are josie_maran,dietcoke,iams,elfcosmetics
Missing brand aquaphorus for 3095827197
Current Position: 28
Brands followed are bareminerals,avoninsider,covergirl,hourglassmakeup,elfcosmetics
Missing brand avoninsider for 1285215506
Current Position: 38
Brands followed are hugoboss,bmwusa,americanapparel,hypehair
Missing brand chobani for 3240233155
Current Position: 46
Brands followed are ralphlauren,cottonelle,armani,covergirl,hugoboss,pringles
Missing brand birdseye for 2596042902
Current Position: 30
Brands followed are dial,cottonelle,clorox,blue_bunny,covergirl,kelloggcompany,herbalessences,hyundai,breyers,dawndish,duracell,kia,farmrichsnacks,bmwusa,chobani,doritos,chrysler,hersheys,downy,dannon,butterfinger,chevrolet,quiltednorthern,fanta,jeep
Missing brand chipsahoy for 1109849240
Current Position: 29
Brands followed are dannon,ctcsquares,hyundai,breyers,jeep,kia,att,pringles,chrysler,doritos
Missing brand americanapparel for 25382705
Current Position: 19
Brands followed are fab,bareminerals,chinaglaze,kerastaseusa
Missing brand dannon for 26740835
Current Position: 21
Brands followed are instyle,americanapparel,chevrolet,bulbrite,jeep,fab,bmsnews,hugoboss,garmin,chobani
Missing brand americanapparel for 2765137877
Current Position: 51
Brands followed are hersheys,herbalessences,fanta,chinaglaze,pringles,doritos
Missing brand 8oclockcoffee for 217508793
Current Position: 17
Brands followed are jrwatkins,derma_e,jvc_usa,dermadoctor,eatwholly,chinaglaze
Missing brand att for 58057783
Current Position: 32
Brands followed are americanapparel,clorox,covergirl,hourglassmakeup,elfcosmetics,kevyn_aucoin
Missing brand covergirl for 25102419
Current Position: 26
Brands followed are jaguarusa,butterfinger,dietcoke,clifbar
Missing brand annieshomegrown for 861730693
Current Position: 27
Brands followed are downy,joico,doritos,elfcosmetics
Missing brand jeep for 1321712900
Current Position: 56
Brands followed are downy,clorox,butterfinger,blue_bunny,ctcsquares,breyers,fanta,kelloggcompany,doritos
Missing brand deliskincare for 242756427
Current Position: 22
Brands followed are bareminerals,covergirl,hourglassmakeup,hugoboss,aveda,chinaglaze,kevyn_aucoin
Missing brand celadrin1 for 39047949
Current Position: 135
Brands followed are deliskincare,pretzelcrisps,davesgourmet,govoskos,babyblanketgift,34degrees,droppslaundry,obagi,drbronner,gardentime1,aquaphorus,bumblebar
Missing brand americanapparel for 512540215
Current Position: 67
Brands followed are clorox,jaguarusa,blue_bunny,covergirl,breyers,herbalessences,advilrelief,haagendazs_us,chinaglaze,duracell,kia,pringles,chobani,hersheys,downy,annieshomegrown,butterfinger,bluediamond
Missing brand airheads for 2737037057
Current Position: 30
Brands followed are hersheys,breyers,fanta,chipsahoy,pringles,doritos
Missing brand dannon for 458047951
Current Position: 23
Brands followed are jaguarusa,downy,dietcoke,kia
Missing brand ctcsquares for 2742209420
Current Position: 38
Brands followed are hersheys,pringles,doritos,fanta
Missing brand aquaphorus for 440219350
Current Position: 17
Brands followed are deliskincare,americanapparel,hourglassmakeup,aveda,chinaglaze,barielle
Missing brand cascadianfarm for 496131851
Current Position: 36
Brands followed are clorox,boogiemom,blue_bunny,flatoutbread,duracell,aquaphorus,kia,farmrichsnacks,bissellclean,chobani,hickoryfarms,doritos,hersheys,downy,birdseye,annieshomegrown,pr_organicsusa,dietpepsi,asus
Missing brand asus for 2861487842
Current Position: 41
Brands followed are doritos,dietcoke,pringles,breyers,fanta
Missing brand fab for 32202890
Current Position: 118
Brands followed are 8thcontinentsoy,illyissimo,derma_e,hourglassmakeup,breyers,chinaglaze,elfcosmetics,drbronner,bluediamond
Missing brand dietcoke for 2610894582
Current Position: 43
Brands followed are cottonelle,blue_bunny,ctcsquares,farmrichsnacks,bluediamond,doritos
Missing brand govoskos for 406698593
Current Position: 68
Brands followed are jaguarusa,dietcoke,duracell,kia
Missing brand faygo for 347350315
Current Position: 64
Brands followed are jaguarusa,hyundai,chrysler,kia
Missing brand allegraotc for 430605717
Current Position: 131
Brands followed are clorox,butterfinger,clifbar,duracell
Missing brand instyle for 58098786
Current Position: 184
Brands followed are realpurity,drbronner,aveda,feelgoodfoods,glutenfreeda
Missing brand instyle for 721401391957159936
Current Position: 54
Brands followed are kevyn_aucoin,chinaglaze,covergirl,hourglassmakeup,elfcosmetics
Missing brand americanapparel for 393484783
Current Position: 71
Brands followed are cottonelle,downy,derma_e,jaguarusa,covergirl,herbalessences,dermadoctor,hourglassmakeup,aveda,pringles
Missing brand att for 721084321
Current Position: 39
Brands followed are bmsnews,hyundai,chobani,dannon
Missing brand colorescience for 2218954851
Current Position: 68
Brands followed are americanapparel,bhcosmetics,bareminerals,covergirl,hourglassmakeup,chinaglaze,elfcosmetics,kevyn_aucoin
Missing brand instyle for 961235252
Current Position: 162
Brands followed are pretzelcrisps,airheads,clifbar,jeep,fab,eatwholly,jif,garmin
Missing brand kia for 443448993
Current Position: 69
Brands followed are dial,downy,boogiemom,dentek,eatwholly,flatoutbread,duracell,friskies
Missing brand drbronner for 20229241
Current Position: 69
