Question

我想知道是否有办法计算一天中不同时间段内我已经使用深度特征综合（即计数，总和，均值等）的所有相同变量？

即早上事件（0-12小时）的计数与晚上事件（13-24）的变量分开。

同样，按照周几，月几日，年月日等方式获取计数最容易。自定义聚合基元？

Answer 1

是的，这是可能的。首先，让我们生成一些随机数据，然后我将逐步介绍如何操作

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是为要计算其特征的线段添加新列

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)

现在我们有一个这样的数据框

   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实体集中

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，我们准备使用interesting_values

设置要为其创建聚合的细分

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

然后，我们可以运行DFS并在where_primitives参数中逐段放置我们想要的聚合原语

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

fm

在生成的特征矩阵中，您现在可以看到我们每天早上，下午和晚上都有聚合

             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

使用Featuretools在一天中的每个时间进行汇总

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