Teradata Package for Python Function Reference on VantageCloud Lake - set_features_active - Teradata Package for Python - Look here for syntax, methods and examples for the functions included in the Teradata Package for Python.

Teradata® Package for Python Function Reference on VantageCloud Lake

Deployment
VantageCloud
Edition
Lake
Product
Teradata Package for Python
Release Number
20.00.00.03
Published
December 2024
ft:locale
en-US
ft:lastEdition
2024-12-19
dita:id
TeradataPython_FxRef_Lake_2000
Product Category
Teradata Vantage
teradataml.store.feature_store.feature_store.FeatureStore.set_features_active = set_features_active(self, names)
DESCRIPTION:
    Mark the feature status as active. Set the status as 'inactive' with
    "set_features_inactive()" method. Note that, inactive features are
    not available for any further processing.
 
PARAMETERS:
    names:
        Required Argument.
        Specifies the name(s) of the feature(s).
        Types: str OR list of str
 
RETURNS:
    bool
 
RAISES:
    teradataMLException
 
EXAMPLES:
    >>> from teradataml import DataFrame, DataSource, FeatureStore, load_example_data
    # Load the admissions data to Vantage.
    >>> load_example_data("dataframe", "admissions_train")
    # Create DataFrame on admissions data.
    >>> df = DataFrame("admissions_train")
    >>> df
       masters   gpa     stats programming  admitted
    id
    34     yes  3.85  Advanced    Beginner         0
    32     yes  3.46  Advanced    Beginner         0
    11      no  3.13  Advanced    Advanced         1
    40     yes  3.95    Novice    Beginner         0
    38     yes  2.65  Advanced    Beginner         1
    36      no  3.00  Advanced      Novice         0
    7      yes  2.33    Novice      Novice         1
    26     yes  3.57  Advanced    Advanced         1
    19     yes  1.98  Advanced    Advanced         0
    13      no  4.00  Advanced      Novice         1
    >>>
    # Create FeatureGroup from DataFrame df.
    >>> fg = FeatureGroup.from_DataFrame(name='admissions', df=df, entity_columns='id')
    # Apply the FeatureGroup to FeatureStore 'vfs_v1'.
    >>> fs = FeatureStore('vfs_v1')
    >>> fs.apply(fg)
    True
    # Get FeatureGroup 'admissions' from FeatureStore.
    >>> fg = fs.get_feature_group('admissions')
    >>> fg
    FeatureGroup(admissions, features=[Feature(name=masters), Feature(name=programming), Feature(name=admitted), Feature(name=stats), Feature(name=gpa)], entity=Entity(name=admissions), data_source=DataSource(name=admissions))
    # Set the Feature 'programming' inactive.
    >>> fs.set_features_inactive('programming')
    True
    # Get FeatureGroup again after setting feature inactive.
    >>> fg = fs.get_feature_group('admissions')
    >>> fg
    FeatureGroup(admissions, features=[Feature(name=masters), Feature(name=stats), Feature(name=admitted), Feature(name=gpa)], entity=Entity(name=admissions), data_source=DataSource(name=admissions))
    >>>
 
    # Mark Feature programming from 'inactive' to 'active'.
    >>> fs.set_features_active('programming')
    # Get FeatureGroup again after setting feature active.
    >>> fg = fs.get_feature_group('admissions')
    >>> fg
    FeatureGroup(admissions, features=[Feature(name=masters), Feature(name=programming), Feature(name=admitted), Feature(name=stats), Feature(name=gpa)], entity=Entity(name=admissions), data_source=DataSource(name=admissions))
    >>>