Brands followed are chobani,blueqness,pretzelcrisps,dannon
Missing brand republicoftea for 2421398091
Current Position: 95
Brands followed are hersheys,dial,cottonelle,hypehair,birdseye,godiva,annieshomegrown,clorox,covergirl,breyers,fanta,farmrichsnacks,att,pringles
Missing brand ge_reports for 181658663
Current Position: 21
Brands followed are jaguarusa,hyundai,bmsnews,kia,acer,bmwusa,chrysler
Missing brand bumble for 571054434
Current Position: 20
Brands followed are alaffiaskincare,hypehair,cremeofnature,drmiracles
Missing brand covergirl for 2174779914
Current Position: 23
Brands followed are dietcoke,jaguarusa,canonusa,clorox,hyundai,breyers,advilrelief,haagendazs_us,bestfoods,dawndish,duracell,kia,chipsahoy,bissellclean,att,pringles,chobani,doritos,hersheys,downy,american_girl,cardinalhealth,dannon,butterfinger,quiltednorthern,fanta,bmsnews,iams,dasaniwater,avoninsider
Missing brand asus for 1705659211
Current Position: 160
Brands followed are downy,droppslaundry,babyganics,flatoutbread,fishernutsbrand,avoninsider
Missing brand kevyn_aucoin for 3035201648
Current Position: 50
Brands followed are bareminerals,boogiemom,fmchocolate,babyganics,advilrelief,bahlsenbiscuits,acer,drbronner,bissellclean,barbasolshave,hickoryfarms
Missing brand pr_organicsusa for 722392625299464192
Current Position: 138
Brands followed are armani,dior,instyle,americanapparel
Missing brand hichew for 183441073
Current Position: 74
Brands followed are cottonelle,clorox,kikkomankitchen,blue_bunny,covergirl,herbalessences,breyers,flatoutbread,chinaglaze,duracell,elfcosmetics,farmrichsnacks,bissellclean,pringles,doritos,gardein,downy,godiva,annieshomegrown,butterfinger,purell,dietpepsi,hugoboss,eatwholly,asus,aveda,puffs,bluediamond
Missing brand gopicnic for 52778489
Current Position: 25
Brands followed are celadrin1,butterfinger,holisticselect,bayescleaners,bulbrite
Missing Count:143/812
[50.98601398601399, 41, 184, 16]



In [50]:

    
y_test_predict = []
y_test_actual = []
correct = 0
for follower_id in test_samples:
    x_test = []
    candidates = brands_to_rank[follower_id]
    
    for record in candidates:
        x_test.append(record)
    x_test = np.asarray(x_test)
    y_test_prob = clf.predict_proba(standardize(x_test))
    result = [(x,y[1]) for x,y in zip(brands_to_lookup[follower_id], y_test_prob)]
    result = sorted(result, key=lambda x: x[1], reverse=True)[0]
    if result[0] == removed_brands_test[follower_id]:
        correct += 1
    print(result[0]+'('+get_sector(result[0])+')', result[1], '[',removed_brands_test[follower_id]+'('+ get_sector(removed_brands_test[follower_id])+')',']')
print('%d/%d ='%(correct, len(test_samples)), correct/len(test_samples))
print('%d/%d ='%(correct, len(test_samples)-len(missing)), correct/(len(test_samples)-len(missing)))









    



kia(car) 1.0 [ kia(car) ]
hersheys(food) 1.0 [ butterfinger(food) ]
kia(car) 1.0 [ kia(car) ]
downy(household_che) 1.0 [ dannon(food) ]
breyers(food) 1.0 [ breyers(food) ]
bareminerals(personal_care) 1.0 [ chinaglaze(personal_care) ]
hersheys(food) 1.0 [ hersheys(food) ]
butterfinger(food) 1.0 [ butterfinger(food) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
hersheys(food) 1.0 [ ralphlauren(apparel) ]
hyundai(car) 1.0 [ bmwusa(car) ]
butterfinger(food) 1.0 [ blue_bunny(food) ]
jeep(car) 1.0 [ jeep(car) ]
farmrichsnacks(food) 1.0 [ iams(pet_food) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
kia(car) 1.0 [ kia(car) ]
bareminerals(personal_care) 1.0 [ herbalessences(personal_care) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
chrysler(car) 1.0 [ bmwusa(car) ]
hyundai(car) 1.0 [ jeep(car) ]
butterfinger(food) 1.0 [ duracell(electronics) ]
kia(car) 1.0 [ kia(car) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
bmwusa(car) 1.0 [ chrysler(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
bahlsenbiscuits(food) 1.0 [ bahlsenbiscuits(food) ]
hyundai(car) 1.0 [ bareminerals(personal_care) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
hersheys(food) 1.0 [ chrysler(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
butterfinger(food) 1.0 [ dietcoke(food) ]
hersheys(food) 1.0 [ jif(food) ]
hyundai(car) 1.0 [ kia(car) ]
hyundai(car) 1.0 [ chevrolet(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
kia(car) 1.0 [ acer(electronics) ]
farmrichsnacks(food) 1.0 [ covergirl(personal_care) ]
auracacia(household_che) 1.0 [ auracacia(household_che) ]
chrysler(car) 1.0 [ canonusa(electronics) ]
hyundai(car) 1.0 [ hyundai(car) ]
kia(car) 1.0 [ fanta(food) ]
doritos(food) 1.0 [ garmin(electronics) ]
kia(car) 1.0 [ haagendazs_us(food) ]
breyers(food) 1.0 [ dietcoke(food) ]
farmrichsnacks(food) 1.0 [ clifbar(food) ]
hersheys(food) 1.0 [ att(electronics) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
pringles(food) 1.0 [ pringles(food) ]
butterfinger(food) 1.0 [ 8oclockcoffee(food) ]
bhcosmetics(personal_care) 1.0 [ dietcoke(food) ]
jeep(car) 1.0 [ jeep(car) ]
jeep(car) 1.0 [ jeep(car) ]
kevyn_aucoin(personal_care) 1.0 [ kevyn_aucoin(personal_care) ]
pringles(food) 1.0 [ pringles(food) ]
hersheys(food) 1.0 [ breyers(food) ]
hourglassmakeup(personal_care) 1.0 [ doritos(food) ]
chinaglaze(personal_care) 1.0 [ chinaglaze(personal_care) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
jeep(car) 1.0 [ jeep(car) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
hourglassmakeup(personal_care) 1.0 [ cottonelle(personal_care) ]
hugoboss(apparel) 1.0 [ hugoboss(apparel) ]
jeep(car) 1.0 [ jeep(car) ]
doritos(food) 1.0 [ doritos(food) ]
hersheys(food) 1.0 [ hersheys(food) ]
hersheys(food) 1.0 [ breyers(food) ]
breyers(food) 1.0 [ breyers(food) ]
breyers(food) 1.0 [ breyers(food) ]
jaguarusa(car) 1.0 [ bmwusa(car) ]
doritos(food) 1.0 [ chobani(food) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
doritos(food) 1.0 [ doritos(food) ]
bissellclean(household_che) 1.0 [ acureorganics(personal_care) ]
hourglassmakeup(personal_care) 1.0 [ hugoboss(apparel) ]
pringles(food) 1.0 [ bareminerals(personal_care) ]
hyundai(car) 1.0 [ kia(car) ]
hersheys(food) 1.0 [ downy(household_che) ]
bissellclean(household_che) 1.0 [ croftersorganic(food) ]
hyundai(car) 1.0 [ hyundai(car) ]
bumble(personal_care) 1.0 [ foodsalive(food) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
hersheys(food) 1.0 [ hersheys(food) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
jeep(car) 1.0 [ jeep(car) ]
armani(apparel) 1.0 [ armani(apparel) ]
jaguarusa(car) 1.0 [ bmwusa(car) ]
hersheys(food) 1.0 [ croftersorganic(food) ]
blue_bunny(food) 1.0 [ pretzelcrisps(food) ]
breyers(food) 1.0 [ duracell(electronics) ]
kia(car) 1.0 [ jewelosco(food) ]
butterfinger(food) 1.0 [ duracell(electronics) ]
kia(car) 1.0 [ armani(apparel) ]
obagi(personal_care) 1.0 [ obagi(personal_care) ]
chrysler(car) 1.0 [ hugoboss(apparel) ]
breyers(food) 1.0 [ dietcoke(food) ]
bmwusa(car) 1.0 [ 5hourenergy(food) ]
jaguarusa(car) 1.0 [ bmwusa(car) ]
bahlsenbiscuits(food) 1.0 [ bahlsenbiscuits(food) ]
butterfinger(food) 1.0 [ clorox(household_che) ]
hourglassmakeup(personal_care) 1.0 [ chinaglaze(personal_care) ]
pringles(food) 1.0 [ kelloggcompany(food) ]
eatwholly(food) 1.0 [ advilrelief(personal_care) ]
jaguarusa(car) 1.0 [ chevrolet(car) ]
doritos(food) 1.0 [ att(electronics) ]
puffs(paper_product) 1.0 [ kelloggcompany(food) ]
kia(car) 1.0 [ chrysler(car) ]
puffs(paper_product) 1.0 [ hyundai(car) ]
elfcosmetics(personal_care) 1.0 [ covergirl(personal_care) ]
hyundai(car) 1.0 [ hyundai(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
elfcosmetics(personal_care) 1.0 [ chinaglaze(personal_care) ]
doritos(food) 1.0 [ doritos(food) ]
blue_bunny(food) 1.0 [ doritos(food) ]
breyers(food) 1.0 [ kikkomankitchen(food) ]
blue_bunny(food) 1.0 [ clorox(household_che) ]
hyundai(car) 1.0 [ chrysler(car) ]
pringles(food) 1.0 [ pringles(food) ]
hourglassmakeup(personal_care) 1.0 [ bhcosmetics(personal_care) ]
jaguarusa(car) 1.0 [ covergirl(personal_care) ]
pringles(food) 1.0 [ pringles(food) ]
breyers(food) 1.0 [ dietcoke(food) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
pringles(food) 1.0 [ pringles(food) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
farmrichsnacks(food) 1.0 [ herbacinusa(personal_care) ]
hyundai(car) 1.0 [ hyundai(car) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
hersheys(food) 1.0 [ clorox(household_che) ]
pringles(food) 1.0 [ pringles(food) ]
farmrichsnacks(food) 1.0 [ pretzelcrisps(food) ]
blue_bunny(food) 1.0 [ blue_bunny(food) ]
doritos(food) 1.0 [ att(electronics) ]
fstg_chips(food) 1.0 [ drbronner(personal_care) ]
breyers(food) 1.0 [ breyers(food) ]
bhcosmetics(personal_care) 1.0 [ puffs(paper_product) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
hersheys(food) 1.0 [ kia(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
farmrichsnacks(food) 1.0 [ hersheys(food) ]
hourglassmakeup(personal_care) 1.0 [ hourglassmakeup(personal_care) ]
jeep(car) 1.0 [ armani(apparel) ]
chevrolet(car) 1.0 [ dasaniwater(food) ]
chrysler(car) 1.0 [ att(electronics) ]
hersheys(food) 1.0 [ bummis(personal_care) ]
butterfinger(food) 1.0 [ jaguarusa(car) ]
kia(car) 1.0 [ kia(car) ]
bmwusa(car) 1.0 [ chrysler(car) ]
hersheys(food) 1.0 [ avoninsider(household_che) ]
hyundai(car) 1.0 [ chrysler(car) ]
doritos(food) 1.0 [ doritos(food) ]
hyundai(car) 1.0 [ kia(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
hyundai(car) 1.0 [ ralphlauren(apparel) ]
bhcosmetics(personal_care) 1.0 [ kevyn_aucoin(personal_care) ]
breyers(food) 1.0 [ jeep(car) ]
bhcosmetics(personal_care) 1.0 [ bumble(personal_care) ]
doritos(food) 1.0 [ fanta(food) ]
hyundai(car) 1.0 [ chobani(food) ]
hyundai(car) 1.0 [ doritos(food) ]
pringles(food) 1.0 [ canonusa(electronics) ]
blue_bunny(food) 1.0 [ blue_bunny(food) ]
hourglassmakeup(personal_care) 1.0 [ bhcosmetics(personal_care) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
hyundai(car) 1.0 [ att(electronics) ]
bhcosmetics(personal_care) 1.0 [ covergirl(personal_care) ]
kia(car) 1.0 [ att(electronics) ]
hersheys(food) 1.0 [ dannon(food) ]
jeep(car) 1.0 [ jeep(car) ]
jaguarusa(car) 1.0 [ jeep(car) ]
hyundai(car) 1.0 [ chevrolet(car) ]
hyundai(car) 1.0 [ hypehair(personal_care) ]
elfcosmetics(personal_care) 1.0 [ 8oclockcoffee(food) ]
hersheys(food) 1.0 [ jeep(car) ]
hersheys(food) 1.0 [ chevrolet(car) ]
bhcosmetics(personal_care) 1.0 [ bhcosmetics(personal_care) ]
hyundai(car) 1.0 [ chrysler(car) ]
kelloggcompany(food) 1.0 [ pretzelcrisps(food) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
butterfinger(food) 1.0 [ butterfinger(food) ]
hyundai(car) 1.0 [ hyundai(car) ]
chevrolet(car) 1.0 [ kia(car) ]
farmrichsnacks(food) 1.0 [ fab(appliances) ]
bmwusa(car) 1.0 [ chevrolet(car) ]
kia(car) 1.0 [ chrysler(car) ]
hersheys(food) 1.0 [ pringles(food) ]
hourglassmakeup(personal_care) 1.0 [ aveda(personal_care) ]
bmwusa(car) 1.0 [ att(electronics) ]
kelloggcompany(food) 1.0 [ eatwholly(food) ]
breyers(food) 1.0 [ downy(household_che) ]
hersheys(food) 1.0 [ hersheys(food) ]
clorox(household_che) 1.0 [ herbalessences(personal_care) ]
jeep(car) 1.0 [ bmsnews(household_che) ]
elfcosmetics(personal_care) 1.0 [ hugoboss(apparel) ]
cascadianfarm(food) 1.0 [ cascadianfarm(food) ]
kia(car) 1.0 [ kia(car) ]
bmwusa(car) 1.0 [ chevrolet(car) ]
elfcosmetics(personal_care) 1.0 [ bhcosmetics(personal_care) ]
hersheys(food) 1.0 [ garmin(electronics) ]
hourglassmakeup(personal_care) 1.0 [ chinaglaze(personal_care) ]
kia(car) 1.0 [ kia(car) ]
jeep(car) 1.0 [ jeep(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
hourglassmakeup(personal_care) 1.0 [ advilrelief(personal_care) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
hersheys(food) 1.0 [ hersheys(food) ]
butterfinger(food) 1.0 [ fanta(food) ]
farmrichsnacks(food) 1.0 [ drbronner(personal_care) ]
bareminerals(personal_care) 1.0 [ bhcosmetics(personal_care) ]
hourglassmakeup(personal_care) 1.0 [ covergirl(personal_care) ]
hyundai(car) 1.0 [ jeep(car) ]
farmrichsnacks(food) 1.0 [ joico(personal_care) ]
bareminerals(personal_care) 1.0 [ hourglassmakeup(personal_care) ]
chrysler(car) 1.0 [ hugoboss(apparel) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
bareminerals(personal_care) 1.0 [ armani(apparel) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
bareminerals(personal_care) 1.0 [ americanapparel(apparel) ]
breyers(food) 1.0 [ herbalessences(personal_care) ]
bhcosmetics(personal_care) 1.0 [ chinaglaze(personal_care) ]
jaguarusa(car) 1.0 [ jeep(car) ]
dior(apparel) 1.0 [ hugoboss(apparel) ]
bhcosmetics(personal_care) 1.0 [ aveda(personal_care) ]
kia(car) 1.0 [ kia(car) ]
pringles(food) 1.0 [ pringles(food) ]
hyundai(car) 1.0 [ jvc_usa(electronics) ]
hyundai(car) 1.0 [ hugoboss(apparel) ]
kia(car) 1.0 [ kia(car) ]
bareminerals(personal_care) 1.0 [ covergirl(personal_care) ]
hersheys(food) 1.0 [ breyers(food) ]
hyundai(car) 1.0 [ chrysler(car) ]
jeep(car) 1.0 [ jeep(car) ]
chrysler(car) 1.0 [ chrysler(car) ]
hersheys(food) 1.0 [ kelloggcompany(food) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
jaguarusa(car) 1.0 [ chevrolet(car) ]
kia(car) 1.0 [ butterfinger(food) ]
downy(household_che) 1.0 [ hersheys(food) ]
blue_bunny(food) 1.0 [ derma_e(personal_care) ]
hersheys(food) 1.0 [ dietcoke(food) ]
chrysler(car) 1.0 [ jaguarusa(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
bmwusa(car) 1.0 [ jeep(car) ]
pringles(food) 1.0 [ kelloggcompany(food) ]
farmrichsnacks(food) 1.0 [ dietpepsi(food) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
armani(apparel) 1.0 [ armani(apparel) ]
jeep(car) 1.0 [ jeep(car) ]
doritos(food) 1.0 [ puffs(paper_product) ]
ralphlauren(apparel) 1.0 [ att(electronics) ]
breyers(food) 1.0 [ breyers(food) ]
farmrichsnacks(food) 1.0 [ downy(household_che) ]
butterfinger(food) 1.0 [ americanapparel(apparel) ]
kia(car) 1.0 [ chrysler(car) ]
doritos(food) 1.0 [ friskies(pet_food) ]
blue_bunny(food) 1.0 [ att(electronics) ]
dior(apparel) 1.0 [ ralphlauren(apparel) ]
jaguarusa(car) 1.0 [ kia(car) ]
farmrichsnacks(food) 1.0 [ puffs(paper_product) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
hersheys(food) 1.0 [ hersheys(food) ]
cwhairlondon(personal_care) 1.0 [ auracacia(household_che) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
bmwusa(car) 1.0 [ att(electronics) ]
hyundai(car) 1.0 [ jaguarusa(car) ]
doritos(food) 1.0 [ puffs(paper_product) ]
jeep(car) 1.0 [ jeep(car) ]
jaguarusa(car) 1.0 [ pringles(food) ]
breyers(food) 1.0 [ breyers(food) ]
elfcosmetics(personal_care) 1.0 [ att(electronics) ]
elfcosmetics(personal_care) 1.0 [ hugoboss(apparel) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
jaguarusa(car) 1.0 [ att(electronics) ]
hyundai(car) 1.0 [ bmwusa(car) ]
chrysler(car) 1.0 [ chrysler(car) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
fstg_chips(food) 1.0 [ fstg_chips(food) ]
hourglassmakeup(personal_care) 1.0 [ clifbar(food) ]
frenchsuk(food) 1.0 [ duracell(electronics) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
chinaglaze(personal_care) 1.0 [ covergirl(personal_care) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
pringles(food) 1.0 [ pringles(food) ]
elfcosmetics(personal_care) 1.0 [ covergirl(personal_care) ]
butterfinger(food) 1.0 [ quiltednorthern(paper_product) ]
kia(car) 1.0 [ kia(car) ]
hersheys(food) 1.0 [ americanapparel(apparel) ]
jaguarusa(car) 1.0 [ doritos(food) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
blue_bunny(food) 1.0 [ kelloggcompany(food) ]
kia(car) 1.0 [ kia(car) ]
breyers(food) 1.0 [ bareminerals(personal_care) ]
doritos(food) 1.0 [ bareminerals(personal_care) ]
farmrichsnacks(food) 1.0 [ farmrichsnacks(food) ]
jaguarusa(car) 1.0 [ chobani(food) ]
covergirl(personal_care) 1.0 [ covergirl(personal_care) ]
pringles(food) 1.0 [ pringles(food) ]
bhcosmetics(personal_care) 1.0 [ doritos(food) ]
bissellclean(household_che) 1.0 [ duracell(electronics) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
blue_bunny(food) 1.0 [ bareminerals(personal_care) ]
breyers(food) 1.0 [ covergirl(personal_care) ]
fstg_chips(food) 1.0 [ barbarasbakery(food) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
