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rtb.py
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import csv
import random
import numpy as np
import pandas as pd
from sklearn import linear_model, tree, lda, naive_bayes
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
CHARTS_DIR_PATH = './charts/'
TABLES_DIR_PATH = './tables/'
DATA_PATH = './data/'
IMP_TRAIN_PATH = './raw_data/imp_train.txt'
CLICK_TRAIN_PATH = './raw_data/click_train.txt'
IMP_TEST_PATH = './raw_data/imp_test.txt'
CLICK_TEST_PATH = './raw_data/click_test.txt'
################################################################################
# DATA PREPROCESSING
################################################################################
def feature_selection_imps(file_path):
'''
This method will take a txt file as input and creates a CSV file n
Output: imps_featureselection.csv
'''
matrix = []
header = ['bid_id',
'ipinyou_id',
'timestamp',
'hour',
'browser_chrome',
'browser_ie',
'browser_safari',
'browser_firefox',
'mobile',
'iphone',
'ipad',
'android',
'windows',
'linux',
'region_id',
'ad_exchange',
'domain',
'ad_slot_id',
'ad_slot_size',
'ad_slot_visibility',
'ad_slot',
'ad_slot_floor_price',
'paying_price']
matrix.append(header)
with open(file_path) as f:
csvreader = csv.reader(f, delimiter='\t')
for row in csvreader:
# label the attributes
bid_id = row[0]
timestamp = row[1]
log_type = row[2]
ipinyou_id = row[3]
useragent = row[4]
ip_address = row[5]
region_id = row[6]
city_id = row[7]
ad_exchange = row[8]
domain = row[9]
url = row[10]
anonymous_url_id = row[11]
ad_slot_id = row[12]
ad_slot_width = row[13]
ad_slot_height = row[14]
ad_slot_visibility = row[15]
ad_slot = row[16]
ad_slot_floor_price = row[17]
creative_id = row[18]
bidding_price = row[19]
paying_price = row[20]
key_page_url = row[21]
advertiser_id = row[22]
user_tags = row[23]
# create derivative attributes
year = timestamp[0:5]
month = timestamp[5:7]
day = timestamp[7:9]
hour = timestamp[8:10]
minute = timestamp[10:12]
if 'Chome' in useragent:
browser_chrome = 1
else:
browser_chrome = 0
if 'MSIE' in useragent:
browser_ie = 1
else:
browser_ie = 0
if 'Safari' in useragent:
browser_safari = 1
else:
browser_safari = 0
if 'Firefox' in useragent:
browser_firefox = 1
else:
browser_firefox = 0
if 'Mobile' in useragent:
mobile = 1
else:
mobile = 0
if 'iPhone' in useragent:
iphone = 1
else:
iphone = 0
if 'iPad' in useragent:
ipad = 1
else:
ipad = 0
if 'Android' in useragent:
android = 1
else:
android = 0
if 'Windows NT' in useragent:
windows = 1
else:
windows = 0
if 'Linux' in useragent:
linux = 1
else:
linux = 0
ad_slot_size = str(ad_slot_width)+'x'+str(ad_slot_height)
vector = [bid_id,
ipinyou_id,
timestamp,
hour,
browser_chrome,
browser_ie,
browser_safari,
browser_firefox,
mobile,
iphone,
ipad,
android,
windows,
linux,
region_id,
ad_exchange,
domain,
ad_slot_id,
ad_slot_size,
ad_slot_visibility,
ad_slot,
ad_slot_floor_price,
paying_price]
matrix.append(vector)
df = pd.DataFrame(matrix)
df.columns = matrix[0]
df = df.ix[1:]
return df
def feature_selection_clicks(file_path):
matrix = []
header = ['bid_id',
'ipinyou_id',
'timestamp']
matrix.append(header)
with open(file_path) as f:
csvreader = csv.reader(f, delimiter='\t')
for row in csvreader:
# label the attributes
bid_id = row[0]
timestamp = row[1]
ipinyou_id = row[3]
vector = [bid_id,
ipinyou_id,
timestamp]
