teradataml.store.feature_store.feature_store.FeatureStore.archive_feature_process = archive_feature_process(self, process_id)

DESCRIPTION:
    Archives the FeatureProcess with the given process_id.
    Notes:
         * Archived FeatureProcess is not available for any further processing.
         * Archived FeatureProcess can be viewed using `FeatureStore.list_feature_processes(archived=True)`.
           method.
         * Same feature can be ingested by multiple processes. If feature associated with
           process "process_id" is also associated with other processes, then this
           function only archives the feature values associated with the process "process_id". Else
           it archives the feature from the feature catalog. Look at `FeatureCatalog.archive_features()`.
           for more details.
 
PARAMETERS:
    process_id:
        Required Argument.
        Specifies the ID of the FeatureProcess to archive from repository.
        Types: str
 
RETURNS:
    bool
 
RAISES:
    TeradataMLException, TypeError, ValueError
 
EXAMPLES:
    >>> load_example_data('dataframe', ['sales'])
    # Create a teradataml DataFrame.
    >>> from teradataml import DataFrame, FeatureProcess, FeatureStore
    >>> df = DataFrame("sales")
 
    # Create FeatureStore for repo 'repo'.
    >>> fs = FeatureStore("repo", data_domain='sales')
    Repo repo does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
    # Setup FeatureStore for this repository.
    >>> fs.setup()
    True
 
    # Run FeatureProcess to ingest features.
    >>> from teradataml import FeatureProcess
    >>> fp = FeatureProcess(repo='repo',
    ...                     data_domain='sales',
    ...                     object=df,
    ...                     entity='accounts',
    ...                     features=['Jan', 'Feb', 'Mar', 'Apr'])
    >>> fp.run()
    Process '2a014f2d-6b71-11f0-aeda-f020ffe7fe09' started.
    Process '2a014f2d-6b71-11f0-aeda-f020ffe7fe09' completed.
 
    # List the available FeatureProcesses.
    >>> fs.list_feature_processes()
                                         description data_domain       process_type data_source entity_id       feature_names feature_ids                     valid_start                       valid_end
    process_id                                                                                                                                                                                           
    2a014f2d-6b71-11f0-aeda-f020ffe7fe09                   sales  denormalized view     "sales"  accounts  Apr, Feb, Jan, Mar        None  2025-07-28 05:10:34.760000+00:  9999-12-31 23:59:59.999999+00:
 
    # Example: Archive the FeatureProcess with process_id '2a014f2d-6b71-11f0-aeda-f020ffe7fe09'.
    >>> fs.archive_feature_process("2a014f2d-6b71-11f0-aeda-f020ffe7fe09")
    Feature 'Jan' is archived from table 'FS_T_a38baff6_821b_3bb7_0850_827fe5372e31'.
    Feature 'Jan' is archived from metadata.
    Feature 'Feb' is archived from table 'FS_T_6003dc24_375e_7fd6_46f0_eeb868305c4a'.
    Feature 'Feb' is archived from metadata.
    Feature 'Mar' is archived from table 'FS_T_a38baff6_821b_3bb7_0850_827fe5372e31'.
    Feature 'Mar' is archived from metadata.
    Feature 'Apr' is archived from table 'FS_T_a38baff6_821b_3bb7_0850_827fe5372e31'.
    Feature 'Apr' is archived from metadata.
    FeatureProcess with process id '2a014f2d-6b71-11f0-aeda-f020ffe7fe09' is archived.
    True