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main.py
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main.py
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import argparse
import codecs
import collections
import csv
import datetime
from functools import reduce
import itertools
import logging
# Logging
logger = logging.getLogger('')
FORMAT = '%(asctime)-15s %(name)s %(message)s'
logging.basicConfig(format=FORMAT)
logger.setLevel(logging.DEBUG)
import operator
import naive_bayes
import naive_bayes_wrapper
import math
import numpy
import random
import sklearn
import sklearn.ensemble
import sklearn.ensemble.weight_boosting
import sys
import util
# TUNABLE PARAMETERS
# Training and test size selection
N = 202020
N = 306000
frac_positive = 0.54
negative_weight = 1.2
test_fraction = 0.01
beta = 3.0
# Estimator parameters
n_estimators1 = 500
n_estimators2 = 2000
min_samples_split = 10
min_samples_leaf = 5
max_features = 10
n_jobs=-1
oob_score=False
# seed=12345678
seed=998877665544
n_positive = int(N*frac_positive) # Number of postive training cases.
n_negative = N - n_positive
# Random number seeds
random_state = random.Random(seed)
classifier_random_state = numpy.random.RandomState(seed=seed)
# Important constants
train_start_date = datetime.datetime(year=2011, month=7, day=3, hour=0, minute=0)
test_start_date = datetime.datetime(year=2012, month=6, day=24)
def random_list_element(random_state, l):
return l[random_state.randrange(len(l))]
class WrappedClassifier:
"""Wrap the brain-dead classifier API to make it look more like a regressor."""
def __init__(self, classifier):
self.classifier = classifier
def __repr__(self):
return ("WrappedClassifier({0})".format(self.classifier))
def fit(self, x, y, sample_weight=None):
self.classifier.fit(x, y, sample_weight=sample_weight)
# self.oob_score_ = self.classifier.oob_score_
if hasattr(self.classifier, "oob_decision_function_"):
self.oob_prediction_ = self.classifier.oob_decision_function_[:,1].reshape(len(y))
if hasattr(self.classifier, "feature_importances_"):
self.feature_importances_ = self.classifier.feature_importances_
def predict(self, x):
return self.classifier.predict_proba(x)[:,1]
def set_params(self, **kwargs):
self.classifier.set_params(**kwargs)
def score(self, x, y):
return self.classifier.score(x, y)
regressors = (
('RandomForestRegressor',
sklearn.ensemble.RandomForestRegressor(
random_state=classifier_random_state,
n_estimators=n_estimators1,
min_samples_leaf=min_samples_leaf,
min_samples_split=min_samples_split,
max_features=max_features,
n_jobs=n_jobs,
oob_score=oob_score
)),
('RandomForestClassifier',
WrappedClassifier(sklearn.ensemble.RandomForestClassifier(
random_state=classifier_random_state,
n_estimators=n_estimators1,
min_samples_leaf=min_samples_leaf,
max_features=max_features,
n_jobs=n_jobs,
criterion='entropy',
oob_score=oob_score
))),
('GradientBootingRegressor',
sklearn.ensemble.gradient_boosting.GradientBoostingRegressor(
random_state=classifier_random_state,
n_estimators=n_estimators1,
min_samples_leaf=min_samples_leaf,
max_features=max_features)),
('GradientBoostingClassifier',
WrappedClassifier(sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(
random_state=classifier_random_state,
n_estimators=n_estimators1,
min_samples_leaf=min_samples_leaf,
max_features=max_features))),
('AdaboostRegressor',
sklearn.ensemble.weight_boosting.AdaBoostRegressor(
n_estimators=n_estimators1,
)),
('AdaboostClassifier',
WrappedClassifier(sklearn.ensemble.weight_boosting.AdaBoostClassifier(
n_estimators=n_estimators1,
))),
)
# For now, don't use boosting methods
regressors = regressors[0:1]
def week_index(date):
return int((date - train_start_date).days / 7.0)
def week_from_index(index):
return train_start_date + index*datetime.timedelta(days=7)
def start_of_week(date):
return week_from_index(week_index(date))
def dump(d):
for k, v in d.items():
logger.debug('{0}:'.format(k))
logger.debug(' {0}'.format(v))