matrix.append(vector)
df = pd.DataFrame(matrix)
df.columns = matrix[0]
df = df.ix[1:]
return df
def merge_impression_click(imps, clicks):
imps = imps.set_index(imps['bid_id'])
imps = imps.drop(['bid_id'], axis=1)
clicks = clicks.set_index(clicks['bid_id'])
clicks = clicks.drop(['bid_id'], axis=1)
click_indicator = []
for index, value in imps.iterrows():
# find the timestamp and userid
timestamp = value['timestamp']
userid = value['ipinyou_id']
# if the userid and (timestamp + 5 minutes) is in the click log
if userid in list(clicks['ipinyou_id']):
click_indicator.append(1)
else:
click_indicator.append(0)
imps['clicks'] = click_indicator
return imps
def transform_ssf(df):
'''
Transforms categorical variables to dummy variables
'''
df = df.set_index(df['timestamp'])
hour_dummies = pd.get_dummies(df['hour'])
hour_dummies_columns = []
for v in hour_dummies.columns.values:
hour_dummies_columns.append('hour_%s' % str(v))
hour_dummies.columns = hour_dummies_columns
region_dummies = pd.get_dummies(df['region_id'])
region_dummies_columns = []
for v in region_dummies.columns.values:
region_dummies_columns.append('region_%s' % str(v))
region_dummies.columns = region_dummies_columns
adexchange_dummies = pd.get_dummies(df['ad_exchange'])
adexchange_dummies_columns = []
for v in adexchange_dummies.columns.values:
adexchange_dummies_columns.append('exchange_%s' % str(v))
adexchange_dummies.columns = adexchange_dummies_columns
# domain_dummies = pd.get_dummies(df['domain'])
# domain_dummies_columns = []
# for v in domain_dummies.columns.values:
# domain_dummies_columns.append('domain_%s' % str(v))
# domain_dummies.columns = domain_dummies_columns
adslotsize_dummies = pd.get_dummies(df['ad_slot_size'])
adslotsize_dummies_columns = []
for v in adslotsize_dummies.columns.values:
adslotsize_dummies_columns.append('adslotsize_%s' % str(v))
adslotsize_dummies.columns = adslotsize_dummies_columns
adslotvisibility_dummies = pd.get_dummies(df['ad_slot_visibility'])
adslotvisibility_dummies_columns = []
for v in adslotvisibility_dummies.columns.values:
adslotvisibility_dummies_columns.append('adslotvis_%s' % str(v))
adslotvisibility_dummies.columns = adslotvisibility_dummies_columns
adslot_dummies = pd.get_dummies(df['ad_slot'])
adslot_dummies_columns = []
for v in adslot_dummies.columns.values:
adslot_dummies_columns.append('adslot_%s' % str(v))
adslot_dummies.columns = adslot_dummies_columns
df = df.drop(['ipinyou_id',
'timestamp',
'hour',
'region_id',
'ad_exchange',
'domain',
'ad_slot_id',
'ad_slot_size',
'ad_slot_visibility',
'ad_slot',
'ad_slot_floor_price'], axis=1)
df_ssf = pd.concat([df,
hour_dummies,
region_dummies,
adexchange_dummies,
adslotsize_dummies,
adslotvisibility_dummies,
adslot_dummies], axis=1)
return df_ssf
def sample_train_data(df, perc_no_clicks=500):
df = df.reset_index()
df_clicks = df[df['clicks'] == 1]
df_noclicks = df[df['clicks'] == 0]
df_noclicks_sample_rows = \
random.sample(df_noclicks.index, int(len(df_clicks)*(perc_no_clicks/100)))
df_noclicks_sample = df_noclicks.ix[df_noclicks_sample_rows]
df = pd.concat([df_clicks, df_noclicks_sample])
df = df.set_index('timestamp')
return df
def partition(df):
df = df.sort_index()
df_length = df.shape[0]
train = df.iloc[0:int(0.8*df_length)]
test = df.iloc[(int(0.8*df_length)+1):]
return train, test
################################################################################