hyundai(car) 1.0 [ hyundai(car) ]
bahlsenbiscuits(food) 1.0 [ bahlsenbiscuits(food) ]
butterfinger(food) 1.0 [ butterfinger(food) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
hersheys(food) 1.0 [ kelloggcompany(food) ]
chrysler(car) 1.0 [ bmwusa(car) ]
doritos(food) 1.0 [ doritos(food) ]
hersheys(food) 1.0 [ clorox(household_che) ]
hourglassmakeup(personal_care) 1.0 [ covergirl(personal_care) ]
hyundai(car) 1.0 [ hyundai(car) ]
jeep(car) 1.0 [ jeep(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
bmwusa(car) 1.0 [ doritos(food) ]
elfcosmetics(personal_care) 1.0 [ armani(apparel) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
chinaglaze(personal_care) 1.0 [ decleorskincare(personal_care) ]
chrysler(car) 1.0 [ chrysler(car) ]
eatwholly(food) 1.0 [ clifbar(food) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
jeep(car) 1.0 [ jeep(car) ]
doritos(food) 1.0 [ pretzelcrisps(food) ]
hourglassmakeup(personal_care) 1.0 [ covergirl(personal_care) ]
ralphlauren(apparel) 1.0 [ ralphlauren(apparel) ]
bissellclean(household_che) 1.0 [ bissellclean(household_che) ]
hyundai(car) 1.0 [ hyundai(car) ]
chrysler(car) 1.0 [ chrysler(car) ]
farmrichsnacks(food) 1.0 [ farmrichsnacks(food) ]
hourglassmakeup(personal_care) 1.0 [ bareminerals(personal_care) ]
blue_bunny(food) 1.0 [ blue_bunny(food) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
butterfinger(food) 1.0 [ butterfinger(food) ]
jaguarusa(car) 1.0 [ hersheys(food) ]
jeep(car) 1.0 [ jeep(car) ]
doritos(food) 1.0 [ doritos(food) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
butterfinger(food) 1.0 [ breyers(food) ]
hourglassmakeup(personal_care) 1.0 [ americanapparel(apparel) ]
doritos(food) 1.0 [ doritos(food) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
jaguarusa(car) 1.0 [ armani(apparel) ]
kia(car) 1.0 [ jeep(car) ]
elfcosmetics(personal_care) 1.0 [ dietcoke(food) ]
eatwholly(food) 1.0 [ fstg_chips(food) ]
jaguarusa(car) 1.0 [ instyle(personal_care) ]
doritos(food) 1.0 [ kelloggcompany(food) ]
butterfinger(food) 1.0 [ puffs(paper_product) ]
clorox(household_che) 1.0 [ pringles(food) ]
pringles(food) 1.0 [ downy(household_che) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
kia(car) 1.0 [ kia(car) ]
aveda(personal_care) 1.0 [ aveda(personal_care) ]
breyers(food) 1.0 [ bareminerals(personal_care) ]
bareminerals(personal_care) 1.0 [ ralphlauren(apparel) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
hersheys(food) 1.0 [ duracell(electronics) ]
aveda(personal_care) 1.0 [ hugoboss(apparel) ]
jeep(car) 1.0 [ jeep(car) ]
jeep(car) 1.0 [ jeep(car) ]
bmwusa(car) 1.0 [ hyundai(car) ]
hourglassmakeup(personal_care) 1.0 [ hourglassmakeup(personal_care) ]
bareminerals(personal_care) 1.0 [ armani(apparel) ]
hyundai(car) 1.0 [ hyundai(car) ]
kia(car) 1.0 [ godiva(food) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
breyers(food) 1.0 [ hersheys(food) ]
jaguarusa(car) 1.0 [ att(electronics) ]
downy(household_che) 1.0 [ pringles(food) ]
jeep(car) 1.0 [ jeep(car) ]
hersheys(food) 1.0 [ annieshomegrown(food) ]
hourglassmakeup(personal_care) 1.0 [ covergirl(personal_care) ]
breyers(food) 1.0 [ breyers(food) ]
breyers(food) 1.0 [ decleorskincare(personal_care) ]
hourglassmakeup(personal_care) 1.0 [ hourglassmakeup(personal_care) ]
kia(car) 1.0 [ asus(electronics) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
breyers(food) 1.0 [ breyers(food) ]
breyers(food) 1.0 [ chevrolet(car) ]
chinaglaze(personal_care) 1.0 [ kevyn_aucoin(personal_care) ]
jaguarusa(car) 1.0 [ chevrolet(car) ]
bhcosmetics(personal_care) 1.0 [ covergirl(personal_care) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
bmwusa(car) 1.0 [ chevrolet(car) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
joico(personal_care) 1.0 [ aveda(personal_care) ]
pringles(food) 1.0 [ pringles(food) ]
hyundai(car) 1.0 [ chrysler(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
breyers(food) 1.0 [ butterfinger(food) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
hourglassmakeup(personal_care) 1.0 [ covergirl(personal_care) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
doritos(food) 1.0 [ doritos(food) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
kia(car) 1.0 [ kia(car) ]
bmwusa(car) 1.0 [ chrysler(car) ]
bareminerals(personal_care) 1.0 [ americanapparel(apparel) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
armani(apparel) 1.0 [ armani(apparel) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
doritos(food) 1.0 [ purinaonedog(pet_food) ]
hersheys(food) 1.0 [ doritos(food) ]
bareminerals(personal_care) 1.0 [ chevrolet(car) ]
butterfinger(food) 1.0 [ puffs(paper_product) ]
pringles(food) 1.0 [ duracell(electronics) ]
hyundai(car) 1.0 [ armani(apparel) ]
bmwusa(car) 1.0 [ chevrolet(car) ]
hersheys(food) 1.0 [ hersheys(food) ]
kia(car) 1.0 [ kia(car) ]
breyers(food) 1.0 [ breyers(food) ]
bmwusa(car) 1.0 [ chevrolet(car) ]
chinaglaze(personal_care) 1.0 [ covergirl(personal_care) ]
joico(personal_care) 1.0 [ elfcosmetics(personal_care) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
pringles(food) 1.0 [ blue_bunny(food) ]
elfcosmetics(personal_care) 1.0 [ bareminerals(personal_care) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
hourglassmakeup(personal_care) 1.0 [ chinaglaze(personal_care) ]
bhcosmetics(personal_care) 1.0 [ hugoboss(apparel) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
