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fba-sub9.py
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'''
README
https://www.kaggle.com/c/facebook-recruiting-iv-human-or-bot/
This script classifies bidders in an online auction as either robot or human
To run the code
bids, bot_or_human = load()
X = build_X(bids, bot_or_human)
make(X)
'''
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import sklearn.preprocessing as preprocessing
from sklearn.linear_model import SGDClassifier
from sklearn.svm import SVC
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression, BayesianRidge
from sklearn.cluster import MiniBatchKMeans
from sklearn.metrics import roc_auc_score
'''
Declare time units
'''
one_day = 4547368124071.8799
startt = 9631916820392676.0
delta = 2.16e10
cutoff = 1.5e8 #cutoff for "small" time scales
'''
How to calculate time units
start and end times for each three-day data segment
-9631916842105263 + 9645558894736842 = 13642052631579 #week 0
-9695580000000000 + 9709222052631578 = 13642052631578 #week 2
-9759243157894736 + 9772885210526315 = 13642052631579 #week 4
gap between end week 0, start week 2: 50021105263158
gap between end week 2, start week 4: 50021105263158
#use linear regression to calculation startt and one_day
from numpy import arange,array,ones,linalg
times = np.array([9631916842105263, 9645558894736842+1, 9695580000000000, 9709222052631578+1, 9759243157894736, 9772885210526315+1])
days = np.array([0, 3, 14, 17, 28, 31]).reshape((-1,1))
from sklearn import linear_model
clf = linear_model.LinearRegression(fit_intercept=True)
clf.fit(days, times)
4547368124071.8799
startt = clf.predict([0])
startt = 9631916820392676.0
'''
#bids, bot_or_human = load()
def load():
bot_or_human = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
test['outcome'] = -1
bids = pd.read_csv('bids.csv')
bids = bids.sort(['auction', 'time'])
bot_or_human = pd.concat([ bot_or_human, test ])
#strip the last five characters from the address and payment accounts
bot_or_human.address = bot_or_human.address.apply(address_strip)
bot_or_human.payment_account = bot_or_human.payment_account.apply(address_strip)
'''
pivot bidder address prefix and bidder payment_account prefix into features
'''
bot_or_human['payment_account_prefix_same_as_address_prefix'] = bot_or_human['address']==bot_or_human['payment_account']
max_relevant = 6
b = bot_or_human[['bidder_id', 'address']].groupby('address').size().reset_index().sort(0, ascending=False)
b = b.rename(columns = {0:'address_count'})
#repeat_addresses = b[b.address_count >= max_relevant].address.values
infrequent_addresses = b[b.address_count >= 2][b.address_count < max_relevant].address.values
rare_addresses = b[b.address_count < 2].address.values
bot_or_human['address'].loc[bot_or_human.address.isin(rare_addresses)] = 'rare_address'
bot_or_human['address'].loc[bot_or_human.address.isin(infrequent_addresses)] = 'infrequent_address'
#b = bot_or_human[['bidder_id', 'address']].pivot(index='bidder_id',columns='address').fillna(0).reset_index()
b = pd.get_dummies(bot_or_human[['address']])
bot_or_human = pd.concat([bot_or_human, b], axis=1)
b = bot_or_human[['bidder_id', 'payment_account']].groupby('payment_account').size().reset_index().sort(0, ascending=False)
b = b.rename(columns = {0:'payment_account_count'})
#repeat_payment_accounts = b[b.payment_account_count >= max_relevant].payment_account.values
infrequent_payment_account = b[b.payment_account_count >= 2][b.payment_account_count < max_relevant].payment_account.values
rare_payment_account = b[b.payment_account_count < 2].payment_account.values
bot_or_human['payment_account'].loc[bot_or_human.payment_account.isin(rare_payment_account)] = 'rare_account'
bot_or_human['payment_account'].loc[bot_or_human.payment_account.isin(infrequent_payment_account)] = 'infrequent_account'
#bot_or_human['payment_account'].loc[~bot_or_human.payment_account.isin(repeat_payment_accounts)] = 'only_once'
b = pd.get_dummies(bot_or_human[['payment_account']])
bot_or_human = pd.concat([bot_or_human, b], axis=1)
return bids, bot_or_human
def address_strip(x):
return x[0:-5]
def dt(X, bids):
bots = X[X.outcome==1].bidder_id.values
humans = X[X.outcome==0].bidder_id.values
'''
auction duration
check if auctions run longer than three days