# DATA EXPLORATION
################################################################################
def data_exploration():
# create data frame from raw data
# file_path = './data/impression_sample.txt'
file_path = './raw_data/impression_sample.txt'
matrix = []
header = ['bid_id',
'year',
'day',
'hour',
'minute',
'log_type',
'ipinyou_id',
'browser_chrome',
'browser_ie',
'browser_safari',
'browser_firefox',
'browser_other',
'mobile',
'iphone',
'ipad',
'android',
'windows',
'linux',
'region_id',
'city_id',
'ad_exchange',
'domain',
'url',
'ad_slot_id',
'ad_slot_size',
'ad_slot_visibility',
'ad_slot',
'ad_slot_floor_price',
'creative_id',
'key_page_url',
'advertiser_id',
'user_tags']
matrix.append(header)
with open(file_path) as f:
csvreader = csv.reader(f, delimiter='\t')
for row in csvreader:
# label the attributes
bid_id = row[0]
timestamp = row[1]
log_type = row[2]
ipinyou_id = row[3]
useragent = row[4]
ip_address = row[5]
region_id = row[6]
city_id = row[7]
ad_exchange = row[8]
domain = row[9]
url = row[10]
anonymous_url_id = row[11]
ad_slot_id = row[12]
ad_slot_width = row[13]
ad_slot_height = row[14]
ad_slot_visibility = row[15]
ad_slot = row[16]
ad_slot_floor_price = row[17]
creative_id = row[18]
bidding_price = row[19]
paying_price = row[20]
key_page_url = row[21]
advertiser_id = row[22]
user_tags = row[23]
# create derivative attributes
year = timestamp[0:4]
month = timestamp[4:6]
day = timestamp[6:8]
hour = timestamp[8:10]
minute = timestamp[10:12]
if 'Chome' in useragent:
browser_chrome = 1
else:
browser_chrome = 0
if 'MSIE' in useragent:
browser_ie = 1
else:
browser_ie = 0
if 'Safari' in useragent:
browser_safari = 1
else:
browser_safari = 0
if 'Firefox' in useragent:
browser_firefox = 1
else:
browser_firefox = 0
if 'Chrome' not in useragent \
and 'MSIE' not in useragent \
and 'Safari' not in useragent \
and 'Firefox' not in useragent:
browser_other = 1
else:
browser_other = 0
if 'Mobile' in useragent:
mobile = 1
else:
mobile = 0
if 'iPhone' in useragent:
iphone = 1
else:
iphone = 0
if 'iPad' in useragent:
ipad = 1
else:
ipad = 0
if 'Android' in useragent:
android = 1
else:
android = 0
if 'Windows NT' in useragent:
windows = 1
else:
windows = 0
if 'Linux' in useragent:
linux = 1
else:
linux = 0
ad_slot_size = str(ad_slot_width) + 'x' + str(ad_slot_height)
vector = [bid_id,
year,
day,
hour,
minute,
log_type,
ipinyou_id,
browser_chrome,
browser_ie,
browser_safari,
browser_firefox,
browser_other,
mobile,
iphone,
ipad,
android,
windows,
linux,
region_id,
city_id,
ad_exchange,
domain,
url,
ad_slot_id,
ad_slot_size,
ad_slot_visibility,
ad_slot,
ad_slot_floor_price,
creative_id,
key_page_url,
advertiser_id,
user_tags]
matrix.append(vector)
# turn matrix into pandas dataframe
df = pd.DataFrame(matrix)
df.columns = matrix[0]
df = df.ix[1:]
df.index = df['bid_id']
df = df.drop(['bid_id'], axis=1)
def histogram(feature):
vc = df[feature].value_counts()
freq = vc.values
plt.hist(freq)
plt.savefig(CHARTS_DIR_PATH + ('de-%s-hist.png' % feature))
plt.clf()
def piechart(feature):
vc = df[feature].value_counts()
vc = vc.sort_index()
labels = vc.index.values
fracs = vc.values
plt.pie(fracs, labels=labels, autopct='%1.1f%%')
plt.savefig(CHARTS_DIR_PATH + ('de_%s-pie.png' % feature))
plt.clf()
# TIMESTAMP
events_per_hour = df['hour'].value_counts()
events_per_hour = events_per_hour.sort_index()
events_per_hour.plot()
plt.savefig(CHARTS_DIR_PATH + 'de_events-per-hour.png')
plt.clf()
# FREQUENCY
histogram('ipinyou_id')
# BROWSER
num_firefox = df['browser_firefox'].sum()
num_safari = df['browser_safari'].sum()
num_ie = df['browser_ie'].sum()
num_chrome = df['browser_chrome'].sum()
num_other = df['browser_other'].sum()
ind = np.array([0,1,2,3,4])
width = 0.75
fig, ax = plt.subplots()
rects = ax.bar(left=ind,
height=[num_firefox, num_safari, num_ie, num_chrome, num_other],
width=width)
ax.set_xticks(ind + (width/2))
ax.set_xticklabels(['Firefox', 'Safari', 'IE', 'Chrome', 'Other'])
plt.savefig(CHARTS_DIR_PATH + 'de_browser-distribution.png')
plt.clf()
# MOBILE VS NONMOBILE
num_mobile = df['mobile'].sum()
num_nonmobile = df['mobile'].count() - num_mobile
labels = 'Mobile', 'Non-mobile'
fracs = [num_mobile, num_nonmobile]
plt.pie(fracs, labels=labels)
plt.savefig(CHARTS_DIR_PATH + 'de_mobile-vs-nonmobile.png')
plt.clf()
# REGIONS AND CITIES
num_regions = len(df['region_id'].unique())
num_cities = len(df['city_id'].unique())
print '### REGIONS AND CITIES ###'
print 'Number of unique regions: %s' % str(num_regions)
print 'Number of unique cities: %s' % str(num_cities)
print
print '--'
# AD EXCHANGE ID
piechart('ad_exchange')
# DOMAIN
histogram('domain')
# URL
histogram('url')
# AD SLOT ID
histogram('ad_slot_id')
# AD SLOT SIZE
piechart('ad_slot_size')
# AD SLOT VISIBILITY
piechart('ad_slot_visibility')
# AD SLOT
piechart('ad_slot')
# AD SLOT FLOOR PRICE
histogram('ad_slot_floor_price')
# CREATIVE ID
print df['creative_id'].value_counts()
# KEY PAGE URL
# ADVERTISER ID
# USER TAGS
print
################################################################################
# MODELS AND MODEL EVALUATION
################################################################################
def fit_model(df, model='logistic_regression', max_depth=5):
# split the target attribute from the explanatory variables
y = df['clicks']
x = df.drop(['clicks', 'paying_price'], axis=1)
# fit the model
if model == 'logistic_regression':
m = linear_model.LogisticRegression()
m.fit(x, y)
df_coef = pd.DataFrame(columns=['attribute','logreg_coef'])
df_coef['attribute'] = x.columns.values
df_coef['logreg_coef'] = np.ravel(m.coef_)
df_coef.to_csv(TABLES_DIR_PATH + 'logreg_coef.csv')
print 'Logistic regression coefficients:'
print df_coef
elif model == 'tree':
m = tree.DecisionTreeClassifier(max_depth=max_depth)
m.fit(x, y)
elif model == 'lda':
m = lda.LDA()
m.fit(x, y)
elif model == 'naivebayes':
m = naive_bayes.GaussianNB()
m.fit(x, y)
return m
def create_propensity_histogram(df_test, model):
# keep a running total of the propensities
propensities_list = []
# for each impression in the log
for index, value in df_test.iterrows():
# grab the attributes of the impression (drop clicks)
attributes = value
attributes = attributes.drop(['clicks', 'paying_price'])
# calculate the impression utility and append to propensities list
imp_propensity = model.predict_proba(attributes)[0][1]
propensities_list.append(imp_propensity)
# append propensities to df_test
df_test['propensity'] = propensities_list
# create histogram of propensities
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(propensities_list)
ax.set_xlabel('Propensity To Click')
ax.set_ylabel('Frequency')
if 'LogisticRegression' in str(type(model)):
ax.set_title('Histogram of Propensity To Click (Logistic Regression)')
