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