def earth_distance(coordinate1, coordinate2):
'''coordinates are (lat,lon) tuples in degrees'''
lat1, lon1 = map(lambda x: x*math.pi/180.0, coordinate1)
lat2, lon2 = map(lambda x: x*math.pi/180.0, coordinate2)
distance = math.acos(math.cos(lat1)*math.cos(lat2) * (math.cos(lon1-lon2)-1)
+ math.cos(lat1 - lat2))
return distance*4000
def prefecture_distance(pref1, pref2):
try:
loc1 = prefecture_locations[pref1]
loc2 = prefecture_locations[pref2]
result = float(int(earth_distance((loc1['LATITUDE'],loc1['LONGITUDE']), (loc2['LATITUDE'],loc2['LONGITUDE']))))
except:
result = -99999.0
return result
def days_accessible(user, coupon, week_start_date):
"""Returns the number of days the coupon was accessible to the user during
the week beginning on week_start_date"""
start = max(week_start_date, user['REG_DATE'], coupon['DISPFROM'])
end = min(week_start_date+datetime.timedelta(days=7), user['WITHDRAW_DATE'], coupon['DISPEND'])
try:
interval = (end-start).total_seconds()/86400.0
except:
print('end: ', end)
print('start: ', start)
print('end-start:', end-start)
return interval if interval > 0 else 0
def coupon_days_accessible(coupon, week_start_date):
"""Returns the number of days the coupon was accessible to anyone during week beginning on week_start_date"""
start = max(week_start_date, coupon['DISPFROM'])
end = min(week_start_date+datetime.timedelta(days=7), coupon['DISPEND'])
try:
interval = (end-start).total_seconds()/86400.0
except:
print('end: ', end)
print('start: ', start)
print('end-start:', end-start)
interval = 0.0
return interval if interval > 0 else 0
parser = argparse.ArgumentParser(description='Train, optionally test, and generate a coupon contest entry')
parser.add_argument('--validate', help='Hold out last week as a test/validation set', action='store_true')
parser.add_argument('--score', help='Generate a submission file with recommendations', action='store_true')
args = parser.parse_args()
if args.validate:
train_end_date = test_start_date - datetime.timedelta(days=7)
else:
train_end_date = test_start_date
train_period_in_weeks = (train_end_date - train_start_date).days // 7
logger.info(
'Parameters: '
'N: {0:,}, '
'frac_positive: {1:,}, '
'n_positive: {2:,}, '
'n_estimators1: {3:,}, '
'test_fraction: {4:6.3f}, '
'min_samples_leaf: {5}, '
'max_features: {6}, '
'n_jobs: {7}, '
'validate: {8}, '
'beta: {9}'
.format(N,
frac_positive,
n_positive,
n_estimators1,
test_fraction,
min_samples_leaf,
max_features,
n_jobs,
args.validate,
beta,
)
)
logger.info('train_period_in_weeks: {0}'.format(train_period_in_weeks))
def user_computed_fields(user_list):
# Add some computed fields in the user records
for u in user_list.values():
u['QUANTIZED_AGE'] = (u['AGE'] // 3) * 3
user = util.read_file('user_list.csv', 'USER_ID_hash')
user_computed_fields(user)
for u in list(itertools.islice(user.values(), 0, 2)):
logger.debug('Sample user record: {0}'.format(u))
def add_coupon_computed_fields(coupon_list):
# Add some computed fields in the coupon records
for c in coupon_list.values():
# Quantized various prices to make it easier to use Naive Bayes
try:
c['QUANTIZED_PRICE_RATE'] = int((c['PRICE_RATE'] // 10) * 10.0)
c['QUANTIZED_DISCOUNT_PRICE'] = int(2 ** (int(math.log2(0.01 + c['DISCOUNT_PRICE']) * 2.0) / 2.0))
c['QUANTIZED_CATALOG_PRICE'] = int(2 ** (int(math.log2(0.01 + c['CATALOG_PRICE']) * 2.0) / 2.0))
except:
print(c['PRICE_RATE'])
print(c['DISCOUNT_PRICE'])
print(c['CATALOG_PRICE'])
raise
coupon = util.read_file('coupon_list_train.csv', 'COUPON_ID_hash')
add_coupon_computed_fields(coupon)
coupon_accessibility_by_week = {}
for c in coupon.values():
for i in range(train_period_in_weeks):
date = week_from_index(i)
if coupon_days_accessible(c, date) > 0:
coupon_accessibility_by_week.setdefault(i, set()).add(c['COUPON_ID_hash'])
for i in coupon_accessibility_by_week.keys():
coupon_accessibility_by_week[i] = list(coupon_accessibility_by_week[i])
coupon_test = util.read_file('coupon_list_test.csv', 'COUPON_ID_hash')
