Tuning Deep Feature Synthesis

There are several parameters that can be tuned to change the output of DFS.

In [1]: import featuretools as ft

In [2]: es = ft.demo.load_mock_customer(return_entityset=True)

In [3]: es
Out[3]: 
Entityset: transactions
  Entities:
    transactions [Rows: 500, Columns: 5]
    products [Rows: 5, Columns: 2]
    sessions [Rows: 35, Columns: 4]
    customers [Rows: 5, Columns: 4]
  Relationships:
    transactions.product_id -> products.product_id
    transactions.session_id -> sessions.session_id
    sessions.customer_id -> customers.customer_id

Using “Seed Features”

Seed features are manually defined, problem specific, features a user provides to DFS. Deep Feature Synthesis will then automatically stack new features on top of these features when it can.

By using seed features, we can include domain specific knowledge in feature engineering automation.

In [4]: expensive_purchase = ft.Feature(es["transactions"]["amount"]) > 125

In [5]: feature_matrix, feature_defs = ft.dfs(entityset=es,
   ...:                                       target_entity="customers",
   ...:                                       agg_primitives=["percent_true"],
   ...:                                       seed_features=[expensive_purchase])
   ...: 

In [6]: feature_matrix[['PERCENT_TRUE(transactions.amount > 125)']]
Out[6]: 
             PERCENT_TRUE(transactions.amount > 125)
customer_id                                         
5                                           0.227848
4                                           0.220183
1                                           0.119048
3                                           0.182796
2                                           0.129032

We can now see that PERCENT_TRUE was automatically applied to this boolean variable.

Add “interesting” values to variables

Sometimes we want to create features that are conditioned on a second value before we calculate. We call this extra filter a “where clause”.

By default, where clauses are built using the interesting_values of a variable.

In [7]: es["sessions"]["device"].interesting_values = ["desktop", "mobile", "tablet"]

We then specify the aggregation primitive to make where clauses for using where_primitives

In [8]: feature_matrix, feature_defs = ft.dfs(entityset=es,
   ...:                                       target_entity="customers",
   ...:                                       agg_primitives=["count", "avg_time_between"],
   ...:                                       where_primitives=["count", "avg_time_between"],
   ...:                                       trans_primitives=[])
   ...: 

In [9]: feature_matrix
Out[9]: 
            zip_code  AVG_TIME_BETWEEN(sessions.session_start)  COUNT(sessions)  AVG_TIME_BETWEEN(transactions.transaction_time)  COUNT(transactions)  AVG_TIME_BETWEEN(sessions.session_start WHERE device = mobile)  AVG_TIME_BETWEEN(sessions.session_start WHERE device = tablet)  AVG_TIME_BETWEEN(sessions.session_start WHERE device = desktop)  COUNT(sessions WHERE device = mobile)  COUNT(sessions WHERE device = tablet)  COUNT(sessions WHERE device = desktop)  AVG_TIME_BETWEEN(transactions.sessions.session_start)  AVG_TIME_BETWEEN(transactions.sessions.session_start WHERE sessions.device = mobile)  AVG_TIME_BETWEEN(transactions.sessions.session_start WHERE sessions.device = tablet)  AVG_TIME_BETWEEN(transactions.sessions.session_start WHERE sessions.device = desktop)  AVG_TIME_BETWEEN(transactions.transaction_time WHERE sessions.device = mobile)  AVG_TIME_BETWEEN(transactions.transaction_time WHERE sessions.device = tablet)  AVG_TIME_BETWEEN(transactions.transaction_time WHERE sessions.device = desktop)  COUNT(transactions WHERE sessions.device = mobile)  COUNT(transactions WHERE sessions.device = tablet)  COUNT(transactions WHERE sessions.device = desktop)
customer_id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         
5              60091                               5577.000000                6                                       363.333333                   79                                       13942.500000                                                             NaN                                                          9685.0                                                    3                                      1                                       2                                         357.500000                                             796.714286                                                                              0.000000                                                                            345.892857                                                                             809.714286                                                                       65.000000                                                                      376.071429                                                                               36                                                  14                                                  29  
4              60091                               2516.428571                8                                       168.518519                  109                                        3336.666667                                                             NaN                                                          4127.5                                                    4                                      1                                       3                                         163.101852                                             192.500000                                                                              0.000000                                                                            223.108108                                                                             206.250000                                                                       65.000000                                                                      238.918919                                                                               53                                                  18                                                  38  
1              60091                               3305.714286                8                                       192.920000                  126                                       11570.000000                                                          8807.5                                                          7150.0                                                    3                                      3                                       2                                         185.120000                                             420.727273                                                                            419.404762                                                                            275.000000                                                                             438.454545                                                                      442.619048                                                                      302.500000                                                                               56                                                  43                                                  27  
3              13244                               5096.000000                6                                       287.554348                   93                                                NaN                                                             NaN                                                          4745.0                                                    1                                      1                                       4                                         276.956522                                               0.000000                                                                              0.000000                                                                            233.360656                                                                              65.000000                                                                       65.000000                                                                      251.475410                                                                               16                                                  15                                                  62  
2              13244                               4907.500000                7                                       328.532609                   93                                        1690.000000                                                          5330.0                                                          6890.0                                                    2                                      2                                       3                                         320.054348                                              56.333333                                                                            197.407407                                                                            417.575758                                                                              82.333333                                                                      226.296296                                                                      435.303030                                                                               31                                                  28                                                  34  