calculate time from bid until time of last bid in the auction
calculate time from bid until time of first bid in the auction
'''
times = bids.groupby('auction').time.min().reset_index()
times=times.rename(columns = {'time':'startt'})
times2 = bids.groupby('auction').time.max().reset_index()
times2=times2.rename(columns = {'time':'endt'})
times = pd.merge(times, times2, on='auction', how='left')
times['duration'] = times.endt - times.startt
times['short'] = 1.0*(times['duration'] < 3.01*one_day)
bids = pd.merge(bids, times[['auction', 'short', 'startt', 'endt']], on='auction', how='left')
b = bids.groupby('bidder_id').short.mean().reset_index()
X = pd.merge(X, b, on='bidder_id', how='left')
#time from bid until end of auction
bids['t_until_end'] = bids.endt - bids.time
bids['t_since_start'] = bids.time - bids.startt
b = bids.groupby('bidder_id').t_until_end.median().reset_index()
b=b.rename(columns = {'t_until_end':'t_until_end_median'})
X = pd.merge(X, b, on='bidder_id', how='left')
b = bids.groupby('bidder_id').t_since_start.median().reset_index()
b=b.rename(columns = {'t_since_start':'t_since_start_median'})
X = pd.merge(X, b, on='bidder_id', how='left')
'''
Useful plots
test_ids = X[X.outcome==-1].bidder_id.values
bot = bids[bids.bidder_id.isin(bots)][bids.country.isin(['de', 'nl', 'se', 'no', 'cz', 'hu'])].t_until_end
human = bids[bids.bidder_id.isin(humans)][bids.country.isin(['de', 'nl', 'se', 'no', 'cz', 'hu'])].t_until_end
bot = bids[bids.bidder_id.isin(bots)][bids.country.isin(['id'])].t_until_end
human = bids[bids.bidder_id.isin(humans)][bids.country.isin(['id'])].t_until_end
bot = bids[bids.bidder_id.isin(bots)].t_until_end
human = bids[bids.bidder_id.isin(humans)].t_until_end
botp = np.histogram(bot, 800, range=[0,8e13], normed=True)
humanp = np.histogram(human, 800, range=[0,8e13], normed=True)
bot = np.mod(bids[bids.bidder_id.isin(bots)].t_until_end.values, 4.55e12)
human = np.mod(bids[bids.bidder_id.isin(humans)].t_until_end.values, 4.55e12)
botp = np.histogram(bot, 300, range=[0,4.9e12], normed=True)
humanp = np.histogram(human, 300, range=[0,4.9e12], normed=True)
plt.plot(botp[1][0:-1], botp[0], 'b.-')
plt.plot(botp[1][0:-1], humanp[0], 'g.-')
bot = bids[bids.bidder_id.isin(bots)].time
human = bids[bids.bidder_id.isin(humans)].time
test = bids[bids.bidder_id.isin(test_ids)].time
bot = bids[bids.bidder_id.isin(bots)][bids.country.isin(['nl'])].time
human = bids[bids.bidder_id.isin(humans)][bids.country.isin(['nl'])].time
bots = ['5354c02817e47f28e60e44a40ca5e48dll4sz']
humans = ['ff58ffde976a4899dcd89597a7877e18lntgz']
bot = np.mod(bids[bids.bidder_id.isin(bots)].time.values, one_day)
human = np.mod(bids[bids.bidder_id.isin(humans)].time.values, one_day)
humanp = np.histogram(human, 48, range=[0, one_day], normed=True)
botp = np.histogram(bot, 48, range=[0, one_day], normed=True)
humanp = np.histogram(human, 4000, range=[9.63e15, 9.78e15], normed=True)
botp = np.histogram(bot, 4000, range=[9.63e15, 9.78e15], normed=True)
testp = np.histogram(test, 4000, range=[9.63e15, 9.78e15], normed=True)
humanp = np.histogram(human, 30, range=[9.63e15, 9.9e15], normed=True)
botp = np.histogram(bot, 30, range=[9.63e15, 9.9e15], normed=True)
humanp = np.histogram(human, 1000, range=[9.631e15, 9.645e15], normed=True)
botp = np.histogram(bot, 1000, range=[9.631e15, 9.645e15], normed=True)
plt.plot(humanp[1][0:-1], testp[0], 'r.-')
plt.plot(humanp[1][0:-1], humanp[0], 'g.-')
plt.plot(humanp[1][0:-1], botp[0], 'b.-')
plt.ylabel('fraction of bids made between t and t+dt')
plt.xlabel('time [arbitrary units]')
plt.legend(['human','bot'], frameon=False)
plt.title('bids over time')
plt.show()
dt = humanp[1][1] - humanp[1][0]
a = np.correlate(humanp[0], humanp[0], mode='full')
plt.plot(dt*np.array(range(0,len(a)-len(a)/2)), a[len(a)/2:], 'k.-')
plt.ylabel('autocorrelation')
plt.xlabel('time')
plt.title('autocorrelation of bids/unit time histogram')
#sample bids per unit time over an auction
plt.hist(bids[bids.auction == bids.auction[3]].time.values, bins=200)
plt.ylabel('bids')
plt.xlabel('time')
plt.title('bids during auction')
'''
'''
number of bids per hour (n=24), half hour (n=48)
'''
times = [72]
for n in times:
print 'hour ', n
b = np.floor((1.0*n/one_day*np.mod(bids.time-startt-delta, one_day)))
b.name = 'hour'+str(n)