add_coupon_computed_fields(coupon_test)
coupon.update(coupon_test)
for c in list(itertools.islice(coupon.values(), 0, 2)):
logger.debug('Sample coupon record: {0}'.format(c))
missing_coupon = dict([('CAPSULE_TEXT', None),
('GENRE_NAME', None),
('PRICE_RATE', -999),
('CATALOG_PRICE', -999),
('DISCOUNT_PRICE', -999),
('DISPFROM', datetime.datetime.min),
('DISPEND', datetime.datetime.max),
('DISPPERIOD', (datetime.datetime.max-datetime.datetime.min).days),
('VALIDFROM', datetime.datetime.min),
('VALIDEND', datetime.datetime.max),
('VALIDPERIOD', (datetime.datetime.max-datetime.datetime.min).days),
('USABLE_DATE_MON', 0),
('USABLE_DATE_TUE', 0),
('USABLE_DATE_WED', 0),
('USABLE_DATE_THU', 0),
('USABLE_DATE_FRI', 0),
('USABLE_DATE_SAT', 0),
('USABLE_DATE_SUN', 0),
('USABLE_DATE_HOLIDAY', 0),
('USABLE_DATE_BEFORE_HOLIDAY', 0),
('large_area_name', None),
('ken_name', None),
('small_area_name', None),
('COUPON_ID_hash', '*MISSING*'),
('QUANTIZED_PRICE_RATE', -999),
('QUANTIZED_DISCOUNT_PRICE', -999),
('QUANTIZED_CATALOG_PRICE', -999),
])
purchase = util.read_file('coupon_detail_train.csv', 'PURCHASEID_hash')
train_area = util.read_file('coupon_area_train.csv')
test_area = util.read_file('coupon_area_test.csv')
validation_purchase = {}
validation_coupon = {}
# Dereference user and coupon references and remove records outside the date range
for k,v in purchase.items():
v['HOUR'] = v['I_DATE'].hour
v['DAY'] = v['I_DATE'].weekday()
v['USER'] = user[v['USER_ID_hash']]
v['COUPON'] = coupon[v['COUPON_ID_hash']]
v['PURCHASE_WEEK_DATE'] = start_of_week(v['I_DATE'])
del v['USER_ID_hash'], v['COUPON_ID_hash']
if v['I_DATE'] < train_start_date or v['I_DATE'] >= train_end_date:
del purchase[k]
if args.validate and v['I_DATE'] >= train_end_date and v['I_DATE'] < test_start_date:
validation_purchase.setdefault(v['USER']['USER_ID_hash'], set()).add(v['COUPON']['COUPON_ID_hash'])
validation_coupon[v['COUPON']['COUPON_ID_hash']] = v['COUPON']
if args.validate:
logger.info(
'Validation week ({0}): {1} purchases; {2} purchasers; {3} distinct items; {4} max per purchaser'.format(
train_end_date,
sum([len(v) for v in validation_purchase.values()]),
len(validation_purchase),
len(validation_coupon),
max([len(v) for v in validation_purchase.values()])
)
)
for k,v in list(itertools.islice(validation_purchase.items(), 0, 2)):
logger.debug('Sample validation week purchases: user: {0}, purchases: {1}'.format(k, v))
for p in list(itertools.islice(purchase.values(), 0, 2)):
logger.debug('Sample purchase record: {0}'.format(p))
logger.info('Retained {0:,} purchase records between {1} and {2}'.format(
len(purchase),
min(p['I_DATE'] for p in purchase.values()),
max(p['I_DATE'] for p in purchase.values())))
# Dereference user and purchase references
visit = util.read_file('coupon_visit_train.csv')
missing_coupons = set()
found_coupons = set()
for v in visit:
v['HOUR'] = v['I_DATE'].hour
v['DAY'] = v['I_DATE'].weekday()
coupon_id_hash = v['VIEW_COUPON_ID_hash']
if coupon_id_hash in coupon:
v['COUPON']= coupon[coupon_id_hash]
found_coupons.add(coupon_id_hash)
else:
v['COUPON']= missing_coupon
missing_coupons.add(coupon_id_hash)
user_id_hash = v['USER_ID_hash']
if user_id_hash in user:
v['USER']= user[user_id_hash]
else:
logger.info('Visit references user ID: {0} but no such user.'.format(user_id_has))
del v['USER_ID_hash'], v['VIEW_COUPON_ID_hash']
if len(found_coupons) > 0:
logger.info ("Visit file references {0} coupons found in the coupon list file.".format(len(found_coupons)))
if len(missing_coupons) > 0:
logger.info ("Visit file references {0} coupons NOT FOUND in the coupon list file (replaced with dummy coupon).".format(len(missing_coupons)))
del missing_coupons
prefecture_set = set()
large_area_name_set = set()
ken_name_set = set()
small_area_name_set = set()
capsule_text_set = set()
genre_name_set = set()
# Scan through data to build indexes for frequently accessed data
# and to accumulate any other useful statistics:
logger.info('Scanning data...')
