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.SKEW(transactions.amount))  SUM(sessions.MEAN(transactions.amount))  SUM(sessions.MAX(transactions.amount))  SUM(sessions.MIN(transactions.amount))  SUM(sessions.STD(transactions.amount))  SUM(sessions.NUM_UNIQUE(transactions.product_id))  STD(sessions.SKEW(transactions.amount))  STD(sessions.MEAN(transactions.amount))  STD(sessions.SUM(transactions.amount))  STD(sessions.COUNT(transactions))  STD(sessions.MAX(transactions.amount))  STD(sessions.MIN(transactions.amount))  STD(sessions.NUM_UNIQUE(transactions.product_id))  MAX(sessions.SKEW(transactions.amount))  MAX(sessions.MEAN(transactions.amount))  MAX(sessions.SUM(transactions.amount))  MAX(sessions.COUNT(transactions))  MAX(sessions.MIN(transactions.amount))  MAX(sessions.STD(transactions.amount))  MAX(sessions.NUM_UNIQUE(transactions.product_id))  SKEW(sessions.MEAN(transactions.amount))  SKEW(sessions.SUM(transactions.amount))  SKEW(sessions.COUNT(transactions))  SKEW(sessions.MAX(transactions.amount))  SKEW(sessions.MIN(transactions.amount))  SKEW(sessions.STD(transactions.amount))  SKEW(sessions.NUM_UNIQUE(transactions.product_id))  MIN(sessions.SKEW(transactions.amount))  MIN(sessions.MEAN(transactions.amount))  MIN(sessions.SUM(transactions.amount))  MIN(sessions.COUNT(transactions))  MIN(sessions.MAX(transactions.amount))  MIN(sessions.STD(transactions.amount))  MIN(sessions.NUM_UNIQUE(transactions.product_id))  MEAN(sessions.SKEW(transactions.amount))  MEAN(sessions.MEAN(transactions.amount))  MEAN(sessions.SUM(transactions.amount))  MEAN(sessions.COUNT(transactions))  MEAN(sessions.MAX(transactions.amount))  MEAN(sessions.MIN(transactions.amount))  MEAN(sessions.STD(transactions.amount))  MEAN(sessions.NUM_UNIQUE(transactions.product_id))  NUM_UNIQUE(sessions.MONTH(session_start))  NUM_UNIQUE(sessions.YEAR(session_start))  NUM_UNIQUE(sessions.MODE(transactions.product_id))  NUM_UNIQUE(sessions.DAY(session_start))  NUM_UNIQUE(sessions.WEEKDAY(session_start))  MODE(sessions.MONTH(session_start))  MODE(sessions.YEAR(session_start))  MODE(sessions.MODE(transactions.product_id))  MODE(sessions.DAY(session_start))  MODE(sessions.WEEKDAY(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                                -0.476122                               582.193117                                 1057.97                                   78.59                              312.745952                                                 40                                 0.589386                                13.759314                              279.510713                           4.062019                                7.322191                                6.954507                                           0.000000                                 0.640252                                88.755625                                 1613.93                                 25                                   26.36                               46.905665                                                  5                                 -0.424949                                 0.778170                            1.946018                                -0.780493                                 2.440005                                -0.312355                                           0.000000                                 -1.038434                                50.623125                                  809.97                                 12                                  118.90                               30.450261                                                  5                                 -0.059515                                 72.774140                              1128.202500                           15.750000                               132.246250                                 9.823750                                39.093244                                           5.000000                                           1                                         1                                                  4                                         1                                            1                                    1                                2014                                             4                                  1                                      2                                              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                                -0.277640                               548.905851                                  931.63                                  154.60                              258.700528                                                 35                                 0.509798                                11.477071                              251.609234                           3.450328                               17.221593                               15.874374                                           0.000000                                 0.755711                                96.581000                                 1320.64                                 18                                   56.46                               47.935920                                                  5                                  0.235296                                -0.440929                           -0.303276                                -1.539467                                 2.154929                                 0.013087                                           0.000000                                 -0.763603                                61.910000                                  634.84                                  8                                  100.04                               27.839228                                                  5                                 -0.039663                                 78.415122                              1028.611429                           13.285714                               133.090000                                22.085714                                36.957218                                           5.000000                                           1                                         1                                                  4                                         1                                            1                                    1                                2014                                             3                                  1                                      2                                              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                                 2.286086                               405.237462                                  847.63                                   66.21                              257.299895                                                 29                                 0.429374                                11.174282                              219.021420                           2.428992                               10.724241                                5.424407                                           0.408248                                 0.854976                                82.109444                                 1477.97                                 18                                   20.06                               50.110120                                                  5                                  0.678544                                 2.246479                           -1.507217                                -0.941078                                 1.000771                                -0.245703                                          -2.449490                                 -0.289466                                55.579412                                  889.21                                 11                                  126.74                               35.704680                                                  4                                  0.381014                                 67.539577                              1039.436667                           15.500000                               141.271667                                11.035000                                42.883316                                           4.833333                                           1                                         1                                                  4                                         1                                            1                                    1                                2014                                             1                                  1                                      2                                              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                                 0.002764                               649.657515                                 1157.99                                  131.51                              356.125829                                                 37                                 0.387884                                13.027258                              235.992478                           3.335416                                3.514421                               16.960575                                           0.517549                                 0.382868                               110.450000                                 1351.46                                 18                                   54.83                               54.293903                                                  5                                  1.980948                                -0.391805                            0.282488                                 0.027256                                 2.103510                                -1.065663                                          -0.644061                                 -0.711744                                70.638182                                  771.68                                 10                                  139.20                               29.026424                                                  4                                  0.000346                                 81.207189                              1090.960000                           13.625000                               144.748750                                16.438750                                44.515729                                           4.625000                                           1                                         1                                                  5                                         1                                            1                                    1                                2014                                             1                                  1                                      2                                              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                                 0.014384                               472.231119                                  839.76                                   86.49                              259.873954                                                 30                                 0.415426                                11.007471                              402.775486                           3.600926                                7.928001                                4.961414                                           0.000000                                 0.602209                                94.481667                                 1700.67                                 18                                   20.65                               51.149250                                                  5                                  0.335175                                 0.472342                           -0.317685                                -0.333796                                -0.470410                                 0.204548                                           0.000000                                 -0.539060                                66.666667                                  543.18                                  8                                  128.51                               36.734681                                                  5                                  0.002397                                 78.705187                              1058.276667                           13.166667                               139.960000                                14.415000                                43.312326                                           5.000000                                           1                                         1                                                  5                                         1                                            1                                    1                                2014                                             3                                  1                                      2                                              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.YEAR(transaction_time))  NUM_UNIQUE(transactions.WEEKDAY(transaction_time))  NUM_UNIQUE(transactions.DAY(transaction_time))  NUM_UNIQUE(transactions.MONTH(transaction_time))  MODE(transactions.YEAR(transaction_time))  MODE(transactions.WEEKDAY(transaction_time))  MODE(transactions.DAY(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(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                                       2014                                             2                                         1                                           1                          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                                       2014                                             2                                         1                                           1                          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                                       2014                                             2                                         1                                           1                          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                                       2014                                             2                                         1                                           1                          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                                       2014                                             2                                         1                                           1                          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?