Representing Data with EntitySets¶
An EntitySet is a collection of entities and the relationships between them. They are useful for preparing raw, structured datasets for feature engineering. While many functions in Featuretools take entities and relationships as separate arguments, it is recommended to create an EntitySet, so you can more easily manipulate your data as needed.
The Raw Data¶
Below we have a two tables of data (represented as Pandas DataFrames) related to customer transactions. The first is a merge of transactions, sessions, and customers so that the result looks like something you might see in a log file:
In [1]: import featuretools as ft
In [2]: data = ft.demo.load_mock_customer()
In [3]: transactions_df = data["transactions"].merge(data["sessions"]).merge(data["customers"])
In [4]: transactions_df.sample(10)
Out[4]:
transaction_id session_id transaction_time product_id amount customer_id device session_start zip_code join_date date_of_birth
264 380 21 2014-01-01 05:14:10 5 57.09 4 desktop 2014-01-01 05:02:15 60091 2011-04-08 20:08:14 2006-08-15
19 244 10 2014-01-01 02:34:55 2 116.95 2 tablet 2014-01-01 02:31:40 13244 2012-04-15 23:31:04 1986-08-18
314 299 6 2014-01-01 01:32:05 4 64.99 1 tablet 2014-01-01 01:23:25 60091 2011-04-17 10:48:33 1994-07-18
290 78 4 2014-01-01 00:54:10 1 37.50 1 mobile 2014-01-01 00:44:25 60091 2011-04-17 10:48:33 1994-07-18
379 457 27 2014-01-01 06:37:35 1 19.16 1 mobile 2014-01-01 06:34:20 60091 2011-04-17 10:48:33 1994-07-18
335 477 9 2014-01-01 02:30:35 3 41.70 1 desktop 2014-01-01 02:15:25 60091 2011-04-17 10:48:33 1994-07-18
293 103 4 2014-01-01 00:57:25 5 20.79 1 mobile 2014-01-01 00:44:25 60091 2011-04-17 10:48:33 1994-07-18
271 390 22 2014-01-01 05:21:45 2 54.83 4 desktop 2014-01-01 05:21:45 60091 2011-04-08 20:08:14 2006-08-15
404 476 29 2014-01-01 07:24:10 4 121.59 1 mobile 2014-01-01 07:10:05 60091 2011-04-17 10:48:33 1994-07-18
179 90 3 2014-01-01 00:35:45 1 75.73 4 mobile 2014-01-01 00:28:10 60091 2011-04-08 20:08:14 2006-08-15
And the second dataframe is a list of products involved in those transactions.
In [5]: products_df = data["products"]
In [6]: products_df
Out[6]:
product_id brand
0 1 B
1 2 B
2 3 B
3 4 B
4 5 A
Creating an EntitySet¶
First, we initialize an EntitySet. If you’d like to give it name, you can optionally provide an id to the constructor.
In [7]: es = ft.EntitySet(id="customer_data")
Adding entities¶
To get started, we load the transactions dataframe as an entity.
In [8]: es = es.entity_from_dataframe(entity_id="transactions",
...: dataframe=transactions_df,
...: index="transaction_id",
...: time_index="transaction_time",
...: variable_types={"product_id": ft.variable_types.Categorical,
...: "zip_code": ft.variable_types.ZIPCode})
...:
In [9]: es
Out[9]:
Entityset: customer_data
Entities:
transactions [Rows: 500, Columns: 11]
Relationships:
No relationships
Note
You can also display your entity set structure graphically by calling EntitySet.plot().
This method loads each column in the dataframe in as a variable. We can see the variables in an entity using the code below.
In [10]: es["transactions"].variables
Out[10]:
[<Variable: transaction_id (dtype = index)>,
<Variable: session_id (dtype = numeric)>,
<Variable: transaction_time (dtype: datetime_time_index, format: None)>,
<Variable: amount (dtype = numeric)>,
<Variable: customer_id (dtype = numeric)>,
<Variable: device (dtype = categorical)>,
<Variable: session_start (dtype: datetime, format: None)>,
<Variable: join_date (dtype: datetime, format: None)>,
<Variable: date_of_birth (dtype: datetime, format: None)>,
<Variable: product_id (dtype = categorical)>,
<Variable: zip_code (dtype = zipcode)>]
In the call to entity_from_dataframe, we specified three important parameters
The
indexparameter specifies the column that uniquely identifies rows in the dataframeThe
time_indexparameter tells Featuretools when the data was created.The
variable_typesparameter indicates that “product_id” should be interpreted as a Categorical variable, even though it just an integer in the underlying data.
