This is the base class that all other custom features inherit from. It's flexible enough to accommodate different types of derived features. Note: All of the derived feature classes (e.g., Multivariate, VectorFeature, etc.) inherit from CustomFeature and thus have its properties, in addition to their specific ones.
| Input Parameter | Type | Default | Description |
|---|---|---|---|
| name | str | None | The name of the custom feature. |
| type | CustomFeatureType | None | The type of custom feature. Must be one of the CustomFeatureType enum values. |
| n_clusters | Optional[int] | 5 | The number of clusters. |
| centroids | Optional[List] | None | Centroids of the clusters in the embedded space. Number of centroids equal to n_clusters. |
| columns | Optional[List[str]] | None | For FROM_COLUMNS type, represents the original columns from which the feature is derived. |
| column | Optional[str] | None | Used for vector-derived features, the original vector column name. |
| source_column | Optional[str] | None | Specifies the original column name for embedding-derived features. |
| n_tags | Optional[int] | 5 | For FROM_TEXT_EMBEDDING type, represents the number of tags for each cluster in the tfidf summarization in drift computation. |
# use from_columns helper function to generate a custom feature combining multiple numeric columns
feature = fdl.CustomFeature.from_columns(
name='my_feature',
columns=['column_1', 'column_2'],
n_clusters=5
)