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Bug/y pred y true size mismatch #16

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652d0d2
take into account dropped rows in the preprocessing
david-behar Jun 11, 2025
84b2fca
update version and release notes
david-behar Jun 11, 2025
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3 changes: 3 additions & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,8 @@
# Changelog

## Version 1.0.9 - 2025-06
- Fix bug when dropping rows during the preprocessing

## Version 1.0.8 - 2022-09-13
- In the model view, use the code env that was used to train the model
- No need to build a Python 3.6 code env anymore
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4 changes: 2 additions & 2 deletions plugin.json
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@@ -1,10 +1,10 @@
{
"id": "model-fairness-report",
"version": "1.0.8",
"version": "1.0.9",
"meta": {
"label": "Model Fairness Report",
"description": "Get insight on the fairness of a model.",
"author": "Dataiku (Du PHAN)",
"author": "Dataiku",
"icon": "icon-group",
"licenseInfo": "Apache Software License",
"url": "https://www.dataiku.com/product/plugins/model-fairness-report/",
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11 changes: 5 additions & 6 deletions python-lib/dku_webapp/utils.py
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Expand Up @@ -93,13 +93,13 @@ def get_histograms(model_accessor, advantageous_outcome, sensitive_column):
test_df = raw_test_df.dropna(subset=[sensitive_column])
target_variable = model_accessor.get_target_variable()

y_true = test_df.loc[:, target_variable]
pred_df = model_accessor.predict(test_df)
y_true = test_df.iloc[pred_df.index].loc[:, target_variable]
y_pred = pred_df.loc[:, DkuWebappConstants.PREDICTION]

advantageous_outcome_proba_col = 'proba_{}'.format(advantageous_outcome)
y_pred_proba = pred_df.loc[:, advantageous_outcome_proba_col]
sensitive_feature_values = test_df[sensitive_column]
sensitive_feature_values = test_df.iloc[pred_df.index][sensitive_column]

return get_histogram_data(y_true, y_pred, y_pred_proba, advantageous_outcome, sensitive_feature_values)

Expand All @@ -109,11 +109,10 @@ def get_metrics(model_accessor, advantageous_outcome, sensitive_column, referenc
target_variable = model_accessor.get_target_variable()
test_df.dropna(subset=[sensitive_column, target_variable], how='any', inplace=True)

y_true = test_df.loc[:, target_variable]
pred_df = model_accessor.predict(test_df)
y_true = test_df.iloc[pred_df.index].loc[:, target_variable]
y_pred = pred_df.loc[:, DkuWebappConstants.PREDICTION]


try: # check whether or not the column can be casted to int
if np.array_equal(test_df[sensitive_column], test_df[sensitive_column].astype(int)):
test_df[sensitive_column] = test_df[sensitive_column].astype(int)
Expand All @@ -124,8 +123,8 @@ def get_metrics(model_accessor, advantageous_outcome, sensitive_column, referenc
except Exception as e:
logger.info('Sensitive column can not be casted to int. ', e)
pass

sensitive_feature_values = test_df[sensitive_column]
sensitive_feature_values = test_df.iloc[pred_df.index][sensitive_column]
model_report = ModelFairnessMetricReport(y_true, y_pred, sensitive_feature_values, advantageous_outcome)
population_names = sensitive_feature_values.unique()

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