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

What’s next?