在Featuretools中使用多个训练窗口计算相同的特征

Question

Featuretools支持已经处理多个截止时间https://docs.featuretools.com/automated_feature_engineering/handling_time.html

In [20]: temporal_cutoffs = ft.make_temporal_cutoffs(cutoffs['customer_id'],
   ....:                                             cutoffs['cutoff_time'],
   ....:                                             window_size='3d',
   ....:                                             num_windows=2)
   ....: 

In [21]: temporal_cutoffs
Out[21]: 
        time  instance_id
0 2011-12-12        13458
1 2011-12-15        13458
2 2012-10-02        13602
3 2012-10-05        13602
4 2012-01-22        15222
5 2012-01-25        15222

In [22]: entityset = ft.demo.load_retail()

In [23]: feature_tensor, feature_defs = ft.dfs(entityset=entityset,
   ....:                                       target_entity='customers',
   ....:                                       cutoff_time=temporal_cutoffs,
   ....:                                       cutoff_time_in_index=True,
   ....:                                       max_features=4)
   ....: 

In [24]: feature_tensor
Out[24]: 
                        MAX(order_products.total)  MIN(order_products.unit_price)  STD(order_products.quantity)  COUNT(order_products)
customer_id time                                                                                                                      
13458.0     2011-12-12                    201.960                          0.3135                     10.053804                    394
            2011-12-15                    201.960                          0.3135                     10.053804                    394
15222.0     2012-01-22                    272.250                          1.1880                     26.832816                      5
            2012-01-25                    272.250                          1.1880                     26.832816                      5
13602.0     2012-10-02                     49.896                          1.0395                      8.732068                     23
            2012-10-05                     49.896                          1.0395                      8.732068                     23

但正如您看到的，对于一个ID，多个时间点，将生成pandas多索引。如何（也许通过支点？）我能得到所有最小值/最大值/。。。生成的列前缀为last_x_days_MIN/MAX/。。。所以每个截止窗口都有附加功能？在

编辑所需的输出格式

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Answer 1

您可以通过使用不同的training_windows对ft.calculate_feature_matrix进行两次调用，然后将生成的特征矩阵连接在一起。例如

import featuretools as ft
import pandas as pd

entityset = ft.demo.load_retail()

cutoffs = pd.DataFrame({
      'customer_name': ["Micheal Nicholson", "Krista Maddox"],
      'cutoff_time': [pd.Timestamp('2011-10-14'), pd.Timestamp('2011-08-18')]
    })

feature_defs = ft.dfs(entityset=entityset,
                      target_entity='customers',
                      agg_primitives=["sum"],
                      trans_primitives=[],
                      max_features=1,
                      features_only=True)



fm_60_days = ft.calculate_feature_matrix(entityset=entityset,
                                         features=feature_defs,
                                         cutoff_time=cutoffs,
                                         training_window="60 days")

fm_30_days = ft.calculate_feature_matrix(entityset=entityset,
                                         features=feature_defs,
                                         cutoff_time=cutoffs,
                                         training_window="30 days")

fm_60_days.merge(fm_30_days, left_index=True, right_index=True, suffixes=("__60_days", "__30_days"))

上面的代码返回这个DataFrame，其中我们使用过去60天和30天的数据来计算相同的特性。在

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注意：这个例子运行在Featuretools（v0.3.1）的最新版本上，我们在其中更新了demo retail数据集，将可解释的名称作为客户id。在