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Add EqualizedOddsImprovement metric #775

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2 changes: 2 additions & 0 deletions sdmetrics/single_table/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,6 +77,7 @@
from sdmetrics.single_table.privacy.numerical_sklearn import NumericalLR, NumericalMLP, NumericalSVR
from sdmetrics.single_table.privacy.radius_nearest_neighbor import NumericalRadiusNearestNeighbor
from sdmetrics.single_table.table_structure import TableStructure
from sdmetrics.single_table.equalized_odds import EqualizedOddsImprovement

__all__ = [
'bayesian_network',
Expand Down Expand Up @@ -140,4 +141,5 @@
'TableStructure',
'DCRBaselineProtection',
'DCROverfittingProtection',
'EqualizedOddsImprovement',
]
88 changes: 16 additions & 72 deletions sdmetrics/single_table/data_augmentation/utils.py
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Original file line number Diff line number Diff line change
@@ -1,33 +1,12 @@
"""Utils method for data augmentation metrics."""

import pandas as pd

from sdmetrics._utils_metadata import _process_data_with_metadata, _validate_single_table_metadata


def _validate_tables(real_training_data, synthetic_data, real_validation_data):
"""Validate the tables of the Data Augmentation metrics."""
tables = [real_training_data, synthetic_data, real_validation_data]
if any(not isinstance(table, pd.DataFrame) for table in tables):
raise ValueError(
'`real_training_data`, `synthetic_data` and `real_validation_data` must be '
'pandas DataFrames.'
)


def _validate_prediction_column_name(prediction_column_name):
"""Validate the prediction column name of the Data Augmentation metrics."""
if not isinstance(prediction_column_name, str):
raise TypeError('`prediction_column_name` must be a string.')


def _validate_classifier(classifier):
"""Validate the classifier of the Data Augmentation metrics."""
if classifier is not None and not isinstance(classifier, str):
raise TypeError('`classifier` must be a string or None.')

if classifier != 'XGBoost':
raise ValueError('Currently only `XGBoost` is supported as classifier.')
from sdmetrics.single_table.utils import (
_validate_classifier,
_validate_data_and_metadata,
_validate_prediction_column_name,
_validate_tables,
)


def _validate_fixed_recall_value(fixed_recall_value):
Expand All @@ -53,51 +32,6 @@ def _validate_parameters(
_validate_fixed_recall_value(fixed_recall_value)


def _validate_data_and_metadata(
real_training_data,
synthetic_data,
real_validation_data,
metadata,
prediction_column_name,
minority_class_label,
):
"""Validate the data and metadata of the Data Augmentation metrics."""
if prediction_column_name not in metadata['columns']:
raise ValueError(
f'The column `{prediction_column_name}` is not described in the metadata.'
' Please update your metadata.'
)

if metadata['columns'][prediction_column_name]['sdtype'] not in ('categorical', 'boolean'):
raise ValueError(
f'The column `{prediction_column_name}` must be either categorical or boolean.'
' Please update your metadata.'
)

if minority_class_label not in real_training_data[prediction_column_name].unique():
raise ValueError(
f'The value `{minority_class_label}` is not present in the column '
f'`{prediction_column_name}` for the real training data.'
)

if minority_class_label not in real_validation_data[prediction_column_name].unique():
raise ValueError(
f"The metric can't be computed because the value `{minority_class_label}` "
f'is not present in the column `{prediction_column_name}` for the real validation data.'
' The `precision` and `recall` are undefined for this case.'
)

synthetic_labels = set(synthetic_data[prediction_column_name].unique())
real_labels = set(real_training_data[prediction_column_name].unique())
if not synthetic_labels.issubset(real_labels):
to_print = "', '".join(sorted(synthetic_labels - real_labels))
raise ValueError(
f'The ``{prediction_column_name}`` column must have the same values in the real '
'and synthetic data. The following values are present in the synthetic data and'
f" not the real data: '{to_print}'"
)


def _validate_inputs(
real_training_data,
synthetic_data,
Expand Down Expand Up @@ -127,6 +61,16 @@ def _validate_inputs(
minority_class_label,
)

synthetic_labels = set(synthetic_data[prediction_column_name].unique())
real_labels = set(real_training_data[prediction_column_name].unique())
if not synthetic_labels.issubset(real_labels):
to_print = "', '".join(sorted(synthetic_labels - real_labels))
raise ValueError(
f'The `{prediction_column_name}` column must have the same values in the real '
'and synthetic data. The following values are present in the synthetic data and'
f" not the real data: '{to_print}'"
)


def _process_data_with_metadata_ml_efficacy_metrics(
real_training_data, synthetic_data, real_validation_data, metadata
Expand Down
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