featuretools
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.
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:
python
import featuretools as ft data = ft.demo.load_mock_customer() transactions_df = data["transactions"].merge(data["sessions"]).merge(data["customers"])
transactions_df.sample(10)
And the second dataframe is a list of products involved in those transactions.
python
products_df = data["products"] products_df
First, we initialize an EntitySet. If you'd like to give it name, you can optionally provide an id to the constructor.
python
es = ft.EntitySet(id="customer_data")
To get started, we load the transactions dataframe as an entity.
python
- 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})
es
This method loads each column in the dataframe in as a variable. We can see the variables in an entity using the code below.
python
es["transactions"].variables
In the call to entity_from_dataframe, we specified three important parameters
- The
indexparameter specifies the column that uniquely identifies rows in the dataframe - The
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
python
- es = es.entity_from_dataframe(entity_id="products",
dataframe=products_df, index="product_id")
es
With two entities in our entity set, we can add a relationship between them.
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.
python
- new_relationship = ft.Relationship(es["products"]["product_id"],
es["transactions"]["product_id"])
es = es.add_relationship(new_relationship) es
Now, we see the relationship has been added to our entity set.
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.
python
- 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"])
es
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.
python
es["transactions"].variables es["sessions"].variables
- 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.
python
es["sessions"].df.head(5) es["transactions"].df.head(5)
To finish preparing this dataset, create a "customers" entity using the same method call.
python
- 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"])
es
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.
python
- feature_matrix, feature_defs = ft.dfs(entityset=es,
target_entity="products")
feature_matrix
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.