bids = pd.concat([bids, b], axis=1)
b = bids.groupby(['bidder_id', 'hour'+str(n)]).size().reset_index()
b=b.rename(columns = {0:'bids_in_hour'})
b=b.pivot('bidder_id', 'hour'+str(n), 'bids_in_hour').fillna(0).reset_index()
old_names = b.columns.values
new_names = b.columns.values
for i in range(1,len(b.columns)):
new_names[i] = str(int(old_names[i])) + '_hour'+str(n)
b = b.rename(columns=dict(zip(old_names, new_names)))
b['max_bids_in_hour'+str(n)] = b.iloc[:, 1:].max(axis=1)
for i in range(n):
b[str(i)+'_hour'+str(n)] = b[str(i)+'_hour'+str(n)]/b['max_bids_in_hour'+str(n)]
#b = b.drop('max_bids_in_hour'+str(n), 1)
X = pd.merge(X, b, on='bidder_id', how='left')
n = times[0]
X['sleep'] = 0
step = int(5.5*n/24)
for i in xrange(n - step):
X['sleep'] += X.loc[:, str(i)+'_hour'+str(n):str(i+step)+'_hour'+str(n)].sum(axis=1) == 0
X['sleep'] = (X['sleep'] >= 1)
'''
dt between a user's bid and the previous bid by another user in the same auction
'''
for week in [0, 2, 4]:
b = bids[bids.week==week].sort(['auction', 'time'])[['auction', 'time', 'bidder_id']]
b['bidder_id_prev'] = pd.Series(np.append([np.nan], b.bidder_id.values[0:-1]), index=b.index)
b['auction_prev'] = pd.Series(np.append([np.nan], b.auction.values[0:-1]), index=b.index)
dt = np.append([np.nan], b.time.values[0:-1])
b['time_prev'] = pd.Series(dt, index=b.index)
b['dt'] = b['time'] - b['time_prev']
if week==0:
c = [b[b.bidder_id != b.bidder_id_prev][b.auction == b.auction_prev]]
else:
c.append(b[b.bidder_id != b.bidder_id_prev][b.auction == b.auction_prev])
c = pd.concat(c)
d = c.groupby('bidder_id').dt.median().reset_index()
d = d.rename(columns = {'dt': 'dt_others_median'})
X = pd.merge(X, d[['bidder_id', 'dt_others_median']], on='bidder_id', how='left')
#X.loc[:,'dt_others_median'] = X.loc[:,'dt_others_median'].fillna(3.0*one_day)
c['dt_others_lt_cutoff'] = np.nan
c.loc[c.dt <= cutoff, 'dt_others_lt_cutoff'] = 1.0
c.loc[c.dt > cutoff, 'dt_others_lt_cutoff'] = 0.0
a= c[['bidder_id', 'dt_others_lt_cutoff']].dropna().groupby('bidder_id').mean().reset_index()
X = pd.merge(X, a, on='bidder_id', how='left')
X=X.rename(columns = {'dt_others_lt_cutoff':'f_dt_others_lt_cutoff'})
#plt.hist(X[X.outcome==0].dt_median.values, bins=100, range=[0, 1e9], normed=True)
#plt.hist(X[X.outcome==1].dt_median.values, bins=100, range=[0, 1e9], normed=True)
#time between bids by same bidder (does not matter if previous bid is in same auction)
for week in [0, 2, 4]:
b = bids[bids.week==week].sort(['bidder_id', 'time'])[['bidder_id', 'time']]
#b['bidder_id_prev'] = np.nan
#b.bidder_id_prev[1:] = b.bidder_id[0:-1]
b['bidder_id_prev'] = pd.Series(np.append([np.nan], b.bidder_id.values[0:-1]), index=b.index)
#b['time_prev'] = np.nan
dt = np.append([np.nan], b.time.values[0:-1])
b['time_prev'] = pd.Series(dt, index=b.index)
b['dt'] = b['time'] - b['time_prev']
if week==0:
c = [b[b.bidder_id == b.bidder_id_prev]]
else:
c.append(b[b.bidder_id == b.bidder_id_prev])
c = pd.concat(c)
d = c.groupby('bidder_id').dt.median().reset_index()
d = d.rename(columns = {'dt': 'dt_self_median'})
X = pd.merge(X, d[['bidder_id', 'dt_self_median']], on='bidder_id', how='left')
d = c.groupby('bidder_id').dt.min().reset_index()
d = d.rename(columns = {'dt': 'dt_self_min'})
X = pd.merge(X, d[['bidder_id', 'dt_self_min']], on='bidder_id', how='left')
#X.loc[:,'dt_self_median'] = X.loc[:,'dt_self_median'].fillna(3.0*one_day)
#X.loc[:,'dt_self_min'] = X.loc[:,'dt_self_min'].fillna(3.0*one_day)
'''
bot_h = np.histogram(bids[bids.bidder_id.isin(bots)].dt.values, bins=100, range=[0, 1e10], normed=True)
human_h = np.histogram(bids[bids.bidder_id.isin(humans)].dt.values, bins=100, range=[0, 1e10], normed=True)
plt.plot(bot_h[1][1:], bot_h[0], 'b.-')
plt.plot(bot_h[1][1:], human_h[0], 'r.-')
'''
return X
def n_bids(X, bids):
a = bids.groupby(['bidder_id', 'auction']).size().reset_index()
a=a.rename(columns = {0:'bids_per_auction'})
b = a.groupby('bidder_id').bids_per_auction.median().reset_index()
b=b.rename(columns = {'bids_per_auction':'bids_per_auction_median'})
X = pd.merge(X, b, on='bidder_id', how='left')
b = a.groupby('bidder_id').mean().reset_index()
b=b.rename(columns = {'bids_per_auction':'bids_per_auction_mean'})
X = pd.merge(X, b, on='bidder_id', how='left')
b = bids.groupby('bidder_id').size().reset_index()