Now, we can do that same thing with our products dataframe
In [11]: es = es.entity_from_dataframe(entity_id="products",
....: dataframe=products_df,
....: index="product_id")
....:
In [12]: es
Out[12]:
Entityset: customer_data
Entities:
transactions [Rows: 500, Columns: 11]
products [Rows: 5, Columns: 2]
Relationships:
No relationships
With two entities in our entity set, we can add a relationship between them.
Adding a Relationship¶
We want to relate these two entities by the columns called “product_id” in each entity. Each product has multiple transactions associated with it, so it is called it the parent entity, while the transactions entity is known as the child entity. When specifying relationships we list the variable in the parent entity first. Note that each ft.Relationship must denote a one-to-many relationship rather than a relationship which is one-to-one or many-to-many.
In [13]: new_relationship = ft.Relationship(es["products"]["product_id"],
....: es["transactions"]["product_id"])
....:
In [14]: es = es.add_relationship(new_relationship)
In [15]: es
Out[15]:
Entityset: customer_data
Entities:
transactions [Rows: 500, Columns: 11]
products [Rows: 5, Columns: 2]
Relationships:
transactions.product_id -> products.product_id
Now, we see the relationship has been added to our entity set.
Creating entity from existing table¶
When working with raw data, it is common to have sufficient information to justify the creation of new entities. In order to create a new entity and relationship for sessions, we “normalize” the transaction entity.
In [16]: es = es.normalize_entity(base_entity_id="transactions",
....: new_entity_id="sessions",
....: index="session_id",
....: make_time_index="session_start",
....: additional_variables=["device", "customer_id", "zip_code", "session_start", "join_date"])
....:
In [17]: es
Out[17]:
Entityset: customer_data
Entities:
transactions [Rows: 500, Columns: 6]
products [Rows: 5, Columns: 2]
sessions [Rows: 35, Columns: 6]
Relationships:
transactions.product_id -> products.product_id
transactions.session_id -> sessions.session_id
Looking at the output above, we see this method did two operations
It created a new entity called “sessions” based on the “session_id” and “session_start” variables in “transactions”
It added a relationship connecting “transactions” and “sessions”.
If we look at the variables in transactions and the new sessions entity, we see two more operations that were performed automatically.
In [18]: es["transactions"].variables
Out[18]:
[<Variable: transaction_id (dtype = index)>,
<Variable: session_id (dtype = id)>,
<Variable: transaction_time (dtype: datetime_time_index, format: None)>,
<Variable: amount (dtype = numeric)>,
<Variable: date_of_birth (dtype: datetime, format: None)>,
<Variable: product_id (dtype = id)>]
In [19]: es["sessions"].variables
Out[19]:
[<Variable: session_id (dtype = index)>,
<Variable: device (dtype = categorical)>,
<Variable: customer_id (dtype = numeric)>,
<Variable: zip_code (dtype = zipcode)>,
<Variable: session_start (dtype: datetime_time_index, format: None)>,
<Variable: join_date (dtype: datetime, format: None)>]
It removed “device”, “customer_id”, “zip_code” and “join_date” from “transactions” and created a new variables in the sessions entity. This reduces redundant information as the those properties of a session don’t change between transactions.
It copied and marked “session_start” as a time index variable into the new sessions entity to indicate the beginning of a session. If the base entity has a time index and
make_time_indexis not set,normalize entitywill create a time index for the new entity. In this case it would create a new time index called “first_transactions_time” using the time of the first transaction of each session. If we don’t want this time index to be created, we can setmake_time_index=False.
If we look at the dataframes, can see what the normalize_entity did to the actual data.
