teradataml.store.feature_store.feature_store.FeatureStore.get_feature_process = get_feature_process(self, object, entity=None, features=None, description=None)

DESCRIPTION:
    Retrieves the FeatureProcess object.
 
PARAMETERS:
    object:
        Required Argument.
        Specifies the source to ingest feature values. It can be one of the following:
            * teradataml DataFrame
            * Feature group
            * Process id
        Notes:
             * If "object" is of type teradataml DataFrame, then "entity"
               and "features" should be provided.
             * If "object" is of type str, then it is considered as
               as process id of an existing FeatureProcess and reruns the
               process. Entity and features are taken from the existing
               feature process. Hence, the arguments "entity" and "features"
               are ignored.
             * If "object" is of type FeatureGroup, then entity and features
               are taken from the FeatureGroup. Hence, the arguments "entity"
               and "features" are ignored.
        Types: DataFrame or FeatureGroup or str
 
    entity:
        Optional Argument.
        Specifies Entity for DataFrame.
        Notes:
             * Ignored when "object" is of type FeatureGroup or str.
             * If a string or list of strings is provided, then "object" should
               have these columns in it.
             * If Entity object is provided, then associated columns in Entity
               object should be present in DataFrame.
        Types: Entity or str or list of str
 
    features:
        Optional Argument.
        Specifies list of features to be considered in feature process. Feature
        ingestion takes place only for these features.
        Note:
            * Ignored when "object" is of type FeatureGroup or str.
        Types: Feature or list of Feature or str or list of str.
 
    description:
        Optional Argument.
        Specifies description for the FeatureProcess.
        Types: str
 
RETURNS:
    FeatureProcess
 
RAISES:
    None.
 
EXAMPLES:
    >>> from teradataml import FeatureStore
    >>> fs = FeatureStore('vfs_v1')
    Repo vfs_v1 does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
    # Setup FeatureStore for this repository.
    >>> fs.setup()
    True
 
    # Load the admissions data to Vantage.
    >>> from teradataml import DataFrame, load_example_data
    >>> load_example_data("dataframe", "admissions_train")
    >>> admission_df = DataFrame("admissions_train")
 
    >>> fp = FeatureProcess(repo='vfs_v1',
    ...                     data_domain='d1',
    ...                     object=admission_df,
    ...                     entity='id',
    ...                     features=['stats', 'programming', 'admitted'])
    >>> fp.run()
    Process '0d365f08-66b0-11f0-88ff-b0dcef8381ea' started.
    Process '0d365f08-66b0-11f0-88ff-b0dcef8381ea' completed.
 
    >>> fs.get_feature_process(object='0d365f08-66b0-11f0-88ff-b0dcef8381ea')
    FeatureProcess(repo=vfs_v1, data_domain=d1, process_id=0d365f08-66b0-11f0-88ff-b0dcef8381ea)