`aif360.datasets`.CompasDataset¶

class aif360.datasets.CompasDataset(label_name='two_year_recid', favorable_classes=[0], protected_attribute_names=['sex', 'race'], privileged_classes=[['Female'], ['Caucasian']], instance_weights_name=None, categorical_features=['age_cat', 'c_charge_degree', 'c_charge_desc'], features_to_keep=['sex', 'age', 'age_cat', 'race', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count', 'c_charge_degree', 'c_charge_desc', 'two_year_recid'], features_to_drop=[], na_values=[], custom_preprocessing=<function default_preprocessing>, metadata={'label_maps': [{1.0: 'Did recid.', 0.0: 'No recid.'}], 'protected_attribute_maps': [{0.0: 'Male', 1.0: 'Female'}, {1.0: 'Caucasian', 0.0: 'Not Caucasian'}]})[source]¶

ProPublica COMPAS Dataset.

See aif360/data/raw/compas/README.md.

See StandardDataset for a description of the arguments.

Note: The label value 0 in this case is considered favorable (no recidivism).

Examples

In some cases, it may be useful to keep track of a mapping from float -> str for protected attributes and/or labels. If our use case differs from the default, we can modify the mapping stored in metadata:

>>> label_map = {1.0: 'Did recid.', 0.0: 'No recid.'}
>>> protected_attribute_maps = [{1.0: 'Male', 0.0: 'Female'}]
>>> cd = CompasDataset(protected_attribute_names=['sex'],
... privileged_classes=[['Male']], metadata={'label_map': label_map,
... 'protected_attribute_maps': protected_attribute_maps})

Now this information will stay attached to the dataset and can be used for more descriptive visualizations.

Methods

`align_datasets`	Align the other dataset features, labels and protected_attributes to this dataset.
`convert_to_dataframe`	Convert the StructuredDataset to a `pandas.DataFrame`.
`copy`	Convenience method to return a copy of this dataset.
`export_dataset`	Export the dataset and supporting attributes TODO: The preferred file format is HDF
`import_dataset`	Import the dataset and supporting attributes TODO: The preferred file format is HDF
`split`	Split this dataset into multiple partitions.
`subset`	Subset of dataset based on position :param indexes: iterable which contains row indexes
`temporarily_ignore`	Temporarily add the fields provided to `ignore_fields`.
`validate_dataset`	Error checking and type validation.

__init__(label_name='two_year_recid', favorable_classes=[0], protected_attribute_names=['sex', 'race'], privileged_classes=[['Female'], ['Caucasian']], instance_weights_name=None, categorical_features=['age_cat', 'c_charge_degree', 'c_charge_desc'], features_to_keep=['sex', 'age', 'age_cat', 'race', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count', 'c_charge_degree', 'c_charge_desc', 'two_year_recid'], features_to_drop=[], na_values=[], custom_preprocessing=<function default_preprocessing>, metadata={'label_maps': [{1.0: 'Did recid.', 0.0: 'No recid.'}], 'protected_attribute_maps': [{0.0: 'Male', 1.0: 'Female'}, {1.0: 'Caucasian', 0.0: 'Not Caucasian'}]})[source]¶