What is Featuretools?

Featuretools

Featuretools is a framework to perform automated feature engineering. It excels at transforming temporal and relational datasets into feature matrices for machine learning.

5 Minute Quick Start

Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions.

In [1]: import featuretools as ft

Load Mock Data

In [2]: data = ft.demo.load_mock_customer()

Prepare data

In this toy dataset, there are 3 tables. Each table is called an entity in Featuretools.

  • customers: unique customers who had sessions

  • sessions: unique sessions and associated attributes

  • transactions: list of events in this session

In [3]: customers_df = data["customers"]

In [4]: customers_df
Out[4]: 
   customer_id zip_code           join_date date_of_birth
0            1    60091 2011-04-17 10:48:33    1994-07-18
1            2    13244 2012-04-15 23:31:04    1986-08-18
2            3    13244 2011-08-13 15:42:34    2003-11-21
3            4    60091 2011-04-08 20:08:14    2006-08-15
4            5    60091 2010-07-17 05:27:50    1984-07-28

In [5]: sessions_df = data["sessions"]

In [6]: sessions_df.sample(5)
Out[6]: 
    session_id  customer_id   device       session_start
13          14            1   tablet 2014-01-01 03:28:00
6            7            3   tablet 2014-01-01 01:39:40
1            2            5   mobile 2014-01-01 00:17:20
28          29            1   mobile 2014-01-01 07:10:05
24          25            3  desktop 2014-01-01 05:59:40

In [7]: transactions_df = data["transactions"]

In [8]: transactions_df.sample(5)
Out[8]: 
     transaction_id  session_id    transaction_time product_id  amount
74              232           5 2014-01-01 01:20:10          1  139.20
231              27          17 2014-01-01 04:10:15          2   90.79
434              36          31 2014-01-01 07:50:10          3   62.35
420              56          30 2014-01-01 07:35:00          3   72.70
54              444           4 2014-01-01 00:58:30          4   43.59

First, we specify a dictionary with all the entities in our dataset.

In [9]: entities = {
   ...:    "customers" : (customers_df, "customer_id"),
   ...:    "sessions" : (sessions_df, "session_id", "session_start"),
   ...:    "transactions" : (transactions_df, "transaction_id", "transaction_time")
   ...: }
   ...: 

Second, we specify how the entities are related. When two entities have a one-to-many relationship, we call the “one” enitity, the “parent entity”. A relationship between a parent and child is defined like this:

(parent_entity, parent_variable, child_entity, child_variable)

In this dataset we have two relationships

In [10]: relationships = [("sessions", "session_id", "transactions", "session_id"),
   ....:                  ("customers", "customer_id", "sessions", "customer_id")]
   ....: 

Note

To manage setting up entities and relationships, we recommend using the EntitySet class which offers convenient APIs for managing data like this. See Representing Data with EntitySets for more information.

Run Deep Feature Synthesis

A minimal input to DFS is a set of entities, a list of relationships, and the “target_entity” to calculate features for. The ouput of DFS is a feature matrix and the corresponding list of feature definitions.

Let’s first create a feature matrix for each customer in the data

In [11]: feature_matrix_customers, features_defs = ft.dfs(entities=entities,
   ....:                                                  relationships=relationships,
   ....:                                                  target_entity="customers")
   ....: 

