aif360.datasets.RegressionDataset

class aif360.datasets.RegressionDataset(df, dep_var_name, protected_attribute_names, privileged_classes, instance_weights_name='', categorical_features=[], na_values=[], custom_preprocessing=None, metadata=None)

Base class for regression datasets.

Subclasses of RegressionDataset should perform the following before calling super().__init__:

1. Load the dataframe from a raw file.

Then, this class will go through a standard preprocessing routine which:

2. (optional) Performs some dataset-specific preprocessing (e.g. renaming columns/values, handling missing data).

3. Drops rows with NA values.

4. Creates a one-hot encoding of the categorical variables.

5. Maps protected attributes to binary privileged/unprivileged values (1/0).

6. Normalizes df values

Parameters:

• df (pandas.DataFrame) – DataFrame on which to perform standard processing.

• dep_var_name – Name of the dependent variable column in df.

• protected_attribute_names (list) – List of names corresponding to protected attribute columns in df.

• privileged_classes (list(list or function)) – Each element is a list of values which are considered privileged or a boolean function which return True if privileged for the corresponding column in protected_attribute_names. All others are unprivileged. Values are mapped to 1 (privileged) and 0 (unprivileged) if they are not already numerical.

• instance_weights_name (optional) – Name of the instance weights column in df.

• categorical_features (optional, list) – List of column names in the DataFrame which are to be expanded into one-hot vectors.

• na_values (optional) – Additional strings to recognize as NA. See pandas.read_csv() for details.

• custom_preprocessing (function) – A function object which acts on and returns a DataFrame (f: DataFrame -> DataFrame). If None, no extra preprocessing is applied.

• metadata (optional) – Additional metadata to append.

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__(df, dep_var_name, protected_attribute_names, privileged_classes, instance_weights_name='', categorical_features=[], na_values=[], custom_preprocessing=None, metadata=None)

Subclasses of RegressionDataset should perform the following before calling super().__init__:

1. Load the dataframe from a raw file.

Then, this class will go through a standard preprocessing routine which:

2. (optional) Performs some dataset-specific preprocessing (e.g. renaming columns/values, handling missing data).

3. Drops rows with NA values.

4. Creates a one-hot encoding of the categorical variables.

5. Maps protected attributes to binary privileged/unprivileged values (1/0).

6. Normalizes df values

Parameters:

• df (pandas.DataFrame) – DataFrame on which to perform standard processing.

• dep_var_name – Name of the dependent variable column in df.

• protected_attribute_names (list) – List of names corresponding to protected attribute columns in df.

• privileged_classes (list(list or function)) – Each element is a list of values which are considered privileged or a boolean function which return True if privileged for the corresponding column in protected_attribute_names. All others are unprivileged. Values are mapped to 1 (privileged) and 0 (unprivileged) if they are not already numerical.

• instance_weights_name (optional) – Name of the instance weights column in df.

• categorical_features (optional, list) – List of column names in the DataFrame which are to be expanded into one-hot vectors.

• na_values (optional) – Additional strings to recognize as NA. See pandas.read_csv() for details.

• custom_preprocessing (function) – A function object which acts on and returns a DataFrame (f: DataFrame -> DataFrame). If None, no extra preprocessing is applied.

• metadata (optional) – Additional metadata to append.