Now, we have several new potentially useful features. For example, the two features below tell us how many sessions a customer completed on a tablet, and the time between those sessions.

In [10]: feature_matrix[["COUNT(sessions WHERE device = tablet)", "AVG_TIME_BETWEEN(sessions.session_start WHERE device = tablet)"]]
Out[10]: 
             COUNT(sessions WHERE device = tablet)  AVG_TIME_BETWEEN(sessions.session_start WHERE device = tablet)
customer_id                                                                                                       
5                                                1                                                NaN             
4                                                1                                                NaN             
1                                                3                                             8807.5             
3                                                1                                                NaN             
2                                                2                                             5330.0             

We can see that customer who only had 0 or 1 sessions on a tablet, had NaN values for average time between such sessions.

Encoding categorical features

Machine learning algorithms typically expect all numeric data. When Deep Feature Synthesis generates categorical features, we need to encode them.

In [11]: feature_matrix, feature_defs = ft.dfs(entityset=es,
   ....:                                       target_entity="customers",
   ....:                                       agg_primitives=["mode"],
   ....:                                       max_depth=1)
   ....: 

In [12]: feature_matrix
Out[12]: 
            zip_code MODE(sessions.device)  DAY(date_of_birth)  DAY(join_date)  MONTH(date_of_birth)  MONTH(join_date)  WEEKDAY(date_of_birth)  WEEKDAY(join_date)  YEAR(date_of_birth)  YEAR(join_date)
customer_id                                                                                                                                                                                             
5              60091                mobile                  28              17                     7                 7                       5                   5                 1984             2010
4              60091                mobile                  15               8                     8                 4                       1                   4                 2006             2011
1              60091                mobile                  18              17                     7                 4                       0                   6                 1994             2011
3              13244               desktop                  21              13                    11                 8                       4                   5                 2003             2011
2              13244               desktop                  18              15                     8                 4                       0                   6                 1986             2012

This feature matrix contains 2 categorical variables, zip_code and MODE(sessions.device). We can use the feature matrix and feature definitions to encode these categorical values. Featuretools offers functionality to apply one hot encoding to the output of DFS.

In [13]: feature_matrix_enc, features_enc = ft.encode_features(feature_matrix, feature_defs)