b=b.rename(columns = {0:'n_bids'})
X = pd.merge(X, b, on='bidder_id', how='left')
return X
def enumerate(x):
x['bid_order'] = 1.0*np.arange(len(x))/len(x)
return x
def fac_div_fac(x,y):
if x == 0:
x = 1
if y == 0:
y = 1
if x > y:
return np.sum( np.log(1.0*np.array(range(int(y+1),int(x+1))) ) )
if y > x:
return -np.sum( np.log(1.0*np.array(range(int(x+1),int(y+1))) ) )
return 0.0
def balance(x):
# x['balance'] = np.std(x)/np.sqrt(np.mean(x))
x0 = int(np.floor(np.sum(x)/len(x)))
base = x0*np.ones(len(x))
base[0:int(np.sum(x)-x0*len(x))] = x0+1
b = 0.0
for i in xrange(len(x)):
#print base[i], x[i]
b += fac_div_fac(base[i], x[i])
#print b
x['balance'] = b
return x
def day(X, bids):
bots = X[X.outcome==1].bidder_id.values
humans = X[X.outcome==0].bidder_id.values
bids['day'] = np.floor((bids['time'] - startt)/one_day)
b = bids.groupby(['bidder_id', 'day']).size().reset_index()
b = b.rename(columns = {0:'daily_bids'})
b = b.pivot('bidder_id', 'day', 'daily_bids').fillna(0).reset_index()
old_names = b.columns.values
new_names = [b.columns[0]]
for i in range(1,len(b.columns)):
new_names.append( str(int(old_names[i])) + '_day' )
b = b.rename(columns=dict(zip(old_names, new_names)))
b['monday'] = (b['0_day'] + b['14_day'] + b['28_day']).fillna(0)
b['tuesday'] = (b['1_day'] + b['15_day'] + b['29_day']).fillna(0)
b['wednesday'] = (b['2_day'] + b['16_day'] + b['30_day']).fillna(0)
b['balance'] = b[['monday', 'tuesday', 'wednesday']].apply(balance, axis=1)[['balance']]
b['balance2'] = b[['0_day', '1_day', '2_day', '14_day', '15_day', '16_day', '28_day', '29_day', '30_day']].apply(balance, axis=1)[['balance']]
X = pd.merge(X, b.loc[:,['bidder_id', 'monday', 'tuesday', 'wednesday', 'balance', 'balance2']], on='bidder_id', how='left')
X[['monday', 'tuesday', 'wednesday', 'balance']] = X[['monday', 'tuesday', 'wednesday', 'balance']].fillna(0)
s = X.loc[:, ['monday', 'tuesday', 'wednesday']].max(axis=1)
X['s_monday'] = X['monday']/s
X['s_tuesday'] = X['tuesday']/s
X['s_wednesday'] = X['wednesday']/s
s = X.loc[:, ['monday', 'tuesday', 'wednesday']].sum(axis=1)
X['f_monday'] = X['monday']/s
X['f_tuesday'] = X['tuesday']/s
X['f_wednesday'] = X['wednesday']/s
# nbids = X.loc[:, ['monday', 'tuesday', 'wednesday']].sum(axis=1)
# X['bids_week0'] = (b['0_day'] + b['1_day'] + b['2_day']).fillna(0)
# X['bids_week2'] = (b['14_day'] + b['15_day'] + b['16_day']).fillna(0)
# X['bids_week4'] = (b['28_day'] + b['29_day'] + b['30_day']).fillna(0)
# s = X.loc[:, ['bids_week0', 'bids_week2', 'bids_week4']].max(axis=1)
# X['s_bids_week0'] = X['bids_week0']/s
# X['s_bids_weeek2'] = X['bids_week2']/s
# X['s_bids_week4'] = X['bids_week4']/s
X.drop('balance2', 1, inplace=True)
return X
def bid_order(X, bids):
bids = bids.groupby('auction').apply(enumerate)
a = bids.sort('time', ascending=True).groupby('auction', as_index=False).first()
b = a.groupby('bidder_id').size().reset_index()
b= b.rename(columns = {0:'num_first_bid'})
X = pd.merge(X, b, on='bidder_id', how='left')
print X.columns
X['num_first_bid'].fillna(0, inplace=True)
return X
def user_countries_per_auction(X, bids):
#a = bids.groupby(['auction', 'bidder_id', 'country']).country.count()
#a = bids.groupby(['auction', 'bidder_id']).country.size()
#bids[bids.bidder_id=='5c28b04424e96267711d5af2db62e022tp36c'][['auction', 'country']].groupby('auction').size()
b = bids[['bidder_id', 'auction', 'country']].groupby(['bidder_id','auction']).country.nunique().reset_index()
b= b.rename(columns = {'country':'countries_per_bidder_per_auction'})
c = b.groupby('bidder_id').countries_per_bidder_per_auction.median().reset_index()
c= c.rename(columns = {'countries_per_bidder_per_auction':'countries_per_bidder_per_auction_median'})
X = pd.merge(X, c, on='bidder_id', how='left')
c = b.groupby('bidder_id').countries_per_bidder_per_auction.mean().reset_index()
c= c.rename(columns = {'countries_per_bidder_per_auction':'countries_per_bidder_per_auction_mean'})
X = pd.merge(X, c, on='bidder_id', how='left')
c = b.groupby('bidder_id').countries_per_bidder_per_auction.max().reset_index()
c= c.rename(columns = {'countries_per_bidder_per_auction':'countries_per_bidder_per_auction_max'})
X = pd.merge(X, c, on='bidder_id', how='left')
a = bids.groupby(['bidder_id']).country.value_counts().reset_index()
b = a.groupby(['bidder_id']).agg(lambda x: x.iloc[0]).reset_index()
