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View Features guide page for feature schema definitions and details.

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Create a feature

Features define the tools and classifications used to annotate your data. To create features for use in an ontology, use the client.create_feature_schema() method. 
from labelbox import Tool

# Defining tool features
# required is an optional field defaults to False if not specified
bbox_tool = Tool(tool=Tool.Type.BBOX, name="dog_box", required=True)
poly_tool = Tool(tool=Tool.Type.POLYGON, name="dog_poly")
seg_tool = Tool(tool=Tool.Type.SEGMENTATION, name="dog_seg")
point_tool = Tool(tool=Tool.Type.POINT, name="dog_center")
line_tool = Tool(tool=Tool.Type.LINE, name="dog_orientation")
ner_tool = Tool(tool=Tool.Type.NER, name="dog_reference", required=True)

# Creating feature schema for each defined tool
feature_schema_bbox = client.create_feature_schema(bbox_tool.asdict())
feature_schema_poly = client.create_feature_schema(poly_tool.asdict())
feature_schema_seg= client.create_feature_schema(seg_tool.asdict())
feature_schema_tool = client.create_feature_schema(point_tool.asdict())
feature_schema_line = client.create_feature_schema(line_tool.asdict())
feature_schema_ner = client.create_feature_schema(ner_tool.asdict())


# Defining classification features
# required is an optional field defaults to False if not specified
from labelbox import Classification, Option

text_classification = Classification(class_type=Classification.Type.TEXT,
                                     name="dog_name", required=True)
radio_classification = Classification(class_type=Classification.Type.RADIO,
                                      name="dog_breed",
                                      options=[Option("poodle")], required=True)
checklist_classification = Classification(
    class_type=Classification.Type.CHECKLIST,
    name="background",
    options=[Option("at_park"), Option("has_leash")])
nested_classification = Classification(
    class_type=Classification.Type.CHECKLIST,
    name="Appliance Features",
    options=[
        Option(
            "HasWarranty",
            options=[  # Nested options
                Option("1 Year"),
                Option("2 Years"),
                Option("5 Years")
            ]
        ),
        Option("EnergyEfficient"),
        Option("SmartHomeCompatible")
    ]
)

# Creating feature schema for each defined classification
feature_schema_text = client.create_feature_schema(text_classification.asdict())
feature_schema_radio = client.create_feature_schema(radio_classification.asdict())
feature_schema_checklist = client.create_feature_schema(checklist_classification.asdict())
feature_schema_nested = client.create_feature_schema(nested_classification.asdict())
After creating features and schemas, you can add them to ontologies. To learn how to upsert features into an ontology, see Ontology.

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Set aUIMode for a feature

You can set a UIMode for your classification, which works similarly like switching the dropdown toggle inside the platform. The UIMode has the following two options:
  1. Classification.UIMode.SEARCHABLE allows the feature to be searched inside a dropdown menu, equivalent to enabling the dropdown toggle.
  2. Classification.UIMode.HOTKEY gives each answer option a dedicated hotkey, equivalent to disabling the dropdown toggle.
radio_classification = Classification(class_type=Classification.Type.RADIO,
                                      name="dog_breed",
                                      options=[Option("poodle")],
                                      required=True,
                                      ui_mode=Classification.UIMode.SEARCHABLE)

checklist_classification = Classification(class_type=Classification.Type.CHECKLIST,
                                        name="background",
                                        options=[Option("at_park"), Option("has_leash")]
                                        ui_mode=Classification.UIMode.HOTKEY)

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Set a likert scale for a feature

You can set the parameter is_likert_scale for your radio classification if all option values are integers. 
classification_features = [
    Classification(
        class_type=lb.Classification.Type.RADIO,
        name="Quality Issues",
        options=[
            Option(value="1", label="Blurry"),
            Option(value="2", label="Distorted"),
            Option(value="3", label="OutOfFocus"),
        ],
        required=True,
        is_likert_scale=True,
    )
]

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Get a feature

You can get the feature schema by name or schema id. Only top-level feature schemas are supported. 
from labelbox import client

client = Client(api_key="<YOUR_API_KEY>")

## Search feature by name in your org
regulatory_sign_feature_schema = next(client.get_feature_schemas("regulatory-sign"))
classification_feature = next(client.get_feature_schemas("Quality Issues"))

## Get feature by feature schema ID. You can get this from the UI
regulatory_sign_feature_schema = client.get_feature_schema("FEATURE_SCHEMA_ID")

print(regulatory_sign_feature_schema)
print(classification_feature)

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Update feature schema name

Updates the title/name of a feature schema. Only top-level feature schemas are supported. 
client.update_feature_schema_title("<feature_id>", "New Title")

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Delete or archive a feature in an ontology

Deletes or archives a feature schema from an ontology. If the feature schema is a root-level node with associated labels, it will be archived. If the feature schema is a nested node in the ontology without associated labels, it will be deleted. If the feature schema is a nested ontology node with associated labels, it will neither be deleted nor archived. To archive a feature means you can unarchive it later on and retrieve annotations made with this feature. If you delete a feature, the feature and its associated annotations cannot be recovered. 
client.delete_feature_schema_from_ontology(ontology_id="<ontology_id>", feature_schema_id="<feature_schema_id>")

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Unarchive a feature in an ontology

Unarchives a feature schema node in an ontology. Only root-level feature schema nodes can be unarchived. 
client.unarchive_feature_schema_node(
  ontology_id="<ontology_id>",
  root_feature_schema_id="<root_feature_schema_id>"
)

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Check whether a feature is archived

Returns True if a feature schema is archived in the specified ontology, returns False otherwise. 
result = client.is_feature_schema_archived(
  ontology_id="<ontology_id>",
  feature_schema_id="<feature_schema_id>"
)

# True or False
print(result)