Use the prune_filters() method to remove all filters with IDs lower than the specified filter_id and reassigns sequential IDs starting from 1 to the remaining filters.
Required Parameter
- filter_id
- Specifies the filter ID used as a threshold to prune filters.
- The remaining filters with filter_id >= specified filter_id will be retained and reassigned new filter_ids starting from 1.
- The 'filter_id' must exist in the filter artifact's table.
Example: Create a FilterManager, Load Filter Definitions, and Prune Filters with filter_id < 2
>>> from teradataml import load_example_data, DataFrame, FeatureStore, FilterManager
>>> load_example_data('dataframe', 'admissions_train')
>>> df = DataFrame("admissions_train")
Create a Feature Store.
>>> fs = FeatureStore(repo='vfs_v1', data_domain='sales')
Repo vfs_v1 does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
Create a FeatureStore.
>>> fs.setup()
Create an instance of FilterManager and load filter definitions.
>>> fm = FilterManager(repo='vfs_v1', name='stats_filter_manager')
Filter Manager 'stats_filter_manager' does not exist. Run FilterManager.load_filter() to create it.
>>> fm.load_filter(df=df.groupby('stats').count()[['stats']])
True
>>> fm.list_filters()
stats filter_id 3 Novice 2 Beginner 1 Advanced
Prune filters with filter_id < 2, keeping only filters with filter_id >= 2.
>>> fm.prune_filters(2)
True
>>> fm.list_filters()
stats filter_id 2 Novice 1 Beginner