b= b.rename(columns = {'level_1':'most_common_country'})
X = pd.merge(X, b[['bidder_id','most_common_country']], on='bidder_id', how='left')
b = bids[['bidder_id', 'country']].groupby(['bidder_id', 'country']).size().reset_index()
b= b.rename(columns = {0:'nbids'})
c = b.pivot('bidder_id','country').fillna(0).reset_index()
c.columns = c.columns.get_level_values(1)
c= c.rename(columns={c.columns[0]:'bidder_id'})
#fraction of bids in each country (for each user)
#c.iloc[:,1:] = c.iloc[:,1:].div(c.iloc[:,1:].sum(axis=1), axis=0)
#number of bids in each country rescaled by the largest number of bids in a country (for each user)
c.iloc[:,1:] = c.iloc[:,1:].div(c.iloc[:,1:].max(axis=1), axis=0)
X = pd.merge(X, c, on='bidder_id', how='left')
#b = bids[['bidder_id', 'country']].groupby(['bidder_id', 'country']).country.nunique().reset_index()
#b = bids[['bidder_id', 'country']].groupby(['bidder_id', 'country']).size().reset_index()
# a = bids.groupby(['bidder_id', 'country']).size().reset_index()
# a= a.rename(columns = {0:'c_count'})
# a.groupby(['bidder_id']).sort('c_count').agg(lambda x: x.iloc[0])
#
# a = bids.groupby(['bidder_id']).country.apply(mode())
# a= a.rename(columns = {0:'bids_in_country'})
# a.groupby(['bidder_id']).agg(lambda x:x.value_counts())
#bids[bids.bidder_id=='0053b78cde37c4384a20d2da9aa4272aym4pb'][bids.auction=='1l3p3'][['time', 'country']]
# hum = np.histogram(X[X.outcome==0].countries_per_bidder_per_auction_max.values, bins=10, normed=True, range=[0,10])
# bot = np.histogram(X[X.outcome==1].countries_per_bidder_per_auction_max.values, bins=10, normed=True, range=[0,10])
# plt.plot(hum[1][0:-1], hum[0], 'g.-')
# plt.plot(hum[1][0:-1], bot[0], 'b.-')
return X
def log_entropy(x):
e = np.sum(np.log(np.array(range(1,np.sum(x)))))
for i in x:
e -= np.sum(np.log(np.array(range(1,i))))
return e
#X = urls(X, bids)
def urls(X, bids):
'''
number of unique referring urls used by bidder
unique referring urls / total_bids
'''
b = bids.groupby('bidder_id').url.nunique().reset_index().fillna(1)
b= b.rename(columns = {'url':'n_urls'})
X = pd.merge(X, b, on='bidder_id', how='left')
b = bids.groupby(['bidder_id']).url.count().reset_index().fillna(1)
b= b.rename(columns = {'url':'n_bids_url'})
X = pd.merge(X, b, on='bidder_id', how='left')
X['f_urls'] = X['n_urls']/X['n_bids_url']
nbids =bids.groupby(['bidder_id']).size().reset_index()
nbids= nbids.rename(columns = {0:'nbids'})
a =bids.groupby(['bidder_id', 'url']).size().reset_index()
a= a.rename(columns = {0:'url_counts'})
a['urls_used'] = 1.0
a = pd.merge(a, nbids, on='bidder_id', how='left')
a['f_urls'] = a['url_counts']/a['nbids']
b = a.groupby('url').urls_used.count().reset_index()
print 'num urls ', b[b.urls_used > 18].url.values.shape[0]
repeat_urls = b[b.urls_used > 18].url.values
c = a[a.url.isin(repeat_urls)].pivot('bidder_id','url','f_urls').reset_index()
X = pd.merge(X, c, on='bidder_id', how='left')
X.iloc[:, -len(repeat_urls):] = X.iloc[:, -len(repeat_urls):].fillna(0)
b = bids.groupby(['bidder_id', 'url']).size().reset_index()
b= b.rename(columns = {0:'url_count'})
c = b.groupby('bidder_id').url_count.apply(log_entropy).reset_index()
c= c.rename(columns = {'url_count':'url_entropy'})
X = pd.merge(X, c, on='bidder_id', how='left')
X['f_urls'].fillna(1.0)
return X
def merch(X, bids):
'''
type of merchandise on first page visited by bidder
'''
a = bids.groupby('bidder_id').first().reset_index()
b = pd.get_dummies(a['merchandise'], columns='merchandise')
X = pd.concat([X, b], axis=1)
return X
def ip(X,bids):
bots = list(X[X.outcome==1].bidder_id.values)
humans = list(X[X.outcome==0].bidder_id.values)
'''
IP fingerprint
uses a clustering algorithm to group bidders which use a similar set of IPs
it's quite time consuming
'''
'''
nbids = bids.groupby('bidder_id').size().reset_index()
nbids= nbids.rename(columns = {0:'n_bids'})
b = bids[bids.bidder_id.isin(bots+humans)].groupby('ip').bidder_id.nunique().reset_index()
ip_many_users = b[b.bidder_id >= 2].ip.values
c = bids[bids.bidder_id.isin(bots+humans)].loc[bids.ip.isin(ip_many_users), :].groupby(['bidder_id', 'ip']).size().reset_index()
#c['v'] = 1
c= c.rename(columns = {0:'counts'})
c = pd.merge(c, nbids, on='bidder_id', how='left')
c['f'] = c['counts']/c['n_bids']