bissellclean(household_che) 1.0 [ gopicnic(food) ]
bissellclean(household_che) 1.0 [ hillshirefarm(food) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
bareminerals(personal_care) 1.0 [ drmiracles(personal_care) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
jaguarusa(car) 1.0 [ hugoboss(apparel) ]
pringles(food) 1.0 [ breyers(food) ]
chevrolet(car) 1.0 [ jeep(car) ]
bhcosmetics(personal_care) 1.0 [ bhcosmetics(personal_care) ]
hyundai(car) 1.0 [ pringles(food) ]
jeep(car) 1.0 [ jeep(car) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
hourglassmakeup(personal_care) 1.0 [ hourglassmakeup(personal_care) ]
doritos(food) 1.0 [ hugoboss(apparel) ]
hourglassmakeup(personal_care) 1.0 [ aveda(personal_care) ]
kia(car) 1.0 [ advilrelief(personal_care) ]
chrysler(car) 1.0 [ jaguarusa(car) ]
pringles(food) 1.0 [ chevrolet(car) ]
hyundai(car) 1.0 [ ge_reports(appliances) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
bhcosmetics(personal_care) 1.0 [ chinaglaze(personal_care) ]
kia(car) 1.0 [ kia(car) ]
hersheys(food) 1.0 [ hersheys(food) ]
breyers(food) 1.0 [ chevrolet(car) ]
hersheys(food) 1.0 [ kia(car) ]
kelloggcompany(food) 1.0 [ godiva(food) ]
bhcosmetics(personal_care) 1.0 [ bhcosmetics(personal_care) ]
blue_bunny(food) 1.0 [ cottonelle(personal_care) ]
pringles(food) 1.0 [ chrysler(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
doritos(food) 1.0 [ kia(car) ]
hourglassmakeup(personal_care) 1.0 [ covergirl(personal_care) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
downy(household_che) 1.0 [ breyers(food) ]
chevrolet(car) 1.0 [ doritos(food) ]
armani(apparel) 1.0 [ armani(apparel) ]
jeep(car) 1.0 [ jeep(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
blue_bunny(food) 1.0 [ doritos(food) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
bareminerals(personal_care) 1.0 [ hourglassmakeup(personal_care) ]
hersheys(food) 1.0 [ elfcosmetics(personal_care) ]
ralphlauren(apparel) 1.0 [ ralphlauren(apparel) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
pringles(food) 1.0 [ pringles(food) ]
hersheys(food) 1.0 [ clorox(household_che) ]
kia(car) 1.0 [ chevrolet(car) ]
elfcosmetics(personal_care) 1.0 [ josie_maran(personal_care) ]
bmwusa(car) 1.0 [ kia(car) ]
breyers(food) 1.0 [ breyers(food) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
jeep(car) 1.0 [ jeep(car) ]
chinaglaze(personal_care) 1.0 [ covergirl(personal_care) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
pringles(food) 1.0 [ kelloggcompany(food) ]
breyers(food) 1.0 [ josie_maran(personal_care) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
breyers(food) 1.0 [ ctcsquares(food) ]
pringles(food) 1.0 [ chrysler(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
kia(car) 1.0 [ kia(car) ]
jeep(car) 1.0 [ jeep(car) ]
joico(personal_care) 1.0 [ joico(personal_care) ]
hyundai(car) 1.0 [ hyundai(car) ]
hersheys(food) 1.0 [ fanta(food) ]
breyers(food) 1.0 [ hyundai(car) ]
farmrichsnacks(food) 1.0 [ bhcosmetics(personal_care) ]
blue_bunny(food) 1.0 [ blue_bunny(food) ]
butterfinger(food) 1.0 [ jif(food) ]
pringles(food) 1.0 [ clifbar(food) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
bareminerals(personal_care) 1.0 [ chinaglaze(personal_care) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
elfcosmetics(personal_care) 1.0 [ covergirl(personal_care) ]
kia(car) 1.0 [ doritos(food) ]
jeep(car) 1.0 [ jeep(car) ]
breyers(food) 1.0 [ hugoboss(apparel) ]
hourglassmakeup(personal_care) 1.0 [ hourglassmakeup(personal_care) ]
breyers(food) 1.0 [ att(electronics) ]
breyers(food) 1.0 [ doritos(food) ]
doritos(food) 1.0 [ hyundai(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
jaguarusa(car) 1.0 [ jeep(car) ]
hyundai(car) 1.0 [ doritos(food) ]
pringles(food) 1.0 [ doritos(food) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
cascadianfarm(food) 1.0 [ fstg_chips(food) ]
chrysler(car) 1.0 [ jaguarusa(car) ]
butterfinger(food) 1.0 [ clifbar(food) ]
kelloggcompany(food) 1.0 [ canonusa(electronics) ]
fstg_chips(food) 1.0 [ auracacia(household_che) ]
hyundai(car) 1.0 [ hyundai(car) ]
butterfinger(food) 1.0 [ essentiaphwater(food) ]
bhcosmetics(personal_care) 1.0 [ bhcosmetics(personal_care) ]
hyundai(car) 1.0 [ chrysler(car) ]
dior(apparel) 1.0 [ covergirl(personal_care) ]
hyundai(car) 1.0 [ hyundai(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
doritos(food) 1.0 [ kikkomankitchen(food) ]
jaguarusa(car) 1.0 [ chrysler(car) ]
doritos(food) 1.0 [ doritos(food) ]
blue_bunny(food) 1.0 [ blue_bunny(food) ]
downy(household_che) 1.0 [ breyers(food) ]
dior(apparel) 1.0 [ covergirl(personal_care) ]
downy(household_che) 1.0 [ dietpepsi(food) ]
bmwusa(car) 1.0 [ jeep(car) ]
farmrichsnacks(food) 1.0 [ boogiemom(baby) ]
hersheys(food) 1.0 [ herbalessences(personal_care) ]
elfcosmetics(personal_care) 1.0 [ bumble(personal_care) ]
breyers(food) 1.0 [ breyers(food) ]
bmwusa(car) 1.0 [ chrysler(car) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
breyers(food) 1.0 [ butterfinger(food) ]
breyers(food) 1.0 [ att(electronics) ]
breyers(food) 1.0 [ dietcoke(food) ]
clorox(household_che) 1.0 [ kia(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
pringles(food) 1.0 [ jaguarusa(car) ]
hourglassmakeup(personal_care) 1.0 [ covergirl(personal_care) ]
hyundai(car) 1.0 [ hyundai(car) ]
