load_filter() | FilterManager Method | Teradata Package for Python - load_filter() - Teradata Package for Python

Use the load_filter_name method to load the filter definitions from teradataml DataFrame into the filter manager.

Required Parameter

df

Specifies the DataFrame containing filter definitions. Each row represents one filter scenario.

Optional Parameter

filter_id

Specifies the name of the column that stores filter identifiers in the filter artifact.

Default value: 'filter_id'

Example: Create a FilterManager To Manage Filters Related to Statistics Features in the Repository 'vfs_v1' with Name 'stats_filter_manager' and Load Filter Definitions into it

>>> from teradataml import load_example_data, DataFrame, FeatureStore, FilterManager
>>> load_example_data('dataframe', 'admissions_train')
>>> df = DataFrame("admissions_train")

Create a FeatureStore.

>>> 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()

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 for statistics features 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)