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 and give it an id

In [7]: es = ft.EntitySet(id="transactions")

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})
   ...: 

In [9]: es
Out[9]: 
Entityset: transactions
  Entities:
    transactions [Rows: 500, Columns: 11]
  Relationships:
    No relationships

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: zip_code (dtype = categorical)>,
 <Variable: join_date (dtype: datetime, format: None)>,
 <Variable: date_of_birth (dtype: datetime, format: None)>,
 <Variable: product_id (dtype = categorical)>]

In the call to entity_from_dataframe, we specified three important parameters

  • The index parameter specifies the column that uniquely identifies rows in the dataframe
  • The time_index parameter tells Featuretools when the data was created.
  • The variable_types parameter 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: transactions
  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: transactions
  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: transactions
  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

  1. It created a new entity called “sessions” based on the “session_id” variable in “transactions”
  2. 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 = categorical)>]

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 = categorical)>,
 <Variable: session_start (dtype: datetime_time_index, format: None)>,
 <Variable: join_date (dtype: datetime, format: None)>]
  1. It removed “device”, “customer_id”, “zip_code”, “session_start” 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.
  2. It marked “session_start” as a time index in the new sessions entity to indicate the beginning of a session. By default, unless it’s explicitly set to another variable, normalize_entity would have made a “first_transactions_time” in this entity. If we don’t want this variable to be created, we can set make_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: transactions
  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.DAY(transaction_time))  NUM_UNIQUE(transactions.DAY(date_of_birth))  NUM_UNIQUE(transactions.YEAR(transaction_time))  NUM_UNIQUE(transactions.YEAR(date_of_birth))  NUM_UNIQUE(transactions.MONTH(transaction_time))  NUM_UNIQUE(transactions.MONTH(date_of_birth))  NUM_UNIQUE(transactions.WEEKDAY(transaction_time))  NUM_UNIQUE(transactions.WEEKDAY(date_of_birth))  NUM_UNIQUE(transactions.sessions.device)  NUM_UNIQUE(transactions.sessions.customer_id)  MODE(transactions.DAY(transaction_time))  MODE(transactions.DAY(date_of_birth))  MODE(transactions.YEAR(transaction_time))  MODE(transactions.YEAR(date_of_birth))  MODE(transactions.MONTH(transaction_time))  MODE(transactions.MONTH(date_of_birth))  MODE(transactions.WEEKDAY(transaction_time))  MODE(transactions.WEEKDAY(date_of_birth)) MODE(transactions.sessions.device)  MODE(transactions.sessions.customer_id)
product_id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      
1              B                   7489.79                 42.479989                    149.56                   0.125525                      6.84                  73.429314                  102                                   34                              3                                               1                                            4                                                1                                             5                                                 1                                              3                                                  1                                                 4                                         3                                              5                                         1                                     18                                       2014                                    1994                                           1                                        7                                             2                                          0                            desktop                                        1
2              B                   7021.43                 46.336308                    149.95                   0.151934                      5.73                  76.319891                   92                                   34                             28                                               1                                            4                                                1                                             5                                                 1                                              3                                                  1                                                 4                                         3                                              5                                         1                                     18                                       2014                                    2006                                           1                                        8                                             2                                          0                            desktop                                        4
3              B                   7008.12                 38.871405                    148.31                   0.223938                      5.89                  73.001250                   96                                   35                              1                                               1                                            4                                                1                                             5                                                 1                                              3                                                  1                                                 4                                         3                                              5                                         1                                     18                                       2014                                    2006                                           1                                        8                                             2                                          0                            desktop                                        4
4              B                   8088.97                 42.492501                    146.46                  -0.132077                      5.81                  76.311038                  106                                   34                             29                                               1                                            4                                                1                                             5                                                 1                                              3                                                  1                                                 4                                         3                                              5                                         1                                     18                                       2014                                    1994                                           1                                        7                                             2                                          0                            desktop                                        1
5              A                   7931.55                 42.131902                    149.02                   0.098248                      5.91                  76.264904                  104                                   34                              4                                               1                                            4                                                1                                             5                                                 1                                              3                                                  1                                                 4                                         3                                              5                                         1                                     18                                       2014                                    1994                                           1                                        7                                             2                                          0                             mobile                                        1

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.