teradataml.store.feature_store.feature_store.FeatureStore.get_dataset_catalog = get_dataset_catalog(self)

DESCRIPTION:
    Retrieves DatasetCatalog based on the feature store's repo and data domain.
 
PARAMETERS:
    None.
 
RETURNS:
    DatasetCatalog
 
RAISES:
    None.
 
EXAMPLES:
    >>> from teradataml import FeatureStore
    # Create FeatureStore for repo 'vfs_v1'.
    >>> fs = FeatureStore('vfs_v1', data_domain='sales')
    Repo vfs_v1 does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
    # Setup FeatureStore for this repository.
    >>> fs.setup()
 
    # Load the sales data to Vantage.
    >>> from teradataml import load_example_data
    >>> load_example_data("dataframe", "sales")
    >>> df = DataFrame("sales")
 
    # Create a feature process.
    >>> from teradataml import FeatureProcess
    >>> fp = FeatureProcess(repo="vfs_v1",
    ...                     data_domain='sales',
    ...                     object=df,
    ...                     entity="accounts",
    ...                     features=["Jan", "Feb", "Mar", "Apr"])
    >>> fp.run()
    Process '5747082b-4acb-11f0-a2d7-f020ffe7fe09' started.
    Process '5747082b-4acb-11f0-a2d7-f020ffe7fe09' completed.
    True
 
    # Get DatasetCatalog from FeatureStore.
    >>> dc = fs.get_dataset_catalog()
 
    # Build dataset using DatasetCatalog object.
    >>> dataset = dc.build_dataset(entity='accounts',
    ...                            selected_features = {
    ...                                 'Jan': fp.process_id,
    ...                                 'Feb': fp.process_id},
    ...                            view_name='ds_jan_feb',
    ...                            description='Dataset with Jan and Feb features')
    >>> dataset
         accounts    Jan    Feb
    0    Blue Inc   50.0   90.0
    1    Alpha Co  200.0  210.0
    2   Jones LLC  150.0  200.0
    3  Yellow Inc    NaN   90.0
    4  Orange Inc    NaN  210.0
    5     Red Inc  150.0  200.0