In [14]: feature_matrix_enc
Out[14]: 
             zip_code = 60091  zip_code = 13244  zip_code is unknown  MODE(sessions.device) = mobile  MODE(sessions.device) = desktop  MODE(sessions.device) is unknown  DAY(date_of_birth) = 18  DAY(date_of_birth) = 28  DAY(date_of_birth) = 21  DAY(date_of_birth) = 15  DAY(date_of_birth) is unknown  DAY(join_date) = 17  DAY(join_date) = 15  DAY(join_date) = 13  DAY(join_date) = 8  DAY(join_date) is unknown  MONTH(date_of_birth) = 8  MONTH(date_of_birth) = 7  MONTH(date_of_birth) = 11  MONTH(date_of_birth) is unknown  MONTH(join_date) = 4  MONTH(join_date) = 8  MONTH(join_date) = 7  MONTH(join_date) is unknown  WEEKDAY(date_of_birth) = 0  WEEKDAY(date_of_birth) = 5  WEEKDAY(date_of_birth) = 4  WEEKDAY(date_of_birth) = 1  WEEKDAY(date_of_birth) is unknown  WEEKDAY(join_date) = 6  WEEKDAY(join_date) = 5  WEEKDAY(join_date) = 4  WEEKDAY(join_date) is unknown  YEAR(date_of_birth) = 2006  YEAR(date_of_birth) = 2003  YEAR(date_of_birth) = 1994  YEAR(date_of_birth) = 1986  YEAR(date_of_birth) = 1984  YEAR(date_of_birth) is unknown  YEAR(join_date) = 2011  YEAR(join_date) = 2012  YEAR(join_date) = 2010  YEAR(join_date) is unknown
customer_id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      
5                        True             False                False                            True                            False                             False                    False                     True                    False                    False                          False                 True                False                False               False                      False                     False                      True                      False                            False                 False                 False                  True                        False                       False                        True                       False                       False                              False                   False                    True                   False                          False                       False                       False                       False                       False                        True                           False                   False                   False                    True                       False
4                        True             False                False                            True                            False                             False                    False                    False                    False                     True                          False                False                False                False                True                      False                      True                     False                      False                            False                  True                 False                 False                        False                       False                       False                       False                        True                              False                   False                   False                    True                          False                        True                       False                       False                       False                       False                           False                    True                   False                   False                       False
1                        True             False                False                            True                            False                             False                     True                    False                    False                    False                          False                 True                False                False               False                      False                     False                      True                      False                            False                  True                 False                 False                        False                        True                       False                       False                       False                              False                    True                   False                   False                          False                       False                       False                        True                       False                       False                           False                    True                   False                   False                       False
3                       False              True                False                           False                             True                             False                    False                    False                     True                    False                          False                False                False                 True               False                      False                     False                     False                       True                            False                 False                  True                 False                        False                       False                       False                        True                       False                              False                   False                    True                   False                          False                       False                        True                       False                       False                       False                           False                    True                   False                   False                       False
2                       False              True                False                           False                             True                             False                     True                    False                    False                    False                          False                False                 True                False               False                      False                      True                     False                      False                            False                  True                 False                 False                        False                        True                       False                       False                       False                              False                    True                   False                   False                          False                       False                       False                       False                        True                       False                           False                   False                    True                   False                       False

The returned feature matrix is now all numeric. Additionally, we get a new set of feature definitions that contain the encoded values.

In [15]: print(features_enc)
[<Feature: zip_code = 60091>, <Feature: zip_code = 13244>, <Feature: zip_code is unknown>, <Feature: MODE(sessions.device) = mobile>, <Feature: MODE(sessions.device) = desktop>, <Feature: MODE(sessions.device) is unknown>, <Feature: DAY(date_of_birth) = 18>, <Feature: DAY(date_of_birth) = 28>, <Feature: DAY(date_of_birth) = 21>, <Feature: DAY(date_of_birth) = 15>, <Feature: DAY(date_of_birth) is unknown>, <Feature: DAY(join_date) = 17>, <Feature: DAY(join_date) = 15>, <Feature: DAY(join_date) = 13>, <Feature: DAY(join_date) = 8>, <Feature: DAY(join_date) is unknown>, <Feature: MONTH(date_of_birth) = 8>, <Feature: MONTH(date_of_birth) = 7>, <Feature: MONTH(date_of_birth) = 11>, <Feature: MONTH(date_of_birth) is unknown>, <Feature: MONTH(join_date) = 4>, <Feature: MONTH(join_date) = 8>, <Feature: MONTH(join_date) = 7>, <Feature: MONTH(join_date) is unknown>, <Feature: WEEKDAY(date_of_birth) = 0>, <Feature: WEEKDAY(date_of_birth) = 5>, <Feature: WEEKDAY(date_of_birth) = 4>, <Feature: WEEKDAY(date_of_birth) = 1>, <Feature: WEEKDAY(date_of_birth) is unknown>, <Feature: WEEKDAY(join_date) = 6>, <Feature: WEEKDAY(join_date) = 5>, <Feature: WEEKDAY(join_date) = 4>, <Feature: WEEKDAY(join_date) is unknown>, <Feature: YEAR(date_of_birth) = 2006>, <Feature: YEAR(date_of_birth) = 2003>, <Feature: YEAR(date_of_birth) = 1994>, <Feature: YEAR(date_of_birth) = 1986>, <Feature: YEAR(date_of_birth) = 1984>, <Feature: YEAR(date_of_birth) is unknown>, <Feature: YEAR(join_date) = 2011>, <Feature: YEAR(join_date) = 2012>, <Feature: YEAR(join_date) = 2010>, <Feature: YEAR(join_date) is unknown>]

These features can be used to calculate the same encoded values on new data. For more information on feature engineering in production, read Deployment.