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

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.YEAR(transaction_time))  NUM_UNIQUE(transactions.DAY(transaction_time))  NUM_UNIQUE(transactions.MONTH(transaction_time))  MODE(transactions.WEEKDAY(transaction_time))  MODE(transactions.YEAR(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                                             2                                       2014                                         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                                             2                                       2014                                         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                                             2                                       2014                                         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                                             2                                       2014                                         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                                             2                                       2014                                         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?