d = c.pivot('bidder_id', 'ip', 'f').reset_index().fillna(0)
print 'starting clustering'
brc = MiniBatchKMeans(n_clusters=100, init='k-means++', max_iter=100, batch_size=100, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init=3, reassignment_ratio=0.01)
brc.fit(d.values[:, 1:])
c = bids.loc[bids.ip.isin(ip_many_users), :].groupby(['bidder_id', 'ip']).size().reset_index()
c['v'] = 1
d = c.pivot('bidder_id', 'ip', 'v').reset_index().fillna(0)
clusters = brc.predict(d.values[:, 1:])
print 'done clustering'
e = d[['bidder_id']]
e['ip_cluster'] = np.nan
e.loc[:,['ip_cluster']] = pd.Series(clusters, index=e.index)
X= pd.merge(X, e[['bidder_id', 'ip_cluster']], on='bidder_id', how='left')
X['ip_cluster'].fillna(-1, inplace=True)
y = pd.get_dummies(X['ip_cluster'])
print 'ip groupings size ', y.shape[1]
X = X.join(y)
X = X.drop('ip_cluster', 1)
print 'done ip fingerprint'
'''
'''
ips per auction for each bidder
'''
a = bids[['bidder_id', 'auction', 'ip']].groupby(['bidder_id','auction', 'ip']).size().reset_index()
a= a.rename(columns = {0:'bids_per_auction_per_ip'})
b = a.groupby(['bidder_id', 'auction']).bids_per_auction_per_ip.apply(log_entropy).reset_index()
b= b.rename(columns = {'bids_per_auction_per_ip':'bids_per_auction_per_ip_entropy'})
c = b.groupby('bidder_id').bids_per_auction_per_ip_entropy.median().reset_index()
c= c.rename(columns = {'bids_per_auction_per_ip_entropy':'bids_per_auction_per_ip_entropy_median'})
X = pd.merge(X, c[['bidder_id','bids_per_auction_per_ip_entropy_median' ]], on='bidder_id', how='left')
c = b.groupby('bidder_id').bids_per_auction_per_ip_entropy.mean().reset_index()
c= c.rename(columns = {'bids_per_auction_per_ip_entropy':'bids_per_auction_per_ip_entropy_mean'})
X = pd.merge(X, c[['bidder_id','bids_per_auction_per_ip_entropy_mean' ]], on='bidder_id', how='left')
b = bids[['bidder_id', 'auction', 'ip']].groupby(['bidder_id','auction']).ip.nunique().reset_index()
b= b.rename(columns = {'ip':'ips_per_bidder_per_auction'})
# b = pd.merge(b,a, on=['bidder_id', 'auction'], how='left')
# b['f_ips_per_bidder_per_auction'] = b['ips_per_bidder_per_auction']/b['bids_per_auction']
c = b.groupby('bidder_id').ips_per_bidder_per_auction.median().reset_index()
c= c.rename(columns = {'ips_per_bidder_per_auction':'ips_per_bidder_per_auction_median'})
X = pd.merge(X, c[['bidder_id', 'ips_per_bidder_per_auction_median']], on='bidder_id', how='left')
c = b.groupby('bidder_id').ips_per_bidder_per_auction.mean().reset_index()
c= c.rename(columns = {'ips_per_bidder_per_auction':'ips_per_bidder_per_auction_mean'})
X = pd.merge(X, c[['bidder_id', 'ips_per_bidder_per_auction_mean']], on='bidder_id', how='left')
'''
fraction of bids placed by user from IP which is rare
'''
#b = bids.groupby(['ip', 'bidder_id']).size().reset_index()
b = bids.groupby('ip').bidder_id.nunique().reset_index()
ip_many_users = b[b.bidder_id > 1].ip.values
#ip_some_users = b[b.bidder_id < 300].ip.values
#ip_few_users = b[b.bidder_id <= 50].ip.values
ip_only_one_user = b[b.bidder_id == 1].ip.values
bids['ip2'] = np.nan
bids.loc[bids.ip.isin(ip_many_users), ['ip2']] = 'many'
#bids.loc[bids.ip.isin(ip_some_users), ['ip2']] = 'some'
#bids.loc[bids.ip.isin(ip_few_users), ['ip2']] = 'few'
bids.loc[bids.ip.isin(ip_only_one_user), ['ip2']] = 'only_one_user'
c = bids.groupby(['bidder_id','ip2']).size().reset_index()
c = c.rename(columns = {0: 'counts'})
d = c.pivot('bidder_id', 'ip2', 'counts').reset_index().fillna(0)
d['ip_only_one_user_counts'] = 1*d['only_one_user']
d.iloc[:,1:] = d.iloc[:,1:].div(d.iloc[:,1:].sum(axis=1), axis=0)
X = pd.merge(X, d, on='bidder_id', how='left')
X.iloc[:, -(d.shape[1]-1):].fillna(0, inplace=True)
X = X.drop('many', 1)
#X = X.drop('some', 1)
#X = X.drop('few', 1)
print 'finished building ip pivot table', d.shape[1]
'''
does the bidder use a network that has a bot on it? (where the bot is not that bidder)
what fraction of networks used by the bidder have bots on them? (where the bot is not that bidder)
'''
bids = pd.merge(bids, X[['bidder_id', 'outcome']], on='bidder_id', how='left')
bids['outcome'] = 1.0*(bids['outcome'] == 1)
b = bids.groupby(['ip', 'bidder_id']).outcome.mean().reset_index()
b.outcome = 1.0*(b.outcome)
c = b.groupby('ip').outcome.sum().reset_index()
c['nbots_on_ip'] = c.outcome
