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

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

Change target entity

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

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

In [14]: feature_matrix_sessions.head(5)
Out[14]: 
            customer_id   device  SUM(transactions.amount)  STD(transactions.amount)  MAX(transactions.amount)  SKEW(transactions.amount)  MIN(transactions.amount)  MEAN(transactions.amount)  COUNT(transactions)  NUM_UNIQUE(transactions.product_id)  MODE(transactions.product_id)  DAY(session_start)  YEAR(session_start)  MONTH(session_start)  WEEKDAY(session_start) customers.zip_code  NUM_UNIQUE(transactions.WEEKDAY(transaction_time))  NUM_UNIQUE(transactions.MONTH(transaction_time))  NUM_UNIQUE(transactions.YEAR(transaction_time))  NUM_UNIQUE(transactions.DAY(transaction_time))  MODE(transactions.WEEKDAY(transaction_time))  MODE(transactions.MONTH(transaction_time))  MODE(transactions.YEAR(transaction_time))  MODE(transactions.DAY(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                                             2                                           1                                       2014                                         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                                             2                                           1                                       2014                                         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                                             2                                           1                                       2014                                         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                                             2                                           1                                       2014                                         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                                             2                                           1                                       2014                                         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?