first_purchase_date = min((p['I_DATE'] for p in purchase.values()))
last_purchase_date = max((p['I_DATE'] for p in purchase.values()))
def range_violation(user, coupon, date):
start_date = max(user['REG_DATE'], coupon['DISPFROM'])
end_date = min(user['WITHDRAW_DATE'], coupon['DISPEND'])
if date > end_date:
return (1, date - end_date)
if date < start_date:
return (-1, start_date-date)
return None
logger.info('First/last purchase dates: {0}/{1}'.format(first_purchase_date, last_purchase_date))
# Purchase
purchase_by_user = collections.OrderedDict()
purchase_by_user_week = collections.OrderedDict()
purchase_by_user_coupon_week = collections.OrderedDict()
for p in purchase.values():
purchase_by_user.setdefault(p['USER']['USER_ID_hash'], []).append(p)
purchase_by_user_week[(p['USER']['USER_ID_hash'], week_index(p['I_DATE']))] = 1
purchase_by_user_coupon_week[(p['USER']['USER_ID_hash'], p['COUPON']['COUPON_ID_hash'], week_index(p['I_DATE']))] = 1
# Verify the assumption that purchases do not occur outside the ad display window or when the user is not registered.
rv = range_violation(p['USER'], p['COUPON'], p['I_DATE'])
if rv:
logger.warning('*** Sale w/o display: '
'{0} days, {1} hours, {2} minutes, {3} seconds {4}'.format(rv[1].days,
rv[1].seconds//3600,
(rv[1].seconds % 3600) // 60,
rv[1].seconds % 60,
'after close' if rv[0]>0 else 'before open'))
logger.warning('\tpurchase date: {0}'.format(p['I_DATE']))
logger.warning('\tuser: {0} reg date range: {1} to {2}'.format(p['USER']['USER_ID_hash'], p['USER']['REG_DATE'], p['USER']['WITHDRAW_DATE']))
logger.warning('\tcoupon: {0} disp date range: {1} to {2}'.format(p['COUPON']['COUPON_ID_hash'], p['COUPON']['DISPFROM'], p['COUPON']['DISPEND']))
# Sort purchase lists
for u,purchase_list in purchase_by_user.items():
purchase_list.sort(key=operator.itemgetter('I_DATE'), reverse=False)
purchase_by_user[u] = tuple(purchase_list)
# Visit
visit_by_user = collections.OrderedDict()
for v in visit:
visit_by_user.setdefault(v['USER']['USER_ID_hash'], []).append(v)
del visit
# Sort visit lists
for u,visit_list in visit_by_user.items():
visit_list.sort(key=operator.itemgetter('I_DATE'), reverse=False)
visit_by_user[u] = tuple(visit_list)
# Area
small_area_by_coupon = collections.OrderedDict()
for a in itertools.chain(train_area, test_area):
small_area_by_coupon.setdefault(a['COUPON_ID_hash'], set()).add(a['SMALL_AREA_NAME'])
prefecture_set.add(a['PREF_NAME'])
small_area_name_set.add(a['SMALL_AREA_NAME'])
# Coupon
for h,c in coupon.items():
capsule_text_set.add(c['CAPSULE_TEXT'])
genre_name_set.add(c['GENRE_NAME'])
large_area_name_set.add(c['large_area_name'])
ken_name_set.add(c['ken_name'])
small_area_name_set.add(c['small_area_name'])
# User
user_history = collections.OrderedDict()
logger.info('Building user history list')
for h,u in user.items():
visit_history = visit_by_user.get(h, ())
purchase_history = purchase_by_user.get(h, ())
user_history[h] = { 'user': u,
'visit': visit_history,
'purchase': purchase_history }
prefecture_set.add(u['PREF_NAME'])
del purchase_by_user, visit_by_user
def log_set(logger, s, name):
# logger.info('{0} ({1}): {2}'.format(name, len(s), ', '.join(sorted(map(str, s)))))
logger.info('{0}: {1} values'.format(name, len(s)))
log_set(logger, prefecture_set, 'Prefecture names')
log_set(logger, large_area_name_set, 'Large area names')
log_set(logger, ken_name_set, 'Ken names')
log_set(logger, capsule_text_set, 'Capsule text')
log_set(logger, genre_name_set, 'Genre names')
log_set(logger, small_area_name_set, 'Small area names')
class CategoryEncoder:
def __init__ (self, categories):
enumerated = tuple(enumerate(categories))
self.mapping = dict((c,i) for i,c in enumerated)
