R tbats模型季节性客户标志无结果

Question

我通过这个博客来确定我时间序列数据中的季节性客户： https://www.kristenkehrer.com/seasonality-code

我的代码是无耻的几乎相同的博客，与一些小的调整，代码如下。我完全可以为2000个客户运行代码。几个小时后，在我的结果中，0个客户被标记为季节性客户。你知道吗

手动查看客户数据随着时间的推移，我相信我有很多季节性客户的例子，应该已经拿起。下面是我使用的数据示例。你知道吗

我错过了什么蠢事吗？作为python的新成员，我是不是有点想尝试一下呢？你知道吗

请注意，我在数据源中添加了“0个月”，但我认为再次检查该函数不会有任何影响。我还没有包括数据源凭据步骤。你知道吗

谢谢

import pandas as pa
import numpy as np
import pyodbc as py

cnxn = py.connect('DRIVER='+driver+';SERVER='+server+';PORT=1433;DATABASE='+database+';UID='+username+';PWD='+ password)

original = pa.read_sql_query('SELECT s.customer_id, s.yr, s.mnth, Case when s.usage<0 then 0 else s.usage end as usage  FROM dbo.Seasonal  s   Join   ( Select Top 2000  customer_id, SUM(usage) as usage   From dbo.Seasonal where Yr!=2018    Group by customer_id ) t ON s.customer_id = t.customer_id Where yr!= 2018 Order by customer_id, yr, mnth', cnxn)

grouped = original.groupby(by='customer_id')

def yearmonth_to_justmonth(year, month):

    return year * 12 + month - 1

def fillInForOwner(group):
    min = group.head(1).iloc[0]
    max = group.tail(1).iloc[0]
    minMonths = yearmonth_to_justmonth(min.yr, min.mnth)
    maxMonths = yearmonth_to_justmonth(max.yr, max.mnth)
    filled_index = pa.Index(np.arange(minMonths, maxMonths, 1), name="filled_months")
    group['months'] = group.yr * 12 + group.mnth - 1
    group = group.set_index('months')
    group = group.reindex(filled_index)
    group.customer_id = min.customer_id
    group.yr = group.index // 12
    group.mnth = group.index % 12 + 1
    group.usage = np.where(group.usage.isnull(), 0, group.usage).astype(int)
    return group

filledIn = grouped.apply(fillInForOwner)
newIndex = pa.Index(np.arange(filledIn.customer_id.count()))

import rpy2 as r
from rpy2.robjects.packages import importr
from rpy2.robjects import r, pandas2ri, globalenv
pandas2ri.activate()

base = importr('base')
colorspace = importr('colorspace')
forecast = importr('forecast')
times = importr('timeSeries')
stats = importr('stats')

outfile = 'results.csv'
df_list = []

for customerid, dataForCustomer in filledIn.groupby(by=['customer_id']):
    startYear = dataForCustomer.head(1).iloc[0].yr
    startMonth = dataForCustomer.head(1).iloc[0].mnth
    endYear = dataForCustomer.tail(1).iloc[0].yr
    endMonth = dataForCustomer.tail(1).iloc[0].mnth


    customerTS = stats.ts(dataForCustomer.usage.astype(int),
                      start=base.c(startYear,startMonth),
                      end=base.c(endYear, endMonth), 
                      frequency=12)
    r.assign('customerTS', customerTS)

    try:
        seasonal = r('''
                        fit<-tbats(customerTS, seasonal.periods = 12, 
                        use.parallel = TRUE)
                        fit$seasonal
                     ''')
    except: 
        seasonal = 1
    df_list.append({'customer_id': customerid, 'seasonal': seasonal})
    print(f' {customerid} | {seasonal} ')

seasonal_output = pa.DataFrame(df_list)
print(seasonal_output)
seasonal_output.to_csv(outfile)

Answer 1

回过头来回答我是如何让它工作的，只是将“原始”数据帧传递到for循环中。我的数据已经有空的$0个月，所以我不需要运行这部分代码。谢谢大家的帮助