teradataml.store.feature_store.models.FeatureCatalog.list_feature_versions = list_feature_versions(self)

DESCRIPTION:
    Lists the details of available feature versions in the repo for the
    corresponding data domain.
 
PARAMETERS:
    None
 
RETURNS:
    DataFrame
 
RAISES:
    TeradataMlException
 
EXAMPLES:
    # Ingest sales data to feature catalog configured for repo 'vfs_v1'.
    >>> from teradataml import load_example_data, FeatureProcess
    >>> load_example_data('dataframe', 'sales')
    >>> df = DataFrame("sales")
    >>> df
                  Feb    Jan    Mar    Apr    datetime
    accounts
    Red Inc     200.0  150.0  140.0    NaN  04/01/2017
    Blue Inc     90.0   50.0   95.0  101.0  04/01/2017
    Alpha Co    210.0  200.0  215.0  250.0  04/01/2017
    Orange Inc  210.0    NaN    NaN  250.0  04/01/2017
    Yellow Inc   90.0    NaN    NaN    NaN  04/01/2017
    Jones LLC   200.0  150.0  140.0  180.0  04/01/2017
 
    # Look at tdtypes before ingesting features.
    >>> df.tdtypes
    accounts    VARCHAR(length=20, charset='LATIN')
    Feb                                     FLOAT()
    Jan                                    BIGINT()
    Mar                                    BIGINT()
    Apr                                    BIGINT()
    datetime                                 DATE()
 
    # Create FeatureStore repo 'vfs_v1'.
    >>> from teradataml import FeatureStore
    >>> fs = FeatureStore(repo='vfs_v1', data_domain='sales')
    Repo vfs does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
    >>> fs.setup()
    True
 
    # Initiate FeatureProcess to ingest features.
    >>> fp = FeatureProcess(repo='vfs_v1', data_domain='sales', object=df, entity='accounts', features=['Jan', 'Feb', 'Mar', 'Apr'])
    # Run the feature process.
    >>> fp.run()
    Process 'a9f29a4e-3f75-11f0-b43b-f020ff57c62c' started.
    Process 'a9f29a4e-3f75-11f0-b43b-f020ff57c62c' completed.
 
    # Example: List features and its versions from the feature catalog.
    >>> from teradataml import FeatureCatalog
    >>> fc = FeatureCatalog(repo='vfs_v1', data_domain='sales')
    >>> fc.list_feature_versions()
      entity_id data_domain      id name                                 table_name                       feature_version
    0  accounts       sales  100001  Feb  FS_T_19fadd83_620c_3603_4ced_95991bf3b44c  a9f29a4e-3f75-11f0-b43b-f020ff57c62c
    1  accounts       sales  300001  Apr  FS_T_fa01ca99_6169_008c_6b72_cff03d7ee9e1  a9f29a4e-3f75-11f0-b43b-f020ff57c62c
    2  accounts       sales       1  Jan  FS_T_fa01ca99_6169_008c_6b72_cff03d7ee9e1  a9f29a4e-3f75-11f0-b43b-f020ff57c62c
    3  accounts       sales  200001  Mar  FS_T_fa01ca99_6169_008c_6b72_cff03d7ee9e1  a9f29a4e-3f75-11f0-b43b-f020ff57c62c