In [20]: es["sessions"].df.head(5)
Out[20]:
session_id device customer_id zip_code session_start join_date
1 1 desktop 2 13244 2014-01-01 00:00:00 2012-04-15 23:31:04
2 2 mobile 5 60091 2014-01-01 00:17:20 2010-07-17 05:27:50
3 3 mobile 4 60091 2014-01-01 00:28:10 2011-04-08 20:08:14
4 4 mobile 1 60091 2014-01-01 00:44:25 2011-04-17 10:48:33
5 5 mobile 4 60091 2014-01-01 01:11:30 2011-04-08 20:08:14
In [21]: es["transactions"].df.head(5)
Out[21]:
transaction_id session_id transaction_time amount date_of_birth product_id
298 298 1 2014-01-01 00:00:00 127.64 1986-08-18 5
2 2 1 2014-01-01 00:01:05 109.48 1986-08-18 2
308 308 1 2014-01-01 00:02:10 95.06 1986-08-18 3
116 116 1 2014-01-01 00:03:15 78.92 1986-08-18 4
371 371 1 2014-01-01 00:04:20 31.54 1986-08-18 3
To finish preparing this dataset, create a “customers” entity using the same method call.
In [22]: es = es.normalize_entity(base_entity_id="sessions",
....: new_entity_id="customers",
....: index="customer_id",
....: make_time_index="join_date",
....: additional_variables=["zip_code", "join_date"])
....:
In [23]: es
Out[23]:
Entityset: customer_data
Entities:
transactions [Rows: 500, Columns: 6]
products [Rows: 5, Columns: 2]
sessions [Rows: 35, Columns: 4]
customers [Rows: 5, Columns: 3]
Relationships:
transactions.product_id -> products.product_id
transactions.session_id -> sessions.session_id
sessions.customer_id -> customers.customer_id
Using the EntitySet¶
Finally, we are ready to use this EntitySet with any functionality within Featuretools. For example, let’s build a feature matrix for each product in our dataset.
In [24]: feature_matrix, feature_defs = ft.dfs(entityset=es,
....: target_entity="products")
....:
In [25]: feature_matrix
Out[25]:
brand SUM(transactions.amount) STD(transactions.amount) MAX(transactions.amount) SKEW(transactions.amount) MIN(transactions.amount) MEAN(transactions.amount) COUNT(transactions) NUM_UNIQUE(transactions.session_id) MODE(transactions.session_id) NUM_UNIQUE(transactions.MONTH(transaction_time)) NUM_UNIQUE(transactions.MONTH(date_of_birth)) NUM_UNIQUE(transactions.sessions.device) NUM_UNIQUE(transactions.WEEKDAY(transaction_time)) NUM_UNIQUE(transactions.WEEKDAY(date_of_birth)) NUM_UNIQUE(transactions.DAY(date_of_birth)) NUM_UNIQUE(transactions.DAY(transaction_time)) NUM_UNIQUE(transactions.YEAR(date_of_birth)) NUM_UNIQUE(transactions.sessions.customer_id) NUM_UNIQUE(transactions.YEAR(transaction_time)) MODE(transactions.MONTH(transaction_time)) MODE(transactions.MONTH(date_of_birth)) MODE(transactions.sessions.device) MODE(transactions.WEEKDAY(transaction_time)) MODE(transactions.WEEKDAY(date_of_birth)) MODE(transactions.DAY(date_of_birth)) MODE(transactions.DAY(transaction_time)) MODE(transactions.YEAR(date_of_birth)) MODE(transactions.sessions.customer_id) MODE(transactions.YEAR(transaction_time))
product_id
1 B 7489.79 42.479989 149.56 0.125525 6.84 73.429314 102 34 3 1 3 3 1 4 4 1 5 5 1 1 7 desktop 2 0 18 1 1994 1 2014
2 B 7021.43 46.336308 149.95 0.151934 5.73 76.319891 92 34 28 1 3 3 1 4 4 1 5 5 1 1 8 desktop 2 0 18 1 2006 4 2014
3 B 7008.12 38.871405 148.31 0.223938 5.89 73.001250 96 35 1 1 3 3 1 4 4 1 5 5 1 1 8 desktop 2 0 18 1 2006 4 2014
4 B 8088.97 42.492501 146.46 -0.132077 5.81 76.311038 106 34 29 1 3 3 1 4 4 1 5 5 1 1 7 desktop 2 0 18 1 1994 1 2014
5 A 7931.55 42.131902 149.02 0.098248 5.91 76.264904 104 34 4 1 3 3 1 4 4 1 5 5 1 1 7 mobile 2 0 18 1 1994 1 2014
As we can see, the features from DFS use the relational structure of our entity set. Therefore it is important to think carefully about the entities that we create.