In [12]: feature_matrix_customers
Out[12]: 
            zip_code  COUNT(sessions)  NUM_UNIQUE(sessions.device) MODE(sessions.device)  SUM(transactions.amount)  STD(transactions.amount)  MAX(transactions.amount)  SKEW(transactions.amount)  MIN(transactions.amount)  MEAN(transactions.amount)  COUNT(transactions)  NUM_UNIQUE(transactions.product_id)  MODE(transactions.product_id)  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)  SUM(sessions.MAX(transactions.amount))  SUM(sessions.NUM_UNIQUE(transactions.product_id))  SUM(sessions.MEAN(transactions.amount))  SUM(sessions.STD(transactions.amount))  SUM(sessions.SKEW(transactions.amount))  SUM(sessions.MIN(transactions.amount))  STD(sessions.MAX(transactions.amount))  STD(sessions.SUM(transactions.amount))  STD(sessions.COUNT(transactions))  STD(sessions.NUM_UNIQUE(transactions.product_id))  STD(sessions.MEAN(transactions.amount))  STD(sessions.SKEW(transactions.amount))  STD(sessions.MIN(transactions.amount))  MAX(sessions.SUM(transactions.amount))  MAX(sessions.COUNT(transactions))  MAX(sessions.NUM_UNIQUE(transactions.product_id))  MAX(sessions.MEAN(transactions.amount))  MAX(sessions.STD(transactions.amount))  MAX(sessions.SKEW(transactions.amount))  MAX(sessions.MIN(transactions.amount))  SKEW(sessions.MAX(transactions.amount))  SKEW(sessions.SUM(transactions.amount))  SKEW(sessions.COUNT(transactions))  SKEW(sessions.NUM_UNIQUE(transactions.product_id))  SKEW(sessions.MEAN(transactions.amount))  SKEW(sessions.STD(transactions.amount))  SKEW(sessions.MIN(transactions.amount))  MIN(sessions.MAX(transactions.amount))  MIN(sessions.SUM(transactions.amount))  MIN(sessions.COUNT(transactions))  MIN(sessions.NUM_UNIQUE(transactions.product_id))  MIN(sessions.MEAN(transactions.amount))  MIN(sessions.STD(transactions.amount))  MIN(sessions.SKEW(transactions.amount))  MEAN(sessions.MAX(transactions.amount))  MEAN(sessions.SUM(transactions.amount))  MEAN(sessions.COUNT(transactions))  MEAN(sessions.NUM_UNIQUE(transactions.product_id))  MEAN(sessions.MEAN(transactions.amount))  MEAN(sessions.STD(transactions.amount))  MEAN(sessions.SKEW(transactions.amount))  MEAN(sessions.MIN(transactions.amount))  NUM_UNIQUE(sessions.MODE(transactions.product_id))  NUM_UNIQUE(sessions.WEEKDAY(session_start))  NUM_UNIQUE(sessions.YEAR(session_start))  NUM_UNIQUE(sessions.DAY(session_start))  NUM_UNIQUE(sessions.MONTH(session_start))  MODE(sessions.MODE(transactions.product_id))  MODE(sessions.WEEKDAY(session_start))  MODE(sessions.YEAR(session_start))  MODE(sessions.DAY(session_start))  MODE(sessions.MONTH(session_start))  NUM_UNIQUE(transactions.sessions.customer_id)  NUM_UNIQUE(transactions.sessions.device)  MODE(transactions.sessions.customer_id) MODE(transactions.sessions.device)
customer_id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          
1              60091                8                            3                mobile                   9025.62                 40.442059                    139.43                   0.019698                      5.81                  71.631905                  126                                    5                              4              17                  18             2011                 1994                 4                     7                   6                       0                                 1057.97                                                 40                               582.193117                              312.745952                                -0.476122                                   78.59                                7.322191                              279.510713                           4.062019                                           0.000000                                13.759314                                 0.589386                                6.954507                                 1613.93                                 25                                                  5                                88.755625                               46.905665                                 0.640252                                   26.36                                -0.780493                                 0.778170                            1.946018                                           0.000000                                  -0.424949                                -0.312355                                 2.440005                                  118.90                                  809.97                                 12                                                  5                                50.623125                               30.450261                                -1.038434                               132.246250                              1128.202500                           15.750000                                           5.000000                                  72.774140                                39.093244                                 -0.059515                                 9.823750                                                  4                                             1                                         1                                        1                                          1                                             4                                      2                                2014                                  1                                    1                                              1                                         3                                        1                             mobile
2              13244                7                            3               desktop                   7200.28                 37.705178                    146.81                   0.098259                      8.73                  77.422366                   93                                    5                              4              15                  18             2012                 1986                 4                     8                   6                       0                                  931.63                                                 35                               548.905851                              258.700528                                -0.277640                                  154.60                               17.221593                              251.609234                           3.450328                                           0.000000                                11.477071                                 0.509798                               15.874374                                 1320.64                                 18                                                  5                                96.581000                               47.935920                                 0.755711                                   56.46                                -1.539467                                -0.440929                           -0.303276                                           0.000000                                   0.235296                                 0.013087                                 2.154929                                  100.04                                  634.84                                  8                                                  5                                61.910000                               27.839228                                -0.763603                               133.090000                              1028.611429                           13.285714                                           5.000000                                  78.415122                                36.957218                                 -0.039663                                22.085714                                                  4                                             1                                         1                                        1                                          1                                             3                                      2                                2014                                  1                                    1                                              1                                         3                                        2                            desktop
