4장 시계열 데이터의 시뮬레이션 (기부)
A unique python library that extends the python programming language and provides utilities that enhance productivity.
import numpy as np
import pandas as pd
## 회원 상태
years = ['2014', '2015', '2016', '2017', '2018']
memberStatus = ['bronze', 'silver', 'gold', 'inactive']
## np.random.choice의 p 인자 값을 조절하여
## 각 부류의 발생 확률을 달리하여 데이터를 생성합니다
memberYears = np.random.choice(years, 1000,
p = [0.1, 0.1, 0.15, 0.30, 0.35])
memberStats = np.random.choice(memberStatus, 1000,
p = [0.5, 0.3, 0.1, 0.1])
yearJoined = pd.DataFrame({'yearJoined': memberYears,
'memberStats': memberStats})
yearJoined
NUM_EMAILS_SENT_WEEKLY = 3
## 서로다른 패턴을 위한 몇 가지 함수를 정의 합니다
def never_opens(period_rng):
return []
def constant_open_rate(period_rng):
n, p = NUM_EMAILS_SENT_WEEKLY, np.random.uniform(0, 1)
num_opened = np.random.binomial(n, p, len(period_rng))
return num_opened
def increasing_open_rate(period_rng):
return open_rate_with_factor_change(period_rng,
np.random.uniform(1.01,
1.30))
def decreasing_open_rate(period_rng):
return open_rate_with_factor_change(period_rng,
np.random.uniform(0.5,
0.99))
def open_rate_with_factor_change(period_rng, fac):
if len(period_rng) < 1 :
return []
times = np.random.randint(0, len(period_rng),
int(0.1 * len(period_rng)))
num_opened = np.zeros(len(period_rng))
for prd in range(0, len(period_rng), 2):
try:
n, p = NUM_EMAILS_SENT_WEEKLY, np.random.uniform(0,
1)
num_opened[prd:(prd + 2)] = np.random.binomial(n, p,
2)
p = max(min(1, p * fac), 0)
except:
num_opened[prd] = np.random.binomial(n, p, 1)
for t in range(len(times)):
num_opened[times[t]] = 0
return num_opened
## 기부 행동
def produce_donations(period_rng, member_behavior, num_emails,
use_id, member_join_year):
donation_amounts = np.array([0, 25, 50, 75, 100, 250, 500,
1000, 1500, 2000])
member_has = np.random.choice(donation_amounts)
email_fraction = num_emails / (NUM_EMAILS_SENT_WEEKLY * len(period_rng))
member_gives = member_has * email_fraction
member_gives_idx = np.where(member_gives >= donation_amounts)[0][-1]
member_gives_idx = max(min(member_gives_idx,
len(donation_amounts) - 2),
1)
num_times_gave = np.random.poisson(2) * (2018 - member_join_year)
times = np.random.randint(0, len(period_rng), num_times_gave)
dons = pd.DataFrame({'member' : [],
'amount' : [],
'timestamp': []})
for n in range(num_times_gave):
donation = donation_amounts[member_gives_idx + np.random.binomial(1, .3)]
ts = str(period_rng[times[n]].start_time + random_weekly_time_delta())
dons = dons.append(pd.DataFrame(
{'member' :[use_id],
'amount' :[donation],
'timestamp': [ts]}))
if dons.shape[0] > 0:
dons = dons[dons.amount != 0]
## we don't report zero donation events as this would not
## be recorded in a real world database
## 기부액이 0인 경우에는 보고하지 않습니다.
## 실세계에서 다룰 때 이러한 정보는 데이터베이스에 반영되지 않습니다.
return dons
def random_weekly_time_delta():
days_of_week = [d for d in range(7)]
hours_of_day = [h for h in range(11, 23)]
minute_of_hour = [m for m in range(60)]
second_of_minute = [s for s in range(60)]
return pd.Timedelta(str(np.random.choice(days_of_week)) + " days" ) + pd.Timedelta(str(np.random.choice(hours_of_day)) + " hours" ) + pd.Timedelta(str(np.random.choice(minute_of_hour)) + " minutes") + pd.Timedelta(str(np.random.choice(second_of_minute)) + " seconds")
behaviors = [never_opens,
constant_open_rate,
increasing_open_rate,
decreasing_open_rate]
member_behaviors = np.random.choice(behaviors, 1000,
[0.2, 0.5, 0.1, 0.2])
rng = pd.period_range('2015-02-14', '2018-06-01', freq = 'W')
emails = pd.DataFrame({'member' : [],
'week' : [],
'emailsOpened': []})
donations = pd.DataFrame({'member' : [],
'amount' : [],
'tunestamp': []})
for idx in range(yearJoined.shape[0]):
## randomly generate the date when a member would have joined
## 회원이 가입한 시기를 무작위로 생성합니다
join_date = pd.Timestamp(yearJoined.iloc[idx].yearJoined) + pd.Timedelta(str(np.random.randint(0, 365)) + ' days')
join_date = min(join_date, pd.Timestamp('2018-06-01')).to_period(freq='W')
## member should not have action timestamps before joining
## 가입 전 어떤 행동에 대한 타임스탬프도 가져서는 안됩니다
member_rng = rng[rng > join_date]
if len(member_rng) < 1:
continue
info = member_behaviors[idx](member_rng)
if len(info) == len(member_rng):
emails = emails.append(pd.DataFrame(
{'member': [idx] * len(info),
'week': [str(r.start_time) for r in member_rng],
'emailsOpened': info}))
donations = donations.append(
produce_donations(member_rng, member_behaviors[idx],
sum(info), idx, join_date.year))
rng = pd.period_range('2015-02-14', '2018-06-01', freq = 'W')
donations.set_index(pd.to_datetime(donations.timestamp), inplace = True)
donations.sort_index(inplace = True)
donations.groupby(pd.Grouper(freq='M')).amount.sum().plot()
