FeatureTools:处理多到多的关系

Question

我有多列采购的数据，包括以下三栏：

 PURCHASE_ID (index of purchase)
 WORKER_ID (index of worker)
 ACCOUNT_ID (index of account)

一个工作人员可以有多个与他们关联的帐户，一个帐户可以有多个工作人员。

如果我创建了员工实体和帐户实体并添加了这些关系，那么我就会得到一个错误：

KeyError: 'Variable: ACCOUNT_ID not found in entity'

到目前为止，我的代码如下：

import pandas as pd
import featuretools as ft
import featuretools.variable_types as vtypes

d = {'PURCHASE_ID': [1, 2], 
     'WORKER_ID': [0, 0], 
     'ACCOUNT_ID': [1, 2], 
     'COST': [5, 10], 
     'PURCHASE_TIME': ['2018-01-01 01:00:00', '2016-01-01 02:00:00']}
df = pd.DataFrame(data=d)

data_variable_types = {'PURCHASE_ID': vtypes.Id,
                       'WORKER_ID': vtypes.Id,
                       'ACCOUNT_ID': vtypes.Id,
                       'COST': vtypes.Numeric,
                       'PURCHASE_TIME': vtypes.Datetime}

es = ft.EntitySet('Purchase')
es = es.entity_from_dataframe(entity_id='purchases',
                               dataframe=df,
                               index='PURCHASE_ID',
                               time_index='PURCHASE_TIME',
                               variable_types=data_variable_types)

es.normalize_entity(base_entity_id='purchases',
                   new_entity_id='workers',
                   index='WORKER_ID',
                   additional_variables=['ACCOUNT_ID'],
                   make_time_index=False)

es.normalize_entity(base_entity_id='purchases',
                   new_entity_id='accounts',
                   index='ACCOUNT_ID',
                   additional_variables=['WORKER_ID'],
                   make_time_index=False)

fm, features = ft.dfs(entityset=es,
                     target_entity='purchases',
                     agg_primitives=['mean'],
                     trans_primitives=[],
                     verbose=True)
features

我如何将实体分开以包含多到多的关系？

Answer 1

您的方法是正确的，但是不需要使用additional_variables变量参数。如果省略它，您的代码将无问题地运行。

additional_variables to EntitySet.normalize_entity的目的是在您要创建的新父实体中包含您想要的其他变量。例如，假设您有关于雇用日期、薪资、地点等的变量，您可以将这些变量作为附加变量，因为对于员工来说，这些变量是静态的。在这种情况下，我不认为你有任何这样的变量。

这是我看到的代码和输出

import pandas as pd
import featuretools as ft
import featuretools.variable_types as vtypes

d = {'PURCHASE_ID': [1, 2], 
     'WORKER_ID': [0, 0], 
     'ACCOUNT_ID': [1, 2], 
     'COST': [5, 10], 
     'PURCHASE_TIME': ['2018-01-01 01:00:00', '2016-01-01 02:00:00']}
df = pd.DataFrame(data=d)

data_variable_types = {'PURCHASE_ID': vtypes.Id,
                       'WORKER_ID': vtypes.Id,
                       'ACCOUNT_ID': vtypes.Id,
                       'COST': vtypes.Numeric,
                       'PURCHASE_TIME': vtypes.Datetime}

es = ft.EntitySet('Purchase')
es = es.entity_from_dataframe(entity_id='purchases',
                               dataframe=df,
                               index='PURCHASE_ID',
                               time_index='PURCHASE_TIME',
                               variable_types=data_variable_types)

es.normalize_entity(base_entity_id='purchases',
                   new_entity_id='workers',
                   index='WORKER_ID',
                   make_time_index=False)

es.normalize_entity(base_entity_id='purchases',
                   new_entity_id='accounts',
                   index='ACCOUNT_ID',
                   make_time_index=False)

fm, features = ft.dfs(entityset=es,
                     target_entity='purchases',
                     agg_primitives=['mean'],
                     trans_primitives=[],
                     verbose=True)
features

这输出

[<Feature: WORKER_ID>,
 <Feature: ACCOUNT_ID>,
 <Feature: COST>,
 <Feature: workers.MEAN(purchases.COST)>,
 <Feature: accounts.MEAN(purchases.COST)>]

如果我们改变目标实体并增加深度

fm, features = ft.dfs(entityset=es,
                     target_entity='workers',
                     agg_primitives=['mean', 'count'],
                     max_depth=3,
                     trans_primitives=[],
                     verbose=True)
features

输出现在是workers实体的特性。

[<Feature: COUNT(purchases)>,
 <Feature: MEAN(purchases.COST)>,
 <Feature: MEAN(purchases.accounts.MEAN(purchases.COST))>,
 <Feature: MEAN(purchases.accounts.COUNT(purchases))>]

让我们解释一下名为MEAN(purchases.accounts.COUNT(purchases))>的特性

1. 对于给定的员工，查找与该员工相关的每一项采购。
2. 对于这些采购中的每一项，计算参与该特定采购的帐户所进行的采购总数。
3. 对所有给定工人的购买量进行平均计算。

换言之，“与这名工人的采购有关的账户平均采购数量是多少”。