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233 | class BiasAuditModule(BaseModel):
output_dir: Path = Field(
description="Output directory for bias audit.",
title="Output Directory",
examples=["output"]
)
sensitive_features: list | None = Field(
description="Sensitive features.",
title="Sensitive Features",
examples=["gender", "race", "ethnicity"]
)
fairness_threshold: float = Field(
description="Fairness threshold.",
default=1.2,
title="Fairness Threshold",
)
relative: bool = Field(
description="Relative.",
default=False,
title="Relative",
)
def model_post_init(self, __context) -> None: # noqa: ANN001
self.output_dir.mkdir(parents=True, exist_ok=True)
def __call__(self, trainer: "TrainerSupervised") -> None:
logger.info("Running Bias Audit Module...")
test_data = (
undummify(trainer.X_test, prefix_sep=trainer.settings.encoding_module.prefix_sep)
if trainer.settings.encoding_module.enabled
else trainer.X_test
)
if self.sensitive_features is None:
logger.info("No sensitive features provided, inferring from data...")
self.sensitive_features = infer_sensitive_features(test_data)
y_pred = None if trainer.settings.task == 'survival' else pd.Series(trainer.infer(test_data))
metrics = ['mean_prediction'] if trainer.settings.task == 'regression' else ['mean_prediction', 'false_positive_rate']
if trainer.settings.task == 'binary':
y_true_array = trainer.y_test.to_numpy()
bias_metric = np.array([
log_loss([y_true_array[idx]], [y_pred[idx]], labels=np.unique(y_true_array))
for idx in range(len(y_true_array))
])
y_pred = (y_pred >= .5).astype(int)
elif trainer.settings.task == 'regression':
bias_metric = np.sqrt((trainer.y_test.to_numpy() - y_pred) ** 2)
for sensitive_feature_name in self.sensitive_features:
_sensitive_column = test_data[sensitive_feature_name]
if trainer.settings.task == 'survival':
self._subgroup_analysis_coxph(trainer.y_test, _sensitive_column)
else:
f_pvalue = self._subgroup_analysis_ols(_sensitive_column, bias_metric)
if f_pvalue < 0.05:
self._generate_violin(_sensitive_column, bias_metric, trainer.settings.task)
result = self._calculate_fair_metrics(trainer.y_test, y_pred, _sensitive_column, self.fairness_threshold, self.relative, metrics)
print(f"\n=== Subgroup Analysis for '{sensitive_feature_name.title()}' using FairLearn ===\n") # noqa: T201
table_output = tabulate(result.iloc[:, :4], headers='keys', tablefmt='grid')
print('\n'.join([' ' + line for line in table_output.split('\n')]), '\n') # noqa: T201
result.to_csv(self.output_dir / f'{sensitive_feature_name}_fm_metrics.csv')
def _generate_violin(self, sensitive_column: pd.Series, bias_metric:np.ndarray, task: str) -> None:
"""Generate a violin plot for the bias metric."""
plt.figure(figsize=(8, 6))
sns.set_theme(style="whitegrid")
sns.violinplot(
x=sensitive_column,
y=bias_metric,
palette="muted",
inner="quart",
linewidth=1.25
)
bias_metric_name = 'log_loss' if task == 'binary' else 'root_mean_squared_error'
plt.title(f'{bias_metric_name.title()} Distribution by {sensitive_column.name}', fontsize=16, weight='bold')
plt.xlabel(f'{sensitive_column.name}', fontsize=14)
plt.ylabel(f'{bias_metric_name.title()} per Patient', fontsize=14)
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.savefig(self.output_dir / f'{sensitive_column.name}_{bias_metric_name}.png')
plt.show()
def _subgroup_analysis_ols(self, sensitive_column: pd.Series, bias_metric: np.ndarray) -> float:
"""Fit a statsmodels OLS model to the bias metric data, using the sensitive feature and print summary based on p_val."""
