Developer guide for creating and modifying features via the Python SDK.
View Features guide page for feature schema definitions and details.
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.
Set aUIMode
for a feature
UIMode
for a featureYou 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:
Classification.UIMode.SEARCHABLE
allows the feature to be searched inside a dropdown menu, equivalent to enabling the dropdown toggle.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)
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)
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")
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>")
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>"
)
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)