Use the processes property to get the list of feature process objects associated with the data domain.
Example setup
Load the data to be used and create the 'sales' DataFrame.
>>> from teradataml import load_example_data, DataFrame
>>> load_example_data('dataframe', ['sales', 'admissions_train'])
>>> admission_df = DataFrame("admissions_train")
>>> df = DataFrame('sales')
Define the repository and data domain.
>>> repo = 'vfs_test' >>> data_domain = 'sales'
Create a feature store.
>>> from teradataml import FeatureStore >>> fs = FeatureStore(repo=repo, data_domain=data_domain)
Repo vfs_test does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
Set up the feature store for this repository.
>>> fs.setup()
True
Run FeatureProcess to ingest features.
>>> from teradataml import FeatureProcess
>>> fp = FeatureProcess(repo=repo, ... data_domain=data_domain, ... object=df, ... entity='accounts', ... features=['Jan', 'Feb', 'Mar', 'Apr'])
>>> fp.run()
Process '4098c3ea-6c8d-11f0-837a-24eb16d15109' started. Process '4098c3ea-6c8d-11f0-837a-24eb16d15109' completed.
Run another FeatureProcess to ingest features into a different DataFrame.
Example: Get the processes in the data domain
Create a data domain object.
>>> from teradataml import DataDomain >>> dd = DataDomain(repo=repo, ... data_domain=data_domain)
List the processes.
>>> dd.processes
[FeatureProcess(repo=vfs_v1, data_domain=sales, process_id=4098c3ea-6c8d-11f0-837a-24eb16d15109), FeatureProcess(repo=vfs_v1, data_domain=sales, process_id=5129c4eb-7d9e-21f1-947b-35fb27e26210)]