bids = pd.merge(bids, c[['ip', 'nbots_on_ip']], on='ip', how='left')
bids['on_ip_that_has_a_bot'] = 1*((bids['nbots_on_ip'] - bids['outcome']) >= 1)
bids['on_ip_that_has_3bots'] = 1*((bids['nbots_on_ip'] - bids['outcome']) >= 3)
bids = bids.drop('outcome', 1)
bids = bids.drop('nbots_on_ip', 1)
b = bids.groupby(['bidder_id', 'ip']).on_ip_that_has_a_bot.mean().reset_index()
c = b.groupby('bidder_id').on_ip_that_has_a_bot.mean().reset_index()
X = pd.merge(X, c[['bidder_id', 'on_ip_that_has_a_bot']], on='bidder_id', how='left')
X['on_ip_that_has_a_bot_mean'] = X['on_ip_that_has_a_bot'].fillna(0)
X['on_ip_that_has_a_bot'] = 1*(X['on_ip_that_has_a_bot'].fillna(0) > 0)
# b = bids.groupby(['bidder_id', 'ip']).on_ip_that_has_3bots.mean().reset_index()
# c = b.groupby('bidder_id').on_ip_that_has_3bots.mean().reset_index()
# X = pd.merge(X, c[['bidder_id', 'on_ip_that_has_3bots']], on='bidder_id', how='left')
# X['on_ip_that_has_3bots_mean'] = X['on_ip_that_has_3bots'].fillna(0)
# X['on_ip_that_has_3bots'] = 1*(X['on_ip_that_has_3bots'].fillna(0) > 0)
'''
IP entropy
'''
b = bids.groupby(['bidder_id', 'ip']).size().reset_index()
b= b.rename(columns = {0:'ip_count'})
c = b.groupby('bidder_id').ip_count.apply(log_entropy).reset_index()
c= c.rename(columns = {'ip_count':'ip_entropy'})
X = pd.merge(X, c, on='bidder_id', how='left')
'''
time between subsequent bids on different IPs for each user
'''
for week in [0, 2, 4]:
b = bids[bids.week==week].sort(['bidder_id', 'time'])[['bidder_id', 'time', 'ip']]
b['bidder_id_prev'] = pd.Series(np.append([np.nan], b.bidder_id.values[0:-1]), index=b.index)
b['ip_prev'] = pd.Series(np.append([np.nan], b.ip.values[0:-1]), index=b.index)
dt = np.append([np.nan], b.time.values[0:-1])
b['time_prev'] = pd.Series(dt, index=b.index)
b['dt'] = b['time'] - b['time_prev']
if week==0:
c = [b[b.bidder_id == b.bidder_id_prev][b.ip != b.ip_prev]]
else:
c.append(b[b.bidder_id == b.bidder_id_prev][b.ip != b.ip_prev])
c = pd.concat(c)
d = c.groupby('bidder_id').dt.median().reset_index()
d = d.rename(columns = {'dt': 'dt_change_ip_median'})
X = pd.merge(X, d[['bidder_id', 'dt_change_ip_median']], on='bidder_id', how='left')
'''
time between bids on the same IP for each user
'''
for week in [0, 2, 4]:
b = bids[bids.week==week].sort(['bidder_id', 'time'])[['bidder_id', 'time', 'ip']]
b['bidder_id_prev'] = pd.Series(np.append([np.nan], b.bidder_id.values[0:-1]), index=b.index)
b['ip_prev'] = pd.Series(np.append([np.nan], b.ip.values[0:-1]), index=b.index)
dt = np.append([np.nan], b.time.values[0:-1])
b['time_prev'] = pd.Series(dt, index=b.index)
b['dt'] = b['time'] - b['time_prev']
if week==0:
c = [b[b.bidder_id == b.bidder_id_prev][b.ip == b.ip_prev]]
else:
c.append(b[b.bidder_id == b.bidder_id_prev][b.ip == b.ip_prev])
c = pd.concat(c)
d = c.groupby('bidder_id').dt.median().reset_index()
d = d.rename(columns = {'dt': 'dt_same_ip_median'})
X = pd.merge(X, d[['bidder_id', 'dt_same_ip_median']], on='bidder_id', how='left')
#X['dt_change_ip-dt_same_ip'] = X['dt_change_ip_median'] - X['dt_same_ip_median']
#X.loc[:,'dt_change_ip_median'] = X.loc[:,'dt_change_ip_median'].fillna(30.0*one_day)
#X.loc[:,'dt_same_ip_median'] = X.loc[:,'dt_same_ip_median'].fillna(30.0*one_day)
#X.loc[:,'dt_change_ip-dt_same_ip'] = X.loc[:,'dt_change_ip-dt_same_ip'].fillna(30.0*one_day)
X['dt_change_ip_median'].fillna(-1, inplace=True)
X['dt_same_ip_median'].fillna(-1, inplace=True)
# X['dt_change_ip-dt_same_ip'].fillna(-31*one_day, inplace=True)
'''
giant pivot table of overlapping IPs
'''
'''
q = bids[bids.bidder_id.isin(bots+humans)].groupby('ip').bidder_id.nunique().reset_index()
ip_many_users = q[q.bidder_id >= 3].ip.values
b = bids.groupby(['ip','bidder_id',]).size().reset_index()
b = b[b.ip.isin(ip_many_users)]
b = b.rename(columns = {0:'count'})
b.drop('count', 1, inplace=True)
b2 = bids[bids.bidder_id.isin(bots+humans)].groupby(['ip','bidder_id',]).size().reset_index()
b2 = b2[b2.ip.isin(ip_many_users)]
b2 = b2.rename(columns = {0:'count'})
b2.drop('count', 1, inplace=True)
b2 = b2.rename(columns = {'bidder_id':'bidder_id2'})
b2['v'] = 1
c = pd.merge(b,b2, on='ip', how='left')
d = c.pivot_table(index='bidder_id', columns='bidder_id2', values='v')
d = d.reset_index().fillna(0)
X = pd.merge(X, d, on='bidder_id', how='left')
X.iloc[:, -d.shape[1]-1:].fillna(0, inplace=True)