self.unmapping = tuple(zip(*enumerated))[1]
def map(self, category):
return self.mapping[category]
def unmap(self, value):
return self.unmapping[int(value)]
prefecture_encoder = CategoryEncoder(prefecture_set)
large_area_name_encoder = CategoryEncoder(large_area_name_set)
capsule_encoder = CategoryEncoder(capsule_text_set)
genre_name_encoder = CategoryEncoder(genre_name_set)
small_area_name_encoder = CategoryEncoder(small_area_name_set)
gender_encoder = CategoryEncoder(('m', 'f'))
class RandomFeatureSet:
def names(self):
return ('random_feature',)
def map (self, user_history, coupon, date):
return (random_state.randrange(2),)
class ExperimentalFeatureSet:
def names(self):
return (
'week_index',
)
def map (self, user_history, coupon, date):
return (
week_index(date),
)
class AvailabilityFeatureSet:
def names(self):
return ('days_accessible_by_user',)
def map (self, user_history, coupon, date):
return (days_accessible(user_history['user'], coupon, date),)
class UserPurchaseHistoryFeatureSet:
def names(self):
return(
# 'number_of_purchases',
# 'number_of_genre_purchases',
# 'number_of_capsule_purchases',
'days_since_purchase',
'days_since_2nd_purchase',
'days_since_genre_purchase',
'days_since_capsule_purchase',
'days_since_small_area_purchase',
'days_since_large_area_purchase',
'days_since_ken_purchase',
'recency_genre_purchase',
'recency_capsule_purchase',
'recency_small_area_purchase',
# 'recency_large_area_purchase',
'recency_ken_purchase',
'percent_from_genre_purchase',
'percent_from_capsule_purchase',
'centered_discount_price',
'centered_price_rate',
'relative_to_previous_discount_price_purchase',
'relative_to_previous_price_rate_purchase',
'relative_to_max_discount_price',
'relative_to_min_price_rate',
'in_previous_purchase_area',
# 'max_qty_genre',
# 'max_qty_capsule',
# 'max_qty_ken',
# 'max_qty_large_area',
# 'max_qty_small_area',
)
def map(self, user_history, coupon, date):
purchase_count = genre_count = capsule_count = 0
discount_price = price_rate = -9999.0
sum_discount_price = 0
sum_price_rate = 0
min_price_rate = 100
max_discount_price = 0
second_last_purchase = last_purchase = datetime.datetime.min
days_since_capsule = days_since_genre = 1 << 31
days_since_small_area_name = days_since_large_area_name = days_since_ken_name = 1 << 31
max_qty_genre = max_qty_capsule = 0
max_qty_ken = max_qty_large_area = max_qty_small_area = 0
previous_purchase_area = set()
for p in user_history['purchase']:
# Consider only the past
if p['I_DATE'] < date:
second_last_purchase = last_purchase
last_purchase = p['I_DATE']
days = (date - last_purchase).total_seconds()/86400.0
discount_price = p['COUPON']['DISCOUNT_PRICE']
price_rate = p['COUPON']['PRICE_RATE']
min_price_rate = min(min_price_rate, price_rate)
max_discount_price = max(max_discount_price, discount_price)
purchase_count += 1
sum_discount_price += discount_price
sum_price_rate += price_rate
item_count = p['ITEM_COUNT']
purchased_coupon = p['COUPON']
if purchased_coupon['GENRE_NAME'] == coupon['GENRE_NAME']:
genre_count += 1
days_since_genre = min(days_since_genre, days)
max_qty_genre = max(max_qty_genre, item_count)
if purchased_coupon['CAPSULE_TEXT'] == coupon['CAPSULE_TEXT']:
capsule_count += 1
days_since_capsule = min(days_since_capsule, days)
max_qty_capsule = max(max_qty_capsule, item_count)
if purchased_coupon['small_area_name'] == coupon['small_area_name']:
days_since_small_area_name = min(days_since_small_area_name, days)
max_qty_small_area = max(max_qty_small_area, item_count)
if purchased_coupon['large_area_name'] == coupon['large_area_name']:
days_since_large_area_name = min(days_since_large_area_name, days)
max_qty_large_area = max(max_qty_large_area, item_count)
