There are several parameters that can be tuned to change the output of DFS.
python
import featuretools as ft es = ft.demo.load_mock_customer(return_entityset=True) es
Seed features are manually defined, problem specific, features a user provides to DFS. Deep Feature Synthesis will then automatically stack new features on top of these features when it can.
By using seed features, we can include domain specific knowledge in feature engineering automation.
python
expensive_purchase = ft.Feature(es["transactions"]["amount"]) > 125
- feature_matrix, feature_defs = ft.dfs(entityset=es,
target_entity="customers", agg_primitives=["percent_true"], seed_features=[expensive_purchase])
feature_matrix[['PERCENT_TRUE(transactions.amount > 125)']]
We can now see that PERCENT_TRUE was automatically applied to this boolean variable.
Sometimes we want to create features that are conditioned on a second value before we calculate. We call this extra filter a "where clause".
By default, where clauses are built using the interesting_values of a variable.
python
es["sessions"]["device"].interesting_values = ["desktop", "mobile", "tablet"]
We then specify the aggregation primitive to make where clauses for using where_primitives
python
- feature_matrix, feature_defs = ft.dfs(entityset=es,
target_entity="customers", agg_primitives=["count", "avg_time_between"], where_primitives=["count", "avg_time_between"], trans_primitives=[])
feature_matrix
Now, we have several new potentially useful features. For example, the two features below tell us how many sessions a customer completed on a tablet, and the time between those sessions.
python
feature_matrix[["COUNT(sessions WHERE device = tablet)", "AVG_TIME_BETWEEN(sessions.session_start WHERE device = tablet)"]]
We can see that customer who only had 0 or 1 sessions on a tablet, had NaN values for average time between such sessions.
Machine learning algorithms typically expect all numeric data. When Deep Feature Synthesis generates categorical features, we need to encode them.
python
- feature_matrix, feature_defs = ft.dfs(entityset=es,
target_entity="customers", agg_primitives=["mode"], max_depth=1)
feature_matrix
This feature matrix contains 2 categorical variables, zip_code and MODE(sessions.device). We can use the feature matrix and feature definitions to encode these categorical values. Featuretools offers functionality to apply one hot encoding to the output of DFS.
python
feature_matrix_enc, features_enc = ft.encode_features(feature_matrix, feature_defs) feature_matrix_enc
The returned feature matrix is now all numeric. Additionally, we get a new set of feature definitions that contain the encoded values.
python
print(features_enc)
These features can be used to calculate the same encoded values on new data. For more information on feature engineering in production, read /guides/deployment.