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