if purchased_coupon['ken_name'] == coupon['ken_name']:
days_since_ken_name = min(days_since_ken_name, days)
max_qty_ken = max(max_qty_ken, item_count)
previous_purchase_area.add(p['SMALL_AREA_NAME'])
days_since_purchase = (date - last_purchase).total_seconds()/86400.0
result = (
# purchase_count,
# genre_count,
# capsule_count,
days_since_purchase,
(date - second_last_purchase).total_seconds()/86400.0,
days_since_genre,
days_since_capsule,
days_since_small_area_name,
days_since_large_area_name,
days_since_ken_name,
days_since_genre - days_since_purchase,
days_since_capsule - days_since_purchase,
days_since_small_area_name - days_since_purchase,
# days_since_large_area_name - days_since_purchase,
days_since_ken_name - days_since_purchase,
float(genre_count) / purchase_count if purchase_count > 0 else -999,
float(capsule_count) / purchase_count if purchase_count > 0 else -999,
coupon['DISCOUNT_PRICE'] - float(sum_discount_price)/ purchase_count if purchase_count > 0 else -999,
coupon['PRICE_RATE'] - float(sum_price_rate)/ purchase_count if purchase_count > 0 else -999,
coupon['DISCOUNT_PRICE'] - discount_price,
coupon['PRICE_RATE'] - price_rate,
coupon['DISCOUNT_PRICE'] - max_discount_price,
coupon['PRICE_RATE'] - min_price_rate,
len(small_area_by_coupon.get(coupon['COUPON_ID_hash'], set()).intersection(previous_purchase_area)),
# max_qty_genre,
# max_qty_capsule,
# max_qty_ken,
# max_qty_large_area,
# max_qty_small_area,
)
return result
class UserVisitHistoryFeatureSet:
def names(self):
return (
'number_of_visits',
# 'number_of_genre_visits',
# 'number_of_capsule_visits',
'days_since_visit',
'days_since_genre_visit',
'days_since_capsule_visit',
'days_since_small_area_visit',
'days_since_large_area_visit',
'days_since_ken_visit',
'recency_genre_visit',
'recency_capsule_visit',
'recency_small_area_visit',
'recency_large_area_visit',
'recency_ken_visit',
# 'days_since_coupon_visit',
'percent_from_genre_visit',
'percent_from_capsule_visit',
'relative_to_previous_discount_price_visit',
'relative_to_previous_price_rate_visit',
'peak_hour',
# 'peak_dow',
)
def map(self, user_history, coupon, date):
genre_count = capsule_count = 0
discount_price = price_rate = -9999.0
days_since_visit = days_since_capsule = days_since_genre = days_since_coupon = 1 << 31
days_since_small_area_name = days_since_large_area_name = days_since_ken_name = 1 << 31
visit_count = 0
peak_dow = peak_hour = -1
peak_dow_count = peak_hour_count = 0
hour_count = {}
dow_count = {}
previous_visit_area = set()
for v in user_history['visit']:
# Consider only the past
if v['I_DATE'] < date:
days = (date - v['I_DATE']).total_seconds()/86400.0
days_since_visit = min(days_since_visit, days)
discount_price = v['COUPON']['DISCOUNT_PRICE']
price_rate = v['COUPON']['PRICE_RATE']
visit_count += 1
hour = v['I_DATE'].hour
hour_count[hour] = hour_count.get(hour, 0) + 1
dow = v['I_DATE'].weekday()
dow_count[dow] = dow_count.get(dow, 0) + 1
visited_coupon = v['COUPON']
if visited_coupon['GENRE_NAME'] == coupon['GENRE_NAME']:
genre_count += 1
days_since_genre = min(days_since_genre, days)
if visited_coupon['CAPSULE_TEXT'] == coupon['CAPSULE_TEXT']:
capsule_count += 1
days_since_capsule = min(days_since_capsule, days)
if visited_coupon['small_area_name'] == coupon['small_area_name']:
days_since_small_area_name = min(days_since_small_area_name, days)
if visited_coupon['large_area_name'] == coupon['large_area_name']:
days_since_large_area_name = min(days_since_large_area_name, days)
if visited_coupon['ken_name'] == coupon['ken_name']:
days_since_ken_name = min(days_since_ken_name, days)
if visited_coupon['COUPON_ID_hash'] == coupon['COUPON_ID_hash']:
days_since_coupon = min(days_since_coupon, days)