hersheys(food) 1.0 [ eas(food) ]
hyundai(car) 1.0 [ hyundai(car) ]
hersheys(food) 1.0 [ 8oclockcoffee(food) ]
hersheys(food) 1.0 [ dietpepsi(food) ]
kia(car) 1.0 [ kia(car) ]
pringles(food) 1.0 [ pringles(food) ]
derma_e(personal_care) 1.0 [ bareminerals(personal_care) ]
elfcosmetics(personal_care) 1.0 [ cwhairlondon(personal_care) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
hourglassmakeup(personal_care) 1.0 [ chinaglaze(personal_care) ]
breyers(food) 1.0 [ american_girl(personal_care) ]
jeep(car) 1.0 [ jeep(car) ]
breyers(food) 1.0 [ dasaniwater(food) ]
downy(household_che) 1.0 [ fttv(food) ]
butterfinger(food) 1.0 [ doritos(food) ]
hyundai(car) 1.0 [ jeep(car) ]
breyers(food) 1.0 [ boogiemom(baby) ]
hyundai(car) 1.0 [ doritos(food) ]
butterfinger(food) 1.0 [ butterfinger(food) ]
chinaglaze(personal_care) 1.0 [ kerastaseusa(personal_care) ]
chobani(food) 1.0 [ auracacia(household_che) ]
pringles(food) 1.0 [ covergirl(personal_care) ]
clorox(household_che) 1.0 [ puffs(paper_product) ]
hyundai(car) 1.0 [ hyundai(car) ]
hersheys(food) 1.0 [ bmwusa(car) ]
chrysler(car) 1.0 [ jaguarusa(car) ]
kevyn_aucoin(personal_care) 1.0 [ ralphlauren(apparel) ]
kia(car) 1.0 [ chrysler(car) ]
blue_bunny(food) 1.0 [ aveda(personal_care) ]
elfcosmetics(personal_care) 1.0 [ americanapparel(apparel) ]
bmwusa(car) 1.0 [ jeep(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
hersheys(food) 1.0 [ jeep(car) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
pringles(food) 1.0 [ pringles(food) ]
butterfinger(food) 1.0 [ butterfinger(food) ]
doritos(food) 1.0 [ doritos(food) ]
pringles(food) 1.0 [ godiva(food) ]
hyundai(car) 1.0 [ bmwusa(car) ]
bareminerals(personal_care) 1.0 [ purell(personal_care) ]
hyundai(car) 1.0 [ doritos(food) ]
bhcosmetics(personal_care) 1.0 [ aquaphorus(personal_care) ]
doritos(food) 1.0 [ herbalessences(personal_care) ]
jaguarusa(car) 1.0 [ avoninsider(household_che) ]
breyers(food) 1.0 [ biooiluk(personal_care) ]
doritos(food) 1.0 [ chobani(food) ]
bissellclean(household_che) 1.0 [ barbarasbakery(food) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
pringles(food) 1.0 [ birdseye(food) ]
hersheys(food) 1.0 [ elfcosmetics(personal_care) ]
chevrolet(car) 1.0 [ armani(apparel) ]
butterfinger(food) 1.0 [ elfcosmetics(personal_care) ]
instyle(personal_care) 1.0 [ instyle(personal_care) ]
breyers(food) 1.0 [ doritos(food) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
chevrolet(car) 1.0 [ chipsahoy(food) ]
hyundai(car) 1.0 [ chrysler(car) ]
breyers(food) 1.0 [ breyers(food) ]
farmrichsnacks(food) 1.0 [ eatwholly(food) ]
butterfinger(food) 1.0 [ butterfinger(food) ]
breyers(food) 1.0 [ cottonelle(personal_care) ]
hersheys(food) 1.0 [ hersheys(food) ]
jeep(car) 1.0 [ ralphlauren(apparel) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
bhcosmetics(personal_care) 1.0 [ ralphlauren(apparel) ]
blue_bunny(food) 1.0 [ elfcosmetics(personal_care) ]
hersheys(food) 1.0 [ hersheys(food) ]
armani(apparel) 1.0 [ armani(apparel) ]
elfcosmetics(personal_care) 1.0 [ aveda(personal_care) ]
bmwusa(car) 1.0 [ jeep(car) ]
elfcosmetics(personal_care) 1.0 [ americanapparel(apparel) ]
hyundai(car) 1.0 [ chrysler(car) ]
breyers(food) 1.0 [ pretzelcrisps(food) ]
butterfinger(food) 1.0 [ cottonelle(personal_care) ]
chrysler(car) 1.0 [ dannon(food) ]
chevrolet(car) 1.0 [ armani(apparel) ]
hourglassmakeup(personal_care) 1.0 [ kia(car) ]
butterfinger(food) 1.0 [ americanapparel(apparel) ]
hyundai(car) 1.0 [ hyundai(car) ]
breyers(food) 1.0 [ boogiemom(baby) ]
farmrichsnacks(food) 1.0 [ 8oclockcoffee(food) ]
jaguarusa(car) 1.0 [ fanta(food) ]
butterfinger(food) 1.0 [ fanta(food) ]
hourglassmakeup(personal_care) 1.0 [ aveda(personal_care) ]
bareminerals(personal_care) 1.0 [ att(electronics) ]
hersheys(food) 1.0 [ clorox(household_che) ]
doritos(food) 1.0 [ bmwusa(car) ]
hersheys(food) 1.0 [ covergirl(personal_care) ]
chinaglaze(personal_care) 1.0 [ chinaglaze(personal_care) ]
hersheys(food) 1.0 [ hersheys(food) ]
kia(car) 1.0 [ chevrolet(car) ]
pringles(food) 1.0 [ annieshomegrown(food) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
butterfinger(food) 1.0 [ clorox(household_che) ]
jaguarusa(car) 1.0 [ jeep(car) ]
hersheys(food) 1.0 [ jeep(car) ]
breyers(food) 1.0 [ doritos(food) ]
hyundai(car) 1.0 [ hyundai(car) ]
hyundai(car) 1.0 [ hyundai(car) ]
elfcosmetics(personal_care) 1.0 [ deliskincare(personal_care) ]
jaguarusa(car) 1.0 [ hyundai(car) ]
farmrichsnacks(food) 1.0 [ celadrin1(household_che) ]
bmwusa(car) 1.0 [ jeep(car) ]
pringles(food) 1.0 [ bissellclean(household_che) ]
blue_bunny(food) 1.0 [ kelloggcompany(food) ]
breyers(food) 1.0 [ dietcoke(food) ]
pringles(food) 1.0 [ butterfinger(food) ]
chrysler(car) 1.0 [ dior(apparel) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
bhcosmetics(personal_care) 1.0 [ covergirl(personal_care) ]
doritos(food) 1.0 [ americanapparel(apparel) ]
hersheys(food) 1.0 [ hersheys(food) ]
chobani(food) 1.0 [ instyle(personal_care) ]
butterfinger(food) 1.0 [ airheads(food) ]
bissellclean(household_che) 1.0 [ dial(personal_care) ]
pringles(food) 1.0 [ pringles(food) ]
blue_bunny(food) 1.0 [ blue_bunny(food) ]
janmarini(personal_care) 1.0 [ colorescience(personal_care) ]
jeep(car) 1.0 [ jeep(car) ]
hyundai(car) 1.0 [ dannon(food) ]
doritos(food) 1.0 [ butterfinger(food) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