3              13244                6                            3               desktop                   6236.62                 43.683296                    149.15                   0.418230                      5.89                  67.060430                   93                                    5                              1              13                  21             2011                 2003                 8                    11                   5                       4                                  847.63                                                 29                               405.237462                              257.299895                                 2.286086                                   66.21                               10.724241                              219.021420                           2.428992                                           0.408248                                11.174282                                 0.429374                                5.424407                                 1477.97                                 18                                                  5                                82.109444                               50.110120                                 0.854976                                   20.06                                -0.941078                                 2.246479                           -1.507217                                          -2.449490                                   0.678544                                -0.245703                                 1.000771                                  126.74                                  889.21                                 11                                                  4                                55.579412                               35.704680                                -0.289466                               141.271667                              1039.436667                           15.500000                                           4.833333                                  67.539577                                42.883316                                  0.381014                                11.035000                                                  4                                             1                                         1                                        1                                          1                                             1                                      2                                2014                                  1                                    1                                              1                                         3                                        3                            desktop
4              60091                8                            3                mobile                   8727.68                 45.068765                    149.95                  -0.036348                      5.73                  80.070459                  109                                    5                              2               8                  15             2011                 2006                 4                     8                   4                       1                                 1157.99                                                 37                               649.657515                              356.125829                                 0.002764                                  131.51                                3.514421                              235.992478                           3.335416                                           0.517549                                13.027258                                 0.387884                               16.960575                                 1351.46                                 18                                                  5                               110.450000                               54.293903                                 0.382868                                   54.83                                 0.027256                                -0.391805                            0.282488                                          -0.644061                                   1.980948                                -1.065663                                 2.103510                                  139.20                                  771.68                                 10                                                  4                                70.638182                               29.026424                                -0.711744                               144.748750                              1090.960000                           13.625000                                           4.625000                                  81.207189                                44.515729                                  0.000346                                16.438750                                                  5                                             1                                         1                                        1                                          1                                             1                                      2                                2014                                  1                                    1                                              1                                         3                                        4                             mobile
5              60091                6                            3                mobile                   6349.66                 44.095630                    149.02                  -0.025941                      7.55                  80.375443                   79                                    5                              5              17                  28             2010                 1984                 7                     7                   5                       5                                  839.76                                                 30                               472.231119                              259.873954                                 0.014384                                   86.49                                7.928001                              402.775486                           3.600926                                           0.000000                                11.007471                                 0.415426                                4.961414                                 1700.67                                 18                                                  5                                94.481667                               51.149250                                 0.602209                                   20.65                                -0.333796                                 0.472342                           -0.317685                                           0.000000                                   0.335175                                 0.204548                                -0.470410                                  128.51                                  543.18                                  8                                                  5                                66.666667                               36.734681                                -0.539060                               139.960000                              1058.276667                           13.166667                                           5.000000                                  78.705187                                43.312326                                  0.002397                                14.415000                                                  5                                             1                                         1                                        1                                          1                                             3                                      2                                2014                                  1                                    1                                              1                                         3                                        5                             mobile