if len(hour_count) > 0:
peak_hour_count, peak_hour = sorted(((c,h) for (h,c) in hour_count.items()), reverse=True)[0]
if len(dow_count) > 0:
peak_dow_count, peak_dow = sorted(((c,d) for (d,c) in dow_count.items()), reverse=True)[0]
result = (
visit_count,
# genre_count,
# capsule_count,
days_since_visit,
days_since_genre,
days_since_capsule,
days_since_small_area_name,
days_since_large_area_name,
days_since_ken_name,
days_since_genre - days_since_visit,
days_since_capsule - days_since_visit,
days_since_small_area_name - days_since_visit,
days_since_large_area_name - days_since_visit,
days_since_ken_name - days_since_visit,
# days_since_coupon,
float(genre_count) / visit_count if visit_count > 0 else 0,
float(capsule_count) / visit_count if visit_count > 0 else 0,
coupon['DISCOUNT_PRICE'] - discount_price,
coupon['PRICE_RATE'] - price_rate,
peak_hour,
# peak_dow,
)
return result
class SimpleUserFeatureSet:
def names(self):
return (
'age',
# 'gender',
'prefecture',
'days_as_member')
def map (self, user_history, coupon, date):
user = user_history['user']
return (
user['AGE'],
# gender_encoder.map(user['SEX_ID']),
prefecture_encoder.map(user['PREF_NAME']),
(date - user['REG_DATE']).total_seconds()/86400.0
)
class SimpleCouponFeatureSet:
def names(self):
return ('capsule_text',
'genre_name',
# 'large_area_name',
'ken_name',
'small_area_name',
'price_rate',
'catalog_price',
'discount_price',
# 'price_reduction',
'valid_period',
'days_on_display',
'display_days_left',
'days_until_valid',
'days_until_expiration',
)
def map (self, user_history, coupon, date):
return (
capsule_encoder.map(coupon['CAPSULE_TEXT']),
genre_name_encoder.map(coupon['GENRE_NAME']),
# large_area_name_encoder.map(coupon['large_area_name']),
prefecture_encoder.map(coupon['ken_name']),
small_area_name_encoder.map(coupon['small_area_name']),
coupon['PRICE_RATE'],
coupon['CATALOG_PRICE'],
coupon['DISCOUNT_PRICE'],
# coupon['CATALOG_PRICE']-coupon['DISCOUNT_PRICE'],
coupon['VALIDPERIOD'],
(date - coupon['DISPFROM']).total_seconds()/86400.0,
(coupon['DISPEND'] - date).total_seconds()/86400.0,
(coupon['VALIDFROM'] - date).total_seconds()/86400.0,
(coupon['VALIDEND'] - date).total_seconds()/86400.0,
)
class CouponUsableDateFeatureSet:
def names(self):
return (
'usable_days_everything',
)
def map (self, user_history, coupon, date):
return (
(coupon['USABLE_DATE_MON']*1 + coupon['USABLE_DATE_TUE']*(1<<3) + coupon['USABLE_DATE_WED']*(1<<6) + coupon['USABLE_DATE_THU']*(1<<9) +
coupon['USABLE_DATE_FRI']*(1<<12) + coupon['USABLE_DATE_SAT']*(1<<15) + coupon['USABLE_DATE_SUN']*(1<<18) + coupon['USABLE_DATE_HOLIDAY']*(1<<21) +
coupon['USABLE_DATE_BEFORE_HOLIDAY']*(1<<24)),
)
class JointFeatureSet:
def names(self):
return ('distance',)
def map (self, user_history, coupon, date):
return (prefecture_distance(user_history['user']['PREF_NAME'], coupon['ken_name']),)
# dump(user_history)
accumulators = (
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'small_area_name', 'small_area_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'large_area_name', 'large_area_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'ken_name', 'ken_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'CAPSULE_TEXT', 'CAPSULE_TEXT'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'GENRE_NAME', 'GENRE_NAME'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'purchase', 'small_area_name', 'small_area_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'purchase', 'large_area_name', 'large_area_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'purchase', 'ken_name', 'ken_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'purchase', 'CAPSULE_TEXT', 'CAPSULE_TEXT'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'purchase', 