`aif360.datasets`¶

Base Class¶

class aif360.datasets.Dataset(**kwargs)[source]¶

Abstract base class for datasets.

copy(deepcopy=False)[source]¶

Convenience method to return a copy of this dataset.

Parameters:	deepcopy (bool, optional) – `deepcopy()` this dataset if True, shallow copy otherwise.
Returns:	A new dataset with fields copied from this object and metadata set accordingly.
Return type:	Dataset

export_dataset()[source]¶: Save this Dataset to disk.

split(num_or_size_splits, shuffle=False)[source]¶

Split this dataset into multiple partitions.

Parameters:	num_or_size_splits (array or int) – If num_or_size_splits is an int, k, the value is the number of equal-sized folds to make (if k does not evenly divide the dataset these folds are approximately equal-sized). If num_or_size_splits is an array of type int, the values are taken as the indices at which to split the dataset. If the values are floats (< 1.), they are considered to be fractional proportions of the dataset at which to split. shuffle (bool, optional) – Randomly shuffle the dataset before splitting.
Returns:	Splits. Contains k or len(num_or_size_splits) + 1 datasets depending on num_or_size_splits.
Return type:	list(Dataset)

validate_dataset()[source]¶: Error checking and type validation.

Structured Dataset¶

class aif360.datasets.StructuredDataset(df, label_names, protected_attribute_names, instance_weights_name=None, scores_names=[], unprivileged_protected_attributes=[], privileged_protected_attributes=[], metadata=None)[source]¶

Base class for all structured datasets.

A StructuredDataset requires data to be stored in numpy.ndarray objects with dtype as float64.

features¶

Dataset features for each instance.

Type:	numpy.ndarray

labels¶

Generic label corresponding to each instance (could be ground-truth, predicted, cluster assignments, etc.).

Type:	numpy.ndarray

scores¶

Probability score associated with each label. Same shape as labels. Only valid for binary labels (this includes one-hot categorical labels as well).

Type:	numpy.ndarray

protected_attributes¶

A subset of features for which fairness is desired.

Type:	numpy.ndarray

feature_names¶

Names describing each dataset feature.

Type:	list(str)

label_names¶

Names describing each label.

Type:	list(str)

protected_attribute_names¶

A subset of feature_names corresponding to protected_attributes.

Type:	list(str)

privileged_protected_attributes¶

A subset of protected attribute values which are considered privileged from a fairness perspective.

Type:	list(numpy.ndarray)

unprivileged_protected_attributes¶

The remaining possible protected attribute values which are not included in privileged_protected_attributes.

Type:	list(numpy.ndarray)

instance_names¶

Indentifiers for each instance. Sequential integers by default.

Type:	list(str)

instance_weights¶

Weighting for each instance. All equal (ones) by default. Pursuant to standard practice in social science data, 1 means one person or entity. These weights are hence person or entity multipliers (see: https://www.ibm.com/support/knowledgecenter/en/SS3RA7_15.0.0/com.ibm.spss.modeler.help/netezza_decisiontrees_weights.htm) These weights may not be normalized to sum to 1 across the entire dataset, rather the nominal (default) weight of each entity/record in the data is 1. This is similar in spirit to the person weight in census microdata samples. https://www.census.gov/programs-surveys/acs/technical-documentation/pums/about.html

Type:	numpy.ndarray

ignore_fields¶

Attribute names to ignore when doing equality comparisons. Always at least contains ‘metadata’.

Type:	set(str)

metadata¶

Details about the creation of this dataset. For example:

{
    'transformer': 'Dataset.__init__',
    'params': kwargs,
    'previous': None
}

Type:	dict

Parameters:

df (pandas.DataFrame) – Input DataFrame with features, labels, and protected attributes. Values should be preprocessed to remove NAs and make all data numerical. Index values are taken as instance names.
label_names (iterable) – Names of the label columns in df.
protected_attribute_names (iterable) – List of names corresponding to protected attribute columns in df.
instance_weights_name (optional) – Column name in df corresponding to instance weights. If not provided, instance_weights will be all set to 1.
unprivileged_protected_attributes (optional) – If not provided, all but the highest numerical value of each protected attribute will be considered not privileged.
privileged_protected_attributes (optional) – If not provided, the highest numerical value of each protected attribute will be considered privileged.
metadata (optional) – Additional metadata to append.

Raises:

TypeError – Certain fields must be np.ndarrays as specified in the class description.
ValueError – ndarray shapes must match.

align_datasets(other)[source]¶

Align the other dataset features, labels and protected_attributes to this dataset.

Parameters:	other (StructuredDataset) – Other dataset that needs to be aligned
Returns:	New aligned dataset
Return type:	StructuredDataset

convert_to_dataframe(de_dummy_code=False, sep='=', set_category=True)[source]¶

Convert the StructuredDataset to a pandas.DataFrame.

Parameters:

de_dummy_code (bool) – Performs de_dummy_coding, converting dummy- coded columns to categories. If de_dummy_code is True and this dataset contains mappings for label and/or protected attribute values to strings in the metadata, this method will convert those as well.
set_category (bool) – Set the de-dummy coded features to categorical type.

Returns:

pandas.DataFrame: Equivalent dataframe for a dataset. All columns will have only numeric values. The protected_attributes field in the dataset will override the values in the features field.
dict: Attributes. Will contain additional information pulled from the dataset such as feature_names, label_names, protected_attribute_names, instance_names, instance_weights, privileged_protected_attributes, unprivileged_protected_attributes. The metadata will not be returned.

Return type:

(pandas.DataFrame, dict)

export_dataset(export_metadata=False)[source]¶: Export the dataset and supporting attributes TODO: The preferred file format is HDF

import_dataset(import_metadata=False)[source]¶: Import the dataset and supporting attributes TODO: The preferred file format is HDF

split(num_or_size_splits, shuffle=False, seed=None)[source]¶

Split the dataset into multiple datasets :param num_or_size_splits: :type num_or_size_splits: list or int :param shuffle: :type shuffle: bool :param seed: takes the same argument as numpy.random.seed() :type seed: int or array_like :param function:

Returns:	Each element of this list is a dataset obtained during the split
Return type:	list

temporarily_ignore(*fields)[source]¶

Temporarily add the fields provided to ignore_fields.

To be used in a with statement. Upon completing the with block, ignore_fields is restored to its original value.

Parameters:	*fields – Additional fields to ignore for equality comparison within the scope of this context manager, e.g. temporarily_ignore(‘features’, ‘labels’). The temporary ignore_fields attribute is the union of the old attribute and the set of these fields.

Examples

>>> sd = StructuredDataset(...)
>>> modified = sd.copy()
>>> modified.labels = sd.labels + 1
>>> assert sd != modified
>>> with sd.temporarily_ignore('labels'):
>>>     assert sd == modified
>>> assert 'labels' not in sd.ignore_fields

validate_dataset()[source]¶

Error checking and type validation.

Raises:	`TypeError` – Certain fields must be np.ndarrays as specified in the class description. `ValueError` – ndarray shapes must match.

Binary Label Dataset¶

class aif360.datasets.BinaryLabelDataset(favorable_label=1.0, unfavorable_label=0.0, **kwargs)[source]¶

Base class for all structured datasets with binary labels.

Parameters:	favorable_label (float) – Label value which is considered favorable (i.e. “positive”). unfavorable_label (float) – Label value which is considered unfavorable (i.e. “negative”). **kwargs – StructuredDataset arguments.