使用Featuretools每天的时间时间聚集

Question

我不知道是否有任何的方式来计算所有相同的变量我已经在用深合成功能（即数量，金额，平均等）在一天内的不同时间段？

即计数早晨事件（0-12小时），为从夜间活动（13-24）一个单独的变量的。

此外，同样内，这将是最简单的按周，月的一天，一年的天，等自定义聚合元获得计数？

Answer 1

是的，这是可能的。首先，让我们产生一些随机数据，然后我将演练如何

import featuretools as ft
import pandas as pd
import numpy as np

# make some random data
n = 100
events_df = pd.DataFrame({
    "id" : range(n),
    "customer_id": np.random.choice(["a", "b", "c"], n),
    "timestamp": pd.date_range("Jan 1, 2019", freq="1h", periods=n),
    "amount": np.random.rand(n) * 100 
})

def to_part_of_day(x):
    if x < 12:
        return "morning"
    elif x < 18:
        return "afternoon"
    else:
        return "evening"

events_df["time_of_day"] = events_df["timestamp"].dt.hour.apply(to_part_of_day)

events_df

我们要做的第一件事情是我们要计算功能，为段添加新列

def to_part_of_day(x):
    if x < 12:
        return "morning"
    elif x < 18:
        return "afternoon"
    else:
        return "evening"

events_df["time_of_day"] = events_df["timestamp"].dt.hour.apply(to_part_of_day)

现在我们有这样一个数据帧

   id customer_id           timestamp     amount time_of_day
0   0           a 2019-01-01 00:00:00  44.713802     morning
1   1           c 2019-01-01 01:00:00  58.776476     morning
2   2           a 2019-01-01 02:00:00  94.671566     morning
3   3           a 2019-01-01 03:00:00  39.271852     morning
4   4           a 2019-01-01 04:00:00  40.773290     morning
5   5           c 2019-01-01 05:00:00  19.815855     morning
6   6           a 2019-01-01 06:00:00  62.457129     morning
7   7           b 2019-01-01 07:00:00  95.114636     morning
8   8           b 2019-01-01 08:00:00  37.824668     morning
9   9           a 2019-01-01 09:00:00  46.502904     morning

接下来，让我们将其加载到我们的EntitySet

es = ft.EntitySet()
es.entity_from_dataframe(entity_id="events",
                         time_index="timestamp",
                         dataframe=events_df)

es.normalize_entity(new_entity_id="customers", index="customer_id", base_entity_id="events")

es.plot()

现在，我们已经准备好使用interesting_values设置我们要创建聚合段的

es["events"]["time_of_day"].interesting_values = ["morning", "afternoon", "evening"]

然后，我们可以运行DFS和地方，我们想要做的聚集基元在每个区间为单位在where_primitives参数

fm, fl = ft.dfs(target_entity="customers",
                entityset=es,
                agg_primitives=["count", "mean", "sum"],
                trans_primitives=[],
                where_primitives=["count", "mean", "sum"])

fm

在得到的特征矩阵，你现在可以看到我们每早上集合，下午和晚上

             COUNT(events)  MEAN(events.amount)  SUM(events.amount)  COUNT(events WHERE time_of_day = afternoon)  COUNT(events WHERE time_of_day = evening)  COUNT(events WHERE time_of_day = morning)  MEAN(events.amount WHERE time_of_day = afternoon)  MEAN(events.amount WHERE time_of_day = evening)  MEAN(events.amount WHERE time_of_day = morning)  SUM(events.amount WHERE time_of_day = afternoon)  SUM(events.amount WHERE time_of_day = evening)  SUM(events.amount WHERE time_of_day = morning)
customer_id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  
a                       37            49.753630         1840.884300                                           12                                          7                                         18                                          35.098923                                        45.861881                                        61.036892                                        421.187073                                      321.033164                                     1098.664063
b                       30            51.241484         1537.244522                                            3                                         10                                         17                                          45.140800                                        46.170996                                        55.300715                                        135.422399                                      461.709963                                      940.112160
c                       33            39.563222         1305.586314                                            9                                          7                                         17                                          50.129136                                        34.593936                                        36.015679                                        451.162220                                      242.157549                                      612.266545