print 'finished cluster fingerprint'
'''
return X
#X = build_X(bids, bot_or_human)
def build_X(bids, bot_or_human):
X = bot_or_human
X = X.drop('payment_account', 1)
X = X.drop('address', 1)
bids['day'] = (np.floor((bids['time'] - startt)/one_day))
bids['week'] = (np.floor(bids['day']/7.0))
#print bids.bidder_id[0]
print 'starting ips'
X = ip(X, bids)
print 'starting bid order'
X = bid_order(X, bids)
print 'starting dt'
X = dt(X, bids)
print 'startin day'
X = day(X, bids)
print 'starting n_bids'
X = n_bids(X, bids)
print 'starting urls'
X = urls(X, bids)
#print 'starting bid order'
#X = bid_order(X, bids)
print 'starting countries'
X = user_countries_per_auction(X, bids)
print 'starting merch'
X = merch(X, bids)
return X
def mean_over_cols(x):
s = np.sum(x)
x['f_bids_first_third'] = x[0]/s
x['f_bids_second_third'] = x[1]/s
x['f_bids_third_third'] = x[2]/s
return x
def make(X):
output = X[X.outcome==-1]['bidder_id'].reset_index()
print X.shape
X.drop(X[X.outcome >=0][X.n_bids.isnull()].index, inplace=True)
print X.shape
#X = X.sort(['n_bids', 'dt_others_median'])
X = X.sort(['dt_others_median'])
#X = X.sort(['on_ip_that_has_a_bot_mean', 'dt_others_median'])
X = X.fillna(method='pad')
# X = X.sort(['on_ip_that_has_a_bot_mean'])
# X = X.fillna(method='pad')
X = X.fillna(method='backfill')
X.sort_index(inplace=True)
X = X.fillna(0)
X = X.drop('most_common_country', 1)
X = X.drop('bidder_id', 1)
#clf0 = GradientBoostingClassifier(n_estimators=100, learning_rate=0.01, max_depth=4, min_samples_split=2)
#clf0 = RandomForestClassifier(n_estimators=100, max_depth=4, min_samples_split=2, random_state=0)
#clf = SGDClassifier(loss="modified_huber", penalty="elasticnet", n_iter=20000, class_weight='auto', alpha=0.1, epsilon=0.01)
#clf = SVC(C=1.0, class_weight='auto', probability=True, kernel='rbf', gamma=0.0, tol=1e-1)
#clf = AdaBoostClassifier(n_estimators=3000)
#clf = GradientBoostingClassifier(n_estimators=3000, learning_rate=0.01, max_depth=None, min_samples_leaf=1)
#clf = ExtraTreesClassifier(n_estimators=3000, max_depth=None, min_samples_leaf=1, random_state=0)
#clf = RandomForestClassifier(n_estimators=2500, max_depth=None, min_samples_leaf=1, random_state=0, criterion='entropy')
clf = []
nclf = 5
for i in xrange(nclf):
clf.append(RandomForestClassifier(n_estimators=800, max_depth=None, min_samples_leaf=1, random_state=i, criterion='entropy') )
scores = []
scores0 = []
Yb = X['outcome'].values
Xb = 1.0*X.drop('outcome', 1)
columns = Xb.columns
Xb = preprocessing.normalize(Xb.values, axis=0)
first_test_item = X[X.outcome>=0].shape[0]
X_test = Xb[first_test_item:, :]
print 'X_test shape ', X_test.shape
Y = Yb[0:first_test_item]
X = Xb[0:first_test_item, :]
n = int(0.8*X.shape[0])
all = range(X.shape[0])
for i in range(100):
np.random.shuffle(all)
train = all[0:n]
valid = all[n:]
X_train = Xb[train, :]
X_valid = Xb[valid, :]
Y_train = Yb[train]
Y_valid = Yb[valid]
X_train2 = X_train
X_valid2 = X_valid
print '--- ', i
pred_valid = np.zeros(X_valid2.shape[0])
for j in range(nclf):
clf[j].fit(X_train2, Y_train) #, sample_weight=(0.95*Y_train+0.05)
#pred_train = clf.predict_proba(X_train2)
#print pred_train[[21,50,51, 100, 101, 102, 103]]
#print Y_train.shape, pred_train.shape
#print roc_auc_score(Y_train, pred_train[:,1])
a = clf[j].predict_proba(X_valid2)[:,1]
pred_valid += a
print roc_auc_score(Y_valid, a)
pred_valid = 1.0*pred_valid/nclf
s = roc_auc_score(Y_valid, pred_valid)
print s
scores.append(s)
print np.mean(scores), 2.0*np.std(scores)
#print np.max(clf.feature_importances_), np.argmax(clf.feature_importances_)
#for i in range(20):
# print columns[i], clf.feature_importances_[i]
v = np.argsort(clf[0].feature_importances_)[::-1]
for i in range(40):
print columns[v[i]], clf[0].feature_importances_[v[i]]
if len(scores) == 100:
print np.sort(np.array(scores))[10]
print np.sort(np.array(scores))[25]
print np.sort(np.array(scores))[50]
print 'making test predictions'
X_train = X
Y_train = Y
pred_test = np.zeros(X_test.shape[0])
for j in range(nclf):
clf[j].fit(X_train, Y_train) #, sample_weight=(0.95*Y_train+0.05)
a = clf[j].predict_proba(X_test)[:,1]
pred_test += a
pred_test = 1.0*pred_test/nclf
#output probability that bidder is a robot
print pred_test.shape,
output['prediction'] = pd.Series(pred_test, index=output.index)