teradataml.store.feature_store.models.FeatureCatalog.archive_features = archive_features(self, features)

DESCRIPTION:
    Archives the feature values from feature catalog.
 
PARAMETERS:
    features:
        Required Argument.
        Specifies name(s) of the feature(s) to be archived from feature catalog.
        Types: str or list of str.
 
RETURNS:
    bool
 
RAISES:
    TeradataMlException
 
EXAMPLES:
    >>> load_example_data('dataframe', ['sales'])
    >>> df = DataFrame("sales")
 
    # Create FeatureStore repo 'vfs_v1'.
    >>> from teradataml import FeatureStore
    >>> fs = FeatureStore(repo='vfs_v1', data_domain='sales')
    Repo vfs_test does not exist. Run FeatureStore.setup() to create the repo and
    >>> fs.setup()
    True
 
    # Create an instance of FeatureCatalog.
    >>> fc = FeatureCatalog(repo='vfs_v1', data_domain='sales')
 
    # Upload features from DataFrame.                                                 
    >>> fp = fc.upload_features(object=df,
    ...                         entity=["accounts"],
    ...                         features=["Feb", "Jan", "Mar", "Apr"])
    Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' started.
    Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' completed.
 
    # List archived features.
    >>> fc.list_features(archived=True)
    feature_id name data_type feature_type valid_start valid_end
 
    # Example 1: Archive the single feature from feature catalog.
    >>> fc.archive_features(features='Apr')
    True
 
    # Validate archived features.
    >>> fc.list_features(archived=True)
                 feature_id name data_type feature_type                     valid_start                       valid_end
    entity_name                                                                                                        
    accounts              4  Apr    BIGINT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:10.190000+00:
 
    # Example 2: Archive multiple feature values from feature catalog.
    >>> fc.archive_features(features=['Jan', 'Feb'])
    True
 
    # Validate archived features.
    >>> fc.list_features(archived=True)
                 feature_id name data_type feature_type                     valid_start                       valid_end
    entity_name                                                                                                        
    accounts              1  Feb     FLOAT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:59.360000+00:
    accounts              2  Jan    BIGINT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:59.360000+00:
    accounts              4  Apr    BIGINT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:10.190000+00: