Postprocessing

Postprocessing turns raw model outputs into human-friendly results and task artefacts. This is an optional final step in the data pipeline and is not required for all models.

info

Postprocessing is currently only supported as an argument to the bitfount.ModelInference algorithm where postprocessing of raw model outputs is most likely to be valuable.

Available postprocessors

Bitfount provides a suite of built-in postprocessors to handle common output-preparation needs. You can mix and match them, even chaining several together.

Built-in postprocessor types

Name	Description	Example Use Case
rename	Rename DataFrame columns.	Change "pred" column to "Prediction".
transform	Apply a transformation function from bitfount.transformations on output columns.	Apply softmax or custom transform to logits.
json_restructure	Move fields between levels in nested JSON structures.	Move a key from nested JSON upwards for flattening.
string_to_json	Parse columns containing JSON as strings into JSON objects.	Safely load prediction results stored as strings.
json_key_rename	Rename keys within JSON fields in columns.	Change "class1" to "cat" inside prediction JSON.
json_wrap_in_list	Wrap JSON fields in an additional list.	Ensure all predictions are in a JSON array format.

Example configuration in YAML

The postprocessors can be supplied as a list of dictionaries. The only required key is type which refers to the name of the postprocessor to use. The other keys are specific the postprocessor and are passed as keyword arguments to the postprocessor. More information about the available postprocessors and their arguments can be found in the API documentation.

algorithm:
  - name: bitfount.ModelInference
    model:
      bitfount_model:
        model_ref: MyModel
        model_version: 2
        username: amin-nejad
      hyperparameters:
        batch_size: 8
    arguments:
      postprocessors:
        - type: rename
          columns: ["logits"]
          mapping:
            logits: probabilities
        - type: transform
          columns: ["probabilities"]
          transform: softmax