CustomFeature - Fiddler Documentation

Base class for all custom feature types in Fiddler models. CustomFeature provides the foundation for creating specialized feature types that enhance model monitoring and analysis. Custom features allow you to define derived metrics, embeddings, and enrichments that extend beyond basic model inputs and outputs for advanced drift detection and analysis. This is an abstract base class that should not be instantiated directly. Instead, use one of its concrete subclasses: Multivariate, VectorFeature, TextEmbedding, ImageEmbedding, or Enrichment.

Examples

Creating a multivariate feature from multiple columns:

feature = CustomFeature.from_columns(
    custom_name="user_behavior_cluster",
    cols=["clicks", "views", "time_spent"],
    n_clusters=5
)

Creating a custom feature from a dictionary:

feature_dict = {
    "name": "text_sentiment",
    "type": "FROM_TEXT_EMBEDDING",
    "column": "embedding_col",
    "source_column": "review_text"
}
feature = CustomFeature.from_dict(feature_dict)

class Config

allow_mutation = False

use_enum_values = True

discriminator = ‘type’

classmethod from_columns(custom_name, cols, n_clusters=5)

Return type: Multivariate

classmethod from_dict(deserialized_json)

Return type: Any

to_dict()

Return type: Dict[str, Any]

Documentation Index

​Examples

​class Config

​allow_mutation = False

​use_enum_values = True

​discriminator = ‘type’

​classmethod from_columns(custom_name, cols, n_clusters=5)

​classmethod from_dict(deserialized_json)

​to_dict()