What is 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.MIN(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MIN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MAX(transactions.amount)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MEAN(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) MODE(sessions.WEEKDAY(session_start)) MODE(sessions.MODE(transactions.product_id)) MODE(sessions.DAY(session_start)) MODE(sessions.MONTH(session_start)) MODE(sessions.YEAR(session_start)) NUM_UNIQUE(transactions.sessions.customer_id) NUM_UNIQUE(transactions.sessions.device) MODE(transactions.sessions.customer_id) MODE(transactions.sessions.device)
customer_id
1 60091 8 3 mobile 9025.62 40.442059 139.43 0.019698 5.81 71.631905 126 5 4 18 17 1994 2011 7 4 0 6 78.59 -0.476122 312.745952 40 1057.97 582.193117 4.062019 6.954507 0.589386 279.510713 0.000000 7.322191 13.759314 25 26.36 0.640252 1613.93 46.905665 5 88.755625 1.946018 2.440005 0.778170 -0.312355 0.000000 -0.780493 -0.424949 12 -1.038434 809.97 30.450261 5 118.90 50.623125 15.750000 9.823750 -0.059515 1128.202500 39.093244 5.000000 132.246250 72.774140 1 4 1 1 1 2 4 1 1 2014 1 3 1 mobile
2 13244 7 3 desktop 7200.28 37.705178 146.81 0.098259 8.73 77.422366 93 5 4 18 15 1986 2012 8 4 0 6 154.60 -0.277640 258.700528 35 931.63 548.905851 3.450328 15.874374 0.509798 251.609234 0.000000 17.221593 11.477071 18 56.46 0.755711 1320.64 47.935920 5 96.581000 -0.303276 2.154929 -0.440929 0.013087 0.000000 -1.539467 0.235296 8 -0.763603 634.84 27.839228 5 100.04 61.910000 13.285714 22.085714 -0.039663 1028.611429 36.957218 5.000000 133.090000 78.415122 1 4 1 1 1 2 3 1 1 2014 1 3 2 desktop
3 13244 6 3 desktop 6236.62 43.683296 149.15 0.418230 5.89 67.060430 93 5 1 21 13 2003 2011 11 8 4 5 66.21 2.286086 257.299895 29 847.63 405.237462 2.428992 5.424407 0.429374 219.021420 0.408248 10.724241 11.174282 18 20.06 0.854976 1477.97 50.110120 5 82.109444 -1.507217 1.000771 2.246479 -0.245703 -2.449490 -0.941078 0.678544 11 -0.289466 889.21 35.704680 4 126.74 55.579412 15.500000 11.035000 0.381014 1039.436667 42.883316 4.833333 141.271667 67.539577 1 4 1 1 1 2 1 1 1 2014 1 3 3 desktop
4 60091 8 3 mobile 8727.68 45.068765 149.95 -0.036348 5.73 80.070459 109 5 2 15 8 2006 2011 8 4 1 4 131.51 0.002764 356.125829 37 1157.99 649.657515 3.335416 16.960575 0.387884 235.992478 0.517549 3.514421 13.027258 18 54.83 0.382868 1351.46 54.293903 5 110.450000 0.282488 2.103510 -0.391805 -1.065663 -0.644061 0.027256 1.980948 10 -0.711744 771.68 29.026424 4 139.20 70.638182 13.625000 16.438750 0.000346 1090.960000 44.515729 4.625000 144.748750 81.207189 1 5 1 1 1 2 1 1 1 2014 1 3 4 mobile
5 60091 6 3 mobile 6349.66 44.095630 149.02 -0.025941 7.55 80.375443 79 5 5 28 17 1984 2010 7 7 5 5 86.49 0.014384 259.873954 30 839.76 472.231119 3.600926 4.961414 0.415426 402.775486 0.000000 7.928001 11.007471 18 20.65 0.602209 1700.67 51.149250 5 94.481667 -0.317685 -0.470410 0.472342 0.204548 0.000000 -0.333796 0.335175 8 -0.539060 543.18 36.734681 5 128.51 66.666667 13.166667 14.415000 0.002397 1058.276667 43.312326 5.000000 139.960000 78.705187 1 5 1 1 1 2 3 1 1 2014 1 3 5 mobile
We now have dozens of new features to describe a customer’s behavior.
Change target entity¶
One of the reasons DFS is so powerful is that it can create a feature matrix for any entity in our data. For example, if we wanted to build features for sessions.
In [13]: feature_matrix_sessions, features_defs = ft.dfs(entities=entities,
....: relationships=relationships,
....: target_entity="sessions")
....:
In [14]: feature_matrix_sessions.head(5)
Out[14]:
customer_id device SUM(transactions.amount) STD(transactions.amount) MAX(transactions.amount) SKEW(transactions.amount) MIN(transactions.amount) MEAN(transactions.amount) COUNT(transactions) NUM_UNIQUE(transactions.product_id) MODE(transactions.product_id) DAY(session_start) YEAR(session_start) MONTH(session_start) WEEKDAY(session_start) customers.zip_code NUM_UNIQUE(transactions.MONTH(transaction_time)) NUM_UNIQUE(transactions.WEEKDAY(transaction_time)) NUM_UNIQUE(transactions.YEAR(transaction_time)) NUM_UNIQUE(transactions.DAY(transaction_time)) MODE(transactions.MONTH(transaction_time)) MODE(transactions.WEEKDAY(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(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 2 2014 1 7 3 desktop 7200.28 37.705178 146.81 0.098259 8.73 77.422366 93 5 4 18 15 1986 2012 8 4 0 6
2 5 mobile 746.96 45.893591 135.25 -0.160550 9.32 74.696000 10 5 5 1 2014 1 2 60091 1 1 1 1 1 2 2014 1 6 3 mobile 6349.66 44.095630 149.02 -0.025941 7.55 80.375443 79 5 5 28 17 1984 2010 7 7 5 5
3 4 mobile 1329.00 46.240016 147.73 -0.324012 8.70 88.600000 15 5 1 1 2014 1 2 60091 1 1 1 1 1 2 2014 1 8 3 mobile 8727.68 45.068765 149.95 -0.036348 5.73 80.070459 109 5 2 15 8 2006 2011 8 4 1 4
4 1 mobile 1613.93 40.187205 129.00 0.234349 6.29 64.557200 25 5 5 1 2014 1 2 60091 1 1 1 1 1 2 2014 1 8 3 mobile 9025.62 40.442059 139.43 0.019698 5.81 71.631905 126 5 4 18 17 1994 2011 7 4 0 6
5 4 mobile 777.02 48.918663 139.20 0.336381 7.43 70.638182 11 5 5 1 2014 1 2 60091 1 1 1 1 1 2 2014 1 8 3 mobile 8727.68 45.068765 149.95 -0.036348 5.73 80.070459 109 5 2 15 8 2006 2011 8 4 1 4
What’s next?¶
Learn about Representing Data with EntitySets
Apply automated feature engineering with Deep Feature Synthesis
Explore runnable demos based on real world use cases
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