plt.savefig(CHARTS_DIR_PATH + 'hist_prop_to_click_logreg.png')
elif 'DecisionTreeClassifier' in str(type(model)):
ax.set_title('Histogram of Propensity To Click (Decision Tree)')
plt.savefig(CHARTS_DIR_PATH + 'hist_prop_to_click_tree.png')
elif 'LDA' in str(type(model)):
ax.set_title('Histogram of Propensity To Click (LDA)')
plt.savefig(CHARTS_DIR_PATH + 'hist_prop_to_click_lda.png')
elif 'GaussianNB' in str(type(model)):
ax.set_title('Histogram of Propensity To Click (Naive Bayes)')
plt.savefig(CHARTS_DIR_PATH + 'hist_prop_to_click_naivebayes.png')
plt.clf()
def evaluate_model_on_full_set(df_test, model):
# for each impression in the log
predictions = []
propensities = []
for index, value in df_test.iterrows():
attributes = value
attributes = attributes.drop(['clicks', 'paying_price'])
propensity = model.predict_proba(attributes)[0][1]
pred = model.predict(attributes)[0]
propensities.append(propensity)
predictions.append(pred)
df_analysis = df_test.copy()
df_analysis['propensity'] = propensities
df_analysis['pred'] = predictions
df_analysis = df_analysis.sort(columns=['propensity'], ascending=False)
df_analysis['cum_click'] = df_analysis['clicks'].cumsum()
df_analysis['cum_click_perc'] = \
100*df_analysis['cum_click'] / df_analysis['clicks'].sum()
ninety_perc_click_threshold = \
df_analysis[df_analysis['cum_click_perc'] < 0.9].shape[0] / float(df_analysis.shape[0])
cm = confusion_matrix(df_analysis['clicks'], df_analysis['pred'])
accuracy = (cm[0][0]+cm[1][1]) / float(cm.sum())
recall = cm[1][1] / float(cm[1][0] + cm[1][1])
precision = cm[1][1] / float(cm[0][1] + cm[1][1])
print cm
return accuracy, recall, precision, cm, ninety_perc_click_threshold*100.0
################################################################################
# SIMULATE PERFORMANCE
################################################################################
def simulate_flat_bid(bid=300, print_output=True):
# create df with all impressions
df = pd.DataFrame.from_csv('./data/test_ssf_100.csv')
# keep running total of ad spend, impressions, clicks
ad_spend = 0
impressions = 0
clicks = 0
# for each impression in the log
for index, value in df.iterrows():
# grab the attributes of the impression (drop clicks)
attributes = value
# calculate the impression utility
imp_utility = bid
# calculate the minimum utility needed
min_utility = 0
adjusted_utility = imp_utility - min_utility
# calculate the bid price
bid_price = adjusted_utility
# find the paying price
paying_price = value['paying_price']
# if bid_price > paying_price, update campaign metrics
if bid_price >= paying_price:
impressions += 1
ad_spend += paying_price/1000.0
if value['clicks'] == 1:
clicks += 1
try:
cpm = float(ad_spend) / (impressions) * 1000
ctr = float(clicks) / impressions
except ZeroDivisionError:
cpm = 0.0
ctr = 0.0
try:
cpc = float(ad_spend) / clicks
except ZeroDivisionError:
cpc = 0.0
if print_output == False:
return ad_spend, impressions, clicks, cpm, ctr, cpc
else:
print 'Total ad spend: %.2f Yen' % ad_spend
print 'Number of impressions delivered: %d' % impressions
print 'Number of clicks: %d' % clicks
print 'eCPM: %.2f Yen' % cpm
print 'CTR: %0.2f %%' % (ctr*100)
print 'CPC: %.2f Yen' % cpc
def simulate_goal_bid(goal, model, print_output=True):
# create df with all impressions
df = pd.DataFrame.from_csv('./data/test_ssf_100.csv')
# keep track of bids
bids = []