kia(car) 1.0 [ kia(car) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
elfcosmetics(personal_care) 1.0 [ bhcosmetics(personal_care) ]
kia(car) 1.0 [ jeep(car) ]
pringles(food) 1.0 [ hersheys(food) ]
fstg_chips(food) 1.0 [ fstg_chips(food) ]
butterfinger(food) 1.0 [ ctcsquares(food) ]
elfcosmetics(personal_care) 1.0 [ aquaphorus(personal_care) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
cascadianfarm(food) 1.0 [ feelgoodfoods(food) ]
breyers(food) 1.0 [ breyers(food) ]
chrysler(car) 1.0 [ chevrolet(car) ]
jeep(car) 1.0 [ jeep(car) ]
hersheys(food) 1.0 [ breyers(food) ]
hersheys(food) 1.0 [ clorox(household_che) ]
breyers(food) 1.0 [ fanta(food) ]
bhcosmetics(personal_care) 1.0 [ covergirl(personal_care) ]
pringles(food) 1.0 [ pringles(food) ]
pringles(food) 1.0 [ pringles(food) ]
pringles(food) 1.0 [ pringles(food) ]
elfcosmetics(personal_care) 1.0 [ elfcosmetics(personal_care) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
hyundai(car) 1.0 [ att(electronics) ]
breyers(food) 1.0 [ cascadianfarm(food) ]
hourglassmakeup(personal_care) 1.0 [ bhcosmetics(personal_care) ]
chrysler(car) 1.0 [ att(electronics) ]
bhcosmetics(personal_care) 1.0 [ chinaglaze(personal_care) ]
bhcosmetics(personal_care) 1.0 [ bhcosmetics(personal_care) ]
jeep(car) 1.0 [ jeep(car) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
hersheys(food) 1.0 [ asus(electronics) ]
jeep(car) 1.0 [ jeep(car) ]
breyers(food) 1.0 [ breyers(food) ]
downy(household_che) 1.0 [ duracell(electronics) ]
bareminerals(personal_care) 1.0 [ fab(appliances) ]
hersheys(food) 1.0 [ eatwholly(food) ]
breyers(food) 1.0 [ dietcoke(food) ]
butterfinger(food) 1.0 [ butterfinger(food) ]
hyundai(car) 1.0 [ hyundai(car) ]
bareminerals(personal_care) 1.0 [ covergirl(personal_care) ]
jaguarusa(car) 1.0 [ chevrolet(car) ]
chevrolet(car) 1.0 [ kia(car) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
hyundai(car) 1.0 [ govoskos(food) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
bmwusa(car) 1.0 [ faygo(food) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
breyers(food) 1.0 [ allegraotc(household_che) ]
jeep(car) 1.0 [ jeep(car) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
hyundai(car) 1.0 [ chrysler(car) ]
acureorganics(personal_care) 1.0 [ instyle(personal_care) ]
hyundai(car) 1.0 [ hyundai(car) ]
bareminerals(personal_care) 1.0 [ instyle(personal_care) ]
elfcosmetics(personal_care) 1.0 [ americanapparel(apparel) ]
kia(car) 1.0 [ kia(car) ]
butterfinger(food) 1.0 [ chevrolet(car) ]
elfcosmetics(personal_care) 1.0 [ hourglassmakeup(personal_care) ]
farmrichsnacks(food) 1.0 [ boogiemom(baby) ]
hyundai(car) 1.0 [ hyundai(car) ]
hyundai(car) 1.0 [ ralphlauren(apparel) ]
chrysler(car) 1.0 [ chrysler(car) ]
kia(car) 1.0 [ att(electronics) ]
bhcosmetics(personal_care) 1.0 [ bhcosmetics(personal_care) ]
jaguarusa(car) 1.0 [ armani(apparel) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
jaguarusa(car) 1.0 [ chevrolet(car) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
aveda(personal_care) 1.0 [ colorescience(personal_care) ]
hyundai(car) 1.0 [ hugoboss(apparel) ]
butterfinger(food) 1.0 [ hourglassmakeup(personal_care) ]
bmwusa(car) 1.0 [ jeep(car) ]
farmrichsnacks(food) 1.0 [ instyle(personal_care) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
farmrichsnacks(food) 1.0 [ kia(car) ]
hyundai(car) 1.0 [ jaguarusa(car) ]
jeep(car) 1.0 [ jeep(car) ]
bhcosmetics(personal_care) 1.0 [ americanapparel(apparel) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
bareminerals(personal_care) 1.0 [ clorox(household_che) ]
hourglassmakeup(personal_care) 1.0 [ hourglassmakeup(personal_care) ]
pringles(food) 1.0 [ hersheys(food) ]
eatwholly(food) 1.0 [ drbronner(personal_care) ]
hersheys(food) 1.0 [ hersheys(food) ]
jaguarusa(car) 1.0 [ jaguarusa(car) ]
butterfinger(food) 1.0 [ hourglassmakeup(personal_care) ]
hourglassmakeup(personal_care) 1.0 [ kenra_haircare(personal_care) ]
doritos(food) 1.0 [ republicoftea(food) ]
pringles(food) 1.0 [ pringles(food) ]
chevrolet(car) 1.0 [ ge_reports(appliances) ]
kia(car) 1.0 [ kia(car) ]
hersheys(food) 1.0 [ jeep(car) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
hersheys(food) 1.0 [ hersheys(food) ]
chrysler(car) 1.0 [ chrysler(car) ]
dior(apparel) 1.0 [ instyle(personal_care) ]
chrysler(car) 1.0 [ jaguarusa(car) ]
butterfinger(food) 1.0 [ butterfinger(food) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
doritos(food) 1.0 [ doritos(food) ]
acureorganics(personal_care) 1.0 [ bumble(personal_care) ]
blue_bunny(food) 1.0 [ covergirl(personal_care) ]
kia(car) 1.0 [ jeep(car) ]
armani(apparel) 1.0 [ covergirl(personal_care) ]
farmrichsnacks(food) 1.0 [ asus(electronics) ]
puffs(paper_product) 1.0 [ duracell(electronics) ]
chevrolet(car) 1.0 [ chevrolet(car) ]
bareminerals(personal_care) 1.0 [ bareminerals(personal_care) ]
farmrichsnacks(food) 1.0 [ kevyn_aucoin(personal_care) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
jeep(car) 1.0 [ jeep(car) ]
ralphlauren(apparel) 1.0 [ pr_organicsusa(food) ]
hersheys(food) 1.0 [ hichew(food) ]
hersheys(food) 1.0 [ clorox(household_che) ]
bmwusa(car) 1.0 [ bmwusa(car) ]
hersheys(food) 1.0 [ gopicnic(food) ]
pringles(food) 1.0 [ duracell(electronics) ]
328/812 = 0.4039408866995074
328/669 = 0.49028400597907323



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