'GENRE_NAME', 'GENRE_NAME'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'visit', 'small_area_name', 'small_area_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'visit', 'large_area_name', 'large_area_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'visit', 'ken_name', 'ken_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'visit', 'CAPSULE_TEXT','CAPSULE_TEXT'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'visit', 'GENRE_NAME', 'GENRE_NAME'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'purchase', 'small_area_name','small_area_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'purchase', 'large_area_name', 'large_area_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'purchase', 'ken_name', 'ken_name'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'purchase', 'CAPSULE_TEXT', 'CAPSULE_TEXT'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.BNStrategy(), user_history, coupon, 'purchase', 'GENRE_NAME', 'GENRE_NAME'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'QUANTIZED_PRICE_RATE', 'QUANTIZED_PRICE_RATE'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'QUANTIZED_DISCOUNT_PRICE', 'QUANTIZED_DISCOUNT_PRICE'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'QUANTIZED_CATALOG_PRICE', 'QUANTIZED_CATALOG_PRICE'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'purchase', 'QUANTIZED_PRICE_RATE', 'QUANTIZED_PRICE_RATE'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'purchase', 'QUANTIZED_DISCOUNT_PRICE', 'QUANTIZED_DISCOUNT_PRICE'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'purchase', 'QUANTIZED_CATALOG_PRICE', 'QUANTIZED_CATALOG_PRICE'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'QUANTIZED_AGE', 'small_area_name'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'QUANTIZED_AGE', 'large_area_name'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'QUANTIZED_AGE', 'ken_name'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'QUANTIZED_AGE', 'CAPSULE_TEXT'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'QUANTIZED_AGE', 'GENRE_NAME'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'SEX_ID', 'small_area_name'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'SEX_ID', 'large_area_name'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'SEX_ID', 'ken_name'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'SEX_ID', 'CAPSULE_TEXT'),
naive_bayes_wrapper.SimpleNBWrapper(user_history, coupon, 'SEX_ID', 'GENRE_NAME'),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'CAPSULE_TEXT', 'DAY', from_coupon=False),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'GENRE_NAME', 'DAY', from_coupon=False),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'CAPSULE_TEXT', 'HOUR', from_coupon=False),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'visit', 'GENRE_NAME', 'HOUR', from_coupon=False),
naive_bayes_wrapper.CacheableWrapper(naive_bayes.MNStrategy(), user_history, coupon, 'purchase', 'small_area_name', 'SMALL_AREA_NAME', from_coupon=False),
)
class NBFeatureSet:
def names(self):
return (
'small_area_visit_history_mn',
'large_area_visit_history_mn',
'ken_visit_history_mn',
'capsule_visit_history_mn',
'genre_visit_history_mn',
'small_area_purchase_history_mn',
'large_area_purchase_history_mn',
'ken_purchase_history_mn',
'capsule_purchase_history_mn',
'genre_purchase_history_mn',
# 'small_area_visit_history_nb',
# 'large_area_visit_history_nb',
# 'ken_visit_history_nb',
# 'capsule_visit_history_nb',
# 'genre_visit_history_nb',
# 'small_area_purchase_history_nb',
# 'large_area_purchase_history_nb',
# 'ken_purchase_history_nb',
# 'capsule_purchase_history_nb',
# 'genre_purchase_history_nb',
'qtz_price_rate_visit_history_mn',