# for each impression in the log
for index, value in df.iterrows():
# grab the attributes of the impression (drop clicks)
attributes = value
attributes = attributes.drop(['clicks', 'paying_price'])
# calculate the bid
imp_utility = model.predict_proba(attributes)[0][1]
bid = imp_utility*goal
bids.append(bid)
df['bid'] = bids
df['impression'] = (df['bid'] > df['paying_price'])*1
df_winning_bids = df[df['impression'] == 1]
impressions = df_winning_bids.shape[0]
clicks = df_winning_bids['clicks'].sum()
ad_spend = df_winning_bids['paying_price'].sum()/1000.0
cpm = float(ad_spend) / (impressions) * 1000
ctr = float(clicks) / impressions
cpc = float(ad_spend) / clicks
if print_output == False:
return ad_spend, impressions, clicks, cpm, ctr, cpc
# create bid and paying_price histogram
plt.hist(list(df['bid']), alpha=0.8, label='bid')
plt.hist(list(df['paying_price']), alpha=0.5, label='actual')
plt.legend(loc='upper right')
plt.xlabel('Price')
plt.ylabel('Frequency')
plt.title('Bid and Price Distribution')
plt.savefig(CHARTS_DIR_PATH + 'bid-distribution_goal_%s.png' % str(goal))
# print output
print 'Goal: %s' % str(goal)
print 'Total ad spend: %.2f Yen' % ad_spend
print 'Number of impressions delivered: %d' % impressions
print 'Number of clicks: %d' % clicks
print 'eCPM: %.2f Yen' % cpm
print 'CTR: %0.2f %%' % (ctr*100)
print 'CPC: %.2f Yen' % cpc
print '-'*50
return ad_spend, impressions, clicks, cpm, ctr, cpc
def calculate_spend_urgency(budget, df_imps, timestamp):
# calculate the total worth of the bids left
total_worth_of_bids_left = \
df_imps[df_imps.index > timestamp]['paying_price'].sum()
# print total_worth
spend_urgency = (budget*1000.0 / total_worth_of_bids_left)*100.0
if spend_urgency > 0:
return spend_urgency
else:
return 0.0
def calculate_cpe_threshold(historical_bids, spend_urgency, model):
# randomly sample historical bids
bids_sample_idx = random.sample(historical_bids.index, 500)
bids_sample = historical_bids.ix[bids_sample_idx]
# calculate propensity to convert for all impressions
propensities = []
for index, value in bids_sample.iterrows():
attributes = value.drop(['clicks', 'paying_price'])
propensity = model.predict_proba(attributes)[0][1]
propensities.append(propensity)
bids_sample['propensity'] = propensities
# calculate cpe for each bid
bids_sample['cpe'] = bids_sample['paying_price'] / bids_sample['propensity']
# sort the impressions be cpe
bids_sample = bids_sample.sort(columns=['cpe'], ascending=True)
# calculate the total ad spend in df
total_ad_spend = bids_sample['paying_price'].sum()
# create a column in df representing cumulative perc of ad spend
bids_sample['cum_spend'] = bids_sample['paying_price'].cumsum()
bids_sample['cum_spend_perc'] = \
100*bids_sample['cum_spend'] / bids_sample['paying_price'].sum()
cpe_threshold = \
bids_sample[bids_sample['cum_spend_perc'] < spend_urgency].iloc[-1]['cpe']
return cpe_threshold
def simulate_dynamic_bid(budget, model, print_output=True):
budget0 = budget
print 'Budget0: %s' % str(budget)
# end date
end_time = 20130609000110181
# create df with all impressions
df = pd.DataFrame.from_csv('./data/test_ssf_100.csv')
# keep track of bids
num_bids = 0
impressions = 0
clicks = 0
bids = []
# define starting cpe threshold
cpe_threshold = 80
# for each impression in the log
for index, value in df.iterrows():
# update max CPE
num_bids += 1
if 0.02 > float(budget) / budget0 > 0.95:
