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.MEAN(transactions.amount)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.STD(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MEAN(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.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.STD(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.MEAN(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.STD(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.MEAN(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.MIN(transactions.amount)) MEAN(sessions.STD(transactions.amount)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.DAY(session_start)) MODE(sessions.MONTH(session_start)) MODE(sessions.WEEKDAY(session_start)) MODE(sessions.YEAR(session_start)) MODE(sessions.MODE(transactions.product_id)) 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 18 17 1994 2011 7 4 0 6 582.193117 1057.97 -0.476122 40 78.59 312.745952 279.510713 13.759314 4.062019 7.322191 0.589386 0.000000 6.954507 1613.93 88.755625 25 0.640252 5 26.36 46.905665 0.778170 -0.424949 1.946018 -0.780493 0.000000 2.440005 -0.312355 809.97 50.623125 12 118.90 -1.038434 5 30.450261 1128.202500 72.774140 15.750000 132.246250 -0.059515 5.000000 9.823750 39.093244 1 1 1 4 1 1 2 2014 4 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 18 15 1986 2012 8 4 0 6 548.905851 931.63 -0.277640 35 154.60 258.700528 251.609234 11.477071 3.450328 17.221593 0.509798 0.000000 15.874374 1320.64 96.581000 18 0.755711 5 56.46 47.935920 -0.440929 0.235296 -0.303276 -1.539467 0.000000 2.154929 0.013087 634.84 61.910000 8 100.04 -0.763603 5 27.839228 1028.611429 78.415122 13.285714 133.090000 -0.039663 5.000000 22.085714 36.957218 1 1 1 4 1 1 2 2014 3 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 21 13 2003 2011 11 8 4 5 405.237462 847.63 2.286086 29 66.21 257.299895 219.021420 11.174282 2.428992 10.724241 0.429374 0.408248 5.424407 1477.97 82.109444 18 0.854976 5 20.06 50.110120 2.246479 0.678544 -1.507217 -0.941078 -2.449490 1.000771 -0.245703 889.21 55.579412 11 126.74 -0.289466 4 35.704680 1039.436667 67.539577 15.500000 141.271667 0.381014 4.833333 11.035000 42.883316 1 1 1 4 1 1 2 2014 1 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 15 8 2006 2011 8 4 1 4 649.657515 1157.99 0.002764 37 131.51 356.125829 235.992478 13.027258 3.335416 3.514421 0.387884 0.517549 16.960575 1351.46 110.450000 18 0.382868 5 54.83 54.293903 -0.391805 1.980948 0.282488 0.027256 -0.644061 2.103510 -1.065663 771.68 70.638182 10 139.20 -0.711744 4 29.026424 1090.960000 81.207189 13.625000 144.748750 0.000346 4.625000 16.438750 44.515729 1 1 1 5 1 1 2 2014 1 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 28 17 1984 2010 7 7 5 5 472.231119 839.76 0.014384 30 86.49 259.873954 402.775486 11.007471 3.600926 7.928001 0.415426 0.000000 4.961414 1700.67 94.481667 18 0.602209 5 20.65 51.149250 0.472342 0.335175 -0.317685 -0.333796 0.000000 -0.470410 0.204548 543.18 66.666667 8 128.51 -0.539060 5 36.734681 1058.276667 78.705187 13.166667 139.960000 0.002397 5.000000 14.415000 43.312326 1 1 1 5 1 1 2 2014 3 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.MONTH(transaction_time)) NUM_UNIQUE(transactions.YEAR(transaction_time)) NUM_UNIQUE(transactions.WEEKDAY(transaction_time)) NUM_UNIQUE(transactions.DAY(transaction_time)) MODE(transactions.MONTH(transaction_time)) MODE(transactions.YEAR(transaction_time)) MODE(transactions.WEEKDAY(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 2014 2 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 2014 2 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 2014 2 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 2014 2 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 2014 2 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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