【发布时间】：2020-07-29 11:59:35
【问题描述】：
即使我使用非常简单的数据进行测试，我也不知道如何计算聚合特征原语。我也查看了 featuretools 代码，但找不到聚合操作发生在哪里。
这里是示例代码：
from sklearn.utils import shuffle
periods = 5
end_date = "2012-04-13"
train_df = pd.DataFrame(
{
"store_id": [0]*periods + [1]*periods + [2]*periods + [3]*periods,
"region": ["A"]*periods+["B"]*periods*3,
"amount": shuffle(range(periods*4)),
"transacted_date": [
"2012-02-05", "2012-02-10", "2012-03-01", "2012-03-18", "2012-04-23",
]*4
}
)
train_df["transacted_date"] = pd.to_datetime(train_df["transacted_date"])
train_df.sort_values(["store_id", "transacted_date"], inplace=True)
def make_retail_cutoffs_amounts(data_df, amount_start_date, amount_end_date):
store_pool = data_df[data_df['transacted_date'] < amount_start_date]['store_id'].unique()
tmp = pd.DataFrame({'store_id': store_pool})
amounts = data_df[
(data_df['store_id'].isin(store_pool)) &
(amount_start_date <= data_df['transacted_date']) &
(data_df['transacted_date'] < amount_end_date)
].groupby('store_id')['amount'].sum().reset_index()
amounts = amounts.merge(tmp, on = 'store_id', how = 'right')
amounts['amount'] = amounts['amount'].fillna(0) # 0으로 채워지는 애는 3개월 다 수익이 없는 녀석!
amounts['cutoff_time'] = pd.to_datetime(amount_start_date)
amounts = amounts[['store_id', 'cutoff_time', 'amount']]
amounts = amounts.rename(columns={"amount":"1month_amount_from_cutoff_time"})
return amounts
amount_start_date = "2012-02-01"
amount_end_date = end_date
agg_month = 1
data_df_list = []
date_list = pd.date_range(amount_start_date, datetime.strptime(end_date, "%Y-%m-%d") + pd.DateOffset(months=1), freq="MS")
for amount_start_date, amount_end_date in zip(date_list[:-agg_month], date_list[agg_month:]):
data_df_list.append(
make_retail_cutoffs_amounts(
train_df, amount_start_date, amount_end_date
)
)
data_df = pd.concat(data_df_list)
data_df.sort_values(["store_id", "cutoff_time", ], inplace=True)
import featuretools as ft
es = ft.EntitySet(id="sale_set")
es = es.entity_from_dataframe(
"sales",
dataframe=train_df,
index="sale_id", make_index=True,
time_index='transacted_date',
)
es.normalize_entity(
new_entity_id="stores",
base_entity_id="sales",
index="store_id",
additional_variables=['region']
)
# When using a training window,
# it is necessary to calculate the last time indexes for the entity set. Adding
es.add_last_time_indexes()
features = ft.dfs(
entityset=es,
target_entity='stores',
cutoff_time=data_df,
verbose=1,
cutoff_time_in_index=True,
n_jobs=1,
max_depth=2,
agg_primitives=["sum",],
trans_primitives=["cum_max"],
training_window="1 month",
)
dfs 工作正常，但无法解释结果特征。
这里是特征的样本数据：
正如您在此处看到的，SUM(sales.amount) 和 SUM(sales.CUM_MAX(amount)) 的第一行分别为 19、37。我想知道它们是如何计算的。
这是我对结果的解释：
如您所见，store_0 有 2 条 2012 年 2 月的销售数据记录。因此，2012 年 3 月 1 日截止时间 store_id=0 的
SUM(sales.amount)应为 0 + 8 = 8，不是 19 岁。同样，
SUM(sales.CUM_MAX(amount))of store_id=0 在 2012-03-01 的截止时间也应该是 SUM(sales.CUM_MAX(amount)) = SUM([0, 8]) = 8，而不是 37 .
我错过了什么吗？它们是如何计算的？
【问题讨论】：
标签： python featuretools