if num_bids % 500 == 0:
# create data frame of historical bids
historical_bids = df[df.index <= index]
# compute the spend urgency
spend_urgency = calculate_spend_urgency(budget, df, index)
# calculate new max CPE threshold
if spend_urgency == 0:
cpe_threshold = np.inf
else:
cpe_threshold = \
calculate_cpe_threshold(historical_bids, spend_urgency, model)
else:
if num_bids % 5000 == 0:
# create data frame of historical bids
historical_bids = df[df.index <= index]
# compute the spend urgency
spend_urgency = calculate_spend_urgency(budget, df, index)
# calculate new max CPE threshold
if spend_urgency == 0:
cpe_threshold = np.inf
else:
cpe_threshold = \
calculate_cpe_threshold(historical_bids, spend_urgency, model)
# grab the attributes of the impression (drop clicks)
attributes = value
price = attributes['paying_price']
attributes = attributes.drop(['clicks', 'paying_price'])
# calculate CPE
propensity = model.predict_proba(attributes)[0][1]
# calculate bid
bid = propensity * cpe_threshold
if budget < 0:
bid = 0.0
bids.append(bid)
# update real time metrics
if bid >= price:
budget -= price/1000.0
impressions += 1
df['bid'] = bids
df['impression'] = (df['bid'] > df['paying_price'])*1
df_winning_bids = df[df['impression'] == 1]
impressions = df_winning_bids.shape[0]
clicks = df_winning_bids['clicks'].sum()
ad_spend = df_winning_bids['paying_price'].sum()/1000.0
cpm = float(ad_spend) / (impressions) * 1000.0
ctr = float(clicks) / impressions
cpc = float(ad_spend) / clicks
if print_output == False:
return ad_spend, impressions, clicks, cpm, ctr, cpc
else:
print 'Total ad spend: %.2f Yen' % ad_spend
print 'Number of impressions delivered: %d' % impressions
print 'Number of clicks: %d' % clicks
print 'eCPM: %.2f Yen' % cpm
print 'CTR: %0.2f %%' % (ctr*100)
print 'CPC: %.2f Yen' % cpc
print '-'*50
return ad_spend, impressions, clicks, cpm, ctr, cpc
################################################################################
# MAIN
################################################################################
def evaluate_models(df_train, df_test):
# logistic regression
model_logreg = fit_model(df_train, model='logistic_regression')
accuracy_unseen_logreg, recall_unseen_logreg, precision_unseen_logreg, \
cm_unseen_logreg, ninety_perc_logreg = \
evaluate_model_on_full_set(df_test, model_logreg)
print '### LOGISTIC REGRESSION ###'
print accuracy_unseen_logreg*100
print recall_unseen_logreg*100
print precision_unseen_logreg*100
print ninety_perc_logreg
print '-'*100
# decision tree (max depth = 2)
model_tree2 = fit_model(df_train, model='tree', max_depth=2)
accuracy_unseen_tree2, recall_unseen_tree2, precision_unseen_tree2, \
cm_unseen_tree2, ninety_perc_tree2 = \
evaluate_model_on_full_set(df_test, model_tree2)
print '### DECISION TREE (max_depth=2) ###'
print accuracy_unseen_tree2*100
print recall_unseen_tree2*100
print precision_unseen_tree2*100
print ninety_perc_tree2
print '-'*100
# decision tree (max depth = 5)
model_tree5 = fit_model(df_train, model='tree', max_depth=5)
accuracy_unseen_tree5, recall_unseen_tree5, precision_unseen_tree5, \
cm_unseen_tree5, ninety_perc_tree5 = \
evaluate_model_on_full_set(df_test, model_tree5)
print '### DECISION TREE (max_depth=5) ###'
print accuracy_unseen_tree5*100
print recall_unseen_tree5*100
print precision_unseen_tree5*100
print ninety_perc_tree5
print '-'*100