one_hot_encoded = pd.get_dummies(sensitive_column, prefix=sensitive_column.name)
X_columns = one_hot_encoded.columns
X = one_hot_encoded.values
y = bias_metric
X = sm.add_constant(X.astype(float), has_constant='add')
model = sm.OLS(y, X).fit()
if model.f_pvalue < 0.05:
output = []
print(f"⚠️ **Possible Bias Detected in {sensitive_column.name.title()}** ⚠️\n") # noqa: T201
output.append(f"=== Subgroup Analysis for '{sensitive_column.name.title()}' Using OLS Regression ===\n")
output.append("Model Statistics:")
output.append(f" R-squared: {model.rsquared:.3f}")
output.append(f" F-statistic: {model.fvalue:.3f}")
output.append(f" F-statistic p-value: {model.f_pvalue:.4f}")
output.append(f" AIC: {model.aic:.2f}")
output.append(f" Log-Likelihood: {model.llf:.2f}")
summary_df = pd.DataFrame({
'Feature': ['const'] + X_columns.tolist(), # Predictor names (includes 'const' if added)
'Coefficient': model.params, # Coefficients
'Standard Error': model.bse # Standard Errors
})
table_output = tabulate(summary_df, headers='keys', tablefmt='grid', showindex=False, floatfmt=".3f")
output.append("Model Coefficients:")
output.append('\n'.join([' ' + line for line in table_output.split('\n')]))
output_text = '\n'.join(output)
print(output_text) # noqa: T201
with (self.output_dir / f'{sensitive_column.name}_OLS_model_summary.txt').open('w') as f:
f.write(output_text)
return model.f_pvalue
def _subgroup_analysis_coxph(self, y_true: pd.Series, sensitive_column: pd.Series) -> None:
"""Fit a CoxPH model using the sensitive feature and print summary based on p_val."""
one_hot_encoded = pd.get_dummies(sensitive_column, prefix=sensitive_column.name)
df_encoded = y_true.join(one_hot_encoded)
cph = CoxPHFitter(penalizer=0.0001)
cph.fit(df_encoded, duration_col='time', event_col='event')
if cph.log_likelihood_ratio_test().p_value < 0.05:
output = []
print(f"⚠️ **Possible Bias Detected in {sensitive_column.name.title()}** ⚠️") # noqa: T201
output.append(f"=== Subgroup Analysis for '{sensitive_column.name.title()}' Using Cox Proportional Hazards Model ===\n")
output.append("Model Statistics:")
output.append(f" AIC (Partial): {cph.AIC_partial_:.2f}")
output.append(f" Log-Likelihood: {cph.log_likelihood_:.2f}")
output.append(f" Log-Likelihood Ratio p-value: {cph.log_likelihood_ratio_test().p_value:.4f}")
output.append(f" Concordance Index (C-index): {cph.concordance_index_:.2f}")
summary_df = pd.DataFrame({
'Feature': cph.summary.index.to_list(),
'Coefficient': cph.summary['coef'].to_list(),
'Standard Error': cph.summary['se(coef)'].to_list()
})
table_output = tabulate(summary_df, headers='keys', tablefmt='grid', showindex=False, floatfmt=".3f")
output.append("Model Coefficients:")
output.append('\n'.join([' ' + line for line in table_output.split('\n')]))
output_text = '\n'.join(output)
print(output_text) # noqa: T201
with (self.output_dir / f'{sensitive_column.name}_Cox_model_summary.txt').open('w') as f:
f.write(output_text)
def _calculate_fair_metrics(
self,
y_true: pd.Series,
y_pred: pd.Series,
sensitive_column: pd.Series,
fairness_threshold: float,
relative: bool,
metrics: list
) -> pd.DataFrame:
"""Calculate the Fairlearn metrics and return the results in a DataFrame."""
_metrics = {metric: get_metric(metric, sensitive_features=sensitive_column) for metric in metrics}
metric_frame = fm.MetricFrame(
metrics=_metrics,
y_true=y_true,
y_pred=y_pred,
sensitive_features=sensitive_column,
)
result = pd.DataFrame(metric_frame.by_group.T, index=_metrics.keys())
result = result.rename(
columns={
"mean_prediction": "Demographic Parity",
"false_positive_rate": "(FPR) Equalized Odds",
"true_positive_rate": "(TPR) Equalized Odds or Equal Opportunity"
}
)
if relative:
largest_feature = sensitive_column.mode().iloc[0]
results_relative = result.T / result[largest_feature]
results_relative = results_relative.applymap(
lambda x: f"{x:.3f} ✅" if x <= fairness_threshold or 1/x <= fairness_threshold
else f"{x:.3f} ❌")
result = pd.concat([result, results_relative.T.rename(index=lambda x: f"Relative {x}")])
return result
|