# 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 [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
1                                           0.119048
2                                           0.129032
3                                           0.182796
4                                           0.220183
5                                           0.227848


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  COUNT(sessions)  AVG_TIME_BETWEEN(sessions.session_start)  COUNT(transactions)  AVG_TIME_BETWEEN(transactions.transaction_time)  COUNT(sessions WHERE device = desktop)  COUNT(sessions WHERE device = tablet)  COUNT(sessions WHERE device = mobile)  AVG_TIME_BETWEEN(sessions.session_start WHERE device = desktop)  AVG_TIME_BETWEEN(sessions.session_start WHERE device = tablet)  AVG_TIME_BETWEEN(sessions.session_start WHERE device = mobile)
customer_id
1              60091                8                               3305.714286                  126                                       192.920000                                       2                                      3                                      3                                             7150.0                                                           8807.5                                                    11570.000000
2              13244                7                               4907.500000                   93                                       328.532609                                       3                                      2                                      2                                             6890.0                                                           5330.0                                                     1690.000000
3              13244                6                               5096.000000                   93                                       287.554348                                       4                                      1                                      1                                             4745.0                                                              NaN                                                             NaN
4              60091                8                               2516.428571                  109                                       168.518519                                       3                                      1                                      4                                             4127.5                                                              NaN                                                     3336.666667
5              60091                6                               5577.000000                   79                                       363.333333                                       2                                      1                                      3                                             9685.0                                                              NaN                                                    13942.500000


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
1                                                3                                             8807.5
2                                                2                                             5330.0
3                                                1                                                NaN
4                                                1                                                NaN
5                                                1                                                NaN


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(join_date)  DAY(date_of_birth)  YEAR(join_date)  YEAR(date_of_birth)  MONTH(join_date)  MONTH(date_of_birth)  WEEKDAY(join_date)  WEEKDAY(date_of_birth)
customer_id
1              60091                mobile              17                  18             2011                 1994                 4                     7                   6                       0
2              13244               desktop              15                  18             2012                 1986                 4                     8                   6                       0
3              13244               desktop              13                  21             2011                 2003                 8                    11                   5                       4
4              60091                mobile               8                  15             2011                 2006                 4                     8                   4                       1
5              60091                mobile              17                  28             2010                 1984                 7                     7                   5                       5


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(join_date) = 17  DAY(join_date) = 15  DAY(join_date) = 13  DAY(join_date) = 8  DAY(join_date) 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  YEAR(join_date) = 2011  YEAR(join_date) = 2012  YEAR(join_date) = 2010  YEAR(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  MONTH(join_date) = 4  MONTH(join_date) = 8  MONTH(join_date) = 7  MONTH(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  WEEKDAY(join_date) = 6  WEEKDAY(join_date) = 5  WEEKDAY(join_date) = 4  WEEKDAY(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
customer_id
1                           1                 0                    0                               1                                0                                 0                    1                    0                    0                   0                          0                        1                        0                        0                        0                              0                       1                       0                       0                           0                           0                           0                           1                           0                           0                               0                     1                     0                     0                            0                         0                         1                          0                                0                       1                       0                       0                              0                           1                           0                           0                           0                                  0
2                           0                 1                    0                               0                                1                                 0                    0                    1                    0                   0                          0                        1                        0                        0                        0                              0                       0                       1                       0                           0                           0                           0                           0                           1                           0                               0                     1                     0                     0                            0                         1                         0                          0                                0                       1                       0                       0                              0                           1                           0                           0                           0                                  0
3                           0                 1                    0                               0                                1                                 0                    0                    0                    1                   0                          0                        0                        0                        1                        0                              0                       1                       0                       0                           0                           0                           1                           0                           0                           0                               0                     0                     1                     0                            0                         0                         0                          1                                0                       0                       1                       0                              0                           0                           0                           1                           0                                  0
4                           1                 0                    0                               1                                0                                 0                    0                    0                    0                   1                          0                        0                        0                        0                        1                              0                       1                       0                       0                           0                           1                           0                           0                           0                           0                               0                     1                     0                     0                            0                         1                         0                          0                                0                       0                       0                       1                              0                           0                           0                           0                           1                                  0
5                           1                 0                    0                               1                                0                                 0                    1                    0                    0                   0                          0                        0                        1                        0                        0                              0                       0                       0                       1                           0                           0                           0                           0                           0                           1                               0                     0                     0                     1                            0                         0                         1                          0                                0                       0                       1                       0                              0                           0                           1                           0                           0                                  0


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(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: 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: YEAR(join_date) = 2011>, <Feature: YEAR(join_date) = 2012>, <Feature: YEAR(join_date) = 2010>, <Feature: YEAR(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: MONTH(join_date) = 4>, <Feature: MONTH(join_date) = 8>, <Feature: MONTH(join_date) = 7>, <Feature: MONTH(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: WEEKDAY(join_date) = 6>, <Feature: WEEKDAY(join_date) = 5>, <Feature: WEEKDAY(join_date) = 4>, <Feature: WEEKDAY(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>]


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