Multi-Label Classification

Multi-label classification models predict multiple outcomes. In Aporia, these models are represented with the multi-label model type.
Examples of multi-label classification problems:
Is this song sad, happy, funny, rock, jazz, or all simultaneously?
Does this movie belong to one or more of the 'romantic', 'comedy', 'documentary', 'thriller' categories, or all simultaneously?
Integration
To monitor a multi-label model, create a new model version with a dict field where keys are different labels and values are the probabilities for each label:
apr_model = aporia.create_model_version(
  model_id="<MODEL_ID>",
  model_version="v1",
  model_type="multi-label"
  features={
     ...
  },
  predictions={
    "genres": "dict"
  },
)
Next, connect to a data source or manually log predictions like so:
apr_model.log_prediction(
  id="<PREDICTION_ID>",
  features={
    ...
  },
  predictions={
    "genres": {
        "action": 0.8,
        "horror": 0.7,
        "thriller": 0.9,
        "drama": 0.2,
        ...
    }
  },
)
If you don't have probabilities for each label, you can log zeros and ones instead. To log actuals for this prediction:
apr_model.log_actuals(
  id="<PREDICTION_ID>",
  actuals={
    "genres": {
        "action": 1.0,
        "horror": 1.0,
        "thriller": 1.0,
        "drama": 0.0,
        ...
    }
  },
)
You can also log multiple dict fields if you have a multi-multi-label model :)
Last modified 4mo ago