We now have dozens of new features to describe a customer’s behavior.

Change target entity

One of the reasons DFS is so powerful is that it can create a feature matrix for any entity in our data. For example, if we wanted to build features for sessions.

In [13]: feature_matrix_sessions, features_defs = ft.dfs(entities=entities,
   ....:                                                 relationships=relationships,
   ....:                                                 target_entity="sessions")
   ....: 

In [14]: feature_matrix_sessions.head(5)
Out[14]: 
            customer_id   device  SUM(transactions.amount)  STD(transactions.amount)  MAX(transactions.amount)  SKEW(transactions.amount)  MIN(transactions.amount)  MEAN(transactions.amount)  COUNT(transactions)  NUM_UNIQUE(transactions.product_id)  MODE(transactions.product_id)  DAY(session_start)  YEAR(session_start)  MONTH(session_start)  WEEKDAY(session_start) customers.zip_code  NUM_UNIQUE(transactions.DAY(transaction_time))  NUM_UNIQUE(transactions.WEEKDAY(transaction_time))  NUM_UNIQUE(transactions.MONTH(transaction_time))  NUM_UNIQUE(transactions.YEAR(transaction_time))  MODE(transactions.DAY(transaction_time))  MODE(transactions.WEEKDAY(transaction_time))  MODE(transactions.MONTH(transaction_time))  MODE(transactions.YEAR(transaction_time))  customers.COUNT(sessions)  customers.NUM_UNIQUE(sessions.device) customers.MODE(sessions.device)  customers.SUM(transactions.amount)  customers.STD(transactions.amount)  customers.MAX(transactions.amount)  customers.SKEW(transactions.amount)  customers.MIN(transactions.amount)  customers.MEAN(transactions.amount)  customers.COUNT(transactions)  customers.NUM_UNIQUE(transactions.product_id)  customers.MODE(transactions.product_id)  customers.DAY(join_date)  customers.DAY(date_of_birth)  customers.YEAR(join_date)  customers.YEAR(date_of_birth)  customers.MONTH(join_date)  customers.MONTH(date_of_birth)  customers.WEEKDAY(join_date)  customers.WEEKDAY(date_of_birth)
session_id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              
1                     2  desktop                   1229.01                 41.600976                    141.66                   0.295458                     20.91                  76.813125                   16                                    5                              3                   1                 2014                     1                       2              13244                                               1                                                  1                                                  1                                                1                                         1                                             2                                           1                                       2014                          7                                      3                         desktop                             7200.28                           37.705178                              146.81                             0.098259                                8.73                            77.422366                             93                                              5                                        4                        15                            18                       2012                           1986                           4                               8                             6                                 0
2                     5   mobile                    746.96                 45.893591                    135.25                  -0.160550                      9.32                  74.696000                   10                                    5                              5                   1                 2014                     1                       2              60091                                               1                                                  1                                                  1                                                1                                         1                                             2                                           1                                       2014                          6                                      3                          mobile                             6349.66                           44.095630                              149.02                            -0.025941                                7.55                            80.375443                             79                                              5                                        5                        17                            28                       2010                           1984                           7                               7                             5                                 5
3                     4   mobile                   1329.00                 46.240016                    147.73                  -0.324012                      8.70                  88.600000                   15                                    5                              1                   1                 2014                     1                       2              60091                                               1                                                  1                                                  1                                                1                                         1                                             2                                           1                                       2014                          8                                      3                          mobile                             8727.68                           45.068765                              149.95                            -0.036348                                5.73                            80.070459                            109                                              5                                        2                         8                            15                       2011                           2006                           4                               8                             4                                 1
4                     1   mobile                   1613.93                 40.187205                    129.00                   0.234349                      6.29                  64.557200                   25                                    5                              5                   1                 2014                     1                       2              60091                                               1                                                  1                                                  1                                                1                                         1                                             2                                           1                                       2014                          8                                      3                          mobile                             9025.62                           40.442059                              139.43                             0.019698                                5.81                            71.631905                            126                                              5                                        4                        17                            18                       2011                           1994                           4                               7                             6                                 0
5                     4   mobile                    777.02                 48.918663                    139.20                   0.336381                      7.43                  70.638182                   11                                    5                              5                   1                 2014                     1                       2              60091                                               1                                                  1                                                  1                                                1                                         1                                             2                                           1                                       2014                          8                                      3                          mobile                             8727.68                           45.068765                              149.95                            -0.036348                                5.73                            80.070459                            109                                              5                                        2                         8                            15                       2011                           2006                           4                               8                             4                                 1

What’s next?