FilterManager Class | Teradata Package for Python - FilterManager Class - Teradata Package for Python

Use the FilterManager class to manage partitioned data processing within a FeatureStore repository. It creates and manages filter artifacts that allow sequential processing of large datasets.

• FilterManager Properties
• FilterManager Methods

Syntax

FilterManager(repo, name)

Required Parameters

repo

Specifies the name of the database where the filter manager resides.

object

Specifies the name of the filter manager to be created.

Example Setup

>>> from teradataml import DataFrame, load_example_data, 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.

>>> fs.setup()

True

Example 1: Create a FilterManager To Manage Filters Related to Statistics Features in the Repository 'vfs_v1' with Name 'stats_filter_manager'

Create an instance of FilterManager.

>>> fm = FilterManager(repo='vfs_v1', name='stats_filter_manager')
>>> fm

Filter Manager 'stats_filter_manager' does not exist. Run FilterManager.load_filter() to create it.
FilterManager(repo=vfs_v1, name=stats_filter_manager, filters=None)

Load filter definitions into the filter manager using a DataFrame.

>>> fm.load_filter(df=df.groupby('stats').count()[['stats']])

True

>>> fm

FilterManager(repo=vfs_v1, name=stats_filter_manager, filters=3)