Calculating same features with multiple training windows in Featuretools

Question

Featuretools supports already handling of multiple cutoff times 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

But as you see for one ID multiple points in time a pandas multi index is generated. How (maybe via a pivot?) can I instead get all the MIN/MAX/... generated columns prefixed with last_x_days_MIN/MAX/... so get additional features per cutoff window?

edit desired output format

initial feature 1,initial feature 2, time_frame_1_<AGGTYPE2>_Feature,time_frame_1_<AGGTYPE1>_Feature,time_frame_2_<AGGTYPE1>_Feature,time_frame_2_<AGGTYPE2>_Feature,time_frame_2_<AGGTYPE1>_Feature,time_frame_2_<AGGTYPE1>_Feature

Answer 1

You can achieve this by making two calls to ft.calculate_feature_matrix with different training_windows and joining the resulting feature matrices together. For example,

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"))

The code above returns this DataFrame where we have the same feature calculated using the last 60 and 30 days of data for calculation.

                  SUM(order_products.quantity)__60_days  SUM(order_products.quantity)__30_days
customer_name                                                                                  
Krista Maddox                                        466                                    306
Micheal Nicholson                                    710                                    539

Note: this example runs on the latest release of Featuretools (v0.3.1) where we updated the demo retail dataset to have interpretable names as customer ids.