NOTICE

The upcoming release of Featuretools 1.0.0 contains several breaking changes. Users are encouraged to test this version prior to release by installing from GitHub:

pip install https://github.com/alteryx/featuretools/archive/woodwork-integration.zip

For details on migrating to the new version, refer to Transitioning to Featuretools Version 1.0. Please report any issues in the Featuretools GitHub repo or by messaging in Alteryx Open Source Slack.


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 = zip_code)>]

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: 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

  1. It created a new entity called “sessions” based on the “session_id” and “session_start” variables 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 = 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 = zip_code)>,
 <Variable: session_start (dtype: datetime_time_index, format: None)>,
 <Variable: join_date (dtype: datetime, format: None)>]
  1. 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.

  2. 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_index is not set, normalize entity will 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 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: 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  COUNT(transactions)  MAX(transactions.amount)  MEAN(transactions.amount)  MIN(transactions.amount)  MODE(transactions.session_id)  NUM_UNIQUE(transactions.session_id)  SKEW(transactions.amount)  STD(transactions.amount)  SUM(transactions.amount)  MODE(transactions.DAY(date_of_birth))  MODE(transactions.DAY(transaction_time))  MODE(transactions.MONTH(date_of_birth))  MODE(transactions.MONTH(transaction_time))  MODE(transactions.WEEKDAY(date_of_birth))  MODE(transactions.WEEKDAY(transaction_time))  MODE(transactions.YEAR(date_of_birth))  MODE(transactions.YEAR(transaction_time))  MODE(transactions.sessions.customer_id) MODE(transactions.sessions.device)  NUM_UNIQUE(transactions.DAY(date_of_birth))  NUM_UNIQUE(transactions.DAY(transaction_time))  NUM_UNIQUE(transactions.MONTH(date_of_birth))  NUM_UNIQUE(transactions.MONTH(transaction_time))  NUM_UNIQUE(transactions.WEEKDAY(date_of_birth))  NUM_UNIQUE(transactions.WEEKDAY(transaction_time))  NUM_UNIQUE(transactions.YEAR(date_of_birth))  NUM_UNIQUE(transactions.YEAR(transaction_time))  NUM_UNIQUE(transactions.sessions.customer_id)  NUM_UNIQUE(transactions.sessions.device)
product_id                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      
1              B                  102                    149.56                  73.429314                      6.84                              3                                   34                   0.125525                 42.479989                   7489.79                                     18                                         1                                        7                                           1                                          0                                             2                                    1994                                       2014                                        1                            desktop                                            4                                               1                                              3                                                 1                                                4                                                  1                                              5                                                1                                              5                                         3
2              B                   92                    149.95                  76.319891                      5.73                             28                                   34                   0.151934                 46.336308                   7021.43                                     18                                         1                                        8                                           1                                          0                                             2                                    2006                                       2014                                        4                            desktop                                            4                                               1                                              3                                                 1                                                4                                                  1                                              5                                                1                                              5                                         3
3              B                   96                    148.31                  73.001250                      5.89                              1                                   35                   0.223938                 38.871405                   7008.12                                     18                                         1                                        8                                           1                                          0                                             2                                    2006                                       2014                                        4                            desktop                                            4                                               1                                              3                                                 1                                                4                                                  1                                              5                                                1                                              5                                         3
4              B                  106                    146.46                  76.311038                      5.81                             29                                   34                  -0.132077                 42.492501                   8088.97                                     18                                         1                                        7                                           1                                          0                                             2                                    1994                                       2014                                        1                            desktop                                            4                                               1                                              3                                                 1                                                4                                                  1                                              5                                                1                                              5                                         3
5              A                  104                    149.02                  76.264904                      5.91                              4                                   34                   0.098248                 42.131902                   7931.55                                     18                                         1                                        7                                           1                                          0                                             2                                    1994                                       2014                                        1                             mobile                                            4                                               1                                              3                                                 1                                                4                                                  1                                              5                                                1                                              5                                         3

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.

Dask and Koalas EntitySets

EntitySets can also be created using Dask dataframes or Koalas dataframes. For more information refer to Using Dask EntitySets (BETA) and Using Koalas EntitySets (BETA).