Specifying Custom Features

"Patented Fiddler Technology"

Vector Monitoring for Unstructured Data

CF1 = fdl.CustomFeature.from_columns(['f1','f2','f3'], custom_name = 'vector1')
CF2 = fdl.CustomFeature.from_columns(['f1','f2','f3'], n_clusters=5, custom_name = 'vector2')
CF3 = fdl.TextEmbedding(name='text_embedding',column='embedding',source_column='text')
CF4 = fdl.ImageEmbedding(name='image_embedding',column='embedding',source_column='image_url')

Passing Custom Features List to Model Info

model_info = fdl.ModelInfo.from_dataset_info(
    dataset_info=dataset_info,
    dataset_id = DATASET_ID,
    features = data_cols,
    target='target',
    outputs='predicted_score',
    custom_features = [CF1,CF2,CF3,CF4]
)

📘
Quick Start for NLP Monitoring
Check out our Quick Start guide for NLP monitoring for a fully functional notebook example.

Updated 5 months ago