Advanced scan usage#

It is possible to customize the configuration of the scan by passing specific configuration at runtime to the scan method.

The following examples show the different options available.

Limiting to a specific group of detectors#

If you want to run only a specific detector (or a group of detectors), you can use the only argument. This argument accepts either a tag or a list of tags:

import giksard as gsk

report = gsk.scan(my_model, my_dataset, only="robustness")

or with multiple tags:

report = gsk.scan(my_model, my_dataset, only=["robustness", "performance"])

Limiting to a selection of model features#

If your model has a great number of features and you want to limit the scan to a specific subset, you can use the features argument:

import giksard as gsk

report = gsk.scan(my_model, my_dataset, features=["feature_1", "feature_2"])

This will produce scan results only for the features feature_1 and feature_2.

Advanced detector configuration#

If you want to customize the configuration of a specific detector, you can use the params argument, which accepts a dictionary where the key is the identifier of the detector and the value is a dictionary of config options that will be passed to the detector upon initialization:

import giksard as gsk

params = {
    "performance_bias": dict(threshold=0.04, metrics=["accuracy", "f1"]),
    "ethical_bias": dict(output_sensitivity=0.5),

report = gsk.scan(my_model, my_dataset, params=params)

You can check in the reference documentation of each detector which options are available for customization.

How to make the scan faster#

If you are dealing with a big dataset, the scan may take a long time to analyze all the vulnerabilities. If this is the case, you may choose to limit the scan to a subset of the features and detectors that are the most relevant to your use case (see above for detailed instructions):

report = gsk.scan(my_model,
    only=["robustness", "performance"],
    features=["feature_1", "feature_2"],

Moreover, certain detectors do a full scan of the dataset, which can be very slow. If this is the case, we recommend use the following configuration which so that these detectors will only scan a random sample of your dataset:

params = {
    "performance_bias": dict(max_dataset_size=100_000),
    "overconfidence": dict(max_dataset_size=100_000),
    "underconfidence": dict(max_dataset_size=100_000),

report = gsk.scan(my_model, my_dataset, params=params)

This will limit the scan to 100,000 samples of your dataset. You can adjust this number to your needs.

Note: for classification models, we will make sure that the sample is balanced between the different classes via stratified sampling.